diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/__pycache__/__init__.cpython-311.pyc b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7b71308bf489410a193a8956bd2c2cbc606d3df8 Binary files /dev/null and b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/__pycache__/__init__.cpython-311.pyc differ diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..880274309cbab486a90df05548a0e4d3f2ea0925 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_musicgen import * + from .modeling_musicgen import * + from .processing_musicgen import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen/processing_musicgen.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen/processing_musicgen.py new file mode 100644 index 0000000000000000000000000000000000000000..180f0a2a95eecc662f71a5b53d553b832fd8ad88 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen/processing_musicgen.py @@ -0,0 +1,86 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Text/audio processor class for MusicGen +""" + +from typing import Any + +import numpy as np + +from ...processing_utils import ProcessorMixin +from ...utils import auto_docstring, to_numpy + + +@auto_docstring +class MusicgenProcessor(ProcessorMixin): + def __init__(self, feature_extractor, tokenizer): + super().__init__(feature_extractor, tokenizer) + + def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True): + return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps) + + @auto_docstring + def __call__(self, *args, **kwargs): + if len(args) > 0: + kwargs["audio"] = args[0] + return super().__call__(*args, **kwargs) + + def batch_decode(self, *args, **kwargs): + """ + This method is used to decode either batches of audio outputs from the MusicGen model, or batches of token ids + from the tokenizer. In the case of decoding token ids, this method forwards all its arguments to T5Tokenizer's + [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. + """ + audio_values = kwargs.pop("audio", None) + padding_mask = kwargs.pop("padding_mask", None) + + if len(args) > 0: + audio_values = args[0] + args = args[1:] + + if audio_values is not None: + return self._decode_audio(audio_values, padding_mask=padding_mask) + else: + return self.tokenizer.batch_decode(*args, **kwargs) + + def _decode_audio(self, audio_values, padding_mask: Any = None) -> list[np.ndarray]: + """ + This method strips any padding from the audio values to return a list of numpy audio arrays. + """ + audio_values = to_numpy(audio_values) + bsz, channels, seq_len = audio_values.shape + + if padding_mask is None: + return list(audio_values) + + padding_mask = to_numpy(padding_mask) + + # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding** + # token (so that the generated audio values are **not** treated as padded tokens) + difference = seq_len - padding_mask.shape[-1] + padding_value = 1 - self.feature_extractor.padding_value + padding_mask = np.pad(padding_mask, ((0, 0), (0, difference)), "constant", constant_values=padding_value) + + audio_values = audio_values.tolist() + for i in range(bsz): + sliced_audio = np.asarray(audio_values[i])[ + padding_mask[i][None, :] != self.feature_extractor.padding_value + ] + audio_values[i] = sliced_audio.reshape(channels, -1) + + return audio_values + + +__all__ = ["MusicgenProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..51456aac76b0e0c5666acadf0d63008a7c3b50d9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_musicgen_melody import * + from .modeling_musicgen_melody import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/configuration_musicgen_melody.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/configuration_musicgen_melody.py new file mode 100644 index 0000000000000000000000000000000000000000..47eee8d7c85c1f89f2f6bbc3884b1438f0df4152 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/configuration_musicgen_melody.py @@ -0,0 +1,163 @@ +# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Musicgen Melody model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto.configuration_auto import AutoConfig + + +@auto_docstring(checkpoint="facebook/musicgen-melody") +@strict +class MusicgenMelodyDecoderConfig(PreTrainedConfig): + r""" + audio_channels (`int`, *optional*, defaults to 1): + Number of audio channels used by the model (either mono or stereo). Stereo models generate a separate + audio stream for the left/right output channels. Mono models generate a single audio stream output. + """ + + model_type = "musicgen_melody_decoder" + base_config_key = "decoder_config" + keys_to_ignore_at_inference = ["past_key_values"] + + vocab_size: int = 2048 + max_position_embeddings: int = 2048 + num_hidden_layers: int = 24 + ffn_dim: int = 4096 + num_attention_heads: int = 16 + layerdrop: float | int = 0.0 + use_cache: bool = True + activation_function: str = "gelu" + hidden_size: int = 1024 + dropout: float | int = 0.1 + attention_dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + initializer_factor: float = 0.02 + scale_embedding: bool = False + num_codebooks: int = 4 + audio_channels: int = 1 + pad_token_id: int | None = 2048 + bos_token_id: int | None = 2048 + eos_token_id: int | list[int] | None = None + tie_word_embeddings: bool = False + is_decoder: bool = False + add_cross_attention: bool = False + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.audio_channels not in [1, 2]: + raise ValueError(f"Expected 1 (mono) or 2 (stereo) audio channels, got {self.audio_channels} channels.") + + +@auto_docstring(checkpoint="facebook/musicgen-melody") +@strict +class MusicgenMelodyConfig(PreTrainedConfig): + r""" + text_encoder (`Union[dict, `PretrainedConfig`]`): + An instance of a configuration object that defines the text encoder config. + audio_encoder (`Union[dict, `PretrainedConfig`]`): + An instance of a configuration object that defines the audio encoder config. + decoder (`Union[dict, `PretrainedConfig`]`): + An instance of a configuration object that defines the decoder config. + num_chroma (`int`, *optional*, defaults to 12): + Number of chroma bins to use. + chroma_length (`int`, *optional*, defaults to 235): + Maximum chroma duration if audio is used to condition the model. Corresponds to the maximum duration used during training. + + Example: + + ```python + >>> from transformers import ( + ... MusicgenMelodyConfig, + ... MusicgenMelodyDecoderConfig, + ... T5Config, + ... EncodecConfig, + ... MusicgenMelodyForConditionalGeneration, + ... ) + + >>> # Initializing text encoder, audio encoder, and decoder model configurations + >>> text_encoder_config = T5Config() + >>> audio_encoder_config = EncodecConfig() + >>> decoder_config = MusicgenMelodyDecoderConfig() + + >>> configuration = MusicgenMelodyConfig( + ... text_encoder=text_encoder_config, audio_encoder=audio_encoder_config, decoder=decoder_config + ... ) + + >>> # Initializing a MusicgenMelodyForConditionalGeneration (with random weights) from the facebook/musicgen-melody style configuration + >>> model = MusicgenMelodyForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + >>> config_text_encoder = model.config.text_encoder + >>> config_audio_encoder = model.config.audio_encoder + >>> config_decoder = model.config.decoder + + >>> # Saving the model, including its configuration + >>> model.save_pretrained("musicgen_melody-model") + + >>> # loading model and config from pretrained folder + >>> musicgen_melody_config = MusicgenMelodyConfig.from_pretrained("musicgen_melody-model") + >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("musicgen_melody-model", config=musicgen_melody_config) + ```""" + + model_type = "musicgen_melody" + sub_configs = { + "text_encoder": AutoConfig, + "audio_encoder": AutoConfig, + "decoder": MusicgenMelodyDecoderConfig, + } + has_no_defaults_at_init = True + + text_encoder: dict | PreTrainedConfig = None + audio_encoder: dict | PreTrainedConfig = None + decoder: dict | PreTrainedConfig = None + num_chroma: int = 12 + chroma_length: int = 235 + initializer_factor: float = 0.02 + + def __post_init__(self, **kwargs): + if isinstance(self.text_encoder, dict): + text_encoder_model_type = self.text_encoder.pop("model_type") + self.text_encoder = AutoConfig.for_model(text_encoder_model_type, **self.text_encoder) + elif self.text_encoder is None: + raise ValueError( + f"A configuration of type {self.model_type} cannot be instantiated because text_encoder is not passed" + ) + + if isinstance(self.audio_encoder, dict): + audio_encoder_model_type = self.audio_encoder.pop("model_type") + self.audio_encoder = AutoConfig.for_model(audio_encoder_model_type, **self.audio_encoder) + elif self.audio_encoder is None: + raise ValueError( + f"A configuration of type {self.model_type} cannot be instantiated because audio_encoder is not passed" + ) + + if isinstance(self.decoder, dict): + self.decoder = MusicgenMelodyDecoderConfig(**self.decoder) + elif self.decoder is None: + self.decoder = MusicgenMelodyDecoderConfig() + + self.is_encoder_decoder = True + super().__post_init__(**kwargs) + + @property + # This is a property because you might want to change the codec model on the fly + def sampling_rate(self): + return self.audio_encoder.sampling_rate + + +__all__ = ["MusicgenMelodyConfig", "MusicgenMelodyDecoderConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py new file mode 100644 index 0000000000000000000000000000000000000000..1811fa11e630e2acb7d0065357493c89c74d54dc --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/feature_extraction_musicgen_melody.py @@ -0,0 +1,334 @@ +# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Feature extractor class for Musicgen Melody +""" + +import copy +from typing import Any + +import numpy as np + +from ...audio_utils import chroma_filter_bank +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...feature_extraction_utils import BatchFeature +from ...utils import TensorType, is_torch_available, is_torchaudio_available, logging +from ...utils.import_utils import requires + + +if is_torch_available(): + import torch + +if is_torchaudio_available(): + import torchaudio + +logger = logging.get_logger(__name__) + + +@requires(backends=("torchaudio",)) +class MusicgenMelodyFeatureExtractor(SequenceFeatureExtractor): + r""" + Constructs a MusicgenMelody feature extractor. + + This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains + most of the main methods. Users should refer to this superclass for more information regarding those methods. + + This class extracts chroma features from audio processed by [Demucs](https://github.com/adefossez/demucs/tree/main) or + directly from raw audio waveform. + + Args: + feature_size (`int`, *optional*, defaults to 12): + The feature dimension of the extracted features. + sampling_rate (`int`, *optional*, defaults to 32000): + The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). + hop_length (`int`, *optional*, defaults to 4096): + Length of the overlapping windows for the STFT used to obtain the Mel Frequency coefficients. + chunk_length (`int`, *optional*, defaults to 30): + The maximum number of chunks of `sampling_rate` samples used to trim and pad longer or shorter audio + sequences. + n_fft (`int`, *optional*, defaults to 16384): + Size of the Fourier transform. + num_chroma (`int`, *optional*, defaults to 12): + Number of chroma bins to use. + padding_value (`float`, *optional*, defaults to 0.0): + Padding value used to pad the audio. + return_attention_mask (`bool`, *optional*, defaults to `False`): + Whether to return the attention mask. Can be overwritten when calling the feature extractor. + + [What are attention masks?](../glossary#attention-mask) + + + + For Whisper models, `attention_mask` should always be passed for batched inference, to avoid subtle + bugs. + + + stem_indices (`list[int]`, *optional*, defaults to `[3, 2]`): + Stem channels to extract if demucs outputs are passed. + """ + + model_input_names = ["input_features"] + + def __init__( + self, + feature_size=12, + sampling_rate=32000, + hop_length=4096, + chunk_length=30, + n_fft=16384, + num_chroma=12, + padding_value=0.0, + return_attention_mask=False, # pad inputs to max length with silence token (zero) and no attention mask + stem_indices=[3, 2], + **kwargs, + ): + super().__init__( + feature_size=feature_size, + sampling_rate=sampling_rate, + padding_value=padding_value, + return_attention_mask=return_attention_mask, + **kwargs, + ) + self.n_fft = n_fft + self.hop_length = hop_length + self.chunk_length = chunk_length + self.n_samples = chunk_length * sampling_rate + self.sampling_rate = sampling_rate + self.chroma_filters = torch.from_numpy( + chroma_filter_bank(sampling_rate=sampling_rate, num_frequency_bins=n_fft, tuning=0, num_chroma=num_chroma) + ).float() + self.spectrogram = torchaudio.transforms.Spectrogram( + n_fft=n_fft, win_length=n_fft, hop_length=hop_length, power=2, center=True, pad=0, normalized=True + ) + self.stem_indices = stem_indices + + def _torch_extract_fbank_features(self, waveform: torch.Tensor) -> torch.Tensor: + """ + Compute the chroma spectrogram of the provided audio using the torchaudio spectrogram implementation and the librosa chroma features. + """ + + # if wav length is not long enough, pad it + wav_length = waveform.shape[-1] + if wav_length < self.n_fft: + pad = self.n_fft - wav_length + rest = 0 if pad % 2 == 0 else 1 + waveform = torch.nn.functional.pad(waveform, (pad // 2, pad // 2 + rest), "constant", 0) + + # squeeze alongside channel dimension + spec = self.spectrogram(waveform).squeeze(1) + + # sum along the frequency dimension + raw_chroma = torch.einsum("cf, ...ft->...ct", self.chroma_filters, spec) + + # normalise with max value + norm_chroma = torch.nn.functional.normalize(raw_chroma, p=float("inf"), dim=-2, eps=1e-6) + + # transpose time and chroma dimension -> (batch, time, chroma) + norm_chroma = norm_chroma.transpose(1, 2) + + # replace max value alongside chroma dimension with 1 and replace the rest with 0 + idx = norm_chroma.argmax(-1, keepdim=True) + norm_chroma[:] = 0 + norm_chroma.scatter_(dim=-1, index=idx, value=1) + + return norm_chroma + + def _extract_stem_indices(self, audio, sampling_rate=None): + """ + Extracts stems from the output of the [Demucs](https://github.com/adefossez/demucs/tree/main) audio separation model, + then converts to mono-channel and resample to the feature extractor sampling rate. + + Args: + audio (`torch.Tensor` of shape `(batch_size, num_stems, channel_size, audio_length)`): + The output of the Demucs model to be processed. + sampling_rate (`int`, *optional*): + Demucs sampling rate. If not specified, defaults to `44000`. + """ + sampling_rate = 44000 if sampling_rate is None else sampling_rate + + # extract "vocals" and "others" sources from audio encoder (demucs) output + # [batch_size, num_stems, channel_size, audio_length] + wav = audio[:, torch.tensor(self.stem_indices)] + + # merge extracted stems to single waveform + wav = wav.sum(1) + + # convert to mono-channel waveform + wav = wav.mean(dim=1, keepdim=True) + + # resample to model sampling rate + # not equivalent to julius.resample + if sampling_rate != self.sampling_rate: + wav = torchaudio.functional.resample( + wav, sampling_rate, self.sampling_rate, rolloff=0.945, lowpass_filter_width=24 + ) + + # [batch_size, 1, audio_length] -> [batch_size, audio_length] + wav = wav.squeeze(1) + + return wav + + def __call__( + self, + audio: np.ndarray | list[float] | list[np.ndarray] | list[list[float]], + truncation: bool = True, + pad_to_multiple_of: int | None = None, + return_tensors: str | TensorType | None = None, + return_attention_mask: bool | None = None, + padding: str | None = True, + max_length: int | None = None, + sampling_rate: int | None = None, + **kwargs, + ) -> BatchFeature: + """ + Main method to featurize and prepare for the model one or several sequence(s). + + Args: + audio (`torch.Tensor`, `np.ndarray`, `list[float]`, `list[np.ndarray]`, `list[torch.Tensor]`, `list[list[float]]`): + The sequence or batch of sequences to be padded. Each sequence can be a torch tensor, a numpy array, a list of float + values, a list of numpy arrays, a list of torch tensors, or a list of list of float values. + If `audio` is the output of Demucs, it has to be a torch tensor of shape `(batch_size, num_stems, channel_size, audio_length)`. + Otherwise, it must be mono or stereo channel audio. + truncation (`bool`, *optional*, default to `True`): + Activates truncation to cut input sequences longer than *max_length* to *max_length*. + pad_to_multiple_of (`int`, *optional*, defaults to None): + If set will pad the sequence to a multiple of the provided value. + + This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability + `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific feature_extractor's default. + + [What are attention masks?](../glossary#attention-mask) + + + For Musicgen Melody models, audio `attention_mask` is not necessary. + + + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding + index) among: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + sampling_rate (`int`, *optional*): + The sampling rate at which the `audio` input was sampled. It is strongly recommended to pass + `sampling_rate` at the forward call to prevent silent errors. + Note that if `audio` is the output of Demucs, `sampling_rate` must be the sampling rate at which Demucs operates. + """ + + if sampling_rate is None: + logger.warning_once( + f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. " + "Failing to do so can result in silent errors that might be hard to debug." + ) + + if isinstance(audio, torch.Tensor) and len(audio.shape) == 4: + logger.warning_once( + "`audio` is a 4-dimensional torch tensor and has thus been recognized as the output of `Demucs`. " + "If this is not the case, make sure to read Musicgen Melody docstrings and " + "to correct `audio` to get the right behaviour." + "Link to the docstrings: https://huggingface.co/docs/transformers/main/en/model_doc/musicgen_melody" + ) + audio = self._extract_stem_indices(audio, sampling_rate=sampling_rate) + elif sampling_rate is not None and sampling_rate != self.sampling_rate: + audio = torchaudio.functional.resample( + audio, sampling_rate, self.sampling_rate, rolloff=0.945, lowpass_filter_width=24 + ) + + is_batched = isinstance(audio, (np.ndarray, torch.Tensor)) and len(audio.shape) > 1 + is_batched = is_batched or ( + isinstance(audio, (list, tuple)) and (isinstance(audio[0], (torch.Tensor, np.ndarray, tuple, list))) + ) + + if is_batched and not isinstance(audio[0], torch.Tensor): + audio = [torch.tensor(speech, dtype=torch.float32).unsqueeze(-1) for speech in audio] + elif is_batched: + audio = [speech.unsqueeze(-1) for speech in audio] + elif not is_batched and not isinstance(audio, torch.Tensor): + audio = torch.tensor(audio, dtype=torch.float32).unsqueeze(-1) + + if isinstance(audio[0], torch.Tensor) and audio[0].dtype is torch.float64: + audio = [speech.to(torch.float32) for speech in audio] + + # always return batch + if not is_batched: + audio = [audio] + + if len(audio[0].shape) == 3: + logger.warning_once( + "`audio` has been detected as a batch of stereo signals. Will be convert to mono signals. " + "If this is an undesired behaviour, make sure to read Musicgen Melody docstrings and " + "to correct `audio` to get the right behaviour." + "Link to the docstrings: https://huggingface.co/docs/transformers/main/en/model_doc/musicgen_melody" + ) + # convert to mono-channel waveform + audio = [stereo.mean(dim=0) for stereo in audio] + + batched_speech = BatchFeature({"input_features": audio}) + + padded_inputs = self.pad( + batched_speech, + padding=padding, + max_length=max_length if max_length else self.n_samples, + truncation=truncation, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + return_tensors="pt", + ) + + input_features = self._torch_extract_fbank_features(padded_inputs["input_features"].squeeze(-1)) + + padded_inputs["input_features"] = input_features + + if return_attention_mask: + # rescale from raw audio length to spectrogram length + padded_inputs["attention_mask"] = padded_inputs["attention_mask"][:, :: self.hop_length] + + if return_tensors is not None: + padded_inputs = padded_inputs.convert_to_tensors(return_tensors) + + return padded_inputs + + def to_dict(self) -> dict[str, Any]: + """ + Serializes this instance to a Python dictionary. Returns: + `dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance. + """ + output = copy.deepcopy(self.__dict__) + output["feature_extractor_type"] = self.__class__.__name__ + if "mel_filters" in output: + del output["mel_filters"] + if "window" in output: + del output["window"] + if "chroma_filters" in output: + del output["chroma_filters"] + if "spectrogram" in output: + del output["spectrogram"] + return output + + +__all__ = ["MusicgenMelodyFeatureExtractor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/modeling_musicgen_melody.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/modeling_musicgen_melody.py new file mode 100644 index 0000000000000000000000000000000000000000..a3e3f5c3251c39f97b0dc445cfbc4f024248cc95 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/modeling_musicgen_melody.py @@ -0,0 +1,2093 @@ +# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Musicgen Melody model.""" + +import inspect +import math +import random +from collections.abc import Callable +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Optional + +import torch +import torch.nn as nn +from torch.nn import CrossEntropyLoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import ( + ClassifierFreeGuidanceLogitsProcessor, + GenerationConfig, + GenerationMixin, + GenerationMode, + LogitsProcessorList, + StoppingCriteriaList, +) +from ...masking_utils import create_causal_mask +from ...modeling_flash_attention_utils import ( + FlashAttentionKwargs, +) +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from ..auto.configuration_auto import AutoConfig +from ..auto.modeling_auto import AutoModel, AutoModelForTextEncoding +from .configuration_musicgen_melody import MusicgenMelodyConfig, MusicgenMelodyDecoderConfig + + +if TYPE_CHECKING: + from ...generation.streamers import BaseStreamer + +logger = logging.get_logger(__name__) + + +@auto_docstring( + custom_intro=""" + Base class for Musicgen Melody autoregressive outputs. + """ +) +@dataclass +class MusicgenMelodyOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of conditional hidden-states representing the concatenation of the projected text encoder output and the projected audio encoder output. + Used as a conditional signal. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + encoder_hidden_states: torch.FloatTensor | None = None + + +# Copied from transformers.models.musicgen.modeling_musicgen.shift_tokens_right +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + # transpose to get (bsz, num_codebooks, seq_len) + input_ids = input_ids.transpose(1, 2) + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() + if decoder_start_token_id is None: + raise ValueError("Make sure to set the decoder_start_token_id attribute of the model's configuration.") + shifted_input_ids[..., 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("Make sure to set the pad_token_id attribute of the model's configuration.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenSinusoidalPositionalEmbedding with Musicgen->MusicgenMelody +class MusicgenMelodySinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length.""" + + def __init__(self, num_positions: int, embedding_dim: int): + super().__init__() + self.embedding_dim = embedding_dim + self.num_positions = num_positions + self.make_weights(num_positions, embedding_dim) + + def make_weights(self, num_embeddings: int, embedding_dim: int): + emb_weights = self.get_embedding(num_embeddings, embedding_dim) + if hasattr(self, "weights"): + # in forward put the weights on the correct dtype and device of the param + emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device) + + self.register_buffer("weights", emb_weights, persistent=False) + + @staticmethod + def get_embedding(num_embeddings: int, embedding_dim: int): + """ + Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the + description in Section 3.5 of "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb) + emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.cos(emb), torch.sin(emb)], dim=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + return emb.to(torch.get_default_dtype()) + + @torch.no_grad() + # Ignore copy + def forward(self, inputs_embeds: torch.Tensor, past_key_values_length: int = 0): + bsz, seq_len, _ = inputs_embeds.size() + # Create the position ids from the input token ids. + position_ids = (torch.arange(seq_len) + past_key_values_length).to(inputs_embeds.device) + # expand embeddings if needed + if seq_len > self.weights.size(0): + self.make_weights(seq_len, self.embedding_dim) + return self.weights.index_select(0, position_ids.view(-1)).detach() + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenAttention with Musicgen->MusicgenMelody +class MusicgenMelodyAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float | None = 0.0, + is_decoder: bool | None = False, + bias: bool | None = True, + is_causal: bool | None = False, + config: MusicgenMelodyConfig | None = None, + layer_idx: int | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = False, + # TODO: we need a refactor so that the different attention modules can get their specific kwargs + # ATM, we have mixed things encoder, decoder, and encoder-decoder attn + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + + # get query proj + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_layer from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = self.k_proj(current_states).view(kv_shape).transpose(1, 2) + value_states = self.v_proj(current_states).view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + # save all key/value_states to cache to be re-used for fast auto-regressive generation + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + output_attentions=output_attentions, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class MusicgenMelodyDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: MusicgenMelodyDecoderConfig, layer_idx=None): + super().__init__() + self.embed_dim = config.hidden_size + + self.self_attn = MusicgenMelodyAttention( + embed_dim=self.embed_dim, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + bias=False, + is_causal=True, + config=config, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=False) + self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=False) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = True, + **kwargs, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + past_key_values (`Cache`): cached past key and value projection states + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + return hidden_states, self_attn_weights + + +@auto_docstring +# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenPreTrainedModel with Musicgen->MusicgenMelody +class MusicgenMelodyPreTrainedModel(PreTrainedModel): + config: MusicgenMelodyDecoderConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["MusicgenMelodyDecoderLayer", "MusicgenMelodyAttention"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module): + std = self.config.initializer_factor + if isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=std) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + init.ones_(module.weight) + init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=std) + # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag + if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): + init.zeros_(module.weight[module.padding_idx]) + elif isinstance(module, MusicgenMelodySinusoidalPositionalEmbedding): + emb_weights = module.get_embedding(module.num_positions, module.embedding_dim) + init.copy_(module.weights, emb_weights) + + +# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenDecoder with MUSICGEN->MUSICGEN_MELODY,Musicgen->MusicgenMelody +class MusicgenMelodyDecoder(MusicgenMelodyPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MusicgenMelodyDecoderLayer`] + """ + + _can_record_outputs = { + "hidden_states": MusicgenMelodyDecoderLayer, + "attentions": OutputRecorder(MusicgenMelodyAttention, index=1, layer_name="self_attn"), + "cross_attentions": OutputRecorder(MusicgenMelodyAttention, index=1, layer_name="encoder_attn"), + } + + def __init__(self, config: MusicgenMelodyDecoderConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.layerdrop + self.max_target_positions = config.max_position_embeddings + self.d_model = config.hidden_size + self.num_codebooks = config.num_codebooks + self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 + + embed_dim = config.vocab_size + 1 + self.embed_tokens = nn.ModuleList( + [nn.Embedding(embed_dim, config.hidden_size) for _ in range(config.num_codebooks)] + ) + + self.embed_positions = MusicgenMelodySinusoidalPositionalEmbedding( + config.max_position_embeddings, + config.hidden_size, + ) + + self.layers = nn.ModuleList( + [MusicgenMelodyDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)] + ) + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.attn_implementation = config._attn_implementation + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + # Ignore copy + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPast: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`): + Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes. + + Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes, + such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details. + + [What are input IDs?](../glossary#input-ids) + + + + The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks, + target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If + you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of + frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks, + target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as + `input_ids`. + + + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states representing the concatenation of the text encoder output and the processed audio encoder output. + Used as a conditional signal and will thus be concatenated to the projected `decoder_input_ids`. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing attention on conditional hidden states. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + """ + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + # (bsz * codebooks, seq_len) -> (bsz, codebooks, seq_len) + input = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1]) + bsz, num_codebooks, seq_len = input.shape + elif inputs_embeds is not None: + input = inputs_embeds[:, :, -1:] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + if inputs_embeds is None: + inputs_embeds = sum(self.embed_tokens[codebook](input[:, codebook]) for codebook in range(num_codebooks)) + + if encoder_hidden_states is not None: + # take care of attention masks + if encoder_attention_mask is not None and attention_mask is None: + attention_mask = torch.ones(inputs_embeds.shape[:2], device=inputs_embeds.device) + + if attention_mask is not None: + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_states.shape[:2], device=attention_mask.device) + attention_mask = torch.cat([encoder_attention_mask, attention_mask], dim=1) + + # fuse encoder_hidden_states and inputs_embeds + inputs_embeds = torch.cat([encoder_hidden_states, inputs_embeds], dim=1) + + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + + # embed positions + positions = self.embed_positions(inputs_embeds, past_key_values_length) + hidden_states = inputs_embeds + positions.to(inputs_embeds.device) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # decoder layers + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + dropout_probability = random.uniform(0, 1) + if self.training and (dropout_probability < self.layerdrop): + continue + + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + hidden_states = layer_outputs[0] + + hidden_states = self.layer_norm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenModel with MUSICGEN->MUSICGEN_MELODY,Musicgen->MusicgenMelody +class MusicgenMelodyModel(MusicgenMelodyPreTrainedModel): + def __init__(self, config: MusicgenMelodyDecoderConfig): + super().__init__(config) + self.decoder = MusicgenMelodyDecoder(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.decoder.embed_tokens = value + + @auto_docstring + # Ignore copy + @capture_outputs + @merge_with_config_defaults + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPast: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`): + Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes. + + Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes, + such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details. + + [What are input IDs?](../glossary#input-ids) + + + + The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks, + target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If + you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of + frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks, + target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as + `input_ids`. + + + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states representing the concatenation of the text encoder output and the processed audio encoder output. + Used as a conditional signal and will thus be concatenated to the projected `decoder_input_ids`. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing attention on conditional hidden states. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + """ + decoder_outputs: BaseModelOutputWithPast = self.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return BaseModelOutputWithPast( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + hidden_states=decoder_outputs.hidden_states, + attentions=decoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The Musicgen Melody decoder model with a language modelling head on top. + """ +) +# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForCausalLM with MUSICGEN->MUSICGEN_MELODY,Musicgen->MusicgenMelody,MusicGen->Musicgen Melody +class MusicgenMelodyForCausalLM(MusicgenMelodyPreTrainedModel, GenerationMixin): + output_modalities = ("audio",) + + def __init__(self, config: MusicgenMelodyDecoderConfig): + super().__init__(config) + + self.model = MusicgenMelodyModel(config) + + self.num_codebooks = config.num_codebooks + self.lm_heads = nn.ModuleList( + [nn.Linear(config.hidden_size, config.vocab_size, bias=False) for _ in range(config.num_codebooks)] + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_heads + + def set_output_embeddings(self, new_embeddings): + self.lm_heads = new_embeddings + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + # Ignore copy + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | MusicgenMelodyOutputWithPast: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, sequence_length)`): + Indices of input sequence tokens in the vocabulary, corresponding to the sequence of audio codes. + + Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes, + such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details. + + [What are input IDs?](../glossary#input-ids) + + + + The `input_ids` will automatically be converted from shape `(batch_size * num_codebooks, + target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If + you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of + frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks, + target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as + `input_ids`. + + + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states representing the concatenation of the text encoder output and the processed audio encoder output. + Used as a conditional signal and will thus be concatenated to the projected `decoder_input_ids`. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing attention on conditional hidden states. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + + if (labels is not None) and (input_ids is None and inputs_embeds is None): + input_ids = shift_tokens_right(labels, self.config.pad_token_id, self.config.bos_token_id) + + outputs: BaseModelOutputWithPast = self.model( + input_ids, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + + lm_logits = torch.stack([head(hidden_states) for head in self.lm_heads], dim=1) + + loss = None + if labels is not None: + # since encoder hidden states have been concatenated to the decoder hidden states, + # we take the last timestamps corresponding to labels + logits = lm_logits[:, :, -labels.shape[1] :] + + loss_fct = CrossEntropyLoss() + loss = torch.zeros([], device=self.device) + + # per codebook cross-entropy + # ref: https://github.com/facebookresearch/audiocraft/blob/69fea8b290ad1b4b40d28f92d1dfc0ab01dbab85/audiocraft/solvers/musicgen.py#L242-L243 + # -100 labels are ignored + labels = labels.masked_fill(labels == self.config.pad_token_id, -100) + + # per codebook cross-entropy + for codebook in range(self.config.num_codebooks): + codebook_logits = logits[:, codebook].contiguous().view(-1, logits.shape[-1]) + codebook_labels = labels[..., codebook].contiguous().view(-1) + loss += loss_fct(codebook_logits, codebook_labels) + + loss = loss / self.config.num_codebooks + + # (bsz, num_codebooks, seq_len, vocab_size) -> (bsz * num_codebooks, seq_len, vocab_size) + lm_logits = lm_logits.reshape(-1, *lm_logits.shape[2:]) + + return MusicgenMelodyOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + # Ignore copy + def prepare_inputs_for_generation( + self, + input_ids, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=True, + delay_pattern_mask=None, + guidance_scale=None, + **kwargs, + ): + # Overwritten -- MusicGen has custom processing + if delay_pattern_mask is None: + input_ids, delay_pattern_mask = self.build_delay_pattern_mask( + input_ids, + pad_token_id=self.generation_config.pad_token_id, + max_length=self.generation_config.max_length, + ) + + # apply the delay pattern mask + input_ids = self.apply_delay_pattern_mask(input_ids, delay_pattern_mask) + + if guidance_scale is not None and guidance_scale > 1: + # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these + # before sampling) + input_ids = input_ids.repeat((2, 1)) + if attention_mask is not None: + attention_mask = attention_mask.repeat((2, 1)) + + if encoder_hidden_states is not None: + encoder_hidden_states = torch.concatenate( + [encoder_hidden_states, torch.zeros_like(encoder_hidden_states)], dim=0 + ) + + if encoder_attention_mask is not None: + encoder_attention_mask = torch.concatenate( + encoder_attention_mask, torch.zeros_like(encoder_attention_mask), dim=0 + ) + + if past_key_values is not None: + input_ids = input_ids[:, -1:] + + # we only want to use conditional signal in the 1st generation step but keeping the attention mask + encoder_hidden_states = None + + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "encoder_hidden_states": encoder_hidden_states, + "encoder_attention_mask": encoder_attention_mask, + "past_key_values": past_key_values, + "use_cache": use_cache, + } + + def build_delay_pattern_mask(self, input_ids: torch.LongTensor, pad_token_id: int, max_length: int | None = None): + """Build a delayed pattern mask to the input_ids. Each codebook is offset by the previous codebook by + one, giving a delayed pattern mask at the start of sequence and end of sequence. Take the example where there + are 4 codebooks and a max sequence length of 8, we have the delayed pattern mask of shape `(codebooks, + seq_len)`: + - [P, -1, -1, -1, -1, P, P, P] + - [P, P, -1, -1, -1, -1, P, P] + - [P, P, P, -1, -1, -1, -1, P] + - [P, P, P, P, -1, -1, -1, -1] + where P is the special padding token id and -1 indicates that the token is valid for prediction. If we include + a prompt (decoder input ids), the -1 positions indicate where new tokens should be predicted. Otherwise, the + mask is set to the value in the prompt: + - [P, a, b, -1, -1, P, P, P] + - [P, P, c, d, -1, -1, P, P] + - [P, P, P, e, f, -1, -1, P] + - [P, P, P, P, g, h, -1, -1] + where a-h indicate the input prompt (decoder input ids) that are offset by 1. Now, we only override the -1 + tokens in our prediction. + """ + # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len) + input_ids = input_ids.reshape(-1, self.num_codebooks, input_ids.shape[-1]) + bsz, num_codebooks, seq_len = input_ids.shape + + max_length = max_length if max_length is not None else self.generation_config.max_length + input_ids_shifted = ( + torch.ones((bsz, num_codebooks, max_length), dtype=torch.long, device=input_ids.device) * -1 + ) + + channel_codebooks = num_codebooks // 2 if self.config.audio_channels == 2 else num_codebooks + # we only apply the mask if we have a large enough seq len - otherwise we return as is + if max_length < 2 * channel_codebooks - 1: + return input_ids.reshape(bsz * num_codebooks, -1), input_ids_shifted.reshape(bsz * num_codebooks, -1) + + # fill the shifted ids with the prompt entries, offset by the codebook idx + for codebook in range(channel_codebooks): + if self.config.audio_channels == 1: + # mono channel - loop over the codebooks one-by-one + input_ids_shifted[:, codebook, codebook : seq_len + codebook] = input_ids[:, codebook] + else: + # left/right channels are interleaved in the generated codebooks, so handle one then the other + input_ids_shifted[:, 2 * codebook, codebook : seq_len + codebook] = input_ids[:, 2 * codebook] + input_ids_shifted[:, 2 * codebook + 1, codebook : seq_len + codebook] = input_ids[:, 2 * codebook + 1] + + # construct a pattern mask that indicates the positions of padding tokens for each codebook + # first fill the upper triangular part (the EOS padding) + delay_pattern = torch.triu( + torch.ones((channel_codebooks, max_length), dtype=torch.bool), diagonal=max_length - channel_codebooks + 1 + ) + # then fill the lower triangular part (the BOS padding) + delay_pattern = delay_pattern + torch.tril(torch.ones((channel_codebooks, max_length), dtype=torch.bool)) + + if self.config.audio_channels == 2: + # for left/right channel we need to duplicate every row of the pattern mask in an interleaved fashion + delay_pattern = delay_pattern.repeat_interleave(2, dim=0) + + mask = ~delay_pattern.to(input_ids.device) + input_ids = mask * input_ids_shifted + ~mask * pad_token_id + + # find the first position to start generating - this is the first place we have the -1 token + # and will always be in the first codebook (since it has no codebook offset) + first_codebook_ids = input_ids[:, 0, :] + start_ids = (first_codebook_ids == -1).nonzero()[:, 1] + if len(start_ids) > 0: + first_start_id = min(start_ids) + else: + # we have no tokens that need to be filled - return entire matrix of input ids + first_start_id = seq_len + + # (bsz * num_codebooks, seq_len) -> (bsz, num_codebooks, seq_len) + pattern_mask = input_ids.reshape(bsz * num_codebooks, -1) + input_ids = input_ids[..., :first_start_id].reshape(bsz * num_codebooks, -1) + return input_ids, pattern_mask + + @staticmethod + def apply_delay_pattern_mask(input_ids, decoder_pad_token_mask): + """Apply a delay pattern mask to the decoder input ids, only preserving predictions where + the mask is set to -1, and otherwise setting to the value detailed in the mask.""" + seq_len = input_ids.shape[-1] + decoder_pad_token_mask = decoder_pad_token_mask[..., :seq_len] + input_ids = torch.where(decoder_pad_token_mask == -1, input_ids, decoder_pad_token_mask) + return input_ids + + @torch.no_grad() + # Ignore copy + def generate( + self, + inputs: torch.Tensor | None = None, + generation_config: GenerationConfig | None = None, + logits_processor: LogitsProcessorList | None = None, + stopping_criteria: StoppingCriteriaList | None = None, + synced_gpus: bool | None = None, + streamer: Optional["BaseStreamer"] = None, + **kwargs, + ): + """ + + Generates sequences of token ids for models with a language modeling head. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Parameters: + inputs (`torch.Tensor` of varying shape depending on the modality, *optional*): + The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the + method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs` + should be in the format `input_ids`. For encoder-decoder models *inputs* can represent any of + `input_ids`, `input_values`, `input_features`, or `pixel_values`. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + logits_processor (`LogitsProcessorList`, *optional*): + Custom logits processors that complement the default logits processors built from arguments and + generation config. If a logit processor is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + stopping_criteria (`StoppingCriteriaList`, *optional*): + Custom stopping criteria that complement the default stopping criteria built from arguments and a + generation config. If a stopping criteria is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + synced_gpus (`bool`, *optional*, defaults to `False`): + Whether to continue running the while loop until max_length (needed to avoid deadlocking with + `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). + streamer (`BaseStreamer`, *optional*): + Streamer object that will be used to stream the generated sequences. Generated tokens are passed + through `streamer.put(token_ids)` and the streamer is responsible for any further processing. + kwargs (`dict[str, Any]`, *optional*): + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder + specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. + + Return: + [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` + or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. + + If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible + [`~utils.ModelOutput`] types are: + + - [`~generation.GenerateDecoderOnlyOutput`], + - [`~generation.GenerateBeamDecoderOnlyOutput`] + + If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible + [`~utils.ModelOutput`] types are: + + - [`~generation.GenerateEncoderDecoderOutput`], + - [`~generation.GenerateBeamEncoderDecoderOutput`] + """ + # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects + generation_config, model_kwargs = self._prepare_generation_config(generation_config, **kwargs) + self._validate_model_kwargs(model_kwargs.copy()) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + requires_attention_mask = "encoder_outputs" not in model_kwargs + kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None + + # 3. Define model inputs` + input_ids, model_input_name, model_kwargs = self._prepare_model_inputs( + inputs, generation_config.bos_token_id, model_kwargs + ) + batch_size = input_ids.shape[0] // self.num_codebooks + self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=input_ids.device) + + # 4. Define other model kwargs + model_kwargs["use_cache"] = generation_config.use_cache + model_kwargs["guidance_scale"] = generation_config.guidance_scale + + if model_kwargs.get("attention_mask", None) is None and requires_attention_mask: + model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation( + input_ids, generation_config, model_kwargs + ) + + # 5. Prepare `max_length` depending on other stopping criteria. + input_ids_length = input_ids.shape[-1] + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None + generation_config = self._prepare_generated_length( + generation_config=generation_config, + has_default_max_length=has_default_max_length, + has_default_min_length=has_default_min_length, + model_input_name=model_input_name, + inputs_tensor=input_ids, + input_ids_length=input_ids_length, + ) + + # 6. Prepare `input_ids` which will be used for auto-regressive generation + # Build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to Musicgen) + input_ids, delay_pattern_mask = self.build_delay_pattern_mask( + input_ids, + pad_token_id=generation_config._decoder_start_token_tensor, + max_length=generation_config.max_length, + ) + + if streamer is not None: + streamer.put(input_ids.cpu()) + + # stash the delay mask so that we don't have to recompute it in each forward pass + model_kwargs["delay_pattern_mask"] = delay_pattern_mask + + # 7. determine generation mode + generation_mode = generation_config.get_generation_mode() + + # 8. prepare batched CFG externally (to enable coexistence with the unbatched CFG) + if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1: + logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale)) + generation_config.guidance_scale = None + + # 9. prepare distribution pre_processing samplers + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_length, + encoder_input_ids=input_ids, + prefix_allowed_tokens_fn=None, + logits_processor=logits_processor, + device=input_ids.device, + ) + + # 10. prepare stopping criteria + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + + if generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH): + # expand input_ids with `num_return_sequences` additional sequences per batch + input_ids, model_kwargs = self._expand_inputs_for_generation( + input_ids=input_ids, + expand_size=generation_config.num_return_sequences, + **model_kwargs, + ) + + # 11. run sample + outputs = self._sample( + input_ids, + logits_processor=logits_processor, + stopping_criteria=stopping_criteria, + generation_config=generation_config, + synced_gpus=synced_gpus, + streamer=streamer, + **model_kwargs, + ) + + else: + raise ValueError( + "Got incompatible mode for generation, should be one of greedy or sampling. " + "Ensure that beam search is de-activated by setting `num_beams=1`." + ) + + if generation_config.return_dict_in_generate: + output_ids = outputs.sequences + else: + output_ids = outputs + + # apply the pattern mask to the final ids + output_ids = self.apply_delay_pattern_mask(output_ids, model_kwargs["delay_pattern_mask"]) + + # revert the pattern delay mask by filtering the pad token id + output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape( + batch_size, self.num_codebooks, -1 + ) + + if generation_config.return_dict_in_generate: + outputs.sequences = output_ids + return outputs + else: + return output_ids + + +@auto_docstring +class MusicgenMelodyForConditionalGeneration(PreTrainedModel, GenerationMixin): + config: MusicgenMelodyConfig + main_input_name = "input_ids" + output_modalities = ("audio",) + supports_gradient_checkpointing = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + def __init__( + self, + config: MusicgenMelodyConfig = None, + text_encoder: PreTrainedModel | None = None, + audio_encoder: PreTrainedModel | None = None, + decoder: MusicgenMelodyForCausalLM | None = None, + ): + r""" + text_encoder (`PreTrainedModel`, *optional*): + The text encoder model that encodes text into hidden states for conditioning. + audio_encoder (`PreTrainedModel`, *optional*): + The audio encoder model that encodes audio into hidden states for conditioning. + decoder (`MusicgenMelodyForCausalLM`, *optional*): + The decoder model that generates audio tokens based on conditioning signals. + """ + if config is None and None in (text_encoder, audio_encoder, decoder): + raise ValueError( + "Either a configuration has to be provided, or all three of text encoder, audio encoder and Musicgen Melody decoder." + ) + if config is None: + config = MusicgenMelodyConfig( + text_encoder=text_encoder.config, audio_encoder=audio_encoder.config, decoder=decoder.config + ) + else: + if not isinstance(config, self.config_class): + raise ValueError(f"Config: {config} has to be of type {self.config_class}") + + # initialize with config + super().__init__(config) + + if text_encoder is None: + text_encoder = AutoModelForTextEncoding.from_config(config.text_encoder) + + if audio_encoder is None: + audio_encoder = AutoModel.from_config(config.audio_encoder) + + if decoder is None: + decoder = MusicgenMelodyForCausalLM._from_config(config.decoder) + + self.text_encoder = text_encoder + self.audio_encoder = audio_encoder + self.decoder = decoder + + # make sure that the individual model's config refers to the shared config + # so that the updates to the config will be synced + self.config.text_encoder._attn_implementation = self.text_encoder.config._attn_implementation + self.config.audio_encoder._attn_implementation = self.audio_encoder.config._attn_implementation + self.config.decoder._attn_implementation = self.decoder.config._attn_implementation + self.text_encoder.config = self.config.text_encoder + self.audio_encoder.config = self.config.audio_encoder + self.decoder.config = self.config.decoder + + # text encoder outputs might need to be projected to different dimension for decoder + if self.text_encoder.config.hidden_size != self.decoder.config.hidden_size: + self.enc_to_dec_proj = nn.Linear(self.text_encoder.config.hidden_size, self.decoder.config.hidden_size) + + # audio encoder outputs after chroma extraction might need to be projected to different dimension for decoder + if self.config.num_chroma != self.decoder.config.hidden_size: + self.audio_enc_to_dec_proj = nn.Linear(self.config.num_chroma, self.decoder.config.hidden_size) + + if self.text_encoder.get_output_embeddings() is not None: + raise ValueError( + f"The encoder {self.text_encoder} should not have a LM Head. Please use a model without and LM Head" + ) + + # Initialize projection layers weights and tie text encoder and decoder weights if set accordingly + self.post_init() + + @torch.no_grad() + def _init_weights(self, module): + # MusicgenMelodyForConditionalGeneration is made of PreTrainedModels that have already been initialized + # Projection layers still need to be initialized. + std = self.decoder.config.initializer_factor + if isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=std) + if module.bias is not None: + init.zeros_(module.bias) + + def get_input_embeddings(self): + return self.text_encoder.get_input_embeddings() + + def get_output_embeddings(self): + return self.decoder.get_output_embeddings() + + def set_output_embeddings(self, new_embeddings): + return self.decoder.set_output_embeddings(new_embeddings) + + @classmethod + # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForConditionalGeneration.from_sub_models_pretrained with Musicgen->MusicgenMelody, musicgen-small->musicgen-melody + def from_sub_models_pretrained( + cls, + text_encoder_pretrained_model_name_or_path: str | None = None, + audio_encoder_pretrained_model_name_or_path: str | None = None, + decoder_pretrained_model_name_or_path: str | None = None, + *model_args, + **kwargs, + ) -> PreTrainedModel: + r""" + Instantiate a text encoder, an audio encoder, and a MusicGen decoder from one, two or three base classes of the + library from pretrained model checkpoints. + + + The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train + the model, you need to first set it back in training mode with `model.train()`. + + Params: + text_encoder_pretrained_model_name_or_path (`str`, *optional*): + Information necessary to initiate the text encoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + + audio_encoder_pretrained_model_name_or_path (`str`, *optional*): + Information necessary to initiate the audio encoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + + decoder_pretrained_model_name_or_path (`str`, *optional*, defaults to `None`): + Information necessary to initiate the decoder. Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + - A path to a *directory* containing model weights saved using + [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`. + + model_args (remaining positional arguments, *optional*): + All remaining positional arguments will be passed to the underlying model's `__init__` method. + + kwargs (remaining dictionary of keyword arguments, *optional*): + Can be used to update the configuration object (after it being loaded) and initiate the model (e.g., + `output_attentions=True`). + + - To update the text encoder configuration, use the prefix *text_encoder_* for each configuration + parameter. + - To update the audio encoder configuration, use the prefix *audio_encoder_* for each configuration + parameter. + - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter. + - To update the parent model configuration, do not use a prefix for each configuration parameter. + + Behaves differently depending on whether a `config` is provided or automatically loaded. + + Example: + + ```python + >>> from transformers import MusicgenMelodyForConditionalGeneration + + >>> # initialize a musicgen model from a t5 text encoder, encodec audio encoder, and musicgen decoder + >>> model = MusicgenMelodyForConditionalGeneration.from_sub_models_pretrained( + ... text_encoder_pretrained_model_name_or_path="google-t5/t5-base", + ... audio_encoder_pretrained_model_name_or_path="facebook/encodec_24khz", + ... decoder_pretrained_model_name_or_path="facebook/musicgen-melody", + ... ) + >>> # saving model after fine-tuning + >>> model.save_pretrained("./musicgen-ft") + >>> # load fine-tuned model + >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("./musicgen-ft") + ```""" + + kwargs_text_encoder = { + argument[len("text_encoder_") :]: value + for argument, value in kwargs.items() + if argument.startswith("text_encoder_") + } + + kwargs_audio_encoder = { + argument[len("audio_encoder_") :]: value + for argument, value in kwargs.items() + if argument.startswith("audio_encoder_") + } + + kwargs_decoder = { + argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") + } + + # remove text encoder, audio encoder and decoder kwargs from kwargs + for key in kwargs_text_encoder: + del kwargs["text_encoder_" + key] + for key in kwargs_audio_encoder: + del kwargs["audio_encoder_" + key] + for key in kwargs_decoder: + del kwargs["decoder_" + key] + + # Load and initialize the encoder and decoder + # The distinction between encoder and decoder at the model level is made + # by the value of the flag `is_decoder` that we need to set correctly. + text_encoder = kwargs_text_encoder.pop("model", None) + if text_encoder is None: + if text_encoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `text_encoder_model` is not defined as an argument, a `text_encoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_text_encoder: + encoder_config, kwargs_text_encoder = AutoConfig.from_pretrained( + text_encoder_pretrained_model_name_or_path, **kwargs_text_encoder, return_unused_kwargs=True + ) + + if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: + logger.info( + f"Initializing {text_encoder_pretrained_model_name_or_path} as a text_encoder model " + "from a decoder model. Cross-attention and causal mask are disabled." + ) + encoder_config.is_decoder = False + encoder_config.add_cross_attention = False + + kwargs_text_encoder["config"] = encoder_config + + text_encoder = AutoModel.from_pretrained( + text_encoder_pretrained_model_name_or_path, *model_args, **kwargs_text_encoder + ) + + audio_encoder = kwargs_audio_encoder.pop("model", None) + if audio_encoder is None: + if audio_encoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `audio_encoder_model` is not defined as an argument, an `audio_encoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_audio_encoder: + encoder_config, kwargs_audio_encoder = AutoConfig.from_pretrained( + audio_encoder_pretrained_model_name_or_path, **kwargs_audio_encoder, return_unused_kwargs=True + ) + + if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True: + logger.info( + f"Initializing {audio_encoder_pretrained_model_name_or_path} as an audio_encoder model " + "from a decoder model. Cross-attention and causal mask are disabled." + ) + encoder_config.is_decoder = False + encoder_config.add_cross_attention = False + + kwargs_audio_encoder["config"] = encoder_config + + audio_encoder = AutoModel.from_pretrained( + audio_encoder_pretrained_model_name_or_path, *model_args, **kwargs_audio_encoder + ) + + decoder = kwargs_decoder.pop("model", None) + if decoder is None: + if decoder_pretrained_model_name_or_path is None: + raise ValueError( + "If `decoder_model` is not defined as an argument, a `decoder_pretrained_model_name_or_path` has " + "to be defined." + ) + + if "config" not in kwargs_decoder: + decoder_config, kwargs_decoder = AutoConfig.from_pretrained( + decoder_pretrained_model_name_or_path, **kwargs_decoder, return_unused_kwargs=True + ) + + if isinstance(decoder_config, MusicgenMelodyConfig): + decoder_config = decoder_config.decoder + + if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False: + logger.info( + f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. Cross attention" + f" layers are added to {decoder_pretrained_model_name_or_path} and randomly initialized if" + f" {decoder_pretrained_model_name_or_path}'s architecture allows for cross attention layers." + ) + decoder_config.is_decoder = True + decoder_config.add_cross_attention = True + + kwargs_decoder["config"] = decoder_config + + if kwargs_decoder["config"].is_decoder is False or kwargs_decoder["config"].add_cross_attention is False: + logger.warning( + f"Decoder model {decoder_pretrained_model_name_or_path} is not initialized as a decoder. " + f"In order to initialize {decoder_pretrained_model_name_or_path} as a decoder, " + "make sure that the attributes `is_decoder` and `add_cross_attention` of `decoder_config` " + "passed to `.from_sub_models_pretrained(...)` are set to `True` or do not pass a " + "`decoder_config` to `.from_sub_models_pretrained(...)`" + ) + + decoder = MusicgenMelodyForCausalLM.from_pretrained( + decoder_pretrained_model_name_or_path, **kwargs_decoder + ) + + # instantiate config with corresponding kwargs + config = MusicgenMelodyConfig( + text_encoder=text_encoder.config, audio_encoder=audio_encoder.config, decoder=decoder.config, **kwargs + ) + return cls(text_encoder=text_encoder, audio_encoder=audio_encoder, decoder=decoder, config=config) + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.BoolTensor | None = None, + input_features: torch.FloatTensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.BoolTensor | None = None, + past_key_values: Cache | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | MusicgenMelodyOutputWithPast: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size * num_codebooks, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary, corresponding to the sequence of audio codes. + + Indices can be obtained by encoding an audio prompt with an audio encoder model to predict audio codes, + such as with the [`EncodecModel`]. See [`EncodecModel.encode`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + + + The `decoder_input_ids` will automatically be converted from shape `(batch_size * num_codebooks, + target_sequence_length)` to `(batch_size, num_codebooks, target_sequence_length)` in the forward pass. If + you obtain audio codes from an audio encoding model, such as [`EncodecModel`], ensure that the number of + frames is equal to 1, and that you reshape the audio codes from `(frames, batch_size, num_codebooks, + target_sequence_length)` to `(batch_size * num_codebooks, target_sequence_length)` prior to passing them as + `decoder_input_ids`. + + + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of conditional hidden-states representing the concatenation of the projected text encoder output and the projected audio encoder output. + Used as a conditional signal and will thus be concatenated to the projected `decoder_input_ids`. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length, num_codebooks)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + + Examples: + ```python + >>> from transformers import AutoProcessor, MusicgenMelodyForConditionalGeneration + >>> import torch + + >>> processor = AutoProcessor.from_pretrained("facebook/musicgen-melody") + >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("facebook/musicgen-melody") + + >>> inputs = processor( + ... text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], + ... padding=True, + ... return_tensors="pt", + ... ) + + >>> pad_token_id = model.generation_config.pad_token_id + >>> decoder_input_ids = ( + ... torch.ones((inputs.input_ids.shape[0] * model.decoder.num_codebooks, 1), dtype=torch.long) + ... * pad_token_id + ... ) + + >>> logits = model(**inputs, decoder_input_ids=decoder_input_ids).logits + >>> logits.shape # (bsz * num_codebooks, encoder_len + tgt_len, vocab_size) + torch.Size([8, 249, 2048]) + ```""" + kwargs_text_encoder = { + argument[len("text_encoder_")]: value + for argument, value in kwargs.items() + if argument.startswith("text_encoder_") + } + + kwargs_decoder = { + argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_") + } + + for passthrough_key in ("output_attentions", "output_hidden_states"): + if passthrough_key in kwargs: + kwargs_text_encoder[passthrough_key] = kwargs[passthrough_key] + kwargs_decoder[passthrough_key] = kwargs[passthrough_key] + + if encoder_hidden_states is None: + if inputs_embeds is not None or input_ids is not None: + encoder_outputs = self.text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + **kwargs_text_encoder, + ) + + encoder_hidden_states = encoder_outputs[0] + + # optionally project encoder_hidden_states + if self.text_encoder.config.hidden_size != self.decoder.config.hidden_size: + encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) + + if attention_mask is not None and encoder_hidden_states is not None: + encoder_hidden_states = encoder_hidden_states * attention_mask[..., None] + + # set a default audio conditional hidden states if text is not None + if encoder_hidden_states is not None and input_features is None: + input_features = torch.zeros( + (encoder_hidden_states.shape[0], 1, self.config.num_chroma), + device=self.device, + dtype=self.dtype, + ) + input_features[:, :, 0] = 1 + + if input_features is not None: + audio_hidden_states = input_features + + # optionally project audio_hidden_states -> + # (batch_size, seq_len, num_chroma) -> (batch_size, seq_len, hidden_size) + if self.config.num_chroma != self.decoder.config.hidden_size: + audio_hidden_states = self.audio_enc_to_dec_proj(audio_hidden_states) + + # pad or truncate to config.chroma_length + if audio_hidden_states.shape[1] < self.config.chroma_length: + n_repeat = int(math.ceil(self.config.chroma_length / audio_hidden_states.shape[1])) + audio_hidden_states = audio_hidden_states.repeat(1, n_repeat, 1) + else: + logger.warning( + f"The conditional audio signal is of length {audio_hidden_states.shape[1]}, which exceeds" + f"the maximum chroma duration of {self.config.chroma_length}." + f"The audio will be truncated to {self.config.chroma_length} frames." + ) + audio_hidden_states = audio_hidden_states[:, : self.config.chroma_length] + + if encoder_hidden_states is not None: + encoder_hidden_states = torch.cat([audio_hidden_states, encoder_hidden_states], dim=1) + else: + encoder_hidden_states = audio_hidden_states + + if (labels is not None) and (decoder_input_ids is None and decoder_inputs_embeds is None): + decoder_input_ids = shift_tokens_right( + labels, self.config.decoder.pad_token_id, self.config.decoder.bos_token_id + ) + + # Decode + decoder_outputs: MusicgenMelodyOutputWithPast = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + past_key_values=past_key_values, + labels=labels, + **kwargs_decoder, + ) + + return MusicgenMelodyOutputWithPast( + loss=decoder_outputs.loss, + logits=decoder_outputs.logits, + past_key_values=decoder_outputs.past_key_values, + hidden_states=decoder_outputs.hidden_states, + attentions=decoder_outputs.attentions, + encoder_hidden_states=encoder_hidden_states, + ) + + def prepare_inputs_for_generation( + self, + decoder_input_ids, + encoder_hidden_states=None, + past_key_values=None, + attention_mask=None, + decoder_attention_mask=None, + use_cache=None, + decoder_delay_pattern_mask=None, + guidance_scale=None, + **kwargs, + ): + # Overwritten -- MusicGen has custom processing + if decoder_delay_pattern_mask is None: + decoder_input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask( + decoder_input_ids, + self.generation_config.pad_token_id, + max_length=self.generation_config.max_length, + ) + + # apply the delay pattern mask + decoder_input_ids = self.decoder.apply_delay_pattern_mask(decoder_input_ids, decoder_delay_pattern_mask) + + if guidance_scale is not None and guidance_scale > 1: + # for classifier free guidance we need to replicate the decoder args across the batch dim (we'll split these + # before sampling) + decoder_input_ids = decoder_input_ids.repeat((2, 1)) + if decoder_attention_mask is not None: + decoder_attention_mask = decoder_attention_mask.repeat((2, 1)) + + if past_key_values is not None: + past_length = past_key_values.get_seq_length() + + # Some generation methods already pass only the last input ID + if decoder_input_ids.shape[1] > past_length: + remove_prefix_length = past_length + else: + # Default to old behavior: keep only final ID + remove_prefix_length = decoder_input_ids.shape[1] - 1 + + decoder_input_ids = decoder_input_ids[:, remove_prefix_length:] + + # we only want to use conditional signal in the 1st generation step but keeping the attention mask + encoder_hidden_states = None + # we also have to update the attention mask + + return { + "input_ids": None, # encoder_hidden_states is defined. input_ids not needed + "encoder_hidden_states": encoder_hidden_states, + "past_key_values": past_key_values, + "decoder_input_ids": decoder_input_ids, + "attention_mask": attention_mask, + "decoder_attention_mask": decoder_attention_mask, + "use_cache": use_cache, + } + + # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForConditionalGeneration._prepare_decoder_input_ids_for_generation + def _prepare_decoder_input_ids_for_generation( + self, + batch_size: int, + model_input_name: str, + model_kwargs: dict[str, torch.Tensor], + decoder_start_token_id: int | None = None, + bos_token_id: int | None = None, + device: torch.device | None = None, + ) -> tuple[torch.LongTensor, dict[str, torch.Tensor]]: + """Prepares `decoder_input_ids` for generation with encoder-decoder models""" + + # 1. Check whether the user has defined `decoder_input_ids` manually. To facilitate in terms of input naming, + # we also allow the user to pass it under `input_ids`, if the encoder does not use it as the main input. + if model_kwargs is not None and "decoder_input_ids" in model_kwargs: + decoder_input_ids = model_kwargs.pop("decoder_input_ids") + elif "input_ids" in model_kwargs and model_input_name != "input_ids": + decoder_input_ids = model_kwargs.pop("input_ids") + else: + decoder_input_ids = None + + # 2. Encoder-decoder models expect the `decoder_input_ids` to start with a special token. Let's ensure that. + decoder_start_token_id = self._get_decoder_start_token_id(decoder_start_token_id, bos_token_id) + if device is None: + device = self.device + decoder_input_ids_start = ( + torch.ones((batch_size * self.decoder.num_codebooks, 1), dtype=torch.long, device=device) + * decoder_start_token_id + ) + + # no user input -> use decoder_start_token_id as decoder_input_ids + if decoder_input_ids is None: + decoder_input_ids = decoder_input_ids_start + + # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust + # decoder_attention_mask if provided) + elif (decoder_input_ids[..., 0] != decoder_start_token_id).all().item(): + decoder_input_ids = torch.cat([decoder_input_ids_start, decoder_input_ids], dim=-1) + if "decoder_attention_mask" in model_kwargs: + decoder_attention_mask = model_kwargs["decoder_attention_mask"] + decoder_attention_mask = torch.cat( + (torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask), + dim=-1, + ) + model_kwargs["decoder_attention_mask"] = decoder_attention_mask + + return decoder_input_ids, model_kwargs + + def _prepare_encoder_hidden_states_kwargs_for_generation( + self, + inputs_tensor: torch.Tensor, + model_kwargs, + model_input_name: str | None, + generation_config: GenerationConfig, + ) -> dict[str, Any]: + encoder_hidden_states = None + # attention mask is consumed once to produce text conditional hidden states through the text encoder + encoder_attention_mask = model_kwargs.pop("attention_mask") + guidance_scale = generation_config.guidance_scale + + # 1. condition on text + if inputs_tensor is not None: + encoder = self.get_encoder() + # Compatibility with Accelerate big model inference: we need the encoder to outputs stuff on the same device + # as the inputs. + if hasattr(encoder, "_hf_hook"): + encoder._hf_hook.io_same_device = True + + # Prepare args and kwargs from model kwargs. + irrelevant_prefix = ["decoder_", "use_cache"] + encoder_kwargs = { + argument: value + for argument, value in model_kwargs.items() + if not any(argument.startswith(p) for p in irrelevant_prefix) + } + encoder_signature = set(inspect.signature(encoder.forward).parameters) + encoder_accepts_wildcard = "kwargs" in encoder_signature or "model_kwargs" in encoder_signature + if not encoder_accepts_wildcard: + encoder_kwargs = { + argument: value for argument, value in encoder_kwargs.items() if argument in encoder_signature + } + encoder_kwargs["output_attentions"] = generation_config.output_attentions + encoder_kwargs["output_hidden_states"] = generation_config.output_hidden_states + + # make sure that encoder returns `ModelOutput` + model_input_name = model_input_name if model_input_name is not None else self.text_encoder.main_input_name + encoder_kwargs["return_dict"] = True + encoder_kwargs[model_input_name] = inputs_tensor + if encoder_attention_mask is not None: + encoder_kwargs["attention_mask"] = encoder_attention_mask + encoder_hidden_states = encoder(**encoder_kwargs).last_hidden_state + + # optionally project encoder_hidden_states + if self.text_encoder.config.hidden_size != self.decoder.config.hidden_size: + encoder_hidden_states = self.enc_to_dec_proj(encoder_hidden_states) + + # for classifier free guidance we need to add a 'null' input to our encoder hidden states + if guidance_scale is not None and guidance_scale > 1: + encoder_hidden_states = torch.concatenate( + [encoder_hidden_states, torch.zeros_like(encoder_hidden_states)], dim=0 + ) + if encoder_attention_mask is not None: + encoder_attention_mask = torch.concatenate( + [encoder_attention_mask, torch.zeros_like(encoder_attention_mask)], dim=0 + ) + if encoder_attention_mask is not None: + encoder_hidden_states = encoder_hidden_states * encoder_attention_mask[..., None] + + # 2. condition on audio + audio_hidden_states = model_kwargs.get("input_features", None) + + if inputs_tensor is not None: + if audio_hidden_states is not None: + null_audio_hidden_states = torch.zeros_like(audio_hidden_states) + else: + null_audio_hidden_states = torch.zeros( + (inputs_tensor.shape[0], 1, self.config.num_chroma), device=self.device, dtype=self.dtype + ) + null_audio_hidden_states[:, :, 0] = 1 + + if audio_hidden_states is None: + audio_hidden_states = null_audio_hidden_states + + if audio_hidden_states is not None: + # for classifier free guidance we need to add a 'null' input to our audio hidden states + if guidance_scale is not None and guidance_scale > 1: + audio_hidden_states = torch.concatenate([audio_hidden_states, null_audio_hidden_states], dim=0) + + # optionally project audio_hidden_states -> + # (batch_size, seq_len, num_chroma) -> (batch_size, seq_len, hidden_size) + if self.config.num_chroma != self.decoder.config.hidden_size: + audio_hidden_states = self.audio_enc_to_dec_proj(audio_hidden_states) + + # pad or truncate to config.chroma_length + if audio_hidden_states.shape[1] < self.config.chroma_length: + n_repeat = int(math.ceil(self.config.chroma_length / audio_hidden_states.shape[1])) + audio_hidden_states = audio_hidden_states.repeat(1, n_repeat, 1) + audio_hidden_states = audio_hidden_states[:, : self.config.chroma_length] + + if encoder_hidden_states is not None: + encoder_hidden_states = torch.cat([audio_hidden_states, encoder_hidden_states], dim=1) + else: + encoder_hidden_states = audio_hidden_states + + model_kwargs["encoder_hidden_states"] = encoder_hidden_states + + return model_kwargs + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.decoder.pad_token_id, self.config.decoder.bos_token_id) + + def resize_token_embeddings(self, *args, **kwargs): + raise NotImplementedError( + "Resizing the embedding layers via the EncoderDecoderModel directly is not supported. Please use the" + " respective methods of the wrapped objects (model.encoder.resize_token_embeddings(...) or" + " model.decoder.resize_token_embeddings(...))" + ) + + def _maybe_initialize_input_ids_for_generation( + self, + inputs: torch.Tensor | None, + bos_token_id: int | None, + model_kwargs: dict[str, torch.Tensor], + ) -> torch.LongTensor: + """Initializes input ids for generation, if necessary.""" + if inputs is not None: + return inputs + + if bos_token_id is None: + raise ValueError("`bos_token_id` has to be defined when no `input_ids` are provided.") + + # If there is some tensor in `model_kwargs`, we can infer the batch size from it. This is helpful with + # soft-prompting or in multimodal implementations built on top of decoder-only language models. + batch_size = 1 + for value in model_kwargs.values(): + if isinstance(value, torch.Tensor): + batch_size = value.shape[0] + break + return torch.ones((batch_size, 1), dtype=torch.long, device=self.device) * bos_token_id + + def freeze_audio_encoder(self): + """ + Freeze the audio encoder weights. + """ + for param in self.audio_encoder.parameters(): + param.requires_grad = False + self.audio_encoder._requires_grad = False + + def freeze_text_encoder(self): + """ + Freeze the text encoder weights. + """ + for param in self.text_encoder.parameters(): + param.requires_grad = False + self.text_encoder._requires_grad = False + + # Copied from transformers.models.musicgen.modeling_musicgen.MusicgenForConditionalGeneration._get_decoder_start_token_id + def _get_decoder_start_token_id( + self, decoder_start_token_id: int | list[int] | None = None, bos_token_id: int | None = None + ) -> int: + decoder_start_token_id = ( + decoder_start_token_id + if decoder_start_token_id is not None + else self.generation_config.decoder_start_token_id + ) + bos_token_id = bos_token_id if bos_token_id is not None else self.generation_config.bos_token_id + + if decoder_start_token_id is not None: + return decoder_start_token_id + elif bos_token_id is not None: + return bos_token_id + raise ValueError( + "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation." + ) + + @torch.no_grad() + def generate( + self, + inputs: torch.Tensor | None = None, + generation_config: GenerationConfig | None = None, + logits_processor: LogitsProcessorList | None = None, + stopping_criteria: StoppingCriteriaList | None = None, + synced_gpus: bool | None = None, + streamer: Optional["BaseStreamer"] = None, + **kwargs, + ): + """ + + Generates sequences of token ids for models with a language modeling head. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Parameters: + inputs (`torch.Tensor` of varying shape depending on the modality, *optional*): + The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the + method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs` + should be in the format `input_ids`. For encoder-decoder models *inputs* can represent any of + `input_ids`, `input_values`, `input_features`, or `pixel_values`. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + logits_processor (`LogitsProcessorList`, *optional*): + Custom logits processors that complement the default logits processors built from arguments and + generation config. If a logit processor is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + stopping_criteria (`StoppingCriteriaList`, *optional*): + Custom stopping criteria that complement the default stopping criteria built from arguments and a + generation config. If a stopping criteria is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + synced_gpus (`bool`, *optional*, defaults to `False`): + Whether to continue running the while loop until max_length (needed to avoid deadlocking with + `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). + streamer (`BaseStreamer`, *optional*): + Streamer object that will be used to stream the generated sequences. Generated tokens are passed + through `streamer.put(token_ids)` and the streamer is responsible for any further processing. + kwargs (`dict[str, Any]`, *optional*): + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder + specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. + + Return: + [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` + or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. + + If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible + [`~utils.ModelOutput`] types are: + + - [`~generation.GenerateDecoderOnlyOutput`], + - [`~generation.GenerateBeamDecoderOnlyOutput`] + + If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible + [`~utils.ModelOutput`] types are: + + - [`~generation.GenerateEncoderDecoderOutput`], + - [`~generation.GenerateBeamEncoderDecoderOutput`] + """ + # 1. Handle `generation_config` and kwargs that might update it, and validate the resulting objects + generation_config, model_kwargs = self._prepare_generation_config(generation_config, **kwargs) + self._validate_model_kwargs(model_kwargs.copy()) + + # 2. Set generation parameters if not already defined + logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList() + stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList() + + requires_attention_mask = "encoder_outputs" not in model_kwargs + kwargs_has_attention_mask = model_kwargs.get("attention_mask", None) is not None + + # 3. Define model inputs + inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs( + inputs, generation_config.bos_token_id, model_kwargs + ) + batch_size = inputs_tensor.shape[0] + self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=inputs_tensor.device) + + # 4. Define other model kwargs + model_kwargs["use_cache"] = generation_config.use_cache + model_kwargs["guidance_scale"] = generation_config.guidance_scale + + if model_kwargs.get("attention_mask", None) is None and requires_attention_mask: + model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation( + inputs_tensor, generation_config, model_kwargs + ) + + if "encoder_hidden_states" not in model_kwargs: + # encoder_hidden_states are created and added to `model_kwargs` + model_kwargs = self._prepare_encoder_hidden_states_kwargs_for_generation( + inputs_tensor, model_kwargs, model_input_name, generation_config + ) + + # 5. Prepare `input_ids` which will be used for auto-regressive generation + input_ids, model_kwargs = self._prepare_decoder_input_ids_for_generation( + batch_size=batch_size, + model_input_name=model_input_name, + model_kwargs=model_kwargs, + decoder_start_token_id=generation_config._decoder_start_token_tensor, + bos_token_id=generation_config._bos_token_tensor, + device=inputs_tensor.device, + ) + + # 6. Prepare `max_length` depending on other stopping criteria. + input_ids_length = input_ids.shape[-1] + has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None + has_default_min_length = kwargs.get("min_length") is None and generation_config.min_length is not None + generation_config = self._prepare_generated_length( + generation_config=generation_config, + has_default_max_length=has_default_max_length, + has_default_min_length=has_default_min_length, + model_input_name=model_input_name, + inputs_tensor=inputs_tensor, + input_ids_length=input_ids_length, + ) + + self._validate_generated_length(generation_config, input_ids_length, has_default_max_length) + + # 7. Prepare the cache. + # - `model_kwargs` may be updated in place with a cache as defined by the parameters in `generation_config`. + # - different models have a different cache name expected by the model (default = "past_key_values") + # - `max_length`, prepared above, is used to determine the maximum cache length + max_cache_length = generation_config.max_length - 1 + if ( + inputs_tensor.shape[1] != input_ids_length + and model_input_name == "inputs_embeds" + and not self.config.is_encoder_decoder + ): + max_cache_length += inputs_tensor.shape[1] + self._prepare_cache_for_generation( + generation_config, + model_kwargs, + generation_mode=None, + batch_size=batch_size, + max_cache_length=max_cache_length, + ) + + # build the delay pattern mask for offsetting each codebook prediction by 1 (this behaviour is specific to MusicGen) + input_ids, decoder_delay_pattern_mask = self.decoder.build_delay_pattern_mask( + input_ids, + pad_token_id=generation_config._decoder_start_token_tensor, + max_length=generation_config.max_length, + ) + # stash the delay mask so that we don't have to recompute in each forward pass + model_kwargs["decoder_delay_pattern_mask"] = decoder_delay_pattern_mask + + # input_ids are ready to be placed on the streamer (if used) + if streamer is not None: + streamer.put(input_ids.cpu()) + + # 8. determine generation mode + generation_mode = generation_config.get_generation_mode() + + # 9. prepare batched CFG externally (to enable coexistence with the unbatched CFG) + if generation_config.guidance_scale is not None and generation_config.guidance_scale > 1: + logits_processor.append(ClassifierFreeGuidanceLogitsProcessor(generation_config.guidance_scale)) + generation_config.guidance_scale = None + + # 10. prepare distribution pre_processing samplers + logits_processor = self._get_logits_processor( + generation_config=generation_config, + input_ids_seq_length=input_ids_length, + encoder_input_ids=inputs_tensor, + prefix_allowed_tokens_fn=None, + logits_processor=logits_processor, + device=input_ids.device, + ) + + # 10. prepare stopping criteria + stopping_criteria = self._get_stopping_criteria( + generation_config=generation_config, stopping_criteria=stopping_criteria + ) + + if generation_mode in (GenerationMode.SAMPLE, GenerationMode.GREEDY_SEARCH): + # expand input_ids with `num_return_sequences` additional sequences per batch + input_ids, model_kwargs = self._expand_inputs_for_generation( + input_ids=input_ids, + expand_size=generation_config.num_return_sequences, + is_encoder_decoder=self.config.is_encoder_decoder, + **model_kwargs, + ) + + # 11. run sample + outputs = self._sample( + input_ids, + logits_processor=logits_processor, + stopping_criteria=stopping_criteria, + generation_config=generation_config, + synced_gpus=synced_gpus, + streamer=streamer, + **model_kwargs, + ) + + else: + raise ValueError( + "Got incompatible mode for generation, should be one of greedy or sampling. " + "Ensure that beam search is de-activated by setting `num_beams=1`." + ) + + if generation_config.return_dict_in_generate: + output_ids = outputs.sequences + else: + output_ids = outputs + + # apply the pattern mask to the final ids + output_ids = self.decoder.apply_delay_pattern_mask(output_ids, model_kwargs["decoder_delay_pattern_mask"]) + + # revert the pattern delay mask by filtering the pad token id + output_ids = output_ids[output_ids != generation_config._pad_token_tensor].reshape( + batch_size, self.decoder.num_codebooks, -1 + ) + + # append the frame dimension back to the audio codes + output_ids = output_ids[None, ...] + + audio_scales = model_kwargs.get("audio_scales") + if audio_scales is None: + audio_scales = [None] * batch_size + + if self.decoder.config.audio_channels == 1: + output_values = self.audio_encoder.decode( + output_ids, + audio_scales=audio_scales, + ).audio_values + else: + codec_outputs_left = self.audio_encoder.decode(output_ids[:, :, ::2, :], audio_scales=audio_scales) + output_values_left = codec_outputs_left.audio_values + + codec_outputs_right = self.audio_encoder.decode(output_ids[:, :, 1::2, :], audio_scales=audio_scales) + output_values_right = codec_outputs_right.audio_values + + output_values = torch.cat([output_values_left, output_values_right], dim=1) + + if generation_config.return_dict_in_generate: + outputs.sequences = output_values + return outputs + else: + return output_values + + +__all__ = [ + "MusicgenMelodyForConditionalGeneration", + "MusicgenMelodyForCausalLM", + "MusicgenMelodyModel", + "MusicgenMelodyPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/processing_musicgen_melody.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/processing_musicgen_melody.py new file mode 100644 index 0000000000000000000000000000000000000000..cf5cc6088b8ea725a42127ddcca3b5abc7ebe278 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/musicgen_melody/processing_musicgen_melody.py @@ -0,0 +1,117 @@ +# Copyright 2024 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Text/audio processor class for MusicGen Melody +""" + +from typing import Any + +import numpy as np + +from ...processing_utils import ProcessorMixin +from ...utils import auto_docstring, to_numpy +from ...utils.import_utils import requires + + +@requires(backends=("torchaudio",)) +@auto_docstring +class MusicgenMelodyProcessor(ProcessorMixin): + def __init__(self, feature_extractor, tokenizer): + super().__init__(feature_extractor, tokenizer) + + # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor.get_decoder_prompt_ids + def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True): + return self.tokenizer.get_decoder_prompt_ids(task=task, language=language, no_timestamps=no_timestamps) + + @auto_docstring + def __call__(self, *args, **kwargs): + if len(args) > 0: + kwargs["audio"] = args[0] + return super().__call__(*args, **kwargs) + + # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor.batch_decode with padding_mask->attention_mask + def batch_decode(self, *args, **kwargs): + """ + This method is used to decode either batches of audio outputs from the MusicGen model, or batches of token ids + from the tokenizer. In the case of decoding token ids, this method forwards all its arguments to T5Tokenizer's + [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. + """ + audio_values = kwargs.pop("audio", None) + attention_mask = kwargs.pop("attention_mask", None) + + if len(args) > 0: + audio_values = args[0] + args = args[1:] + + if audio_values is not None: + return self._decode_audio(audio_values, attention_mask=attention_mask) + else: + return self.tokenizer.batch_decode(*args, **kwargs) + + # Copied from transformers.models.musicgen.processing_musicgen.MusicgenProcessor._decode_audio with padding_mask->attention_mask + def _decode_audio(self, audio_values, attention_mask: Any = None) -> list[np.ndarray]: + """ + This method strips any padding from the audio values to return a list of numpy audio arrays. + """ + audio_values = to_numpy(audio_values) + bsz, channels, seq_len = audio_values.shape + + if attention_mask is None: + return list(audio_values) + + attention_mask = to_numpy(attention_mask) + + # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding** + # token (so that the generated audio values are **not** treated as padded tokens) + difference = seq_len - attention_mask.shape[-1] + padding_value = 1 - self.feature_extractor.padding_value + attention_mask = np.pad(attention_mask, ((0, 0), (0, difference)), "constant", constant_values=padding_value) + + audio_values = audio_values.tolist() + for i in range(bsz): + sliced_audio = np.asarray(audio_values[i])[ + attention_mask[i][None, :] != self.feature_extractor.padding_value + ] + audio_values[i] = sliced_audio.reshape(channels, -1) + + return audio_values + + def get_unconditional_inputs(self, num_samples=1, return_tensors="pt"): + """ + Helper function to get null inputs for unconditional generation, enabling the model to be used without the + feature extractor or tokenizer. + + Args: + num_samples (int, *optional*): + Number of audio samples to unconditionally generate. + + Example: + ```python + >>> from transformers import MusicgenMelodyForConditionalGeneration, MusicgenMelodyProcessor + + >>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("facebook/musicgen-melody") + + >>> # get the unconditional (or 'null') inputs for the model + >>> processor = MusicgenMelodyProcessor.from_pretrained("facebook/musicgen-melody") + >>> unconditional_inputs = processor.get_unconditional_inputs(num_samples=1) + + >>> audio_samples = model.generate(**unconditional_inputs, max_new_tokens=256) + ```""" + inputs = self.tokenizer([""] * num_samples, return_tensors=return_tensors, return_attention_mask=True) + inputs["attention_mask"][:] = 0 + + return inputs + + +__all__ = ["MusicgenMelodyProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4f0d850d308f6582e6e7620193bdc60da1f45403 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from ..roberta.tokenization_roberta import RobertaTokenizer as MvpTokenizer + from .configuration_mvp import * + from .modeling_mvp import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/configuration_mvp.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/configuration_mvp.py new file mode 100644 index 0000000000000000000000000000000000000000..a5d7271046499857f8c316688c69009cab824a59 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/configuration_mvp.py @@ -0,0 +1,87 @@ +# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""MVP model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="RUCAIBox/mvp") +@strict +class MvpConfig(PreTrainedConfig): + r""" + use_prompt (`bool`, *optional*, defaults to `False`): + Whether or not to use prompt. + prompt_length (`int`, *optional*, defaults to 100): + The length of prompt. + prompt_mid_dim (`int`, *optional*, defaults to 800): + Dimensionality of the "intermediate" layer in prompt. + + Example: + + ```python + >>> from transformers import MvpConfig, MvpModel + + >>> # Initializing a MVP RUCAIBox/mvp style configuration + >>> configuration = MvpConfig() + + >>> # Initializing a model (with random weights) from the RUCAIBox/mvp style configuration + >>> model = MvpModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "mvp" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "num_attention_heads": "encoder_attention_heads", + "hidden_size": "d_model", + "num_hidden_layers": "encoder_layers", + } + + vocab_size: int = 50267 + max_position_embeddings: int = 1024 + encoder_layers: int = 12 + encoder_ffn_dim: int = 4096 + encoder_attention_heads: int = 16 + decoder_layers: int = 12 + decoder_ffn_dim: int = 4096 + decoder_attention_heads: int = 16 + encoder_layerdrop: float | int = 0.0 + decoder_layerdrop: float | int = 0.0 + activation_function: str = "gelu" + d_model: int = 1024 + dropout: float | int = 0.1 + attention_dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + init_std: float = 0.02 + classifier_dropout: float | int = 0.0 + scale_embedding: bool = False + use_cache: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 2 + is_encoder_decoder: bool = True + decoder_start_token_id: int | None = 2 + use_prompt: bool = False + prompt_length: int = 100 + prompt_mid_dim: int = 800 + is_decoder: bool = False + tie_word_embeddings: bool = True + + +__all__ = ["MvpConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/modeling_mvp.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/modeling_mvp.py new file mode 100644 index 0000000000000000000000000000000000000000..cf6fcaddf2f4cdddc0100e1cabab9c969b3e5be0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/mvp/modeling_mvp.py @@ -0,0 +1,1630 @@ +# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch MVP model.""" + +import math + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, + Seq2SeqQuestionAnsweringModelOutput, + Seq2SeqSequenceClassifierOutput, +) +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, logging, torch_compilable_check +from .configuration_mvp import MvpConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.bart.modeling_bart.shift_tokens_right +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +# Copied from transformers.models.bart.modeling_bart.BartLearnedPositionalEmbedding with Bart->Mvp +class MvpLearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int): + # Mvp is set up so that if padding_idx is specified then offset the embedding ids by 2 + # and adjust num_embeddings appropriately. Other models don't have this hack + self.offset = 2 + super().__init__(num_embeddings + self.offset, embedding_dim) + + def forward( + self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor | None = None + ): + """`input_ids' shape is expected to be [bsz x seqlen].""" + + if position_ids is None: + bsz, seq_len = input_ids.shape[:2] + position_ids = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device + ).expand(bsz, -1) + else: + position_ids = position_ids.unsqueeze(0) + + return super().forward(position_ids + self.offset) + + +class MvpAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float | None = 0.0, + is_decoder: bool | None = False, + bias: bool | None = True, + layer_idx: bool | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.layer_idx = layer_idx + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + attn_prompt: torch.Tensor | None = None, + output_attentions: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.k_proj(current_states) + value_states = self.v_proj(current_states) + key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if past_key_values is not None: + # save all key/value_states to cache to be re-used for fast auto-regressive generation + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + if attn_prompt is not None: + key_states = torch.cat([attn_prompt[0].expand(bsz, -1, -1, -1), key_states], dim=2) + value_states = torch.cat([attn_prompt[1].expand(bsz, -1, -1, -1), value_states], dim=2) + if attention_mask is not None: + prompt_mask = torch.zeros(bsz, 1, tgt_len, attn_prompt[0].size(1)).to(attention_mask.device) + attention_mask = torch.cat([prompt_mask, attention_mask], dim=(-1)) + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = query_states.view(bsz, tgt_len, self.num_heads, self.head_dim).transpose(1, 2) + query_states = query_states.reshape(*proj_shape) + key_states = key_states.reshape(*proj_shape) + value_states = value_states.reshape(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped + + +class MvpEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: MvpConfig): + super().__init__() + self.embed_dim = config.d_model + self.self_attn = MvpAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: torch.FloatTensor, + self_attn_prompt: torch.FloatTensor, + output_attentions: bool | None = False, + ) -> tuple[torch.FloatTensor, torch.FloatTensor | None]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + self_attn_prompt (`torch.FloatTensor`): prompt of self attention of shape + `(2, encoder_attention_heads, pro_len, head_dim)`. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + attn_prompt=self_attn_prompt, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + if hidden_states.dtype == torch.float16 and not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states, attn_weights + + +class MvpDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: MvpConfig, layer_idx=None): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = MvpAttention( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = MvpAttention( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + layer_idx=layer_idx, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + self_attn_prompt: torch.Tensor | None = None, + cross_attn_prompt: torch.Tensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool | None = False, + use_cache: bool | None = True, + **kwargs, + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + self_attn_prompt (`torch.FloatTensor`): prompt of self attention of shape + `(2, decoder_attention_heads, pro_len, head_dim)`. + cross_attn_prompt (`torch.FloatTensor`): prompt of cross attention of shape + `(2, decoder_attention_heads, pro_len, head_dim)`. + past_key_values (`Cache`): cached past key and value projection states + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + # Self Attention + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + attn_prompt=self_attn_prompt, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Cross-Attention Block + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + + hidden_states, cross_attn_weights = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + attn_prompt=cross_attn_prompt, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + return outputs + + +# Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->MVP +class MvpClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__( + self, + input_dim: int, + inner_dim: int, + num_classes: int, + pooler_dropout: float, + ): + super().__init__() + self.dense = nn.Linear(input_dim, inner_dim) + self.dropout = nn.Dropout(p=pooler_dropout) + self.out_proj = nn.Linear(inner_dim, num_classes) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dropout(hidden_states) + hidden_states = self.dense(hidden_states) + hidden_states = torch.tanh(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.out_proj(hidden_states) + return hidden_states + + +class MvpPrompt(nn.Module): + """Layer-wise prompt for encoder or decoder.""" + + def __init__(self, config, num_layers, num_heads): + super().__init__() + self.prompt_length = config.prompt_length + self.num_layers = num_layers + self.num_heads = num_heads + self.head_dim = config.d_model // num_heads + self.dropout = nn.Dropout(p=config.dropout) + self.prompt_embedding = nn.Embedding(config.prompt_length, config.d_model) + self.prompt_trans = nn.Sequential( + nn.Linear(config.d_model, config.prompt_mid_dim), + nn.GELU(), + nn.Linear(config.prompt_mid_dim, num_layers * 2 * config.d_model), + ) + + def forward(self, prompt_ids: torch.Tensor) -> tuple[torch.Tensor]: + prompt = self.prompt_trans(self.prompt_embedding(prompt_ids)) + prompt = prompt.view(self.prompt_length, self.num_layers * 2, self.num_heads, self.head_dim) + prompt = self.dropout(prompt) + prompt = prompt.permute([1, 2, 0, 3]).split(2) + return prompt + + +@auto_docstring +class MvpPreTrainedModel(PreTrainedModel): + config: MvpConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, MvpForConditionalGeneration): + init.zeros_(module.final_logits_bias) + + @property + def dummy_inputs(self): + pad_token = self.config.pad_token_id + input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device) + dummy_inputs = { + "attention_mask": input_ids.ne(pad_token), + "input_ids": input_ids, + } + return dummy_inputs + + +class MvpEncoder(MvpPreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`MvpEncoderLayer`]. + + Args: + config: MvpConfig + embed_tokens (nn.Embedding): output embedding + use_prompt (bool): whether to use prompt + """ + + def __init__(self, config: MvpConfig, embed_tokens: nn.Embedding | None = None, use_prompt: bool | None = False): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + self.padding_idx = config.pad_token_id + self.max_source_positions = config.max_position_embeddings + self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) + + self.embed_positions = MvpLearnedPositionalEmbedding( + config.max_position_embeddings, + embed_dim, + ) + self.layers = nn.ModuleList([MvpEncoderLayer(config) for _ in range(config.encoder_layers)]) + self.layernorm_embedding = nn.LayerNorm(embed_dim) + + self.use_prompt = use_prompt + if use_prompt: + self.prompt_length = config.prompt_length + self.self_attn_prompt = MvpPrompt( + config, + config.encoder_layers, + config.encoder_attention_heads, + ) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_shape = input.shape + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale + + embed_pos = self.embed_positions(input) + + hidden_states = inputs_embeds + embed_pos + hidden_states = self.layernorm_embedding(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # layer-wise prompt + if self.use_prompt: + prompt_ids = torch.arange(self.prompt_length).to(self.device) + self_attn_prompt = self.self_attn_prompt(prompt_ids) + + # expand attention_mask + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + ) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + self_attn_prompt=(self_attn_prompt[idx] if self.use_prompt else None), + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +class MvpDecoder(MvpPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`MvpDecoderLayer`] + + Args: + config: MvpConfig + embed_tokens (nn.Embedding): output embedding + use_prompt (bool): whether to use prompt + """ + + def __init__(self, config: MvpConfig, use_prompt: bool | None = False): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) + self.embed_positions = MvpLearnedPositionalEmbedding( + config.max_position_embeddings, + config.d_model, + ) + self.layers = nn.ModuleList([MvpDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)]) + self.layernorm_embedding = nn.LayerNorm(config.d_model) + + self.use_prompt = use_prompt + if use_prompt: + self.prompt_length = config.prompt_length + self.self_attn_prompt = MvpPrompt( + config, + config.decoder_layers, + config.decoder_attention_heads, + ) + self.cross_attn_prompt = MvpPrompt( + config, + config.decoder_layers, + config.decoder_attention_heads, + ) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPastAndCrossAttentions: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_shape = input_ids.shape + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if use_cache and past_key_values is None: + past_key_values = ( + EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + if encoder_hidden_states is not None or self.config.is_encoder_decoder + else DynamicCache(config=self.config) + ) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + + # expand encoder attention mask + if encoder_hidden_states is not None and encoder_attention_mask is not None: + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + # embed positions + positions = self.embed_positions(input, past_key_values_length) + + hidden_states = inputs_embeds + positions + hidden_states = self.layernorm_embedding(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # layer-wise prompt + if self.use_prompt: + prompt_ids = torch.arange(self.prompt_length).to(self.device) + self_attn_prompt = self.self_attn_prompt(prompt_ids) + cross_attn_prompt = self.cross_attn_prompt(prompt_ids) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + layer_outputs = decoder_layer( + hidden_states, + attention_mask, + encoder_hidden_states, # as positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + self_attn_prompt=(self_attn_prompt[idx] if self.use_prompt else None), + cross_attn_prompt=(cross_attn_prompt[idx] if self.use_prompt else None), + past_key_values=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + +@auto_docstring +class MvpModel(MvpPreTrainedModel): + _keys_to_ignore_on_load_unexpected = ["final_logits_bias"] + _tied_weights_keys = { + "encoder.embed_tokens.weight": "shared.weight", + "decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: MvpConfig): + super().__init__(config) + + padding_idx, vocab_size = config.pad_token_id, config.vocab_size + self.use_prompt = config.use_prompt + self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) + + self.encoder = MvpEncoder(config, config.use_prompt) + self.decoder = MvpDecoder(config, config.use_prompt) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, value): + self.shared = value + self.encoder.embed_tokens = self.shared + self.decoder.embed_tokens = self.shared + + def set_lightweight_tuning(self): + assert self.use_prompt, "If you want to use lightweight tuning, make sure that `use_prompt=True`." + + self.requires_grad_(False) + self.encoder.self_attn_prompt.requires_grad_(True) + self.decoder.self_attn_prompt.requires_grad_(True) + self.decoder.cross_attn_prompt.requires_grad_(True) + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | Seq2SeqModelOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` + is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more + information on the default strategy. + """ + # different to other models, Mvp automatically creates decoder_input_ids from + # input_ids if no decoder_input_ids are provided + if decoder_input_ids is None and decoder_inputs_embeds is None: + if input_ids is None: + raise ValueError( + "If no `decoder_input_ids` or `decoder_inputs_embeds` are " + "passed, `input_ids` cannot be `None`. Please pass either " + "`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`." + ) + + decoder_input_ids = shift_tokens_right( + input_ids, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The MVP Model with a language modeling head. Can be used for various text generation tasks. + """ +) +class MvpForConditionalGeneration(MvpPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "lm_head.weight": "model.shared.weight", + } + + def __init__(self, config: MvpConfig): + super().__init__(config) + self.model = MvpModel(config) + self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) + self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def resize_token_embeddings( + self, new_num_tokens: int, pad_to_multiple_of: int | None = None, mean_resizing: bool = True + ) -> nn.Embedding: + new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing) + self._resize_final_logits_bias(new_num_tokens) + return new_embeddings + + def _resize_final_logits_bias(self, new_num_tokens: int) -> None: + old_num_tokens = self.final_logits_bias.shape[-1] + if new_num_tokens <= old_num_tokens: + new_bias = self.final_logits_bias[:, :new_num_tokens] + else: + extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) + new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) + self.register_buffer("final_logits_bias", new_bias) + + def set_lightweight_tuning(self): + self.model.set_lightweight_tuning() + self.lm_head.requires_grad_(False) + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | Seq2SeqLMOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` + is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more + information on the default strategy. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example of summarization: + + Fine-tuning a model + ```python + >>> import torch + >>> from transformers import AutoTokenizer, MvpForConditionalGeneration + + >>> tokenizer = AutoTokenizer.from_pretrained("RUCAIBox/mvp") + >>> model = MvpForConditionalGeneration.from_pretrained("RUCAIBox/mvp") + + >>> inputs = tokenizer( + ... "Summarize: You may want to stick it to your boss and leave your job, but don't do it if these are your reasons.", + ... return_tensors="pt", + ... ) + >>> labels = tokenizer("Bad Reasons To Quit Your Job", return_tensors="pt")["input_ids"] + + >>> loss = model(**inputs, labels=labels).loss + >>> loss.backward() + ``` + + Inference after the model fine-tuned + ```python + >>> with torch.no_grad(): + ... generated_ids = model.generate(**inputs) + + >>> generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) + ``` + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + lm_logits = self.lm_head(outputs[0]) + self.final_logits_bias + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) + + +@auto_docstring( + custom_intro=""" + Mvp model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE + tasks. + """ +) +class MvpForSequenceClassification(MvpPreTrainedModel): + def __init__(self, config: MvpConfig, **kwargs): + super().__init__(config, **kwargs) + self.model = MvpModel(config) + self.classification_head = MvpClassificationHead( + config.d_model, + config.d_model, + config.num_labels, + config.classifier_dropout, + ) + + # Initialize weights and apply final processing + self.post_init() + + def set_lightweight_tuning(self): + self.model.set_lightweight_tuning() + self.classification_head.requires_grad_(False) + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | Seq2SeqSequenceClassifierOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` + is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more + information on the default strategy. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Example of single-label classification: + + Fine-tuning a model on `num_labels` classes + ```python + >>> import torch + >>> from transformers import AutoTokenizer, MvpForSequenceClassification + + >>> num_labels = 2 # for example, this is a binary classification task + >>> tokenizer = AutoTokenizer.from_pretrained("RUCAIBox/mvp") + >>> model = MvpForSequenceClassification.from_pretrained("RUCAIBox/mvp", num_labels=num_labels) + + >>> inputs = tokenizer("Classify: Hello, my dog is cute", return_tensors="pt") + >>> labels = torch.tensor(1) # the real label for inputs + + >>> loss = model(**inputs, labels=labels).loss + >>> loss.backward() + ``` + + Inference after the model fine-tuned + ```python + >>> with torch.no_grad(): + ... logits = model(**inputs).logits + + >>> predicted_class_id = logits.argmax() + ``` + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + if labels is not None: + use_cache = False + + if input_ids is None and inputs_embeds is not None: + raise NotImplementedError( + f"Passing input embeddings is currently not supported for {self.__class__.__name__}" + ) + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = outputs[0] # last hidden state + + eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device) + + torch_compilable_check( + torch.unique_consecutive(eos_mask.sum(1)).numel() == 1, + "All examples must have the same number of tokens.", + ) + sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[ + :, -1, : + ] + logits = self.classification_head(sentence_representation) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.config.num_labels == 1: + self.config.problem_type = "regression" + elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.config.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return Seq2SeqSequenceClassifierOutput( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + +@auto_docstring +class MvpForQuestionAnswering(MvpPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + config.num_labels = 2 + self.num_labels = config.num_labels + + self.model = MvpModel(config) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + def set_lightweight_tuning(self): + self.model.set_lightweight_tuning() + self.qa_outputs.requires_grad_(False) + + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + start_positions: torch.LongTensor | None = None, + end_positions: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | Seq2SeqQuestionAnsweringModelOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Mvp uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` + is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + If you want to change padding behavior, you should read [`modeling_mvp._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more + information on the default strategy. + + Example: + + Fine-tuning a model for extrative question answering, and our model also supports generative question answering + using `BartForConditionalGeneration` + ```python + >>> import torch + >>> from transformers import AutoTokenizer, MvpForQuestionAnswering + + >>> tokenizer = AutoTokenizer.from_pretrained("RUCAIBox/mvp") + >>> model = MvpForQuestionAnswering.from_pretrained("RUCAIBox/mvp") + + >>> inputs = tokenizer( + ... "Answer the following question: Who was Jim Henson? [SEP] Jim Henson was a nice puppet", + ... return_tensors="pt", + ... ) + >>> target_start_index = torch.tensor([18]) + >>> target_end_index = torch.tensor([19]) + + >>> loss = model(**inputs, start_positions=target_start_index, end_positions=target_end_index).loss + >>> loss.backward() + ``` + + Inference after the model fine-tuned + ```python + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> answer_start_index = outputs.start_logits.argmax() + >>> answer_end_index = outputs.end_logits.argmax() + + >>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] + >>> predict_answer = tokenizer.decode(predict_answer_tokens) + ``` + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + if start_positions is not None and end_positions is not None: + use_cache = False + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = ( + start_logits, + end_logits, + ) + outputs[1:] + return ((total_loss,) + output) if total_loss is not None else output + + return Seq2SeqQuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + +# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Mvp +class MvpDecoderWrapper(MvpPreTrainedModel): + """ + This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is + used in combination with the [`EncoderDecoderModel`] framework. + """ + + def __init__(self, config): + super().__init__(config) + self.decoder = MvpDecoder(config) + self.post_init() + + def forward(self, *args, **kwargs): + return self.decoder(*args, **kwargs) + + +class MvpForCausalLM(MvpPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.decoder.embed_tokens.weight"} + + def __init__(self, config): + config.is_decoder = True + config.is_encoder_decoder = False + super().__init__(config) + self.model = MvpDecoderWrapper(config) + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + def set_lightweight_tuning(self): + self.model.set_lightweight_tuning() + self.lm_head.requires_grad_(False) + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | CausalLMOutputWithCrossAttentions: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, MvpForCausalLM + + >>> tokenizer = AutoTokenizer.from_pretrained("RUCAIBox/mvp") + >>> model = MvpForCausalLM.from_pretrained("RUCAIBox/mvp") + + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 8, 50267] + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + # Only compute necessary logits + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +__all__ = [ + "MvpForCausalLM", + "MvpForConditionalGeneration", + "MvpForQuestionAnswering", + "MvpForSequenceClassification", + "MvpModel", + "MvpPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/myt5/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/myt5/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..65c8190ee6d94d7f63727202392a625d04aecdb4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/myt5/__init__.py @@ -0,0 +1,26 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .tokenization_myt5 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/myt5/tokenization_myt5.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/myt5/tokenization_myt5.py new file mode 100644 index 0000000000000000000000000000000000000000..93289a037ac9f59381271956a16f5cbacf182d0e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/myt5/tokenization_myt5.py @@ -0,0 +1,378 @@ +# Copyright 2024 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for model MyT5.""" + +import json +import os +import warnings +from collections import defaultdict + +from ...tokenization_python import AddedToken, PreTrainedTokenizer +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +VOCAB_FILES_NAMES = {"vocab_file": "byte_maps.json"} + + +class ByteRewriter: + """ + Byte rewriter class for MyT5 tokenizer. + This class is used to rewrite bytes using a hash tree. The hash tree is constructed from a set of rewriting rules. + + Args: + rewriting_rules (`str` or `dict[str, str]`): + A path to a json file containing the rewriting rules or a dictionary containing the rewriting rules. + + """ + + LEAF = "[LEAF]" + + def __init__(self, rewriting_rules: str | dict[str, str]): + if isinstance(rewriting_rules, str): + with open(rewriting_rules, "r") as f: + rewriting_rules = json.load(f) + elif not isinstance(rewriting_rules, dict): + raise TypeError( + f"rewriting_rules should be either a path to json file or a dict, got {type(rewriting_rules)}" + ) + + self.hash_tree = self.construct_hash_tree(rewriting_rules) + reverse_rewriting_rules = {v: k for k, v in rewriting_rules.items()} + self.reverse_hash_tree = self.construct_hash_tree(reverse_rewriting_rules) + + def add_leaf(self, hash_tree: dict[str, dict | list[str]], byte_in_sequence: str, byte_out_sequence: str): + """ + Add a leaf with the output byte sequence to the hash tree. + """ + byte_in_list = byte_in_sequence.split(" ") + byte_out_list = byte_out_sequence.split(" ") + + tree_pointer = hash_tree + for b in byte_in_list: + if b not in tree_pointer: + tree_pointer[b] = {} + tree_pointer = tree_pointer[b] + + tree_pointer[self.LEAF] = byte_out_list + + def construct_hash_tree(self, rewriting_rules: dict[str, str]) -> dict[str, dict | list[str]]: + """ + Construct a hash tree for rewritten byte sequences. + """ + hash_tree = defaultdict(dict) + for b in (f"{x:02x}" for x in range(256)): + hash_tree[b][self.LEAF] = [b] + + for in_sequence, out_sequence in rewriting_rules.items(): + self.add_leaf(hash_tree, in_sequence, out_sequence) + + return hash_tree + + def search_hash_tree(self, byte_sequence: list[str]) -> None | list[str]: + """ + Search the hash tree and return the rewritten byte sequence if found. + """ + tree_pointer = self.hash_tree + for b in byte_sequence: + if b in tree_pointer: + tree_pointer = tree_pointer[b] + else: + return None + + return tree_pointer[self.LEAF] + + def rewrite_bytes(self, in_bytes: list[str], reverse=False) -> list[str]: + """ + Rewrite a sequence of bytes using the hash tree. + + Args: + in_bytes (`list[str]`): A list of bytes to be rewritten. + reverse (`bool`): If True, decoding is performed with the reverse hash tree. + Returns: + `list[str]`: The rewritten byte sequence. + """ + out_bytes = [] + b_start = 0 + b_end = 0 + + while b_start < len(in_bytes): + tree_pointer = self.hash_tree if not reverse else self.reverse_hash_tree + for j in range(b_start, len(in_bytes)): + b = in_bytes[j] + if b in tree_pointer: + tree_pointer = tree_pointer[b] + elif j == b_start: + cur_leaf = [b] + b_end = j + break + else: + break + if self.LEAF in tree_pointer: + cur_leaf = tree_pointer[self.LEAF] + b_end = j + out_bytes.extend(cur_leaf) + b_start = b_end + 1 + + return out_bytes + + +class MyT5Tokenizer(PreTrainedTokenizer): + """ + Construct a MyT5 tokenizer. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): The file containing the byte rewriting rules. + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + extra_ids (`int`, *optional*, defaults to 125): + Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are + accessible as "" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are + indexed from the end of the vocabulary up to beginning ("" is the last token in the vocabulary + like in ByT5 preprocessing see + [here](https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117)). + additional_special_tokens (`list[str]`, *optional*): + Additional special tokens used by the tokenizer. + """ + + model_input_names = ["input_ids", "attention_mask"] + vocab_files_names = VOCAB_FILES_NAMES + + def __init__( + self, + vocab_file, + eos_token="", + unk_token="", + pad_token="", + extra_ids=125, + additional_special_tokens=None, + **kwargs, + ) -> None: + # Add extra_ids to the special token list + if extra_ids > 0 and additional_special_tokens is None: + additional_special_tokens = [f"" for i in range(extra_ids)] + elif extra_ids > 0 and additional_special_tokens is not None and len(additional_special_tokens) > 0: + # Check that we have the right number of extra_id special tokens + extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens))) + if extra_tokens != extra_ids: + raise ValueError( + f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are" + " provided to MyT5Tokenizer. In this case the additional_special_tokens must include the" + " extra_ids tokens" + ) + + pad_token = AddedToken(pad_token, lstrip=True, rstrip=True) if isinstance(pad_token, str) else pad_token + eos_token = AddedToken(eos_token, lstrip=True, rstrip=True) if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, lstrip=True, rstrip=True) if isinstance(unk_token, str) else unk_token + # unk token needs to be in the vocab with correct index + self._added_tokens_decoder = {0: pad_token, 1: eos_token, 2: unk_token} + self.offset = len(self._added_tokens_decoder) + self._utf_vocab_size = 2**8 # utf is 8 bits + + # Load byte maps + self.byte_maps = json.load(open(vocab_file, "r")) + + self.decompose_rewriter = ByteRewriter(self.byte_maps["decompose_map"]) + self.merge_rewriter = ByteRewriter(self.byte_maps["merge_map"]) + + super().__init__( + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + extra_ids=0, + additional_special_tokens=additional_special_tokens, + **kwargs, + ) + + @property + def vocab_size(self): + return self._utf_vocab_size + + # Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.get_vocab + def get_vocab(self): + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size + self.offset)} + vocab.update(self.added_tokens_encoder) + return vocab + + # Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.get_special_tokens_mask + def get_special_tokens_mask( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None, already_has_special_tokens: bool = False + ) -> list[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + # normal case: some special tokens + if token_ids_1 is None: + return ([0] * len(token_ids_0)) + [1] + return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + + def _add_eos_if_not_present(self, token_ids: list[int]) -> list[int]: + """Do not add eos again if user already added it.""" + if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: + warnings.warn( + f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated" + " eos tokens being added." + ) + return token_ids + else: + return token_ids + [self.eos_token_id] + + def create_token_type_ids_from_sequences( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. MyT5 does not + make use of token type ids, therefore a list of zeros is returned. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of zeros. + """ + eos = [self.eos_token_id] + + if token_ids_1 is None: + return len(token_ids_0 + eos) * [0] + return len(token_ids_0 + eos + token_ids_1 + eos) * [0] + + # Copied from transformers.models.byt5.tokenization_byt5.ByT5Tokenizer.build_inputs_with_special_tokens + def build_inputs_with_special_tokens( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A sequence has the following format: + + - single sequence: `X ` + - pair of sequences: `A B ` + + Args: + token_ids_0 (`list[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + token_ids_0 = self._add_eos_if_not_present(token_ids_0) + if token_ids_1 is None: + return token_ids_0 + else: + token_ids_1 = self._add_eos_if_not_present(token_ids_1) + return token_ids_0 + token_ids_1 + + def _tokenize(self, text: str, **kwargs) -> list[str]: + """Take as input a string and return a list of strings (tokens) for words/sub-words. + Represents tokens in two character hex format""" + + tokens = [f"{i:02x}" for i in text.encode("utf-8")] + tokens = self.morphological_encode(tokens) + return tokens + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + + if len(token) != 2: + token_id = None + else: + token_id = int(token, 16) + self.offset + + return token_id + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = f"{index - self.offset:02x}" + return token + + def morphological_encode(self, indices: list[str]) -> list[str]: + # Decompose and merge morphological sequences + indices = self.decompose_rewriter.rewrite_bytes(indices, reverse=False) + indices = self.merge_rewriter.rewrite_bytes(indices, reverse=False) + return indices + + def morphological_decode(self, indices: list[str]) -> list[str]: + # Demerge and compose morphological sequences + indices = self.merge_rewriter.rewrite_bytes(indices, reverse=True) + indices = self.decompose_rewriter.rewrite_bytes(indices, reverse=True) + return indices + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + bstring = b"" + + out_tokens = [] + for token in tokens: + if token in self.added_tokens_decoder: + out_tokens.append(self.added_tokens_decoder[token]) + elif token in self.added_tokens_encoder: + out_tokens.append(token) + else: + out_tokens.append(token) + + out_tokens = self.morphological_decode(out_tokens) + _added_tokens = set(self.added_tokens_decoder.values()) | set(self.added_tokens_encoder) + for token in out_tokens: + if token in _added_tokens: + bstring += bytes(token, "utf-8") + else: + bstring += bytes.fromhex(token) + string = bstring.decode("utf-8", errors="ignore") + return string + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + else: + vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory + with open(vocab_file, "w", encoding="utf-8") as writer: + writer.write(json.dumps(self.byte_maps, indent=2, ensure_ascii=False)) + return (vocab_file,) + + +__all__ = ["MyT5Tokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6610a71fa701d0f8854c96ed5b63ef389666cc90 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/__init__.py @@ -0,0 +1,14 @@ +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_nanochat import * + from .modeling_nanochat import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/configuration_nanochat.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/configuration_nanochat.py new file mode 100644 index 0000000000000000000000000000000000000000..24a0ab7b6d097afbda06b1a0a9977cb43240d2ce --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/configuration_nanochat.py @@ -0,0 +1,81 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="karpathy/nanochat-d32") +@strict +class NanoChatConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import NanoChatModel, NanoChatConfig + + >>> # Initializing a NanoChat style configuration + >>> configuration = NanoChatConfig() + + >>> # Initializing a model from the NanoChat style configuration + >>> model = NanoChatModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "nanochat" + keys_to_ignore_at_inference = ["past_key_values"] + + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.fc1": "colwise", + "layers.*.mlp.fc2": "rowwise", + } + + vocab_size: int = 50304 + hidden_size: int = 768 + intermediate_size: int = 8192 + num_hidden_layers: int = 12 + num_attention_heads: int = 6 + num_key_value_heads: int | None = None + max_position_embeddings: int = 2048 + hidden_act: str = "relu2" + attention_dropout: float | int = 0.0 + rms_norm_eps: float = 1e-6 + initializer_range: float = 0.02 + rope_parameters: RopeParameters | dict | None = None + use_cache: bool = True + final_logit_softcapping: float | None = 15.0 + attention_bias: bool = False + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 1 + pad_token_id: int | None = 1 + tie_word_embeddings: bool = False + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + super().__post_init__(**kwargs) + + +__all__ = ["NanoChatConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/modeling_nanochat.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/modeling_nanochat.py new file mode 100644 index 0000000000000000000000000000000000000000..9205b89cd360e1363fcda18dc7826adf01dd2724 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/modeling_nanochat.py @@ -0,0 +1,518 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/nanochat/modular_nanochat.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_nanochat.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn as nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_func_from_hub, use_kernelized_func +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import can_return_tuple, maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_nanochat import NanoChatConfig + + +class NanoChatRMSNorm(torch.nn.Module): + def __init__(self, eps: float = 1e-6): + super().__init__() + self.eps = eps + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + return self._norm(x.float()).type_as(x) + + def extra_repr(self): + return f"eps={self.eps}" + + +class NanoChatRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: NanoChatConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: NanoChatConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def rotate_half(x): + """Rotates half the hidden dims of the input with flipped signs for NanoChat.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((x2, -x1), dim=-1) + + +@use_kernelized_func(apply_rotary_pos_emb) +class NanoChatAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: NanoChatConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + + self.q_norm = NanoChatRMSNorm(eps=config.rms_norm_eps) + self.k_norm = NanoChatRMSNorm(eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + # RoPE -> Norm (instead of usual Norm -> RoPE) + query_states = self.q_norm(query_states) + key_states = self.k_norm(key_states) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class NanoChatMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class NanoChatDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: NanoChatConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = NanoChatAttention(config=config, layer_idx=layer_idx) + + self.mlp = NanoChatMLP(config) + + self.input_layernorm = NanoChatRMSNorm(eps=config.rms_norm_eps) + self.post_attention_layernorm = NanoChatRMSNorm(eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class NanoChatPreTrainedModel(PreTrainedModel): + config: NanoChatConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["NanoChatDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": NanoChatDecoderLayer, + "attentions": NanoChatAttention, + } + + def _init_weights(self, module: nn.Module) -> None: + super()._init_weights(module) + if isinstance(module, NanoChatAttention): + init.normal_( + module.o_proj.weight, + mean=0.0, + std=self.config.initializer_range / math.sqrt(2 * self.config.num_hidden_layers), + ) + + +@auto_docstring +class NanoChatModel(NanoChatPreTrainedModel): + def __init__(self, config: NanoChatConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [NanoChatDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + + self.norm = NanoChatRMSNorm(eps=config.rms_norm_eps) + self.rotary_emb = NanoChatRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + hidden_states = self.norm(hidden_states) # Additional norm before the layers + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_embeddings=position_embeddings, + position_ids=position_ids, + past_key_values=past_key_values, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class NanoChatForCausalLM(NanoChatPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = NanoChatModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, AutoModelForCausalLM + + >>> model = AutoModelForCausalLM.from_pretrained("karpathy/nanochat-d32") + + >>> tokenizer = AutoTokenizer.from_pretrained("karpathy/nanochat-d32") + + >>> conversation = [ + {"role": "user", "content": "What is the capital of France?"}, + ] + + >>> inputs = tokenizer.apply_chat_template( + conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" + ).to(device) + + >>> with torch.no_grad(): + >>> outputs = model.generate(**inputs, max_new_tokens=64, do_sample=False) + + >>> generated_tokens = outputs[0, inputs["input_ids"].shape[1] :] + >>> output = tokenizer.decode(generated_tokens, skip_special_tokens=True) + ```""" + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + if self.config.final_logit_softcapping is not None: + logits = logits / self.config.final_logit_softcapping + logits = torch.tanh(logits) + logits = logits * self.config.final_logit_softcapping + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = ["NanoChatPreTrainedModel", "NanoChatModel", "NanoChatForCausalLM"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/modular_nanochat.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/modular_nanochat.py new file mode 100644 index 0000000000000000000000000000000000000000..713cc29b81eb304e2d92f506294021858bb58c92 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nanochat/modular_nanochat.py @@ -0,0 +1,235 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable + +import torch +import torch.nn as nn + +from ... import initialization as init +from ...cache_utils import Cache, DynamicCache +from ...masking_utils import create_causal_mask +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ..clip.modeling_clip import CLIPMLP +from ..gemma2.modeling_gemma2 import Gemma2ForCausalLM +from ..llama.modeling_llama import ( + LlamaDecoderLayer, + LlamaModel, + LlamaPreTrainedModel, + LlamaRotaryEmbedding, + apply_rotary_pos_emb, + eager_attention_forward, +) +from ..llama4.modeling_llama4 import Llama4TextL2Norm +from ..qwen3.modeling_qwen3 import Qwen3Attention +from .configuration_nanochat import NanoChatConfig + + +class NanoChatRMSNorm(Llama4TextL2Norm): + pass + + +class NanoChatRotaryEmbedding(LlamaRotaryEmbedding): + pass + + +def rotate_half(x): + """Rotates half the hidden dims of the input with flipped signs for NanoChat.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((x2, -x1), dim=-1) + + +class NanoChatAttention(Qwen3Attention): + def __init__(self, config: NanoChatConfig, layer_idx: int): + super().__init__(config, layer_idx) + del self.sliding_window + del self.layer_type + + self.q_norm = NanoChatRMSNorm(eps=config.rms_norm_eps) + self.k_norm = NanoChatRMSNorm(eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + # RoPE -> Norm (instead of usual Norm -> RoPE) + query_states = self.q_norm(query_states) + key_states = self.k_norm(key_states) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class NanoChatMLP(CLIPMLP): + def __init__(self, config): + super().__init__(config) + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + + +class NanoChatDecoderLayer(LlamaDecoderLayer): + def __init__(self, config: NanoChatConfig, layer_idx: int): + super().__init__() + + self.input_layernorm = NanoChatRMSNorm(eps=config.rms_norm_eps) + self.post_attention_layernorm = NanoChatRMSNorm(eps=config.rms_norm_eps) + + +@auto_docstring +class NanoChatPreTrainedModel(LlamaPreTrainedModel): + def _init_weights(self, module: nn.Module) -> None: + PreTrainedModel._init_weights(self, module) + if isinstance(module, NanoChatAttention): + init.normal_( + module.o_proj.weight, + mean=0.0, + std=self.config.initializer_range / math.sqrt(2 * self.config.num_hidden_layers), + ) + + +@auto_docstring +class NanoChatModel(LlamaModel): + def __init__(self, config: NanoChatConfig): + super().__init__(config) + + self.norm = NanoChatRMSNorm(eps=config.rms_norm_eps) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + hidden_states = self.norm(hidden_states) # Additional norm before the layers + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_embeddings=position_embeddings, + position_ids=position_ids, + past_key_values=past_key_values, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class NanoChatForCausalLM(Gemma2ForCausalLM): + _tp_plan = {"lm_head": "colwise_gather_output"} + + def forward(self, **super_kwargs) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, AutoModelForCausalLM + + >>> model = AutoModelForCausalLM.from_pretrained("karpathy/nanochat-d32") + + >>> tokenizer = AutoTokenizer.from_pretrained("karpathy/nanochat-d32") + + >>> conversation = [ + {"role": "user", "content": "What is the capital of France?"}, + ] + + >>> inputs = tokenizer.apply_chat_template( + conversation, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" + ).to(device) + + >>> with torch.no_grad(): + >>> outputs = model.generate(**inputs, max_new_tokens=64, do_sample=False) + + >>> generated_tokens = outputs[0, inputs["input_ids"].shape[1] :] + >>> output = tokenizer.decode(generated_tokens, skip_special_tokens=True) + ```""" + super().forward(**super_kwargs) + + +__all__ = [ + "NanoChatPreTrainedModel", + "NanoChatModel", + "NanoChatForCausalLM", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2bd66ddd622e067fc6b2863ffdc745a9e09f2307 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_nemotron import * + from .modeling_nemotron import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/configuration_nemotron.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/configuration_nemotron.py new file mode 100644 index 0000000000000000000000000000000000000000..8d4cf07c1f05f1928486aacc672065f6e50f6404 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/configuration_nemotron.py @@ -0,0 +1,73 @@ +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Nemotron model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="thhaus/nemotron3-8b") +@strict +class NemotronConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import NemotronModel, NemotronConfig + + >>> # Initializing a Nemotron nemotron-15b style configuration + >>> configuration = NemotronConfig() + + >>> # Initializing a model from the nemotron-15b style configuration + >>> model = NemotronModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "nemotron" + keys_to_ignore_at_inference = ["past_key_values"] + + vocab_size: int = 256000 + hidden_size: int = 6144 + intermediate_size: int = 24576 + num_hidden_layers: int = 32 + num_attention_heads: int = 48 + head_dim: int | None = None + num_key_value_heads: int | None = None + hidden_act: str = "relu2" + max_position_embeddings: int = 4096 + initializer_range: float = 0.0134 + norm_eps: float = 1e-5 + use_cache: bool = True + pad_token_id: int | None = None + bos_token_id: int | None = 2 + eos_token_id: int | list[int] | None = 3 + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + mlp_bias: bool = False + + def __post_init__(self, **kwargs): + self.head_dim = self.head_dim if self.head_dim is not None else self.hidden_size // self.num_attention_heads + kwargs.setdefault("partial_rotary_factor", 0.5) # assign default for BC + super().__post_init__(**kwargs) + + +__all__ = ["NemotronConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/modeling_nemotron.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/modeling_nemotron.py new file mode 100644 index 0000000000000000000000000000000000000000..b91b45ffd18364836de3b1cf2a16bbe3511ec82d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron/modeling_nemotron.py @@ -0,0 +1,731 @@ +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Nemotron model.""" + +import math +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import Size, Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, StaticCache +from ...generation import GenerationMixin +from ...masking_utils import create_causal_mask +from ...modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask +from ...modeling_layers import ( + GenericForQuestionAnswering, + GenericForSequenceClassification, + GenericForTokenClassification, + GradientCheckpointingLayer, +) +from ...modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from ...modeling_rope_utils import ( + ROPE_INIT_FUNCTIONS, + dynamic_rope_update, +) +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_nemotron import NemotronConfig + + +logger = logging.get_logger(__name__) + + +def _cast_if_autocast_enabled(device_type, *args): + if not torch.is_autocast_enabled(): + return args + else: + target_dtype = torch.get_autocast_dtype(device_type) + return torch.amp.autocast_mode._cast(args, device_type, target_dtype) + + +class NemotronLayerNorm1P(nn.LayerNorm): + def __init__( + self, + normalized_shape: int | list[int] | Size, + eps: float = 1e-5, + elementwise_affine: bool = True, + bias: bool = True, + device=None, + dtype=None, + ): + super().__init__(normalized_shape, eps, elementwise_affine, bias, device, dtype) + + def forward(self, input: Tensor) -> Tensor: + device_type = input.device.type if input.device.type != "mps" else "cpu" + args = _cast_if_autocast_enabled( + device_type, input, self.normalized_shape, self.weight + 1, self.bias, self.eps + ) + with maybe_autocast(device_type=input.device.type, enabled=False): + return F.layer_norm(*args) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: NemotronConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + # Ignore copy + def compute_default_rope_parameters( + config: NemotronConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0) + head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + dim = int(head_dim * partial_rotary_factor) + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + rot_dim = cos.shape[-1] + # If q_pass/k_pass is empty, rotary pos embedding is applied to all tensor q/k + q, q_pass = q[..., :rot_dim], q[..., rot_dim:] + k, k_pass = k[..., :rot_dim], k[..., rot_dim:] + + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return torch.cat((q_embed, q_pass), dim=-1), torch.cat((k_embed, k_pass), dim=-1) + + +class NemotronMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.up_proj(x))) + + +# Copied from transformers.models.llama.modeling_llama.repeat_kv +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class NemotronAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: NemotronConfig, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = config.head_dim + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + + self.partial_rotary_factor = config.rope_parameters["partial_rotary_factor"] + self.is_causal = True + self.rotary_emb = NemotronRotaryEmbedding(config=config) + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.o_proj = nn.Linear(self.head_dim * self.num_heads, self.hidden_size, bias=config.attention_bias) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + attn_output = attn_output.transpose(1, 2).contiguous() + + attn_output = attn_output.reshape(bsz, q_len, -1) + + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +# NO LONGER EXIST Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2 with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +# TODO cyril: modular +class NemotronFlashAttention2(NemotronAttention): + """ + Nemotron flash attention module. This module inherits from `NemotronAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignment, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = flash_attn_supports_top_left_mask() + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + if isinstance(past_key_values, StaticCache): + raise ValueError( + "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` " + "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers" + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (NemotronRMSNorm handles it correctly) + + input_dtype = query_states.dtype + device_type = query_states.device.type if query_states.device.type != "mps" else "cpu" + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_dtype(device_type) + # Handle the case where the model is quantized + elif hasattr(self.config, "_is_quantized"): + target_dtype = self.config.dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + position_ids=position_ids, + dropout=dropout_rate, + sliding_window=getattr(self, "sliding_window", None), + use_top_left_mask=self._flash_attn_uses_top_left_mask, + is_causal=self.is_causal, + ) + + attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, None + + +# NO LONGER EXIST Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +# TODO cyril: modular +class NemotronSdpaAttention(NemotronAttention): + """ + Nemotron attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `NemotronAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + if output_attentions: + logger.warning_once( + f"{self.__class__.__name__} does not support `output_attentions=True`. The returned attention weights will " + "be `None`. If you want to get attention weights, please set `attn_implementation='eager'` when loading the model." + ) + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + is_causal = causal_mask is None and q_len > 1 + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(bsz, q_len, -1) + + attn_output = self.o_proj(attn_output) + + return attn_output, None + + +NEMOTRON_ATTENTION_CLASSES = { + "eager": NemotronAttention, + "flash_attention_2": NemotronFlashAttention2, + "sdpa": NemotronSdpaAttention, +} + + +# copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +# no longer copied after attention refactors +class NemotronDecoderLayer(GradientCheckpointingLayer): + # Ignore copy + def __init__(self, config: NemotronConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = NEMOTRON_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) + + self.mlp = NemotronMLP(config) + self.input_layernorm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps) + self.post_attention_layernorm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs, + ) -> torch.Tensor: + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + ) + + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class NemotronPreTrainedModel(PreTrainedModel): + config: NemotronConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["NemotronDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + + _can_compile_fullgraph = True + _can_record_outputs = { + "hidden_states": NemotronDecoderLayer, + "attentions": NemotronAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, NemotronLayerNorm1P): + init.ones_(module.weight) + init.zeros_(module.bias) + + +@auto_docstring +class NemotronModel(NemotronPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`NemotronDecoderLayer`] + + Args: + config: NemotronConfig + """ + + def __init__(self, config: NemotronConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [NemotronDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = NemotronLayerNorm1P(config.hidden_size, eps=config.norm_eps) + self.rotary_emb = NemotronRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +# TODO: re-enable check: Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->NEMOTRON,Llama->Nemotron,llama->nemotron +class NemotronForCausalLM(NemotronPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + + def __init__(self, config): + super().__init__(config) + self.model = NemotronModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, NemotronForCausalLM + + >>> model = NemotronForCausalLM.from_pretrained("thhaus/nemotron3-8b") + >>> tokenizer = AutoTokenizer.from_pretrained("thhaus/nemotron3-8b") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class NemotronForSequenceClassification(GenericForSequenceClassification, NemotronPreTrainedModel): ... + + +class NemotronForQuestionAnswering(GenericForQuestionAnswering, NemotronPreTrainedModel): + base_model_prefix = "transformer" + + +class NemotronForTokenClassification(GenericForTokenClassification, NemotronPreTrainedModel): ... + + +__all__ = [ + "NemotronForQuestionAnswering", + "NemotronForCausalLM", + "NemotronModel", + "NemotronPreTrainedModel", + "NemotronForSequenceClassification", + "NemotronForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bf5b9326beeae718ed8a95cb1a3bac078075f94e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_nemotron_h import * + from .modeling_nemotron_h import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/configuration_nemotron_h.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/configuration_nemotron_h.py new file mode 100644 index 0000000000000000000000000000000000000000..1c39584c45ac6a208723e765aac74db374257196 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/configuration_nemotron_h.py @@ -0,0 +1,271 @@ +# Copyright 2024-2025 NVIDIA Corporation and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""NemotronH model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16") +@strict +class NemotronHConfig(PreTrainedConfig): + r""" + layers_block_type (`list`, *optional*): + Explicit list of layer types for each layer. Each element must be one of: "mlp", "mamba", "attention", or "moe". + The number of layers is determined by the length of this list. + num_logits_to_keep (`int`, *optional*, defaults to 1): + Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. + use_mamba_kernels (`bool`, *optional*, defaults to `True`): + Flag indicating whether or not to use the fast mamba kernels. + ssm_state_size (`int`, *optional*, defaults to 128): + The dimension of the mamba state space latents. + mamba_hidden_act (`str`, *optional*, defaults to `"silu"`): + The non-linear activation function in the Mamba layers. + n_groups (`int`, *optional*, defaults to 8): + Number of groups for the evolution matrices of the Mamba layers. + expand (`int`, *optional*, defaults to 2): + Expanding factor used to determine the intermediate size in the Mamba layers. + use_conv_bias (`bool`, *optional*, defaults to `True`): + Whether or not to use bias in the convolution layer of the Mamba mixer block. + chunk_size (`int`, *optional*, defaults to 128): + Size of the chunks that will comprise the sequence in the Mamba layers. + mamba_ssm_cache_dtype (`str`, *optional*, defaults to `"float32"`): + Data type for Mamba SSM cache states. + moe_shared_expert_intermediate_size (`int`, *optional*, defaults to 7688): + Dimension of the MLP representations in shared experts. + moe_latent_size (`int`, *optional*): + Latent size for MoE expert projections. If `None`, uses `hidden_size`. + moe_shared_expert_overlap (`bool`, *optional*, defaults to `True`): + Whether shared experts overlap with routed experts. + n_group (`int`, *optional*, defaults to 1): + Number of groups for expert routing. + num_nextn_predict_layers (`int`, *optional*, defaults to 0): + Number of additional layers for multi-token prediction. If 0, multi-token prediction is disabled. + mtp_layers_block_type (`list`, *optional*, defaults to `['attention', 'moe']`): + Explicit list of layer types for multi-token prediction layers when `num_nextn_predict_layers` > 0. + use_bias (`bool`, *optional*, defaults to `False`): + Whether to use bias in the model. + residual_in_fp32 (`bool`, *optional*, defaults to `False`): + Whether or not residuals should be in `float32`. + rescale_prenorm_residual (`bool`, *optional*, defaults to `True`): + Whether to rescale the pre-normalization residual connections. + + ```python + >>> from transformers import NemotronHModel, NemotronHConfig + + >>> # Initializing a NemotronH configuration + >>> configuration = NemotronHConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = NemotronHModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "nemotron_h" + attribute_map = {"layer_types": "layers_block_type"} + keys_to_ignore_at_inference = ["past_key_values"] + + vocab_size: int = 131072 + hidden_size: int = 4096 + layers_block_type: list[str] | None = None + tie_word_embeddings: bool = False + use_cache: bool = True + num_logits_to_keep: int = 1 + pad_token_id: int | None = 0 + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 2 + num_attention_heads: int = 32 + num_key_value_heads: int = 8 + head_dim: int = 128 + max_position_embeddings: int = 4096 + attention_bias: bool = False + attention_dropout: float | int = 0.0 + sliding_window: int | None = None + intermediate_size: int = 21504 + mlp_hidden_act: str = "relu2" + mlp_bias: bool = False + use_mamba_kernels: bool = True + ssm_state_size: int = 128 + mamba_num_heads: int = 128 + mamba_head_dim: int = 64 + mamba_hidden_act: str = "silu" + n_groups: int = 8 + conv_kernel: int = 4 + expand: int = 2 + time_step_min: float = 0.001 + time_step_max: float = 0.1 + time_step_limit: list[float] | tuple[float, ...] = (0.0, float("inf")) + time_step_floor: float = 1e-4 + use_conv_bias: bool = True + chunk_size: int = 128 + mamba_proj_bias: bool = False + mamba_ssm_cache_dtype: str = "float32" + n_routed_experts: int = 8 + n_shared_experts: int = 1 + moe_intermediate_size: int = 7688 + moe_shared_expert_intermediate_size: int = 7688 + moe_latent_size: int | None = None + moe_shared_expert_overlap: bool = True + num_experts_per_tok: int = 2 + routed_scaling_factor: float | int = 1.0 + n_group: int = 1 + topk_group: int = 1 + norm_topk_prob: bool = True + num_nextn_predict_layers: int = 0 + mtp_layers_block_type: list[str] | None = None + use_bias: bool = False + initializer_range: float = 0.02 + layer_norm_epsilon: float = 1e-5 + residual_in_fp32: bool = False + hidden_dropout: float | int = 0.0 + rescale_prenorm_residual: bool = True + + def __post_init__(self, **kwargs): + # Backward compatibility; configs expect different names for these fields when init + # but they have to be re-names when creating/saving the config. + self.n_groups = kwargs.pop("mamba_n_groups") if "mamba_n_groups" in kwargs else self.n_groups + self.conv_kernel = kwargs.pop("mamba_d_conv") if "mamba_d_conv" in kwargs else self.conv_kernel + self.expand = kwargs.pop("mamba_expand") if "mamba_expand" in kwargs else self.expand + self.time_step_min = kwargs.pop("mamba_dt_min") if "mamba_dt_min" in kwargs else self.time_step_min + self.time_step_max = kwargs.pop("mamba_dt_max") if "mamba_dt_max" in kwargs else self.time_step_max + self.time_step_limit = kwargs.pop("mamba_dt_limit") if "mamba_dt_limit" in kwargs else self.time_step_limit + self.time_step_floor = ( + kwargs.pop("mamba_dt_init_floor") if "mamba_dt_init_floor" in kwargs else self.time_step_floor + ) + self.use_conv_bias = kwargs.pop("mamba_conv_bias") if "mamba_conv_bias" in kwargs else self.use_conv_bias + self.chunk_size = kwargs.pop("mamba_chunk_size") if "mamba_chunk_size" in kwargs else self.chunk_size + + # Backward compatibility: convert hybrid_override_pattern to layers_block_type + # Always pop hybrid_override_pattern from kwargs to prevent it from being set as an attribute + if "hybrid_override_pattern" in kwargs: + pattern = kwargs.pop("hybrid_override_pattern") + if self.layer_types is None: + self.layer_types = self._pattern_to_list(pattern) + elif self.layer_types is None: + # Default layers_block_type if not provided + self.layer_types = ["mamba", "moe", "attention", "mlp"] + + # Note: num_hidden_layers is deprecated and ignored if layers_block_type is explicitly provided + # It's only kept for backward compatibility when loading old configs + if self.num_hidden_layers is not None: + # Warn if num_hidden_layers is provided but doesn't match layers_block_type + if len(self.layer_types) != self.num_hidden_layers: + logger.warning( + f"num_hidden_layers ({self.num_hidden_layers}) is deprecated and doesn't match " + f"layer_types length ({len(self.layer_types)}). Using layers_block_type length." + ) + + # Backward compatibility: convert mtp_hybrid_override_pattern to mtp_layers_block_type + # Always pop mtp_hybrid_override_pattern from kwargs to prevent it from being set as an attribute + if self.mtp_layers_block_type is None: + self.mtp_layers_block_type = ["attention", "moe"] + + if "mtp_hybrid_override_pattern" in kwargs: + pattern = kwargs.pop("mtp_hybrid_override_pattern") + if self.mtp_layers_block_type == ["attention", "moe"]: + self.mtp_layers_block_type = self._pattern_to_list(pattern) + + # for backward compatibility + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + super().__post_init__(**kwargs) + + @staticmethod + def validate_layer_type(self): + """ + Validate layers_block_type list. + """ + if not isinstance(self.layer_types, list): + raise ValueError(f"`layers_block_type` must be a list of strings. Got type: {type(self.layer_types)}") + + valid_types = {"mamba", "attention", "moe", "mlp"} + if not all(block_type in valid_types for block_type in self.layer_types): + invalid = set(self.layer_types) - valid_types + raise ValueError(f"`layers_block_type` contains invalid types: {invalid}. Must be one of: {valid_types}") + + if self.num_nextn_predict_layers > 0: + if self.mtp_layers_block_type is None: + raise ValueError( + "mtp_layers_block_type is required when num_nextn_predict_layers > 0. " + "Please provide an explicit list of layer types for MTP layers. " + "Example: mtp_layers_block_type=['attention', 'moe']" + ) + + if not isinstance(self.mtp_layers_block_type, list): + raise ValueError( + f"`mtp_layers_block_type` must be a list of strings. Got type: {type(self.mtp_layers_block_type)}" + ) + + if not all(block_type in valid_types for block_type in self.mtp_layers_block_type): + invalid = set(self.mtp_layers_block_type) - valid_types + raise ValueError( + f"`mtp_layers_block_type` contains invalid types: {invalid}. Must be one of: {valid_types}" + ) + + @property + def num_hidden_layers(self) -> int: + """ + Number of hidden layers derived from the length of layers_block_type. + This property replaces the deprecated num_hidden_layers parameter. + """ + return len(self.layers_block_type) + + @num_hidden_layers.setter + def num_hidden_layers(self, value): + """ + Setter for backward compatibility when loading configs. + The value is ignored since num_hidden_layers is computed from layers_block_type. + """ + # Ignore the value - num_hidden_layers is always derived from layers_block_type + pass + + @property + def hybrid_override_pattern(self) -> str: + """ + Backward compatibility property. + Returns the pattern string representation of layers_block_type. + """ + return self._list_to_pattern(self.layers_block_type) + + @property + def mtp_hybrid_override_pattern(self) -> str: + """ + Backward compatibility property. + Returns the pattern string representation of mtp_layers_block_type. + """ + return self._list_to_pattern(self.mtp_layers_block_type) + + @staticmethod + def _list_to_pattern(layers_list: list) -> str: + """Convert list of layer types back to pattern string (for backward compatibility).""" + reverse_mapping = {"mamba": "M", "moe": "E", "attention": "*", "mlp": "-"} + return "".join(reverse_mapping[layer_type] for layer_type in layers_list) + + @staticmethod + def _pattern_to_list(pattern: str) -> list: + """Convert pattern string to list of layer types (for backward compatibility).""" + pattern_mapping = {"M": "mamba", "E": "moe", "*": "attention", "-": "mlp"} + return [pattern_mapping[char] for char in pattern] + + +__all__ = ["NemotronHConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/modeling_nemotron_h.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/modeling_nemotron_h.py new file mode 100644 index 0000000000000000000000000000000000000000..0f4554a50397b695adfaf40d53f1566d40842d44 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/modeling_nemotron_h.py @@ -0,0 +1,1231 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/nemotron_h/modular_nemotron_h.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_nemotron_h.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from collections.abc import Callable + +import torch +import torch.nn.functional as F +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import ( + lazy_load_kernel, + use_experts_implementation, + use_kernel_forward_from_hub, + use_kernel_func_from_hub, + use_kernelized_func, +) +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...models.zamba2.modeling_zamba2 import Zamba2RMSNormGated +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging +from ...utils.generic import merge_with_config_defaults +from ...utils.import_utils import resolve_internal_import +from ...utils.output_capturing import capture_outputs +from .configuration_nemotron_h import NemotronHConfig + + +logger = logging.get_logger(__name__) + + +# Helper methods for segment sum computation + + +def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int): + """ + Padding x tensor with `pad_size` on the seq_len dim (dim=1) + + Assumes that we only have tensors of either size 4 or 3 + """ + pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(input_tensor.shape) == 4 else (0, 0, 0, pad_size, 0, 0) + + return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0) + + +def reshape_into_chunks(input_tensor, pad_size, chunk_size): + """ + Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and + simultaneously splitting it into chunk sequences. + + Assumes that we only have tensors of either size 4 or 3 + """ + # [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...] + input_tensor = pad_tensor_by_size(input_tensor, pad_size) + + if len(input_tensor.shape) == 3: + # [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads] + return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2]) + else: + # [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> [bsz, -1, chunk_size, num_heads, head_dim or state_size] + return input_tensor.reshape( + input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3] + ) + + +def segment_sum(input_tensor): + """ + More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions. + """ + chunk_size = input_tensor.size(-1) + # 1. expand input tensor to have an additional dimension and repeat along that dimension + # [..., chunk_size] -> [..., chunk_size, chunk_size] + input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size) + # 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag + mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=-1) + input_tensor = input_tensor.masked_fill(~mask, 0) + # 3. compute actual cumsum + tensor_segsum = torch.cumsum(input_tensor, dim=-2) + + # 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time) + mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=0) + tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf) + return tensor_segsum + + +is_fast_path_available = False + + +class NemotronHMamba2Mixer(nn.Module): + """ + Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`. + A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective) + ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4, + and is why Mamba is called **selective** state spaces) + """ + + def __init__(self, config: NemotronHConfig, layer_idx: int | None = None): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.ssm_state_size = config.ssm_state_size + self.conv_kernel_size = config.conv_kernel + self.intermediate_size = config.mamba_num_heads * config.mamba_head_dim + self.layer_idx = layer_idx + self.use_conv_bias = config.use_conv_bias + self.activation = config.mamba_hidden_act + self.act = ACT2FN[config.mamba_hidden_act] + self.use_mem_eff_path = True + + self.n_groups = config.n_groups + self.head_dim = config.mamba_head_dim + self.num_heads = config.mamba_num_heads + self.chunk_size = config.chunk_size + + self.time_step_limit = config.time_step_limit + self.time_step_min = config.time_step_min + self.time_step_max = config.time_step_max + + self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + + self.conv1d = nn.Conv1d( + in_channels=self.conv_dim, + out_channels=self.conv_dim, + bias=config.use_conv_bias, + kernel_size=self.conv_kernel_size, + groups=self.conv_dim, + padding=self.conv_kernel_size - 1, + ) + + # projection of the input hidden states + projection_size = self.intermediate_size + self.conv_dim + self.num_heads + + self.in_proj = nn.Linear( + self.hidden_size, + projection_size, + bias=config.use_bias, + ) + # selective projection used to make dt, B and C input dependent + + # time step projection (discretization) + # instantiate once and copy inv_dt in init_weights of PretrainedModel + self.dt_bias = nn.Parameter(torch.ones(self.num_heads)) + + # S4D real initialization. These are not discretized! + # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded + A = torch.arange(1, self.num_heads + 1) + self.A_log = nn.Parameter(torch.log(A)) + + self.norm = Zamba2RMSNormGated( + self.intermediate_size, group_size=self.intermediate_size // self.n_groups, eps=config.layer_norm_epsilon + ) + self.D = nn.Parameter(torch.ones(self.num_heads)) + + self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias) + + global causal_conv1d_update, causal_conv1d_fn + global selective_state_update, mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined + global is_fast_path_available + + if config.use_mamba_kernels: + causal_conv1d = lazy_load_kernel("causal-conv1d") + causal_conv1d_update = getattr(causal_conv1d, "causal_conv1d_update", None) + causal_conv1d_fn = getattr(causal_conv1d, "causal_conv1d_fn", None) + + mamba_ssm = lazy_load_kernel("mamba-ssm") + selective_state_update = resolve_internal_import( + mamba_ssm, chained_path="ops.triton.selective_state_update.selective_state_update" + ) + mamba_chunk_scan_combined = resolve_internal_import( + mamba_ssm, chained_path="ops.triton.ssd_combined.mamba_chunk_scan_combined" + ) + mamba_split_conv1d_scan_combined = resolve_internal_import( + mamba_ssm, chained_path="ops.triton.ssd_combined.mamba_split_conv1d_scan_combined" + ) + + is_fast_path_available = all( + ( + selective_state_update, + mamba_chunk_scan_combined, + mamba_split_conv1d_scan_combined, + causal_conv1d_fn, + causal_conv1d_update, + ) + ) + else: + causal_conv1d_update = None + causal_conv1d_fn = None + selective_state_update = None + mamba_chunk_scan_combined = None + mamba_split_conv1d_scan_combined = None + is_fast_path_available = False + + if getattr(config, "use_mamba_kernels", True) and not is_fast_path_available: + logger.warning_once( + "The fast path is not available because one of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)`" + " is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and" + " https://github.com/Dao-AILab/causal-conv1d" + ) + + def cuda_kernels_forward( + self, + hidden_states: torch.Tensor, + cache_params: Cache | None = None, + attention_mask: torch.Tensor | None = None, + ): + # set up dimensions for reshapes later + + batch_size, seq_len, _ = hidden_states.shape + groups_time_state_size = self.n_groups * self.ssm_state_size + d_to_remove = 2 * self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + self.num_heads + + # getting projected states from cache if it exists + if cache_params is not None and cache_params.has_previous_state(self.layer_idx): + in_projected_states = self.in_proj(hidden_states.squeeze(1)) # (B 2D) + d_mlp = (in_projected_states.shape[-1] - d_to_remove) // 2 + split_projection_dim = [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads] + _, _, gate, hidden_states_B_C, dt = torch.split(in_projected_states, split_projection_dim, dim=-1) + + hidden_states_B_C = causal_conv1d_update( + hidden_states_B_C, + cache_params.layers[self.layer_idx].conv_states, + self.conv1d.weight.squeeze(1), + self.conv1d.bias, + self.activation, + ) + + hidden_states, B, C = torch.split( + hidden_states_B_C, + [self.intermediate_size, groups_time_state_size, groups_time_state_size], + dim=-1, + ) + A = -torch.exp(self.A_log.float()) # (nheads,) + + A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32) + dt = dt[:, :, None].expand(-1, -1, self.head_dim) + dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim) + D = self.D[:, None, ...].expand(-1, self.head_dim) + B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups) + C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups) + hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim) + hidden_states = selective_state_update( + cache_params.layers[self.layer_idx].recurrent_states, + hidden_states_reshaped, + dt, + A, + B, + C, + D, + z=None, + dt_bias=dt_bias, + dt_softplus=True, + ) + hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim) + hidden_states = self.norm(hidden_states, gate) + out = self.out_proj(hidden_states)[:, None, ...] + # if no cache is found, calling the kernel + else: + if attention_mask is not None and not torch.all(attention_mask == 1): + # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66 + dtype = hidden_states.dtype + hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) + # 1. Gated MLP's linear projection + projected_states = self.in_proj(hidden_states) + A = -torch.exp(self.A_log.float()) # (num_heads) or (intermediate_size, state_size) + dt_limit_kwargs = {} if self.time_step_limit is None else {"dt_limit": self.time_step_limit} + if attention_mask is not None: + input_not_masked = torch.all(attention_mask == 1) + else: + input_not_masked = True + + if self.use_mem_eff_path and self.training and cache_params is None and input_not_masked: + out, ssm_state = mamba_split_conv1d_scan_combined( + projected_states, + self.conv1d.weight.squeeze(1), + self.conv1d.bias, + self.dt_bias, + A, + D=self.D, + chunk_size=self.chunk_size, + seq_idx=None, + activation=self.activation, + rmsnorm_weight=self.norm.weight, + rmsnorm_eps=self.norm.variance_epsilon, + outproj_weight=self.out_proj.weight, + outproj_bias=self.out_proj.bias, + headdim=self.head_dim, + ngroups=self.n_groups, + norm_before_gate=False, + return_final_states=True, + **dt_limit_kwargs, + ) + + else: + gate, hidden_states_B_C, time_step = torch.split( + projected_states, + [self.intermediate_size, self.conv_dim, self.num_heads], + dim=-1, + ) + + # 1D Convolution + if cache_params is not None: + hidden_states_B_C_t = hidden_states_B_C.transpose(1, 2) + conv_state = nn.functional.pad( + hidden_states_B_C_t, (self.conv_kernel_size - hidden_states_B_C_t.shape[-1], 0) + ) + conv_state = cache_params.update_conv_state(conv_state, self.layer_idx) + if causal_conv1d_fn is None or self.activation not in ["silu", "swish"]: + hidden_states_B_C = self.act( + self.conv1d(hidden_states_B_C.transpose(1, 2)).transpose(1, 2)[:, :seq_len] + ) # (B, L, self.d_inner + 2 * ngroups * d_state) + else: + hidden_states_B_C = causal_conv1d_fn( + x=hidden_states_B_C.transpose(1, 2), + weight=self.conv1d.weight.squeeze(1), + bias=self.conv1d.bias, + activation=self.activation, + ).transpose(1, 2)[:, :seq_len] + hidden_states, B, C = torch.split( + hidden_states_B_C, + [self.intermediate_size, groups_time_state_size, groups_time_state_size], + dim=-1, + ) + if attention_mask is not None and not torch.all(attention_mask == 1): + # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66 + dtype = hidden_states.dtype + hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) + scan_output, ssm_state = mamba_chunk_scan_combined( + hidden_states.view(batch_size, seq_len, -1, self.head_dim), + time_step, + A, + B.view(batch_size, seq_len, self.n_groups, -1), + C.view(batch_size, seq_len, self.n_groups, -1), + chunk_size=self.chunk_size, + D=self.D, + z=None, + seq_idx=None, + return_final_states=True, + dt_bias=self.dt_bias, + dt_softplus=True, + **dt_limit_kwargs, + ) + if ssm_state is not None and cache_params is not None: + cache_params.update_recurrent_state(ssm_state, self.layer_idx) + scan_output = scan_output.view(batch_size, seq_len, -1) + # Multiply "gate" branch and apply extra normalization layer + scan_output = self.norm(scan_output, gate) + out = self.out_proj(scan_output) + return out + + # fmt: off + def torch_forward(self, input_states, cache_params: Cache | None=None, attention_mask: torch.Tensor | None = None): + batch_size, seq_len, _ = input_states.shape + dtype = input_states.dtype + # Gated MLP's linear projection + if cache_params is not None and cache_params.has_previous_state(self.layer_idx): + projected_states = self.in_proj(input_states) + else: + if attention_mask is not None: + # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66 + input_states = (input_states * attention_mask[:, :, None]).to(dtype) + projected_states = self.in_proj(input_states) + d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size- self.num_heads) // 2 + _, _, gate, hidden_states, dt = projected_states.split( + [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads], dim=-1 + ) + hidden_states = hidden_states.transpose(1, 2) + + use_precomputed_state = cache_params is not None and cache_params.has_previous_state(self.layer_idx) + + # Convolution sequence transformation + if use_precomputed_state: + conv_state = cache_params.update_conv_state(hidden_states, self.layer_idx) + hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1) + if self.use_conv_bias: + hidden_states += self.conv1d.bias + hidden_states = self.act(hidden_states).to(dtype)[:, None, ...] # [batch, 1, intermediate_size] : decoding + else: + if cache_params is not None: + conv_state = nn.functional.pad( + hidden_states, + (self.conv_kernel_size - hidden_states.shape[-1], 0) + ) + conv_state = cache_params.update_conv_state(conv_state, self.layer_idx) + + hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len].transpose(1, 2)) + if attention_mask is not None: + dtype = hidden_states.dtype + # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66 + hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) + + hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1) + A = -torch.exp(self.A_log.float()) # [num_heads] + if use_precomputed_state: + # Note: there is no need to pad parameter matrices here, as there is just one new token + # for batched generation + dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...] + dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim) + # [num_heads] -> [num_heads, head_dim] + dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim) + + dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype)) + dt = torch.clamp(dt, self.time_step_min) #, self.time_step_max) + A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32) + # [bsz, num_heads, head_dim, state_size] + dA = torch.exp(dt[..., None] * A) + + # Discretize B + # [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] -> + # -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size] + B = B.reshape(batch_size, self.n_groups, -1)[..., None, :] + B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous() + B = B.reshape(batch_size, -1, B.shape[-1]) + # [bsz, num_heads, head_dim, state_size] + dB = dt[..., None] * B[..., None, :] + + # Discretize x into dB + # [bsz, intermediate_size] -> [bsz, num_heads, head_dim] + hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim) + dBx = dB * hidden_states[..., None] + + # State calculation + ssm_states = cache_params.layers[self.layer_idx].recurrent_states.clone() + ssm_states = ssm_states * dA + dBx + ssm_states = cache_params.update_recurrent_state(ssm_states, self.layer_idx) + + # Subsequent output + # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size] + C = C.reshape(batch_size, self.n_groups, -1)[..., None, :] + C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous() + C = C.reshape(batch_size, -1, C.shape[-1]) + # [bsz, num_heads, head_dim] + + ssm_states = ssm_states.to(C.dtype) # Shape: [b, h, d, n] + # Reshape ssm_states to merge the first two dimensions + ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size) # Shape: [b*h, d, n] + C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1) # Shape: [b*h, n, 1] + y = torch.bmm(ssm_states_reshaped, C_reshaped) + y = y.view(batch_size, self.num_heads, self.head_dim) + + # D skip connection + # [num_heads] -> [num_heads, head_dim] + D = self.D[..., None].expand(self.D.shape[0], self.head_dim) + y = (y + hidden_states * D).to(y.dtype) + + # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size] + y = y.reshape(batch_size, -1)[:, None, ...] + else: + # begin ssd naive implementation without einsums + dt = nn.functional.softplus(dt + self.dt_bias) + dt = torch.clamp(dt, self.time_step_min) + hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float() + B = B.reshape(batch_size, seq_len, -1, self.ssm_state_size).float() + C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float() + B = B.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads) + C = C.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads) + pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size + + D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size) + + # Discretize x and A + hidden_states = hidden_states * dt[..., None] + A = A.to(hidden_states.dtype) * dt + + # Rearrange into blocks/chunks + hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)] + + + # [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size] + A = A.permute(0, 3, 1, 2) + A_cumsum = torch.cumsum(A, dim=-1) + + # 1. Compute the output for each intra-chunk (diagonal blocks) + # This is the analog of a causal mask + L = torch.exp(segment_sum(A)) + + # First, contraction of C and B to get G (attention-weights like) + G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, : ,:] # shape: (b, c, l, s, h, n) + G = G_intermediate.sum(dim=-1) # shape: (b, c, l, s, h) + + + # Step 2: Compute M, equivalent to applying attention mask to weights + M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None] + M = M_intermediate.sum(dim=-1) + + # Step 3: Compute Y_diag (apply to values) + Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(3) + + # (right term of low-rank factorization of off-diagonal blocks; B terms) + + decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum) + B_decay_contraction = B * decay_states.permute(0, 2, 3, 1)[..., None] + # permute back B * decay states + states = (B_decay_contraction.permute(0, 1, 3, 2, 4)[..., None] * hidden_states.permute(0, 1, 3, 2, 4)[..., None, :]).sum(dim=3).permute(0, 1, 2, 4, 3) + previous_states = torch.zeros_like(states[:, :1]) + states = torch.cat([previous_states, states], dim=1) + decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0)))) + + states_permuted = states.permute(0, 2, 1, 3, 4) + result = (decay_chunk[..., None, None] * states_permuted[:, :, None, ...]).sum(dim=2) + new_states = result.permute(0, 2, 1, 3, 4) + states, ssm_state = new_states[:, :-1], new_states[:, -1] + + # Compute state -> output conversion per chunk + # (left term of low-rank factorization of off-diagonal blocks; C terms) + state_decay_out = torch.exp(A_cumsum) + # compute Yoff + C_times_states = (C[..., None, :] * states[:, :, None, ...]) + state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1) + Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None]) + # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks) + + y = Y_diag + Y_off + # [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim] + y = y.reshape(batch_size, -1, self.num_heads, self.head_dim) + + y = y + D_residual + # Cutting off padded chunks + if pad_size > 0: + y = y[:, :seq_len, :, :] + y = y.reshape(batch_size, seq_len, -1) + if ssm_state is not None and cache_params is not None: + cache_params.update_recurrent_state(ssm_state, self.layer_idx) + + scan_output = self.norm(y, gate) + + # end ssd naive + + # 4. Final linear projection + contextualized_states = self.out_proj(scan_output.to(dtype)) # [batch, seq_len, hidden_size] + return contextualized_states + # fmt: on + + def forward( + self, + hidden_states, + cache_params: Cache | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ): + if is_fast_path_available and "cuda" in self.in_proj.weight.device.type and not is_torchdynamo_compiling(): + # Use cuda stream to avoid NaN when using multiple GPUs, which is caused by multi-GPU synchronization issue. + # Mamba might launch on the default cuda stream that not strictly respect the current Pytorch cuda stream. + # This leads to kernel reading uninitialized memory before the data transfer is complete. + with torch.cuda.stream(torch.cuda.default_stream(hidden_states.device)): + return self.cuda_kernels_forward(hidden_states, cache_params, attention_mask) + + return self.torch_forward(hidden_states, cache_params, attention_mask) + + +@use_kernel_forward_from_hub("RMSNorm") +class NemotronHRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + NemotronHRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class NemotronHMLP(nn.Module): + def __init__(self, config, intermediate_size=None, **kwargs): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = intermediate_size or config.intermediate_size + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.mlp_hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.up_proj(x))) + + +@use_experts_implementation(has_gate=False) +class NemotronHExperts(nn.Module): + """ + Collection of expert weights stored as 3D tensors. + + **Architecture Note**: Unlike Mixtral or DeepSeek which use gated MLPs, + NemotronH uses a standard MLP architecture with only up_proj and down_proj + """ + + def __init__(self, config): + super().__init__() + self.num_experts = config.n_routed_experts + self.hidden_dim = config.hidden_size + self.intermediate_dim = config.moe_intermediate_size + + # Determine input/output dimension based on whether latent projection is used + input_dim = config.moe_latent_size if config.moe_latent_size is not None else config.hidden_size + + # All expert weights stored as 3D tensors: (num_experts, out_dim, in_dim) + # up_proj: (num_experts, intermediate_dim, input_dim) + self.up_proj = nn.Parameter(torch.empty(self.num_experts, self.intermediate_dim, input_dim)) + # down_proj: (num_experts, input_dim, intermediate_dim) + self.down_proj = nn.Parameter(torch.empty(self.num_experts, input_dim, self.intermediate_dim)) + + self.act_fn = ACT2FN[config.mlp_hidden_act] + + def forward(self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor): + final_hidden_states = torch.zeros_like(hidden_states, dtype=top_k_weights.dtype) + + # Create expert mask to identify which tokens go to which experts + with torch.no_grad(): + expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts) + expert_mask = expert_mask.permute(2, 1, 0) # (num_experts, num_experts_per_tok, num_tokens) + # Only iterate over experts that have at least one token assigned + expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero().squeeze(-1) + + for expert_idx in expert_hit: + expert_idx = expert_idx.item() + # Find which tokens are routed to this expert + top_k_pos, token_idx = torch.where(expert_mask[expert_idx]) + + if token_idx.numel() == 0: + continue + + # Get input for this expert + current_state = hidden_states[token_idx] + + # Expert computation: down_proj(act_fn(up_proj(x))) + # No gating mechanism unlike Mixtral which uses: down_proj(act_fn(gate_proj(x)) * up_proj(x)) + current_hidden_states = torch.nn.functional.linear(current_state, self.up_proj[expert_idx]) + current_hidden_states = self.act_fn(current_hidden_states) + current_hidden_states = torch.nn.functional.linear(current_hidden_states, self.down_proj[expert_idx]) + + # Apply routing weights + current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None] + + # Accumulate into final output + final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype)) + + return final_hidden_states.to(hidden_states.dtype) + + +class NemotronHMoE(nn.Module): + """ + Mixture-of-Experts (MoE) module for NemotronH. + + Unique architectures: + - Uses non-gated MLP experts (NemotronHExperts) instead of gated experts + - Adds optional latent projection for computational efficiency + """ + + def __init__(self, config, layer_idx: int | None = None): + super().__init__() + self.config = config + + # Replace with NemotronH-specific experts (non-gated MLP architecture) + self.experts = NemotronHExperts(config) + self.gate = NemotronHTopkRouter(config) + + # Override shared_experts to use NemotronHMLP with correct intermediate size + self.shared_experts = NemotronHMLP(config=config, intermediate_size=config.moe_shared_expert_intermediate_size) + self.n_routed_experts = config.n_routed_experts + self.n_group = config.n_group + self.topk_group = config.topk_group + self.norm_topk_prob = config.norm_topk_prob + self.routed_scaling_factor = config.routed_scaling_factor + self.top_k = config.num_experts_per_tok + + # NemotronH-specific latent projection layers + if config.moe_latent_size is not None: + self.fc1_latent_proj = nn.Linear(config.hidden_size, config.moe_latent_size, bias=config.mlp_bias) + self.fc2_latent_proj = nn.Linear(config.moe_latent_size, config.hidden_size, bias=config.mlp_bias) + else: + self.fc1_latent_proj = nn.Identity() + self.fc2_latent_proj = nn.Identity() + + def route_tokens_to_experts(self, router_logits): + router_logits = router_logits.sigmoid() + router_logits_for_choice = router_logits + self.gate.e_score_correction_bias + group_scores = ( + router_logits_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group) + .topk(2, dim=-1)[0] + .sum(dim=-1) + ) + group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1] + group_mask = torch.zeros_like(group_scores) + group_mask.scatter_(1, group_idx, 1) + score_mask = ( + group_mask.unsqueeze(-1) + .expand(-1, self.n_group, self.n_routed_experts // self.n_group) + .reshape(-1, self.n_routed_experts) + ) + scores_for_choice = router_logits_for_choice.masked_fill(~score_mask.bool(), float("-inf")) + topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1] + topk_weights = router_logits.gather(1, topk_indices) + if self.norm_topk_prob: + denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20 + topk_weights /= denominator + topk_weights = topk_weights * self.routed_scaling_factor + return topk_indices, topk_weights + + def forward(self, hidden_states): + residuals = hidden_states + orig_shape = hidden_states.shape + router_logits = self.gate(hidden_states) + topk_indices, topk_weights = self.route_tokens_to_experts(router_logits) + hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) + + # NemotronH-specific: latent projection + hidden_states = self.fc1_latent_proj(hidden_states) + hidden_states = self.experts(hidden_states, topk_indices, topk_weights) + hidden_states = self.fc2_latent_proj(hidden_states) + + hidden_states = hidden_states.view(*orig_shape) + hidden_states = hidden_states + self.shared_experts(residuals) + return hidden_states + + +class NemotronHTopkRouter(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.n_routed_experts = config.n_routed_experts + + self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size))) + self.register_buffer("e_score_correction_bias", torch.zeros(self.n_routed_experts)) + + def forward(self, hidden_states): + hidden_states = hidden_states.view(-1, self.config.hidden_size) + router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32)) + return router_logits + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class NemotronHAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: NemotronHConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +MIXER_TYPES = { + "mamba": NemotronHMamba2Mixer, + "attention": NemotronHAttention, + "moe": NemotronHMoE, + "mlp": NemotronHMLP, +} + + +class NemotronHBlock(GradientCheckpointingLayer): + """ + A single transformer block in the NemotronH model. + + This block can contain different types of mixers (Mamba, Attention, MLP, or MoE) + depending on the configuration. Each block applies pre-normalization followed by + the mixer, then adds a residual connection. + + Args: + config (`NemotronHConfig`): + Model configuration specifying the block architecture. + layer_idx (`int`): + Index of this block in the model. Used to determine the block type from + `config.layers_block_type[layer_idx]`. + """ + + def __init__(self, config, layer_idx): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.norm = NemotronHRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) + + self.block_type = config.layers_block_type[layer_idx] + self.mixer = MIXER_TYPES[self.block_type](config, layer_idx=layer_idx) + + def forward( + self, + hidden_states, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + use_cache: bool | None = False, + **kwargs: Unpack[TransformersKwargs], + ): + residual = hidden_states + hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype)) + + if self.block_type == "mamba": + hidden_states = self.mixer(hidden_states, cache_params=past_key_values, attention_mask=attention_mask) + elif self.block_type == "attention": + hidden_states, _ = self.mixer( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + position_ids=position_ids, + user_cache=use_cache, + **kwargs, + ) + else: + hidden_states = self.mixer(hidden_states) + + hidden_states = residual + hidden_states + + return hidden_states + + +class NemotronHPreTrainedModel(PreTrainedModel): + config: NemotronHConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["NemotronHBlock"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_flex_attn = True + _is_stateful = True + _can_record_outputs = { + "hidden_states": NemotronHBlock, + "attentions": NemotronHAttention, + } + _keep_in_fp32_modules_strict = [ + "e_score_correction_bias", + ] + _keys_to_ignore_on_load_unexpected = [r"mtp.*"] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights.""" + super()._init_weights(module) + if isinstance(module, NemotronHMamba2Mixer): + # Only re-initialise params that were NOT loaded from a checkpoint. + # `_is_hf_initialized` is set by `from_pretrained` on each loaded + # parameter; without this guard a post-load safety pass of + # `_init_weights` would overwrite checkpoint values of + # A_log / D / dt_bias with fresh random draws. + if not getattr(module.A_log, "_is_hf_initialized", False): + A = torch.arange(1, self.config.mamba_num_heads + 1) + init.copy_(module.A_log, torch.log(A)) + if not getattr(module.D, "_is_hf_initialized", False): + init.ones_(module.D) + if not getattr(module.dt_bias, "_is_hf_initialized", False): + dt = torch.exp( + torch.rand(self.config.mamba_num_heads) + * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min)) + + math.log(self.config.time_step_min) + ).clamp(min=self.config.time_step_floor) + + # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + with torch.no_grad(): + init.copy_(module.dt_bias, inv_dt) + elif isinstance(module, NemotronHTopkRouter): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + init.zeros_(module.e_score_correction_bias) + elif isinstance(module, NemotronHExperts): + # Initialize expert weights + init.normal_(module.up_proj, mean=0.0, std=self.config.initializer_range) + init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range) + + if isinstance(module, nn.Linear): + if module.bias is not None: + if not getattr(module.bias, "_is_hf_initialized", False): + init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, std=self.config.initializer_range) + + if self.config.rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + for name, p in module.named_parameters(): + if name == "out_proj.weight": + # Skip checkpoint-loaded weights so a post-load safety + # pass of `_init_weights` doesn't silently overwrite them. + if getattr(p, "_is_hf_initialized", False): + continue + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p_new = p / math.sqrt(self.config.num_hidden_layers) + init.copy_(p, p_new) + + +class NemotronHModel(NemotronHPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.layers = nn.ModuleList([NemotronHBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)]) + + self.norm_f = NemotronHRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, new_embeddings): + self.embeddings = new_embeddings + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids: torch.LongTensor | None = None, + inputs_embeds: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + hidden_states = inputs_embeds + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + input_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + mamba_mask = self._update_mamba_mask(attention_mask, past_key_values) + + # Map block types to their corresponding masks + block_type_to_mask = { + "mamba": mamba_mask, + "attention": causal_mask, + "moe": None, + "mlp": None, + } + + for layer_idx, mixer_block in enumerate(self.layers): + layer_mask = block_type_to_mask[mixer_block.block_type] + + hidden_states = mixer_block( + hidden_states, + attention_mask=layer_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm_f(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + def _update_mamba_mask(self, attention_mask, past_key_values): + """ + No need for zeroing states when + 1. Cached forward + 2. Attending to all inputs + """ + mamba_mask = attention_mask + if (past_key_values is not None and past_key_values.has_previous_state()) or ( + attention_mask is not None and torch.all(attention_mask == 1) + ): + mamba_mask = None + return mamba_mask + + +# Adapted from transformers.models.jamba.modeling_jamba.JambaForCausalLM with Jamba->NemotronH, JAMBA->NEMOTRON_H +class NemotronHForCausalLM(NemotronHPreTrainedModel, GenerationMixin): + _tied_weights_keys = {} + + def __init__(self, config: NemotronHConfig): + super().__init__(config) + self.model = NemotronHModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, NemotronHForCausalLM + + >>> model = NemotronHForCausalLM.from_pretrained("Zyphra/NemotronH-7B-v1") + >>> tokenizer = AutoTokenizer.from_pretrained("Zyphra/NemotronH-7B-v1") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]).float() + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + is_first_iteration=False, + **kwargs, + ): + kwargs["logits_to_keep"] = self.config.num_logits_to_keep + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + use_cache=use_cache, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + return model_inputs + + +__all__ = ["NemotronHPreTrainedModel", "NemotronHModel", "NemotronHForCausalLM"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/modular_nemotron_h.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/modular_nemotron_h.py new file mode 100644 index 0000000000000000000000000000000000000000..9b013dfd55bd4adceeb15219049c3ce22c996afe --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nemotron_h/modular_nemotron_h.py @@ -0,0 +1,531 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import math + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...integrations import use_experts_implementation +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_utils import PreTrainedModel +from ...models.deepseek_v3.modeling_deepseek_v3 import DeepseekV3MoE, DeepseekV3TopkRouter +from ...models.jamba.modeling_jamba import JambaAttention +from ...models.llama.modeling_llama import LlamaRMSNorm +from ...models.nemotron.modeling_nemotron import NemotronMLP +from ...models.zamba.modeling_zamba import ZambaForCausalLM +from ...models.zamba2.modeling_zamba2 import Zamba2MambaMixer, Zamba2RMSNormGated +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_nemotron_h import NemotronHConfig + + +logger = logging.get_logger(__name__) + +is_fast_path_available = False + + +class NemotronHMamba2Mixer(Zamba2MambaMixer): + def __init__(self, config: NemotronHConfig, layer_idx: int | None = None): + super().__init__(config, layer_idx) + self.ssm_state_size = config.ssm_state_size + self.conv_kernel_size = config.conv_kernel + self.intermediate_size = config.mamba_num_heads * config.mamba_head_dim + self.use_conv_bias = config.use_conv_bias + self.activation = config.mamba_hidden_act + self.act = ACT2FN[config.mamba_hidden_act] + self.use_mem_eff_path = True + + self.n_groups = config.n_groups + self.head_dim = config.mamba_head_dim + self.num_heads = config.mamba_num_heads + + self.conv1d = nn.Conv1d( + in_channels=self.conv_dim, + out_channels=self.conv_dim, + bias=config.use_conv_bias, + kernel_size=self.conv_kernel_size, + groups=self.conv_dim, + padding=self.conv_kernel_size - 1, + ) + + # projection of the input hidden states + projection_size = self.intermediate_size + self.conv_dim + self.num_heads + + self.in_proj = nn.Linear( + self.hidden_size, + projection_size, + bias=config.use_bias, + ) + + self.norm = Zamba2RMSNormGated( + self.intermediate_size, group_size=self.intermediate_size // self.n_groups, eps=config.layer_norm_epsilon + ) + + self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias) + + def forward( + self, + hidden_states, + cache_params: Cache | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ): + if is_fast_path_available and "cuda" in self.in_proj.weight.device.type and not is_torchdynamo_compiling(): + # Use cuda stream to avoid NaN when using multiple GPUs, which is caused by multi-GPU synchronization issue. + # Mamba might launch on the default cuda stream that not strictly respect the current Pytorch cuda stream. + # This leads to kernel reading uninitialized memory before the data transfer is complete. + with torch.cuda.stream(torch.cuda.default_stream(hidden_states.device)): + return self.cuda_kernels_forward(hidden_states, cache_params, attention_mask) + + return self.torch_forward(hidden_states, cache_params, attention_mask) + + +class NemotronHRMSNorm(LlamaRMSNorm): + pass + + +class NemotronHMLP(NemotronMLP, nn.Module): + def __init__(self, config, intermediate_size=None, **kwargs): + nn.Module.__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = intermediate_size or config.intermediate_size + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.mlp_hidden_act] + + +@use_experts_implementation(has_gate=False) +class NemotronHExperts(nn.Module): + """ + Collection of expert weights stored as 3D tensors. + + **Architecture Note**: Unlike Mixtral or DeepSeek which use gated MLPs, + NemotronH uses a standard MLP architecture with only up_proj and down_proj + """ + + def __init__(self, config): + super().__init__() + self.num_experts = config.n_routed_experts + self.hidden_dim = config.hidden_size + self.intermediate_dim = config.moe_intermediate_size + + # Determine input/output dimension based on whether latent projection is used + input_dim = config.moe_latent_size if config.moe_latent_size is not None else config.hidden_size + + # All expert weights stored as 3D tensors: (num_experts, out_dim, in_dim) + # up_proj: (num_experts, intermediate_dim, input_dim) + self.up_proj = nn.Parameter(torch.empty(self.num_experts, self.intermediate_dim, input_dim)) + # down_proj: (num_experts, input_dim, intermediate_dim) + self.down_proj = nn.Parameter(torch.empty(self.num_experts, input_dim, self.intermediate_dim)) + + self.act_fn = ACT2FN[config.mlp_hidden_act] + + def forward(self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor): + final_hidden_states = torch.zeros_like(hidden_states, dtype=top_k_weights.dtype) + + # Create expert mask to identify which tokens go to which experts + with torch.no_grad(): + expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts) + expert_mask = expert_mask.permute(2, 1, 0) # (num_experts, num_experts_per_tok, num_tokens) + # Only iterate over experts that have at least one token assigned + expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero().squeeze(-1) + + for expert_idx in expert_hit: + expert_idx = expert_idx.item() + # Find which tokens are routed to this expert + top_k_pos, token_idx = torch.where(expert_mask[expert_idx]) + + if token_idx.numel() == 0: + continue + + # Get input for this expert + current_state = hidden_states[token_idx] + + # Expert computation: down_proj(act_fn(up_proj(x))) + # No gating mechanism unlike Mixtral which uses: down_proj(act_fn(gate_proj(x)) * up_proj(x)) + current_hidden_states = torch.nn.functional.linear(current_state, self.up_proj[expert_idx]) + current_hidden_states = self.act_fn(current_hidden_states) + current_hidden_states = torch.nn.functional.linear(current_hidden_states, self.down_proj[expert_idx]) + + # Apply routing weights + current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None] + + # Accumulate into final output + final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype)) + + return final_hidden_states.to(hidden_states.dtype) + + +class NemotronHMoE(DeepseekV3MoE): + """ + Mixture-of-Experts (MoE) module for NemotronH. + + Unique architectures: + - Uses non-gated MLP experts (NemotronHExperts) instead of gated experts + - Adds optional latent projection for computational efficiency + """ + + def __init__(self, config, layer_idx: int | None = None): + super().__init__(config) + + # Replace with NemotronH-specific experts (non-gated MLP architecture) + self.experts = NemotronHExperts(config) + self.gate = NemotronHTopkRouter(config) + + # Override shared_experts to use NemotronHMLP with correct intermediate size + self.shared_experts = NemotronHMLP(config=config, intermediate_size=config.moe_shared_expert_intermediate_size) + + # NemotronH-specific latent projection layers + if config.moe_latent_size is not None: + self.fc1_latent_proj = nn.Linear(config.hidden_size, config.moe_latent_size, bias=config.mlp_bias) + self.fc2_latent_proj = nn.Linear(config.moe_latent_size, config.hidden_size, bias=config.mlp_bias) + else: + self.fc1_latent_proj = nn.Identity() + self.fc2_latent_proj = nn.Identity() + + def forward(self, hidden_states): + residuals = hidden_states + orig_shape = hidden_states.shape + router_logits = self.gate(hidden_states) + topk_indices, topk_weights = self.route_tokens_to_experts(router_logits) + hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) + + # NemotronH-specific: latent projection + hidden_states = self.fc1_latent_proj(hidden_states) + hidden_states = self.experts(hidden_states, topk_indices, topk_weights) + hidden_states = self.fc2_latent_proj(hidden_states) + + hidden_states = hidden_states.view(*orig_shape) + hidden_states = hidden_states + self.shared_experts(residuals) + return hidden_states + + +class NemotronHTopkRouter(DeepseekV3TopkRouter): + pass + + +class NemotronHAttention(JambaAttention): + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + return super().forward(hidden_states, attention_mask, past_key_values, **kwargs) + + +MIXER_TYPES = { + "mamba": NemotronHMamba2Mixer, + "attention": NemotronHAttention, + "moe": NemotronHMoE, + "mlp": NemotronHMLP, +} + + +class NemotronHBlock(GradientCheckpointingLayer): + """ + A single transformer block in the NemotronH model. + + This block can contain different types of mixers (Mamba, Attention, MLP, or MoE) + depending on the configuration. Each block applies pre-normalization followed by + the mixer, then adds a residual connection. + + Args: + config (`NemotronHConfig`): + Model configuration specifying the block architecture. + layer_idx (`int`): + Index of this block in the model. Used to determine the block type from + `config.layers_block_type[layer_idx]`. + """ + + def __init__(self, config, layer_idx): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.norm = NemotronHRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) + + self.block_type = config.layers_block_type[layer_idx] + self.mixer = MIXER_TYPES[self.block_type](config, layer_idx=layer_idx) + + def forward( + self, + hidden_states, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + use_cache: bool | None = False, + **kwargs: Unpack[TransformersKwargs], + ): + residual = hidden_states + hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype)) + + if self.block_type == "mamba": + hidden_states = self.mixer(hidden_states, cache_params=past_key_values, attention_mask=attention_mask) + elif self.block_type == "attention": + hidden_states, _ = self.mixer( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + position_ids=position_ids, + user_cache=use_cache, + **kwargs, + ) + else: + hidden_states = self.mixer(hidden_states) + + hidden_states = residual + hidden_states + + return hidden_states + + +class NemotronHPreTrainedModel(PreTrainedModel): + config: NemotronHConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["NemotronHBlock"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_flex_attn = True + _is_stateful = True + _can_record_outputs = { + "hidden_states": NemotronHBlock, + "attentions": NemotronHAttention, + } + _keep_in_fp32_modules_strict = [ + "e_score_correction_bias", + ] + _keys_to_ignore_on_load_unexpected = [r"mtp.*"] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights.""" + super()._init_weights(module) + if isinstance(module, NemotronHMamba2Mixer): + # Only re-initialise params that were NOT loaded from a checkpoint. + # `_is_hf_initialized` is set by `from_pretrained` on each loaded + # parameter; without this guard a post-load safety pass of + # `_init_weights` would overwrite checkpoint values of + # A_log / D / dt_bias with fresh random draws. + if not getattr(module.A_log, "_is_hf_initialized", False): + A = torch.arange(1, self.config.mamba_num_heads + 1) + init.copy_(module.A_log, torch.log(A)) + if not getattr(module.D, "_is_hf_initialized", False): + init.ones_(module.D) + if not getattr(module.dt_bias, "_is_hf_initialized", False): + dt = torch.exp( + torch.rand(self.config.mamba_num_heads) + * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min)) + + math.log(self.config.time_step_min) + ).clamp(min=self.config.time_step_floor) + + # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759 + inv_dt = dt + torch.log(-torch.expm1(-dt)) + with torch.no_grad(): + init.copy_(module.dt_bias, inv_dt) + elif isinstance(module, NemotronHTopkRouter): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + init.zeros_(module.e_score_correction_bias) + elif isinstance(module, NemotronHExperts): + # Initialize expert weights + init.normal_(module.up_proj, mean=0.0, std=self.config.initializer_range) + init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range) + + if isinstance(module, nn.Linear): + if module.bias is not None: + if not getattr(module.bias, "_is_hf_initialized", False): + init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, std=self.config.initializer_range) + + if self.config.rescale_prenorm_residual: + # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme: + # > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale + # > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers. + # > -- GPT-2 :: https://openai.com/blog/better-language-models/ + # + # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py + for name, p in module.named_parameters(): + if name == "out_proj.weight": + # Skip checkpoint-loaded weights so a post-load safety + # pass of `_init_weights` doesn't silently overwrite them. + if getattr(p, "_is_hf_initialized", False): + continue + # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block + # Following Pytorch init, except scale by 1/sqrt(2 * n_layer) + init.kaiming_uniform_(p, a=math.sqrt(5)) + with torch.no_grad(): + p_new = p / math.sqrt(self.config.num_hidden_layers) + init.copy_(p, p_new) + + +class NemotronHModel(NemotronHPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.layers = nn.ModuleList([NemotronHBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)]) + + self.norm_f = NemotronHRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, new_embeddings): + self.embeddings = new_embeddings + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids: torch.LongTensor | None = None, + inputs_embeds: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): # ^ is python for xor + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + hidden_states = inputs_embeds + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + input_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + mamba_mask = self._update_mamba_mask(attention_mask, past_key_values) + + # Map block types to their corresponding masks + block_type_to_mask = { + "mamba": mamba_mask, + "attention": causal_mask, + "moe": None, + "mlp": None, + } + + for layer_idx, mixer_block in enumerate(self.layers): + layer_mask = block_type_to_mask[mixer_block.block_type] + + hidden_states = mixer_block( + hidden_states, + attention_mask=layer_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm_f(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + def _update_mamba_mask(self, attention_mask, past_key_values): + """ + No need for zeroing states when + 1. Cached forward + 2. Attending to all inputs + """ + mamba_mask = attention_mask + if (past_key_values is not None and past_key_values.has_previous_state()) or ( + attention_mask is not None and torch.all(attention_mask == 1) + ): + mamba_mask = None + return mamba_mask + + +class NemotronHForCausalLM(ZambaForCausalLM): + _tied_weights_keys = {} + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | CausalLMOutputWithPast: + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]).float() + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "NemotronHPreTrainedModel", + "NemotronHModel", + "NemotronHForCausalLM", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9fe4c4b7f9381425cf34bb6099c0d5d5f8417db6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb/__init__.py @@ -0,0 +1,26 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .tokenization_nllb import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb/tokenization_nllb.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb/tokenization_nllb.py new file mode 100644 index 0000000000000000000000000000000000000000..4a4f4d67e9b97ac97f0a6149835de21e8b336461 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb/tokenization_nllb.py @@ -0,0 +1,318 @@ +# Copyright 2022 The Facebook AI Research Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors +from tokenizers.models import BPE + +from ...tokenization_python import AddedToken, BatchEncoding +from ...tokenization_utils_tokenizers import TokenizersBackend +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} + + +FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] # fmt: skip + + +class NllbTokenizer(TokenizersBackend): + """ + Construct an NLLB tokenizer (backed by HuggingFace's *tokenizers* library). Based on + [Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). + + This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + The tokenization method is ` ` for source language documents, and ` + ` for target language documents. + + Examples: + + ```python + >>> from transformers import NllbTokenizer + + >>> tokenizer = NllbTokenizer.from_pretrained( + ... "facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn" + ... ) + >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria" + >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie." + >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt") + ``` + + Args: + vocab_file (`str`, *optional*): + Path to the vocabulary file. + bos_token (`str`, *optional*, defaults to `""`): + The beginning of sequence token that was used during pretraining. + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + sep_token (`str`, *optional*, defaults to `""`): + The separator token. + cls_token (`str`, *optional*, defaults to `""`): + The classifier token. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding. + mask_token (`str`, *optional*, defaults to `""`): + The token used for masking values. + src_lang (`str`, *optional*): + The language to use as source language for translation. + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + legacy_behaviour (`bool`, *optional*, defaults to `False`): + Whether to use legacy behaviour (suffix pattern) or new behaviour (prefix pattern). + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + model = BPE + + prefix_tokens: list[int] = [] + suffix_tokens: list[int] = [] + + def __init__( + self, + vocab: str | dict[str, int] | None = None, + merges: str | list[str] | None = None, + bos_token="", + eos_token="", + sep_token="", + cls_token="", + unk_token="", + pad_token="", + mask_token="", + src_lang=None, + tgt_lang=None, + _spm_precompiled_charsmap: str | None = None, + additional_special_tokens=None, + extra_special_tokens=None, + legacy_behaviour=False, + **kwargs, + ): + # V5: extra_special_tokens takes precedence over additional_special_tokens (deprecated) + # Handle case where both are passed (ie. from config and user override) + if extra_special_tokens is not None: + additional_special_tokens = extra_special_tokens + elif additional_special_tokens is None: + additional_special_tokens = FAIRSEQ_LANGUAGE_CODES + + mask_token = ( + AddedToken(mask_token, normalized=True, lstrip=True, special=True) + if isinstance(mask_token, str) + else mask_token + ) + self.legacy_behaviour = legacy_behaviour + + if vocab is None: + vocab = { + str(bos_token): 0, + str(pad_token): 1, + str(eos_token): 2, + str(unk_token): 3, + } + self._vocab = vocab + self._merges = merges or [] + + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + dropout=None, + unk_token=str(unk_token), + fuse_unk=True, + byte_fallback=False, + ) + ) + + if _spm_precompiled_charsmap is not None: + self._tokenizer.normalizer = normalizers.Sequence( + [ + normalizers.Precompiled(_spm_precompiled_charsmap), + normalizers.Replace(Regex(r" {2,}"), " "), + ] + ) + + self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="always", split=True) + self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="always", split=True) + + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + sep_token=sep_token, + cls_token=cls_token, + unk_token=unk_token, + pad_token=pad_token, + src_lang=src_lang, + tgt_lang=tgt_lang, + mask_token=mask_token, + extra_special_tokens=additional_special_tokens, + legacy_behaviour=legacy_behaviour, + **kwargs, + ) + + # Build fairseq mappings for backward compatibility + self.fairseq_offset = 1 + self.fairseq_tokens_to_ids = { + "": 0, + "": 1, + "": 2, + "": 3, + } + self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} + + self._src_lang = src_lang if src_lang is not None else "eng_Latn" + self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang) + self.tgt_lang = tgt_lang + self.set_src_lang_special_tokens(self._src_lang) + + @property + def src_lang(self) -> str: + return self._src_lang + + @src_lang.setter + def src_lang(self, new_src_lang: str) -> None: + self._src_lang = new_src_lang + self.set_src_lang_special_tokens(self._src_lang) + + def _build_translation_inputs( + self, raw_inputs, return_tensors: str, src_lang: str | None, tgt_lang: str | None, **extra_kwargs + ): + """Used by translation pipeline, to prepare inputs for the generate function""" + if src_lang is None or tgt_lang is None: + raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") + self.src_lang = src_lang + inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs) + tgt_lang_id = self.convert_tokens_to_ids(tgt_lang) + inputs["forced_bos_token_id"] = tgt_lang_id + return inputs + + def prepare_seq2seq_batch( + self, + src_texts: list[str], + src_lang: str = "eng_Latn", + tgt_texts: list[str] | None = None, + tgt_lang: str = "fra_Latn", + max_length: int | None = None, + max_target_length: int | None = None, + padding: str = "longest", + return_tensors: str | None = None, + truncation: bool = True, + **kwargs, + ) -> BatchEncoding: + self.src_lang = src_lang + self.tgt_lang = tgt_lang + + if max_length is None: + max_length = self.model_max_length + + model_inputs = self( + src_texts, + add_special_tokens=True, + return_tensors=return_tensors, + max_length=max_length, + padding=padding, + truncation=truncation, + **kwargs, + ) + + if tgt_texts is None: + return model_inputs + + # Process tgt_texts + if max_target_length is None: + max_target_length = max_length + + # Switch to target mode to set the right special tokens + self._switch_to_target_mode() + labels = self( + tgt_texts, + add_special_tokens=True, + return_tensors=return_tensors, + padding=padding, + max_length=max_target_length, + truncation=truncation, + **kwargs, + ) + model_inputs["labels"] = labels["input_ids"] + + # Switch back to input mode + self._switch_to_input_mode() + + return model_inputs + + def _switch_to_input_mode(self): + return self.set_src_lang_special_tokens(self.src_lang) + + def _switch_to_target_mode(self): + if self.tgt_lang is None: + self.tgt_lang = self._src_lang + return self.set_tgt_lang_special_tokens(self.tgt_lang) + + def set_src_lang_special_tokens(self, src_lang) -> None: + """Reset the special tokens to the source lang setting. + - In legacy mode: No prefix and suffix=[eos, src_lang_code]. + - In default mode: Prefix=[src_lang_code], suffix = [eos] + """ + self.cur_lang_code = self.convert_tokens_to_ids(src_lang) + lang_code_token = src_lang + + if self.legacy_behaviour: + self.prefix_tokens = [] + self.suffix_tokens = [self.eos_token_id, self.cur_lang_code] + self._tokenizer.post_processor = processors.TemplateProcessing( + single=["$A", self.eos_token, lang_code_token], + pair=["$A", "$B", self.eos_token, lang_code_token], + special_tokens=[(self.eos_token, self.eos_token_id), (lang_code_token, self.cur_lang_code)], + ) + else: + self.prefix_tokens = [self.cur_lang_code] + self.suffix_tokens = [self.eos_token_id] + self._tokenizer.post_processor = processors.TemplateProcessing( + single=[lang_code_token, "$A", self.eos_token], + pair=[lang_code_token, "$A", "$B", self.eos_token], + special_tokens=[(self.eos_token, self.eos_token_id), (lang_code_token, self.cur_lang_code)], + ) + + def set_tgt_lang_special_tokens(self, lang: str) -> None: + """Reset the special tokens to the target lang setting. + - In legacy mode: No prefix and suffix=[eos, tgt_lang_code]. + - In default mode: Prefix=[tgt_lang_code], suffix = [eos] + """ + self.cur_lang_code = self.convert_tokens_to_ids(lang) + lang_code_token = lang + + if self.legacy_behaviour: + self.prefix_tokens = [] + self.suffix_tokens = [self.eos_token_id, self.cur_lang_code] + self._tokenizer.post_processor = processors.TemplateProcessing( + single=["$A", self.eos_token, lang_code_token], + pair=["$A", "$B", self.eos_token, lang_code_token], + special_tokens=[(self.eos_token, self.eos_token_id), (lang_code_token, self.cur_lang_code)], + ) + else: + self.prefix_tokens = [self.cur_lang_code] + self.suffix_tokens = [self.eos_token_id] + self._tokenizer.post_processor = processors.TemplateProcessing( + single=[lang_code_token, "$A", self.eos_token], + pair=[lang_code_token, "$A", "$B", self.eos_token], + special_tokens=[(self.eos_token, self.eos_token_id), (lang_code_token, self.cur_lang_code)], + ) + + +__all__ = ["NllbTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e21bde18010d3c81455b0a66eed8263c47a0e5de --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_nllb_moe import * + from .modeling_nllb_moe import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/configuration_nllb_moe.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/configuration_nllb_moe.py new file mode 100644 index 0000000000000000000000000000000000000000..8faba5d9cd6012fcd5771c4cccb34ea030c6d153 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/configuration_nllb_moe.py @@ -0,0 +1,122 @@ +# Copyright 2023, HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""NLLB-MoE model configuration""" + +from typing import Literal + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="facebook/nllb-moe-54b") +@strict +class NllbMoeConfig(PreTrainedConfig): + r""" + router_bias (`bool`, *optional*, defaults to `False`): + Whether or not the classifier of the router should have a bias. + router_dtype (`str`, *optional*, default to `"float32"`): + The `dtype` used for the routers. It is preferable to keep the `dtype` to `"float32"` as specified in the + *selective precision* discussion in [the paper](https://huggingface.co/papers/2101.03961). + router_ignore_padding_tokens (`bool`, *optional*, defaults to `False`): + Whether to ignore padding tokens when routing. if `False`, the padding tokens are not routed to any + experts. + expert_capacity (`int`, *optional*, defaults to 64): + Number of tokens that can be stored in each expert. + encoder_sparse_step (`int`, *optional*, defaults to 4): + Frequency of the sparse layers in the encoder. 4 means that one out of 4 layers will be sparse. + decoder_sparse_step (`int`, *optional*, defaults to 4): + Frequency of the sparse layers in the decoder. 4 means that one out of 4 layers will be sparse. + second_expert_policy (`str`, *optional*, default to `"all"`): + The policy used for the sampling the probability of being sampled to a second expert for each token. + normalize_router_prob_before_dropping (`bool`, *optional*, defaults to `True`): + Whether or not to normalize the router probabilities before applying a mask based on the experts capacity + (capacity dropping). + batch_prioritized_routing (`bool`, *optional*, defaults to `True`): + Whether or not to orders the tokens by their router probabilities before capacity dropping. This means that + the tokens that have the highest probabilities will be routed before other tokens that might be further in + the sequence. + moe_eval_capacity_token_fraction (`float`, *optional*, defaults to 1.0): + Fraction of tokens as capacity during validation, if set to negative, uses the same as training. Should be + in range: (0.0, 1.0]. + moe_token_dropout (`float`, *optional*, default to 0.2): + Masking rate for MoE expert output masking (EOM), which is implemented via a Dropout2d on the expert + outputs. + + Example: + + ```python + >>> from transformers import NllbMoeModel, NllbMoeConfig + + >>> # Initializing a NllbMoe facebook/nllb-moe-54b style configuration + >>> configuration = NllbMoeConfig() + + >>> # Initializing a model from the facebook/nllb-moe-54b style configuration + >>> model = NllbMoeModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "nllb-moe" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "num_attention_heads": "encoder_attention_heads", + "hidden_size": "d_model", + "num_hidden_layers": "encoder_layers", + } + + vocab_size: int = 128112 + max_position_embeddings: int = 1024 + encoder_layers: int = 12 + encoder_ffn_dim: int = 4096 + encoder_attention_heads: int = 16 + decoder_layers: int = 12 + decoder_ffn_dim: int = 4096 + decoder_attention_heads: int = 16 + encoder_layerdrop: float | int = 0.05 + decoder_layerdrop: float | int = 0.05 + use_cache: bool = True + is_encoder_decoder: bool = True + activation_function: str = "relu" + d_model: int = 1024 + dropout: float | int = 0.1 + attention_dropout: float | int = 0.1 + activation_dropout: float | int = 0.0 + init_std: float = 0.02 + decoder_start_token_id: int | None = 2 + scale_embedding: bool = True + router_bias: bool = False + router_dtype: Literal["float32", "float16", "bfloat16"] = "float32" + router_ignore_padding_tokens: bool = False + num_experts: int = 128 + expert_capacity: int = 64 + encoder_sparse_step: int = 4 + decoder_sparse_step: int = 4 + router_z_loss_coef: float = 0.001 + router_aux_loss_coef: float = 0.001 + second_expert_policy: str = "all" + normalize_router_prob_before_dropping: bool = False + batch_prioritized_routing: bool = False + moe_eval_capacity_token_fraction: float = 1.0 + moe_token_dropout: float | int = 0.2 + pad_token_id: int | None = 1 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 2 + tie_word_embeddings: bool = True + output_router_logits: bool = False + + +__all__ = ["NllbMoeConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/modeling_nllb_moe.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/modeling_nllb_moe.py new file mode 100644 index 0000000000000000000000000000000000000000..d4ef4927ce676c3595aaef1f57e389509c2a6e07 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nllb_moe/modeling_nllb_moe.py @@ -0,0 +1,1143 @@ +# Copyright 2023 NllbMoe Authors and HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable + +import torch +import torch.nn as nn +from torch.nn import CrossEntropyLoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...integrations.fsdp import is_fsdp_managed_module +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + MoEModelOutput, + MoEModelOutputWithPastAndCrossAttentions, + Seq2SeqMoEModelOutput, + Seq2SeqMoEOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_nllb_moe import NllbMoeConfig + + +logger = logging.get_logger(__name__) + + +class NllbMoeScaledWordEmbedding(nn.Embedding): + """ + This module overrides nn.Embeddings' forward by multiplying with embeddings scale. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float | None = 1.0): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.embed_scale = embed_scale + + def forward(self, input_ids: torch.Tensor): + return super().forward(input_ids) * self.embed_scale + + +# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding with M2M100->NllbMoe +class NllbMoeSinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length.""" + + def __init__(self, num_positions: int, embedding_dim: int, padding_idx: int | None = None): + super().__init__() + self.offset = 2 + self.num_positions = num_positions + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.make_weights(num_positions + self.offset, embedding_dim, padding_idx) + + def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: int | None = None): + emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx) + if hasattr(self, "weights"): + # in forward put the weights on the correct dtype and device of the param + emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device) + + self.register_buffer("weights", emb_weights, persistent=False) + + @staticmethod + def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: int | None = None): + """ + Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of + "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb) + emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + + return emb.to(torch.get_default_dtype()) + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values_length: int = 0, + ): + if input_ids is not None: + bsz, seq_len = input_ids.size() + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ).to(input_ids.device) + else: + bsz, seq_len = inputs_embeds.size()[:-1] + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds, past_key_values_length, self.padding_idx + ) + + # expand embeddings if needed + max_pos = self.padding_idx + 1 + seq_len + past_key_values_length + if max_pos > self.weights.size(0): + self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx) + + return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach() + + @staticmethod + def create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length, padding_idx): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length + + @staticmethod + # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_input_ids + def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx + + +class NllbMoeTop2Router(nn.Module): + """ + Router using tokens choose top-2 experts assignment. + + This router uses the same mechanism as in NLLB-MoE from the fairseq repository. Items are sorted by router_probs + and then routed to their choice of expert until the expert's expert_capacity is reached. **There is no guarantee + that each token is processed by an expert**, or that each expert receives at least one token. + + The router combining weights are also returned to make sure that the states that are not updated will be masked. + + """ + + def __init__(self, config: NllbMoeConfig): + super().__init__() + self.num_experts = config.num_experts + self.expert_capacity = config.expert_capacity + self.classifier = nn.Linear(config.hidden_size, self.num_experts, bias=config.router_bias) + self.router_ignore_padding_tokens = config.router_ignore_padding_tokens + self.dtype = getattr(torch, config.router_dtype) + + self.second_expert_policy = config.second_expert_policy + self.normalize_router_prob_before_dropping = config.normalize_router_prob_before_dropping + self.batch_prioritized_routing = config.batch_prioritized_routing + self.moe_eval_capacity_token_fraction = config.moe_eval_capacity_token_fraction + + def _cast_classifier(self): + r""" + `bitsandbytes` `Linear8bitLt` layers does not support manual casting Therefore we need to check if they are an + instance of the `Linear8bitLt` class by checking special attributes. + """ + if not (hasattr(self.classifier, "SCB") or hasattr(self.classifier, "CB")): + self.classifier = self.classifier.to(self.dtype) + + def normalize_router_probabilities(self, router_probs, top_1_mask, top_2_mask): + top_1_max_probs = (router_probs * top_1_mask).sum(dim=1) + top_2_max_probs = (router_probs * top_2_mask).sum(dim=1) + denom_s = torch.clamp(top_1_max_probs + top_2_max_probs, min=torch.finfo(router_probs.dtype).eps) + top_1_max_probs = top_1_max_probs / denom_s + top_2_max_probs = top_2_max_probs / denom_s + return top_1_max_probs, top_2_max_probs + + def route_tokens( + self, + router_logits: torch.Tensor, + input_dtype: torch.dtype = torch.float32, + padding_mask: torch.LongTensor | None = None, + ) -> tuple: + """ + Computes the `dispatch_mask` and the `dispatch_weights` for each experts. The masks are adapted to the expert + capacity. + """ + nb_tokens = router_logits.shape[0] + # Apply Softmax and cast back to the original `dtype` + router_probs = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(input_dtype) + top_1_expert_index = torch.argmax(router_probs, dim=-1) + top_1_mask = torch.nn.functional.one_hot(top_1_expert_index, num_classes=self.num_experts) + + if self.second_expert_policy == "sampling": + gumbel = torch.distributions.gumbel.Gumbel(0, 1).rsample + router_logits += gumbel(router_logits.shape).to(router_logits.device) + + # replace top_1_expert_index with min values + logits_except_top_1 = router_logits.masked_fill(top_1_mask.bool(), float("-inf")) + top_2_expert_index = torch.argmax(logits_except_top_1, dim=-1) + top_2_mask = torch.nn.functional.one_hot(top_2_expert_index, num_classes=self.num_experts) + + if self.normalize_router_prob_before_dropping: + top_1_max_probs, top_2_max_probs = self.normalize_router_probabilities( + router_probs, top_1_mask, top_2_mask + ) + + if self.second_expert_policy == "random": + top_2_max_probs = (router_probs * top_2_mask).sum(dim=1) + sampled = (2 * top_2_max_probs) > torch.rand_like(top_2_max_probs.float()) + top_2_mask = top_2_mask * sampled.repeat(self.num_experts, 1).transpose(1, 0) + + if padding_mask is not None and not self.router_ignore_padding_tokens: + if len(padding_mask.shape) == 4: + # only get the last causal mask + padding_mask = padding_mask[:, :, -1, :].reshape(-1)[-nb_tokens:] + non_padding = ~padding_mask.bool() + top_1_mask = top_1_mask * non_padding.unsqueeze(-1).to(top_1_mask.dtype) + top_2_mask = top_2_mask * non_padding.unsqueeze(-1).to(top_1_mask.dtype) + + if self.batch_prioritized_routing: + # sort tokens based on their routing probability + # to make sure important tokens are routed, first + importance_scores = -1 * router_probs.max(dim=1)[0] + sorted_top_1_mask = top_1_mask[importance_scores.argsort(dim=0)] + sorted_cumsum1 = (torch.cumsum(sorted_top_1_mask, dim=0) - 1) * sorted_top_1_mask + locations1 = sorted_cumsum1[importance_scores.argsort(dim=0).argsort(dim=0)] + + sorted_top_2_mask = top_2_mask[importance_scores.argsort(dim=0)] + sorted_cumsum2 = (torch.cumsum(sorted_top_2_mask, dim=0) - 1) * sorted_top_2_mask + locations2 = sorted_cumsum2[importance_scores.argsort(dim=0).argsort(dim=0)] + # Update 2nd's location by accounting for locations of 1st + locations2 += torch.sum(top_1_mask, dim=0, keepdim=True) + + else: + locations1 = torch.cumsum(top_1_mask, dim=0) - 1 + locations2 = torch.cumsum(top_2_mask, dim=0) - 1 + # Update 2nd's location by accounting for locations of 1st + locations2 += torch.sum(top_1_mask, dim=0, keepdim=True) + + if not self.training and self.moe_eval_capacity_token_fraction > 0: + self.expert_capacity = math.ceil(self.moe_eval_capacity_token_fraction * nb_tokens) + else: + capacity = 2 * math.ceil(nb_tokens / self.num_experts) + self.expert_capacity = capacity if self.expert_capacity is None else self.expert_capacity + + # Remove locations outside capacity from ( cumsum < capacity = False will not be routed) + top_1_mask = top_1_mask * torch.lt(locations1, self.expert_capacity) + top_2_mask = top_2_mask * torch.lt(locations2, self.expert_capacity) + + if not self.normalize_router_prob_before_dropping: + top_1_max_probs, top_2_max_probs = self.normalize_router_probabilities( + router_probs, top_1_mask, top_2_mask + ) + + # Calculate combine_weights and dispatch_mask + gates1 = top_1_max_probs[:, None] * top_1_mask + gates2 = top_2_max_probs[:, None] * top_2_mask + router_probs = gates1 + gates2 + + return top_1_mask, router_probs + + def forward(self, hidden_states: torch.Tensor, padding_mask: torch.LongTensor | None = None) -> tuple: + r""" + The hidden states are reshaped to simplify the computation of the router probabilities (combining weights for + each experts.) + + Args: + hidden_states (`torch.Tensor`): + (batch_size, sequence_length, hidden_dim) from which router probabilities are computed. + Returns: + top_1_mask (`torch.Tensor` of shape (batch_size, sequence_length)): + Index tensor of shape [batch_size, sequence_length] corresponding to the expert selected for each token + using the top1 probabilities of the router. + router_probabilities (`torch.Tensor` of shape (batch_size, sequence_length, nump_experts)): + Tensor of shape (batch_size, sequence_length, num_experts) corresponding to the probabilities for each + token and expert. Used for routing tokens to experts. + router_logits (`torch.Tensor` of shape (batch_size, sequence_length))): + Logits tensor of shape (batch_size, sequence_length, num_experts) corresponding to raw router logits. + This is used later for computing router z-loss. + """ + self.input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(self.dtype) + self._cast_classifier() + router_logits = self.classifier(hidden_states) + top_1_mask, router_probs = self.route_tokens(router_logits, self.input_dtype, padding_mask) + return top_1_mask, router_probs, router_logits + + +class NllbMoeDenseActDense(nn.Module): + def __init__(self, config: NllbMoeConfig, ffn_dim: int): + super().__init__() + self.fc1 = nn.Linear(config.d_model, ffn_dim) + self.fc2 = nn.Linear(ffn_dim, config.d_model) + self.dropout = nn.Dropout(config.activation_dropout) + self.act = ACT2FN[config.activation_function] + + def forward(self, hidden_states: torch.Tensor): + hidden_states = self.fc1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.dropout(hidden_states) + if ( + isinstance(self.fc2.weight, torch.Tensor) + and hidden_states.dtype != self.fc2.weight.dtype + and (self.fc2.weight.dtype != torch.int8 and self.fc2.weight.dtype != torch.uint8) + ): + hidden_states = hidden_states.to(self.fc2.weight.dtype) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class NllbMoeExperts(nn.ModuleDict): + def __init__(self, config: NllbMoeConfig, ffn_dim: int): + super().__init__() + self.num_experts = config.num_experts + for idx in range(self.num_experts): + self[f"expert_{idx}"] = NllbMoeDenseActDense(config, ffn_dim) + self.moe_token_dropout = config.moe_token_dropout + self.token_dropout = nn.Dropout(self.moe_token_dropout) + + def forward(self, hidden_states: torch.Tensor, router_mask: torch.Tensor, router_probs: torch.Tensor): + final_hidden_states = torch.zeros_like(hidden_states) + expert_mask = torch.nn.functional.one_hot(router_mask, num_classes=self.num_experts).permute(2, 1, 0) + + expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero() + for expert_idx in expert_hit: + idx, top_x = torch.where(expert_mask[expert_idx].squeeze(0)) + current_state = hidden_states[None, top_x].reshape(-1, hidden_states.shape[-1]) + current_hidden_states = self[f"expert_{expert_idx[0]}"](current_state) * router_probs[top_x, idx, None] + if self.moe_token_dropout > 0: + if self.training: + current_hidden_states = self.token_dropout(current_hidden_states) + else: + current_hidden_states *= 1 - self.moe_token_dropout + final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype)) + return final_hidden_states + + +class NllbMoeSparseMLP(nn.Module): + r""" + Implementation of the NLLB-MoE sparse MLP module. + """ + + def __init__(self, config: NllbMoeConfig, ffn_dim: int): + super().__init__() + self.router = NllbMoeTop2Router(config) + self.num_experts = config.num_experts + self.experts = NllbMoeExperts(config, ffn_dim) + + def forward(self, hidden_states: torch.Tensor, padding_mask: torch.Tensor | None = None): + batch_size, sequence_length, hidden_dim = hidden_states.shape + hidden_states = hidden_states.view(-1, hidden_dim) + top_1_mask, router_probs, _ = self.router(hidden_states, padding_mask) + hidden_states = self.experts(hidden_states, top_1_mask, router_probs) + return hidden_states.reshape(batch_size, sequence_length, hidden_dim) + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class NllbMoeAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float | None = 0.0, + is_decoder: bool | None = False, + bias: bool | None = True, + is_causal: bool | None = False, + config: NllbMoeConfig | None = None, + layer_idx: int | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + is_cross_attention = key_value_states is not None + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_layer from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = self.k_proj(current_states).view(kv_shape).transpose(1, 2) + value_states = self.v_proj(current_states).view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + # save all key/value_states to cache to be re-used for fast auto-regressive generation + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + return attn_output, attn_weights + + +class NllbMoeEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: NllbMoeConfig, is_sparse: bool = False, layer_idx: int = 0): + super().__init__() + self.embed_dim = config.d_model + self.is_sparse = is_sparse + self.self_attn = NllbMoeAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + layer_idx=layer_idx, + ) + self.attn_dropout = nn.Dropout(config.dropout) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + if not self.is_sparse: + self.ffn = NllbMoeDenseActDense(config, ffn_dim=config.encoder_ffn_dim) + else: + self.ffn = NllbMoeSparseMLP(config, ffn_dim=config.encoder_ffn_dim) + self.ff_layer_norm = nn.LayerNorm(config.d_model) + self.ff_dropout = nn.Dropout(config.activation_dropout) + + def forward( + self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, **kwargs: Unpack[TransformersKwargs] + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, _ = self.self_attn(hidden_states, attention_mask=attention_mask, **kwargs) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + residual = hidden_states + + hidden_states = self.ff_layer_norm(hidden_states) + if self.is_sparse: + hidden_states = self.ffn(hidden_states, attention_mask) + else: + hidden_states = self.ffn(hidden_states) + hidden_states = self.ff_dropout(hidden_states) + hidden_states = residual + hidden_states + if hidden_states.dtype == torch.float16 and not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + return hidden_states + + +class NllbMoeDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: NllbMoeConfig, is_sparse: bool = False, layer_idx: int | None = None): + super().__init__() + self.embed_dim = config.d_model + self.is_sparse = is_sparse + self.self_attn = NllbMoeAttention( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + config=config, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.attn_dropout = nn.Dropout(config.dropout) + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.cross_attention = NllbMoeAttention( + self.embed_dim, + config.decoder_attention_heads, + config.attention_dropout, + is_decoder=True, + config=config, + layer_idx=layer_idx, + ) + self.cross_attention_layer_norm = nn.LayerNorm(self.embed_dim) + if not self.is_sparse: + self.ffn = NllbMoeDenseActDense(config, ffn_dim=config.decoder_ffn_dim) + else: + self.ffn = NllbMoeSparseMLP(config, ffn_dim=config.decoder_ffn_dim) + self.ff_layer_norm = nn.LayerNorm(config.d_model) + self.ff_dropout = nn.Dropout(config.activation_dropout) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.cross_attention_layer_norm(hidden_states) + + hidden_states, _ = self.cross_attention( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + past_key_values=past_key_values, + attention_mask=encoder_attention_mask, + **kwargs, + ) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.ff_layer_norm(hidden_states) + if self.is_sparse: + hidden_states = self.ffn(hidden_states, attention_mask) + else: + hidden_states = self.ffn(hidden_states) + + hidden_states = self.ff_dropout(hidden_states) + hidden_states = residual + hidden_states + + # clamp inf values to enable fp16 training + if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +@auto_docstring +class NllbMoePreTrainedModel(PreTrainedModel): + config: NllbMoeConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["NllbMoeEncoderLayer", "NllbMoeDecoderLayer"] + # TODO: If anyone is up to it to make sure tests pass etc + # Flash attention has problems due to not preparing masks the same way as eager/sdpa + # SDPA has more flaky logits which requires more time to look into tests + _supports_flash_attn = False + _supports_sdpa = False + _supports_flex_attn = False + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, NllbMoeSinusoidalPositionalEmbedding): + emb_weights = module.get_embedding( + module.num_positions + module.offset, module.embedding_dim, module.padding_idx + ) + init.copy_(module.weights, emb_weights) + + +class NllbMoeEncoder(NllbMoePreTrainedModel): + _can_record_outputs = { + "hidden_states": NllbMoeEncoderLayer, + "router_logits": OutputRecorder(NllbMoeTop2Router, index=2), + "attentions": NllbMoeAttention, + } + + def __init__(self, config: NllbMoeConfig): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + self.padding_idx = config.pad_token_id + self.max_source_positions = config.max_position_embeddings + embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = NllbMoeScaledWordEmbedding( + config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale + ) + + self.embed_positions = NllbMoeSinusoidalPositionalEmbedding( + config.max_position_embeddings, + embed_dim, + self.padding_idx, + ) + sparse_step = config.encoder_sparse_step + self.layers = nn.ModuleList() + for i in range(config.encoder_layers): + is_sparse = (i + 1) % sparse_step == 0 if sparse_step > 0 else False + self.layers.append(NllbMoeEncoderLayer(config, is_sparse, layer_idx=i)) + + self.layer_norm = nn.LayerNorm(config.d_model) + self.gradient_checkpointing = False + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ): + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + embed_pos = self.embed_positions(input_ids, inputs_embeds) + embed_pos = embed_pos.to(inputs_embeds.device) + + hidden_states = inputs_embeds + embed_pos + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + for encoder_layer in self.layers: + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + dropout_probability = torch.rand([]) + if self.training and (dropout_probability < self.layerdrop): # skip the layer + continue + else: + hidden_states = encoder_layer(hidden_states, attention_mask, **kwargs) + + last_hidden_state = self.layer_norm(hidden_states) + return MoEModelOutput(last_hidden_state=last_hidden_state) + + +class NllbMoeDecoder(NllbMoePreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`NllbMoeDecoderLayer`] + + Args: + config: + NllbMoeConfig + embed_tokens (nn.Embedding): + output embedding + """ + + _can_record_outputs = { + "hidden_states": NllbMoeDecoderLayer, + "attentions": OutputRecorder(NllbMoeAttention, layer_name="self_attn", index=1), + "router_logits": OutputRecorder(NllbMoeTop2Router, index=2), + "cross_attentions": OutputRecorder(NllbMoeAttention, layer_name="cross_attention", index=1), + } + + def __init__(self, config: NllbMoeConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = NllbMoeScaledWordEmbedding( + config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale + ) + + self.embed_positions = NllbMoeSinusoidalPositionalEmbedding( + config.max_position_embeddings, + config.d_model, + self.padding_idx, + ) + + sparse_step = config.decoder_sparse_step + self.layers = nn.ModuleList() + for i in range(config.decoder_layers): + is_sparse = (i + 1) % sparse_step == 0 if sparse_step > 0 else False + self.layers.append(NllbMoeDecoderLayer(config, is_sparse, layer_idx=i)) + + self.layer_norm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPastAndCrossAttentions: + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # initialize `past_key_values` + if use_cache and past_key_values is None: + past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + # embed positions + positions = self.embed_positions(input_ids, inputs_embeds, past_key_values_length) + positions = positions.to(inputs_embeds.device) + + hidden_states = inputs_embeds + positions + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self) + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + dropout_probability = torch.rand([]) + skip_the_layer = self.training and dropout_probability < self.layerdrop + if not skip_the_layer or synced_gpus: + hidden_states = decoder_layer( + hidden_states, + attention_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + if skip_the_layer: + continue + + last_hidden_states = self.layer_norm(hidden_states) + + return MoEModelOutputWithPastAndCrossAttentions( + last_hidden_state=last_hidden_states, past_key_values=past_key_values + ) + + +@auto_docstring +class NllbMoeModel(NllbMoePreTrainedModel): + _tied_weights_keys = { + "encoder.embed_tokens.weight": "shared.weight", + "decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: NllbMoeConfig): + super().__init__(config) + + padding_idx, vocab_size = config.pad_token_id, config.vocab_size + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + self.shared = NllbMoeScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale) + + self.encoder = NllbMoeEncoder(config) + self.decoder = NllbMoeDecoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, value): + self.shared = value + self.encoder.embed_tokens = self.shared + self.decoder.embed_tokens = self.shared + + @auto_docstring + @can_return_tuple + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | Seq2SeqMoEModelOutput: + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + **kwargs, + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs.last_hidden_state, + encoder_attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Seq2SeqMoEModelOutput( + past_key_values=decoder_outputs.past_key_values, + cross_attentions=decoder_outputs.cross_attentions, + last_hidden_state=decoder_outputs.last_hidden_state, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + decoder_hidden_states=decoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + decoder_attentions=decoder_outputs.attentions, + encoder_router_logits=encoder_outputs.router_logits, + decoder_router_logits=decoder_outputs.router_logits, + ) + + +def load_balancing_loss_func( + gate_logits: torch.Tensor | tuple[torch.Tensor] | None, + num_experts: int | None = None, + top_k=2, + attention_mask: torch.Tensor | None = None, +) -> torch.Tensor | int: + r""" + Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. + + See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss + function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between + experts is too unbalanced. + + Args: + gate_logits: + Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of + shape [batch_size X sequence_length, num_experts]. + num_experts: + Number of experts + top_k: + The number of experts to route per-token, can be also interpreted as the `top-k` routing + parameter. + attention_mask (`torch.Tensor`, *optional*): + The attention_mask used in forward function + shape [batch_size X sequence_length] if not None. + + Returns: + The auxiliary loss. + """ + if gate_logits is None or not isinstance(gate_logits, tuple): + return 0 + + if isinstance(gate_logits, tuple): + compute_device = gate_logits[0].device + concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) + + routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) + + _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) + + expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) + + if attention_mask is None: + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.mean(expert_mask.float(), dim=0) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.mean(routing_weights, dim=0) + else: + batch_size, sequence_length = attention_mask.shape + num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) + + # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask + expert_attention_mask = ( + attention_mask[None, :, :, None, None] + .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) + .reshape(-1, top_k, num_experts) + .to(compute_device) + ) + + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( + expert_attention_mask, dim=0 + ) + + # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert + router_per_expert_attention_mask = ( + attention_mask[None, :, :, None] + .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) + .reshape(-1, num_experts) + .to(compute_device) + ) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( + router_per_expert_attention_mask, dim=0 + ) + + overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) + return overall_loss * num_experts + + +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +@auto_docstring( + custom_intro=""" + The NllbMoe Model with a language modeling head. Can be used for summarization. + """ +) +class NllbMoeForConditionalGeneration(NllbMoePreTrainedModel, GenerationMixin): + base_model_prefix = "model" + _tied_weights_keys = { + "lm_head.weight": "model.shared.weight", + } + + def __init__(self, config: NllbMoeConfig): + super().__init__(config) + self.model = NllbMoeModel(config) + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + self.num_experts = config.num_experts + self.router_z_loss_coef = config.router_z_loss_coef + self.router_aux_loss_coef = config.router_aux_loss_coef + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_router_logits: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | Seq2SeqMoEOutput: + output_router_logits = ( + output_router_logits if output_router_logits is not None else self.config.output_router_logits + ) + if labels is not None: + if decoder_input_ids is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_router_logits=output_router_logits, + **kwargs, + ) + lm_logits = self.lm_head(outputs[0]) + + loss = None + encoder_aux_loss = None + decoder_aux_loss = None + + if labels is not None: + loss_fct = CrossEntropyLoss(ignore_index=-100) + # todo check in the config if router loss enables + + if output_router_logits: + encoder_router_logits = outputs.encoder_router_logits + decoder_router_logits = outputs.decoder_router_logits + encoder_aux_loss = load_balancing_loss_func( + encoder_router_logits, self.num_experts, top_k=2, attention_mask=attention_mask + ) + decoder_aux_loss = load_balancing_loss_func( + decoder_router_logits, self.num_experts, top_k=2, attention_mask=decoder_attention_mask + ) + + loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) + + if output_router_logits and labels is not None: + aux_loss = self.router_aux_loss_coef * (encoder_aux_loss + decoder_aux_loss) + loss = loss + aux_loss + + return Seq2SeqMoEOutput( + loss=loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + cross_attentions=outputs.cross_attentions, + encoder_aux_loss=encoder_aux_loss, + decoder_aux_loss=decoder_aux_loss, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + decoder_hidden_states=outputs.decoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + decoder_attentions=outputs.decoder_attentions, + encoder_router_logits=outputs.encoder_router_logits, + decoder_router_logits=outputs.decoder_router_logits, + ) + + +__all__ = [ + "NllbMoeForConditionalGeneration", + "NllbMoeModel", + "NllbMoePreTrainedModel", + "NllbMoeTop2Router", + "NllbMoeSparseMLP", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..70984a23cf9de8eab531cf017870301061d98938 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_nomic_bert import * + from .modeling_nomic_bert import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/configuration_nomic_bert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/configuration_nomic_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..8e13aa47e3f1d6073885acc3c95bc6a6354c8cec --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/configuration_nomic_bert.py @@ -0,0 +1,77 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/nomic_bert/modular_nomic_bert.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_nomic_bert.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="nomic-ai/nomic-embed-text-v1.5") +@strict +class NomicBertConfig(PreTrainedConfig): + r""" + Examples: + + ```python + >>> from transformers import NomicBertConfig, NomicBertModel + + >>> # Initializing a Nomic BERT nomic-ai/nomic-embed-text-v1.5 style configuration + >>> configuration = NomicBertConfig() + + >>> # Initializing a model (with random weights) from the nomic-ai/nomic-embed-text-v1.5 style configuration + >>> model = NomicBertModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "nomic_bert" + + vocab_size: int = 30528 + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + hidden_act: str = "silu" + hidden_dropout_prob: float = 0.0 + attention_probs_dropout_prob: float = 0.0 + max_position_embeddings: int = 2048 + type_vocab_size: int = 2 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-12 + pad_token_id: int = 0 + classifier_dropout: float | None = None + bos_token_id: int | None = None + eos_token_id: int | None = None + tie_word_embeddings = True + default_theta = 1000.0 + rope_parameters: RopeParameters | dict | None = None + head_dim: int | None = None + + def __post_init__(self, **kwargs): + super().__post_init__(**kwargs) + if self.head_dim is None: + self.head_dim = self.hidden_size // self.num_attention_heads + + +__all__ = ["NomicBertConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/modeling_nomic_bert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/modeling_nomic_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..f2836a1ec0f6e65d592ce96b2c28ea1a04e9c5bf --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/modeling_nomic_bert.py @@ -0,0 +1,721 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/nomic_bert/modular_nomic_bert.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_nomic_bert.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn as nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...integrations import use_kernel_func_from_hub, use_kernelized_func +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPooling, + MaskedLMOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import can_return_tuple, maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_nomic_bert import NomicBertConfig + + +class NomicBertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, config: NomicBertConfig): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + self.register_buffer( + "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + ) -> torch.Tensor: + embeddings = inputs_embeds + if inputs_embeds is None: + embeddings = self.word_embeddings(input_ids) + + input_shape = embeddings.shape[:-1] + device = embeddings.device + + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0]) + buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1) + buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids) + token_type_ids = buffered_token_type_ids.expand(*input_shape) + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = embeddings + token_type_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + + return embeddings + + +class NomicBertRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: NomicBertConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: NomicBertConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class NomicBertAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_probs_dropout_prob + self.is_causal = False + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class NomicBertMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class NomicBertLayer(GradientCheckpointingLayer): + def __init__(self, config: NomicBertConfig): + super().__init__() + self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.post_attention_dropout = nn.Dropout(config.hidden_dropout_prob) + self.post_mlp_dropout = nn.Dropout(config.hidden_dropout_prob) + self.mlp = NomicBertMLP(config) + self.self_attn = NomicBertAttention(config=config) + self.post_mlp_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + attention_output, _ = self.self_attn( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + self.post_attention_dropout(attention_output) + hidden_states = self.post_attention_layernorm(hidden_states) + + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.post_mlp_dropout(hidden_states) + hidden_states = self.post_mlp_layernorm(hidden_states) + return hidden_states + + +class NomicBertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = NomicBertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=True) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +@auto_docstring +class NomicBertPreTrainedModel(PreTrainedModel): + config_class = NomicBertConfig + base_model_prefix = "nomic_bert" + supports_gradient_checkpointing = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": NomicBertLayer, + "attentions": NomicBertAttention, + } + + # Are kept as non-persistent buffers to avoid being saved in the state dict + # and causing mismatch when loading from a checkpoint that doesn't have them + _keys_to_ignore_on_load_unexpected = ["inv_freq", "original_inv_freq"] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, NomicBertLMPredictionHead): + init.zeros_(module.bias) + elif isinstance(module, NomicBertEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + init.zeros_(module.token_type_ids) + + +class NomicBertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +@auto_docstring +class NomicBertModel(NomicBertPreTrainedModel): + _no_split_modules = ["NomicBertEmbeddings", "NomicBertLayer"] + + def __init__(self, config, add_pooling_layer=False): + r""" + add_pooling_layer (`bool`, *optional*, defaults to `False`): + Whether to add a pooling layer. + """ + super().__init__(config) + self.config = config + self.gradient_checkpointing = False + + self.embeddings = NomicBertEmbeddings(config) + + self.pooler = NomicBertPooler(config) if add_pooling_layer else None + self.rotary_emb = NomicBertRotaryEmbedding(config) + self.layers = nn.ModuleList([NomicBertLayer(config) for _ in range(config.num_hidden_layers)]) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | BaseModelOutputWithPooling: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if input_ids is not None: + seq_length = input_ids.shape[1] + device = input_ids.device + else: + seq_length = inputs_embeds.shape[1] + device = inputs_embeds.device + + if position_ids is None: + position_ids = torch.arange(seq_length, dtype=torch.long, device=device)[None, :] + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + ) + + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=attention_mask, + ) + + hidden_states = embedding_output + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for encoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + position_ids=position_ids, + **kwargs, + ) + + sequence_output = hidden_states + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=pooled_output, + ) + + +class NomicBertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + # Use layer_norm rather than LayerNorm to avoid bert legacy mappings weights and bias to gamma and beta + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.layer_norm(hidden_states) + return hidden_states + + +class NomicBertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = NomicBertLMPredictionHead(config) + + def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +@auto_docstring +class NomicBertForMaskedLM(NomicBertPreTrainedModel): + _tied_weights_keys = { + "cls.predictions.decoder.weight": "nomic_bert.embeddings.word_embeddings.weight", + "cls.predictions.decoder.bias": "cls.predictions.bias", + } + + def __init__(self, config): + super().__init__(config) + + self.nomic_bert = NomicBertModel(config) + self.cls = NomicBertOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + self.cls.predictions.bias = new_embeddings.bias + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MaskedLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + outputs = self.nomic_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + NomicBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled + output) e.g. for GLUE tasks. + """ +) +class NomicBertForSequenceClassification(NomicBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + self.nomic_bert = NomicBertModel(config, add_pooling_layer=True) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | SequenceClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + outputs = self.nomic_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class NomicBertForTokenClassification(NomicBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.nomic_bert = NomicBertModel(config, add_pooling_layer=False) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | TokenClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + outputs = self.nomic_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "NomicBertPreTrainedModel", + "NomicBertModel", + "NomicBertForMaskedLM", + "NomicBertForSequenceClassification", + "NomicBertForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/modular_nomic_bert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/modular_nomic_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..bd27e8034b626faf55d299919430aa489baa2086 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nomic_bert/modular_nomic_bert.py @@ -0,0 +1,299 @@ +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn +from huggingface_hub.dataclasses import strict +from torch.nn import CrossEntropyLoss + +from ...configuration_utils import PreTrainedConfig +from ...masking_utils import create_bidirectional_mask +from ...modeling_outputs import ( + BaseModelOutputWithPooling, + MaskedLMOutput, +) +from ...modeling_rope_utils import RopeParameters +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..bert.configuration_bert import BertConfig +from ..bert.modeling_bert import ( + BertForMaskedLM, + BertForSequenceClassification, + BertForTokenClassification, + BertOnlyMLMHead, + BertPredictionHeadTransform, + BertPreTrainedModel, +) +from ..gemma.modeling_gemma import GemmaMLP +from ..jina_embeddings_v3.modeling_jina_embeddings_v3 import ( + JinaEmbeddingsV3Attention, + JinaEmbeddingsV3Embeddings, + JinaEmbeddingsV3Layer, + JinaEmbeddingsV3Model, +) +from ..llama.modeling_llama import LlamaRotaryEmbedding + + +@auto_docstring(checkpoint="nomic-ai/nomic-embed-text-v1.5") +@strict +class NomicBertConfig(BertConfig): + r""" + Examples: + + ```python + >>> from transformers import NomicBertConfig, NomicBertModel + + >>> # Initializing a Nomic BERT nomic-ai/nomic-embed-text-v1.5 style configuration + >>> configuration = NomicBertConfig() + + >>> # Initializing a model (with random weights) from the nomic-ai/nomic-embed-text-v1.5 style configuration + >>> model = NomicBertModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "nomic_bert" + default_theta = 1000.0 + + vocab_size: int = 30528 + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + hidden_act: str = "silu" + hidden_dropout_prob: float = 0.0 + attention_probs_dropout_prob: float = 0.0 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-12 + classifier_dropout: float | None = None + type_vocab_size: int = 2 + bos_token_id: int | None = None + eos_token_id: int | None = None + pad_token_id: int = 0 + tie_word_embeddings = True + rope_parameters: RopeParameters | dict | None = None + max_position_embeddings: int = 2048 + head_dim: int | None = None + is_decoder = AttributeError() + add_cross_attention = AttributeError() + use_cache = AttributeError() + + def __post_init__(self, **kwargs): + PreTrainedConfig.__post_init__(self, **kwargs) + if self.head_dim is None: + self.head_dim = self.hidden_size // self.num_attention_heads + + +class NomicBertEmbeddings(JinaEmbeddingsV3Embeddings): + pass + + +class NomicBertRotaryEmbedding(LlamaRotaryEmbedding): + pass + + +class NomicBertAttention(JinaEmbeddingsV3Attention): + def __init__(self, config): + super().__init__(config) + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + self.k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) + + +class NomicBertMLP(GemmaMLP): + pass + + +class NomicBertLayer(JinaEmbeddingsV3Layer): + pass + + +class NomicBertPreTrainedModel(BertPreTrainedModel): + config_class = NomicBertConfig + base_model_prefix = "nomic_bert" + + # Are kept as non-persistent buffers to avoid being saved in the state dict + # and causing mismatch when loading from a checkpoint that doesn't have them + _keys_to_ignore_on_load_unexpected = ["inv_freq", "original_inv_freq"] + _can_record_outputs = { + "hidden_states": NomicBertLayer, + "attentions": NomicBertAttention, + } + + +@auto_docstring +class NomicBertModel(JinaEmbeddingsV3Model): + def __init__(self, config, add_pooling_layer=False): + r""" + add_pooling_layer (`bool`, *optional*, defaults to `False`): + Whether to add a pooling layer. + """ + super().__init__(config, add_pooling_layer=add_pooling_layer) + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | BaseModelOutputWithPooling: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if input_ids is not None: + seq_length = input_ids.shape[1] + device = input_ids.device + else: + seq_length = inputs_embeds.shape[1] + device = inputs_embeds.device + + if position_ids is None: + position_ids = torch.arange(seq_length, dtype=torch.long, device=device)[None, :] + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + ) + + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=attention_mask, + ) + + hidden_states = embedding_output + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for encoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + position_ids=position_ids, + **kwargs, + ) + + sequence_output = hidden_states + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=pooled_output, + ) + + +class NomicBertPredictionHeadTransform(BertPredictionHeadTransform): + def __init__(self, config): + super().__init__(config) + # Use layer_norm rather than LayerNorm to avoid bert legacy mappings weights and bias to gamma and beta + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + del self.LayerNorm + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.layer_norm(hidden_states) + return hidden_states + + +class NomicBertOnlyMLMHead(BertOnlyMLMHead): + pass + + +@auto_docstring +class NomicBertForMaskedLM(BertForMaskedLM): + def __init__(self, config): + PreTrainedModel.__init__(self, config) + + self.nomic_bert = NomicBertModel(config) + self.cls = NomicBertOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MaskedLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + outputs = self.nomic_bert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class NomicBertForSequenceClassification(BertForSequenceClassification): + def __init__(self, config): + super().__init__(config) + self.nomic_bert = NomicBertModel(config, add_pooling_layer=True) + + +class NomicBertForTokenClassification(BertForTokenClassification): + pass + + +__all__ = [ + "NomicBertConfig", + "NomicBertPreTrainedModel", + "NomicBertModel", + "NomicBertForMaskedLM", + "NomicBertForSequenceClassification", + "NomicBertForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9a7fc4fbdcf203f27d6a9e58c82af2bd41a742d4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .image_processing_nougat import * + from .image_processing_pil_nougat import * + from .processing_nougat import * + from .tokenization_nougat import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/configuration_nougat.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/configuration_nougat.py new file mode 100644 index 0000000000000000000000000000000000000000..a1603eb2565270d6f5d30504a84f1f4294292550 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/configuration_nougat.py @@ -0,0 +1,87 @@ +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging +from ..auto.configuration_auto import AutoConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="facebook/nougat-base") +@strict +class NougatConfig(PreTrainedConfig): + r""" + encoder (`dict | PreTrainedConfig`): + The config object or dictionary of the encoder backbone. + decoder (`dict | PreTrainedConfig`): + The config object or dictionary of the decoder backbone. + + Examples: + + ```python + >>> from transformers import NougatConfig, VisionEncoderDecoderModel + + >>> # Initializing a Nougat configuration + >>> config = NougatConfig() + + >>> # Initializing a VisionEncoderDecoder model (with random weights) from a Nougat configurations + >>> model = VisionEncoderDecoderModel(config=config) + ```""" + + model_type = "nougat" + sub_configs = {"encoder": AutoConfig, "decoder": AutoConfig} + + encoder: dict | PreTrainedConfig | None = None + decoder: dict | PreTrainedConfig | None = None + is_encoder_decoder: bool = True + + def __post_init__(self, **kwargs): + if self.encoder is None or self.decoder is None: + raise ValueError( + f"A configuration of type {self.model_type} cannot be instantiated because " + f"one of both `encoder` or `decoder` sub-configurations is not passed." + ) + + if isinstance(self.encoder, dict): + encoder_model_type = self.encoder.pop("model_type") + self.encoder = AutoConfig.for_model(encoder_model_type, **self.encoder) + if isinstance(self.decoder, dict): + decoder_model_type = self.decoder.pop("model_type") + self.decoder = AutoConfig.for_model(decoder_model_type, **self.decoder) + super().__post_init__(**kwargs) + + @classmethod + def from_encoder_decoder_configs( + cls, encoder_config: PreTrainedConfig, decoder_config: PreTrainedConfig, **kwargs + ) -> PreTrainedConfig: + r""" + Instantiate a [`VisionEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model + configuration and decoder model configuration. + + Returns: + [`VisionEncoderDecoderConfig`]: An instance of a configuration object + """ + logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config") + decoder_config.is_decoder = True + decoder_config.add_cross_attention = True + + return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs) + + +__all__ = ["NougatConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/image_processing_nougat.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/image_processing_nougat.py new file mode 100644 index 0000000000000000000000000000000000000000..8e738dbf18380b24ca7d65dfe6556e79704be38d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/image_processing_nougat.py @@ -0,0 +1,304 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Nougat.""" + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import ( + get_resize_output_image_size, + group_images_by_shape, + reorder_images, +) +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TensorType, + auto_docstring, +) + + +class NougatImageProcessorKwargs(ImagesKwargs, total=False): + r""" + do_crop_margin (`bool`, *optional*, defaults to `self.do_crop_margin`): + Whether to crop the image margins. + do_thumbnail (`bool`, *optional*, defaults to `self.do_thumbnail`): + Whether to resize the image using thumbnail method. + do_align_long_axis (`bool`, *optional*, defaults to `self.do_align_long_axis`): + Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees. + """ + + do_crop_margin: bool + do_thumbnail: bool + do_align_long_axis: bool + + +@auto_docstring +class NougatImageProcessor(TorchvisionBackend): + valid_kwargs = NougatImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 896, "width": 672} + do_resize = True + do_normalize = True + do_thumbnail = True + do_align_long_axis = False + do_pad = True + do_rescale = True + do_crop_margin = True + + def __init__(self, **kwargs: Unpack[NougatImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[NougatImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def python_find_non_zero( + self, + image: "torch.Tensor", + ): + """This is a reimplementation of a findNonZero function equivalent to cv2.""" + + non_zero_indices = torch.nonzero(image, as_tuple=False) + idxvec = non_zero_indices[:, [2, 1]] + idxvec = idxvec.reshape(-1, 1, 2) + return idxvec + + def python_bounding_rect(self, coordinates): + """This is a reimplementation of a BoundingRect function equivalent to cv2.""" + + min_values = torch.amin(coordinates, axis=(0, 1)).to(torch.int) + max_values = torch.amax(coordinates, axis=(0, 1)).to(torch.int) + + x_min, y_min = min_values[0], min_values[1] + width = max_values[0] - x_min + 1 + height = max_values[1] - y_min + 1 + return x_min, y_min, width, height + + def crop_margin( + self, + image: "torch.Tensor", + gray_threshold: int = 200, + ) -> "torch.Tensor": + """ + Crops the margin of the image. Gray pixels are considered margin (i.e., pixels with a value below the + threshold). + + Args: + image (`torch.Tensor`): + The image to be cropped. + gray_threshold (`int`, *optional*, defaults to `200`) + Value below which pixels are considered to be gray. + """ + data = tvF.rgb_to_grayscale(image, num_output_channels=1) + + max_val = torch.max(data) + min_val = torch.min(data) + + if max_val == min_val: + return image + data = (data - min_val) / (max_val - min_val) * 255 + gray = data < gray_threshold + coords = self.python_find_non_zero(gray) + x_min, y_min, width, height = self.python_bounding_rect(coords) + image = image[:, y_min : y_min + height, x_min : x_min + width] + + return image + + def align_long_axis( + self, + image: "torch.Tensor", + size: SizeDict, + ) -> "torch.Tensor": + """ + Align the long axis of the image to the longest axis of the specified size. + + Args: + image (`torch.Tensor`): + The image to be aligned. + size (`SizeDict`): + The size to align the long axis to. + Returns: + `torch.Tensor`: The aligned image. + """ + input_height, input_width = image.shape[-2:] + output_height, output_width = size.height, size.width + + if (output_width < output_height and input_width > input_height) or ( + output_width > output_height and input_width < input_height + ): + image = torch.rot90(image, 3, dims=[1, 2]) + + return image + + def thumbnail( + self, + image: "torch.Tensor", + size: SizeDict, + ) -> "torch.Tensor": + """ + Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any + corresponding dimension of the specified size. + + Args: + image (`torch.tensor`): + The image to be resized. + size (`SizeDict`): + The size to resize the image to. + """ + + input_height, input_width = image.shape[-2:] + output_height, output_width = size.height, size.width + + # We always resize to the smallest of either the input or output size. + height = min(input_height, output_height) + width = min(input_width, output_width) + + if height == input_height and width == input_width: + return image + + if input_height > input_width: + width = int(input_width * height / input_height) + elif input_width > input_height: + height = int(input_height * width / input_width) + + new_size = (height, width) + + return tvF.resize(image, new_size, interpolation=tvF.InterpolationMode.BICUBIC) + + def pad_images( + self, + image: "torch.Tensor", + size: SizeDict, + ) -> "torch.Tensor": + """ + Pads a batch of images to the specified size at the top, bottom, left and right. + + Args: + image (`torch.tensor`): + The image to be padded. + size (`SizeDict`): + The size to pad the image to. + """ + input_height, input_width = image.shape[-2:] + output_height, output_width = size.height, size.width + + delta_width = output_width - input_width + delta_height = output_height - input_height + + pad_top = delta_height // 2 + pad_left = delta_width // 2 + + pad_bottom = delta_height - pad_top + pad_right = delta_width - pad_left + + padding = (pad_left, pad_top, pad_right, pad_bottom) + return tvF.pad(image, padding) + + def resize( + self, + image: "torch.Tensor", + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + antialias: bool = True, + **kwargs, + ) -> "torch.Tensor": + """ + Resize an image to `(size.height, size.width)`. + + Args: + image (`torch.Tensor`): + Image to resize. + size (`SizeDict`): + Size of the output image. + resample (`PILImageResampling | tvF.InterpolationMode | int`, *optional*): + Resampling filter to use when resizing the image. + Returns: + `torch.Tensor`: The resized image. + """ + shortest_edge = min(size.height, size.width) + + new_size = get_resize_output_image_size( + image, size=shortest_edge, default_to_square=False, input_data_format=ChannelDimension.FIRST + ) + return super().resize( + image, SizeDict(height=new_size[0], width=new_size[1]), resample=resample, antialias=antialias, **kwargs + ) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + do_align_long_axis: bool = False, + do_thumbnail: bool = True, + do_crop_margin: bool = True, + **kwargs, + ) -> BatchFeature: + # Crop images + if do_crop_margin: + images = [self.crop_margin(image) for image in images] + + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_align_long_axis: + stacked_images = self.align_long_axis(image=stacked_images, size=size) + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample) + if do_thumbnail: + stacked_images = self.thumbnail(image=stacked_images, size=size) + if do_pad: + stacked_images = self.pad_images(image=stacked_images, size=size) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["NougatImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/image_processing_pil_nougat.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/image_processing_pil_nougat.py new file mode 100644 index 0000000000000000000000000000000000000000..a4c240229b32b4570746a90312595536ebdc8a8f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/image_processing_pil_nougat.py @@ -0,0 +1,293 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Nougat.""" + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import ( + get_resize_output_image_size, + pad, + to_channel_dimension_format, + to_pil_image, +) +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + get_image_size, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TensorType, + auto_docstring, +) + + +# Adapted from transformers.models.nougat.image_processing_nougat.NougatImageProcessorKwargs +class NougatImageProcessorKwargs(ImagesKwargs, total=False): + r""" + do_crop_margin (`bool`, *optional*, defaults to `self.do_crop_margin`): + Whether to crop the image margins. + do_thumbnail (`bool`, *optional*, defaults to `self.do_thumbnail`): + Whether to resize the image using thumbnail method. + do_align_long_axis (`bool`, *optional*, defaults to `self.do_align_long_axis`): + Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees. + """ + + do_crop_margin: bool + do_thumbnail: bool + do_align_long_axis: bool + + +@auto_docstring +class NougatImageProcessorPil(PilBackend): + valid_kwargs = NougatImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 896, "width": 672} + do_resize = True + do_normalize = True + do_thumbnail = True + do_align_long_axis = False + do_pad = True + do_rescale = True + do_crop_margin = True + + def __init__(self, **kwargs: Unpack[NougatImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[NougatImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def python_find_non_zero(self, image: np.ndarray): + """This is a reimplementation of a findNonZero function equivalent to cv2.""" + non_zero_indices = np.column_stack(np.nonzero(image)) + idxvec = non_zero_indices[:, [1, 0]] + idxvec = idxvec.reshape(-1, 1, 2) + return idxvec + + def python_bounding_rect(self, coordinates): + """This is a reimplementation of a BoundingRect function equivalent to cv2.""" + min_values = np.min(coordinates, axis=(0, 1)).astype(int) + max_values = np.max(coordinates, axis=(0, 1)).astype(int) + x_min, y_min = min_values[0], min_values[1] + width = max_values[0] - x_min + 1 + height = max_values[1] - y_min + 1 + return x_min, y_min, width, height + + def crop_margin( + self, + image: np.ndarray, + gray_threshold: int = 200, + ) -> np.ndarray: + """ + Crops the margin of the image. Gray pixels are considered margin (i.e., pixels with a value below the + threshold). + + Args: + image (`np.ndarray`): + The image to be cropped. + gray_threshold (`int`, *optional*, defaults to `200`) + Value below which pixels are considered to be gray. + """ + image_pil = to_pil_image(image, input_data_format=ChannelDimension.FIRST) + data = np.array(image_pil.convert("L")).astype(np.uint8) + max_val = data.max() + min_val = data.min() + if max_val == min_val: + return image + data = (data - min_val) / (max_val - min_val) * 255 + gray = data < gray_threshold + coords = self.python_find_non_zero(gray) + x_min, y_min, width, height = self.python_bounding_rect(coords) + image_pil = image_pil.crop((x_min, y_min, x_min + width, y_min + height)) + image = np.array(image_pil).astype(np.uint8) + image = to_channel_dimension_format(image, ChannelDimension.FIRST, input_channel_dim=ChannelDimension.LAST) + return image + + def align_long_axis( + self, + image: np.ndarray, + size: SizeDict, + ) -> np.ndarray: + """ + Align the long axis of the image to the longest axis of the specified size. + + Args: + image (`np.ndarray`): + The image to be aligned. + size (`SizeDict`): + The size to align the long axis to. + Returns: + `np.ndarray`: The aligned image. + """ + input_height, input_width = get_image_size(image, channel_dim=ChannelDimension.FIRST) + output_height, output_width = size.height, size.width + + if (output_width < output_height and input_width > input_height) or ( + output_width > output_height and input_width < input_height + ): + image = np.rot90(image, 3, axes=(1, 2)) + + return image + + def thumbnail( + self, + image: np.ndarray, + size: SizeDict, + ) -> np.ndarray: + """ + Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any + corresponding dimension of the specified size. + + Args: + image (`np.ndarray`): + The image to be resized. + size (`SizeDict`): + The size to resize the image to. + """ + + input_height, input_width = get_image_size(image, channel_dim=ChannelDimension.FIRST) + output_height, output_width = size.height, size.width + + # We always resize to the smallest of either the input or output size. + height = min(input_height, output_height) + width = min(input_width, output_width) + + if height == input_height and width == input_width: + return image + + if input_height > input_width: + width = int(input_width * height / input_height) + elif input_width > input_height: + height = int(input_height * width / input_width) + + # Use np_resize for exact dimensions; self.resize uses shortest-edge logic and would produce + # different output due to rounding in get_resize_output_image_size. + return super().resize( + image, SizeDict(height=height, width=width), resample=PILImageResampling.BICUBIC, reducing_gap=2.0 + ) + + def pad_images( + self, + image: np.ndarray, + size: SizeDict, + ) -> np.ndarray: + """ + Pads a batch of images to the specified size at the top, bottom, left and right. + + Args: + image (`np.ndarray`): + The image to be padded. + size (`SizeDict`): + The size to pad the image to. + """ + + input_height, input_width = get_image_size(image, channel_dim=ChannelDimension.FIRST) + output_height, output_width = size.height, size.width + + delta_width = output_width - input_width + delta_height = output_height - input_height + + pad_top = delta_height // 2 + pad_left = delta_width // 2 + + pad_bottom = delta_height - pad_top + pad_right = delta_width - pad_left + + padding = ((pad_top, pad_bottom), (pad_left, pad_right)) + return pad(image, padding, input_data_format=ChannelDimension.FIRST, data_format=ChannelDimension.FIRST) + + def resize( + self, + image: np.ndarray, + size: SizeDict, + resample: "PILImageResampling | None" = None, + reducing_gap: int | None = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image to `(size.height, size.width)`. + + Args: + image (`np.ndarray`): + Image to resize. + size (`SizeDict`): + Size of the output image. + resample (`PILImageResampling | int`, *optional*): + Resampling filter to use when resizing the image. + Returns: + `np.ndarray`: The resized image. + """ + + shortest_edge = min(size.height, size.width) + + new_size = get_resize_output_image_size( + image, size=shortest_edge, default_to_square=False, input_data_format=ChannelDimension.FIRST + ) + return super().resize( + image, + SizeDict(height=new_size[0], width=new_size[1]), + resample=resample, + reducing_gap=reducing_gap, + **kwargs, + ) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + do_align_long_axis: bool = False, + do_thumbnail: bool = True, + do_crop_margin: bool = True, + do_pad: bool | None = None, + **kwargs, + ) -> BatchFeature: + processed_images = [] + for image in images: + if do_crop_margin: + image = self.crop_margin(image) + if do_align_long_axis: + image = self.align_long_axis(image, size) + if do_resize: + image = self.resize(image, size, resample) + if do_thumbnail: + image = self.thumbnail(image, size) + if do_pad: + image = self.pad_images(image, size) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["NougatImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/processing_nougat.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/processing_nougat.py new file mode 100644 index 0000000000000000000000000000000000000000..64b9d122e100a1f419b95482e07c3586d1d01b39 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/processing_nougat.py @@ -0,0 +1,142 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for Nougat. +""" + +from typing import Optional, Union + +from transformers.tokenization_utils_base import PreTokenizedInput, TextInput, TruncationStrategy + +from ...processing_utils import ProcessorMixin +from ...utils import PaddingStrategy, TensorType, auto_docstring + + +@auto_docstring +class NougatProcessor(ProcessorMixin): + def __init__(self, image_processor, tokenizer): + super().__init__(image_processor, tokenizer) + + @auto_docstring + def __call__( + self, + images=None, + text=None, + do_crop_margin: bool | None = None, + do_resize: bool | None = None, + size: dict[str, int] | None = None, + resample: "PILImageResampling" = None, # noqa: F821 + do_thumbnail: bool | None = None, + do_align_long_axis: bool | None = None, + do_pad: bool | None = None, + do_rescale: bool | None = None, + rescale_factor: int | float | None = None, + do_normalize: bool | None = None, + image_mean: float | list[float] | None = None, + image_std: float | list[float] | None = None, + data_format: Optional["ChannelDimension"] = "channels_first", # noqa: F821 + input_data_format: Union[str, "ChannelDimension"] | None = None, # noqa: F821 + text_pair: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + text_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + text_pair_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + add_special_tokens: bool = True, + padding: bool | str | PaddingStrategy = False, + truncation: bool | str | TruncationStrategy | None = None, + max_length: int | None = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: int | None = None, + return_tensors: str | TensorType | None = None, + return_token_type_ids: bool | None = None, + return_attention_mask: bool | None = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + ): + r""" + do_crop_margin (`bool`, *optional*): + Whether to automatically crop white margins from document images. When enabled, the processor detects + and removes white space around the edges of document pages, which is useful for processing scanned + documents or PDFs with large margins. + do_thumbnail (`bool`, *optional*): + Whether to create a thumbnail version of the image. When enabled, a smaller version of the image is + generated alongside the main processed image, which can be useful for preview or faster processing. + do_align_long_axis (`bool`, *optional*): + Whether to automatically align images so that the longer axis is horizontal. When enabled, portrait + images are rotated to landscape orientation, which is typically better for document processing tasks. + """ + if images is None and text is None: + raise ValueError("You need to specify either an `images` or `text` input to process.") + + if images is not None: + inputs = self.image_processor( + images, + do_crop_margin=do_crop_margin, + do_resize=do_resize, + size=size, + resample=resample, + do_thumbnail=do_thumbnail, + do_align_long_axis=do_align_long_axis, + do_pad=do_pad, + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + return_tensors=return_tensors, + data_format=data_format, + input_data_format=input_data_format, + ) + if text is not None: + encodings = self.tokenizer( + text, + text_pair=text_pair, + text_target=text_target, + text_pair_target=text_pair_target, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + ) + + if text is None: + return inputs + elif images is None: + return encodings + else: + inputs["labels"] = encodings["input_ids"] + return inputs + + def post_process_generation(self, *args, **kwargs): + """ + This method forwards all its arguments to NougatTokenizer's [`~PreTrainedTokenizer.post_process_generation`]. + Please refer to the docstring of this method for more information. + """ + return self.tokenizer.post_process_generation(*args, **kwargs) + + +__all__ = ["NougatProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/tokenization_nougat.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/tokenization_nougat.py new file mode 100644 index 0000000000000000000000000000000000000000..f9ed3d3fc5fdc611fefdc29ccf776bb729447a0f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nougat/tokenization_nougat.py @@ -0,0 +1,660 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Tokenizer class for Nougat. +""" + +import re +from functools import partial +from multiprocessing import Pool + +import numpy as np +from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors +from tokenizers.models import BPE + +from ...tokenization_utils_tokenizers import TokenizersBackend +from ...utils import is_levenshtein_available, is_nltk_available, logging, requires_backends + + +if is_levenshtein_available(): + from Levenshtein import ratio + +if is_nltk_available(): + import nltk + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} + + +def markdown_compatible(text: str) -> str: + """ + Make text compatible with Markdown formatting. + + This function makes various text formatting adjustments to make it compatible with Markdown. + + Args: + text (`str`): + The input text to be made Markdown-compatible. + + Returns: + `str`: The Markdown-compatible text. + """ + # equation tag + # Replace lines that start with a pattern like (decimal) \[some text\] with \[[some text] \tag{decimal}\]. + text = re.sub(r"^\(([\d.]+[a-zA-Z]?)\) \\\[(.+?)\\\]$", r"\[\2 \\tag{\1}\]", text, flags=re.MULTILINE) + # Replace lines that start with a pattern like \[some text\] (decimal) with \[[some text] \tag{decimal}\]. + text = re.sub(r"^\\\[(.+?)\\\] \(([\d.]+[a-zA-Z]?)\)$", r"\[\1 \\tag{\2}\]", text, flags=re.MULTILINE) + # Replace lines that start with a pattern like \[some text\] (digits) \[another text\] with \[[some text] \tag{digits}\] [another text]. + text = re.sub( + r"^\\\[(.+?)\\\] \(([\d.]+[a-zA-Z]?)\) (\\\[.+?\\\])$", + r"\[\1 \\tag{\2}\] \3", + text, + flags=re.MULTILINE, + ) + # multi line + text = text.replace(r"\. ", ". ") + # bold formatting + text = text.replace(r"\bm{", r"\mathbf{").replace(r"{\\bm ", r"\mathbf{") + text = re.sub(r"\\mbox{ ?\\boldmath\$(.*?)\$}", r"\\mathbf{\1}", text) + # Reformat urls (http, ftp and https only) to markdown [url](url) clickable format + text = re.sub( + r"((?:http|ftp|https):\/\/(?:[\w_-]+(?:(?:\.[\w_-]+)+))(?:[\w.,@?^=%&:\/~+#-]*[\w@?^=%&\/~+#-]))", + r"[\1](\1)", + text, + ) + # algorithms + text = re.sub(r"```\s*(.+?)\s*```", r"```\n\1\n```", text, flags=re.DOTALL) + + return text + + +def normalize_list_like_lines(generation): + """ + Normalize lines in the given text that resemble list items. The function looks for lines that start optionally with + '-' or '*', possibly followed by Roman numerals or digits indicating nesting levels. The function reformats such + lines to make them more structured. + + Args: + generation (str): The input text containing lines that need to be normalized. + + Returns: + str: The input text with the list-like lines normalized. + + Note: + The function uses regular expressions to identify and reformat the list-like lines. The patterns capture + optional bullet points, nesting levels indicated by numerals, and the actual list item content. The + normalization adjusts the bullet point style and nesting levels based on the captured patterns. + """ + + lines = generation.split("\n") + output_lines = [] + for line_no, line in enumerate(lines): + match = re.search(r". ([-*]) ", line) + if not match or line[0] not in ("-", "*"): + output_lines.append(line) + continue # Doesn't fit the pattern we want, no changes + delim = match.group(1) + " " + splits = line.split(delim)[1:] + replacement = "" + delim1 = line[0] + " " + + for i, item in enumerate(splits): + level = 0 + potential_numeral, _, rest = item.strip().partition(" ") + if not rest: + continue + # Infer current nesting level based on detected numbering + if re.match(r"^[\dixv]+((?:\.[\dixv])?)+$", potential_numeral, flags=re.IGNORECASE | re.MULTILINE): + level = potential_numeral.count(".") + + replacement += ( + ("\n" if i > 0 else "") + ("\t" * level) + (delim if i > 0 or line_no == 0 else delim1) + item.strip() + ) + + if line_no == len(lines) - 1: # If this is the last line in the generation + replacement += "\n" # Add an empty line to the end of the generation + + output_lines.append(replacement) + + return "\n".join(output_lines) + + +def find_next_punctuation(text: str, start_idx=0): + """ + Find the index of the next punctuation mark. + + Args: + text (`str`): + String to examine + start_idx (`int`, *optional*) + Index where to start + """ + + for i in range(start_idx, len(text)): + if text[i] in [".", "?", "!", "\n"]: + return i + + return None + + +def truncate_repetitions(text: str, min_len: int = 30) -> str: + """ + Attempt to truncate repeating segments in the input string. + + This function looks for the longest repeating substring at the end of the input string and truncates it to appear + only once. To be considered for removal, repetitions need to be continuous. + + Args: + text (`str`): + The input raw prediction to be truncated. + min_len (int): + The minimum length of the repeating segment. + + Returns: + `str`: The input string with repeated segments truncated. + """ + text_lower = text.lower() + text_length = len(text_lower) + + if text_length < 2 * min_len: + return text + + # try to find a length at which the tail is repeating + max_repetition_length = None + for repetition_length in range(min_len, int(text_length / 2)): + # check if there is a repetition at the end + same = True + for i in range(0, repetition_length): + if text_lower[text_length - repetition_length - i - 1] != text_lower[text_length - i - 1]: + same = False + break + + if same: + max_repetition_length = repetition_length + + if max_repetition_length is None: + return text + + lcs = text_lower[-max_repetition_length:] + + # remove all but the last repetition + substituted_text = text + substituted_text_lower = text_lower + while substituted_text_lower.endswith(lcs): + substituted_text = substituted_text[:-max_repetition_length] + substituted_text_lower = substituted_text_lower[:-max_repetition_length] + + # this is the tail with the repetitions + repeating_tail = text_lower[len(substituted_text_lower) :] + + # add until next punctuation and make sure last sentence is not repeating + substituted_text_lower_out = substituted_text_lower + while True: + sentence_end = find_next_punctuation(text_lower, len(substituted_text_lower_out)) + sentence_start = find_next_punctuation(text_lower[::-1], len(substituted_text_lower_out)) + if sentence_end and sentence_start: + sentence = text_lower[sentence_start:sentence_end] + substituted_text_lower_out = text_lower[: sentence_end + 1] + if sentence in repeating_tail: + break + else: + break + + text_out = text[: len(substituted_text_lower_out)] + + return text_out + + +def remove_numbers(lines): + def _clean(s): + return re.sub(r"(?:[\d_]|\*\*)", "", s).strip() + + if isinstance(lines, str): + return _clean(lines) + out = [] + for l in lines: + out.append(_clean(l)) + return out + + +def get_slices(lines, clean_lines): + """ + Get slices of text based on specific criteria within the lines. + + This function identifies and returns slices of text from the input lines based on certain conditions. + + These conditions were chosen by the Nougat authors: + - The slice is less than 200 characters long. + - The slice is more than 3 characters long. + - The slice does not start with "[MISSING_PAGE". + - The slice is either the same as the next slice or the ratio of the two in terms of Levenshtein distance is + greater than 0.9. + + Args: + lines (`list[str]`): + The list of lines containing the text. + clean_lines (`list[str]`): + A cleaned version of the text (without numbers). + + Returns: + `list[tuple]`: A list of tuples representing the start and end indices of text slices. + """ + indices = np.zeros(len(lines)) + for i in range(len(lines) - 1): + j = i + 1 + while not clean_lines[j] and j < len(lines) - 1: + j += 1 + if ( + len(clean_lines[i]) < 200 + and len(clean_lines[i]) > 3 + and len(clean_lines[j]) < 200 + and len(clean_lines[j]) > 3 + and not clean_lines[i].startswith("[MISSING_PAGE") + and (clean_lines[i] == clean_lines[j] or ratio(clean_lines[i], clean_lines[j]) > 0.9) + ): + indices[i:j] = 1 + ids = np.where(indices)[0] + slices = [] + if len(ids) == 0: + return slices + j0 = 0 + for j, x in enumerate(np.diff(ids) > 3): + if x: + slices.append((ids[j0], ids[j] + 2)) + j0 = j + 1 + slices.append((ids[j0], ids[-1] + 2)) + return [sli for sli in slices if sli[1] - sli[0] > 15] + + +def remove_slice_from_lines(lines, clean_text, slice) -> str: + """ + Remove a slice of text from the lines based on specific criteria. + + This function identifies a slice of text within the lines and removes it based on certain conditions. + + Args: + lines (list of str): The list of lines containing the text. + clean_text (list of str): A cleaned version of the text (without numbers). + slice (tuple): A tuple representing the start and end indices of the slice to be removed. + + Returns: + str: The removed slice of text as a single string. + """ + base = clean_text[slice[0]] + section = list(slice) + check_start_flag = False + # backwards pass, at most 5 lines + for line_idx in range(max(0, slice[0] - 1), max(0, slice[0] - 5), -1): + if not lines[line_idx]: + continue + if lines[line_idx] == "## References": + section[0] = line_idx + break + elif ratio(base, remove_numbers(lines[line_idx])) < 0.9: + section[0] = line_idx + 1 + potential_ref = remove_numbers(lines[max(0, line_idx - 1)].partition("* [")[-1]) + if len(potential_ref) >= 0.75 * len(base) and ratio(base, potential_ref) < 0.9: + section[0] = line_idx + check_start_flag = True + break + # forward pass, at most 5 lines + for line_idx in range(min(len(lines), slice[1]), min(len(lines), slice[1] + 5)): + if ratio(base, remove_numbers(lines[line_idx])) < 0.9: + section[1] = line_idx + break + if len(lines) <= section[1]: + section[1] = len(lines) - 1 + to_delete = "\n".join(lines[section[0] : section[1] + 1]) + # cut off next page content + itera, iterb = enumerate(lines[section[1] - 1]), enumerate(lines[section[1]]) + while True: + try: + (ia, a) = next(itera) + while a.isnumeric(): + (ia, a) = next(itera) + (ib, b) = next(iterb) + while b.isnumeric(): + (ib, b) = next(iterb) + if a != b: + break + except StopIteration: + break + if check_start_flag and "* [" in to_delete: + to_delete = "* [" + to_delete.partition("* [")[-1] + try: + delta = len(lines[section[1]]) - ib - 1 + if delta > 0: + to_delete = to_delete[:-delta] + except UnboundLocalError: + pass + + return to_delete.strip() + + +class NougatTokenizer(TokenizersBackend): + """ + Tokenizer for Nougat (backed by HuggingFace tokenizers library). + + This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. This class mainly adds Nougat-specific + methods for postprocessing the generated text. + + Args: + vocab_file (`str`, *optional*): + Path to the vocabulary file. + merges_file (`str`, *optional*): + Path to the merges file. + tokenizer_file (`str`, *optional*): + [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that + contains everything needed to load the tokenizer. + + clean_up_tokenization_spaces (`str`, *optional*, defaults to `False`): + Whether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra + spaces. + + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + + bos_token (`str`, *optional*, defaults to `""`): + The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. + + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + + vocab (`str`, `dict` or `list`, *optional*): + Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file. + + merges (`str` or `list`, *optional*): + Custom merges list. If not provided, merges are loaded from merges_file. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + model = BPE + + def __init__( + self, + errors: str = "replace", + unk_token: str = "", + bos_token: str = "", + eos_token: str = "", + pad_token: str = "", + vocab: str | dict | list | None = None, + merges: str | list | None = None, + **kwargs, + ): + self._vocab = ( + vocab + if vocab is not None + else { + str(bos_token): 0, + str(pad_token): 1, + str(eos_token): 2, + str(unk_token): 3, + "[START_REF]": 4, + } + ) + self._merges = merges or [] + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + dropout=None, + continuing_subword_prefix="", + end_of_word_suffix="", + fuse_unk=False, + ) + ) + + self._tokenizer.normalizer = normalizers.NFKC() + self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence( + [ + pre_tokenizers.Split(pattern="SPL1T-TH1S-Pl3A5E", behavior="removed", invert=False), + pre_tokenizers.Digits(individual_digits=True), + pre_tokenizers.Split( + pattern=r"[\(\)\[\]\{\}]|([!\"#\$%\&'\*\+,\-\./:;<=>\?\\\^_`\|\~])\1*", + behavior="isolated", + invert=False, + ), + pre_tokenizers.Split(pattern="\n", behavior="isolated", invert=False), + pre_tokenizers.ByteLevel(add_prefix_space=False, trim_offsets=True, use_regex=True), + ] + ) + self._tokenizer.decoder = decoders.ByteLevel(add_prefix_space=True, trim_offsets=True, use_regex=True) + + super().__init__( + errors=errors, + unk_token=unk_token, + bos_token=bos_token, + eos_token=eos_token, + pad_token=pad_token, + **kwargs, + ) + self._tokenizer.post_processor = processors.TemplateProcessing( + single=f"{bos_token}:0 $A:0 {eos_token}:0", + pair="$A:0 $B:1", + special_tokens=[ + (str(eos_token), self.eos_token_id), + (str(bos_token), self.bos_token_id), + ], + ) + + # Enable truncation and padding + self._tokenizer.enable_truncation(max_length=4096) + self._tokenizer.enable_padding(length=4096, pad_id=self.pad_token_id, pad_token=str(pad_token)) + + def remove_hallucinated_references(self, text: str) -> str: + """ + Remove hallucinated or missing references from the text. + + This function identifies and removes references that are marked as missing or hallucinated from the input text. + + Args: + text (`str`): + The input text containing references. + + Returns: + `str`: The text with hallucinated references removed. + """ + lines = text.split("\n") + if len(lines) == 0: + return "" + clean_lines = remove_numbers(lines) + slices = get_slices(lines, clean_lines) + to_delete = [] + for slice in slices: + to_delete.append(remove_slice_from_lines(lines, clean_lines, slice)) + for to_delete in reversed(to_delete): + text = text.replace(to_delete, "\n\n[MISSING_PAGE_POST]\n\n") + text = re.sub( + r"## References\n+\[MISSING_PAGE_POST(:\d+)?\]", + "\n\n[MISSING_PAGE_POST\\1]", + text, + ) + return text + + def correct_tables(self, generation: str) -> str: + """ + Takes a generated string and fixes tables/tabulars to make them match the markdown format needed. + + Args: + generation (str): The generated text to be postprocessed. + + Returns: + str: The postprocessed text. + + Example: + + ```python + correct_tables("\\begin{table} \\begin{tabular}{l l} & \\ \\end{tabular} \\end{table}") + "\\begin{table}\n\\begin{tabular}{l l} & \\ \\end{tabular}\n\\end{table}" + ``` + """ + # remove obvious wrong tables + for l in generation.split("\n"): + if l.count("\\begin{tabular}") > 15 or l.count("\\multicolumn") > 60 or l.count("&") > 400: + generation = generation.replace(l, "") + # whitespace corrections + + generation = generation.replace("\\begin{table} \\begin{tabular}", "\\begin{table}\n\\begin{tabular}") + generation = generation.replace("\\end{tabular} \\end{table}", "\\end{tabular}\n\\end{table}") + generation = generation.replace("\\end{table} Tab", "\\end{table}\nTab") + + generation = re.sub(r"(^.+)\\begin{tab", r"\1\n\\begin{tab", generation, flags=re.MULTILINE) + + # Remove left-aligned empty LaTeX tabular blocks. + generation = generation.replace(r"\begin{tabular}{l l} & \\ \end{tabular}", "") + # Remove tabulars with just 2 newline characters. + generation = generation.replace("\\begin{tabular}{}\n\n\\end{tabular}", "") + return generation + + def post_process_single(self, generation: str, fix_markdown: bool = True) -> str: + """ + Postprocess a single generated text. Regular expressions used here are taken directly from the Nougat article + authors. These expressions are commented for clarity and tested end-to-end in most cases. + + Args: + generation (str): The generated text to be postprocessed. + fix_markdown (bool, optional): Whether to perform Markdown formatting fixes. Default is True. + + Returns: + str: The postprocessed text. + """ + generation = re.sub( + r"(?:\n|^)#+ \d*\W? ?(.{100,})", r"\n\1", generation + ) # too long section titles probably are none + generation = generation.strip() + # Remove LaTeX left margin tag + generation = generation.replace("\n* [leftmargin=*]\n", "\n") + # Remove lines with markdown headings starting with #, with numerals, + # and possibly roman numerals with trailing spaces and newlines + generation = re.sub(r"^#+ (?:[\d+\.]+|[ixv\.]+)?\s*(?:$|\n\s*)", "", generation, flags=re.MULTILINE) + # most likely hallucinated titles + lines = generation.split("\n") + if lines[-1].startswith("#") and lines[-1].lstrip("#").startswith(" ") and len(lines) > 1: + logger.info("Likely hallucinated title at the end of the page: " + lines[-1]) + generation = "\n".join(lines[:-1]) + # obvious repetition detection + generation = truncate_repetitions(generation) + # Reference corrections + generation = self.remove_hallucinated_references(generation) + # Remove lines starting with asterisks and numbers like "*[1]" and followed by capital letters and periods (ie too long references) + generation = re.sub(r"^\* \[\d+\](\s?[A-W]\.+\s?){10,}.*$", "", generation, flags=re.MULTILINE) + # Remove empty brackets after a reference number in brackets. *[12][]ABC will become *[12]ABC + generation = re.sub(r"^(\* \[\d+\])\[\](.*)$", r"\1\2", generation, flags=re.MULTILINE) + # Remove single characters before or after 2 new lines + generation = re.sub(r"(^\w\n\n|\n\n\w$)", "", generation) + # pmc math artifact correction + generation = re.sub( + r"([\s.,()])_([a-zA-Z0-9])__([a-zA-Z0-9]){1,3}_([\s.,:()])", + r"\1\(\2_{\3}\)\4", + generation, + ) + generation = re.sub(r"([\s.,\d])_([a-zA-Z0-9])_([\s.,\d;])", r"\1\(\2\)\3", generation) + # footnote mistakes + generation = re.sub( + r"(\nFootnote .*?:) (?:footnotetext|thanks):\W*(.*(?:\n\n|$))", + r"\1 \2", + generation, + ) + # TODO Come up with footnote formatting inside a table + generation = re.sub(r"\[FOOTNOTE:.+?\](.*?)\[ENDFOOTNOTE\]", "", generation) + # itemize post processing + generation = normalize_list_like_lines(generation) + + if generation.endswith((".", "}")): + generation += "\n\n" + if re.match(r"[A-Z0-9,;:]$", generation): + # add space in case it there is a comma or word ending + generation += " " + elif generation.startswith(("#", "**", "\\begin")): + generation = "\n\n" + generation + elif generation.split("\n")[-1].startswith(("#", "Figure", "Table")): + generation = generation + "\n\n" + else: + try: + last_word = generation.split(" ")[-1] + if last_word in nltk.corpus.words.words(): + generation += " " + except LookupError: + # add space just in case. Will split words but better than concatenating them + generation += " " + + # table corrections + generation = self.correct_tables(generation) + # Remove optional, empty square brackets after begin{array} + generation = generation.replace("\\begin{array}[]{", "\\begin{array}{") + # Remove empty or malformed LaTeX tabular blocks with 2 or more columns specified, with spaces and ampersands. + generation = re.sub( + r"\\begin{tabular}{([clr ]){2,}}\s*[& ]*\s*(\\\\)? \\end{tabular}", + "", + generation, + ) + # Remove lines containing "S.A.B." one or more times. Was included in Nougat's code. + generation = re.sub(r"(\*\*S\. A\. B\.\*\*\n+){2,}", "", generation) + # Remove markdown-style headers that are incomplete or empty on multiple lines. + generation = re.sub(r"^#+( [\[\d\w])?$", "", generation, flags=re.MULTILINE) + # Remove lines with just one period. + generation = re.sub(r"^\.\s*$", "", generation, flags=re.MULTILINE) + # Replace instances of three or more newlines with just two newlines. + generation = re.sub(r"\n{3,}", "\n\n", generation) + if fix_markdown: + return markdown_compatible(generation) + else: + return generation + + def post_process_generation( + self, + generation: str | list[str], + fix_markdown: bool = True, + num_workers: int | None = None, + ) -> str | list[str]: + """ + Postprocess a generated text or a list of generated texts. + + This function can be used to perform postprocessing on generated text, such as fixing Markdown formatting. + + Postprocessing is quite slow so it is recommended to use multiprocessing to speed up the process. + + Args: + generation (Union[str, list[str]]): + The generated text or a list of generated texts. + fix_markdown (`bool`, *optional*, defaults to `True`): + Whether to perform Markdown formatting fixes. + num_workers (`int`, *optional*): + Optional number of workers to pass to leverage multiprocessing (postprocessing several texts in + parallel). + + Returns: + Union[str, list[str]]: The postprocessed text or list of postprocessed texts. + """ + requires_backends(self, ["nltk", "levenshtein"]) + + if isinstance(generation, list): + if num_workers is not None and isinstance(num_workers, int): + with Pool(num_workers) as p: + return p.map(partial(self.post_process_single, fix_markdown=fix_markdown), generation) + else: + return [self.post_process_single(s, fix_markdown=fix_markdown) for s in generation] + else: + return self.post_process_single(generation, fix_markdown=fix_markdown) + + +__all__ = ["NougatTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c32df6d8db909207e4867e119d2a7fca4a0bd907 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_nystromformer import * + from .modeling_nystromformer import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/configuration_nystromformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/configuration_nystromformer.py new file mode 100644 index 0000000000000000000000000000000000000000..64d625ffeb1ee69c3b54a6c04006a2fd3813fb86 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/configuration_nystromformer.py @@ -0,0 +1,77 @@ +# Copyright 2022 UW-Madison and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Nystromformer model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="uw-madison/nystromformer-512") +@strict +class NystromformerConfig(PreTrainedConfig): + r""" + segment_means_seq_len (`int`, *optional*, defaults to 64): + Sequence length used in segment-means. + num_landmarks (`int`, *optional*, defaults to 64): + The number of landmark (or Nystrom) points to use in Nystrom approximation of the softmax self-attention + matrix. + conv_kernel_size (`int`, *optional*, defaults to 65): + The kernel size of depthwise convolution used in Nystrom approximation. + inv_coeff_init_option (`bool`, *optional*, defaults to `False`): + Whether or not to use exact coefficient computation for the initial values for the iterative method of + calculating the Moore-Penrose inverse of a matrix. + + Example: + + ```python + >>> from transformers import NystromformerModel, NystromformerConfig + + >>> # Initializing a Nystromformer uw-madison/nystromformer-512 style configuration + >>> configuration = NystromformerConfig() + + >>> # Initializing a model from the uw-madison/nystromformer-512 style configuration + >>> model = NystromformerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "nystromformer" + + vocab_size: int = 30000 + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + hidden_act: str = "gelu_new" + hidden_dropout_prob: float | int = 0.1 + attention_probs_dropout_prob: float | int = 0.1 + max_position_embeddings: int = 510 + type_vocab_size: int = 2 + segment_means_seq_len: int = 64 + num_landmarks: int = 64 + conv_kernel_size: int = 65 + inv_coeff_init_option: bool = False + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-5 + pad_token_id: int | None = 1 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 2 + add_cross_attention: bool = False + tie_word_embeddings: bool = True + + +__all__ = ["NystromformerConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/modeling_nystromformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/modeling_nystromformer.py new file mode 100644 index 0000000000000000000000000000000000000000..7bc78d27fa8d5f3cc1f1f90d583ea11c7ad2f3ca --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/nystromformer/modeling_nystromformer.py @@ -0,0 +1,944 @@ +# Copyright 2022 UW-Madison The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Nystromformer model.""" + +import math + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import apply_chunking_to_forward +from ...utils import ( + auto_docstring, + logging, +) +from .configuration_nystromformer import NystromformerConfig + + +logger = logging.get_logger(__name__) + + +class NystromformerEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings + 2, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + 2, persistent=False + ) + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device), + persistent=False, + ) + + def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + buffered_token_type_ids = self.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = inputs_embeds + token_type_embeddings + + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class NystromformerSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.num_landmarks = config.num_landmarks + self.seq_len = config.segment_means_seq_len + self.conv_kernel_size = config.conv_kernel_size + + if config.inv_coeff_init_option: + self.init_option = config["inv_init_coeff_option"] + else: + self.init_option = "original" + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + if self.conv_kernel_size is not None: + self.conv = nn.Conv2d( + in_channels=self.num_attention_heads, + out_channels=self.num_attention_heads, + kernel_size=(self.conv_kernel_size, 1), + padding=(self.conv_kernel_size // 2, 0), + bias=False, + groups=self.num_attention_heads, + ) + + # Function to approximate Moore-Penrose inverse via the iterative method + def iterative_inv(self, mat, n_iter=6): + identity = torch.eye(mat.size(-1), device=mat.device) + key = mat + + # The entries of key are positive and ||key||_{\infty} = 1 due to softmax + if self.init_option == "original": + # This original implementation is more conservative to compute coefficient of Z_0. + value = 1 / torch.max(torch.sum(key, dim=-2)) * key.transpose(-1, -2) + else: + # This is the exact coefficient computation, 1 / ||key||_1, of initialization of Z_0, leading to faster convergence. + value = 1 / torch.max(torch.sum(key, dim=-2), dim=-1).values[:, :, None, None] * key.transpose(-1, -2) + + for _ in range(n_iter): + key_value = torch.matmul(key, value) + value = torch.matmul( + 0.25 * value, + 13 * identity + - torch.matmul(key_value, 15 * identity - torch.matmul(key_value, 7 * identity - key_value)), + ) + return value + + def forward(self, hidden_states, attention_mask=None, output_attentions=False): + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.attention_head_size) + query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + key_layer = self.key(hidden_states).view(hidden_shape).transpose(1, 2) + value_layer = self.value(hidden_states).view(hidden_shape).transpose(1, 2) + + query_layer = query_layer / math.sqrt(math.sqrt(self.attention_head_size)) + key_layer = key_layer / math.sqrt(math.sqrt(self.attention_head_size)) + + if self.num_landmarks == self.seq_len: + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in NystromformerModel forward() function) + attention_scores = attention_scores + attention_mask + + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + context_layer = torch.matmul(attention_probs, value_layer) + + else: + q_landmarks = query_layer.reshape( + -1, + self.num_attention_heads, + self.num_landmarks, + self.seq_len // self.num_landmarks, + self.attention_head_size, + ).mean(dim=-2) + k_landmarks = key_layer.reshape( + -1, + self.num_attention_heads, + self.num_landmarks, + self.seq_len // self.num_landmarks, + self.attention_head_size, + ).mean(dim=-2) + + kernel_1 = torch.nn.functional.softmax(torch.matmul(query_layer, k_landmarks.transpose(-1, -2)), dim=-1) + kernel_2 = torch.nn.functional.softmax(torch.matmul(q_landmarks, k_landmarks.transpose(-1, -2)), dim=-1) + + attention_scores = torch.matmul(q_landmarks, key_layer.transpose(-1, -2)) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in NystromformerModel forward() function) + attention_scores = attention_scores + attention_mask + + kernel_3 = nn.functional.softmax(attention_scores, dim=-1) + attention_probs = torch.matmul(kernel_1, self.iterative_inv(kernel_2)) + new_value_layer = torch.matmul(kernel_3, value_layer) + context_layer = torch.matmul(attention_probs, new_value_layer) + + if self.conv_kernel_size is not None: + context_layer += self.conv(value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput +class NystromformerSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class NystromformerAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.self = NystromformerSelfAttention(config) + self.output = NystromformerSelfOutput(config) + + def forward(self, hidden_states, attention_mask=None, output_attentions=False): + self_outputs = self.self(hidden_states, attention_mask, output_attentions) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->Nystromformer +class NystromformerIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->Nystromformer +class NystromformerOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class NystromformerLayer(GradientCheckpointingLayer): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = NystromformerAttention(config) + self.add_cross_attention = config.add_cross_attention + self.intermediate = NystromformerIntermediate(config) + self.output = NystromformerOutput(config) + + def forward(self, hidden_states, attention_mask=None, output_attentions=False): + self_attention_outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class NystromformerEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([NystromformerLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module(hidden_states, attention_mask, output_attentions) + + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->Nystromformer +class NystromformerPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->Nystromformer +class NystromformerLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = NystromformerPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=True) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->Nystromformer +class NystromformerOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = NystromformerLMPredictionHead(config) + + def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +@auto_docstring +class NystromformerPreTrainedModel(PreTrainedModel): + config: NystromformerConfig + base_model_prefix = "nystromformer" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, NystromformerEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)) + 2) + init.zeros_(module.token_type_ids) + + +@auto_docstring +class NystromformerModel(NystromformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.embeddings = NystromformerEmbeddings(config) + self.encoder = NystromformerEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | BaseModelOutputWithPastAndCrossAttentions: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length)), device=device) + + if token_type_ids is None: + if hasattr(self.embeddings, "token_type_ids"): + buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] + buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) + token_type_ids = buffered_token_type_ids_expanded + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + if not return_dict: + return (sequence_output,) + encoder_outputs[1:] + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +@auto_docstring +class NystromformerForMaskedLM(NystromformerPreTrainedModel): + _tied_weights_keys = { + "cls.predictions.decoder.weight": "nystromformer.embeddings.word_embeddings.weight", + "cls.predictions.decoder.bias": "cls.predictions.bias", + } + + def __init__(self, config): + super().__init__(config) + + self.nystromformer = NystromformerModel(config) + self.cls = NystromformerOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + self.cls.predictions.bias = new_embeddings.bias + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | MaskedLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.nystromformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class NystromformerClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + self.config = config + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = ACT2FN[self.config.hidden_act](x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +@auto_docstring( + custom_intro=""" + Nyströmformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the + pooled output) e.g. for GLUE tasks. + """ +) +class NystromformerForSequenceClassification(NystromformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.nystromformer = NystromformerModel(config) + self.classifier = NystromformerClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | SequenceClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.nystromformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class NystromformerForMultipleChoice(NystromformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.nystromformer = NystromformerModel(config) + self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size) + self.classifier = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | MultipleChoiceModelOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert *input_ids* indices into associated vectors than the + model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + outputs = self.nystromformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_state = outputs[0] # (bs * num_choices, seq_len, dim) + pooled_output = hidden_state[:, 0] # (bs * num_choices, dim) + pooled_output = self.pre_classifier(pooled_output) # (bs * num_choices, dim) + pooled_output = nn.ReLU()(pooled_output) # (bs * num_choices, dim) + logits = self.classifier(pooled_output) + + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + if not return_dict: + output = (reshaped_logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class NystromformerForTokenClassification(NystromformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.nystromformer = NystromformerModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | TokenClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.nystromformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class NystromformerForQuestionAnswering(NystromformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + config.num_labels = 2 + self.num_labels = config.num_labels + + self.nystromformer = NystromformerModel(config) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + start_positions: torch.LongTensor | None = None, + end_positions: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput: + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.nystromformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[1:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "NystromformerForMaskedLM", + "NystromformerForMultipleChoice", + "NystromformerForQuestionAnswering", + "NystromformerForSequenceClassification", + "NystromformerForTokenClassification", + "NystromformerLayer", + "NystromformerModel", + "NystromformerPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..139af5473e41fabbe978ee4b9c956dfd9b6e4453 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_olmo import * + from .modeling_olmo import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/configuration_olmo.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/configuration_olmo.py new file mode 100644 index 0000000000000000000000000000000000000000..186cc3a704fb2e79b2a2a6c6a66568788dbdfbe6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/configuration_olmo.py @@ -0,0 +1,92 @@ +# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""OLMo model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="allenai/OLMo-7B-hf") +@strict +class OlmoConfig(PreTrainedConfig): + r""" + clip_qkv (`float`, *optional*): + If not `None`, elements of query, key and value attention states are clipped so that their + absolute value does not exceed this value. + + ```python + >>> from transformers import OlmoModel, OlmoConfig + + >>> # Initializing a OLMo 7B style configuration + >>> configuration = OlmoConfig() + + >>> # Initializing a model from the OLMo 7B style configuration + >>> model = OlmoModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "olmo" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 50304 + hidden_size: int = 4096 + intermediate_size: int = 11008 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_key_value_heads: int | None = None + hidden_act: str = "silu" + max_position_embeddings: int = 2048 + initializer_range: float = 0.02 + use_cache: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = 50279 + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + clip_qkv: float | None = None + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + super().__post_init__(**kwargs) + + +__all__ = ["OlmoConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/modeling_olmo.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/modeling_olmo.py new file mode 100644 index 0000000000000000000000000000000000000000..0a886321bfc305863c2b2d73e8c797e68aaa3f42 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/modeling_olmo.py @@ -0,0 +1,503 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/olmo/modular_olmo.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_olmo.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernelized_func +from ...masking_utils import create_causal_mask +from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_olmo import OlmoConfig + + +class OlmoLayerNorm(nn.Module): + """LayerNorm but with no learnable weight or bias.""" + + def __init__(self, hidden_size: int) -> None: + super().__init__() + self.normalized_shape = (hidden_size,) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + orig_dtype = hidden_states.dtype + return F.layer_norm(hidden_states.to(dtype=torch.float32), self.normalized_shape, None, None, eps=1e-5).to( + orig_dtype + ) + + +class OlmoMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class OlmoRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: OlmoConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: OlmoConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + return cos, sin + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + q_type, k_type = q.dtype, k.dtype + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed.to(q_type), k_embed.to(k_type) + + +@use_kernelized_func(apply_rotary_pos_emb) +class OlmoAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: OlmoConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + if self.config.clip_qkv is not None: + query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class OlmoDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: OlmoConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = OlmoAttention(config=config, layer_idx=layer_idx) + + self.mlp = OlmoMLP(config) + self.input_layernorm = OlmoLayerNorm(config.hidden_size) + self.post_attention_layernorm = OlmoLayerNorm(config.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class OlmoPreTrainedModel(PreTrainedModel): + config: OlmoConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["OlmoDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": OlmoDecoderLayer, + "attentions": OlmoAttention, + } + + +@auto_docstring +class OlmoModel(OlmoPreTrainedModel): + def __init__(self, config: OlmoConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [OlmoDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = OlmoLayerNorm(config.hidden_size) + self.rotary_emb = OlmoRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_embeddings=position_embeddings, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class OlmoForCausalLM(OlmoPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = OlmoModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, OlmoForCausalLM + + >>> model = OlmoForCausalLM.from_pretrained("meta-olmo/Olmo-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo/Olmo-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class OlmoForSequenceClassification(GenericForSequenceClassification, OlmoPreTrainedModel): + pass + + +__all__ = ["OlmoForCausalLM", "OlmoForSequenceClassification", "OlmoModel", "OlmoPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/modular_olmo.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/modular_olmo.py new file mode 100644 index 0000000000000000000000000000000000000000..a0495d624c17628016b4e97138786eff976330c8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo/modular_olmo.py @@ -0,0 +1,195 @@ +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...cache_utils import Cache +from ...modeling_rope_utils import dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...utils import logging +from ...utils.generic import maybe_autocast +from ..llama.modeling_llama import ( + LlamaAttention, + LlamaDecoderLayer, + LlamaForCausalLM, + LlamaForSequenceClassification, + LlamaMLP, + LlamaModel, + LlamaRotaryEmbedding, + eager_attention_forward, + rotate_half, +) +from .configuration_olmo import OlmoConfig + + +logger = logging.get_logger(__name__) + + +class OlmoLayerNorm(nn.Module): + """LayerNorm but with no learnable weight or bias.""" + + def __init__(self, hidden_size: int) -> None: + super().__init__() + self.normalized_shape = (hidden_size,) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + orig_dtype = hidden_states.dtype + return F.layer_norm(hidden_states.to(dtype=torch.float32), self.normalized_shape, None, None, eps=1e-5).to( + orig_dtype + ) + + +class OlmoMLP(LlamaMLP): + def __init__(self, config): + super().__init__(config) + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + + +# This is identical to LlamaRotaryEmbedding except the output cos and sin are returned +# as float32 rather than the input type. +class OlmoRotaryEmbedding(LlamaRotaryEmbedding): + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + return cos, sin + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + q_type, k_type = q.dtype, k.dtype + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed.to(q_type), k_embed.to(k_type) + + +class OlmoAttention(LlamaAttention): + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + if self.config.clip_qkv is not None: + query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class OlmoDecoderLayer(LlamaDecoderLayer): + def __init__(self, config: OlmoConfig, layer_idx: int): + super().__init__(config, layer_idx) + self.input_layernorm = OlmoLayerNorm(config.hidden_size) + self.post_attention_layernorm = OlmoLayerNorm(config.hidden_size) + self.self_attn = OlmoAttention(config=config, layer_idx=layer_idx) + + +class OlmoModel(LlamaModel): + def __init__(self, config: OlmoConfig): + super().__init__(config) + self.layers = nn.ModuleList( + [OlmoDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = OlmoLayerNorm(config.hidden_size) + + +class OlmoForCausalLM(LlamaForCausalLM): + pass + + +class OlmoForSequenceClassification(LlamaForSequenceClassification): + pass + + +__all__ = [ + "OlmoForCausalLM", + "OlmoForSequenceClassification", + "OlmoModel", + "OlmoPreTrainedModel", # noqa: F822 +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e2161a4948b5e32f600af135c33330c2e2c353c7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 EleutherAI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_olmo2 import * + from .modeling_olmo2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/configuration_olmo2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/configuration_olmo2.py new file mode 100644 index 0000000000000000000000000000000000000000..f879c0b8367f44047b58a449d812f92882cd4eca --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/configuration_olmo2.py @@ -0,0 +1,96 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/olmo2/modular_olmo2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_olmo2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="allenai/Olmo2-7B-1124-hf") +@strict +class Olmo2Config(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Olmo2Model, Olmo2Config + + >>> # Initializing a Olmo2 7B style configuration + >>> configuration = Olmo2Config() + + >>> # Initializing a model from the Olmo2 7B style configuration + >>> model = Olmo2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "olmo2" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.k_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.v_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the added norm on q and k + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 50304 + hidden_size: int = 4096 + intermediate_size: int = 11008 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_key_value_heads: int | None = None + hidden_act: str = "silu" + max_position_embeddings: int = 2048 + initializer_range: float = 0.02 + use_cache: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = 50279 + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + + rms_norm_eps: float = 1e-5 + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + super().__post_init__(**kwargs) + + +__all__ = ["Olmo2Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/modeling_olmo2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/modeling_olmo2.py new file mode 100644 index 0000000000000000000000000000000000000000..9901212ff737647a62280b44981249b8a243b7ea --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/modeling_olmo2.py @@ -0,0 +1,507 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/olmo2/modular_olmo2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_olmo2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn as nn + +from transformers.utils.generic import TransformersKwargs + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub, use_kernelized_func +from ...masking_utils import create_causal_mask +from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_olmo2 import Olmo2Config + + +@use_kernel_forward_from_hub("RMSNorm") +class Olmo2RMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + Olmo2RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return (self.weight * hidden_states).to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class Olmo2RotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: Olmo2Config, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: Olmo2Config | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + return cos, sin + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + q_type, k_type = q.dtype, k.dtype + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed.to(q_type), k_embed.to(k_type) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernelized_func(apply_rotary_pos_emb) +class Olmo2Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: Olmo2Config, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + self.q_norm = Olmo2RMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps) + self.k_norm = Olmo2RMSNorm(config.num_key_value_heads * self.head_dim, config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Olmo2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class Olmo2DecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Olmo2Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = Olmo2Attention(config=config, layer_idx=layer_idx) + + self.mlp = Olmo2MLP(config) + self.post_attention_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_feedforward_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = self.post_feedforward_layernorm(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class Olmo2PreTrainedModel(PreTrainedModel): + config: Olmo2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Olmo2DecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": Olmo2DecoderLayer, + "attentions": Olmo2Attention, + } + + +@auto_docstring +class Olmo2Model(Olmo2PreTrainedModel): + def __init__(self, config: Olmo2Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Olmo2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = Olmo2RotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_embeddings=position_embeddings, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class Olmo2ForCausalLM(Olmo2PreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = Olmo2Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, Olmo2ForCausalLM + + >>> model = Olmo2ForCausalLM.from_pretrained("meta-olmo2/Olmo2-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo2/Olmo2-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class Olmo2ForSequenceClassification(GenericForSequenceClassification, Olmo2PreTrainedModel): + pass + + +__all__ = ["Olmo2ForCausalLM", "Olmo2ForSequenceClassification", "Olmo2Model", "Olmo2PreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/modular_olmo2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/modular_olmo2.py new file mode 100644 index 0000000000000000000000000000000000000000..4ac66c2e4608f89557d92a4d215fdd4c0d351dfd --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo2/modular_olmo2.py @@ -0,0 +1,236 @@ +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable + +import torch +import torch.nn as nn +from huggingface_hub.dataclasses import strict + +from transformers.utils.generic import TransformersKwargs + +from ...cache_utils import Cache +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...processing_utils import Unpack +from ...utils import auto_docstring, logging +from ..llama.modeling_llama import LlamaPreTrainedModel, LlamaRMSNorm, eager_attention_forward +from ..olmo.configuration_olmo import OlmoConfig +from ..olmo.modeling_olmo import ( + OlmoAttention, + OlmoDecoderLayer, + OlmoForCausalLM, + OlmoForSequenceClassification, + OlmoModel, + OlmoRotaryEmbedding, + apply_rotary_pos_emb, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="allenai/Olmo2-7B-1124-hf") +@strict +class Olmo2Config(OlmoConfig): + r""" + Example: + + ```python + >>> from transformers import Olmo2Model, Olmo2Config + + >>> # Initializing a Olmo2 7B style configuration + >>> configuration = Olmo2Config() + + >>> # Initializing a model from the Olmo2 7B style configuration + >>> model = Olmo2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "olmo2" + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.k_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.v_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the added norm on q and k + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + rms_norm_eps: float = 1e-5 + clip_qkv = AttributeError() + + +# OLMo2 RMS norm is identical to Llama RMS norm except: +# - Weight and hidden states are multiplied before converting back to the input dtype, rather than after. +class Olmo2RMSNorm(LlamaRMSNorm): + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return (self.weight * hidden_states).to(input_dtype) + + +class Olmo2RotaryEmbedding(OlmoRotaryEmbedding): + pass + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Olmo2 attention is identical to OLMo attention except: +# - Norm is applied to attention queries and keys. +# - No qkv clipping. +class Olmo2Attention(OlmoAttention): + def __init__(self, config: Olmo2Config, layer_idx: int | None = None): + super().__init__(config, layer_idx=layer_idx) + self.q_norm = Olmo2RMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps) + self.k_norm = Olmo2RMSNorm(config.num_key_value_heads * self.head_dim, config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +# The OLMo2 layers are identical to those of the OLMo model except: +# - RMSNorm is used instead of standard layer norm. +# - Norm is applied after attention/feedforward rather than before. +class Olmo2DecoderLayer(OlmoDecoderLayer): + def __init__(self, config: Olmo2Config, layer_idx: int): + super().__init__(config, layer_idx=layer_idx) + self.post_attention_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_feedforward_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.self_attn = Olmo2Attention(config=config, layer_idx=layer_idx) + del self.input_layernorm + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = self.post_feedforward_layernorm(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +class Olmo2PreTrainedModel(LlamaPreTrainedModel): + pass + + +# The OLMo2 model is identical to the OLMo model, except RMSNorm is used instead of +# standard layer norm for the output norm. +class Olmo2Model(OlmoModel): + def __init__(self, config: Olmo2Config): + super().__init__(config) + self.norm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.layers = nn.ModuleList( + [Olmo2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + + +# The heads now only need to redefine the model inside to the correct `RobertaModel` +class Olmo2ForCausalLM(OlmoForCausalLM): + pass + + +class Olmo2ForSequenceClassification(OlmoForSequenceClassification): + pass + + +__all__ = [ + "Olmo2Config", + "Olmo2ForCausalLM", + "Olmo2ForSequenceClassification", + "Olmo2Model", + "Olmo2PreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6e6ea471ca246002a9cc3c1644344cd06d7b9274 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_olmo3 import * + from .modeling_olmo3 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/configuration_olmo3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/configuration_olmo3.py new file mode 100644 index 0000000000000000000000000000000000000000..2f45be450a0bcd57cbbd8c9af67d73a2dd97cc70 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/configuration_olmo3.py @@ -0,0 +1,101 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/olmo3/modular_olmo3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_olmo3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="allenai/Olmo-3-7B-Instruct") +@strict +class Olmo3Config(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Olmo3Model, Olmo3Config + + >>> # Initializing a Olmo3 7B style configuration + >>> configuration = Olmo3Config() + + >>> # Initializing a model from the Olmo3 7B style configuration + >>> model = Olmo3Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "olmo3" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.k_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.v_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the added norm on q and k + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 50304 + hidden_size: int = 4096 + intermediate_size: int = 11008 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_key_value_heads: int | None = None + hidden_act: str = "silu" + max_position_embeddings: int = 2048 + initializer_range: float = 0.02 + use_cache: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = 50279 + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + + rms_norm_eps: float = 1e-5 + + sliding_window: int | None = 4096 + layer_types: list[str] | None = None + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" if (i + 1) % 4 != 0 else "full_attention" for i in range(self.num_hidden_layers) + ] + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + super().__post_init__(**kwargs) + + +__all__ = ["Olmo3Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/modeling_olmo3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/modeling_olmo3.py new file mode 100644 index 0000000000000000000000000000000000000000..78ade3570f97eeea0152a5b815768dbe4a269f24 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/modeling_olmo3.py @@ -0,0 +1,511 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/olmo3/modular_olmo3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_olmo3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn as nn + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub, use_kernelized_func +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import auto_docstring, can_return_tuple +from ...utils.generic import TransformersKwargs, maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_olmo3 import Olmo3Config + + +@use_kernel_forward_from_hub("RMSNorm") +class Olmo3RMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + Olmo3RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return (self.weight * hidden_states).to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + q_type, k_type = q.dtype, k.dtype + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed.to(q_type), k_embed.to(k_type) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernelized_func(apply_rotary_pos_emb) +class Olmo3Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: Olmo3Config, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + self.q_norm = Olmo3RMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps) + self.k_norm = Olmo3RMSNorm(config.num_key_value_heads * self.head_dim, config.rms_norm_eps) + self.attention_type = config.layer_types[layer_idx] + self.sliding_window = config.sliding_window if self.attention_type == "sliding_attention" else None + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Olmo3MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class Olmo3DecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Olmo3Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = Olmo3Attention(config=config, layer_idx=layer_idx) + + self.mlp = Olmo3MLP(config) + self.post_attention_layernorm = Olmo3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_feedforward_layernorm = Olmo3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = self.post_feedforward_layernorm(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +class Olmo3RotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: Olmo3Config, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: Olmo3Config | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +@auto_docstring +class Olmo3PreTrainedModel(PreTrainedModel): + config: Olmo3Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Olmo3DecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": Olmo3DecoderLayer, + "attentions": Olmo3Attention, + } + + +@auto_docstring +class Olmo3Model(Olmo3PreTrainedModel): + def __init__(self, config: Olmo3Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Olmo3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = Olmo3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = Olmo3RotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs), + } + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[self.config.layer_types[i]], + position_ids=position_ids, + past_key_values=past_key_values, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class Olmo3ForCausalLM(Olmo3PreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = Olmo3Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, Olmo3ForCausalLM + + >>> model = Olmo3ForCausalLM.from_pretrained("meta-olmo3/Olmo3-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo3/Olmo3-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class Olmo3ForSequenceClassification(GenericForSequenceClassification, Olmo3PreTrainedModel): + pass + + +__all__ = ["Olmo3ForCausalLM", "Olmo3ForSequenceClassification", "Olmo3Model", "Olmo3PreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/modular_olmo3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/modular_olmo3.py new file mode 100644 index 0000000000000000000000000000000000000000..f5934880c5f58aaa9a780c316631b2e00a50195a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo3/modular_olmo3.py @@ -0,0 +1,251 @@ +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable + +import torch +import torch.nn as nn +from huggingface_hub.dataclasses import strict + +from ...cache_utils import Cache, DynamicCache +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_outputs import BaseModelOutputWithPast +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...processing_utils import Unpack +from ...utils import auto_docstring +from ...utils.generic import TransformersKwargs +from ..gemma2.modeling_gemma2 import Gemma2RotaryEmbedding +from ..olmo2.configuration_olmo2 import Olmo2Config +from ..olmo2.modeling_olmo2 import ( + Olmo2Attention, + Olmo2DecoderLayer, + Olmo2ForCausalLM, + Olmo2ForSequenceClassification, + Olmo2Model, + Olmo2PreTrainedModel, + Olmo2RMSNorm, + apply_rotary_pos_emb, + eager_attention_forward, +) + + +@auto_docstring(checkpoint="allenai/Olmo-3-7B-Instruct") +@strict +class Olmo3Config(Olmo2Config): + r""" + Example: + + ```python + >>> from transformers import Olmo3Model, Olmo3Config + + >>> # Initializing a Olmo3 7B style configuration + >>> configuration = Olmo3Config() + + >>> # Initializing a model from the Olmo3 7B style configuration + >>> model = Olmo3Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "olmo3" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.k_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.v_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the added norm on q and k + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + sliding_window: int | None = 4096 + layer_types: list[str] | None = None + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" if (i + 1) % 4 != 0 else "full_attention" for i in range(self.num_hidden_layers) + ] + + super().__post_init__(**kwargs) + + +class Olmo3RMSNorm(Olmo2RMSNorm): + pass + + +# Olmo3 attention is identical to OLMo 2 attention except: +# - Sliding window attention is used for 3 out of 4 layers. +class Olmo3Attention(Olmo2Attention): + def __init__(self, config: Olmo3Config, layer_idx: int): + super().__init__(config, layer_idx=layer_idx) + self.attention_type = config.layer_types[layer_idx] + self.sliding_window = config.sliding_window if self.attention_type == "sliding_attention" else None + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Olmo3DecoderLayer(Olmo2DecoderLayer): + pass + + +class Olmo3RotaryEmbedding(Gemma2RotaryEmbedding): + pass + + +class Olmo3PreTrainedModel(Olmo2PreTrainedModel): + pass + + +# The OLMo 3 model is identical to the OLMo 2 model, except: +# - Sliding window attention is used for 3 out of 4 layers. +# - RoPE scaling is not applied to sliding window attention layers. +class Olmo3Model(Olmo2Model): + def __init__(self, config: Olmo3Config): + super().__init__(config) + self.norm = Olmo3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.layers = nn.ModuleList( + [Olmo3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.rotary_emb = Olmo3RotaryEmbedding(config=config) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + "sliding_attention": create_sliding_window_causal_mask(**mask_kwargs), + } + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[self.config.layer_types[i]], + position_ids=position_ids, + past_key_values=past_key_values, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class Olmo3ForCausalLM(Olmo2ForCausalLM): + pass + + +class Olmo3ForSequenceClassification(Olmo2ForSequenceClassification): + pass + + +__all__ = [ + "Olmo3Config", + "Olmo3ForCausalLM", + "Olmo3ForSequenceClassification", + "Olmo3Model", + "Olmo3PreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a1306d88e936ac344f76e70f2b94ef33c52a5918 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_olmo_hybrid import * + from .modeling_olmo_hybrid import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/configuration_olmo_hybrid.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/configuration_olmo_hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..0f7c32e8799e3c19c21bffd8e57f5fc68ba36e1b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/configuration_olmo_hybrid.py @@ -0,0 +1,153 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_olmo_hybrid.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring +from ...utils.type_validators import interval + + +@auto_docstring(checkpoint="allenai/Olmo-Hybrid-7B") +@strict +class OlmoHybridConfig(PreTrainedConfig): + r""" + linear_num_key_heads (`int`, *optional*): + Number of key heads for the linear attention layers. Defaults to `num_attention_heads`. + linear_num_value_heads (`int`, *optional*): + Number of value heads for the linear attention layers. Defaults to `num_attention_heads`. + linear_key_head_dim (`int`, *optional*): + Dimension of each key head in linear attention layers. Defaults to `0.75 * hidden_size / linear_num_key_heads`. + linear_value_head_dim (`int`, *optional*): + Dimension of each value head in linear attention layers. Defaults to `2 * linear_key_head_dim`. + linear_a_log_min (`float`, *optional*, defaults to 0.0): + Minimum value for uniform initialization of A_log in GatedDeltaNet layers. + linear_a_log_max (`float`, *optional*, defaults to 16.0): + Maximum value for uniform initialization of A_log in GatedDeltaNet layers. + linear_dt_min (`float`, *optional*, defaults to 0.001): + Minimum value for dt initialization in GatedDeltaNet layers. + linear_dt_max (`float`, *optional*, defaults to 0.1): + Maximum value for dt initialization in GatedDeltaNet layers. + linear_dt_init_floor (`float`, *optional*, defaults to 0.0001): + Floor value for clamping dt during initialization in GatedDeltaNet layers. + linear_conv_kernel_dim (`int`, *optional*, defaults to 4): + Kernel size for the short convolution applied to queries, keys, and values in linear attention layers. + linear_allow_neg_eigval (`bool`, *optional*, defaults to `True`): + Whether to allow negative eigenvalues in the GatedDeltaNet recurrence. When `True`, the beta + parameter is scaled by 2.0 to allow values in range [0, 2] instead of [0, 1]. + + Example: + + ```python + >>> from transformers import OlmoHybridModel, OlmoHybridConfig + + >>> # Initializing an OlmoHybrid style configuration + >>> configuration = OlmoHybridConfig() + + >>> # Initializing a model from the OlmoHybrid style configuration + >>> model = OlmoHybridModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "olmo_hybrid" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.k_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.v_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the added norm on q and k + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 100352 + hidden_size: int = 3840 + intermediate_size: int = 11008 + num_hidden_layers: int = 32 + num_attention_heads: int = 30 + num_key_value_heads: int | None = None + hidden_act: str = "silu" + max_position_embeddings: int = 65536 + initializer_range: float = interval(min=0.0, max=1.0)(default=0.02) + rms_norm_eps: float = 1e-06 + use_cache: bool = True + pad_token_id: int | None = 100277 + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = 100257 + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: int | float | None = 0.0 + layer_types: list[str] | None = None + linear_num_key_heads: int | None = None + linear_num_value_heads: int | None = None + linear_key_head_dim: int | None = None + linear_value_head_dim: int | None = None + linear_a_log_min: float = 0.0 + linear_a_log_max: float = 16.0 + linear_dt_min: float = 0.001 + linear_dt_max: float = 0.1 + linear_dt_init_floor: float = 1e-4 + linear_conv_kernel_dim: int = 4 + linear_allow_neg_eigval: bool = True + + def __post_init__(self, **kwargs): + if self.layer_types is None: + # Default: linear attention for most layers, full attention every 4th layer + self.layer_types = ["linear_attention"] * int(self.num_hidden_layers) + for i in range(int(self.num_hidden_layers)): + if i % 4 == 3: + self.layer_types[i] = "full_attention" + # Ensure at least one full attention layer for small num_hidden_layers + if "full_attention" not in self.layer_types: + self.layer_types[-1] = "full_attention" + + if self.linear_num_key_heads is None: + self.linear_num_key_heads = self.num_attention_heads + if self.linear_num_value_heads is None: + self.linear_num_value_heads = self.num_attention_heads + if self.linear_key_head_dim is None: + self.linear_key_head_dim = int(0.75 * self.hidden_size / self.linear_num_key_heads) + if self.linear_value_head_dim is None: + self.linear_value_head_dim = 2 * self.linear_key_head_dim + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if "linear_attention" not in self.layer_types: + raise ValueError("OLMoHybrid expects at least one 'linear_attention' layer.") + if all(t == "linear_attention" for t in self.layer_types): + raise ValueError("OLMoHybrid expects at least one attention layer.") + + +__all__ = ["OlmoHybridConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..5c76f0a8ca221193137e4b3cf1e7804df4105cef --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py @@ -0,0 +1,1118 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_olmo_hybrid.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from collections.abc import Callable +from typing import Any, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub, use_kernelized_func +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.import_utils import is_flash_linear_attention_available +from ...utils.output_capturing import capture_outputs +from .configuration_olmo_hybrid import OlmoHybridConfig + + +if is_flash_linear_attention_available(): + from fla.modules import FusedRMSNormGated, ShortConvolution + from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule +else: + chunk_gated_delta_rule, fused_recurrent_gated_delta_rule = None, None + FusedRMSNormGated = None + ShortConvolution = None + + +logger = logging.get_logger(__name__) + + +class OlmoHybridDynamicCache: + """ + Cache for hybrid model supporting both attention KV cache and linear attention state. + + The main difference is that this cache stores separate conv states for q, k, v (instead of a single conv_states). + """ + + is_compileable = False + + def __init__(self, config: OlmoHybridConfig): + super().__init__() + self.layer_types = config.layer_types + self.transformer_layers = [ + i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention" + ] + self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention") + self.recurrent_states = [None for _ in range(config.num_hidden_layers)] + self.key_cache = [None for _ in range(config.num_hidden_layers)] + self.value_cache = [None for _ in range(config.num_hidden_layers)] + # Replace single conv_states with separate q, k, v conv states + self.conv_states_q = [None for _ in range(config.num_hidden_layers)] + self.conv_states_k = [None for _ in range(config.num_hidden_layers)] + self.conv_states_v = [None for _ in range(config.num_hidden_layers)] + + def __len__(self): + return len(self.layer_types) + + def update( + self, + key_states: torch.Tensor, + value_states: torch.Tensor, + layer_idx: int, + cache_kwargs: dict[str, Any] | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + if self.key_cache[layer_idx] is None: + self.key_cache[layer_idx] = key_states + self.value_cache[layer_idx] = value_states + else: + self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2) + self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2) + + return self.key_cache[layer_idx], self.value_cache[layer_idx] + + def reorder_cache(self, beam_idx: torch.LongTensor): + """Reorders the cache for beam search, given the selected beam indices.""" + batch_size = beam_idx.shape[0] + for layer_idx in range(len(self.key_cache)): + if self.key_cache[layer_idx] is not None: + if self.key_cache[layer_idx].shape[0] < batch_size: + expand_ratio = batch_size // self.key_cache[layer_idx].shape[0] + self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(expand_ratio, dim=0) + self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(expand_ratio, dim=0) + device = self.key_cache[layer_idx].device + self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device)) + self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device)) + if self.conv_states_q[layer_idx] is not None: + if self.conv_states_q[layer_idx].shape[0] < batch_size: + expand_ratio = batch_size // self.conv_states_q[layer_idx].shape[0] + self.conv_states_q[layer_idx] = self.conv_states_q[layer_idx].repeat_interleave( + expand_ratio, dim=0 + ) + self.conv_states_k[layer_idx] = self.conv_states_k[layer_idx].repeat_interleave( + expand_ratio, dim=0 + ) + self.conv_states_v[layer_idx] = self.conv_states_v[layer_idx].repeat_interleave( + expand_ratio, dim=0 + ) + self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].repeat_interleave( + expand_ratio, dim=0 + ) + device = self.conv_states_q[layer_idx].device + self.conv_states_q[layer_idx] = self.conv_states_q[layer_idx].index_select(0, beam_idx.to(device)) + self.conv_states_k[layer_idx] = self.conv_states_k[layer_idx].index_select(0, beam_idx.to(device)) + self.conv_states_v[layer_idx] = self.conv_states_v[layer_idx].index_select(0, beam_idx.to(device)) + self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select( + 0, beam_idx.to(device) + ) + + def get_seq_length(self, layer_idx: int | None = 0) -> int: + """Returns the sequence length of the cached states. A layer index can be optionally passed.""" + # take any layer that contains cache and not empty tensor + layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx + if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None: + return 0 + return self.key_cache[layer_idx].shape[-2] + + def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]: + """ + Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for + the given layer at `layer_idx`. + The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer. + """ + kv_offset = 0 + past_seen_tokens = self.get_seq_length(layer_idx) + kv_length = query_length + past_seen_tokens + return kv_length, kv_offset + + def has_previous_state(self): + """We have a previous state if the last linear (conv) layer was already updated.""" + return self.conv_states_q[self.last_linear_layer] is not None + + +class OlmoHybridRMSNormGated(nn.Module): + def __init__(self, hidden_size, eps=1e-6, **kwargs): + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states, gate=None): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + # Norm before gate + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + hidden_states = self.weight * hidden_states.to(input_dtype) + hidden_states = hidden_states * F.silu(gate.to(torch.float32)) + + return hidden_states.to(input_dtype) + + +@use_kernel_forward_from_hub("RMSNorm") +class OlmoHybridRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + OlmoHybridRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return (self.weight * hidden_states).to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class OlmoHybridShortConvolution(nn.Conv1d): + def __init__( + self, + hidden_size: int, + kernel_size: int, + bias: bool = False, + activation: str | None = "silu", + ): + super().__init__( + in_channels=hidden_size, + out_channels=hidden_size, + kernel_size=kernel_size, + groups=hidden_size, + padding=kernel_size - 1, + bias=bias, + ) + self.hidden_size = hidden_size + self.conv_kernel_size = kernel_size + self.act_fn = ACT2FN[activation] + + def forward( + self, + hidden_states: torch.Tensor, + cache: torch.Tensor | None = None, + use_precomputed: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor]: + seq_len, dim = hidden_states.shape[-2:] + + hidden_states = hidden_states.transpose(1, 2) + + if use_precomputed and seq_len == 1: + # Single-token decode: rolling-window update against the cached context. + x_with_state = torch.cat([cache, hidden_states], dim=-1) + out = F.conv1d( + x_with_state, + self.weight, + self.bias, + padding=0, + groups=dim, + ) + conv_state = x_with_state[:, :, 1:] + else: + # Multi-token forward (prefill, or chunked-tokens decode when the cache has prior state). + if use_precomputed: + # Cached chunked-tokens decode: prepend the cached conv context so the causal conv + # sees the correct left-context rather than zero-padding. Dropped from the output + # at the end of this branch. + hidden_states = torch.cat([cache, hidden_states], dim=-1) + out = F.conv1d(hidden_states, self.weight, self.bias, padding=self.conv_kernel_size - 1, groups=dim) + out = out[:, :, : hidden_states.shape[-1]] + conv_state = F.pad(hidden_states, (self.conv_kernel_size - 1 - hidden_states.shape[-1], 0)) + if use_precomputed: + out = out[:, :, -seq_len:] + + out = self.act_fn(out) + + return out.transpose(1, 2), conv_state + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + q_type, k_type = q.dtype, k.dtype + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed.to(q_type), k_embed.to(k_type) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernelized_func(apply_rotary_pos_emb) +class OlmoHybridAttention(nn.Module): + """ + Multi-headed attention for OLMo Hybrid that supports optional RoPE (NoPE mode). + + Inherits from Olmo3Attention. The only behavioral difference is that when + position_embeddings is None, rotary position embeddings are skipped entirely, + enabling NoPE mode for long context extension. + """ + + def __init__(self, config: OlmoHybridConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + self.q_norm = OlmoHybridRMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps) + self.k_norm = OlmoHybridRMSNorm(config.num_key_value_heads * self.head_dim, config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None, + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + # NoPE mode: skip RoPE when position_embeddings is None + cos, sin = None, None + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class OlmoHybridRotaryEmbedding(nn.Module): + """ + RoPE for OLMo Hybrid that returns float32 cos/sin to match OLMo-core. + """ + + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: OlmoHybridConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: OlmoHybridConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + # KEY difference from parent: return float32, don't cast to x.dtype + return cos, sin + + +def apply_mask_to_padding_states(hidden_states, attention_mask): + """ + Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66 + """ + # NOTE: attention mask is a 2D boolean tensor + if attention_mask is not None and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1: + dtype = hidden_states.dtype + hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype) + + return hidden_states + + +def l2norm(x: torch.FloatTensor, dim: int = -1, eps: float = 1e-6): + """This function is intended to align with the l2norm implementation in the FLA library.""" + inv_norm = torch.rsqrt((x * x).sum(dim=dim, keepdim=True) + eps) + return x * inv_norm + + +def torch_chunk_gated_delta_rule( + query, + key, + value, + g, + beta, + chunk_size=64, + initial_state=None, + output_final_state=False, + use_qk_l2norm_in_kernel=False, +): + initial_dtype = query.dtype + if use_qk_l2norm_in_kernel: + query = l2norm(query, dim=-1, eps=1e-6) + key = l2norm(key, dim=-1, eps=1e-6) + query, key, value, beta, g = [ + x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g) + ] + + batch_size, num_heads, sequence_length, k_head_dim = key.shape + v_head_dim = value.shape[-1] + pad_size = (chunk_size - sequence_length % chunk_size) % chunk_size + query = F.pad(query, (0, 0, 0, pad_size)) + key = F.pad(key, (0, 0, 0, pad_size)) + value = F.pad(value, (0, 0, 0, pad_size)) + beta = F.pad(beta, (0, pad_size)) + g = F.pad(g, (0, pad_size)) + total_sequence_length = sequence_length + pad_size + scale = 1 / (query.shape[-1] ** 0.5) + query = query * scale + + v_beta = value * beta.unsqueeze(-1) + k_beta = key * beta.unsqueeze(-1) + # reshape to chunks + query, key, value, k_beta, v_beta = [ + x.reshape(x.shape[0], x.shape[1], -1, chunk_size, x.shape[-1]) for x in (query, key, value, k_beta, v_beta) + ] + g = g.reshape(g.shape[0], g.shape[1], -1, chunk_size) + mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=0) + + # chunk decay + g = g.cumsum(dim=-1) + decay_mask = ((g.unsqueeze(-1) - g.unsqueeze(-2)).tril().exp().float()).tril() + attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0) + for i in range(1, chunk_size): + row = attn[..., i, :i].clone() + sub = attn[..., :i, :i].clone() + attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2) + attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device) + value = attn @ v_beta + k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1)) + last_recurrent_state = ( + torch.zeros(batch_size, num_heads, k_head_dim, v_head_dim, dtype=value.dtype, device=value.device) + if initial_state is None + else initial_state.to(value) + ) + core_attn_out = torch.zeros_like(value) + mask = torch.triu(torch.ones(chunk_size, chunk_size, dtype=torch.bool, device=query.device), diagonal=1) + + # for each chunk + for i in range(0, total_sequence_length // chunk_size): + q_i, k_i, v_i = query[:, :, i], key[:, :, i], value[:, :, i] + attn = q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i] + v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state + v_new = v_i - v_prime + attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state + core_attn_out[:, :, i] = attn_inter + attn @ v_new + last_recurrent_state = ( + last_recurrent_state * g[:, :, i, -1, None, None].exp() + + (k_i * (g[:, :, i, -1, None] - g[:, :, i]).exp()[..., None]).transpose(-1, -2) @ v_new + ) + + if not output_final_state: + last_recurrent_state = None + core_attn_out = core_attn_out.reshape(core_attn_out.shape[0], core_attn_out.shape[1], -1, core_attn_out.shape[-1]) + core_attn_out = core_attn_out[:, :, :sequence_length] + core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype) + return core_attn_out, last_recurrent_state + + +def torch_recurrent_gated_delta_rule( + query, key, value, g, beta, initial_state, output_final_state, use_qk_l2norm_in_kernel=False +): + initial_dtype = query.dtype + if use_qk_l2norm_in_kernel: + query = l2norm(query, dim=-1, eps=1e-6) + key = l2norm(key, dim=-1, eps=1e-6) + query, key, value, beta, g = [ + x.transpose(1, 2).contiguous().to(torch.float32) for x in (query, key, value, beta, g) + ] + + batch_size, num_heads, sequence_length, k_head_dim = key.shape + v_head_dim = value.shape[-1] + scale = 1 / (query.shape[-1] ** 0.5) + query = query * scale + + core_attn_out = torch.zeros( + batch_size, num_heads, sequence_length, v_head_dim, dtype=value.dtype, device=value.device + ) + last_recurrent_state = ( + torch.zeros(batch_size, num_heads, k_head_dim, v_head_dim, dtype=value.dtype, device=value.device) + if initial_state is None + else initial_state.to(value) + ) + + for i in range(sequence_length): + q_t = query[:, :, i] + k_t = key[:, :, i] + v_t = value[:, :, i] + g_t = g[:, :, i].exp().unsqueeze(-1).unsqueeze(-1) + beta_t = beta[:, :, i].unsqueeze(-1) + + last_recurrent_state = last_recurrent_state * g_t + kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2) + delta = (v_t - kv_mem) * beta_t + last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta.unsqueeze(-2) + core_attn_out[:, :, i] = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2) + + if not output_final_state: + last_recurrent_state = None + core_attn_out = core_attn_out.transpose(1, 2).contiguous().to(initial_dtype) + return core_attn_out, last_recurrent_state + + +is_fast_path_available = all( + (ShortConvolution, chunk_gated_delta_rule, fused_recurrent_gated_delta_rule, FusedRMSNormGated) +) + + +class OlmoHybridGatedDeltaNet(nn.Module): + """ + GatedDeltaNet linear attention for OLMo Hybrid. + + Key differences from Qwen3NextGatedDeltaNet: + - Fully separate q/k/v/a/b projections (vs. fused qkvz + partially split ba) + - Per-projection conv1d for q, k, v (vs. single conv1d over concatenated qkv) + - Dedicated g_proj gate (vs. z derived from the fused qkvz projection) + - Supports allow_neg_eigval: scales beta by 2.0 to allow range [0, 2] + """ + + def __init__(self, config: OlmoHybridConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.num_v_heads = config.linear_num_value_heads + self.num_k_heads = config.linear_num_key_heads + self.head_k_dim = config.linear_key_head_dim + self.head_v_dim = config.linear_value_head_dim + self.key_dim = self.head_k_dim * self.num_k_heads + self.value_dim = self.head_v_dim * self.num_v_heads + self.layer_idx = layer_idx + self.conv_kernel_size = config.linear_conv_kernel_dim + self.allow_neg_eigval = config.linear_allow_neg_eigval + self.eps = config.rms_norm_eps + + self.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False) + self.a_proj = nn.Linear(self.hidden_size, self.num_v_heads, bias=False) + self.b_proj = nn.Linear(self.hidden_size, self.num_v_heads, bias=False) + + self.g_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False) + + self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False) + + Conv1dClass = ShortConvolution if ShortConvolution is not None else OlmoHybridShortConvolution + + self.q_conv1d = Conv1dClass( + hidden_size=self.key_dim, + kernel_size=self.conv_kernel_size, + bias=False, + activation="silu", + ) + self.k_conv1d = Conv1dClass( + hidden_size=self.key_dim, + kernel_size=self.conv_kernel_size, + bias=False, + activation="silu", + ) + self.v_conv1d = Conv1dClass( + hidden_size=self.value_dim, + kernel_size=self.conv_kernel_size, + bias=False, + activation="silu", + ) + + A = torch.empty(self.num_v_heads, dtype=torch.float32).uniform_( + config.linear_a_log_min, config.linear_a_log_max + ) + self.A_log = nn.Parameter(torch.log(A)) + + dt = torch.exp( + torch.rand(self.num_v_heads) * (math.log(config.linear_dt_max) - math.log(config.linear_dt_min)) + + math.log(config.linear_dt_min) + ) + dt = torch.clamp(dt, min=config.linear_dt_init_floor) + inv_dt = dt + torch.log(-torch.expm1(-dt)) + self.dt_bias = nn.Parameter(inv_dt) + + # Output norm - NOTE: FLA's FusedRMSNormGated uses eps=1e-5 by default + self.o_norm = ( + OlmoHybridRMSNormGated(self.head_v_dim, eps=1e-5) + if FusedRMSNormGated is None + else FusedRMSNormGated( + self.head_v_dim, + eps=1e-5, + device=torch.cuda.current_device(), + dtype=config.dtype if config.dtype is not None else torch.get_default_dtype(), + ) + ) + + self.chunk_gated_delta_rule = chunk_gated_delta_rule or torch_chunk_gated_delta_rule + self.recurrent_gated_delta_rule = fused_recurrent_gated_delta_rule or torch_recurrent_gated_delta_rule + + if not is_fast_path_available: + logger.warning_once( + "The fast path is not available because one of the required libraries is not installed. " + "Falling back to torch implementation. To install, follow: " + "https://github.com/fla-org/flash-linear-attention#installation" + ) + + def forward( + self, + hidden_states: torch.Tensor, + cache_params: OlmoHybridDynamicCache | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + # Requires LEFT padding to work correctly + hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask) + + batch_size, seq_len, _ = hidden_states.shape + + use_cache = cache_params is not None + # Reads "we have cached conv/recurrent state to continue from". Single-token vs multi-token + # branching lives inside `ShortConvolution` and in the recurrent-vs-chunk kernel dispatch + # below, each of which gates on `seq_len == 1` locally. + use_precomputed = use_cache and cache_params.has_previous_state() + + conv_state_q = cache_params.conv_states_q[self.layer_idx] if cache_params else None + conv_state_k = cache_params.conv_states_k[self.layer_idx] if cache_params else None + conv_state_v = cache_params.conv_states_v[self.layer_idx] if cache_params else None + recurrent_state = cache_params.recurrent_states[self.layer_idx] if cache_params else None + + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + + q, new_conv_state_q = self.q_conv1d( + q, cache=conv_state_q, use_precomputed=use_precomputed, output_final_state=use_cache + ) + k, new_conv_state_k = self.k_conv1d( + k, cache=conv_state_k, use_precomputed=use_precomputed, output_final_state=use_cache + ) + v, new_conv_state_v = self.v_conv1d( + v, cache=conv_state_v, use_precomputed=use_precomputed, output_final_state=use_cache + ) + + if cache_params is not None: + cache_params.conv_states_q[self.layer_idx] = new_conv_state_q + cache_params.conv_states_k[self.layer_idx] = new_conv_state_k + cache_params.conv_states_v[self.layer_idx] = new_conv_state_v + + q = q.view(batch_size, seq_len, -1, self.head_k_dim) + k = k.view(batch_size, seq_len, -1, self.head_k_dim) + v = v.view(batch_size, seq_len, -1, self.head_v_dim) + + if self.num_v_heads > self.num_k_heads: + expand_ratio = self.num_v_heads // self.num_k_heads + q = q.repeat_interleave(expand_ratio, dim=2) + k = k.repeat_interleave(expand_ratio, dim=2) + + beta = self.b_proj(hidden_states).sigmoid() + if self.allow_neg_eigval: + beta = beta * 2.0 + + g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias) + + if use_precomputed and seq_len == 1: + output, new_recurrent_state = self.recurrent_gated_delta_rule( + q, + k, + v, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + use_qk_l2norm_in_kernel=True, + ) + else: + output, new_recurrent_state = self.chunk_gated_delta_rule( + q, + k, + v, + g=g, + beta=beta, + initial_state=recurrent_state if use_precomputed else None, + output_final_state=use_cache, + use_qk_l2norm_in_kernel=True, + ) + + if cache_params is not None: + cache_params.recurrent_states[self.layer_idx] = new_recurrent_state + + gate = self.g_proj(hidden_states) + output = output.reshape(-1, self.head_v_dim) + gate = gate.reshape(-1, self.head_v_dim) + output = self.o_norm(output, gate) + output = output.reshape(batch_size, seq_len, -1) + + output = self.o_proj(output) + + return output + + +class OlmoHybridMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class OlmoHybridAttentionDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: OlmoHybridConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = OlmoHybridAttention(config=config, layer_idx=layer_idx) + + self.mlp = OlmoHybridMLP(config) + self.post_attention_layernorm = OlmoHybridRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_feedforward_layernorm = OlmoHybridRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.layer_type = "full_attention" + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = self.post_feedforward_layernorm(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +class OlmoHybridLinearAttentionDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: OlmoHybridConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.mlp = OlmoHybridMLP(config) + self.input_layernorm = OlmoHybridRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = OlmoHybridRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.layer_type = "linear_attention" + self.linear_attn = OlmoHybridGatedDeltaNet(config, layer_idx=layer_idx) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + output_attentions: bool | None = False, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Main difference to llama - signature (`cache_params`) and linear attention + hidden_states = self.linear_attn( + hidden_states=hidden_states, + cache_params=past_key_values, + attention_mask=attention_mask, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class OlmoHybridPreTrainedModel(PreTrainedModel): + config: OlmoHybridConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["OlmoHybridAttentionDecoderLayer", "OlmoHybridLinearAttentionDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn = True + _supports_sdpa = True + _keys_to_ignore_on_load_unexpected = [r"^mtp.*"] + _can_record_outputs = { + "hidden_states": (OlmoHybridAttentionDecoderLayer, OlmoHybridLinearAttentionDecoderLayer), + "attentions": OlmoHybridAttention, + } + _is_stateful = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, OlmoHybridGatedDeltaNet): + cfg = self.config + init.copy_( + module.A_log, + torch.empty_like(module.A_log).uniform_(cfg.linear_a_log_min, cfg.linear_a_log_max).log_(), + ) + dt = torch.exp( + torch.rand_like(module.dt_bias) * (math.log(cfg.linear_dt_max) - math.log(cfg.linear_dt_min)) + + math.log(cfg.linear_dt_min) + ) + dt = torch.clamp(dt, min=cfg.linear_dt_init_floor) + inv_dt = dt + torch.log(-torch.expm1(-dt)) + init.copy_(module.dt_bias, inv_dt) + + +class OlmoHybridModel(OlmoHybridPreTrainedModel): + def __init__(self, config: OlmoHybridConfig): + super().__init__(config) + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + self.layers = nn.ModuleList( + [ + OlmoHybridLinearAttentionDecoderLayer(config, layer_idx) + if config.layer_types[layer_idx] == "linear_attention" + else OlmoHybridAttentionDecoderLayer(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self.norm = OlmoHybridRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = ( + OlmoHybridRotaryEmbedding(config=config) + if getattr(config, "rope_parameters", None) is not None + and config.rope_parameters.get("rope_theta") is not None + else None + ) + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = OlmoHybridDynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + input_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + linear_attn_mask = self._update_linear_attn_mask(attention_mask, past_key_values) + + hidden_states = inputs_embeds + # RoPE or NoPE + position_embeddings = self.rotary_emb(hidden_states, position_ids) if self.rotary_emb is not None else None + + for i, decoder_layer in enumerate(self.layers): + layer_mask = linear_attn_mask if self.config.layer_types[i] == "linear_attention" else causal_mask + layer_position_embeddings = position_embeddings if self.config.layer_types[i] == "full_attention" else None + + hidden_states = decoder_layer( + hidden_states, + position_embeddings=layer_position_embeddings, + attention_mask=layer_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + def _update_linear_attn_mask(self, attention_mask, past_key_values): + """ + NOTE: Left-padding is used for linear attention mask. + No need for zeroing states when + 1. Cached forward + 2. Attending to all inputs + """ + linear_attn_mask = attention_mask + if (past_key_values is not None and past_key_values.has_previous_state()) or ( + attention_mask is not None and torch.all(attention_mask == 1) + ): + linear_attn_mask = None + return linear_attn_mask + + +@auto_docstring +class OlmoHybridForCausalLM(OlmoHybridPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = OlmoHybridModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, OlmoHybridForCausalLM + + >>> model = OlmoHybridForCausalLM.from_pretrained("meta-olmo_hybrid/OlmoHybrid-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-olmo_hybrid/OlmoHybrid-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = ["OlmoHybridForCausalLM", "OlmoHybridModel", "OlmoHybridPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/modular_olmo_hybrid.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/modular_olmo_hybrid.py new file mode 100644 index 0000000000000000000000000000000000000000..7e40d6d61f5d8b231ac8f76b2752520e8ddf8a54 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmo_hybrid/modular_olmo_hybrid.py @@ -0,0 +1,797 @@ +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +import math +from collections.abc import Callable +from typing import Any + +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...configuration_utils import PreTrainedConfig +from ...masking_utils import create_causal_mask +from ...modeling_outputs import BaseModelOutputWithPast +from ...modeling_rope_utils import dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.import_utils import is_flash_linear_attention_available +from ...utils.output_capturing import capture_outputs +from ..llama.configuration_llama import LlamaConfig +from ..llama.modeling_llama import LlamaDecoderLayer +from ..olmo3.modeling_olmo3 import ( + Olmo3Attention, + Olmo3DecoderLayer, + Olmo3ForCausalLM, + Olmo3MLP, + Olmo3RMSNorm, + Olmo3RotaryEmbedding, + apply_rotary_pos_emb, + eager_attention_forward, +) +from ..qwen3_next.modeling_qwen3_next import ( + Qwen3NextModel, + Qwen3NextPreTrainedModel, + Qwen3NextRMSNormGated, + apply_mask_to_padding_states, + torch_chunk_gated_delta_rule, + torch_recurrent_gated_delta_rule, +) + + +if is_flash_linear_attention_available(): + from fla.modules import FusedRMSNormGated, ShortConvolution + from fla.ops.gated_delta_rule import chunk_gated_delta_rule, fused_recurrent_gated_delta_rule +else: + chunk_gated_delta_rule, fused_recurrent_gated_delta_rule = None, None + FusedRMSNormGated = None + ShortConvolution = None + +is_fast_path_available = all( + (ShortConvolution, chunk_gated_delta_rule, fused_recurrent_gated_delta_rule, FusedRMSNormGated) +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="allenai/Olmo-Hybrid-7B") +@strict +class OlmoHybridConfig(LlamaConfig): + r""" + linear_num_key_heads (`int`, *optional*): + Number of key heads for the linear attention layers. Defaults to `num_attention_heads`. + linear_num_value_heads (`int`, *optional*): + Number of value heads for the linear attention layers. Defaults to `num_attention_heads`. + linear_key_head_dim (`int`, *optional*): + Dimension of each key head in linear attention layers. Defaults to `0.75 * hidden_size / linear_num_key_heads`. + linear_value_head_dim (`int`, *optional*): + Dimension of each value head in linear attention layers. Defaults to `2 * linear_key_head_dim`. + linear_a_log_min (`float`, *optional*, defaults to 0.0): + Minimum value for uniform initialization of A_log in GatedDeltaNet layers. + linear_a_log_max (`float`, *optional*, defaults to 16.0): + Maximum value for uniform initialization of A_log in GatedDeltaNet layers. + linear_dt_min (`float`, *optional*, defaults to 0.001): + Minimum value for dt initialization in GatedDeltaNet layers. + linear_dt_max (`float`, *optional*, defaults to 0.1): + Maximum value for dt initialization in GatedDeltaNet layers. + linear_dt_init_floor (`float`, *optional*, defaults to 0.0001): + Floor value for clamping dt during initialization in GatedDeltaNet layers. + linear_conv_kernel_dim (`int`, *optional*, defaults to 4): + Kernel size for the short convolution applied to queries, keys, and values in linear attention layers. + linear_allow_neg_eigval (`bool`, *optional*, defaults to `True`): + Whether to allow negative eigenvalues in the GatedDeltaNet recurrence. When `True`, the beta + parameter is scaled by 2.0 to allow values in range [0, 2] instead of [0, 1]. + + Example: + + ```python + >>> from transformers import OlmoHybridModel, OlmoHybridConfig + + >>> # Initializing an OlmoHybrid style configuration + >>> configuration = OlmoHybridConfig() + + >>> # Initializing a model from the OlmoHybrid style configuration + >>> model = OlmoHybridModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "olmo_hybrid" + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.k_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.v_proj": "colwise_gather_output", # we need to replicate here due to the added norm on q and k + "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the added norm on q and k + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + + vocab_size: int = 100352 + hidden_size: int = 3840 + intermediate_size: int = 11008 + num_hidden_layers: int = 32 + num_attention_heads: int = 30 + num_key_value_heads: int | None = None + max_position_embeddings: int = 65536 + pad_token_id: int | None = 100277 + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = 100257 + rms_norm_eps: float = 1e-06 + layer_types: list[str] | None = None + linear_num_key_heads: int | None = None + linear_num_value_heads: int | None = None + linear_key_head_dim: int | None = None + linear_value_head_dim: int | None = None + linear_a_log_min: float = 0.0 + linear_a_log_max: float = 16.0 + linear_dt_min: float = 0.001 + linear_dt_max: float = 0.1 + linear_dt_init_floor: float = 1e-4 + linear_conv_kernel_dim: int = 4 + linear_allow_neg_eigval: bool = True + + pretraining_tp = AttributeError() + mlp_bias = AttributeError() + head_dim = AttributeError() + + def __post_init__(self, **kwargs): + if self.layer_types is None: + # Default: linear attention for most layers, full attention every 4th layer + self.layer_types = ["linear_attention"] * int(self.num_hidden_layers) + for i in range(int(self.num_hidden_layers)): + if i % 4 == 3: + self.layer_types[i] = "full_attention" + # Ensure at least one full attention layer for small num_hidden_layers + if "full_attention" not in self.layer_types: + self.layer_types[-1] = "full_attention" + + if self.linear_num_key_heads is None: + self.linear_num_key_heads = self.num_attention_heads + if self.linear_num_value_heads is None: + self.linear_num_value_heads = self.num_attention_heads + if self.linear_key_head_dim is None: + self.linear_key_head_dim = int(0.75 * self.hidden_size / self.linear_num_key_heads) + if self.linear_value_head_dim is None: + self.linear_value_head_dim = 2 * self.linear_key_head_dim + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + PreTrainedConfig.__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if "linear_attention" not in self.layer_types: + raise ValueError("OLMoHybrid expects at least one 'linear_attention' layer.") + if all(t == "linear_attention" for t in self.layer_types): + raise ValueError("OLMoHybrid expects at least one attention layer.") + + +class OlmoHybridDynamicCache: + """ + Cache for hybrid model supporting both attention KV cache and linear attention state. + + The main difference is that this cache stores separate conv states for q, k, v (instead of a single conv_states). + """ + + is_compileable = False + + def __init__(self, config: OlmoHybridConfig): + super().__init__() + self.layer_types = config.layer_types + self.transformer_layers = [ + i for i in range(config.num_hidden_layers) if self.layer_types[i] == "full_attention" + ] + self.last_linear_layer = len(self.layer_types) - 1 - self.layer_types[::-1].index("linear_attention") + self.recurrent_states = [None for _ in range(config.num_hidden_layers)] + self.key_cache = [None for _ in range(config.num_hidden_layers)] + self.value_cache = [None for _ in range(config.num_hidden_layers)] + # Replace single conv_states with separate q, k, v conv states + self.conv_states_q = [None for _ in range(config.num_hidden_layers)] + self.conv_states_k = [None for _ in range(config.num_hidden_layers)] + self.conv_states_v = [None for _ in range(config.num_hidden_layers)] + + def __len__(self): + return len(self.layer_types) + + def update( + self, + key_states: torch.Tensor, + value_states: torch.Tensor, + layer_idx: int, + cache_kwargs: dict[str, Any] | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + if self.key_cache[layer_idx] is None: + self.key_cache[layer_idx] = key_states + self.value_cache[layer_idx] = value_states + else: + self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2) + self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2) + + return self.key_cache[layer_idx], self.value_cache[layer_idx] + + def reorder_cache(self, beam_idx: torch.LongTensor): + """Reorders the cache for beam search, given the selected beam indices.""" + batch_size = beam_idx.shape[0] + for layer_idx in range(len(self.key_cache)): + if self.key_cache[layer_idx] is not None: + if self.key_cache[layer_idx].shape[0] < batch_size: + expand_ratio = batch_size // self.key_cache[layer_idx].shape[0] + self.key_cache[layer_idx] = self.key_cache[layer_idx].repeat_interleave(expand_ratio, dim=0) + self.value_cache[layer_idx] = self.value_cache[layer_idx].repeat_interleave(expand_ratio, dim=0) + device = self.key_cache[layer_idx].device + self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device)) + self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device)) + if self.conv_states_q[layer_idx] is not None: + if self.conv_states_q[layer_idx].shape[0] < batch_size: + expand_ratio = batch_size // self.conv_states_q[layer_idx].shape[0] + self.conv_states_q[layer_idx] = self.conv_states_q[layer_idx].repeat_interleave( + expand_ratio, dim=0 + ) + self.conv_states_k[layer_idx] = self.conv_states_k[layer_idx].repeat_interleave( + expand_ratio, dim=0 + ) + self.conv_states_v[layer_idx] = self.conv_states_v[layer_idx].repeat_interleave( + expand_ratio, dim=0 + ) + self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].repeat_interleave( + expand_ratio, dim=0 + ) + device = self.conv_states_q[layer_idx].device + self.conv_states_q[layer_idx] = self.conv_states_q[layer_idx].index_select(0, beam_idx.to(device)) + self.conv_states_k[layer_idx] = self.conv_states_k[layer_idx].index_select(0, beam_idx.to(device)) + self.conv_states_v[layer_idx] = self.conv_states_v[layer_idx].index_select(0, beam_idx.to(device)) + self.recurrent_states[layer_idx] = self.recurrent_states[layer_idx].index_select( + 0, beam_idx.to(device) + ) + + def get_seq_length(self, layer_idx: int | None = 0) -> int: + """Returns the sequence length of the cached states. A layer index can be optionally passed.""" + # take any layer that contains cache and not empty tensor + layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx + if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx] is None: + return 0 + return self.key_cache[layer_idx].shape[-2] + + def get_mask_sizes(self, query_length: int, layer_idx: int) -> tuple[int, int]: + """ + Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for + the given layer at `layer_idx`. + The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns for each layer. + """ + kv_offset = 0 + past_seen_tokens = self.get_seq_length(layer_idx) + kv_length = query_length + past_seen_tokens + return kv_length, kv_offset + + def has_previous_state(self): + """We have a previous state if the last linear (conv) layer was already updated.""" + return self.conv_states_q[self.last_linear_layer] is not None + + +class OlmoHybridRMSNormGated(Qwen3NextRMSNormGated): + pass + + +class OlmoHybridRMSNorm(Olmo3RMSNorm): + pass + + +class OlmoHybridShortConvolution(nn.Conv1d): + def __init__( + self, + hidden_size: int, + kernel_size: int, + bias: bool = False, + activation: str | None = "silu", + ): + super().__init__( + in_channels=hidden_size, + out_channels=hidden_size, + kernel_size=kernel_size, + groups=hidden_size, + padding=kernel_size - 1, + bias=bias, + ) + self.hidden_size = hidden_size + self.conv_kernel_size = kernel_size + self.act_fn = ACT2FN[activation] + + def forward( + self, + hidden_states: torch.Tensor, + cache: torch.Tensor | None = None, + use_precomputed: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor]: + seq_len, dim = hidden_states.shape[-2:] + + hidden_states = hidden_states.transpose(1, 2) + + if use_precomputed and seq_len == 1: + # Single-token decode: rolling-window update against the cached context. + x_with_state = torch.cat([cache, hidden_states], dim=-1) + out = F.conv1d( + x_with_state, + self.weight, + self.bias, + padding=0, + groups=dim, + ) + conv_state = x_with_state[:, :, 1:] + else: + # Multi-token forward (prefill, or chunked-tokens decode when the cache has prior state). + if use_precomputed: + # Cached chunked-tokens decode: prepend the cached conv context so the causal conv + # sees the correct left-context rather than zero-padding. Dropped from the output + # at the end of this branch. + hidden_states = torch.cat([cache, hidden_states], dim=-1) + out = F.conv1d(hidden_states, self.weight, self.bias, padding=self.conv_kernel_size - 1, groups=dim) + out = out[:, :, : hidden_states.shape[-1]] + conv_state = F.pad(hidden_states, (self.conv_kernel_size - 1 - hidden_states.shape[-1], 0)) + if use_precomputed: + out = out[:, :, -seq_len:] + + out = self.act_fn(out) + + return out.transpose(1, 2), conv_state + + +class OlmoHybridAttention(Olmo3Attention): + """ + Multi-headed attention for OLMo Hybrid that supports optional RoPE (NoPE mode). + + Inherits from Olmo3Attention. The only behavioral difference is that when + position_embeddings is None, rotary position embeddings are skipped entirely, + enabling NoPE mode for long context extension. + """ + + def __init__(self, config: OlmoHybridConfig, layer_idx: int): + super().__init__(config, layer_idx) + # Hybrid model doesn't use sliding window attention + del self.sliding_window + del self.attention_type + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None, + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + # NoPE mode: skip RoPE when position_embeddings is None + cos, sin = None, None + if position_embeddings is not None: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class OlmoHybridRotaryEmbedding(Olmo3RotaryEmbedding): + """ + RoPE for OLMo Hybrid that returns float32 cos/sin to match OLMo-core. + """ + + @torch.no_grad() + @dynamic_rope_update + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + # KEY difference from parent: return float32, don't cast to x.dtype + return cos, sin + + +class OlmoHybridGatedDeltaNet(nn.Module): + """ + GatedDeltaNet linear attention for OLMo Hybrid. + + Key differences from Qwen3NextGatedDeltaNet: + - Fully separate q/k/v/a/b projections (vs. fused qkvz + partially split ba) + - Per-projection conv1d for q, k, v (vs. single conv1d over concatenated qkv) + - Dedicated g_proj gate (vs. z derived from the fused qkvz projection) + - Supports allow_neg_eigval: scales beta by 2.0 to allow range [0, 2] + """ + + def __init__(self, config: OlmoHybridConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.num_v_heads = config.linear_num_value_heads + self.num_k_heads = config.linear_num_key_heads + self.head_k_dim = config.linear_key_head_dim + self.head_v_dim = config.linear_value_head_dim + self.key_dim = self.head_k_dim * self.num_k_heads + self.value_dim = self.head_v_dim * self.num_v_heads + self.layer_idx = layer_idx + self.conv_kernel_size = config.linear_conv_kernel_dim + self.allow_neg_eigval = config.linear_allow_neg_eigval + self.eps = config.rms_norm_eps + + self.q_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) + self.k_proj = nn.Linear(self.hidden_size, self.key_dim, bias=False) + self.v_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False) + self.a_proj = nn.Linear(self.hidden_size, self.num_v_heads, bias=False) + self.b_proj = nn.Linear(self.hidden_size, self.num_v_heads, bias=False) + + self.g_proj = nn.Linear(self.hidden_size, self.value_dim, bias=False) + + self.o_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False) + + Conv1dClass = ShortConvolution if ShortConvolution is not None else OlmoHybridShortConvolution + + self.q_conv1d = Conv1dClass( + hidden_size=self.key_dim, + kernel_size=self.conv_kernel_size, + bias=False, + activation="silu", + ) + self.k_conv1d = Conv1dClass( + hidden_size=self.key_dim, + kernel_size=self.conv_kernel_size, + bias=False, + activation="silu", + ) + self.v_conv1d = Conv1dClass( + hidden_size=self.value_dim, + kernel_size=self.conv_kernel_size, + bias=False, + activation="silu", + ) + + A = torch.empty(self.num_v_heads, dtype=torch.float32).uniform_( + config.linear_a_log_min, config.linear_a_log_max + ) + self.A_log = nn.Parameter(torch.log(A)) + + dt = torch.exp( + torch.rand(self.num_v_heads) * (math.log(config.linear_dt_max) - math.log(config.linear_dt_min)) + + math.log(config.linear_dt_min) + ) + dt = torch.clamp(dt, min=config.linear_dt_init_floor) + inv_dt = dt + torch.log(-torch.expm1(-dt)) + self.dt_bias = nn.Parameter(inv_dt) + + # Output norm - NOTE: FLA's FusedRMSNormGated uses eps=1e-5 by default + self.o_norm = ( + OlmoHybridRMSNormGated(self.head_v_dim, eps=1e-5) + if FusedRMSNormGated is None + else FusedRMSNormGated( + self.head_v_dim, + eps=1e-5, + device=torch.cuda.current_device(), + dtype=config.dtype if config.dtype is not None else torch.get_default_dtype(), + ) + ) + + self.chunk_gated_delta_rule = chunk_gated_delta_rule or torch_chunk_gated_delta_rule + self.recurrent_gated_delta_rule = fused_recurrent_gated_delta_rule or torch_recurrent_gated_delta_rule + + if not is_fast_path_available: + logger.warning_once( + "The fast path is not available because one of the required libraries is not installed. " + "Falling back to torch implementation. To install, follow: " + "https://github.com/fla-org/flash-linear-attention#installation" + ) + + def forward( + self, + hidden_states: torch.Tensor, + cache_params: OlmoHybridDynamicCache | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + # Requires LEFT padding to work correctly + hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask) + + batch_size, seq_len, _ = hidden_states.shape + + use_cache = cache_params is not None + # Reads "we have cached conv/recurrent state to continue from". Single-token vs multi-token + # branching lives inside `ShortConvolution` and in the recurrent-vs-chunk kernel dispatch + # below, each of which gates on `seq_len == 1` locally. + use_precomputed = use_cache and cache_params.has_previous_state() + + conv_state_q = cache_params.conv_states_q[self.layer_idx] if cache_params else None + conv_state_k = cache_params.conv_states_k[self.layer_idx] if cache_params else None + conv_state_v = cache_params.conv_states_v[self.layer_idx] if cache_params else None + recurrent_state = cache_params.recurrent_states[self.layer_idx] if cache_params else None + + q = self.q_proj(hidden_states) + k = self.k_proj(hidden_states) + v = self.v_proj(hidden_states) + + q, new_conv_state_q = self.q_conv1d( + q, cache=conv_state_q, use_precomputed=use_precomputed, output_final_state=use_cache + ) + k, new_conv_state_k = self.k_conv1d( + k, cache=conv_state_k, use_precomputed=use_precomputed, output_final_state=use_cache + ) + v, new_conv_state_v = self.v_conv1d( + v, cache=conv_state_v, use_precomputed=use_precomputed, output_final_state=use_cache + ) + + if cache_params is not None: + cache_params.conv_states_q[self.layer_idx] = new_conv_state_q + cache_params.conv_states_k[self.layer_idx] = new_conv_state_k + cache_params.conv_states_v[self.layer_idx] = new_conv_state_v + + q = q.view(batch_size, seq_len, -1, self.head_k_dim) + k = k.view(batch_size, seq_len, -1, self.head_k_dim) + v = v.view(batch_size, seq_len, -1, self.head_v_dim) + + if self.num_v_heads > self.num_k_heads: + expand_ratio = self.num_v_heads // self.num_k_heads + q = q.repeat_interleave(expand_ratio, dim=2) + k = k.repeat_interleave(expand_ratio, dim=2) + + beta = self.b_proj(hidden_states).sigmoid() + if self.allow_neg_eigval: + beta = beta * 2.0 + + g = -self.A_log.float().exp() * F.softplus(self.a_proj(hidden_states).float() + self.dt_bias) + + if use_precomputed and seq_len == 1: + output, new_recurrent_state = self.recurrent_gated_delta_rule( + q, + k, + v, + g=g, + beta=beta, + initial_state=recurrent_state, + output_final_state=use_cache, + use_qk_l2norm_in_kernel=True, + ) + else: + output, new_recurrent_state = self.chunk_gated_delta_rule( + q, + k, + v, + g=g, + beta=beta, + initial_state=recurrent_state if use_precomputed else None, + output_final_state=use_cache, + use_qk_l2norm_in_kernel=True, + ) + + if cache_params is not None: + cache_params.recurrent_states[self.layer_idx] = new_recurrent_state + + gate = self.g_proj(hidden_states) + output = output.reshape(-1, self.head_v_dim) + gate = gate.reshape(-1, self.head_v_dim) + output = self.o_norm(output, gate) + output = output.reshape(batch_size, seq_len, -1) + + output = self.o_proj(output) + + return output + + +class OlmoHybridMLP(Olmo3MLP): + pass + + +class OlmoHybridAttentionDecoderLayer(Olmo3DecoderLayer): + def __init__(self, config: OlmoHybridConfig, layer_idx: int): + super().__init__(config, layer_idx) + self.layer_type = "full_attention" + self.self_attn = OlmoHybridAttention(config=config, layer_idx=layer_idx) + + +class OlmoHybridLinearAttentionDecoderLayer(LlamaDecoderLayer): + def __init__(self, config: OlmoHybridConfig, layer_idx: int): + super().__init__(config, layer_idx) + self.layer_type = "linear_attention" + del self.self_attn + self.linear_attn = OlmoHybridGatedDeltaNet(config, layer_idx=layer_idx) + self.input_layernorm = OlmoHybridRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = OlmoHybridRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.mlp = OlmoHybridMLP(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + output_attentions: bool | None = False, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Main difference to llama - signature (`cache_params`) and linear attention + hidden_states = self.linear_attn( + hidden_states=hidden_states, + cache_params=past_key_values, + attention_mask=attention_mask, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class OlmoHybridPreTrainedModel(Qwen3NextPreTrainedModel): + _is_stateful = True + _no_split_modules = ["OlmoHybridAttentionDecoderLayer", "OlmoHybridLinearAttentionDecoderLayer"] + _can_record_outputs = { + "hidden_states": (OlmoHybridAttentionDecoderLayer, OlmoHybridLinearAttentionDecoderLayer), + "attentions": OlmoHybridAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + PreTrainedModel._init_weights(self, module) + if isinstance(module, OlmoHybridGatedDeltaNet): + cfg = self.config + init.copy_( + module.A_log, + torch.empty_like(module.A_log).uniform_(cfg.linear_a_log_min, cfg.linear_a_log_max).log_(), + ) + dt = torch.exp( + torch.rand_like(module.dt_bias) * (math.log(cfg.linear_dt_max) - math.log(cfg.linear_dt_min)) + + math.log(cfg.linear_dt_min) + ) + dt = torch.clamp(dt, min=cfg.linear_dt_init_floor) + inv_dt = dt + torch.log(-torch.expm1(-dt)) + init.copy_(module.dt_bias, inv_dt) + + +class OlmoHybridModel(Qwen3NextModel): + def __init__(self, config: OlmoHybridConfig): + super().__init__(config) + self.layers = nn.ModuleList( + [ + OlmoHybridLinearAttentionDecoderLayer(config, layer_idx) + if config.layer_types[layer_idx] == "linear_attention" + else OlmoHybridAttentionDecoderLayer(config, layer_idx) + for layer_idx in range(config.num_hidden_layers) + ] + ) + self.rotary_emb = ( + OlmoHybridRotaryEmbedding(config=config) + if getattr(config, "rope_parameters", None) is not None + and config.rope_parameters.get("rope_theta") is not None + else None + ) + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = OlmoHybridDynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + input_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + linear_attn_mask = self._update_linear_attn_mask(attention_mask, past_key_values) + + hidden_states = inputs_embeds + # RoPE or NoPE + position_embeddings = self.rotary_emb(hidden_states, position_ids) if self.rotary_emb is not None else None + + for i, decoder_layer in enumerate(self.layers): + layer_mask = linear_attn_mask if self.config.layer_types[i] == "linear_attention" else causal_mask + layer_position_embeddings = position_embeddings if self.config.layer_types[i] == "full_attention" else None + + hidden_states = decoder_layer( + hidden_states, + position_embeddings=layer_position_embeddings, + attention_mask=layer_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class OlmoHybridForCausalLM(Olmo3ForCausalLM): + pass + + +__all__ = [ + "OlmoHybridConfig", + "OlmoHybridForCausalLM", + "OlmoHybridModel", + "OlmoHybridPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e0e18d77cbad11bf218b6cd304f703b4d7935eef --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_olmoe import * + from .modeling_olmoe import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/configuration_olmoe.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/configuration_olmoe.py new file mode 100644 index 0000000000000000000000000000000000000000..16bedbe698f8cf52299963c504421cd6f3e75d26 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/configuration_olmoe.py @@ -0,0 +1,89 @@ +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""OLMoE model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="allenai/OLMoE-1B-7B-0924") +@strict +class OlmoeConfig(PreTrainedConfig): + r""" + clip_qkv (`float`, *optional*): + If not `None`, elements of query, key and value attention states are clipped so that their + absolute value does not exceed this value. + + ```python + >>> from transformers import OlmoeModel, OlmoeConfig + + >>> # Initializing a OLMoE 7B A1B style configuration + >>> configuration = OlmoeConfig() + + >>> # Initializing a model from the OLMoE 7B A1B style configuration + >>> model = OlmoeModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "olmoe" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = {"num_local_experts": "num_experts"} + + # Default tensor parallel plan for base model `Olmoe` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise_gather_output", # due to the norm, we have to gather + "layers.*.self_attn.k_proj": "colwise_gather_output", # due to the norm, we have to gather + "layers.*.self_attn.v_proj": "colwise_gather_output", # due to the norm, we have to gather + "layers.*.self_attn.o_proj": "rowwise_split_input", # due to the norm, we have to gather + "layers.*.mlp.experts.gate_up_proj": "packed_colwise", + "layers.*.mlp.experts.down_proj": "rowwise", + "layers.*.mlp.experts": "moe_tp_experts", + } + + vocab_size: int = 50304 + hidden_size: int = 2048 + intermediate_size: int = 2048 + num_hidden_layers: int = 16 + num_attention_heads: int = 16 + num_key_value_heads: int | None = None + hidden_act: str = "silu" + max_position_embeddings: int = 4096 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-05 + use_cache: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = 50279 + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + clip_qkv: float | None = None + num_experts_per_tok: int = 8 + num_experts: int = 64 + output_router_logits: bool = False + router_aux_loss_coef: float = 0.01 + norm_topk_prob: bool = False + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + super().__post_init__(**kwargs) + + +__all__ = ["OlmoeConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/modeling_olmoe.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/modeling_olmoe.py new file mode 100644 index 0000000000000000000000000000000000000000..5d89ec741529d873b428935fde4619abc6c1de1b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/modeling_olmoe.py @@ -0,0 +1,708 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/olmoe/modular_olmoe.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_olmoe.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import ( + use_experts_implementation, + use_kernel_forward_from_hub, + use_kernel_func_from_hub, + use_kernelized_func, +) +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_olmoe import OlmoeConfig + + +@use_kernel_forward_from_hub("RMSNorm") +class OlmoeRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-5) -> None: + """ + OlmoeRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class OlmoeRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: OlmoeConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: OlmoeConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class OlmoeMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class OlmoeAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: OlmoeConfig, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + self.q_norm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.k_norm = OlmoeRMSNorm( + (config.hidden_size // config.num_attention_heads) * config.num_key_value_heads, eps=config.rms_norm_eps + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + if self.config.clip_qkv is not None: # Diff with llama + query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + + query_states = query_states.view(*hidden_shape).transpose(1, 2) + key_states = key_states.view(*hidden_shape).transpose(1, 2) + value_states = value_states.view(*hidden_shape).transpose(1, 2) + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=getattr(self.config, "sliding_window", None), # main diff with Llama + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +@use_experts_implementation +class OlmoeExperts(nn.Module): + """Collection of expert weights stored as 3D tensors.""" + + def __init__(self, config: OlmoeConfig): + super().__init__() + self.num_experts = config.num_local_experts + self.hidden_dim = config.hidden_size + self.intermediate_dim = config.intermediate_size + self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim)) + self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim)) + self.act_fn = ACT2FN[config.hidden_act] + + def forward( + self, + hidden_states: torch.Tensor, + top_k_index: torch.Tensor, + top_k_weights: torch.Tensor, + ) -> torch.Tensor: + final_hidden_states = torch.zeros_like(hidden_states) + with torch.no_grad(): + expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts) + expert_mask = expert_mask.permute(2, 1, 0) + expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero() + + for expert_idx in expert_hit: + expert_idx = expert_idx[0] + if expert_idx == self.num_experts: + continue + top_k_pos, token_idx = torch.where(expert_mask[expert_idx]) + current_state = hidden_states[token_idx] + gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1) + current_hidden_states = self.act_fn(gate) * up + current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx]) + current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None] + final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype)) + + return final_hidden_states + + +class OlmoeTopKRouter(nn.Module): + def __init__(self, config): + super().__init__() + self.top_k = config.num_experts_per_tok + self.num_experts = config.num_experts + self.norm_topk_prob = config.norm_topk_prob + self.hidden_dim = config.hidden_size + self.weight = nn.Parameter(torch.zeros(self.num_experts, self.hidden_dim)) + + def forward(self, hidden_states): + hidden_states = hidden_states.reshape(-1, self.hidden_dim) + router_logits = F.linear(hidden_states, self.weight) # (seq_len, num_experts) + router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1) + router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1) # (seq_len, top_k) + if self.norm_topk_prob: + router_top_value /= router_top_value.sum(dim=-1, keepdim=True) + router_top_value = router_top_value.to(router_logits.dtype) + router_scores = router_top_value + return router_logits, router_scores, router_indices + + +class OlmoeSparseMoeBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.gate = OlmoeTopKRouter(config) + self.experts = OlmoeExperts(config) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + batch_size, sequence_length, hidden_dim = hidden_states.shape + hidden_states = hidden_states.view(-1, hidden_dim) + _, top_k_weights, top_k_index = self.gate(hidden_states) + final_hidden_states = self.experts(hidden_states, top_k_index, top_k_weights).reshape( + batch_size, sequence_length, hidden_dim + ) + return final_hidden_states + + +class OlmoeDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: OlmoeConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = OlmoeAttention(config=config, layer_idx=layer_idx) + self.mlp = OlmoeSparseMoeBlock(config) + self.input_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class OlmoePreTrainedModel(PreTrainedModel): + config: OlmoeConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["OlmoeDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _can_record_outputs = { + "router_logits": OutputRecorder(OlmoeTopKRouter, index=0), + "hidden_states": OlmoeDecoderLayer, + "attentions": OlmoeAttention, + } + + _supports_attention_backend = True + + @torch.no_grad() + def _init_weights(self, module): + PreTrainedModel._init_weights(self, module) + if isinstance(module, OlmoeExperts): + init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range) + init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range) + elif isinstance(module, OlmoeTopKRouter): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + + +@auto_docstring +class OlmoeModel(OlmoePreTrainedModel): + def __init__(self, config: OlmoeConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [OlmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = OlmoeRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> MoeModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( # diff with mixtral: no sliding + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + + # create position embeddings to be shared across the decoder layers + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + position_embeddings=position_embeddings, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +def load_balancing_loss_func( + gate_logits: torch.Tensor | tuple[torch.Tensor] | None, + num_experts: int | None = None, + top_k=2, + attention_mask: torch.Tensor | None = None, +) -> torch.Tensor | int: + r""" + Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. + + See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss + function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between + experts is too unbalanced. + + Args: + gate_logits: + Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of + shape [batch_size X sequence_length, num_experts]. + num_experts: + Number of experts + top_k: + The number of experts to route per-token, can be also interpreted as the `top-k` routing + parameter. + attention_mask (`torch.Tensor`, *optional*): + The attention_mask used in forward function + shape [batch_size X sequence_length] if not None. + + Returns: + The auxiliary loss. + """ + if gate_logits is None or not isinstance(gate_logits, tuple): + return 0 + + if isinstance(gate_logits, tuple): + compute_device = gate_logits[0].device + concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) + + routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) + + _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) + + expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) + + if attention_mask is None: + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.mean(expert_mask.float(), dim=0) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.mean(routing_weights, dim=0) + else: + batch_size, sequence_length = attention_mask.shape + num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) + + # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask + expert_attention_mask = ( + attention_mask[None, :, :, None, None] + .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) + .reshape(-1, top_k, num_experts) + .to(compute_device) + ) + + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( + expert_attention_mask, dim=0 + ) + + # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert + router_per_expert_attention_mask = ( + attention_mask[None, :, :, None] + .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) + .reshape(-1, num_experts) + .to(compute_device) + ) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( + router_per_expert_attention_mask, dim=0 + ) + + overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) + return overall_loss * num_experts + + +@auto_docstring +class OlmoeForCausalLM(OlmoePreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = OlmoeModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.router_aux_loss_coef = config.router_aux_loss_coef + self.num_experts = config.num_experts + self.num_experts_per_tok = config.num_experts_per_tok + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_router_logits: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> MoeCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, OlmoeForCausalLM + + >>> model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924") + >>> tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + 'Hey, are you conscious? Can you talk to me?\nI’m not sure if you’re conscious of this, but I’m' + ``` + """ + + output_router_logits = ( + output_router_logits if output_router_logits is not None else self.config.output_router_logits + ) + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs: MoeModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_router_logits=output_router_logits, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) + + aux_loss = None + if output_router_logits: + aux_loss = load_balancing_loss_func( + outputs.router_logits, + self.num_experts, + self.num_experts_per_tok, + attention_mask, + ) + if labels is not None: + loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device + + return MoeCausalLMOutputWithPast( + loss=loss, + aux_loss=aux_loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + ) + + +__all__ = ["OlmoeForCausalLM", "OlmoeModel", "OlmoePreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/modular_olmoe.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/modular_olmoe.py new file mode 100644 index 0000000000000000000000000000000000000000..9fee40493496aa4a476ab2621473c522d0cb3d40 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/olmoe/modular_olmoe.py @@ -0,0 +1,279 @@ +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch OLMoE model.""" + +from collections.abc import Callable + +import torch +from torch import nn + +from ... import initialization as init +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...masking_utils import create_causal_mask +from ...modeling_outputs import MoeModelOutputWithPast +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.output_capturing import OutputRecorder +from ..gemma.modeling_gemma import GemmaMLP +from ..llama.modeling_llama import ( + LlamaAttention, + LlamaDecoderLayer, + LlamaRMSNorm, + LlamaRotaryEmbedding, + apply_rotary_pos_emb, + eager_attention_forward, +) +from ..mixtral.modeling_mixtral import MixtralExperts, MixtralForCausalLM, MixtralModel +from ..qwen2_moe.modeling_qwen2_moe import Qwen2MoeTopKRouter +from .configuration_olmoe import OlmoeConfig + + +logger = logging.get_logger(__name__) + + +class OlmoeRMSNorm(LlamaRMSNorm): + def __init__(self, hidden_size, eps=1e-5): + super().__init__(hidden_size, eps) + + +class OlmoeRotaryEmbedding(LlamaRotaryEmbedding): + pass + + +class OlmoeMLP(GemmaMLP): + pass + + +class OlmoeAttention(LlamaAttention): + def __init__(self, config: OlmoeConfig, layer_idx: int | None = None): + super().__init__(config, layer_idx) + self.q_norm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.k_norm = OlmoeRMSNorm( + (config.hidden_size // config.num_attention_heads) * config.num_key_value_heads, eps=config.rms_norm_eps + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + if self.config.clip_qkv is not None: # Diff with llama + query_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + key_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + value_states.clamp_(min=-self.config.clip_qkv, max=self.config.clip_qkv) + + query_states = query_states.view(*hidden_shape).transpose(1, 2) + key_states = key_states.view(*hidden_shape).transpose(1, 2) + value_states = value_states.view(*hidden_shape).transpose(1, 2) + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=getattr(self.config, "sliding_window", None), # main diff with Llama + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class OlmoeExperts(MixtralExperts): + pass + + +class OlmoeTopKRouter(Qwen2MoeTopKRouter): + pass + + +class OlmoeSparseMoeBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.gate = OlmoeTopKRouter(config) + self.experts = OlmoeExperts(config) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + batch_size, sequence_length, hidden_dim = hidden_states.shape + hidden_states = hidden_states.view(-1, hidden_dim) + _, top_k_weights, top_k_index = self.gate(hidden_states) + final_hidden_states = self.experts(hidden_states, top_k_index, top_k_weights).reshape( + batch_size, sequence_length, hidden_dim + ) + return final_hidden_states + + +class OlmoeDecoderLayer(LlamaDecoderLayer): + def __init__(self, config: OlmoeConfig, layer_idx: int): + super().__init__(config, layer_idx) + self.hidden_size = config.hidden_size + self.self_attn = OlmoeAttention(config=config, layer_idx=layer_idx) + self.mlp = OlmoeSparseMoeBlock(config) + self.input_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + +@auto_docstring +class OlmoePreTrainedModel(PreTrainedModel): + config: OlmoeConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["OlmoeDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _can_record_outputs = { + "router_logits": OutputRecorder(OlmoeTopKRouter, index=0), + "hidden_states": OlmoeDecoderLayer, + "attentions": OlmoeAttention, + } + + _supports_attention_backend = True + + @torch.no_grad() + def _init_weights(self, module): + PreTrainedModel._init_weights(self, module) + if isinstance(module, OlmoeExperts): + init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range) + init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range) + elif isinstance(module, OlmoeTopKRouter): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + + +@auto_docstring +class OlmoeModel(MixtralModel): + def __init__(self, config: OlmoeConfig): + super().__init__(config) + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [OlmoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = OlmoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = OlmoeRotaryEmbedding(config=config) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> MoeModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( # diff with mixtral: no sliding + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + + # create position embeddings to be shared across the decoder layers + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + position_embeddings=position_embeddings, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class OlmoeForCausalLM(MixtralForCausalLM, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + + def __init__(self, config): + super().__init__(config) + self.model = OlmoeModel(config) + self.num_experts = config.num_experts + + def forward(self, **super_kwargs): + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, OlmoeForCausalLM + + >>> model = OlmoeForCausalLM.from_pretrained("allenai/OLMoE-1B-7B-0924") + >>> tokenizer = AutoTokenizer.from_pretrained("allenai/OLMoE-1B-7B-0924") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + 'Hey, are you conscious? Can you talk to me?\nI’m not sure if you’re conscious of this, but I’m' + ``` + """ + return super().forward(**super_kwargs) + + +__all__ = ["OlmoeForCausalLM", "OlmoeModel", "OlmoePreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a8b49479b2599b9cc689f9ef281b61fd32ae01c9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_omdet_turbo import * + from .modeling_omdet_turbo import * + from .processing_omdet_turbo import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/configuration_omdet_turbo.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/configuration_omdet_turbo.py new file mode 100644 index 0000000000000000000000000000000000000000..7be495e0770b752bdc18428041d96d451fc55413 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/configuration_omdet_turbo.py @@ -0,0 +1,189 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""OmDet-Turbo model configuration""" + +from typing import Literal + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging +from ..auto import CONFIG_MAPPING, AutoConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="omlab/omdet-turbo-swin-tiny-hf") +@strict +class OmDetTurboConfig(PreTrainedConfig): + r""" + apply_layernorm_after_vision_backbone (`bool`, *optional*, defaults to `True`): + Whether to apply layer normalization on the feature maps of the vision backbone output. + disable_custom_kernels (`bool`, *optional*, defaults to `False`): + Whether to disable custom kernels. + text_projection_in_dim (`int`, *optional*, defaults to 512): + The input dimension for the text projection. + text_projection_out_dim (`int`, *optional*, defaults to 512): + The output dimension for the text projection. + task_encoder_hidden_dim (`int`, *optional*, defaults to 1024): + The feedforward dimension for the task encoder. + class_embed_dim (`int`, *optional*, defaults to 512): + The dimension of the classes embeddings. + class_distance_type (`str`, *optional*, defaults to `"cosine"`): + The type of distance to compare predicted classes to projected classes embeddings. + Can be `"cosine"` or `"dot"`. + num_queries (`int`, *optional*, defaults to 900): + The number of queries. + csp_activation (`str`, *optional*, defaults to `"silu"`): + The activation function of the Cross Stage Partial (CSP) networks of the encoder. + conv_norm_activation (`str`, *optional*, defaults to `"gelu"`): + The activation function of the ConvNormLayer layers of the encoder. + encoder_feedforward_activation (`str`, *optional*, defaults to `"relu"`): + The activation function for the feedforward network of the encoder. + encoder_feedforward_dropout (`float`, *optional*, defaults to 0.0): + The dropout rate following the activation of the encoder feedforward network. + hidden_expansion (`int`, *optional*, defaults to 1): + The hidden expansion of the CSP networks in the encoder. + vision_features_channels (`tuple(int)`, *optional*, defaults to `[256, 256, 256]`): + The projected vision features channels used as inputs for the decoder. + encoder_in_channels (`List(int)`, *optional*, defaults to `[192, 384, 768]`): + The input channels for the encoder. + encoder_projection_indices (`List(int)`, *optional*, defaults to `[2]`): + The indices of the input features projected by each layers. + encoder_dim_feedforward (`int`, *optional*, defaults to 2048): + The feedforward dimension for the encoder. + positional_encoding_temperature (`int`, *optional*, defaults to 10000): + The positional encoding temperature in the encoder. + num_feature_levels (`int`, *optional*, defaults to 3): + The number of feature levels for the multi-scale deformable attention module of the decoder. + decoder_activation (`str`, *optional*, defaults to `"relu"`): + The activation function for the decoder. + decoder_dim_feedforward (`int`, *optional*, defaults to 2048): + The feedforward dimension for the decoder. + decoder_num_points (`int`, *optional*, defaults to 4): + The number of points sampled in the decoder multi-scale deformable attention module. + decoder_dropout (`float`, *optional*, defaults to 0.0): + The dropout rate for the decoder. + eval_size (`tuple[int, int]`, *optional*): + Height and width used to computes the effective height and width of the position embeddings after taking + into account the stride (see RTDetr). + learn_initial_query (`bool`, *optional*, defaults to `False`): + Whether to learn the initial query. + cache_size (`int`, *optional*, defaults to 100): + The cache size for the classes and prompts caches. + + Examples: + + ```python + >>> from transformers import OmDetTurboConfig, OmDetTurboForObjectDetection + + >>> # Initializing a OmDet-Turbo omlab/omdet-turbo-swin-tiny-hf style configuration + >>> configuration = OmDetTurboConfig() + + >>> # Initializing a model (with random weights) from the omlab/omdet-turbo-swin-tiny-hf style configuration + >>> model = OmDetTurboForObjectDetection(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "omdet-turbo" + sub_configs = {"backbone_config": AutoConfig, "text_config": AutoConfig} + attribute_map = { + "encoder_hidden_dim": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + text_config: dict | PreTrainedConfig | None = None + backbone_config: dict | PreTrainedConfig | None = None + apply_layernorm_after_vision_backbone: bool = True + image_size: int | list[int] | tuple[int, int] = 640 + disable_custom_kernels: bool = False + layer_norm_eps: float = 1e-5 + batch_norm_eps: float = 1e-5 + init_std: float = 0.02 + text_projection_in_dim: int = 512 + text_projection_out_dim: int = 512 + task_encoder_hidden_dim: int = 1024 + class_embed_dim: int = 512 + class_distance_type: Literal["cosine", "dot"] = "cosine" + num_queries: int = 900 + csp_activation: str = "silu" + conv_norm_activation: str = "gelu" + encoder_feedforward_activation: str = "relu" + encoder_feedforward_dropout: float | int = 0.0 + encoder_dropout: float | int = 0.0 + hidden_expansion: int = 1 + encoder_hidden_dim: int = 256 + vision_features_channels: list[int] | tuple[int, ...] = (256, 256, 256) + encoder_in_channels: list[int] | tuple[int, ...] = (192, 384, 768) + encoder_projection_indices: list[int] | tuple[int, ...] = (2,) + encoder_attention_heads: int = 8 + encoder_dim_feedforward: int = 2048 + encoder_layers: int = 1 + positional_encoding_temperature: int = 10000 + num_feature_levels: int = 3 + decoder_hidden_dim: int = 256 + decoder_num_heads: int = 8 + decoder_num_layers: int = 6 + decoder_activation: str = "relu" + decoder_dim_feedforward: int = 2048 + decoder_num_points: int = 4 + decoder_dropout: float | int = 0.0 + eval_size: int | None = None + learn_initial_query: bool = False + cache_size: int = 100 + is_encoder_decoder: bool = True + + def __post_init__(self, **kwargs): + # Init timm backbone with hardcoded values for BC + timm_default_kwargs = { + "out_indices": [1, 2, 3], + "img_size": self.image_size, + "always_partition": True, + } + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_backbone="swin_tiny_patch4_window7_224", + default_config_type="swin", + default_config_kwargs={"image_size": self.image_size, "out_indices": [2, 3, 4]}, + timm_default_kwargs=timm_default_kwargs, + **kwargs, + ) + + # Extract timm.create_model kwargs; TimmBackbone doesn't forward arbitrary config attrs to timm + self.timm_kwargs = {} + if getattr(self.backbone_config, "model_type", None) == "timm_backbone": + for attr in ("img_size", "always_partition"): + if hasattr(self.backbone_config, attr): + self.timm_kwargs[attr] = getattr(self.backbone_config, attr) + + if self.text_config is None: + logger.info("`text_config` is `None`. Initializing the config with the default `clip_text_model`") + self.text_config = CONFIG_MAPPING["clip_text_model"]() + elif isinstance(self.text_config, dict): + text_model_type = self.text_config.get("model_type") + self.text_config = CONFIG_MAPPING[text_model_type](**self.text_config) + + super().__post_init__(**kwargs) + + def to_dict(self): + output = super().to_dict() + output.pop("timm_kwargs", None) + return output + + +__all__ = ["OmDetTurboConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/modeling_omdet_turbo.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/modeling_omdet_turbo.py new file mode 100644 index 0000000000000000000000000000000000000000..a00c0ab17c773f49ea14ce35a910f9aa2230971e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/modeling_omdet_turbo.py @@ -0,0 +1,1657 @@ +# Copyright 2024 Om Research Lab and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch OmDet-Turbo model.""" + +import math +import warnings +from collections import OrderedDict +from dataclasses import dataclass +from functools import lru_cache + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ... import initialization as init +from ...activations import ACT2CLS, ACT2FN +from ...integrations import use_kernel_forward_from_hub +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + logging, + torch_compilable_check, +) +from ..auto import AutoBackbone, AutoModel +from .configuration_omdet_turbo import OmDetTurboConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the OmDetTurboHybridEncoder. + """ +) +@dataclass +class OmDetTurboEncoderOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor`): + Last hidden states of the encoder. + extracted_states (`tuple[torch.FloatTensor]`): + The extracted states from the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) of the encoder. + """ + + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + extracted_states: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the OmDetTurboDecoder. + """ +) +@dataclass +class OmDetTurboDecoderOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder. + decoder_coords (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + The predicted coordinates of the objects. + decoder_classes (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes)`): + The predicted classes of the objects. + encoder_coord_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + The predicted coordinates of the objects from the encoder. + encoder_class_logits (`tuple[torch.FloatTensor]` of shape `(batch_size, num_queries, num_classes)`): + The predicted class of the objects from the encoder. + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + The initial reference points. + intermediate_reference_points (`tuple[tuple[torch.FloatTensor]]`): + The intermediate reference points. + """ + + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[tuple[torch.FloatTensor]] | None = None + decoder_coords: torch.FloatTensor | None = None + decoder_classes: torch.FloatTensor | None = None + encoder_coord_logits: torch.FloatTensor | None = None + encoder_class_logits: tuple[torch.FloatTensor] | None = None + init_reference_points: torch.FloatTensor | None = None + intermediate_reference_points: tuple[tuple[torch.FloatTensor]] = None + + +@auto_docstring( + custom_intro=""" + Output type of [`OmDetTurboObjectDetectionOutput`]. + """ +) +@dataclass +class OmDetTurboObjectDetectionOutput(ModelOutput): + r""" + loss (`torch.FloatTensor`): + The loss value. + decoder_coord_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + The predicted coordinates logits of the objects. + decoder_class_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes)`): + The predicted class of the objects. + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + The initial reference points. + intermediate_reference_points (`tuple[tuple[torch.FloatTensor]]`): + The intermediate reference points. + encoder_coord_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + The predicted coordinates of the objects from the encoder. + encoder_class_logits (`tuple[torch.FloatTensor]`): + The predicted class of the objects from the encoder. + encoder_extracted_states (`torch.FloatTensor`): + The extracted states from the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) of the encoder. + decoder_hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape + `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + decoder_attentions (`tuple[tuple[torch.FloatTensor]]`, *optional*): + Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads, + sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the + weighted average in the self-attention, cross-attention and multi-scale deformable attention heads. + encoder_hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of shape + `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + encoder_attentions (`tuple[tuple[torch.FloatTensor]]`, *optional*): + Tuple of tuples of `torch.FloatTensor` (one for attention for each layer) of shape `(batch_size, num_heads, + sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the + weighted average in the self-attention, cross-attention and multi-scale deformable attention heads. + classes_structure (`torch.LongTensor`, *optional*): + The number of queried classes for each image. + """ + + loss: torch.FloatTensor | None = None + decoder_coord_logits: torch.FloatTensor | None = None + decoder_class_logits: torch.FloatTensor | None = None + init_reference_points: torch.FloatTensor | None = None + intermediate_reference_points: tuple[tuple[torch.FloatTensor]] | None = None + encoder_coord_logits: torch.FloatTensor | None = None + encoder_class_logits: tuple[torch.FloatTensor] | None = None + encoder_extracted_states: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[tuple[torch.FloatTensor]] | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[tuple[torch.FloatTensor]] | None = None + classes_structure: torch.LongTensor | None = None + + +@use_kernel_forward_from_hub("MultiScaleDeformableAttention") +# Copied from transformers.models.deformable_detr.modeling_deformable_detr.MultiScaleDeformableAttention +class MultiScaleDeformableAttention(nn.Module): + def forward( + self, + value: Tensor, + value_spatial_shapes: Tensor, + value_spatial_shapes_list: list[tuple], + level_start_index: Tensor, + sampling_locations: Tensor, + attention_weights: Tensor, + im2col_step: int, + ): + batch_size, _, num_heads, hidden_dim = value.shape + _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape + value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level_id, (height, width) in enumerate(value_spatial_shapes_list): + # batch_size, height*width, num_heads, hidden_dim + # -> batch_size, height*width, num_heads*hidden_dim + # -> batch_size, num_heads*hidden_dim, height*width + # -> batch_size*num_heads, hidden_dim, height, width + value_l_ = ( + value_list[level_id] + .flatten(2) + .transpose(1, 2) + .reshape(batch_size * num_heads, hidden_dim, height, width) + ) + # batch_size, num_queries, num_heads, num_points, 2 + # -> batch_size, num_heads, num_queries, num_points, 2 + # -> batch_size*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1) + # batch_size*num_heads, hidden_dim, num_queries, num_points + sampling_value_l_ = nn.functional.grid_sample( + value_l_, + sampling_grid_l_, + mode="bilinear", + padding_mode="zeros", + align_corners=False, + ) + sampling_value_list.append(sampling_value_l_) + # (batch_size, num_queries, num_heads, num_levels, num_points) + # -> (batch_size, num_heads, num_queries, num_levels, num_points) + # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.transpose(1, 2).reshape( + batch_size * num_heads, 1, num_queries, num_levels * num_points + ) + output = ( + (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) + .sum(-1) + .view(batch_size, num_heads * hidden_dim, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +class OmDetTurboLRUCache: + def __init__(self, capacity: int): + self.cache = OrderedDict() + self.capacity = capacity + self.current_load = 0 + + def has(self, key) -> bool: + return key in self.cache + + def get(self, key): + """ + Get the value of the key if the key exists in the cache, otherwise return None. + Move the key to the end of the cache to show that it was recently used. + """ + if key not in self.cache: + return None + self.cache.move_to_end(key) + return self.cache[key] + + def put(self, key, value) -> None: + """ + Add the key-value pair to the cache. + Move the key to the end of the cache to show that it was recently used. + If the cache is full, remove the first key (least recently used). + """ + if key not in self.cache: + self.current_load += 1 + if self.current_load > self.capacity: + self.cache.popitem(last=False) + self.current_load -= 1 + + self.cache[key] = value + self.cache.move_to_end(key) + + +class OmDetTurboLanguageBackbone(nn.Module): + def __init__(self, config: OmDetTurboConfig): + super().__init__() + self.model = AutoModel.from_config(config.text_config) + self.text_projection = nn.Parameter(torch.zeros(config.text_projection_in_dim, config.text_projection_out_dim)) + + def forward(self, hidden_states, mask=None, encode_type="task"): + text_outputs = self.model(hidden_states) + pooled_output = text_outputs[0] + if encode_type == "task": + if mask is None: + raise ValueError("mask is required for task encoding") + max_len = (mask != 0).sum(1).max().item() + truncated_mask = mask[:, :max_len] + truncated_output = pooled_output[:, :max_len, :] + return truncated_output.transpose(0, 1), truncated_mask + elif encode_type == "class": + max_pooled_output = pooled_output[torch.arange(pooled_output.shape[0]), hidden_states.argmax(dim=-1)] + projected_output = max_pooled_output @ self.text_projection + return projected_output + else: + raise ValueError(f"encode_type {encode_type} is not supported") + + +class OmDetTurboVisionBackbone(nn.Module): + def __init__(self, config: OmDetTurboConfig): + super().__init__() + self.apply_layernorm_after_vision_backbone = config.apply_layernorm_after_vision_backbone + self.vision_backbone = AutoBackbone.from_config(config.backbone_config, **getattr(config, "timm_kwargs", {})) + self.layer_norms = nn.ModuleList( + [nn.LayerNorm(in_channel_dim, eps=config.layer_norm_eps) for in_channel_dim in config.encoder_in_channels] + ) + + def forward(self, pixel_values): + outputs = self.vision_backbone(pixel_values).feature_maps + if self.apply_layernorm_after_vision_backbone: + outputs = [ + layer_norm(output).permute(0, 3, 1, 2).contiguous() + for layer_norm, output in zip(self.layer_norms, outputs) + ] + + return outputs + + +# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrMultiscaleDeformableAttention with DeformableDetr->OmDetTurbo, Deformable DETR->OmDet-Turbo +class OmDetTurboMultiscaleDeformableAttention(nn.Module): + """ + Multiscale deformable attention as proposed in Deformable DETR. + """ + + def __init__(self, config: OmDetTurboConfig, num_heads: int, n_points: int): + super().__init__() + + self.attn = MultiScaleDeformableAttention() + + if config.d_model % num_heads != 0: + raise ValueError( + f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" + ) + dim_per_head = config.d_model // num_heads + # check if dim_per_head is power of 2 + if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): + warnings.warn( + "You'd better set embed_dim (d_model) in OmDetTurboMultiscaleDeformableAttention to make the" + " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" + " implementation." + ) + + self.im2col_step = 64 + + self.d_model = config.d_model + self.n_levels = config.num_feature_levels + self.n_heads = num_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) + self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) + self.value_proj = nn.Linear(config.d_model, config.d_model) + self.output_proj = nn.Linear(config.d_model, config.d_model) + + self.disable_custom_kernels = config.disable_custom_kernels + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states=None, + encoder_attention_mask=None, + position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # add position embeddings to the hidden states before projecting to queries and keys + if position_embeddings is not None: + hidden_states = hidden_states + position_embeddings + + batch_size, num_queries, _ = hidden_states.shape + batch_size, sequence_length, _ = encoder_hidden_states.shape + # Ignore copy + total_elements = sum(shape[0] * shape[1] for shape in spatial_shapes_list) + torch_compilable_check( + total_elements == sequence_length, + "Make sure to align the spatial shapes with the sequence length of the encoder hidden states", + ) + + value = self.value_proj(encoder_hidden_states) + if attention_mask is not None: + # we invert the attention_mask + value = value.masked_fill(~attention_mask[..., None], float(0)) + value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2 + ) + attention_weights = self.attention_weights(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points + ) + attention_weights = F.softmax(attention_weights, -1).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points + ) + # batch_size, num_queries, n_heads, n_levels, n_points, 2 + num_coordinates = reference_points.shape[-1] + if num_coordinates == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif num_coordinates == 4: + sampling_locations = ( + reference_points[:, :, None, :, None, :2] + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + ) + else: + raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") + + output = self.attn( + value, + spatial_shapes, + spatial_shapes_list, + level_start_index, + sampling_locations, + attention_weights, + self.im2col_step, + ) + + output = self.output_proj(output) + + return output, attention_weights + + +# Copied from transformers.models.rt_detr.modeling_rt_detr.RTDetrConvNormLayer with RTDetr->OmDetTurbo +class OmDetTurboConvNormLayer(nn.Module): + def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride, + padding=(kernel_size - 1) // 2 if padding is None else padding, + bias=False, + ) + self.norm = nn.BatchNorm2d(out_channels, config.batch_norm_eps) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_state): + hidden_state = self.conv(hidden_state) + hidden_state = self.norm(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +# Copied from transformers.models.rt_detr.modeling_rt_detr.RTDetrRepVggBlock with RTDetr->OmDetTurbo, activation_function->csp_activation +class OmDetTurboRepVggBlock(nn.Module): + """ + RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again". + """ + + def __init__(self, config: OmDetTurboConfig): + super().__init__() + + activation = config.csp_activation + hidden_channels = int(config.encoder_hidden_dim * config.hidden_expansion) + self.conv1 = OmDetTurboConvNormLayer(config, hidden_channels, hidden_channels, 3, 1, padding=1) + self.conv2 = OmDetTurboConvNormLayer(config, hidden_channels, hidden_channels, 1, 1, padding=0) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, x): + y = self.conv1(x) + self.conv2(x) + return self.activation(y) + + +# Copied from transformers.models.rt_detr.modeling_rt_detr.RTDetrCSPRepLayer with RTDetr->OmDetTurbo, activation_function->csp_activation +class OmDetTurboCSPRepLayer(nn.Module): + """ + Cross Stage Partial (CSP) network layer with RepVGG blocks. + """ + + def __init__(self, config: OmDetTurboConfig): + super().__init__() + + in_channels = config.encoder_hidden_dim * 2 + out_channels = config.encoder_hidden_dim + num_blocks = 3 + activation = config.csp_activation + + hidden_channels = int(out_channels * config.hidden_expansion) + self.conv1 = OmDetTurboConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.conv2 = OmDetTurboConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.bottlenecks = nn.Sequential(*[OmDetTurboRepVggBlock(config) for _ in range(num_blocks)]) + if hidden_channels != out_channels: + self.conv3 = OmDetTurboConvNormLayer(config, hidden_channels, out_channels, 1, 1, activation=activation) + else: + self.conv3 = nn.Identity() + + def forward(self, hidden_state): + hidden_state_1 = self.conv1(hidden_state) + hidden_state_1 = self.bottlenecks(hidden_state_1) + hidden_state_2 = self.conv2(hidden_state) + return self.conv3(hidden_state_1 + hidden_state_2) + + +class OmDetTurboMultiheadAttention(nn.Module): + """Equivalent implementation of nn.MultiheadAttention with `batch_first=True`.""" + + def __init__(self, config, hidden_size, num_attention_heads, dropout): + super().__init__() + if hidden_size % num_attention_heads != 0: + raise ValueError( + f"The hidden size ({hidden_size}) is not a multiple of the number of attention " + f"heads ({num_attention_heads})" + ) + self.num_attention_heads = num_attention_heads + self.attention_head_size = int(hidden_size / num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.query = nn.Linear(hidden_size, self.all_head_size) + self.key = nn.Linear(hidden_size, self.all_head_size) + self.value = nn.Linear(hidden_size, self.all_head_size) + self.out_proj = nn.Linear(hidden_size, hidden_size) + self.dropout = nn.Dropout(dropout) + + def forward( + self, + queries: torch.Tensor, + keys: torch.Tensor, + values: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + ) -> tuple[torch.Tensor]: + batch_size, seq_length, _ = queries.shape + query_layer = ( + self.query(queries) + .view(batch_size, -1, self.num_attention_heads, self.attention_head_size) + .transpose(1, 2) + ) + key_layer = ( + self.key(keys).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2) + ) + value_layer = ( + self.value(values).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2) + ) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + if attention_mask is not None: + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + context_layer = self.out_proj(context_layer) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class OmDetTurboEncoderLayer(nn.Module): + def __init__(self, config: OmDetTurboConfig): + super().__init__() + self.self_attn = OmDetTurboMultiheadAttention( + config, + hidden_size=config.encoder_hidden_dim, + num_attention_heads=config.num_attention_heads, + dropout=config.encoder_dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(config.encoder_hidden_dim, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.encoder_dropout) + self.activation_fn = ACT2FN[config.encoder_feedforward_activation] + self.encoder_feedforward_dropout = nn.Dropout(config.encoder_feedforward_dropout) + self.fc1 = nn.Linear(config.encoder_hidden_dim, config.encoder_dim_feedforward) + self.fc2 = nn.Linear(config.encoder_dim_feedforward, config.encoder_hidden_dim) + self.final_layer_norm = nn.LayerNorm(config.encoder_hidden_dim, eps=config.layer_norm_eps) + + @staticmethod + def with_pos_embed(tensor, pos_embed): + return tensor if pos_embed is None else tensor + pos_embed + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + position_embeddings: torch.Tensor | None = None, + output_attentions: bool = False, + ): + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + position_embeddings (`torch.FloatTensor`, *optional*): + Object queries (also called content embeddings), to be added to the hidden states. + output_attentions (`bool`, *optional*, defaults to `False`): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + query = key = self.with_pos_embed(hidden_states, position_embeddings) + + hidden_states = self.self_attn( + queries=query, + keys=key, + values=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states, attentions = hidden_states if output_attentions else (hidden_states[0], None) + hidden_states = self.dropout(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = self.encoder_feedforward_dropout(hidden_states) + hidden_states = self.fc2(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + if self.training: + if not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + if output_attentions: + return hidden_states, attentions + + return (hidden_states,) + + +class OmDetTurboEncoder(nn.Module): + def __init__(self, config: OmDetTurboConfig): + super().__init__() + + self.layers = nn.ModuleList([OmDetTurboEncoderLayer(config) for _ in range(config.encoder_layers)]) + + def forward( + self, src, src_mask=None, pos_embed=None, output_attentions: bool = False + ) -> tuple[torch.Tensor | tuple[torch.Tensor]]: + hidden_states = src + attention = () if output_attentions else None + for layer in self.layers: + hidden_states = layer( + hidden_states, + attention_mask=src_mask, + position_embeddings=pos_embed, + output_attentions=output_attentions, + ) + if output_attentions: + attention = attention + (hidden_states[1],) + hidden_states = hidden_states[0] + + return hidden_states, attention + + +class OmDetTurboHybridEncoder(nn.Module): + """ + Encoder consisting of channel projection layers, a set of `OmDetTurboEncoder`, a top-down Feature Pyramid Network + (FPN) and a bottom-up Path Aggregation Network (PAN). More details on the paper: https://huggingface.co/papers/2304.08069 + + Args: + config: OmDetTurboConfig + """ + + def __init__(self, config: OmDetTurboConfig): + super().__init__() + self.config = config + self.in_channels = config.encoder_in_channels + self.encoder_hidden_dim = config.encoder_hidden_dim + self.encoder_projection_indices = config.encoder_projection_indices + self.positional_encoding_temperature = config.positional_encoding_temperature + self.eval_size = config.eval_size + self.out_channels = [self.encoder_hidden_dim for _ in self.in_channels] + + self.channel_projection_layers = nn.ModuleList() + for in_channel in self.in_channels: + self.channel_projection_layers.append( + nn.Sequential( + nn.Conv2d(in_channel, self.encoder_hidden_dim, kernel_size=(1, 1), bias=False), + nn.BatchNorm2d(self.encoder_hidden_dim), + ) + ) + + # encoder transformer + self.encoder = nn.ModuleList([OmDetTurboEncoder(config) for _ in range(len(self.encoder_projection_indices))]) + # top-down fpn + self.lateral_convs = nn.ModuleList() + self.fpn_blocks = nn.ModuleList() + for _ in range(len(self.in_channels) - 1, 0, -1): + self.lateral_convs.append( + OmDetTurboConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=1, + stride=1, + activation=config.conv_norm_activation, + ) + ) + self.fpn_blocks.append(OmDetTurboCSPRepLayer(config)) + + # bottom-up pan + self.downsample_convs = nn.ModuleList() + self.pan_blocks = nn.ModuleList() + for _ in range(len(self.in_channels) - 1): + self.downsample_convs.append( + OmDetTurboConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=3, + stride=2, + activation=config.conv_norm_activation, + ) + ) + self.pan_blocks.append(OmDetTurboCSPRepLayer(config)) + + @staticmethod + def build_2d_sincos_position_embedding( + width, height, embed_dim=256, temperature=10000.0, device="cpu", dtype=torch.float32 + ): + grid_w = torch.arange(int(width), dtype=dtype, device=device) + grid_h = torch.arange(int(height), dtype=dtype, device=device) + grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij") + if embed_dim % 4 != 0: + raise ValueError("Embed dimension must be divisible by 4 for 2D sin-cos position embedding") + pos_dim = embed_dim // 4 + omega = torch.arange(pos_dim, dtype=dtype, device=device) / pos_dim + omega = 1.0 / (temperature**omega) + + out_w = grid_w.flatten()[..., None] @ omega[None] + out_h = grid_h.flatten()[..., None] @ omega[None] + + return torch.concat([out_w.sin(), out_w.cos(), out_h.sin(), out_h.cos()], dim=1)[None, :, :] + + def forward( + self, + inputs_embeddings=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Args: + inputs_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layers) that is passed to the encoder. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + hidden_states = inputs_embeddings + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + # get projection features + projected_features = [self.channel_projection_layers[i](feature) for i, feature in enumerate(hidden_states)] + # encoder + for encoder_layer_index, feature_to_project_index in enumerate(self.encoder_projection_indices): + if output_hidden_states: + encoder_states = encoder_states + (projected_features[feature_to_project_index],) + height, width = projected_features[feature_to_project_index].shape[2:] + # flatten [batch, channel, height, width] to [batch, height*width, channel] + src_flatten = projected_features[feature_to_project_index].flatten(2).permute(0, 2, 1) + if self.training or self.eval_size is None: + pos_embed = self.build_2d_sincos_position_embedding( + width, + height, + self.encoder_hidden_dim, + self.positional_encoding_temperature, + device=src_flatten.device, + dtype=src_flatten.dtype, + ).to(src_flatten.device, src_flatten.dtype) + else: + pos_embed = None + layer_outputs = self.encoder[encoder_layer_index]( + src_flatten, + pos_embed=pos_embed, + output_attentions=output_attentions, + ) + projected_features[feature_to_project_index] = ( + layer_outputs[0].permute(0, 2, 1).reshape(-1, self.encoder_hidden_dim, height, width).contiguous() + ) + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (projected_features[feature_to_project_index],) + + # Feature Pyramid Network (FPN) + fpn_feature_maps = [projected_features[-1]] + for idx in range(len(self.in_channels) - 1, 0, -1): + feat_high = fpn_feature_maps[0] + feat_low = projected_features[idx - 1] + feat_high = self.lateral_convs[len(self.in_channels) - 1 - idx](feat_high) + fpn_feature_maps[0] = feat_high + upsample_feat = F.interpolate(feat_high, scale_factor=2.0, mode="nearest") + fps_map = self.fpn_blocks[len(self.in_channels) - 1 - idx](torch.concat([upsample_feat, feat_low], dim=1)) + fpn_feature_maps.insert(0, fps_map) + + # Path Aggregation Network (PAN) + fpn_states = [fpn_feature_maps[0]] + for idx in range(len(self.in_channels) - 1): + feat_low = fpn_states[-1] + feat_high = fpn_feature_maps[idx + 1] + downsample_feat = self.downsample_convs[idx](feat_low) + hidden_states = self.pan_blocks[idx]( + torch.concat([downsample_feat, feat_high.to(downsample_feat.device)], dim=1) + ) + fpn_states.append(hidden_states) + if not return_dict: + return (fpn_states[-1], encoder_states, all_attentions, fpn_states) + return OmDetTurboEncoderOutput( + last_hidden_state=fpn_states[-1], + hidden_states=encoder_states, + attentions=all_attentions, + extracted_states=fpn_states, + ) + + +class OmDetTurboMLPWithDropout(nn.Module): + def __init__(self, config): + super().__init__() + self.linear1 = nn.Linear(config.class_embed_dim, config.task_encoder_hidden_dim) + self.activation = ACT2FN[config.decoder_activation] + self.dropout = nn.Dropout(config.decoder_dropout) + self.linear2 = nn.Linear(config.task_encoder_hidden_dim, config.class_embed_dim) + + def forward(self, x): + return self.linear2(self.dropout(self.activation(self.linear1(x)))) + + +class OmDetTurboMLP(nn.Module): + """Very simple multi-layer perceptron (also called FFN)""" + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + hidden_layers_dims = [hidden_dim] * (num_layers - 1) + layers_dims = [input_dim] + hidden_layers_dims + [output_dim] + self.layers = nn.ModuleList( + [nn.Linear(in_dim, out_dim) for in_dim, out_dim in zip(layers_dims[:-1], layers_dims[1:])] + ) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +class OmDetTurboResidualLayer(nn.Module): + """ + A residual connection followed by a layer norm. + """ + + def __init__(self, config): + super().__init__() + self.norm1 = nn.LayerNorm(config.class_embed_dim, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.decoder_dropout) + + def forward(self, x, y): + return self.norm1(x + self.dropout(y)) + + +class OmDetTurboTaskEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.mlp = OmDetTurboMLPWithDropout(config) + self.res1 = OmDetTurboResidualLayer(config) + + def forward(self, x): + mlp_out = self.mlp(x) + x = self.res1(x, mlp_out) + return x + + +class OmDetTurboDeformableTransformerDecoderLayer(GradientCheckpointingLayer): + """ + A single layer of the Deformable Transformer Decoder. + """ + + def __init__(self, config): + super().__init__() + # self attention + self.self_attn = OmDetTurboMultiheadAttention( + config, + hidden_size=config.decoder_hidden_dim, + num_attention_heads=config.decoder_num_heads, + dropout=config.decoder_dropout, + ) + self.dropout1 = nn.Dropout(config.decoder_dropout) + self.norm1 = nn.LayerNorm(config.decoder_hidden_dim, eps=config.layer_norm_eps) + + # cross attention + self.cross_attn = OmDetTurboMultiscaleDeformableAttention( + config, num_heads=config.decoder_num_heads, n_points=config.decoder_num_points + ) + self.dropout2 = nn.Dropout(config.decoder_dropout) + self.norm2 = nn.LayerNorm(config.decoder_hidden_dim, eps=config.layer_norm_eps) + + # feed forward network + self.linear1 = nn.Linear(config.decoder_hidden_dim, config.decoder_dim_feedforward) + self.act = ACT2FN[config.decoder_activation] + self.dropout3 = nn.Dropout(config.decoder_dropout) + self.linear2 = nn.Linear(config.decoder_dim_feedforward, config.decoder_hidden_dim) + self.dropout4 = nn.Dropout(config.decoder_dropout) + self.norm3 = nn.LayerNorm(config.decoder_hidden_dim, eps=config.layer_norm_eps) + + self.output_attentions = config.output_attentions + self.output_hidden_states = config.output_hidden_states + + @staticmethod + def with_pos_embed(tensor, pos): + return tensor if pos is None else tensor + pos + + def forward( + self, + decoder_embeddings, + task_features, + reference_points, + vision_features, + vision_shapes, + vision_shapes_list, + level_start_index=None, + attention_mask=None, + padding_mask=None, + query_position=None, + output_attentions=None, + output_hidden_states=None, + ): + output_attentions = output_attentions if output_attentions is not None else self.output_attentions + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states + + origin_embedding_len = decoder_embeddings.shape[1] + + # self attention + query = key = self.with_pos_embed(decoder_embeddings, query_position) + # combine task_features with query, key, value + task_features = task_features.transpose(0, 1) + query = torch.cat((query, task_features), dim=1) + key = torch.cat((key, task_features), dim=1) + decoder_embeddings = torch.cat((decoder_embeddings, task_features), dim=1) + + outputs = self.self_attn( + query, + key, + decoder_embeddings, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + context, self_attention = outputs if output_attentions else (outputs[0], None) + decoder_embeddings = decoder_embeddings + self.dropout1(context) + decoder_embeddings = self.norm1(decoder_embeddings) + + task_features = decoder_embeddings[:, origin_embedding_len:, :].transpose(0, 1) + decoder_embeddings = decoder_embeddings[:, :origin_embedding_len, :] + + # cross attention + hidden_states = self.with_pos_embed(decoder_embeddings, query_position) + reference_points = reference_points.unsqueeze(2) + outputs, cross_attention = self.cross_attn( + hidden_states=hidden_states, + attention_mask=padding_mask, + encoder_hidden_states=vision_features, + reference_points=reference_points, + spatial_shapes=vision_shapes, + spatial_shapes_list=vision_shapes_list, + level_start_index=level_start_index, + ) + decoder_embeddings = decoder_embeddings + self.dropout2(outputs) + residual = self.norm2(decoder_embeddings) + + # feed forward network + decoder_embeddings = self.linear2(self.dropout3(self.act(self.linear1(residual)))) + decoder_embeddings = residual + self.dropout4(decoder_embeddings) + decoder_embeddings = self.norm3(decoder_embeddings) + + return ( + decoder_embeddings, + task_features, + self_attention if output_attentions else None, + cross_attention if output_attentions else None, + ) + + +@auto_docstring +class OmDetTurboPreTrainedModel(PreTrainedModel): + config: OmDetTurboConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ("image", "text") + + @torch.no_grad() + def _init_weights(self, module): + def linear_init_(module_to_init): + bound = 1 / math.sqrt(module_to_init.weight.shape[0]) + init.uniform_(module_to_init.weight, -bound, bound) + if hasattr(module_to_init, "bias") and module_to_init.bias is not None: + init.uniform_(module_to_init.bias, -bound, bound) + + if isinstance(module, OmDetTurboEncoderLayer): + linear_init_(module.fc1) + linear_init_(module.fc2) + elif isinstance(module, OmDetTurboDecoder): + init.constant_(module.encoder_bbox_head.layers[-1].weight, 0.0) + init.constant_(module.encoder_bbox_head.layers[-1].bias, 0.0) + for mlp in module.decoder_bbox_head: + init.constant_(mlp.layers[-1].weight, 0.0) + init.constant_(mlp.layers[-1].bias, 0.0) + linear_init_(module.encoder_vision_features[0]) + init.xavier_uniform_(module.encoder_vision_features[0].weight) + if module.learn_initial_query: + init.xavier_uniform_(module.tgt_embed.weight) + init.xavier_uniform_(module.query_position_head.layers[0].weight) + init.xavier_uniform_(module.query_position_head.layers[1].weight) + for layer in module.channel_projection_layers: + init.xavier_uniform_(layer[0].weight) + elif isinstance(module, OmDetTurboLanguageBackbone): + init.normal_(module.text_projection, std=self.config.text_projection_in_dim**-0.5) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + init.normal_(module.weight, mean=0.0, std=self.config.init_std) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d)): + init.ones_(module.weight) + init.zeros_(module.bias) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, OmDetTurboDecoder): + module.gradient_checkpointing = value + + @staticmethod + def _get_cache_key_at_index(input_ids, attention_mask, index): + input_ids = input_ids[index] + input_mask = attention_mask[index] + cache_key = tuple(input_ids[input_mask != 0].tolist()) + return cache_key + + def get_cached_class_embeddings(self, classes_input_ids, classes_attention_mask): + not_cached_index = [] + not_cached_classes = [] + total_embeddings = [] + for idx, _ in enumerate(classes_input_ids): + cache_key = self._get_cache_key_at_index(classes_input_ids, classes_attention_mask, idx) + if self.language_cache_class.has(cache_key): + total_embeddings.append(self.language_cache_class.get(cache_key)) + else: + total_embeddings.append(None) + not_cached_index.append(idx) + not_cached_classes.append(cache_key) + + if not_cached_classes: + not_cached_classes_ids = torch.stack([classes_input_ids[idx] for idx in not_cached_index]) + embeddings = self.language_backbone(not_cached_classes_ids, encode_type="class") + for idx, emb in enumerate(embeddings): + idx_to_put = not_cached_index[idx] + total_embeddings[idx_to_put] = emb + self.language_cache_class.put(not_cached_classes[idx], emb) + + total_class_embs = torch.stack(total_embeddings).to(self.device) + return total_class_embs + + def get_cached_task_embeddings(self, tasks_input_ids, tasks_attention_mask): + not_cached_index = [] + not_cached_tasks = [] + total_task_features = [] + total_task_masks = [] + for idx, _ in enumerate(tasks_input_ids): + cache_key = self._get_cache_key_at_index(tasks_input_ids, tasks_attention_mask, idx) + if self.language_cache_prompt.has(cache_key): + task_feature, task_mask = self.language_cache_prompt.get(cache_key) + total_task_features.append(task_feature) + total_task_masks.append(task_mask) + else: + total_task_features.append(None) + total_task_masks.append(None) + not_cached_index.append(idx) + not_cached_tasks.append(cache_key) + + if not_cached_tasks: + not_cached_index_ids = torch.stack([tasks_input_ids[idx] for idx in not_cached_index]) + not_cached_mask = torch.stack([tasks_attention_mask[idx] for idx in not_cached_index]) + embeddings, masks = self.language_backbone(not_cached_index_ids, mask=not_cached_mask, encode_type="task") + + for idx in range(embeddings.shape[1]): + emb = embeddings[:, [idx], :] + idx_to_put = not_cached_index[idx] + cur_mask = torch.unsqueeze(masks[idx], dim=0).to(self.device) + total_task_features[idx_to_put] = emb + total_task_masks[idx_to_put] = cur_mask + self.language_cache_prompt.put(not_cached_tasks[idx], (emb, cur_mask)) + + # pad before concat if needed + max_len = max(task.shape[0] for task in total_task_features) + for idx, task in enumerate(total_task_features): + if task.shape[0] < max_len: + pad_size = max_len - task.shape[0] + total_task_features[idx] = F.pad(task, (0, 0, 0, 0, 0, pad_size)) + total_task_masks[idx] = F.pad(total_task_masks[idx], (0, pad_size)) + + total_task_features = torch.cat(total_task_features, dim=1).to(self.device) + total_task_masks = torch.cat(total_task_masks, dim=0).to(self.device) + + return total_task_features, total_task_masks + + def get_language_embedding( + self, + classes_input_ids, + classes_attention_mask, + tasks_input_ids, + tasks_attention_mask, + classes_structure, + ): + batched_classes_embeddings = self.get_cached_class_embeddings(classes_input_ids, classes_attention_mask) + # regroup class embeddings using saved structure + max_class_size = torch.max(classes_structure) + class_embeddings_regrouped = [] + start = 0 + for size in classes_structure: + pad_size = max_class_size - size + class_embeddings_regrouped.append( + F.pad(batched_classes_embeddings[start : start + size], (0, 0, 0, pad_size)).unsqueeze(1) + ) + start += size + class_embeddings = torch.cat(class_embeddings_regrouped, dim=1) + + task_embeddings, task_mask = self.get_cached_task_embeddings(tasks_input_ids, tasks_attention_mask) + + return class_embeddings, task_embeddings, task_mask + + +def _cosine_similarity_scaled(a, b, logit_scale): + a = a / a.norm(dim=2, keepdim=True).clamp_min(1e-12) + b = b / b.norm(dim=1, keepdim=True).clamp_min(1e-12) + logit_scale = logit_scale.exp() + logits_per_image = logit_scale * torch.bmm(a, b) + return logits_per_image + + +def get_class_similarity(class_distance_type, cls_feature, class_proj): + logit_scale = torch.tensor(1 / 0.07).log() + if class_distance_type == "cosine": + class_logits = _cosine_similarity_scaled(cls_feature, class_proj, logit_scale) + elif class_distance_type == "dot": + class_logits = torch.bmm(cls_feature, class_proj) + else: + raise Exception(f"Unknown class_distance_type {class_distance_type}") + return class_logits + + +def _inverse_sigmoid(x, eps=1e-5): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +class OmDetTurboDecoder(OmDetTurboPreTrainedModel): + def __init__(self, config: OmDetTurboConfig): + self.config = config + super().__init__(config) + self.gradient_checkpointing = False + + hidden_dim = config.decoder_hidden_dim + self.num_queries = config.num_queries + self.class_distance_type = config.class_distance_type + self.learn_initial_query = config.learn_initial_query + + # backbone feature projection + self.channel_projection_layers = nn.ModuleList( + nn.Sequential(nn.Conv2d(x, hidden_dim, 1, bias=False), nn.BatchNorm2d(hidden_dim)) + for x in config.vision_features_channels + ) + self.task_encoder = OmDetTurboTaskEncoder(config) + if config.class_embed_dim != hidden_dim: + self.task_project = nn.Linear(config.class_embed_dim, hidden_dim) + + # Transformer module + self.layers = nn.ModuleList( + [OmDetTurboDeformableTransformerDecoderLayer(config) for _ in range(config.decoder_num_layers)] + ) + self.decoder_num_layers = config.decoder_num_layers + # decoder embedding + if self.learn_initial_query: + self.tgt_embed = nn.Embedding(self.num_queries, hidden_dim) + self.query_position_head = OmDetTurboMLP( + input_dim=4, hidden_dim=2 * hidden_dim, output_dim=hidden_dim, num_layers=2 + ) + + # encoder head + self.encoder_vision_features = nn.Sequential( + nn.Linear(hidden_dim, hidden_dim), nn.LayerNorm(hidden_dim, eps=config.layer_norm_eps) + ) + self.encoder_class_head = nn.Linear(config.class_embed_dim, hidden_dim) + self.encoder_bbox_head = OmDetTurboMLP(input_dim=hidden_dim, hidden_dim=hidden_dim, output_dim=4, num_layers=3) + + # decoder head + self.decoder_class_head = nn.ModuleList( + [nn.Linear(config.class_embed_dim, hidden_dim) for _ in range(config.decoder_num_layers)] + ) + self.decoder_bbox_head = nn.ModuleList( + [OmDetTurboMLP(hidden_dim, hidden_dim, 4, num_layers=3) for _ in range(config.decoder_num_layers)] + ) + + # Initialize weights and apply final processing + self.post_init() + + @lru_cache(maxsize=32) + def generate_anchors(self, spatial_shapes=None, grid_size=0.05, device="cpu", dtype=torch.float32): + # We always generate anchors in float32 to preserve equivalence between + # dynamic and static anchor inference + # Ignore copy + if spatial_shapes is None: + raise ValueError("spatial_shapes must be provided") + + anchors = [] + for level, (height, width) in enumerate(spatial_shapes): + grid_y, grid_x = torch.meshgrid( + torch.arange(end=height, dtype=dtype, device=device), + torch.arange(end=width, dtype=dtype, device=device), + indexing="ij", + ) + grid_xy = torch.stack([grid_x, grid_y], -1) + valid_wh = torch.tensor([width, height], dtype=dtype, device=device) + grid_xy = (grid_xy.unsqueeze(0) + 0.5) / valid_wh + wh = torch.ones_like(grid_xy, dtype=dtype, device=device) * grid_size * (2.0**level) + anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, height * width, 4)) + # define the valid range for anchor coordinates + eps = 1e-2 + anchors = torch.concat(anchors, 1) + valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True) + anchors = torch.log(anchors / (1 - anchors)) + anchors = torch.where(valid_mask, anchors, torch.inf) + + return anchors, valid_mask + + def _get_encoder_input(self, vision_features): + # get projection features + vision_features = [self.channel_projection_layers[i](feat) for i, feat in enumerate(vision_features)] + # get encoder inputs + new_vision_features = [] + new_vision_shapes_list = [] + for feat in vision_features: + height, width = feat.shape[2:] + # [batch_size, channels, height, width] -> [batch_size, height*width, channels] + new_vision_features.append(feat.flatten(2).permute(0, 2, 1)) + # [num_feature_levels, 2] + new_vision_shapes_list.append((height, width)) + + # [batch_size, height*width, channels] + new_vision_features = torch.cat(new_vision_features, 1) + new_vision_shapes = torch.tensor(new_vision_shapes_list, dtype=torch.int64, device=vision_features[0].device) + level_start_index = torch.cat((new_vision_shapes.new_zeros((1,)), new_vision_shapes.prod(1).cumsum(0)[:-1])) + + return new_vision_features, new_vision_shapes, new_vision_shapes_list, level_start_index + + def _get_decoder_input( + self, vision_features, vision_shapes, class_features, denoise_embeddings=None, denoise_bboxes=None + ): + batch_size = len(vision_features) + # prepare input for decoder + anchors, valid_mask = self.generate_anchors( + vision_shapes, device=vision_features.device, dtype=vision_features.dtype + ) + predicted_class_features = self.encoder_vision_features( + torch.where( + valid_mask, + vision_features, + torch.tensor(0.0, dtype=vision_features.dtype, device=vision_features.device), + ) + ) + + original_class_projected = self.encoder_class_head(class_features).permute(1, 2, 0) + encoder_class_similarity = get_class_similarity( + self.class_distance_type, predicted_class_features, original_class_projected + ) + + # dynamic anchors + static content + # (batch_size, height*width, 4) + encoder_outputs_bboxes = self.encoder_bbox_head(predicted_class_features) + anchors + + # query selection + # (batch_size, num_queries) + topk_ind = torch.topk(encoder_class_similarity.max(-1).values, self.num_queries, dim=1).indices.view(-1) + # (batch_size, num_queries) + batch_ind = ( + torch.arange(end=batch_size, dtype=topk_ind.dtype, device=topk_ind.device) + .unsqueeze(-1) + .repeat(1, self.num_queries) + .view(-1) + ) + + reference_points = encoder_outputs_bboxes[batch_ind, topk_ind].view(batch_size, self.num_queries, -1) + encoder_bboxes = reference_points.sigmoid() + if denoise_bboxes is not None: + reference_points = torch.cat([denoise_bboxes, reference_points], 1) + if self.training: + reference_points = reference_points.detach() + encoder_class_similarity = encoder_class_similarity[batch_ind, topk_ind].view(batch_size, self.num_queries, -1) + + if self.learn_initial_query: + embeddings = self.tgt_embed.weight.unsqueeze(0).repeat(batch_size, 1, 1) + else: + embeddings = predicted_class_features[batch_ind, topk_ind].view(batch_size, self.num_queries, -1) + if self.training: + embeddings = embeddings.detach() + if denoise_embeddings is not None: + embeddings = torch.cat([denoise_embeddings, embeddings], 1) + + return embeddings, reference_points, encoder_bboxes, encoder_class_similarity, anchors + + def forward( + self, + vision_features, + class_features, + task_features, + task_mask, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + """ + Args: + vision_features (`torch.FloatTensor`): The sequence of vision features. shape depends on the vision + backbone. + class_features (`torch.FloatTensor`): The sequence of class features of shape + `(class_sequence_length, batch_size, class_embed_dim)`. + task_features (`torch.FloatTensor`): The sequence of task features of shape + `(task_sequence_length, batch_size, decoder_hidden_dim)`. + task_mask (`torch.LongTensor`): The mask for the task features of shape `(batch_size, task_sequence_length)`. + output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention + layers. See `attentions` under returned tensors for more detail. + output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See + `hidden_states` under returned tensors for more detail. + return_dict (`bool`, *optional*): Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain + tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + vision_features, vision_shapes, vision_shapes_list, level_start_index = self._get_encoder_input( + vision_features + ) + + # todo add denoising for training + denoise_embeddings, denoise_bboxes, key_padding_mask = None, None, None + batch_size = task_mask.shape[0] + + # compose attn_mask for vision_emb and task_emb fusion + task_features = self.task_encoder(task_features) + if self.task_project is not None: + task_features = self.task_project(task_features) + src_key_mask = (task_mask == 0).detach() + attn_mask_len = self.num_queries + fusion_size = attn_mask_len + task_features.shape[0] + key_padding_mask = torch.zeros([batch_size, fusion_size], dtype=torch.bool).to(task_features.device) + key_padding_mask[:, attn_mask_len:] = src_key_mask + decoder_embeddings, reference_points, encoder_bboxes, encoder_class_similarity, init_reference_points = ( + self._get_decoder_input( + vision_features, tuple(vision_shapes_list), class_features, denoise_embeddings, denoise_bboxes + ) + ) + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=torch.ones_like(key_padding_mask, dtype=decoder_embeddings.dtype)[..., None], + attention_mask=~key_padding_mask, + ) + + all_hidden_states = () if output_hidden_states else None + all_attns = () if output_attentions else None + all_self_attns = () if output_attentions else None + all_cross_attns = () if output_attentions else None + predicted_class_features = decoder_embeddings + + if output_hidden_states: + all_hidden_states = all_hidden_states + (predicted_class_features,) + decoder_bboxes = [] + decoder_classes = [] + last_refined_bbox = None + reference_points = reference_points.sigmoid() + for i, layer in enumerate(self.layers): + predicted_class_features, task_features, self_attention, cross_attention = layer( + predicted_class_features, + task_features, + reference_points, + vision_features, + vision_shapes, + vision_shapes_list, + level_start_index=level_start_index, + attention_mask=attention_mask, + query_position=self.query_position_head(reference_points), + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + ) + if output_attentions: + all_self_attns = all_self_attns + (self_attention,) + all_cross_attns = all_cross_attns + (cross_attention,) + if output_hidden_states: + all_hidden_states = all_hidden_states + (predicted_class_features,) + + refined_bbox = torch.sigmoid( + self.decoder_bbox_head[i](predicted_class_features) + _inverse_sigmoid(reference_points) + ) + original_class_projected = self.decoder_class_head[i](class_features).permute(1, 2, 0) + if self.training: + decoder_classes.append( + get_class_similarity( + class_distance_type=self.class_distance_type, + cls_feature=predicted_class_features, + class_proj=original_class_projected, + ) + ) + if i == 0: + decoder_bboxes.append(refined_bbox) + else: + decoder_bboxes.append( + torch.sigmoid( + self.decoder_bbox_head[i](predicted_class_features) + _inverse_sigmoid(last_refined_bbox) + ) + ) + elif i == self.decoder_num_layers - 1: + decoder_classes.append( + get_class_similarity(self.class_distance_type, predicted_class_features, original_class_projected) + ) + decoder_bboxes.append(refined_bbox) + break + last_refined_bbox = refined_bbox + reference_points = refined_bbox.detach() if self.training else refined_bbox + if output_attentions: + all_attns += (all_self_attns, all_cross_attns) + + last_hidden_state = predicted_class_features + decoder_bboxes = torch.stack(decoder_bboxes) + decoder_classes = torch.stack(decoder_classes) + + if not return_dict: + return ( + last_hidden_state, + all_hidden_states, + all_attns, + decoder_bboxes, + decoder_classes, + encoder_bboxes, + encoder_class_similarity, + init_reference_points, + reference_points, + ) + + return OmDetTurboDecoderOutput( + last_hidden_state=last_hidden_state, + hidden_states=all_hidden_states, + attentions=all_attns, + decoder_coords=decoder_bboxes, + decoder_classes=decoder_classes, + encoder_coord_logits=encoder_bboxes, + encoder_class_logits=encoder_class_similarity, + init_reference_points=init_reference_points, + intermediate_reference_points=reference_points, + ) + + +@auto_docstring( + custom_intro=""" + OmDetTurbo Model (consisting of a vision and a text backbone, and encoder-decoder architecture) outputting + bounding boxes and classes scores for tasks such as COCO detection. + """ +) +class OmDetTurboForObjectDetection(OmDetTurboPreTrainedModel): + def __init__(self, config: OmDetTurboConfig): + super().__init__(config) + self.vision_backbone = OmDetTurboVisionBackbone(config) + self.language_backbone = OmDetTurboLanguageBackbone(config) + self.encoder = OmDetTurboHybridEncoder(config) + self.decoder = OmDetTurboDecoder(config) + self.num_queries = config.num_queries + + self.language_cache_class = OmDetTurboLRUCache(config.cache_size) + self.language_cache_prompt = OmDetTurboLRUCache(config.cache_size) + self.vocab_size = config.text_config.vocab_size + self.post_init() + + def get_input_embeddings(self): + return self.language_backbone.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_backbone.model.set_input_embeddings(value) + + def resize_token_embeddings( + self, new_num_tokens: int | None = None, pad_to_multiple_of=None, mean_resizing: bool = True + ) -> nn.Embedding: + model_embeds = self.language_backbone.model.resize_token_embeddings( + new_num_tokens=new_num_tokens, pad_to_multiple_of=pad_to_multiple_of, mean_resizing=mean_resizing + ) + self.config.text_config.vocab_size = model_embeds.num_embeddings + self.vocab_size = model_embeds.num_embeddings + return model_embeds + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + classes_input_ids: torch.LongTensor, + classes_attention_mask: torch.LongTensor, + tasks_input_ids: torch.LongTensor, + tasks_attention_mask: torch.LongTensor, + classes_structure: torch.LongTensor, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.FloatTensor] | OmDetTurboObjectDetectionOutput: + r""" + classes_input_ids (`torch.LongTensor` of shape `(total_classes (>= batch_size), sequence_length)`): + Indices of input classes sequence tokens in the vocabulary of the language model. + Several classes can be provided for each tasks, thus the tokenized classes are flattened + and the structure of the classes is provided in the `classes_structure` argument. + + Indices can be obtained using [`OmDetTurboProcessor`]. See [`OmDetTurboProcessor.__call__`] for + details. + + [What are input IDs?](../glossary#input-ids) + classes_attention_mask (`torch.BoolTensor` of shape `(total_classes (>= batch_size), num_classes, sequence_length)`): + Attention mask for the classes. This is a binary mask that indicates which tokens should be attended to, + and which should not. + tasks_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input tasks sequence tokens in the vocabulary of the language model. + + Indices can be obtained using [`OmDetTurboProcessor`]. See [`OmDetTurboProcessor.__call__`] for + details. + + [What are input IDs?](../glossary#input-ids) + tasks_attention_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`): + Attention mask for the tasks. This is a binary mask that indicates which tokens should be attended to, + and which should not. + classes_structure (torch.LongTensor of shape `(batch_size)`): + Structure of the classes. This tensor indicates the number of classes for each task. + + Examples: + + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + + >>> from transformers import AutoProcessor, OmDetTurboForObjectDetection + + >>> processor = AutoProcessor.from_pretrained("omlab/omdet-turbo-swin-tiny-hf") + >>> model = OmDetTurboForObjectDetection.from_pretrained("omlab/omdet-turbo-swin-tiny-hf") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> classes = ["cat", "remote"] + >>> task = "Detect {}.".format(", ".join(classes)) + >>> inputs = processor(image, text=classes, task=task, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> # convert outputs (bounding boxes and class logits) + >>> results = processor.post_process_grounded_object_detection( + ... outputs, + ... classes=classes, + ... target_sizes=[image.size[::-1]], + ... score_threshold=0.3, + ... nms_threshold=0.3, + >>> )[0] + >>> for score, class_name, box in zip(results["scores"], results["classes"], results["boxes"]): + ... box = [round(i, 1) for i in box.tolist()] + ... print( + ... f"Detected {class_name} with confidence " + ... f"{round(score.item(), 2)} at location {box}" + ... ) + Detected remote with confidence 0.76 at location [39.9, 71.3, 176.5, 117.9] + Detected cat with confidence 0.72 at location [345.1, 22.5, 639.7, 371.9] + Detected cat with confidence 0.65 at location [12.7, 53.8, 315.5, 475.3] + Detected remote with confidence 0.57 at location [333.4, 75.6, 370.7, 187.0] + ```""" + if labels is not None: + raise NotImplementedError("Training is not implemented yet") + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + loss = None + image_features = self.vision_backbone(pixel_values) + encoder_outputs = self.encoder( + image_features, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + class_features, task_features, task_mask = self.get_language_embedding( + classes_input_ids, + classes_attention_mask, + tasks_input_ids, + tasks_attention_mask, + classes_structure, + ) + encoder_extracted_states = encoder_outputs.extracted_states if return_dict else encoder_outputs[-1] + decoder_outputs = self.decoder( + encoder_extracted_states, + class_features, + task_features, + task_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return tuple( + output + for output in [ + loss, + decoder_outputs[3][-1], + decoder_outputs[4][-1], + decoder_outputs[7], + decoder_outputs[8], + decoder_outputs[5], + decoder_outputs[6], + encoder_outputs[-1], + decoder_outputs[1], + decoder_outputs[2], + encoder_outputs[1], + encoder_outputs[2], + classes_structure, + ] + if output is not None + ) + + return OmDetTurboObjectDetectionOutput( + loss=loss, + decoder_coord_logits=decoder_outputs.decoder_coords[-1], + decoder_class_logits=decoder_outputs.decoder_classes[-1], + init_reference_points=decoder_outputs.init_reference_points, + intermediate_reference_points=decoder_outputs.intermediate_reference_points, + encoder_coord_logits=decoder_outputs.encoder_coord_logits, + encoder_class_logits=decoder_outputs.encoder_class_logits, + encoder_extracted_states=encoder_outputs.extracted_states, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + classes_structure=classes_structure, + ) + + +__all__ = ["OmDetTurboForObjectDetection", "OmDetTurboPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/processing_omdet_turbo.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/processing_omdet_turbo.py new file mode 100644 index 0000000000000000000000000000000000000000..6c154978cedbbc0ca12c728bbf0fe45fa92baa67 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/omdet_turbo/processing_omdet_turbo.py @@ -0,0 +1,353 @@ +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for OmDet-Turbo. +""" + +from typing import TYPE_CHECKING + +from ...feature_extraction_utils import BatchFeature +from ...image_transforms import center_to_corners_format +from ...image_utils import ImageInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import ( + TensorType, + auto_docstring, + is_torch_available, + is_torchvision_available, +) +from ...utils.import_utils import requires + + +if TYPE_CHECKING: + from .modeling_omdet_turbo import OmDetTurboObjectDetectionOutput + + +class OmDetTurboTextKwargs(TextKwargs, total=False): + """ + task (`str`, `list[str]`, `TextInput`, or `PreTokenizedInput`, *optional*): + The detection task description(s) to encode. If not provided, a default task description is generated + from the `text` input (e.g., "Detect {text}."). Can be a single string, a list of strings (one per image), + or pre-tokenized input. The task description guides the model on what objects to detect in the images. + """ + + task: str | list[str] | TextInput | PreTokenizedInput | None + + +if is_torch_available(): + import torch + + +if is_torchvision_available(): + from torchvision.ops.boxes import batched_nms + + +class OmDetTurboProcessorKwargs(ProcessingKwargs, total=False): + text_kwargs: OmDetTurboTextKwargs + _defaults = { + "text_kwargs": { + "add_special_tokens": True, + "padding": "max_length", + "truncation": True, + "max_length": 77, + "stride": 0, + "return_overflowing_tokens": False, + "return_special_tokens_mask": False, + "return_offsets_mapping": False, + "return_token_type_ids": False, + "return_length": False, + "verbose": True, + "task": None, + }, + } + + +def clip_boxes(box, box_size: tuple[int, int]): + """ + Clip the boxes by limiting x coordinates to the range [0, width] + and y coordinates to the range [0, height]. + + Args: + box (Tensor): The box to be clipped. + box_size (height, width): The clipping box's size. + """ + assert torch.isfinite(box).all(), "Box tensor contains infinite or NaN!" + height, width = box_size + x1 = box[:, 0].clamp(min=0, max=width) + y1 = box[:, 1].clamp(min=0, max=height) + x2 = box[:, 2].clamp(min=0, max=width) + y2 = box[:, 3].clamp(min=0, max=height) + box = torch.stack((x1, y1, x2, y2), dim=-1) + + return box + + +def compute_score(boxes): + """ + Compute logit scores per class for each box (proposal) and an array of class indices + corresponding to each proposal, flattened across the proposal_num. + The indices in `classes` will later be used to filter and match the predicted classes + with the input class names. + """ + num_classes = boxes.shape[2] + proposal_num = boxes.shape[1] + scores = torch.sigmoid(boxes) + classes = torch.arange(num_classes, device=boxes.device).unsqueeze(0).repeat(proposal_num, 1).flatten(0, 1) + return scores, classes + + +def _post_process_boxes_for_image( + boxes: "torch.Tensor", + scores: "torch.Tensor", + labels: "torch.Tensor", + image_num_classes: int, + image_size: tuple[int, int], + threshold: float, + nms_threshold: float, + max_num_det: int | None = None, +) -> tuple["torch.Tensor", "torch.Tensor", "torch.Tensor"]: + """ + Filter predicted results using given thresholds and NMS. + + Args: + boxes (`torch.Tensor`): + A Tensor of predicted class-specific or class-agnostic boxes for the image. + Shape (num_queries, max_num_classes_in_batch * 4) if doing class-specific regression, + or (num_queries, 4) if doing class-agnostic regression. + scores (`torch.Tensor` of shape (num_queries, max_num_classes_in_batch + 1)): + A Tensor of predicted class scores for the image. + labels (`torch.Tensor` of shape (num_queries * (max_num_classes_in_batch + 1),)): + A Tensor of predicted labels for the image. + image_num_classes (`int`): + The number of classes queried for detection on the image. + image_size (`tuple[int, int]`): + A tuple of (height, width) for the image. + threshold (`float`): + Only return detections with a confidence score exceeding this threshold. + nms_threshold (`float`): + The threshold to use for box non-maximum suppression. Value in [0, 1]. + max_num_det (`int`, *optional*): + The maximum number of detections to return. Default is None. + + Returns: + Tuple: A tuple with the following: + "boxes" (Tensor): A tensor of shape (num_filtered_objects, 4), containing the predicted boxes in (x1, y1, x2, y2) format. + "scores" (Tensor): A tensor of shape (num_filtered_objects,), containing the predicted confidence scores for each detection. + "labels" (Tensor): A tensor of ids, where each id is the predicted class id for the corresponding detection + """ + + # Filter by max number of detections + proposal_num = len(boxes) if max_num_det is None else max_num_det + scores_per_image, topk_indices = scores.flatten(0, 1).topk(proposal_num, sorted=False) + labels_per_image = labels[topk_indices] + boxes_per_image = boxes.view(-1, 1, 4).repeat(1, scores.shape[1], 1).view(-1, 4) + boxes_per_image = boxes_per_image[topk_indices] + + # Convert and scale boxes to original image size + boxes_per_image = center_to_corners_format(boxes_per_image) + boxes_per_image = boxes_per_image * torch.tensor(image_size[::-1]).repeat(2).to(boxes_per_image.device) + + # Filtering by confidence score + filter_mask = scores_per_image > threshold # R x K + score_keep = filter_mask.nonzero(as_tuple=False).view(-1) + boxes_per_image = boxes_per_image[score_keep] + scores_per_image = scores_per_image[score_keep] + labels_per_image = labels_per_image[score_keep] + + # Ensure we did not overflow to non existing classes + filter_classes_mask = labels_per_image < image_num_classes + classes_keep = filter_classes_mask.nonzero(as_tuple=False).view(-1) + boxes_per_image = boxes_per_image[classes_keep] + scores_per_image = scores_per_image[classes_keep] + labels_per_image = labels_per_image[classes_keep] + + # NMS + keep = batched_nms(boxes_per_image, scores_per_image, labels_per_image, nms_threshold) + boxes_per_image = boxes_per_image[keep] + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + + # Clip to image size + boxes_per_image = clip_boxes(boxes_per_image, image_size) + + return boxes_per_image, scores_per_image, labels_per_image + + +@requires(backends=("vision", "torchvision")) +@auto_docstring +class OmDetTurboProcessor(ProcessorMixin): + def __init__(self, image_processor, tokenizer): + super().__init__(image_processor, tokenizer) + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: list[str] | list[list[str]] | None = None, + **kwargs: Unpack[OmDetTurboProcessorKwargs], + ) -> BatchFeature: + if images is None or text is None: + raise ValueError("You have to specify both `images` and `text`") + + output_kwargs = self._merge_kwargs( + OmDetTurboProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + if isinstance(text, str): + text = text.strip(" ").split(",") + + if not (len(text) and isinstance(text[0], (list, tuple))): + text = [text] + + task = output_kwargs["text_kwargs"].pop("task", None) + if task is None: + task = [f"Detect {', '.join(text_single)}." for text_single in text] + elif not isinstance(task, (list, tuple)): + task = [task] + + encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"]) + tasks_encoding = self.tokenizer(text=task, **output_kwargs["text_kwargs"]) + + classes = text + + classes_structure = torch.tensor([len(class_single) for class_single in classes], dtype=torch.long) + classes_flattened = [class_single for class_batch in classes for class_single in class_batch] + classes_encoding = self.tokenizer(text=classes_flattened, **output_kwargs["text_kwargs"]) + + encoding = BatchFeature() + encoding.update({f"tasks_{key}": value for key, value in tasks_encoding.items()}) + encoding.update({f"classes_{key}": value for key, value in classes_encoding.items()}) + encoding.update({"classes_structure": classes_structure}) + encoding.update(encoding_image_processor) + + return encoding + + @property + def model_input_names(self): + image_processor_input_names = self.image_processor.model_input_names + tokenizer_input_names = [ + "classes_attention_mask", + "tasks_attention_mask", + "tasks_input_ids", + "classes_input_ids", + "classes_structure", + ] + return tokenizer_input_names + image_processor_input_names + + def _get_default_image_size(self) -> tuple[int, int]: + height = ( + self.image_processor.size["height"] + if "height" in self.image_processor.size + else self.image_processor.size["shortest_edge"] + ) + width = ( + self.image_processor.size["width"] + if "width" in self.image_processor.size + else self.image_processor.size["longest_edge"] + ) + return height, width + + def post_process_grounded_object_detection( + self, + outputs: "OmDetTurboObjectDetectionOutput", + text_labels: list[str] | list[list[str]] | None = None, + threshold: float = 0.3, + nms_threshold: float = 0.5, + target_sizes: TensorType | list[tuple] | None = None, + max_num_det: int | None = None, + ): + """ + Converts the raw output of [`OmDetTurboForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format and get the associated text class. + + Args: + outputs ([`OmDetTurboObjectDetectionOutput`]): + Raw outputs of the model. + text_labels (Union[list[str], list[list[str]]], *optional*): + The input classes names. If not provided, `text_labels` will be set to `None` in `outputs`. + threshold (float, defaults to 0.3): + Only return detections with a confidence score exceeding this threshold. + nms_threshold (float, defaults to 0.5): + The threshold to use for box non-maximum suppression. Value in [0, 1]. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + max_num_det (`int`, *optional*): + The maximum number of detections to return. + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, classes and boxes for an image + in the batch as predicted by the model. + """ + + batch_size = len(outputs.decoder_coord_logits) + + # Inputs consistency check for target sizes + if target_sizes is None: + height, width = self._get_default_image_size() + target_sizes = [(height, width)] * batch_size + + if any(len(image_size) != 2 for image_size in target_sizes): + raise ValueError( + "Each element of target_sizes must contain the size (height, width) of each image of the batch" + ) + + if len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as output sequences") + + # Inputs consistency check for text labels + if text_labels is not None and isinstance(text_labels[0], str): + text_labels = [text_labels] + + if text_labels is not None and len(text_labels) != batch_size: + raise ValueError("Make sure that you pass in as many classes group as output sequences") + + # Convert target_sizes to list for easier handling + if isinstance(target_sizes, torch.Tensor): + target_sizes = target_sizes.tolist() + + batch_boxes = outputs.decoder_coord_logits + batch_logits = outputs.decoder_class_logits + batch_num_classes = outputs.classes_structure + + batch_scores, batch_labels = compute_score(batch_logits) + + results = [] + for boxes, scores, image_size, image_num_classes in zip( + batch_boxes, batch_scores, target_sizes, batch_num_classes + ): + boxes, scores, labels = _post_process_boxes_for_image( + boxes=boxes, + scores=scores, + labels=batch_labels, + image_num_classes=image_num_classes, + image_size=image_size, + threshold=threshold, + nms_threshold=nms_threshold, + max_num_det=max_num_det, + ) + result = {"boxes": boxes, "scores": scores, "labels": labels, "text_labels": None} + results.append(result) + + # Add text labels + if text_labels is not None: + for result, image_text_labels in zip(results, text_labels): + result["text_labels"] = [image_text_labels[idx] for idx in result["labels"]] + + return results + + +__all__ = ["OmDetTurboProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b8dcfac01991d238e12406df77467e8d0827cfae --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_oneformer import * + from .image_processing_oneformer import * + from .image_processing_pil_oneformer import * + from .modeling_oneformer import * + from .processing_oneformer import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/configuration_oneformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/configuration_oneformer.py new file mode 100644 index 0000000000000000000000000000000000000000..f3ac0e9bae888c917994fdd80d60054edabcbd41 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/configuration_oneformer.py @@ -0,0 +1,158 @@ +# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""OneFormer model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="shi-labs/oneformer_ade20k_swin_tiny") +@strict +class OneFormerConfig(PreTrainedConfig): + r""" + ignore_value (`int`, *optional*, defaults to 255): + Values to be ignored in GT label while calculating loss. + num_queries (`int`, *optional*, defaults to 150): + Number of object queries. + contrastive_weight (`float`, *optional*, defaults to 0.5): + Weight for contrastive loss. + contrastive_temperature (`float`, *optional*, defaults to 0.07): + Initial value for scaling the contrastive logits. + train_num_points (`int`, *optional*, defaults to 12544): + Number of points to sample while calculating losses on mask predictions. + oversample_ratio (`float`, *optional*, defaults to 3.0): + Ratio to decide how many points to oversample. + importance_sample_ratio (`float`, *optional*, defaults to 0.75): + Ratio of points that are sampled via importance sampling.. + is_training (`bool`, *optional*, defaults to `False`): + Whether to run in training or inference mode. + output_auxiliary_logits (`bool`, *optional*, defaults to `True`): + Whether to return intermediate predictions from transformer decoder. + strides (`list`, *optional*, defaults to `[4, 8, 16, 32]`): + List containing the strides for feature maps in the encoder. + task_seq_len (`int`, *optional*, defaults to 77): + Sequence length for tokenizing text list input. + text_encoder_width (`int`, *optional*, defaults to 256): + Hidden size for text encoder. + text_encoder_context_length (`int`, *optional*, defaults to 77): + Input sequence length for text encoder. + text_encoder_num_layers (`int`, *optional*, defaults to 6): + Number of layers for transformer in text encoder. + text_encoder_vocab_size (`int`, *optional*, defaults to 49408): + Vocabulary size for tokenizer. + text_encoder_proj_layers (`int`, *optional*, defaults to 2): + Number of layers in MLP for project text queries. + text_encoder_n_ctx (`int`, *optional*, defaults to 16): + Number of learnable text context queries. + conv_dim (`int`, *optional*, defaults to 256): + Feature map dimension to map outputs from the backbone. + mask_dim (`int`, *optional*, defaults to 256): + Dimension for feature maps in pixel decoder. + hidden_dim (`int`, *optional*, defaults to 256): + Dimension for hidden states in transformer decoder. + encoder_feedforward_dim (`int`, *optional*, defaults to 1024): + Dimension for FFN layer in pixel decoder. + norm (`str`, *optional*, defaults to `"GN"`): + Type of normalization. + use_task_norm (`bool`, *optional*, defaults to `True`): + Whether to normalize the task token. + dim_feedforward (`int`, *optional*, defaults to 2048): + Dimension for FFN layer in transformer decoder. + pre_norm (`bool`, *optional*, defaults to `False`): + Whether to normalize hidden states before attention layers in transformer decoder. + enforce_input_proj (`bool`, *optional*, defaults to `False`): + Whether to project hidden states in transformer decoder. + query_dec_layers (`int`, *optional*, defaults to 2): + Number of layers in query transformer. + common_stride (`int`, *optional*, defaults to 4): + Common stride used for features in pixel decoder. + + Examples: + ```python + >>> from transformers import OneFormerConfig, OneFormerModel + + >>> # Initializing a OneFormer shi-labs/oneformer_ade20k_swin_tiny configuration + >>> configuration = OneFormerConfig() + >>> # Initializing a model (with random weights) from the shi-labs/oneformer_ade20k_swin_tiny style configuration + >>> model = OneFormerModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "oneformer" + sub_configs = {"backbone_config": AutoConfig} + attribute_map = {"hidden_size": "hidden_dim", "num_hidden_layers": "decoder_layers"} + + backbone_config: dict | PreTrainedConfig | None = None + ignore_value: int = 255 + num_queries: int = 150 + no_object_weight: float = 0.1 + class_weight: float = 2.0 + mask_weight: float = 5.0 + dice_weight: float = 5.0 + contrastive_weight: float = 0.5 + contrastive_temperature: float = 0.07 + train_num_points: int = 12544 + oversample_ratio: float = 3.0 + importance_sample_ratio: float = 0.75 + init_std: float = 0.02 + init_xavier_std: float = 1.0 + layer_norm_eps: float = 1e-05 + is_training: bool = False + use_auxiliary_loss: bool = True + output_auxiliary_logits: bool = True + strides: list[int] | tuple[int, ...] = (4, 8, 16, 32) + task_seq_len: int = 77 + text_encoder_width: int = 256 + text_encoder_context_length: int = 77 + text_encoder_num_layers: int = 6 + text_encoder_vocab_size: int = 49408 + text_encoder_proj_layers: int = 2 + text_encoder_n_ctx: int = 16 + conv_dim: int = 256 + mask_dim: int = 256 + hidden_dim: int = 256 + encoder_feedforward_dim: int = 1024 + norm: str = "GN" + encoder_layers: int = 6 + decoder_layers: int = 10 + use_task_norm: bool = True + num_attention_heads: int = 8 + dropout: float | int = 0.1 + dim_feedforward: int = 2048 + pre_norm: bool = False + enforce_input_proj: bool = False + query_dec_layers: int = 2 + common_stride: int = 4 + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="swin", + default_config_kwargs={ + "drop_path_rate": 0.3, + "out_features": ["stage1", "stage2", "stage3", "stage4"], + }, + **kwargs, + ) + + super().__post_init__(**kwargs) + + +__all__ = ["OneFormerConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/image_processing_oneformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/image_processing_oneformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ccee798bc39ec853f11f6e6fc4d7c5413cc952da --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/image_processing_oneformer.py @@ -0,0 +1,969 @@ +# Copyright 2025 SHI Labs and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for OneFormer.""" + +import json +import os +from typing import Union + +import torch +from torch import nn +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + get_max_height_width, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TensorType, + auto_docstring, + logging, +) + + +try: + from huggingface_hub import hf_hub_download + from huggingface_hub.utils import RepositoryNotFoundError +except ImportError: + hf_hub_download = None + RepositoryNotFoundError = None + + +logger = logging.get_logger(__name__) + + +class OneFormerImageProcessorKwargs(ImagesKwargs, total=False): + r""" + repo_path (`str`, *optional*, defaults to `shi-labs/oneformer_demo`): + Path to a local directory or HuggingFace Hub repository containing model metadata. + class_info_file (`str`, *optional*): + Path to the JSON file within the repository that contains class metadata. + num_text (`int`, *optional*): + Number of text queries for the text encoder, used as task-guiding prompts. + num_labels (`int`, *optional*): + Number of semantic classes for segmentation, determining the output layer's size. + ignore_index (`int`, *optional*): + Label to ignore in segmentation maps, often used for padding. + do_reduce_labels (`bool`, *optional*, defaults to `False`): + Whether to decrement all label values by 1, mapping the background class to `ignore_index`. + """ + + repo_path: str | None + class_info_file: str | None + num_text: int | None + num_labels: int | None + ignore_index: int | None + do_reduce_labels: bool + + +def prepare_metadata(class_info): + metadata = {} + class_names = [] + thing_ids = [] + for key, info in class_info.items(): + metadata[key] = info["name"] + class_names.append(info["name"]) + if info["isthing"]: + thing_ids.append(int(key)) + metadata["thing_ids"] = thing_ids + metadata["class_names"] = class_names + return metadata + + +def load_metadata(repo_id, class_info_file): + fname = os.path.join("" if repo_id is None else repo_id, class_info_file) + + if not os.path.exists(fname) or not os.path.isfile(fname): + if repo_id is None: + raise ValueError(f"Could not file {fname} locally. repo_id must be defined if loading from the hub") + if hf_hub_download is None: + raise ImportError( + "huggingface_hub is required to download metadata files. Install it with `pip install huggingface_hub`" + ) + # We try downloading from a dataset by default for backward compatibility + try: + fname = hf_hub_download(repo_id, class_info_file, repo_type="dataset") + except RepositoryNotFoundError: + fname = hf_hub_download(repo_id, class_info_file) + + with open(fname, "r") as f: + class_info = json.load(f) + + return class_info + + +def make_pixel_mask(image: "torch.Tensor", output_size: tuple[int, int]) -> "torch.Tensor": + """ + Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. + + Args: + image (`torch.Tensor`): + Image to make the pixel mask for. + output_size (`Tuple[int, int]`): + Output size of the mask. + """ + + input_height, input_width = image.shape[-2], image.shape[-1] + mask = torch.zeros(output_size, dtype=torch.int64) + mask[:input_height, :input_width] = 1 + return mask + + +def binary_mask_to_rle(mask): + """ + Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format. + + Args: + mask (`torch.Tensor` or `numpy.array`): + A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target + segment_id or class_id. + Returns: + `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE + format. + """ + import numpy as np + + # Convert to torch tensor if numpy array + if isinstance(mask, np.ndarray): + mask = torch.from_numpy(mask) + + pixels = mask.flatten() + pixels = torch.concat( + [ + torch.tensor([0], device=pixels.device, dtype=pixels.dtype), + pixels, + torch.tensor([0], device=pixels.device, dtype=pixels.dtype), + ] + ) + runs = torch.where(pixels[1:] != pixels[:-1])[0] + 1 + runs[1::2] -= runs[::2] + return list(runs) + + +def convert_segmentation_to_rle(segmentation): + """ + Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format. + + Args: + segmentation (`torch.Tensor` or `numpy.array`): + A segmentation map of shape `(height, width)` where each value denotes a segment or class id. + Returns: + `List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id. + """ + segment_ids = torch.unique(segmentation) + + run_length_encodings = [] + for idx in segment_ids: + mask = torch.where(segmentation == idx, 1, 0) + rle = binary_mask_to_rle(mask) + run_length_encodings.append(rle) + + return run_length_encodings + + +def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels): + """ + Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and + `labels`. + + Args: + masks (`torch.Tensor`): + A tensor of shape `(num_queries, height, width)`. + scores (`torch.Tensor`): + A tensor of shape `(num_queries)`. + labels (`torch.Tensor`): + A tensor of shape `(num_queries)`. + object_mask_threshold (`float`): + A number between 0 and 1 used to binarize the masks. + Raises: + `ValueError`: Raised when the first dimension doesn't match in all input tensors. + Returns: + `Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region + < `object_mask_threshold`. + """ + if not (masks.shape[0] == scores.shape[0] == labels.shape[0]): + raise ValueError("mask, scores and labels must have the same shape!") + + to_keep = labels.ne(num_labels) & (scores > object_mask_threshold) + + return masks[to_keep], scores[to_keep], labels[to_keep] + + +def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8): + # Get the mask associated with the k class + mask_k = mask_labels == k + mask_k_area = mask_k.sum() + + # Compute the area of all the stuff in query k + original_area = (mask_probs[k] >= mask_threshold).sum() + mask_exists = mask_k_area > 0 and original_area > 0 + + # Eliminate disconnected tiny segments + if mask_exists: + area_ratio = mask_k_area / original_area + if not area_ratio.item() > overlap_mask_area_threshold: + mask_exists = False + + return mask_exists, mask_k + + +def compute_segments( + mask_probs, + pred_scores, + pred_labels, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + label_ids_to_fuse: set[int] | None = None, + target_size: tuple[int, int] | None = None, +): + height = mask_probs.shape[1] if target_size is None else target_size[0] + width = mask_probs.shape[2] if target_size is None else target_size[1] + + segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device) + segments: list[dict] = [] + + if target_size is not None: + mask_probs = tvF.resize( + mask_probs.unsqueeze(0), + size=target_size, + interpolation=tvF.InterpolationMode.BILINEAR, + )[0] + + current_segment_id = 0 + + mask_probs *= pred_scores.view(-1, 1, 1) + mask_labels = mask_probs.argmax(0) # [height, width] + + stuff_memory_list: dict[str, int] = {} + for k in range(pred_labels.shape[0]): + pred_class = pred_labels[k].item() + should_fuse = pred_class in label_ids_to_fuse + + mask_exists, mask_k = check_segment_validity( + mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold + ) + + if mask_exists: + if pred_class in stuff_memory_list: + current_segment_id = stuff_memory_list[pred_class] + else: + current_segment_id += 1 + + segmentation[mask_k] = current_segment_id + segment_score = round(pred_scores[k].item(), 6) + segments.append( + { + "id": current_segment_id, + "label_id": pred_class, + "was_fused": should_fuse, + "score": segment_score, + } + ) + if should_fuse: + stuff_memory_list[pred_class] = current_segment_id + + return segmentation, segments + + +@auto_docstring +class OneFormerImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"shortest_edge": 800, "longest_edge": 1333} + crop_size = None + do_resize = True + do_rescale = True + do_normalize = True + default_to_square = False + do_center_crop = False + do_convert_rgb = True + rescale_factor = 1 / 255 + ignore_index = None + do_reduce_labels = False + repo_path = "shi-labs/oneformer_demo" + class_info_file = None + num_text = None + num_labels = None + valid_kwargs = OneFormerImageProcessorKwargs + model_input_names = ["pixel_values", "pixel_mask", "task_inputs"] + + def __init__(self, **kwargs: Unpack[OneFormerImageProcessorKwargs]): + super().__init__(**kwargs) + if self.class_info_file: + self.metadata = prepare_metadata(load_metadata(self.repo_path, self.class_info_file)) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + task_inputs: list[str] | None = None, + segmentation_maps: ImageInput | None = None, + instance_id_to_semantic_id: list[dict[int, int]] | dict[int, int] | None = None, + **kwargs: Unpack[OneFormerImageProcessorKwargs], + ) -> BatchFeature: + r""" + task_inputs (`list[str]`, *optional*): + List of tasks (`"panoptic"`, `"instance"`, `"semantic"`) for each image in the batch. + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps. + instance_id_to_semantic_id (`Union[list[dict[int, int]], dict[int, int]]`, *optional*): + A mapping from instance IDs to semantic IDs. + """ + return super().preprocess(images, task_inputs, segmentation_maps, instance_id_to_semantic_id, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + task_inputs: list[str] | None, + segmentation_maps: ImageInput, + instance_id_to_semantic_id: list[dict[int, int]] | dict[int, int] | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + device: Union[str, "torch.device"] | None = None, + **kwargs: Unpack[OneFormerImageProcessorKwargs], + ) -> BatchFeature: + """ + Preprocess image-like inputs. + To be overridden by subclasses when image-like inputs other than images should be processed. + It can be used for segmentation maps, depth maps, etc. + """ + # Prepare input images + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device + ) + if segmentation_maps is not None: + segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + return self._preprocess(images, task_inputs, segmentation_maps, instance_id_to_semantic_id, **kwargs) + + def _preprocess( + self, + images: list["torch.Tensor"], + task_inputs: list[str] | None, + segmentation_maps: list["torch.Tensor"], + instance_id_to_semantic_id: list[dict[int, int]] | dict[int, int] | None, + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + ignore_index: int | None, + do_reduce_labels: bool | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + + processed_images_grouped = {} + + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample) + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + processed_segmentation_maps = None + if segmentation_maps is not None: + grouped_segmentation_maps, grouped_segmentation_maps_index = group_images_by_shape( + segmentation_maps, disable_grouping=disable_grouping + ) + processed_segmentation_maps_grouped = {} + for shape, stacked_segmentation_maps in grouped_segmentation_maps.items(): + if do_resize: + stacked_segmentation_maps = self.resize( + stacked_segmentation_maps, size=size, resample=tvF.InterpolationMode.NEAREST_EXACT + ) + processed_segmentation_maps_grouped[shape] = stacked_segmentation_maps + processed_segmentation_maps = reorder_images( + processed_segmentation_maps_grouped, grouped_segmentation_maps_index + ) + + encoded_inputs = self.encode_inputs( + processed_images, + task_inputs, + segmentation_maps=processed_segmentation_maps, + instance_id_to_semantic_id=instance_id_to_semantic_id, + ignore_index=ignore_index, + do_reduce_labels=do_reduce_labels, + return_tensors=return_tensors, + ) + + return encoded_inputs + + def _pad_image( + self, + image: "torch.Tensor", + output_size: tuple[int, int], + constant_values: float = 0, + ) -> "torch.Tensor": + """ + Pad an image with zeros to the given size using torch operations. + + Args: + image (`torch.Tensor`): + Image tensor in channel-first format (C, H, W). + output_size (`tuple[int, int]`): + Target output size (height, width). + constant_values (`float`, *optional*, defaults to 0): + The value to use for padding. + + Returns: + `torch.Tensor`: The padded image. + """ + input_height, input_width = image.shape[1], image.shape[2] + output_height, output_width = output_size + + pad_bottom = output_height - input_height + pad_right = output_width - input_width + + padded_image = tvF.pad(image, padding=[0, 0, pad_right, pad_bottom], fill=constant_values) + + return padded_image + + def pad( + self, + images: list["torch.Tensor"], + return_pixel_mask: bool = True, + return_tensors: str | TensorType | None = None, + ) -> BatchFeature: + """ + Pad a batch of images to the same size using torch operations. + + Args: + images (`List[torch.Tensor]`): + List of image tensors in channel-first format. + return_pixel_mask (`bool`, *optional*, defaults to `True`): + Whether to return pixel masks. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. + + Returns: + `BatchFeature`: Padded images and optional pixel masks. + """ + outputs = super().pad(images, return_mask=return_pixel_mask) + padded_images = outputs[0] if return_pixel_mask else outputs + pixel_masks = outputs[1] if return_pixel_mask else None + + if return_tensors: + padded_images = torch.stack(padded_images, dim=0) + if return_pixel_mask: + pixel_masks = torch.stack(pixel_masks, dim=0) + + data = {"pixel_values": padded_images} + if return_pixel_mask: + data["pixel_mask"] = pixel_masks + + return BatchFeature(data=data, tensor_type=return_tensors) + + def convert_segmentation_map_to_binary_masks( + self, + segmentation_map: "torch.Tensor", + instance_id_to_semantic_id: dict[int, int] | None = None, + ignore_index: int | None = None, + do_reduce_labels: bool = False, + ): + if do_reduce_labels and ignore_index is None: + raise ValueError("If `do_reduce_labels` is True, `ignore_index` must be provided.") + + if do_reduce_labels: + segmentation_map = torch.where(segmentation_map == 0, ignore_index, segmentation_map - 1) + + all_labels = torch.unique(segmentation_map) + + if ignore_index is not None: + all_labels = all_labels[all_labels != ignore_index] + + binary_masks = [(segmentation_map == i) for i in all_labels] + + if binary_masks: + binary_masks = torch.stack(binary_masks, dim=0) + else: + binary_masks = torch.zeros((0, *segmentation_map.shape), device=segmentation_map.device) + + # Convert instance ids to class ids + if instance_id_to_semantic_id is not None: + labels = torch.zeros(all_labels.shape[0], device=segmentation_map.device) + + for i, label in enumerate(all_labels): + class_id = instance_id_to_semantic_id[(label.item() + 1 if do_reduce_labels else label.item())] + labels[i] = class_id - 1 if do_reduce_labels else class_id + else: + labels = all_labels + + return ( + binary_masks.float(), + labels.long(), + ) + + def get_semantic_annotations(self, label, num_class_obj): + annotation_classes = label["classes"] + annotation_masks = label["masks"] + + texts = ["a semantic photo"] * self.num_text + classes = [] + masks = [] + + for idx in range(len(annotation_classes)): + class_id = annotation_classes[idx] + mask = annotation_masks[idx] + if not torch.all(mask == 0): + if class_id not in classes: + cls_name = self.metadata[str(class_id.cpu().item())] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + else: + idx = classes.index(class_id) + masks[idx] += mask + masks[idx] = torch.clamp(masks[idx], 0, 1) + + num = 0 + for i, cls_name in enumerate(self.metadata["class_names"]): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = torch.stack(classes) + masks = torch.stack(masks) + return classes, masks, texts + + def get_instance_annotations(self, label, num_class_obj): + annotation_classes = label["classes"] + annotation_masks = label["masks"] + + texts = ["an instance photo"] * self.num_text + classes = [] + masks = [] + + for idx in range(len(annotation_classes)): + class_id = annotation_classes[idx] + mask = annotation_masks[idx] + + if class_id in self.metadata["thing_ids"]: + if not torch.all(mask == 0): + cls_name = self.metadata[str(class_id.cpu().item())] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + + num = 0 + for i, cls_name in enumerate(self.metadata["class_names"]): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = torch.stack(classes) + masks = torch.stack(masks) + return classes, masks, texts + + def get_panoptic_annotations(self, label, num_class_obj): + annotation_classes = label["classes"] + annotation_masks = label["masks"] + + texts = ["an panoptic photo"] * self.num_text + classes = [] + masks = [] + for idx in range(len(annotation_classes)): + class_id = annotation_classes[idx] + mask = annotation_masks[idx] if hasattr(annotation_masks[idx], "data") else annotation_masks[idx] + if not torch.all(mask == 0): + cls_name = self.metadata[str(class_id.cpu().item())] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + + num = 0 + for i, cls_name in enumerate(self.metadata["class_names"]): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = torch.stack(classes) + masks = torch.stack(masks) + return classes, masks, texts + + def encode_inputs( + self, + pixel_values_list: list["ImageInput"], + task_inputs: list[str] | None = None, + segmentation_maps: list["ImageInput"] | None = None, + instance_id_to_semantic_id: list[dict[int, int]] | dict[int, int] | None = None, + ignore_index: int | None = None, + do_reduce_labels: bool = False, + return_tensors: str | TensorType | None = None, + ) -> BatchFeature: + ignore_index = self.ignore_index if ignore_index is None else ignore_index + do_reduce_labels = self.do_reduce_labels if do_reduce_labels is None else do_reduce_labels + if task_inputs is None: + task_inputs = ["panoptic"] + pixel_values_list = self._prepare_image_like_inputs( + pixel_values_list, input_data_format=ChannelDimension.FIRST + ) + if segmentation_maps is not None: + segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + pad_size = get_max_height_width(pixel_values_list) + encoded_inputs = self.pad(pixel_values_list, return_tensors=return_tensors) + + annotations = None + if segmentation_maps is not None: + annotations = [] + for idx, segmentation_map in enumerate(segmentation_maps): + # Use instance2class_id mapping per image + if isinstance(instance_id_to_semantic_id, list): + instance_id = instance_id_to_semantic_id[idx] + else: + instance_id = instance_id_to_semantic_id + + # Convert segmentation map to binary masks using torch operations + masks, classes = self.convert_segmentation_map_to_binary_masks( + segmentation_map, + instance_id, + ignore_index=ignore_index, + do_reduce_labels=do_reduce_labels, + ) + + annotations.append({"masks": masks, "classes": classes}) + + if annotations is not None: + mask_labels = [] + class_labels = [] + text_inputs = [] + num_class_obj = dict.fromkeys(self.metadata["class_names"], 0) + + for i, label in enumerate(annotations): + task = task_inputs[i] + + if task == "semantic": + classes, masks, texts = self.get_semantic_annotations(label, num_class_obj) + elif task == "instance": + classes, masks, texts = self.get_instance_annotations(label, num_class_obj) + elif task == "panoptic": + classes, masks, texts = self.get_panoptic_annotations(label, num_class_obj) + else: + raise ValueError(f"{task} was not expected, expected `semantic`, `instance` or `panoptic`") + # Pad masks to max size using torch operations + padded_masks = [ + self._pad_image(image=mask, output_size=pad_size, constant_values=ignore_index) for mask in masks + ] + padded_masks = torch.cat(padded_masks, dim=0) + mask_labels.append(padded_masks) + class_labels.append(classes) + text_inputs.append(texts) + + encoded_inputs["mask_labels"] = mask_labels + encoded_inputs["class_labels"] = class_labels + encoded_inputs["text_inputs"] = text_inputs + + encoded_inputs["task_inputs"] = [f"the task is {task_input}" for task_input in task_inputs] + return encoded_inputs + + def post_process_semantic_segmentation( + self, outputs, target_sizes: list[tuple[int, int]] | None = None + ) -> "torch.Tensor": + """ + Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports + PyTorch. + + Args: + outputs ([`MaskFormerForInstanceSegmentation`]): + Raw outputs of the model. + target_sizes (`List[Tuple[int, int]]`, *optional*): + List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested + final size (height, width) of each prediction. If left to None, predictions will not be resized. + Returns: + `List[torch.Tensor]`: + A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) + corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each + `torch.Tensor` correspond to a semantic class id. + """ + class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width] + + # Remove the null class `[..., :-1]` + masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1] + masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width] + + # Semantic segmentation logits of shape (batch_size, num_classes, height, width) + segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs) + batch_size = class_queries_logits.shape[0] + + # Resize logits and compute semantic segmentation maps + if target_sizes is not None: + if batch_size != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + + semantic_segmentation = [] + for idx in range(batch_size): + resized_logits = tvF.resize( + segmentation[idx].unsqueeze(dim=0), + size=target_sizes[idx], + interpolation=tvF.InterpolationMode.BILINEAR, + ) + semantic_map = resized_logits[0].argmax(dim=0) + semantic_segmentation.append(semantic_map) + else: + semantic_segmentation = segmentation.argmax(dim=1) + semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] + + return semantic_segmentation + + def post_process_instance_segmentation( + self, + outputs, + task_type: str = "instance", + is_demo: bool = True, + threshold: float = 0.5, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + target_sizes: list[tuple[int, int]] | None = None, + return_coco_annotation: bool | None = False, + ): + """ + Converts the output of [`OneFormerForUniversalSegmentationOutput`] into image instance segmentation + predictions. Only supports PyTorch. + + Args: + outputs ([`OneFormerForUniversalSegmentationOutput`]): + The outputs from [`OneFormerForUniversalSegmentationOutput`]. + task_type (`str`, *optional*, defaults to "instance"): + The post processing depends on the task token input. If the `task_type` is "panoptic", we need to + ignore the stuff predictions. + is_demo (`bool`, *optional)*, defaults to `True`): + Whether the model is in demo mode. If true, use threshold to predict final masks. + threshold (`float`, *optional*, defaults to 0.5): + The probability score threshold to keep predicted instance masks. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold to use when turning the predicted masks into binary values. + overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8): + The overlap mask area threshold to merge or discard small disconnected parts within each binary + instance mask. + target_sizes (`List[Tuple]`, *optional*): + List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested + final size (height, width) of each prediction in batch. If left to None, predictions will not be + resized. + return_coco_annotation (`bool`, *optional)*, defaults to `False`): + Whether to return predictions in COCO format. + + Returns: + `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys: + - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set + to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized + to the corresponding `target_sizes` entry. + - **segments_info** -- A dictionary that contains additional information on each segment. + - **id** -- an integer representing the `segment_id`. + - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`. + - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise. + Multiple instances of the same class / label were fused and assigned a single `segment_id`. + - **score** -- Prediction score of segment with `segment_id`. + """ + class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width] + + device = masks_queries_logits.device + batch_size = class_queries_logits.shape[0] + num_queries = class_queries_logits.shape[1] + num_classes = class_queries_logits.shape[-1] - 1 + + # Loop over items in batch size + results: list[dict[str, torch.Tensor]] = [] + + for i in range(batch_size): + # [Q, K] + scores = nn.functional.softmax(class_queries_logits[i], dim=-1)[:, :-1] + labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1) + + # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False) + scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False) + labels_per_image = labels[topk_indices] + + topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor") + # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1) + mask_pred = masks_queries_logits[i][topk_indices] + + # Only consider scores with confidence over [threshold] for demo + if is_demo: + keep = scores_per_image > threshold + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + mask_pred = mask_pred[keep] + + # if this is panoptic segmentation, we only keep the "thing" classes + if task_type == "panoptic": + keep = torch.zeros_like(scores_per_image).bool() + for j, lab in enumerate(labels_per_image): + keep[j] = lab in self.metadata["thing_ids"] + + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + mask_pred = mask_pred[keep] + + if mask_pred.shape[0] <= 0: + height, width = target_sizes[i] if target_sizes is not None else mask_pred.shape[1:] + segmentation = torch.zeros((height, width)) - 1 + results.append({"segmentation": segmentation, "segments_info": []}) + continue + + if "ade20k" in self.class_info_file and not is_demo and "instance" in task_type: + for j in range(labels_per_image.shape[0]): + labels_per_image[j] = self.metadata["thing_ids"].index(labels_per_image[j].item()) + + # Get segmentation map and segment information of batch item + target_size = target_sizes[i] if target_sizes is not None else None + segmentation, segments = compute_segments( + mask_pred, + scores_per_image, + labels_per_image, + mask_threshold, + overlap_mask_area_threshold, + set(), + target_size, + ) + + # Return segmentation map in run-length encoding (RLE) format + if return_coco_annotation: + segmentation = convert_segmentation_to_rle(segmentation) + + results.append({"segmentation": segmentation, "segments_info": segments}) + return results + + # Copied from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_panoptic_segmentation + def post_process_panoptic_segmentation( + self, + outputs, + threshold: float = 0.5, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + label_ids_to_fuse: set[int] | None = None, + target_sizes: list[tuple[int, int]] | None = None, + ) -> list[dict]: + """ + Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation + predictions. Only supports PyTorch. + + Args: + outputs ([`MaskFormerForInstanceSegmentationOutput`]): + The outputs from [`MaskFormerForInstanceSegmentation`]. + threshold (`float`, *optional*, defaults to 0.5): + The probability score threshold to keep predicted instance masks. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold to use when turning the predicted masks into binary values. + overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8): + The overlap mask area threshold to merge or discard small disconnected parts within each binary + instance mask. + label_ids_to_fuse (`Set[int]`, *optional*): + The labels in this state will have all their instances be fused together. For instance we could say + there can only be one sky in an image, but several persons, so the label ID for sky would be in that + set, but not the one for person. + target_sizes (`list[Tuple]`, *optional*): + List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested + final size (height, width) of each prediction in batch. If left to None, predictions will not be + resized. + + Returns: + `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys: + - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set + to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized + to the corresponding `target_sizes` entry. + - **segments_info** -- A dictionary that contains additional information on each segment. + - **id** -- an integer representing the `segment_id`. + - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`. + - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise. + Multiple instances of the same class / label were fused and assigned a single `segment_id`. + - **score** -- Prediction score of segment with `segment_id`. + """ + + if label_ids_to_fuse is None: + logger.warning("`label_ids_to_fuse` unset. No instance will be fused.") + label_ids_to_fuse = set() + + class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width] + + batch_size = class_queries_logits.shape[0] + num_labels = class_queries_logits.shape[-1] - 1 + + mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width] + + # Predicted label and score of each query (batch_size, num_queries) + pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1) + + # Loop over items in batch size + results: list[dict[str, TensorType]] = [] + + for i in range(batch_size): + mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects( + mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels + ) + + # No mask found + if mask_probs_item.shape[0] <= 0: + height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:] + segmentation = torch.zeros((height, width)) - 1 + results.append({"segmentation": segmentation, "segments_info": []}) + continue + + # Get segmentation map and segment information of batch item + target_size = target_sizes[i] if target_sizes is not None else None + segmentation, segments = compute_segments( + mask_probs=mask_probs_item, + pred_scores=pred_scores_item, + pred_labels=pred_labels_item, + mask_threshold=mask_threshold, + overlap_mask_area_threshold=overlap_mask_area_threshold, + label_ids_to_fuse=label_ids_to_fuse, + target_size=target_size, + ) + + results.append({"segmentation": segmentation, "segments_info": segments}) + return results + + +__all__ = ["OneFormerImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/image_processing_pil_oneformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/image_processing_pil_oneformer.py new file mode 100644 index 0000000000000000000000000000000000000000..bed17ab5f4eb0fbf2f15483efb226c6bc0d4c8b7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/image_processing_pil_oneformer.py @@ -0,0 +1,1008 @@ +# Copyright 2025 SHI Labs and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for OneFormer.""" + +import json +import os + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import PaddingMode +from ...image_transforms import pad as np_pad +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + get_image_size, + get_max_height_width, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_torch_available, is_torchvision_available, logging +from ...utils.import_utils import requires + + +logger = logging.get_logger(__name__) + +if is_torch_available(): + import torch + from torch import nn + +if is_torchvision_available(): + import torchvision.transforms.v2.functional as tvF + +try: + from huggingface_hub import hf_hub_download + from huggingface_hub.utils import RepositoryNotFoundError +except ImportError: + hf_hub_download = None + RepositoryNotFoundError = None + + +def make_pixel_mask(image: np.ndarray, output_size: tuple[int, int]) -> np.ndarray: + """ + Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. + + Args: + image (`np.ndarray`): + Image to make the pixel mask for. + output_size (`Tuple[int, int]`): + Output size of the mask. + """ + input_height, input_width = get_image_size(image, channel_dim=ChannelDimension.FIRST) + mask = np.zeros(output_size, dtype=np.int64) + mask[:input_height, :input_width] = 1 + return mask + + +# Adapted from transformers.models.oneformer.image_processing_oneformer.OneFormerImageProcessorKwargs +class OneFormerImageProcessorKwargs(ImagesKwargs, total=False): + r""" + repo_path (`str`, *optional*, defaults to `shi-labs/oneformer_demo`): + Path to a local directory or HuggingFace Hub repository containing model metadata. + class_info_file (`str`, *optional*): + Path to the JSON file within the repository that contains class metadata. + num_text (`int`, *optional*): + Number of text queries for the text encoder, used as task-guiding prompts. + num_labels (`int`, *optional*): + Number of semantic classes for segmentation, determining the output layer's size. + ignore_index (`int`, *optional*): + Label to ignore in segmentation maps, often used for padding. + do_reduce_labels (`bool`, *optional*, defaults to `False`): + Whether to decrement all label values by 1, mapping the background class to `ignore_index`. + """ + + repo_path: str | None + class_info_file: str | None + num_text: int | None + num_labels: int | None + ignore_index: int | None + do_reduce_labels: bool + + +# Adapted from transformers.models.oneformer.image_processing_oneformer.binary_mask_to_rle +def binary_mask_to_rle(mask): + """ + Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format. + + Args: + mask (`torch.Tensor` or `numpy.array`): + A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target + segment_id or class_id. + Returns: + `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE + format. + """ + from ...utils import is_torch_tensor + + if is_torch_tensor(mask): + mask = mask.numpy() + + pixels = mask.flatten() + pixels = np.concatenate([[0], pixels, [0]]) + runs = np.where(pixels[1:] != pixels[:-1])[0] + 1 + runs[1::2] -= runs[::2] + return list(runs) + + +# Adapted from transformers.models.oneformer.image_processing_oneformer.check_segment_validity +def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8): + # Get the mask associated with the k class + mask_k = mask_labels == k + mask_k_area = mask_k.sum() + + # Compute the area of all the stuff in query k + original_area = (mask_probs[k] >= mask_threshold).sum() + mask_exists = mask_k_area > 0 and original_area > 0 + + # Eliminate disconnected tiny segments + if mask_exists: + area_ratio = mask_k_area / original_area + if not area_ratio.item() > overlap_mask_area_threshold: + mask_exists = False + + return mask_exists, mask_k + + +# Adapted from transformers.models.oneformer.image_processing_oneformer.compute_segments +def compute_segments( + mask_probs, + pred_scores, + pred_labels, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + label_ids_to_fuse: set[int] | None = None, + target_size: tuple[int, int] | None = None, +): + height = mask_probs.shape[1] if target_size is None else target_size[0] + width = mask_probs.shape[2] if target_size is None else target_size[1] + + segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device) + segments: list[dict] = [] + + if target_size is not None: + mask_probs = tvF.resize( + mask_probs.unsqueeze(0), + size=target_size, + interpolation=tvF.InterpolationMode.BILINEAR, + )[0] + + current_segment_id = 0 + + mask_probs *= pred_scores.view(-1, 1, 1) + mask_labels = mask_probs.argmax(0) # [height, width] + + stuff_memory_list: dict[str, int] = {} + for k in range(pred_labels.shape[0]): + pred_class = pred_labels[k].item() + should_fuse = pred_class in label_ids_to_fuse + + mask_exists, mask_k = check_segment_validity( + mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold + ) + + if mask_exists: + if pred_class in stuff_memory_list: + current_segment_id = stuff_memory_list[pred_class] + else: + current_segment_id += 1 + + segmentation[mask_k] = current_segment_id + segment_score = round(pred_scores[k].item(), 6) + segments.append( + { + "id": current_segment_id, + "label_id": pred_class, + "was_fused": should_fuse, + "score": segment_score, + } + ) + if should_fuse: + stuff_memory_list[pred_class] = current_segment_id + + return segmentation, segments + + +# Adapted from transformers.models.oneformer.image_processing_oneformer.convert_segmentation_to_rle +def convert_segmentation_to_rle(segmentation): + """ + Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format. + + Args: + segmentation (`torch.Tensor` or `numpy.array`): + A segmentation map of shape `(height, width)` where each value denotes a segment or class id. + Returns: + `List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id. + """ + segment_ids = torch.unique(segmentation) + + run_length_encodings = [] + for idx in segment_ids: + mask = torch.where(segmentation == idx, 1, 0) + rle = binary_mask_to_rle(mask) + run_length_encodings.append(rle) + + return run_length_encodings + + +# Adapted from transformers.models.oneformer.image_processing_oneformer.load_metadata +def load_metadata(repo_id, class_info_file): + fname = os.path.join("" if repo_id is None else repo_id, class_info_file) + + if not os.path.exists(fname) or not os.path.isfile(fname): + if repo_id is None: + raise ValueError(f"Could not file {fname} locally. repo_id must be defined if loading from the hub") + if hf_hub_download is None: + raise ImportError( + "huggingface_hub is required to download metadata files. Install it with `pip install huggingface_hub`" + ) + # We try downloading from a dataset by default for backward compatibility + try: + fname = hf_hub_download(repo_id, class_info_file, repo_type="dataset") + except RepositoryNotFoundError: + fname = hf_hub_download(repo_id, class_info_file) + + with open(fname, "r") as f: + class_info = json.load(f) + + return class_info + + +# Adapted from transformers.models.oneformer.image_processing_oneformer.prepare_metadata +def prepare_metadata(class_info): + metadata = {} + class_names = [] + thing_ids = [] + for key, info in class_info.items(): + metadata[key] = info["name"] + class_names.append(info["name"]) + if info["isthing"]: + thing_ids.append(int(key)) + metadata["thing_ids"] = thing_ids + metadata["class_names"] = class_names + return metadata + + +# Adapted from transformers.models.oneformer.image_processing_oneformer.remove_low_and_no_objects +def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels): + """ + Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and + `labels`. + + Args: + masks (`torch.Tensor`): + A tensor of shape `(num_queries, height, width)`. + scores (`torch.Tensor`): + A tensor of shape `(num_queries)`. + labels (`torch.Tensor`): + A tensor of shape `(num_queries)`. + object_mask_threshold (`float`): + A number between 0 and 1 used to binarize the masks. + Raises: + `ValueError`: Raised when the first dimension doesn't match in all input tensors. + Returns: + `Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region + < `object_mask_threshold`. + """ + if not (masks.shape[0] == scores.shape[0] == labels.shape[0]): + raise ValueError("mask, scores and labels must have the same shape!") + + to_keep = labels.ne(num_labels) & (scores > object_mask_threshold) + + return masks[to_keep], scores[to_keep], labels[to_keep] + + +@auto_docstring +@requires(backends=("torch",)) +class OneFormerImageProcessorPil(PilBackend): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"shortest_edge": 800, "longest_edge": 1333} + crop_size = None + do_resize = True + do_rescale = True + do_normalize = True + default_to_square = False + do_center_crop = False + do_convert_rgb = True + rescale_factor = 1 / 255 + ignore_index = None + do_reduce_labels = False + repo_path = "shi-labs/oneformer_demo" + class_info_file = None + num_text = None + num_labels = None + valid_kwargs = OneFormerImageProcessorKwargs + model_input_names = ["pixel_values", "pixel_mask", "task_inputs"] + + def __init__(self, **kwargs: Unpack[OneFormerImageProcessorKwargs]): + super().__init__(**kwargs) + if self.class_info_file: + self.metadata = prepare_metadata(load_metadata(self.repo_path, self.class_info_file)) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + task_inputs: list[str] | None = None, + segmentation_maps: ImageInput | None = None, + instance_id_to_semantic_id: list[dict[int, int]] | dict[int, int] | None = None, + **kwargs: Unpack[OneFormerImageProcessorKwargs], + ) -> BatchFeature: + r""" + task_inputs (`list[str]`, *optional*): + List of tasks (`"panoptic"`, `"instance"`, `"semantic"`) for each image in the batch. + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps. + instance_id_to_semantic_id (`Union[list[dict[int, int]], dict[int, int]]`, *optional*): + A mapping from instance IDs to semantic IDs. + """ + return super().preprocess(images, task_inputs, segmentation_maps, instance_id_to_semantic_id, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + task_inputs: list[str] | None, + segmentation_maps: ImageInput, + instance_id_to_semantic_id: list[dict[int, int]] | dict[int, int] | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + **kwargs: Unpack[OneFormerImageProcessorKwargs], + ) -> BatchFeature: + """ + Preprocess image-like inputs. + To be overridden by subclasses when image-like inputs other than images should be processed. + It can be used for segmentation maps, depth maps, etc. + """ + # Prepare input images + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format + ) + if segmentation_maps is not None: + segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + return self._preprocess(images, task_inputs, segmentation_maps, instance_id_to_semantic_id, **kwargs) + + def _preprocess( + self, + images: list[np.ndarray], + task_inputs: list[str] | None, + segmentation_maps: list[np.ndarray], + instance_id_to_semantic_id: list[dict[int, int]] | dict[int, int] | None, + do_resize: bool, + size: SizeDict, + resample: PILImageResampling | None, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + ignore_index: int | None, + do_reduce_labels: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + # Process images one by one (no batching in PIL backend) + processed_images = [] + processed_segmentation_maps = None + if segmentation_maps is not None: + processed_segmentation_maps = [] + + for idx, image in enumerate(images): + if do_resize: + image = self.resize(image=image, size=size, resample=resample) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + + if segmentation_maps is not None: + seg_map = segmentation_maps[idx] + if do_resize: + seg_map = self.resize(image=seg_map, size=size, resample=PILImageResampling.NEAREST) + processed_segmentation_maps.append(seg_map) + + encoded_inputs = self.encode_inputs( + processed_images, + task_inputs, + segmentation_maps=processed_segmentation_maps, + instance_id_to_semantic_id=instance_id_to_semantic_id, + ignore_index=ignore_index, + do_reduce_labels=do_reduce_labels, + return_tensors=return_tensors, + ) + + return encoded_inputs + + def _pad_image(self, image: np.ndarray, output_size: tuple[int, int], constant_values: float = 0) -> np.ndarray: + """ + Pad an image with zeros to the given size using numpy operations. + + Args: + image (`np.ndarray`): + Image array in channel-first format (C, H, W) or (H, W). + output_size (`tuple[int, int]`): + Target output size (height, width). + constant_values (`float`, *optional*, defaults to 0): + The value to use for padding. + + Returns: + `np.ndarray`: The padded image. + """ + input_height, input_width = get_image_size(image, channel_dim=ChannelDimension.FIRST) + output_height, output_width = output_size + + pad_bottom = output_height - input_height + pad_right = output_width - input_width + + # For 2D arrays (masks), use np.pad directly + # For 3D arrays (images), use np_pad which handles channel dimension + if image.ndim == 2: + padding = ((0, pad_bottom), (0, pad_right)) + padded_image = np.pad(image, padding, mode="constant", constant_values=constant_values) + else: + padding = ((0, pad_bottom), (0, pad_right)) + padded_image = np_pad( + image, + padding, + mode=PaddingMode.CONSTANT, + constant_values=constant_values, + data_format=ChannelDimension.FIRST, + input_data_format=ChannelDimension.FIRST, + ) + + return padded_image + + def pad( + self, images: list[np.ndarray], return_pixel_mask: bool = True, return_tensors: str | TensorType | None = None + ) -> BatchFeature: + """ + Pad a batch of images to the same size using numpy operations. + + Args: + images (`List[np.ndarray]`): + List of image arrays in channel-first format. + return_pixel_mask (`bool`, *optional*, defaults to `True`): + Whether to return pixel masks. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. + + Returns: + `BatchFeature`: Padded images and optional pixel masks. + """ + pad_size = get_max_height_width(images, input_data_format=ChannelDimension.FIRST) + + padded_images = [] + pixel_masks = [] + for image in images: + padded_image = self._pad_image(image, pad_size, constant_values=0) + padded_images.append(padded_image) + if return_pixel_mask: + pixel_mask = make_pixel_mask(image, pad_size) + pixel_masks.append(pixel_mask) + + if return_tensors == "pt": + padded_images = [torch.from_numpy(img) for img in padded_images] + padded_images = torch.stack(padded_images, dim=0) + if return_pixel_mask: + pixel_masks = [torch.from_numpy(mask) for mask in pixel_masks] + pixel_masks = torch.stack(pixel_masks, dim=0) + + data = {"pixel_values": padded_images} + if return_pixel_mask: + data["pixel_mask"] = pixel_masks + + return BatchFeature(data=data, tensor_type=return_tensors) + + def convert_segmentation_map_to_binary_masks( + self, + segmentation_map: np.ndarray, + instance_id_to_semantic_id: dict[int, int] | None = None, + ignore_index: int | None = None, + do_reduce_labels: bool = False, + ): + """Convert segmentation map to binary masks using NumPy operations.""" + if do_reduce_labels and ignore_index is None: + raise ValueError("If `do_reduce_labels` is True, `ignore_index` must be provided.") + + if do_reduce_labels: + segmentation_map = np.where(segmentation_map == 0, ignore_index, segmentation_map - 1) + + all_labels = np.unique(segmentation_map) + + if ignore_index is not None: + all_labels = all_labels[all_labels != ignore_index] + + binary_masks = [(segmentation_map == i) for i in all_labels] + if binary_masks: + binary_masks = np.stack(binary_masks, axis=0) + else: + binary_masks = np.zeros((0, *segmentation_map.shape), dtype=np.float32) + + # Convert instance ids to class ids + if instance_id_to_semantic_id is not None: + labels = np.zeros(all_labels.shape[0], dtype=np.int64) + + for i, label in enumerate(all_labels): + class_id = instance_id_to_semantic_id[(int(label) + 1 if do_reduce_labels else int(label))] + labels[i] = class_id - 1 if do_reduce_labels else class_id + else: + labels = all_labels.astype(np.int64) + return binary_masks.astype(np.float32), labels + + def get_semantic_annotations(self, label, num_class_obj): + annotation_classes = label["classes"] + annotation_masks = label["masks"] + + texts = ["a semantic photo"] * self.num_text + classes = [] + masks = [] + + for idx in range(len(annotation_classes)): + class_id = annotation_classes[idx] + mask = annotation_masks[idx] + if not np.all(mask == 0): + if class_id not in classes: + cls_name = self.metadata[str(class_id)] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + else: + idx = classes.index(class_id) + masks[idx] += mask + masks[idx] = np.clip(masks[idx], 0, 1) + + num = 0 + for i, cls_name in enumerate(self.metadata["class_names"]): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) if classes else np.array([], dtype=np.int64) + # Stack masks into a 3D array (num_masks, H, W) to match torchvision version + if masks: + masks = np.stack(masks, axis=0) + else: + # Empty masks - use shape from first annotation mask if available + if annotation_masks and len(annotation_masks) > 0: + mask_shape = annotation_masks[0].shape[-2:] if hasattr(annotation_masks[0], "shape") else (0, 0) + else: + mask_shape = (0, 0) + masks = np.zeros((0, *mask_shape), dtype=np.float32) + return classes, masks, texts + + def get_instance_annotations(self, label, num_class_obj): + annotation_classes = label["classes"] + annotation_masks = label["masks"] + + texts = ["an instance photo"] * self.num_text + classes = [] + masks = [] + + for idx in range(len(annotation_classes)): + class_id = annotation_classes[idx] + mask = annotation_masks[idx] + + if class_id in self.metadata["thing_ids"]: + if not np.all(mask == 0): + cls_name = self.metadata[str(class_id)] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + + num = 0 + for i, cls_name in enumerate(self.metadata["class_names"]): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) if classes else np.array([], dtype=np.int64) + # Stack masks into a 3D array (num_masks, H, W) to match torchvision version + if masks: + masks = np.stack(masks, axis=0) + else: + # Empty masks - use shape from first annotation mask if available + if annotation_masks and len(annotation_masks) > 0: + mask_shape = annotation_masks[0].shape[-2:] if hasattr(annotation_masks[0], "shape") else (0, 0) + else: + mask_shape = (0, 0) + masks = np.zeros((0, *mask_shape), dtype=np.float32) + return classes, masks, texts + + def get_panoptic_annotations(self, label, num_class_obj): + annotation_classes = label["classes"] + annotation_masks = label["masks"] + + texts = ["an panoptic photo"] * self.num_text + classes = [] + masks = [] + for idx in range(len(annotation_classes)): + class_id = annotation_classes[idx] + mask = annotation_masks[idx] if hasattr(annotation_masks[idx], "data") else annotation_masks[idx] + if not np.all(mask == 0): + cls_name = self.metadata[str(class_id)] + classes.append(class_id) + masks.append(mask) + num_class_obj[cls_name] += 1 + + num = 0 + for i, cls_name in enumerate(self.metadata["class_names"]): + if num_class_obj[cls_name] > 0: + for _ in range(num_class_obj[cls_name]): + if num >= len(texts): + break + texts[num] = f"a photo with a {cls_name}" + num += 1 + + classes = np.array(classes) if classes else np.array([], dtype=np.int64) + # Stack masks into a 3D array (num_masks, H, W) to match torchvision version + if masks: + masks = np.stack(masks, axis=0) + else: + # Empty masks - use shape from first annotation mask if available + if annotation_masks and len(annotation_masks) > 0: + mask_shape = annotation_masks[0].shape[-2:] if hasattr(annotation_masks[0], "shape") else (0, 0) + else: + mask_shape = (0, 0) + masks = np.zeros((0, *mask_shape), dtype=np.float32) + return classes, masks, texts + + def encode_inputs( + self, + pixel_values_list: list[np.ndarray], + task_inputs: list[str] | None = None, + segmentation_maps: list[np.ndarray] | None = None, + instance_id_to_semantic_id: list[dict[int, int]] | dict[int, int] | None = None, + ignore_index: int | None = None, + do_reduce_labels: bool = False, + return_tensors: str | TensorType | None = None, + ) -> BatchFeature: + ignore_index = self.ignore_index if ignore_index is None else ignore_index + do_reduce_labels = self.do_reduce_labels if do_reduce_labels is None else do_reduce_labels + if task_inputs is None: + task_inputs = ["panoptic"] + pixel_values_list = self._prepare_image_like_inputs( + pixel_values_list, input_data_format=ChannelDimension.FIRST + ) + if segmentation_maps is not None: + segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + pad_size = get_max_height_width(pixel_values_list, input_data_format=ChannelDimension.FIRST) + encoded_inputs = self.pad(pixel_values_list, return_tensors=return_tensors) + + annotations = None + if segmentation_maps is not None: + annotations = [] + for idx, segmentation_map in enumerate(segmentation_maps): + # Use instance2class_id mapping per image + if isinstance(instance_id_to_semantic_id, list): + instance_id = instance_id_to_semantic_id[idx] + else: + instance_id = instance_id_to_semantic_id + + # Squeeze channel dimension if present + if segmentation_map.ndim == 3 and segmentation_map.shape[0] == 1: + segmentation_map = segmentation_map.squeeze(0) + + # Convert segmentation map to binary masks using numpy operations + masks, classes = self.convert_segmentation_map_to_binary_masks( + segmentation_map, instance_id, ignore_index=ignore_index, do_reduce_labels=do_reduce_labels + ) + + annotations.append({"masks": masks, "classes": classes}) + + if annotations is not None: + mask_labels = [] + class_labels = [] + text_inputs = [] + num_class_obj = dict.fromkeys(self.metadata["class_names"], 0) + + for i, label in enumerate(annotations): + task = task_inputs[i] + + if task == "semantic": + classes, masks, texts = self.get_semantic_annotations(label, num_class_obj) + elif task == "instance": + classes, masks, texts = self.get_instance_annotations(label, num_class_obj) + elif task == "panoptic": + classes, masks, texts = self.get_panoptic_annotations(label, num_class_obj) + else: + raise ValueError(f"{task} was not expected, expected `semantic`, `instance` or `panoptic`") + # Pad masks to max size using numpy operations + # masks is a 3D array (num_masks, H, W), iterate to get 2D slices + padded_masks = [ + self._pad_image(image=mask, output_size=pad_size, constant_values=ignore_index) for mask in masks + ] + # Stack padded masks back into 3D array (num_masks, padded_H, padded_W) + padded_masks = ( + np.stack(padded_masks, axis=0) if padded_masks else np.zeros((0, *pad_size), dtype=np.float32) + ) + mask_labels.append(padded_masks) + class_labels.append(classes) + text_inputs.append(texts) + + encoded_inputs["mask_labels"] = [ + torch.from_numpy(mask_label) if return_tensors == "pt" else mask_label for mask_label in mask_labels + ] + encoded_inputs["class_labels"] = [ + torch.from_numpy(class_label) if return_tensors == "pt" else class_label + for class_label in class_labels + ] + encoded_inputs["text_inputs"] = text_inputs + + encoded_inputs["task_inputs"] = [f"the task is {task_input}" for task_input in task_inputs] + return encoded_inputs + + def post_process_semantic_segmentation( + self, outputs, target_sizes: list[tuple[int, int]] | None = None + ) -> "torch.Tensor": + """ + Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports + PyTorch. + + Args: + outputs ([`MaskFormerForInstanceSegmentation`]): + Raw outputs of the model. + target_sizes (`List[Tuple[int, int]]`, *optional*): + List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested + final size (height, width) of each prediction. If left to None, predictions will not be resized. + Returns: + `List[torch.Tensor]`: + A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) + corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each + `torch.Tensor` correspond to a semantic class id. + """ + class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width] + + # Remove the null class `[..., :-1]` + masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1] + masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width] + + # Semantic segmentation logits of shape (batch_size, num_classes, height, width) + segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs) + batch_size = class_queries_logits.shape[0] + + # Resize logits and compute semantic segmentation maps + if target_sizes is not None: + if batch_size != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + + semantic_segmentation = [] + for idx in range(batch_size): + resized_logits = torch.nn.functional.interpolate( + segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False + ) + semantic_map = resized_logits[0].argmax(dim=0) + semantic_segmentation.append(semantic_map) + else: + semantic_segmentation = segmentation.argmax(dim=1) + semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] + + return semantic_segmentation + + def post_process_instance_segmentation( + self, + outputs, + task_type: str = "instance", + is_demo: bool = True, + threshold: float = 0.5, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + target_sizes: list[tuple[int, int]] | None = None, + return_coco_annotation: bool | None = False, + ): + """ + Converts the output of [`OneFormerForUniversalSegmentationOutput`] into image instance segmentation + predictions. Only supports PyTorch. + + Args: + outputs ([`OneFormerForUniversalSegmentationOutput`]): + The outputs from [`OneFormerForUniversalSegmentationOutput`]. + task_type (`str`, *optional*, defaults to "instance"): + The post processing depends on the task token input. If the `task_type` is "panoptic", we need to + ignore the stuff predictions. + is_demo (`bool`, *optional)*, defaults to `True`): + Whether the model is in demo mode. If true, use threshold to predict final masks. + threshold (`float`, *optional*, defaults to 0.5): + The probability score threshold to keep predicted instance masks. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold to use when turning the predicted masks into binary values. + overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8): + The overlap mask area threshold to merge or discard small disconnected parts within each binary + instance mask. + target_sizes (`List[Tuple]`, *optional*): + List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested + final size (height, width) of each prediction in batch. If left to None, predictions will not be + resized. + return_coco_annotation (`bool`, *optional)*, defaults to `False`): + Whether to return predictions in COCO format. + + Returns: + `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys: + - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set + to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized + to the corresponding `target_sizes` entry. + - **segments_info** -- A dictionary that contains additional information on each segment. + - **id** -- an integer representing the `segment_id`. + - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`. + - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise. + Multiple instances of the same class / label were fused and assigned a single `segment_id`. + - **score** -- Prediction score of segment with `segment_id`. + """ + class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width] + + device = masks_queries_logits.device + batch_size = class_queries_logits.shape[0] + num_queries = class_queries_logits.shape[1] + num_classes = class_queries_logits.shape[-1] - 1 + + # Loop over items in batch size + results: list[dict[str, torch.Tensor]] = [] + + for i in range(batch_size): + # [Q, K] + scores = nn.functional.softmax(class_queries_logits[i], dim=-1)[:, :-1] + labels = torch.arange(num_classes, device=device).unsqueeze(0).repeat(num_queries, 1).flatten(0, 1) + + # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False) + scores_per_image, topk_indices = scores.flatten(0, 1).topk(num_queries, sorted=False) + labels_per_image = labels[topk_indices] + + topk_indices = torch.div(topk_indices, num_classes, rounding_mode="floor") + # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1) + mask_pred = masks_queries_logits[i][topk_indices] + + # Only consider scores with confidence over [threshold] for demo + if is_demo: + keep = scores_per_image > threshold + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + mask_pred = mask_pred[keep] + + # if this is panoptic segmentation, we only keep the "thing" classes + if task_type == "panoptic": + keep = torch.zeros_like(scores_per_image).bool() + for j, lab in enumerate(labels_per_image): + keep[j] = lab in self.metadata["thing_ids"] + + scores_per_image = scores_per_image[keep] + labels_per_image = labels_per_image[keep] + mask_pred = mask_pred[keep] + + if mask_pred.shape[0] <= 0: + height, width = target_sizes[i] if target_sizes is not None else mask_pred.shape[1:] + segmentation = torch.zeros((height, width)) - 1 + results.append({"segmentation": segmentation, "segments_info": []}) + continue + + if "ade20k" in self.class_info_file and not is_demo and "instance" in task_type: + for j in range(labels_per_image.shape[0]): + labels_per_image[j] = self.metadata["thing_ids"].index(labels_per_image[j].item()) + + # Get segmentation map and segment information of batch item + target_size = target_sizes[i] if target_sizes is not None else None + segmentation, segments = compute_segments( + mask_pred, + scores_per_image, + labels_per_image, + mask_threshold, + overlap_mask_area_threshold, + set(), + target_size, + ) + + # Return segmentation map in run-length encoding (RLE) format + if return_coco_annotation: + segmentation = convert_segmentation_to_rle(segmentation) + + results.append({"segmentation": segmentation, "segments_info": segments}) + return results + + # Adapted from transformers.models.maskformer.image_processing_maskformer.MaskFormerImageProcessor.post_process_panoptic_segmentation + def post_process_panoptic_segmentation( + self, + outputs, + threshold: float = 0.5, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + label_ids_to_fuse: set[int] | None = None, + target_sizes: list[tuple[int, int]] | None = None, + ) -> list[dict]: + """ + Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation + predictions. Only supports PyTorch. + + Args: + outputs ([`MaskFormerForInstanceSegmentationOutput`]): + The outputs from [`MaskFormerForInstanceSegmentation`]. + threshold (`float`, *optional*, defaults to 0.5): + The probability score threshold to keep predicted instance masks. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold to use when turning the predicted masks into binary values. + overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8): + The overlap mask area threshold to merge or discard small disconnected parts within each binary + instance mask. + label_ids_to_fuse (`Set[int]`, *optional*): + The labels in this state will have all their instances be fused together. For instance we could say + there can only be one sky in an image, but several persons, so the label ID for sky would be in that + set, but not the one for person. + target_sizes (`list[Tuple]`, *optional*): + List of length (batch_size), where each list item (`tuple[int, int]]`) corresponds to the requested + final size (height, width) of each prediction in batch. If left to None, predictions will not be + resized. + + Returns: + `list[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys: + - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id`, set + to `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized + to the corresponding `target_sizes` entry. + - **segments_info** -- A dictionary that contains additional information on each segment. + - **id** -- an integer representing the `segment_id`. + - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`. + - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise. + Multiple instances of the same class / label were fused and assigned a single `segment_id`. + - **score** -- Prediction score of segment with `segment_id`. + """ + + if label_ids_to_fuse is None: + logger.warning("`label_ids_to_fuse` unset. No instance will be fused.") + label_ids_to_fuse = set() + + class_queries_logits = outputs.class_queries_logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.masks_queries_logits # [batch_size, num_queries, height, width] + + batch_size = class_queries_logits.shape[0] + num_labels = class_queries_logits.shape[-1] - 1 + + mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width] + + # Predicted label and score of each query (batch_size, num_queries) + pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1) + + # Loop over items in batch size + results: list[dict[str, TensorType]] = [] + + for i in range(batch_size): + mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects( + mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels + ) + + # No mask found + if mask_probs_item.shape[0] <= 0: + height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:] + segmentation = torch.zeros((height, width)) - 1 + results.append({"segmentation": segmentation, "segments_info": []}) + continue + + # Get segmentation map and segment information of batch item + target_size = target_sizes[i] if target_sizes is not None else None + segmentation, segments = compute_segments( + mask_probs=mask_probs_item, + pred_scores=pred_scores_item, + pred_labels=pred_labels_item, + mask_threshold=mask_threshold, + overlap_mask_area_threshold=overlap_mask_area_threshold, + label_ids_to_fuse=label_ids_to_fuse, + target_size=target_size, + ) + + results.append({"segmentation": segmentation, "segments_info": segments}) + return results + + +__all__ = ["OneFormerImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/modeling_oneformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/modeling_oneformer.py new file mode 100644 index 0000000000000000000000000000000000000000..fb5a159c1bef145ab39ea88b465b58ae7e109119 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/modeling_oneformer.py @@ -0,0 +1,3182 @@ +# Copyright 2022 SHI Labs and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch OneFormer model.""" + +import copy +import math +import warnings +from dataclasses import dataclass + +import numpy as np +import torch +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...backbone_utils import load_backbone +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import ( + ModelOutput, + auto_docstring, + is_accelerate_available, + is_scipy_available, + logging, + requires_backends, + torch_compilable_check, +) +from ...utils.generic import maybe_autocast +from .configuration_oneformer import OneFormerConfig + + +if is_accelerate_available(): + from accelerate import PartialState + from accelerate.utils import reduce + +logger = logging.get_logger(__name__) + + +if is_scipy_available(): + from scipy.optimize import linear_sum_assignment + + +def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +def multi_scale_deformable_attention( + value: Tensor, + value_spatial_shapes: Tensor | list[tuple], + sampling_locations: Tensor, + attention_weights: Tensor, +) -> Tensor: + batch_size, _, num_heads, hidden_dim = value.shape + _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape + value_list = value.split([height * width for height, width in value_spatial_shapes], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level_id, (height, width) in enumerate(value_spatial_shapes): + # batch_size, height*width, num_heads, hidden_dim + # -> batch_size, height*width, num_heads*hidden_dim + # -> batch_size, num_heads*hidden_dim, height*width + # -> batch_size*num_heads, hidden_dim, height, width + value_l_ = ( + value_list[level_id].flatten(2).transpose(1, 2).reshape(batch_size * num_heads, hidden_dim, height, width) + ) + # batch_size, num_queries, num_heads, num_points, 2 + # -> batch_size, num_heads, num_queries, num_points, 2 + # -> batch_size*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1) + # batch_size*num_heads, hidden_dim, num_queries, num_points + sampling_value_l_ = nn.functional.grid_sample( + value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False + ) + sampling_value_list.append(sampling_value_l_) + # (batch_size, num_queries, num_heads, num_levels, num_points) + # -> (batch_size, num_heads, num_queries, num_levels, num_points) + # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.transpose(1, 2).reshape( + batch_size * num_heads, 1, num_queries, num_levels * num_points + ) + output = ( + (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) + .sum(-1) + .view(batch_size, num_heads * hidden_dim, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +# Copied from transformers.models.maskformer.modeling_maskformer.dice_loss +def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor: + r""" + Compute the DICE loss, similar to generalized IOU for masks as follows: + + $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$ + + In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow + + $$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * y }{x + y + 1}} $$ + + Args: + inputs (`torch.Tensor`): + A tensor representing a mask. + labels (`torch.Tensor`): + A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs + (0 for the negative class and 1 for the positive class). + num_masks (`int`): + The number of masks present in the current batch, used for normalization. + + Returns: + `torch.Tensor`: The computed loss. + """ + probs = inputs.sigmoid().flatten(1) + numerator = 2 * (probs * labels).sum(-1) + denominator = probs.sum(-1) + labels.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + loss = loss.sum() / num_masks + return loss + + +# Copied from transformers.models.mask2former.modeling_mask2former.sigmoid_cross_entropy_loss +def sigmoid_cross_entropy_loss(inputs: torch.Tensor, labels: torch.Tensor, num_masks: int) -> torch.Tensor: + r""" + Args: + inputs (`torch.Tensor`): + A float tensor of arbitrary shape. + labels (`torch.Tensor`): + A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs + (0 for the negative class and 1 for the positive class). + + Returns: + loss (`torch.Tensor`): The computed loss. + """ + criterion = nn.BCEWithLogitsLoss(reduction="none") + cross_entropy_loss = criterion(inputs, labels) + + loss = cross_entropy_loss.mean(1).sum() / num_masks + return loss + + +# Copied from transformers.models.maskformer.modeling_maskformer.pair_wise_dice_loss +def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor: + """ + A pair wise version of the dice loss, see `dice_loss` for usage. + + Args: + inputs (`torch.Tensor`): + A tensor representing a mask + labels (`torch.Tensor`): + A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs + (0 for the negative class and 1 for the positive class). + + Returns: + `torch.Tensor`: The computed loss between each pairs. + """ + inputs = inputs.sigmoid().flatten(1) + numerator = 2 * torch.matmul(inputs, labels.T) + # using broadcasting to get a [num_queries, NUM_CLASSES] matrix + denominator = inputs.sum(-1)[:, None] + labels.sum(-1)[None, :] + loss = 1 - (numerator + 1) / (denominator + 1) + return loss + + +# Copied from transformers.models.mask2former.modeling_mask2former.pair_wise_sigmoid_cross_entropy_loss +def pair_wise_sigmoid_cross_entropy_loss(inputs: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + r""" + A pair wise version of the cross entropy loss, see `sigmoid_cross_entropy_loss` for usage. + + Args: + inputs (`torch.Tensor`): + A tensor representing a mask. + labels (`torch.Tensor`): + A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs + (0 for the negative class and 1 for the positive class). + + Returns: + loss (`torch.Tensor`): The computed loss between each pairs. + """ + + height_and_width = inputs.shape[1] + + criterion = nn.BCEWithLogitsLoss(reduction="none") + cross_entropy_loss_pos = criterion(inputs, torch.ones_like(inputs)) + cross_entropy_loss_neg = criterion(inputs, torch.zeros_like(inputs)) + + loss_pos = torch.matmul(cross_entropy_loss_pos / height_and_width, labels.T) + loss_neg = torch.matmul(cross_entropy_loss_neg / height_and_width, (1 - labels).T) + loss = loss_pos + loss_neg + return loss + + +# Copied from transformers.models.mask2former.modeling_mask2former.sample_point +def sample_point( + input_features: torch.Tensor, point_coordinates: torch.Tensor, add_dim=False, **kwargs +) -> torch.Tensor: + """ + A wrapper around `torch.nn.functional.grid_sample` to support 3D point_coordinates tensors. + + Args: + input_features (`torch.Tensor` of shape (batch_size, channels, height, width)): + A tensor that contains features map on a height * width grid + point_coordinates (`torch.Tensor` of shape (batch_size, num_points, 2) or (batch_size, grid_height, grid_width,: + 2)): + A tensor that contains [0, 1] * [0, 1] normalized point coordinates + add_dim (`bool`): + boolean value to keep track of added dimension + + Returns: + point_features (`torch.Tensor` of shape (batch_size, channels, num_points) or (batch_size, channels, + height_grid, width_grid): + A tensor that contains features for points in `point_coordinates`. + """ + if point_coordinates.dim() == 3: + add_dim = True + point_coordinates = point_coordinates.unsqueeze(2) + + # use nn.function.grid_sample to get features for points in `point_coordinates` via bilinear interpolation + point_features = torch.nn.functional.grid_sample(input_features, 2.0 * point_coordinates - 1.0, **kwargs) + if add_dim: + point_features = point_features.squeeze(3) + + return point_features + + +# Refactored from https://github.com/SHI-Labs/OneFormer/blob/33ebb56ed34f970a30ae103e786c0cb64c653d9a/oneformer/modeling/matcher.py#L93 +class OneFormerHungarianMatcher(nn.Module): + def __init__( + self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0, num_points: int = 12544 + ): + """This class computes an assignment between the labels and the predictions of the network. + + For efficiency reasons, the labels don't include the no_object. Because of this, in general, there are more + predictions than labels. In this case, we do a 1-to-1 matching of the best predictions, while the others are + un-matched (and thus treated as non-objects). + + Params: + cost_class (float, *optional*, defaults to 1.0): + This is the relative weight of the classification error in the matching cost. + cost_mask (float, *optional*, defaults to 1.0): + This is the relative weight of the sigmoid ce loss of the binary mask in the matching cost. + cost_dice (float, *optional*, defaults to 1.0): + This is the relative weight of the dice loss of the binary mask in the matching cost + num_points (int, *optional*, defaults to 12544): + Number of points to be sampled for dice and mask loss matching cost. + """ + super().__init__() + if cost_class == 0 and cost_mask == 0 and cost_dice == 0: + raise ValueError("All costs can't be 0") + self.cost_class = cost_class + self.cost_mask = cost_mask + self.cost_dice = cost_dice + self.num_points = num_points + + @torch.no_grad() + def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> list[tuple[Tensor]]: + """Performs the matching + + Params: + masks_queries_logits (`torch.Tensor`): + A tensor` of dim `batch_size, num_queries, num_labels` with the + classification logits. + class_queries_logits (`torch.Tensor`): + A tensor` of dim `batch_size, num_queries, height, width` with the + predicted masks. + + class_labels (`torch.Tensor`): + A tensor` of dim `num_target_boxes` (where num_target_boxes is the number + of ground-truth objects in the target) containing the class labels. + mask_labels (`torch.Tensor`): + A tensor` of dim `num_target_boxes, height, width` containing the target + masks. + + Returns: + `list[tuple[Tensor]]`: A list of size batch_size, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected labels (in order) + For each batch element, it holds: + len(index_i) = len(index_j) = min(num_queries, num_targets). + """ + indices: list[tuple[np.array]] = [] + + num_queries = class_queries_logits.shape[1] + + preds_masks = masks_queries_logits + preds_probs = class_queries_logits + # iterate through batch size + for pred_probs, pred_mask, target_mask, labels in zip(preds_probs, preds_masks, mask_labels, class_labels): + pred_probs = pred_probs.softmax(-1) + # Compute the classification cost. Contrary to the loss, we don't use the NLL, + # but approximate it in 1 - proba[target class]. + # The 1 is a constant that doesn't change the matching, it can be omitted. + cost_class = -pred_probs[:, labels] + + pred_mask = pred_mask[:, None] + target_mask = target_mask[:, None].to(pred_mask.device) + + # all masks share the same set of points for efficient matching! + point_coords = torch.rand(1, self.num_points, 2, device=pred_mask.device) + + # get ground truth labels + target_mask = sample_point( + target_mask, + point_coords.repeat(target_mask.shape[0], 1, 1), + align_corners=False, + ).squeeze(1) + + pred_mask = sample_point( + pred_mask, + point_coords.repeat(pred_mask.shape[0], 1, 1), + align_corners=False, + ).squeeze(1) + + with maybe_autocast(device_type="cuda", enabled=False): + pred_mask = pred_mask.float() + target_mask = target_mask.float() + + # compute the sigmoid ce loss + cost_mask = pair_wise_sigmoid_cross_entropy_loss(pred_mask, target_mask) + # Compute the dice loss + cost_dice = pair_wise_dice_loss(pred_mask, target_mask) + # final cost matrix + cost_matrix = self.cost_mask * cost_mask + self.cost_class * cost_class + self.cost_dice * cost_dice + cost_matrix = cost_matrix.reshape(num_queries, -1).cpu() + # do the assigmented using the hungarian algorithm in scipy + assigned_indices: tuple[np.array] = linear_sum_assignment(cost_matrix.cpu()) + indices.append(assigned_indices) + + # It could be stacked in one tensor + matched_indices = [ + (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices + ] + return matched_indices + + +class OneFormerLoss(nn.Module): + def __init__( + self, + num_classes: int, + matcher: OneFormerHungarianMatcher, + weight_dict: dict[str, float], + eos_coef: float, + num_points: int, + oversample_ratio: float, + importance_sample_ratio: float, + contrastive_temperature: float | None = None, + ): + """ + This class computes the losses using the class predictions, mask predictions and the contrastive queries. + + Oneformer calculates the classification CE loss on the class predictions. Mask predictions are used for + calculating the binary CE loss and dice loss. The contrastive queries are used for calculating the contrastive + loss. + + Args: + num_labels (`int`): + The number of classes. + matcher (`OneFormerHungarianMatcher`): + A torch module that computes the assignments between the predictions and labels. + weight_dict (`dict[str, float]`): + A dictionary of weights to be applied to the different losses. + eos_coef (`float`): + Weight to apply to the null class. + num_points (`int`): + Number of points to be sampled for dice and mask loss calculations. + oversample_ratio (`float`): + Required for pointwise loss calculation. + importance_sample_ratio (`float`): + Required for pointwise loss calculation. + contrastive_temperature (`float`): + Temperature for scaling the contrastive logits. + """ + requires_backends(self, ["scipy"]) + super().__init__() + self.num_classes = num_classes + self.matcher = matcher + self.weight_dict = weight_dict + self.eos_coef = eos_coef + empty_weight = torch.ones(self.num_classes + 1) + empty_weight[-1] = self.eos_coef + self.register_buffer("empty_weight", empty_weight) + + # pointwise mask loss parameters + self.num_points = num_points + self.oversample_ratio = oversample_ratio + self.importance_sample_ratio = importance_sample_ratio + self.contrastive_temperature = contrastive_temperature + if self.contrastive_temperature is not None: + self.logit_scale = nn.Parameter(torch.tensor(np.log(1 / contrastive_temperature))) + + def _max_by_axis(self, the_list: list[list[int]]) -> list[int]: + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + def _pad_images_to_max_in_batch(self, tensors: list[Tensor]) -> tuple[Tensor, Tensor]: + # get the maximum size in the batch + max_size = self._max_by_axis([list(tensor.shape) for tensor in tensors]) + batch_size = len(tensors) + # compute finel size + batch_shape = [batch_size] + max_size + b, _, h, w = batch_shape + # get metadata + dtype = tensors[0].dtype + device = tensors[0].device + padded_tensors = torch.zeros(batch_shape, dtype=dtype, device=device) + padding_masks = torch.ones((b, h, w), dtype=torch.bool, device=device) + # pad the tensors to the size of the biggest one + for tensor, padded_tensor, padding_mask in zip(tensors, padded_tensors, padding_masks): + padded_tensor[: tensor.shape[0], : tensor.shape[1], : tensor.shape[2]].copy_(tensor) + padding_mask[: tensor.shape[1], : tensor.shape[2]] = False + + return padded_tensors, padding_masks + + def loss_contrastive(self, contrastive_queries_logits: Tensor, text_queries: Tensor): + """Compute the query-text contrastive loss. + + Args: + contrastive_queries_logits (`torch.Tensor`): + A tensor of shape `batch_size, num_queries, hidden_dim` + text_queries (`torch.Tensor`): + A tensor of shape `batch_size, num_queries, hidden_dim` + Returns: + `dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key: + - **loss_contrastive** -- The query-text contrastive loss computed using task-guided queries + and text queries derived from input text list. + """ + + image_queries = contrastive_queries_logits.float() + + # [batch_size, hidden_dim] + image_queries = nn.functional.normalize(image_queries.flatten(1), dim=-1) + text_queries = nn.functional.normalize(text_queries.flatten(1), dim=-1) + + logit_scale = torch.clamp(self.logit_scale.exp(), max=100) + + logits_per_text = torch.matmul(text_queries, image_queries.t()) * logit_scale + logits_per_img = logits_per_text.t() + + loss_img = nn.functional.cross_entropy( + logits_per_img, torch.arange(len(logits_per_img), device=logits_per_text.device) + ) + loss_text = nn.functional.cross_entropy( + logits_per_text, torch.arange(len(logits_per_text), device=logits_per_text.device) + ) + + loss_contrastive = loss_img + loss_text + + losses = {"loss_contrastive": loss_contrastive} + return losses + + def loss_labels( + self, class_queries_logits: Tensor, class_labels: list[Tensor], indices: tuple[np.array] + ) -> dict[str, Tensor]: + """Compute the losses related to the labels using cross entropy. + + Args: + class_queries_logits (`torch.Tensor`): + A tensor of shape `batch_size, num_queries, num_labels` + class_labels (`list[torch.Tensor]`): + List of class labels of shape `(labels)`. + indices (`tuple[np.array])`: + The indices computed by the Hungarian matcher. + + Returns: + `dict[str, Tensor]`: A dict of `torch.Tensor` containing the following key: + - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels. + """ + pred_logits = class_queries_logits + batch_size, num_queries, _ = pred_logits.shape + criterion = nn.CrossEntropyLoss(weight=self.empty_weight) + idx = self._get_predictions_permutation_indices(indices) + + # shape = (batch_size, num_queries) + target_classes_o = torch.cat([target[j] for target, (_, j) in zip(class_labels, indices)]) + # shape = (batch_size, num_queries) + target_classes = torch.full( + (batch_size, num_queries), fill_value=self.num_classes, dtype=torch.int64, device=pred_logits.device + ) + target_classes[idx] = target_classes_o + # permute pred_logits (batch_size, num_queries, num_labels) -> (batch_size, num_labels, num_queries) + pred_logits_transposed = pred_logits.transpose(1, 2) + loss_ce = criterion(pred_logits_transposed, target_classes) + losses = {"loss_cross_entropy": loss_ce} + return losses + + def loss_masks( + self, masks_queries_logits: Tensor, mask_labels: list[Tensor], indices: tuple[np.array], num_masks: int + ) -> dict[str, Tensor]: + """Compute the losses related to the masks using focal and dice loss. + + Args: + masks_queries_logits (`torch.Tensor`): + A tensor of shape `batch_size, num_queries, height, width` + mask_labels (`torch.Tensor`): + List of mask labels of shape `(labels, height, width)`. + indices (`tuple[np.array])`: + The indices computed by the Hungarian matcher. + num_masks (`int)`: + The number of masks, used for normalization. + + Returns: + `dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys: + - **loss_mask** -- The loss computed using sigmoid ce loss on the predicted and ground truth masks. + - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth + masks. + """ + src_idx = self._get_predictions_permutation_indices(indices) + tgt_idx = self._get_targets_permutation_indices(indices) + # shape (batch_size * num_queries, height, width) + pred_masks = masks_queries_logits[src_idx] + # shape (batch_size, num_queries, height, width) + # pad all and stack the targets to the num_labels dimension + # upsample predictions to the target size, we have to add one dim to use interpolate + target_masks, _ = self._pad_images_to_max_in_batch(mask_labels) + target_masks = target_masks[tgt_idx] + + pred_masks = pred_masks[:, None] + target_masks = target_masks[:, None] + + with torch.no_grad(): + # sample point_coords + point_coords = self.sample_points_using_uncertainty( + pred_masks, + self.calculate_uncertainty, + self.num_points, + self.oversample_ratio, + self.importance_sample_ratio, + ) + # get ground-truth labels + point_labels = sample_point(target_masks, point_coords, align_corners=False).squeeze(1) + + point_logits = sample_point(pred_masks, point_coords, align_corners=False).squeeze(1) + + losses = { + "loss_mask": sigmoid_cross_entropy_loss(point_logits, point_labels, num_masks), + "loss_dice": dice_loss(point_logits, point_labels, num_masks), + } + + del pred_masks + del target_masks + return losses + + # Copied from transformers.models.mask2former.modeling_mask2former.Mask2FormerLoss.calculate_uncertainty + def calculate_uncertainty(self, logits: torch.Tensor) -> torch.Tensor: + """ + In Mask2Former paper, uncertainty is estimated as L1 distance between 0.0 and the logit prediction in 'logits' + for the foreground class in `classes`. + + Args: + logits (`torch.Tensor`): + A tensor of shape (R, 1, ...) for class-specific or class-agnostic, where R is the total number of predicted masks in all images and C is: + the number of foreground classes. The values are logits. + + Returns: + scores (`torch.Tensor`): A tensor of shape (R, 1, ...) that contains uncertainty scores with the most + uncertain locations having the highest uncertainty score. + """ + uncertainty_scores = -(torch.abs(logits)) + return uncertainty_scores + + # Copied from transformers.models.mask2former.modeling_mask2former.Mask2FormerLoss.sample_points_using_uncertainty + def sample_points_using_uncertainty( + self, + logits: torch.Tensor, + uncertainty_function, + num_points: int, + oversample_ratio: int, + importance_sample_ratio: float, + ) -> torch.Tensor: + """ + This function is meant for sampling points in [0, 1] * [0, 1] coordinate space based on their uncertainty. The + uncertainty is calculated for each point using the passed `uncertainty function` that takes points logit + prediction as input. + + Args: + logits (`float`): + Logit predictions for P points. + uncertainty_function: + A function that takes logit predictions for P points and returns their uncertainties. + num_points (`int`): + The number of points P to sample. + oversample_ratio (`int`): + Oversampling parameter. + importance_sample_ratio (`float`): + Ratio of points that are sampled via importance sampling. + + Returns: + point_coordinates (`torch.Tensor`): + Coordinates for P sampled points. + """ + + num_boxes = logits.shape[0] + num_points_sampled = int(num_points * oversample_ratio) + + # Get random point coordinates + point_coordinates = torch.rand(num_boxes, num_points_sampled, 2, device=logits.device) + # Get sampled prediction value for the point coordinates + point_logits = sample_point(logits, point_coordinates, align_corners=False) + # Calculate the uncertainties based on the sampled prediction values of the points + point_uncertainties = uncertainty_function(point_logits) + + num_uncertain_points = int(importance_sample_ratio * num_points) + num_random_points = num_points - num_uncertain_points + + idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1] + shift = num_points_sampled * torch.arange(num_boxes, dtype=torch.long, device=logits.device) + idx += shift[:, None] + point_coordinates = point_coordinates.view(-1, 2)[idx.view(-1), :].view(num_boxes, num_uncertain_points, 2) + + if num_random_points > 0: + point_coordinates = torch.cat( + [point_coordinates, torch.rand(num_boxes, num_random_points, 2, device=logits.device)], + dim=1, + ) + return point_coordinates + + def _get_predictions_permutation_indices(self, indices): + # permute predictions following indices + batch_indices = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)]) + predictions_indices = torch.cat([src for (src, _) in indices]) + return batch_indices, predictions_indices + + def _get_targets_permutation_indices(self, indices): + # permute labels following indices + batch_indices = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)]) + target_indices = torch.cat([tgt for (_, tgt) in indices]) + return batch_indices, target_indices + + def forward( + self, + masks_queries_logits: Tensor, + class_queries_logits: Tensor, + contrastive_queries_logits: Tensor, + mask_labels: list[Tensor], + class_labels: list[Tensor], + text_queries: Tensor, + auxiliary_predictions: dict[str, Tensor] | None = None, + calculate_contrastive_loss: bool = True, + ) -> dict[str, Tensor]: + """ + This performs the loss computation. + + Args: + masks_queries_logits (`torch.Tensor`): + A tensor of shape `batch_size, num_queries, height, width` + class_queries_logits (`torch.Tensor`): + A tensor of shape `batch_size, num_queries, num_labels` + contrastive_queries_logits (`torch.Tensor`): + A tensor of shape `batch_size, num_queries, hidden_dim` + mask_labels (`torch.Tensor`): + List of mask labels of shape `(labels, height, width)`. + class_labels (`list[torch.Tensor]`): + List of class labels of shape `(labels)`. + text_queries (`torch.Tensor`): + A tensor of shape `batch_size, num_queries, hidden_dim` + auxiliary_predictions (`dict[str, torch.Tensor]`, *optional*): + if `use_auxiliary_loss` was set to `true` in [`OneFormerConfig`], then it contains the logits from the + inner layers of the Detr's Decoder. + calculate_contrastive_loss (`bool`, *optional*, defaults to `True`): + Whether or not to calculate the contrastive loss. + + Returns: + `dict[str, Tensor]`: A dict of `torch.Tensor` containing two keys: + - **loss_cross_entropy** -- The loss computed using cross entropy on the predicted and ground truth labels. + - **loss_mask** -- The loss computed using sigmoid ce loss on the predicted and ground truth masks. + - **loss_dice** -- The loss computed using dice loss on the predicted on the predicted and ground truth + masks. + - **loss_contrastive** -- The query-text contrstive loss computed using object and text queries. + if `use_auxiliary_loss` was set to `true` in [`OneFormerConfig`], the dictionary contains additional losses + for each auxiliary predictions. + """ + + # retrieve the matching between the outputs of the last layer and the labels + indices = self.matcher(masks_queries_logits, class_queries_logits, mask_labels, class_labels) + # compute the average number of target masks for normalization purposes + num_masks = self.get_num_masks(class_labels, device=class_labels[0].device) + # get all the losses + losses: dict[str, Tensor] = { + **self.loss_masks(masks_queries_logits, mask_labels, indices, num_masks), + **self.loss_labels(class_queries_logits, class_labels, indices), + } + if calculate_contrastive_loss: + losses = {**losses, **self.loss_contrastive(contrastive_queries_logits, text_queries)} + + # in case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if auxiliary_predictions is not None: + for idx, aux_outputs in enumerate(auxiliary_predictions): + masks_queries_logits = aux_outputs["masks_queries_logits"] + class_queries_logits = aux_outputs["class_queries_logits"] + loss_dict = self.forward( + masks_queries_logits, + class_queries_logits, + None, + mask_labels, + class_labels, + None, + calculate_contrastive_loss=False, + ) + loss_dict = {f"{key}_{idx}": value for key, value in loss_dict.items()} + losses.update(loss_dict) + + return losses + + def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor: + """ + Computes the average number of target masks across the batch, for normalization purposes. + """ + num_masks = sum(len(classes) for classes in class_labels) + num_masks = torch.as_tensor([num_masks], dtype=torch.float, device=device) + world_size = 1 + if is_accelerate_available(): + if PartialState._shared_state != {}: + num_masks = reduce(num_masks) + world_size = PartialState().num_processes + + num_masks = torch.clamp(num_masks / world_size, min=1) + return num_masks + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for outputs of the Transformer decoder. This class adds attributes for class predictions, mask + predictions and contrastive logits to BaseModelOutputWithCrossAttentions. + """ +) +class OneFormerTransformerDecoderOutput(BaseModelOutput): + r""" + object_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`): + Queries representation for the region proposals. + contrastive_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`): + Queries representation for the contrastive loss. + prediction_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`): + Mask predictions from last layer of the transformer decoder. + prediction_class (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes+1)`): + Class predictions from last layer of the transformer decoder. + auxiliary_predictions (Tuple of Dict of `str, torch.FloatTensor`, *optional*): + Tuple of class and mask predictions from each layer of the transformer decoder. + """ + + object_queries: torch.FloatTensor | None = None + contrastive_logits: torch.FloatTensor | None = None + prediction_masks: torch.FloatTensor | None = None + prediction_class: torch.FloatTensor | None = None + auxiliary_predictions: tuple[dict[str, torch.FloatTensor]] | None = None + + +@dataclass +@auto_docstring( + custom_intro=""" + OneFormer's pixel decoder module output, practically a Multi-Scale Deformable Attention based decoder. It returns + the mask features and the multiscale features. + """ +) +# Copied from transformers.models.mask2former.modeling_mask2former.Mask2FormerPixelDecoderOutput with Mask2->One +class OneFormerPixelDecoderOutput(ModelOutput): + r""" + multi_scale_features (`tuple(torch.FloatTensor)`): + Tuple of multi-scale features of scales [1/8, 1/16, 1/32] and shape `(batch_size, num_channels, height, + width)`from the Multi-Scale Deformable Attenntion based Pixel Decoder. + mask_features (`torch.FloatTensor`): + Tensor of shape `(batch_size, num_channels, height, width)`, 1/4 scale features from the last Pixel Decoder + Layer. + attentions (`tuple(torch.FloatTensor)`, *optional*): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights from pixel decoder. Returned when `output_attentions=True` is passed + or when `config.output_attentions=True` + """ + + multi_scale_features: tuple[torch.FloatTensor] | None = None + mask_features: torch.FloatTensor | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + OneFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the + `encoder` and `decoder`. By default, the `encoder` is a Swin/Dinat Backbone and the `decoder` is a Multi-Scale + Deformable Attention based decoder. + """ +) +@dataclass +class OneFormerPixelLevelModuleOutput(ModelOutput): + r""" + encoder_features (List of `(torch.FloatTensor)`): + List of `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`. Hidden-states (also + called feature maps) of the model at the output of each stage. + decoder_features (List of `(torch.FloatTensor)`): + List of `torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`. Hidden-states (also + called feature maps) of the model at the output of each stage. + decoder_last_feature (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)): + 1/4 scale features from the last Pixel Decoder Layer. + """ + + encoder_features: list[torch.FloatTensor] | None = None + decoder_features: list[torch.FloatTensor] | None = None + decoder_last_feature: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Class for outputs of [`OneFormerModel`]. This class returns all the needed hidden states to compute the logits. + """ +) +@dataclass +class OneFormerModelOutput(ModelOutput): + r""" + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of + shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder + model at the output of each stage. + pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of + shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel + decoder model at the output of each stage. + transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the + transformer decoder at the output of each stage. + transformer_decoder_object_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`): + Output object queries from the last layer in the transformer decoder. + transformer_decoder_contrastive_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`): + Contrastive queries from the transformer decoder. + transformer_decoder_mask_predictions (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`): + Mask Predictions from the last layer in the transformer decoder. + transformer_decoder_class_predictions (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes+1)`): + Class Predictions from the last layer in the transformer decoder. + transformer_decoder_auxiliary_predictions (Tuple of Dict of `str, torch.FloatTensor`, *optional*): + Tuple of class and mask predictions from each layer of the transformer decoder. + text_queries (`torch.FloatTensor`, *optional* of shape `(batch_size, num_queries, hidden_dim)`): + Text queries derived from the input text list used for calculating contrastive loss during training. + task_token (`torch.FloatTensor` of shape `(batch_size, hidden_dim)`): + 1D task token to condition the queries. + attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Self and Cross Attentions weights from transformer decoder. + """ + + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + pixel_decoder_hidden_states: tuple[torch.FloatTensor] | None = None + transformer_decoder_hidden_states: torch.FloatTensor | None = None + transformer_decoder_object_queries: torch.FloatTensor | None = None + transformer_decoder_contrastive_queries: torch.FloatTensor | None = None + transformer_decoder_mask_predictions: torch.FloatTensor | None = None + transformer_decoder_class_predictions: torch.FloatTensor | None = None + transformer_decoder_auxiliary_predictions: tuple[dict[str, torch.FloatTensor]] | None = None + text_queries: torch.FloatTensor | None = None + task_token: torch.FloatTensor | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@dataclass +@auto_docstring( + custom_intro=""" + Class for outputs of [`OneFormerForUniversalSegmentationOutput`]. + + This output can be directly passed to [`~OneFormerImageProcessor.post_process_semantic_segmentation`] or + [`~OneFormerImageProcessor.post_process_instance_segmentation`] or + [`~OneFormerImageProcessor.post_process_panoptic_segmentation`] depending on the task. Please, see + [`~OneFormerImageProcessor] for details regarding usage. + """ +) +class OneFormerForUniversalSegmentationOutput(ModelOutput): + r""" + loss (`torch.Tensor`, *optional*): + The computed loss, returned when labels are present. + class_queries_logits (`torch.FloatTensor`): + A tensor of shape `(batch_size, num_queries, num_labels + 1)` representing the proposed classes for each + query. Note the `+ 1` is needed because we incorporate the null class. + masks_queries_logits (`torch.FloatTensor`): + A tensor of shape `(batch_size, num_queries, height, width)` representing the proposed masks for each + query. + auxiliary_predictions (List of Dict of `str, torch.FloatTensor`, *optional*): + List of class and mask predictions from each layer of the transformer decoder. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of + shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the encoder + model at the output of each stage. + pixel_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of + shape `(batch_size, num_channels, height, width)`. Hidden-states (also called feature maps) of the pixel + decoder model at the output of each stage. + transformer_decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each stage) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the + transformer decoder at the output of each stage. + transformer_decoder_object_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`): + Output object queries from the last layer in the transformer decoder. + transformer_decoder_contrastive_queries (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_dim)`): + Contrastive queries from the transformer decoder. + transformer_decoder_mask_predictions (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`): + Mask Predictions from the last layer in the transformer decoder. + transformer_decoder_class_predictions (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes+1)`): + Class Predictions from the last layer in the transformer decoder. + transformer_decoder_auxiliary_predictions (List of Dict of `str, torch.FloatTensor`, *optional*): + List of class and mask predictions from each layer of the transformer decoder. + text_queries (`torch.FloatTensor`, *optional* of shape `(batch_size, num_queries, hidden_dim)`): + Text queries derived from the input text list used for calculating contrastive loss during training. + task_token (`torch.FloatTensor` of shape `(batch_size, hidden_dim)`): + 1D task token to condition the queries. + attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `tuple(torch.FloatTensor)` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Self and Cross Attentions weights from transformer decoder. + """ + + loss: torch.FloatTensor | None = None + class_queries_logits: torch.FloatTensor | None = None + masks_queries_logits: torch.FloatTensor | None = None + auxiliary_predictions: list[dict[str, torch.FloatTensor]] = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + pixel_decoder_hidden_states: list[torch.FloatTensor] | None = None + transformer_decoder_hidden_states: torch.FloatTensor | None = None + transformer_decoder_object_queries: torch.FloatTensor | None = None + transformer_decoder_contrastive_queries: torch.FloatTensor | None = None + transformer_decoder_mask_predictions: torch.FloatTensor | None = None + transformer_decoder_class_predictions: torch.FloatTensor | None = None + transformer_decoder_auxiliary_predictions: list[dict[str, torch.FloatTensor]] | None = None + text_queries: torch.FloatTensor | None = None + task_token: torch.FloatTensor | None = None + attentions: tuple[tuple[torch.FloatTensor]] | None = None + + +# Modified from transformers.models.detr.modeling_deformable_detr.DeformableDetrMultiscaleDeformableAttention with DeformableDetr->OneFormerPixelDecoderEncoder +class OneFormerPixelDecoderEncoderMultiscaleDeformableAttention(nn.Module): + """ + Multiscale deformable attention as proposed in Deformable DETR. + """ + + def __init__(self, embed_dim: int, num_heads: int, n_levels: int, n_points: int): + super().__init__() + if embed_dim % num_heads != 0: + raise ValueError( + f"embed_dim (d_model) must be divisible by num_heads, but got {embed_dim} and {num_heads}" + ) + dim_per_head = embed_dim // num_heads + # check if dim_per_head is power of 2 + if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): + warnings.warn( + "You'd better set embed_dim (d_model) in DeformableDetrMultiscaleDeformableAttention to make the" + " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" + " implementation." + ) + + self.im2col_step = 128 + + self.d_model = embed_dim + self.n_levels = n_levels + self.n_heads = num_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(embed_dim, num_heads * n_levels * n_points * 2) + self.attention_weights = nn.Linear(embed_dim, num_heads * n_levels * n_points) + self.value_proj = nn.Linear(embed_dim, embed_dim) + self.output_proj = nn.Linear(embed_dim, embed_dim) + + def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Tensor | None): + return tensor if position_embeddings is None else tensor + position_embeddings + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states=None, + encoder_attention_mask=None, + position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + level_start_index=None, + output_attentions: bool = False, + ): + # add position embeddings to the hidden states before projecting to queries and keys + if position_embeddings is not None: + hidden_states = self.with_pos_embed(hidden_states, position_embeddings) + + batch_size, num_queries, _ = hidden_states.shape + batch_size, sequence_length, _ = encoder_hidden_states.shape + torch_compilable_check( + (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == sequence_length, + "Make sure to align the spatial shapes with the sequence length of the encoder hidden states", + ) + + value = self.value_proj(encoder_hidden_states) + if attention_mask is not None: + # we invert the attention_mask + value = value.masked_fill(attention_mask[..., None], float(0)) + value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2 + ) + attention_weights = self.attention_weights(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points + ) + attention_weights = nn.functional.softmax(attention_weights, -1).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points + ) + # batch_size, num_queries, n_heads, n_levels, n_points, 2 + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif reference_points.shape[-1] == 4: + sampling_locations = ( + reference_points[:, :, None, :, None, :2] + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + ) + else: + raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") + # PyTorch implementation + output = multi_scale_deformable_attention(value, spatial_shapes, sampling_locations, attention_weights) + output = self.output_proj(output) + + return output, attention_weights + + +class OneFormerPixelDecoderEncoderLayer(nn.Module): + def __init__(self, config: OneFormerConfig): + super().__init__() + self.embed_dim = config.conv_dim + self.self_attn = OneFormerPixelDecoderEncoderMultiscaleDeformableAttention( + embed_dim=self.embed_dim, + num_heads=config.num_attention_heads, + n_levels=3, + n_points=4, + ) + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.dropout = config.dropout + self.activation_fn = nn.functional.relu + self.activation_dropout = config.dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_feedforward_dim) + self.fc2 = nn.Linear(config.encoder_feedforward_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + self.is_training = config.is_training + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + level_start_index=None, + output_attentions: bool = False, + ): + """ + Args: + hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Input to the layer. + attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Attention mask. + position_embeddings (`torch.FloatTensor`, *optional*): + Position embeddings, to be added to `hidden_states`. + reference_points (`torch.FloatTensor`, *optional*): + Reference points. + spatial_shapes (`torch.LongTensor`, *optional*): + Spatial shapes of the backbone feature maps. + level_start_index (`torch.LongTensor`, *optional*): + Level start index. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + # Apply Multi-scale Deformable Attention Module on the multi-scale feature maps. + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=hidden_states, + encoder_attention_mask=attention_mask, + position_embeddings=position_embeddings, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + level_start_index=level_start_index, + output_attentions=output_attentions, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.is_training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.is_training) + + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.is_training) + + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + if self.is_training: + if not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +# Modified from from transformers.models.detr.modeling_deformable_detr.DeformableDetrEncoder with DeformableDetrEncoder->OneFormerPixelDecoderEncoderOnly +class OneFormerPixelDecoderEncoderOnly(nn.Module): + """ + Transformer encoder consisting of *config.encoder_layers* deformable attention layers. Each layer is a + [`OneFormerPixelDecoderEncoderLayer`]. + + The encoder updates the flattened multi-scale feature maps through multiple deformable attention layers. + + Args: + config: OneFormerConfig + """ + + def __init__(self, config: OneFormerConfig): + super().__init__() + + self.config = config + self.dropout = config.dropout + self.layers = nn.ModuleList([OneFormerPixelDecoderEncoderLayer(config) for _ in range(config.encoder_layers)]) + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + """ + Get reference points for each feature map. Used in decoder. + + Args: + spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`): + Spatial shapes of each feature map. + valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`): + Valid ratios of each feature map. + device (`torch.device`): + Device on which to create the tensors. + Returns: + `torch.FloatTensor` of shape `(batch_size, num_queries, num_feature_levels, 2)` + """ + reference_points_list = [] + for lvl, (height, width) in enumerate(spatial_shapes): + ref_y, ref_x = torch.meshgrid( + torch.linspace(0.5, height - 0.5, height, dtype=valid_ratios.dtype, device=device), + torch.linspace(0.5, width - 0.5, width, dtype=valid_ratios.dtype, device=device), + indexing="ij", + ) + ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * height) + ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * width) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def forward( + self, + inputs_embeds=None, + attention_mask=None, + position_embeddings=None, + spatial_shapes=None, + level_start_index=None, + valid_ratios=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`: + - 1 for pixel features that are real (i.e. **not masked**), + - 0 for pixel features that are padding (i.e. **masked**). + [What are attention masks?](../glossary#attention-mask) + position_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Position embeddings that are added to the queries and keys in each self-attention layer. + spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`): + Spatial shapes of each feature map. + level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`): + Starting index of each feature map. + valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`): + Ratio of valid area in each feature level. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + hidden_states = inputs_embeds + reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=inputs_embeds.device) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + for i, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + position_embeddings=position_embeddings, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + level_start_index=level_start_index, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +# Modified from from transformers.models.mask2former.modeling_mask2former.Mask2FormerPixelDecoder with Mask2->One +class OneFormerPixelDecoder(nn.Module): + def __init__(self, config: OneFormerConfig, feature_channels): + super().__init__() + + self.config = config + + # positional encoding + self.position_embedding = OneFormerSinePositionEmbedding(num_pos_feats=config.conv_dim // 2, normalize=True) + self.num_feature_levels = 3 + transformer_in_channels = feature_channels[-self.num_feature_levels :] + self.transformer_feature_strides = config.strides[-self.num_feature_levels :] + self.feature_channels = feature_channels + self.level_embed = nn.Parameter(torch.Tensor(self.num_feature_levels, config.conv_dim)) + + # Create input projection layers + if self.num_feature_levels > 1: + input_projections_list = [] + for in_channels in transformer_in_channels[::-1]: + input_projections_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.conv_dim, kernel_size=1), + nn.GroupNorm(32, config.conv_dim), + ) + ) + self.input_projections = nn.ModuleList(input_projections_list) + else: + self.input_projections = nn.ModuleList( + [ + nn.Sequential( + nn.Conv2d(transformer_in_channels[-1], config.conv_dim, kernel_size=1), + nn.GroupNorm(32, config.conv_dim), + ) + ] + ) + + self.encoder = OneFormerPixelDecoderEncoderOnly(config) + + self.mask_projection = nn.Conv2d( + config.conv_dim, + config.mask_dim, + kernel_size=1, + stride=1, + padding=0, + ) + + self.common_stride = config.common_stride + + # extra fpn levels + stride = min(self.transformer_feature_strides) + self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride)) + + lateral_convs = [] + output_convs = [] + + for idx, in_channels in enumerate(self.feature_channels[: self.num_fpn_levels]): + lateral_conv = nn.Sequential( + nn.Conv2d( + in_channels, + config.conv_dim, + kernel_size=1, + bias=False, + ), + nn.GroupNorm(32, config.conv_dim), + ) + output_conv = nn.Sequential( + nn.Conv2d( + config.conv_dim, + config.conv_dim, + kernel_size=3, + stride=1, + padding=1, + bias=False, + ), + nn.GroupNorm(32, config.conv_dim), + nn.ReLU(), + ) + self.add_module(f"adapter_{idx + 1}", lateral_conv) + self.add_module(f"layer_{idx + 1}", output_conv) + + lateral_convs.append(lateral_conv) + output_convs.append(output_conv) + # Place convs into top-down order (from low to high resolution) + # to make the top-down computation in forward clearer. + self.lateral_convs = lateral_convs[::-1] + self.output_convs = output_convs[::-1] + + def get_valid_ratio(self, mask, dtype=torch.float32): + """Get the valid ratio of all feature maps.""" + + _, height, width = mask.shape + valid_height = torch.sum(~mask[:, :, 0], 1) + valid_width = torch.sum(~mask[:, 0, :], 1) + valid_ratio_height = valid_height.to(dtype) / height + valid_ratio_width = valid_width.to(dtype) / width + valid_ratio = torch.stack([valid_ratio_width, valid_ratio_height], -1) + return valid_ratio + + def forward( + self, + features, + encoder_outputs=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + # Then, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) + sources = [] + position_embeddings_list = [] + for level, source in enumerate(features[::-1][: self.num_feature_levels]): + sources.append(self.input_projections[level](source)) + position_embeddings_list.append(self.position_embedding(source.shape, source.device, source.dtype)) + + masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in sources] + + # Prepare encoder inputs (by flattening) + source_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for level, (source, mask, pos_embed) in enumerate(zip(sources, masks, position_embeddings_list)): + batch_size, num_channels, height, width = source.shape + spatial_shape = (height, width) + spatial_shapes.append(spatial_shape) + source = source.flatten(2).transpose(1, 2) + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + lvl_pos_embed = pos_embed + self.level_embed[level].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + source_flatten.append(source) + mask_flatten.append(mask) + source_flatten = torch.cat(source_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=source_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack([self.get_valid_ratio(m, dtype=source_flatten.dtype) for m in masks], 1) + + # Fourth, sent source_flatten + mask_flatten + lvl_pos_embed_flatten (backbone + proj layer output) through encoder + # Also provide spatial_shapes, level_start_index and valid_ratios + if encoder_outputs is None: + encoder_outputs = self.encoder( + inputs_embeds=source_flatten, + attention_mask=mask_flatten, + position_embeddings=lvl_pos_embed_flatten, + spatial_shapes=spatial_shapes, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + y = encoder_outputs.last_hidden_state + bs = y.shape[0] + + split_size_or_sections = [None] * self.num_feature_levels + for i in range(self.num_feature_levels): + if i < self.num_feature_levels - 1: + split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i] + else: + split_size_or_sections[i] = y.shape[1] - level_start_index[i] + y = torch.split(y, split_size_or_sections, dim=1) + + out = [] + multi_scale_features = [] + num_cur_levels = 0 + for i, z in enumerate(y): + out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1])) + + # append `out` with extra FPN levels + # Reverse feature maps into top-down order (from low to high resolution) + for idx, feats in enumerate(features[: self.num_fpn_levels][::-1]): + lateral_conv = self.lateral_convs[idx] + output_conv = self.output_convs[idx] + cur_fpn = lateral_conv(feats) + # Following FPN implementation, we use nearest upsampling here + y = cur_fpn + nn.functional.interpolate( + out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False + ) + y = output_conv(y) + out.append(y) + + for o in out: + if num_cur_levels < self.num_feature_levels: + multi_scale_features.append(o) + num_cur_levels += 1 + + return OneFormerPixelDecoderOutput( + mask_features=self.mask_projection(out[-1]), + multi_scale_features=multi_scale_features, + attentions=encoder_outputs.attentions, + ) + + +# Modified from from transformers.models.mask2former.modeling_mask2former.Mask2FormerPixelLevelModule with Mask2->One +class OneFormerPixelLevelModule(nn.Module): + def __init__(self, config: OneFormerConfig): + """ + Pixel Level Module proposed in [Masked-attention Mask Transformer for Universal Image + Segmentation](https://huggingface.co/papers/2112.01527). It runs the input image through a backbone and a pixel + decoder, generating multi-scale feature maps and pixel embeddings. + + Args: + config ([`OneFormerConfig`]): + The configuration used to instantiate this model. + """ + super().__init__() + self.encoder = load_backbone(config) + self.decoder = OneFormerPixelDecoder(config, feature_channels=self.encoder.channels) + + def forward(self, pixel_values: Tensor, output_hidden_states: bool = False) -> OneFormerPixelLevelModuleOutput: + features: list[Tensor] = self.encoder(pixel_values).feature_maps + decoder_output: OneFormerPixelDecoderOutput = self.decoder(features, output_hidden_states=output_hidden_states) + return OneFormerPixelLevelModuleOutput( + encoder_features=tuple(features), + decoder_features=decoder_output.multi_scale_features, + decoder_last_feature=decoder_output.mask_features, + ) + + +# Modified from transformers.models.detr.modeling_detr.DetrAttention with Detr->OneFormer +class OneFormerAttention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Here, we add position embeddings to the queries and + keys (as explained in the DETR paper). + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + if self.head_dim * num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): + return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def with_pos_embed(self, tensor: torch.Tensor, position_embeddings: Tensor | None): + return tensor if position_embeddings is None else tensor + position_embeddings + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: torch.Tensor | None = None, + key_value_states: torch.Tensor | None = None, + key_value_position_embeddings: torch.Tensor | None = None, + output_attentions: bool = False, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + hidden_states = hidden_states.permute(1, 0, 2) if hidden_states is not None else None + position_embeddings = position_embeddings.permute(1, 0, 2) if position_embeddings is not None else None + key_value_states = key_value_states.permute(1, 0, 2) if key_value_states is not None else None + key_value_position_embeddings = ( + key_value_position_embeddings.permute(1, 0, 2) if key_value_position_embeddings is not None else None + ) + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + batch_size, target_len, embed_dim = hidden_states.size() + + # add position embeddings to the hidden states before projecting to queries and keys + if position_embeddings is not None: + hidden_states_original = hidden_states + hidden_states = self.with_pos_embed(hidden_states, position_embeddings) + + # add key-value position embeddings to the key value states + if key_value_position_embeddings is not None: + key_value_states_original = key_value_states + key_value_states = self.with_pos_embed(key_value_states, key_value_position_embeddings) + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + if is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) + value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) + value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) + + proj_shape = (batch_size * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*proj_shape) + + source_len = key_states.size(1) + + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): + raise ValueError( + f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (batch_size * self.num_heads, target_len, source_len): + raise ValueError( + f"Attention mask should be of size {(target_len, batch_size * self.num_heads, source_len)}, but is" + f" {attention_mask.size()}" + ) + attn_weights += attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(batch_size, target_len, embed_dim) + + attn_output = self.out_proj(attn_output).permute(1, 0, 2) + + return attn_output, attn_weights_reshaped + + +class OneFormerTransformerDecoderSelfAttentionLayer(nn.Module): + def __init__( + self, embed_dim, num_heads, dropout=0.0, activation="relu", normalize_before=False, layer_norm_eps=1e-05 + ): + super().__init__() + self.self_attn = OneFormerAttention(embed_dim=embed_dim, num_heads=num_heads, dropout=dropout, is_decoder=True) + + self.norm = nn.LayerNorm(embed_dim, eps=layer_norm_eps) + self.dropout = nn.Dropout(dropout) + + self.activation = ACT2FN[activation] + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Tensor | None): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + output, + output_mask: Tensor | None = None, + output_key_padding_mask: Tensor | None = None, + query_pos: Tensor | None = None, + ): + output2, attention_weights = self.self_attn( + hidden_states=output, position_embeddings=query_pos, attention_mask=output_mask, output_attentions=True + ) + output = output + self.dropout(output2) + output = self.norm(output) + + return output, attention_weights + + def forward_pre( + self, + output, + output_mask: Tensor | None = None, + output_key_padding_mask: Tensor | None = None, + query_pos: Tensor | None = None, + ): + output2 = self.norm(output) + output2, attention_weights = self.self_attn( + hidden_states=output2, position_embeddings=query_pos, attention_mask=output_mask, output_attentions=True + ) + output = output + self.dropout(output2) + + return output, attention_weights + + def forward( + self, + output, + output_mask: Tensor | None = None, + output_key_padding_mask: Tensor | None = None, + query_pos: Tensor | None = None, + ): + if self.normalize_before: + return self.forward_pre(output, output_mask, output_key_padding_mask, query_pos) + return self.forward_post(output, output_mask, output_key_padding_mask, query_pos) + + +class OneFormerTransformerDecoderCrossAttentionLayer(nn.Module): + def __init__( + self, embed_dim, num_heads, dropout=0.0, activation="relu", normalize_before=False, layer_norm_eps=1e-05 + ): + super().__init__() + self.multihead_attn = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout) + + self.norm = nn.LayerNorm(embed_dim, eps=layer_norm_eps) + self.dropout = nn.Dropout(dropout) + + self.activation = ACT2FN[activation] + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Tensor | None): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + output, + memory, + memory_mask: Tensor | None = None, + memory_key_padding_mask: Tensor | None = None, + pos: Tensor | None = None, + query_pos: Tensor | None = None, + ): + output2, attention_weights = self.multihead_attn( + query=self.with_pos_embed(output, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + ) + output = output + self.dropout(output2) + output = self.norm(output) + + return output, attention_weights + + def forward_pre( + self, + output, + memory, + memory_mask: Tensor | None = None, + memory_key_padding_mask: Tensor | None = None, + pos: Tensor | None = None, + query_pos: Tensor | None = None, + ): + output2 = self.norm(output) + output2, attention_weights = self.multihead_attn( + query=self.with_pos_embed(output2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + ) + output = output + self.dropout(output2) + + return output, attention_weights + + def forward( + self, + output, + memory, + memory_mask: Tensor | None = None, + memory_key_padding_mask: Tensor | None = None, + pos: Tensor | None = None, + query_pos: Tensor | None = None, + ): + if self.normalize_before: + return self.forward_pre(output, memory, memory_mask, memory_key_padding_mask, pos, query_pos) + return self.forward_post(output, memory, memory_mask, memory_key_padding_mask, pos, query_pos) + + +class OneFormerTransformerDecoderFFNLayer(nn.Module): + def __init__( + self, + d_model, + dim_feedforward=2048, + dropout=0.0, + activation="relu", + normalize_before=False, + layer_norm_eps=1e-05, + ): + super().__init__() + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm = nn.LayerNorm(d_model, eps=layer_norm_eps) + + self.activation = ACT2FN[activation] + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Tensor | None): + return tensor if pos is None else tensor + pos + + def forward_post(self, output): + output2 = self.linear2(self.dropout(self.activation(self.linear1(output)))) + output = output + self.dropout(output2) + output = self.norm(output) + return output + + def forward_pre(self, output): + output2 = self.norm(output) + output2 = self.linear2(self.dropout(self.activation(self.linear1(output2)))) + output = output + self.dropout(output2) + return output + + def forward(self, output): + if self.normalize_before: + return self.forward_pre(output) + return self.forward_post(output) + + +class OneFormerMLPPredictionHead(nn.Module): + def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3): + """ + A classic Multi Layer Perceptron (MLP). + + Args: + input_dim (`int`): + The input dimensions. + hidden_dim (`int`): + The hidden dimensions. + output_dim (`int`): + The output dimensions. + num_layers (int, *optional*, defaults to 3): + The number of layers. + """ + super().__init__() + in_dims = [input_dim] + [hidden_dim] * (num_layers - 1) + out_dims = [hidden_dim] * (num_layers - 1) + [output_dim] + + layers = [] + for i, (in_dim, out_dim) in enumerate(zip(in_dims, out_dims)): + layers.append( + PredictionBlock(in_dim, out_dim, activation=nn.ReLU() if i < num_layers - 1 else nn.Identity()) + ) + + self.layers = nn.Sequential(*layers) + + def forward(self, input: Tensor) -> Tensor: + return self.layers(input) + + +# refactored from original implementation +class OneFormerTransformerDecoderLayer(nn.Module): + def __init__(self, config: OneFormerConfig): + super().__init__() + self.embed_dim = config.hidden_dim + self.num_feature_levels = 3 + + self.cross_attn = OneFormerTransformerDecoderCrossAttentionLayer( + embed_dim=self.embed_dim, + num_heads=config.num_attention_heads, + dropout=0.0, + normalize_before=config.pre_norm, + layer_norm_eps=config.layer_norm_eps, + ) + + self.self_attn = OneFormerTransformerDecoderSelfAttentionLayer( + embed_dim=self.embed_dim, + num_heads=config.num_attention_heads, + dropout=0.0, + normalize_before=config.pre_norm, + layer_norm_eps=config.layer_norm_eps, + ) + + self.ffn = OneFormerTransformerDecoderFFNLayer( + d_model=self.embed_dim, + dim_feedforward=config.dim_feedforward, + dropout=0.0, + normalize_before=config.pre_norm, + layer_norm_eps=config.layer_norm_eps, + ) + + def forward( + self, + index: int, + output: torch.Tensor, + multi_stage_features: list[torch.Tensor], + multi_stage_positional_embeddings: list[torch.Tensor], + attention_mask: torch.Tensor | None = None, + query_embeddings: torch.Tensor | None = None, + output_attentions: bool | None = False, + ): + """ + Args: + index (`int`): index of the layer in the Transformer decoder. + output (`torch.FloatTensor`): the object queries of shape `(N, batch, hidden_dim)` + multi_stage_features (`list[torch.Tensor]`): the multi-scale features from the pixel decoder. + multi_stage_positional_embeddings (`list[torch.Tensor]`): + positional embeddings for the multi_stage_features + attention_mask (`torch.FloatTensor`): attention mask for the masked cross attention layer + query_embeddings (`torch.FloatTensor`, *optional*): + position embeddings that are added to the queries and keys in the self-attention layer. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + + level_index = index % self.num_feature_levels + attention_mask[torch.where(attention_mask.sum(-1) == attention_mask.shape[-1])] = False + + # Masked Cross Attention + output, cross_attn_weights = self.cross_attn( + output, + multi_stage_features[level_index], + memory_mask=attention_mask, + memory_key_padding_mask=None, # here we do not apply masking on padded region + pos=multi_stage_positional_embeddings[level_index], + query_pos=query_embeddings, + ) + + # Self Attention + output, self_attn_weights = self.self_attn( + output, + output_mask=None, + output_key_padding_mask=None, + query_pos=query_embeddings, + ) + + # Fully Connected + output = self.ffn(output) + + outputs = (output,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + return outputs + + +class OneFormerTransformerDecoderQueryTransformerDecoder(nn.Module): + def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False): + super().__init__() + self.layers = _get_clones(decoder_layer, num_layers) + self.num_layers = num_layers + self.norm = norm + self.return_intermediate = return_intermediate + + def forward( + self, + output, + memory, + output_mask: Tensor | None = None, + memory_mask: Tensor | None = None, + output_key_padding_mask: Tensor | None = None, + memory_key_padding_mask: Tensor | None = None, + pos: Tensor | None = None, + query_pos: Tensor | None = None, + ): + intermediate = [] + + for layer in self.layers: + output = layer( + output, + memory, + output_mask=output_mask, + memory_mask=memory_mask, + output_key_padding_mask=output_key_padding_mask, + memory_key_padding_mask=memory_key_padding_mask, + pos=pos, + query_pos=query_pos, + ) + if self.return_intermediate: + intermediate.append(self.norm(output)) + + if self.norm is not None: + output = self.norm(output) + if self.return_intermediate: + intermediate.pop() + intermediate.append(output) + + if self.return_intermediate: + return torch.stack(intermediate) + + return output.unsqueeze(0) + + +class OneFormerTransformerDecoderQueryTransformerDecoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + layer_norm_eps=1e-05, + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = ACT2FN[activation] + self.normalize_before = normalize_before + + def with_pos_embed(self, tensor, pos: Tensor | None): + return tensor if pos is None else tensor + pos + + def forward_post( + self, + output, + memory, + output_mask: Tensor | None = None, + memory_mask: Tensor | None = None, + output_key_padding_mask: Tensor | None = None, + memory_key_padding_mask: Tensor | None = None, + pos: Tensor | None = None, + query_pos: Tensor | None = None, + ): + q = k = self.with_pos_embed(output, query_pos) + output2 = self.self_attn(q, k, value=output, attn_mask=output_mask, key_padding_mask=output_key_padding_mask) + output2 = output2[0] + output = output + self.dropout1(output2) + output = self.norm1(output) + output2 = self.multihead_attn( + query=self.with_pos_embed(output, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + ) + output2 = output2[0] + output = output + self.dropout2(output2) + output = self.norm2(output) + output2 = self.linear2(self.dropout(self.activation(self.linear1(output)))) + output = output + self.dropout3(output2) + output = self.norm3(output) + return output + + def forward_pre( + self, + output, + memory, + output_mask: Tensor | None = None, + memory_mask: Tensor | None = None, + output_key_padding_mask: Tensor | None = None, + memory_key_padding_mask: Tensor | None = None, + pos: Tensor | None = None, + query_pos: Tensor | None = None, + ): + output2 = self.norm1(output) + q = k = self.with_pos_embed(output2, query_pos) + output2 = self.self_attn(q, k, value=output2, attn_mask=output_mask, key_padding_mask=output_key_padding_mask) + output2 = output2[0] + output = output + self.dropout1(output2) + output2 = self.norm2(output) + output2 = self.multihead_attn( + query=self.with_pos_embed(output2, query_pos), + key=self.with_pos_embed(memory, pos), + value=memory, + attn_mask=memory_mask, + key_padding_mask=memory_key_padding_mask, + ) + output2 = output2[0] + output = output + self.dropout2(output2) + output2 = self.norm3(output) + output2 = self.linear2(self.dropout(self.activation(self.linear1(output2)))) + output = output + self.dropout3(output2) + return output + + def forward( + self, + output, + memory, + output_mask: Tensor | None = None, + memory_mask: Tensor | None = None, + output_key_padding_mask: Tensor | None = None, + memory_key_padding_mask: Tensor | None = None, + pos: Tensor | None = None, + query_pos: Tensor | None = None, + ): + if self.normalize_before: + return self.forward_pre( + output, + memory, + output_mask, + memory_mask, + output_key_padding_mask, + memory_key_padding_mask, + pos, + query_pos, + ) + return self.forward_post( + output, + memory, + output_mask, + memory_mask, + output_key_padding_mask, + memory_key_padding_mask, + pos, + query_pos, + ) + + +class OneFormerTransformerDecoderQueryTransformer(nn.Module): + def __init__( + self, + d_model=512, + nhead=8, + num_decoder_layers=6, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + normalize_before=False, + return_intermediate_dec=False, + layer_norm_eps=1e-05, + ): + super().__init__() + + decoder_layer = OneFormerTransformerDecoderQueryTransformerDecoderLayer( + d_model, nhead, dim_feedforward, dropout, activation, normalize_before, layer_norm_eps + ) + decoder_norm = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.decoder = OneFormerTransformerDecoderQueryTransformerDecoder( + decoder_layer, + num_decoder_layers, + decoder_norm, + return_intermediate=return_intermediate_dec, + ) + + self.d_model = d_model + self.nhead = nhead + + def forward(self, src, mask, query_embed, pos_embed, task_token=None): + batch_size = src.shape[0] + src = src.flatten(2).permute(2, 0, 1) + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + query_embed = query_embed.unsqueeze(1).repeat(1, batch_size, 1) + if mask is not None: + mask = mask.flatten(1) + + if task_token is None: + queries = torch.zeros_like(query_embed) + else: + queries = task_token.repeat(query_embed.shape[0], 1, 1) + + queries = self.decoder(queries, src, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed) + return queries.transpose(1, 2) + + +class OneFormerTransformerDecoder(nn.Module): + """ + Transformer decoder + """ + + def __init__(self, in_channels: int, config: OneFormerConfig): + super().__init__() + self.config = config + + self.dropout = config.dropout + self.num_heads = config.num_attention_heads + self.is_training = config.is_training + self.use_task_norm = config.use_task_norm + self.use_auxiliary_loss = config.use_auxiliary_loss + + self.query_transformer = OneFormerTransformerDecoderQueryTransformer( + d_model=config.hidden_dim, + dropout=config.dropout, + nhead=config.num_attention_heads, + dim_feedforward=config.dim_feedforward, + num_decoder_layers=config.query_dec_layers, + normalize_before=config.pre_norm, + return_intermediate_dec=False, + layer_norm_eps=config.layer_norm_eps, + ) + + self.decoder_norm = nn.LayerNorm(config.hidden_dim, eps=config.layer_norm_eps) + + self.num_feature_levels = 3 + + self.layers = nn.ModuleList( + [OneFormerTransformerDecoderLayer(config) for _ in range(config.decoder_layers - 1)] + ) + + self.query_input_projection = nn.Conv2d(in_channels, config.hidden_dim, kernel_size=1) + + self.class_embed = nn.Linear(config.hidden_dim, config.num_labels + 1) + self.mask_embed = OneFormerMLPPredictionHead( + config.hidden_dim, + config.hidden_dim, + config.mask_dim, + 3, + ) + + def forward( + self, + task_token=None, + multi_stage_features=None, + multi_stage_positional_embeddings=None, + mask_features=None, + query_features=None, + query_embeddings=None, + query_embedder=None, + size_list=None, + output_attentions=None, + ): + if self.use_task_norm: + task_token = self.decoder_norm(task_token) + + object_queries = self.query_transformer( + query_features, + None, + query_embedder.weight[:-1], + self.query_input_projection(mask_features), + task_token if self.use_task_norm else None, + ) + + object_queries = object_queries[0].permute(1, 0, 2) + + queries = torch.cat([object_queries, task_token], dim=0) + + output = queries.clone() + + intermediate_class_predictions = [] + intermediate_mask_predictions = [] + + # prediction heads on learnable query features + outputs_class, outputs_mask, attention_mask = self.forward_prediction_heads( + output, mask_features, attention_mask_target_size=size_list[0] + ) + intermediate_class_predictions.append(outputs_class) + intermediate_mask_predictions.append(outputs_mask) + + attentions = () + + for index, layer in enumerate(self.layers): + layer_outputs = layer( + index=index, + output=output, + multi_stage_features=multi_stage_features, + multi_stage_positional_embeddings=multi_stage_positional_embeddings, + attention_mask=attention_mask, + query_embeddings=query_embeddings, + output_attentions=output_attentions, + ) + + output = layer_outputs[0] + attentions += (layer_outputs[1:],) + + outputs_class, outputs_mask, attention_mask = self.forward_prediction_heads( + output, mask_features, attention_mask_target_size=size_list[(index + 1) % self.num_feature_levels] + ) + intermediate_class_predictions.append(outputs_class) + intermediate_mask_predictions.append(outputs_mask) + + if not len(intermediate_mask_predictions) == len(self.layers) + 1: + raise ValueError( + "Intermediate predictions in the transformer decoder must have the same number of elements as number" + " of layers" + ) + + object_queries = layer_outputs[0].permute(1, 0, 2) + + contrastive_logits = queries.permute(1, 0, 2) + + return OneFormerTransformerDecoderOutput( + object_queries=object_queries, + contrastive_logits=contrastive_logits, + prediction_masks=intermediate_mask_predictions[-1], + prediction_class=intermediate_class_predictions[-1], + auxiliary_predictions=self._get_aux_predictions( + intermediate_class_predictions, intermediate_mask_predictions + ) + if self.use_auxiliary_loss + else None, + attentions=attentions, + ) + + def forward_prediction_heads(self, output, mask_features, attention_mask_target_size): + decoder_output = self.decoder_norm(output) + decoder_output = decoder_output.transpose(0, 1) + outputs_class = self.class_embed(decoder_output) + mask_embed = self.mask_embed(decoder_output) + outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features) + + attention_mask = nn.functional.interpolate( + outputs_mask, size=attention_mask_target_size, mode="bilinear", align_corners=False + ) + + # must use bool type + # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged. + attention_mask = ( + attention_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5 + ).bool() + attention_mask = attention_mask.detach() + + return outputs_class, outputs_mask, attention_mask + + def _get_aux_predictions(self, outputs_class, outputs_seg_masks): + aux_list = [ + {"class_queries_logits": a, "masks_queries_logits": b} + for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1]) + ] + return tuple(aux_list) + + +class OneFormerTransformerModule(nn.Module): + """ + The OneFormer's transformer module. + """ + + def __init__(self, in_features: int, config: OneFormerConfig): + super().__init__() + hidden_dim = config.hidden_dim + self.num_feature_levels = 3 + self.position_embedder = OneFormerSinePositionEmbedding(num_pos_feats=hidden_dim // 2, normalize=True) + self.queries_embedder = nn.Embedding(config.num_queries, hidden_dim) + self.input_projections = [] + + for _ in range(self.num_feature_levels): + if in_features != hidden_dim or config.enforce_input_proj: + self.input_projections.append(nn.Conv2d(in_features, hidden_dim, kernel_size=1)) + else: + self.input_projections.append(nn.Sequential()) + + self.decoder = OneFormerTransformerDecoder(in_channels=in_features, config=config) + self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim) + + def forward( + self, + multi_scale_features: list[Tensor], + mask_features: Tensor, + task_token: Tensor, + output_attentions: bool = False, + ) -> OneFormerTransformerDecoderOutput: + if not len(multi_scale_features) == self.num_feature_levels: + raise ValueError( + f"Number of elements in multi_scale_features ({len(multi_scale_features)}) and num_feature_levels" + f" ({self.num_feature_levels}) do not match!" + ) + multi_stage_features = [] + multi_stage_positional_embeddings = [] + size_list = [] + + for i in range(self.num_feature_levels): + size_list.append(multi_scale_features[i].shape[-2:]) + multi_stage_positional_embeddings.append( + self.position_embedder( + multi_scale_features[i].shape, multi_scale_features[i].device, multi_scale_features[i].dtype, None + ).flatten(2) + ) + multi_stage_features.append( + self.input_projections[i](multi_scale_features[i]).flatten(2) + + self.level_embed.weight[i][None, :, None] + ) + + # flatten NxCxHxW to HWxNxC + multi_stage_positional_embeddings[-1] = multi_stage_positional_embeddings[-1].permute(2, 0, 1) + multi_stage_features[-1] = multi_stage_features[-1].permute(2, 0, 1) + + _, batch_size, _ = multi_stage_features[0].shape + + # QxNxC + query_embeddings = self.queries_embedder.weight.unsqueeze(1).repeat(1, batch_size, 1) + task_token = task_token.unsqueeze(0) + + query_features = self.position_embedder(mask_features.shape, mask_features.device, mask_features.dtype, None) + + return self.decoder( + task_token=task_token, + multi_stage_features=multi_stage_features, + multi_stage_positional_embeddings=multi_stage_positional_embeddings, + mask_features=mask_features, + query_features=query_features, + query_embeddings=query_embeddings, + query_embedder=self.queries_embedder, + size_list=size_list, + output_attentions=output_attentions, + ) + + +# Copied from transformers.models.maskformer.modeling_maskformer.MaskFormerSinePositionEmbedding with Mask->One +class OneFormerSinePositionEmbedding(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one used by the Attention is all you + need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: float | None = None + ): + super().__init__() + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + self.scale = 2 * math.pi if scale is None else scale + + @compile_compatible_method_lru_cache(maxsize=1) + def forward( + self, + shape: torch.Size, + device: torch.device | str, + dtype: torch.dtype, + mask: Tensor | None = None, + ) -> Tensor: + if mask is None: + mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool) + not_mask = (~mask).to(dtype) + y_embed = not_mask.cumsum(1) + x_embed = not_mask.cumsum(2) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype) + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +# Copied from transformers.models.maskformer.modeling_maskformer.PredictionBlock +class PredictionBlock(nn.Module): + def __init__(self, in_dim: int, out_dim: int, activation: nn.Module) -> None: + super().__init__() + self.layers = [nn.Linear(in_dim, out_dim), activation] + # Maintain submodule indexing as if part of a Sequential block + for i, layer in enumerate(self.layers): + self.add_module(str(i), layer) + + def forward(self, input: Tensor) -> Tensor: + hidden_state = input + for layer in self.layers: + hidden_state = layer(hidden_state) + return hidden_state + + +class OneFormerTextMapperAttention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim**-0.5 + + self.q_proj = nn.Linear(dim, dim, bias=qkv_bias) + self.k_proj = nn.Linear(dim, dim, bias=qkv_bias) + self.v_proj = nn.Linear(dim, dim, bias=qkv_bias) + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, q, k, v): + batch_size, q_sequence_length, num_channels = q.shape + if not k.shape == v.shape: + raise ValueError(f"keys ({list(k.shape)}) and values ({list(v.shape)}) have different shapes!") + batch_size, k_sequence_length, num_channels = k.shape + q = self.q_proj(q).reshape(batch_size, q_sequence_length, self.num_heads, num_channels // self.num_heads) + k = self.k_proj(k).reshape(batch_size, k_sequence_length, self.num_heads, num_channels // self.num_heads) + v = self.v_proj(v).reshape(batch_size, k_sequence_length, self.num_heads, num_channels // self.num_heads) + + attn = torch.einsum("bnkc,bmkc->bknm", q, k) * self.scale + + attn = attn.softmax(dim=-1) + + output = torch.einsum("bknm,bmkc->bnkc", attn, v).reshape(batch_size, q_sequence_length, num_channels) + + output = self.proj(output) + output = self.proj_drop(output) + return output + + +class OneFormerTextTransformerDecoderLayer(nn.Module): + def __init__( + self, + d_model, + nhead, + dropout=0.1, + layer_norm_eps=1e-05, + ): + super().__init__() + self.self_attn = OneFormerTextMapperAttention(d_model, nhead, proj_drop=dropout) + self.cross_attn = OneFormerTextMapperAttention(d_model, nhead, proj_drop=dropout) + + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps) + self.dropout = nn.Dropout(dropout) + + self.mlp = nn.Sequential( + nn.Linear(d_model, d_model * 4), nn.GELU(), nn.Dropout(dropout), nn.Linear(d_model * 4, d_model) + ) + + def forward(self, hidden_state, mem): + q = k = v = self.norm1(hidden_state) + hidden_state = hidden_state + self.self_attn(q, k, v) + q = self.norm2(hidden_state) + hidden_state = hidden_state + self.cross_attn(q, mem, mem) + hidden_state = hidden_state + self.dropout(self.mlp(self.norm3(hidden_state))) + return hidden_state + + +class OneFormerTextContextDecoder(nn.Module): + def __init__( + self, + transformer_width=256, + transformer_heads=4, + transformer_layers=6, + visual_dim=1024, + dropout=0.1, + layer_norm_eps=1e-05, + **kwargs, + ): + super().__init__() + + self.memory_proj = nn.Sequential( + nn.LayerNorm(visual_dim, eps=layer_norm_eps), + nn.Linear(visual_dim, transformer_width), + nn.LayerNorm(transformer_width, eps=layer_norm_eps), + ) + + self.text_proj = nn.Sequential( + nn.LayerNorm(visual_dim, eps=layer_norm_eps), + nn.Linear(visual_dim, transformer_width), + ) + + self.decoder = nn.ModuleList( + [ + OneFormerTextTransformerDecoderLayer(transformer_width, transformer_heads, dropout, layer_norm_eps) + for _ in range(transformer_layers) + ] + ) + + self.out_proj = nn.Sequential( + nn.LayerNorm(transformer_width, eps=layer_norm_eps), nn.Linear(transformer_width, visual_dim) + ) + + def forward(self, text, visual): + visual = self.memory_proj(visual) + hidden_state = self.text_proj(text) + + for layer in self.decoder: + hidden_state = layer(hidden_state, visual) + + return self.out_proj(hidden_state) + + +class OneFormerTextMLP(nn.Module): + def __init__( + self, + hidden_size: int | None = None, + intermediate_size: int | None = None, + output_size: int | None = None, + ): + super().__init__() + self.activation_fn = ACT2FN["quick_gelu"] + self.fc1 = nn.Linear(hidden_size, intermediate_size) + self.fc2 = nn.Linear(intermediate_size, output_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class OneFormerTextTransformerLayer(GradientCheckpointingLayer): + def __init__(self, width: int, heads: int, attn_mask: torch.Tensor, layer_norm_eps=1e-05): + super().__init__() + self.self_attn = nn.MultiheadAttention(width, heads) + self.layer_norm1 = nn.LayerNorm(width, eps=layer_norm_eps) + self.mlp = OneFormerTextMLP(width, width * 4, width) + self.layer_norm2 = nn.LayerNorm(width, eps=layer_norm_eps) + self.attn_mask = attn_mask + + def forward( + self, + hidden_states: torch.Tensor, + key_padding_mask: torch.Tensor | None = None, + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.self_attn( + hidden_states, + hidden_states, + hidden_states, + need_weights=False, + key_padding_mask=key_padding_mask, + )[0] + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class OneFormerTextTransformer(nn.Module): + def __init__( + self, + width: int, + layers: int, + heads: int, + attn_mask: torch.Tensor | None = None, + use_checkpoint=False, + layer_norm_eps=1e-05, + ): + super().__init__() + self.width = width + self.num_layers = layers + self.layers = nn.Sequential( + *[OneFormerTextTransformerLayer(width, heads, attn_mask, layer_norm_eps) for _ in range(layers)] + ) + self.use_checkpoint = use_checkpoint + + def forward(self, hidden_states: torch.Tensor): + for layer in self.layers: + hidden_states = layer(hidden_states) + return hidden_states + + +class OneFormerTextEncoder(nn.Module): + def __init__( + self, + context_length: int, + width: int, + layers: int, + vocab_size, + use_checkpoint=False, + layer_norm_eps=1e-05, + ): + super().__init__() + heads = width // 64 + self.context_length = context_length + self.width = width + self.transformer = OneFormerTextTransformer( + width=width, + layers=layers, + heads=heads, + attn_mask=self.build_attention_mask(), + use_checkpoint=use_checkpoint, + layer_norm_eps=layer_norm_eps, + ) + + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width)) + self.ln_final = nn.LayerNorm(width, eps=layer_norm_eps) + self.token_embedding = nn.Embedding(vocab_size, width) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + def forward(self, text): + hidden_state = self.token_embedding(text) + hidden_state = hidden_state + self.positional_embedding + hidden_state = hidden_state.permute(1, 0, 2) + hidden_state = self.transformer(hidden_state) + hidden_state = hidden_state.permute(1, 0, 2) + hidden_state = self.ln_final(hidden_state) + hidden_state = hidden_state[torch.arange(hidden_state.shape[0]), text.argmax(dim=-1)] + + return hidden_state + + +class OneFormerTextMapper(nn.Module): + def __init__(self, config: OneFormerConfig): + super().__init__() + self.text_encoder = OneFormerTextEncoder( + context_length=config.text_encoder_context_length, + width=config.text_encoder_width, + layers=config.text_encoder_num_layers, + vocab_size=config.text_encoder_vocab_size, + layer_norm_eps=config.layer_norm_eps, + ) + + self.text_projector = OneFormerMLPPredictionHead( + config.text_encoder_width, + config.hidden_dim, + config.hidden_dim, + config.text_encoder_proj_layers, + ) + if config.text_encoder_n_ctx > 0: + self.prompt_ctx = nn.Embedding( + config.text_encoder_n_ctx, + config.text_encoder_width, + ) + else: + self.prompt_ctx = None + + def forward( + self, + inputs: Tensor, + ) -> Tensor: + text_queries = self.encode_text(inputs) + + return text_queries + + def encode_text(self, text): + if text.ndim is None: + raise ValueError("text must not be NoneType") + if text.ndim not in [2, 3]: + raise ValueError("Number of dimensions in text must be 2 or 3") + squeeze_dim = False + num_text = 1 + if text.ndim == 3: + num_text = text.shape[1] + batch_size, num_text, hidden_dim = text.shape + text = text.reshape(batch_size * num_text, hidden_dim) + squeeze_dim = True + + # [batch_size, num_channels] + encoded_text = self.text_encoder(text) + + text_queries = self.text_projector(encoded_text) + + if squeeze_dim: + _, hidden_dim = text_queries.shape + text_queries = text_queries.reshape(batch_size, num_text, hidden_dim) + if self.prompt_ctx is not None: + text_queries_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_queries.shape[0], 1, 1) + text_queries = torch.cat([text_queries, text_queries_ctx], dim=1) + + return text_queries + + +class OneFormerTaskModel(nn.Module): + def __init__(self, config: OneFormerConfig): + super().__init__() + self.task_mlp = OneFormerMLPPredictionHead( + config.task_seq_len, + config.hidden_dim, + config.hidden_dim, + 2, + ) + + def forward(self, inputs: Tensor) -> Tensor: + task_tokens = self.task_mlp(inputs) + return task_tokens + + +@auto_docstring +class OneFormerPreTrainedModel(PreTrainedModel): + config: OneFormerConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ("image",) + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + xavier_std = self.config.init_xavier_std + std = self.config.init_std + if isinstance(module, OneFormerTransformerModule): + if module.input_projections is not None: + for input_projection in module.input_projections: + if not isinstance(input_projection, nn.Sequential): + init.xavier_uniform_(input_projection.weight, gain=xavier_std) + init.constant_(input_projection.bias, 0) + elif isinstance(module, OneFormerTransformerDecoder): + init.xavier_uniform_(module.query_input_projection.weight, gain=xavier_std) + init.constant_(module.query_input_projection.bias, 0) + elif isinstance(module, OneFormerPixelDecoderEncoderMultiscaleDeformableAttention): + init.constant_(module.sampling_offsets.weight, 0.0) + thetas = torch.arange(module.n_heads, dtype=torch.int64).float() * (2.0 * math.pi / module.n_heads) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(module.n_heads, 1, 1, 2) + .repeat(1, module.n_levels, module.n_points, 1) + ) + for i in range(module.n_points): + grid_init[:, :, i, :] *= i + 1 + + init.copy_(module.sampling_offsets.bias, grid_init.view(-1)) + init.constant_(module.attention_weights.weight, 0.0) + init.constant_(module.attention_weights.bias, 0.0) + init.xavier_uniform_(module.value_proj.weight) + init.constant_(module.value_proj.bias, 0.0) + init.xavier_uniform_(module.output_proj.weight) + init.constant_(module.output_proj.bias, 0.0) + elif isinstance(module, OneFormerPixelDecoder): + init.normal_(module.level_embed, std=0) + elif isinstance(module, (OneFormerTransformerDecoderLayer, OneFormerTransformerDecoderQueryTransformer)): + for p in module.parameters(): + if p.dim() > 1: + init.xavier_uniform_(p, gain=xavier_std) + elif isinstance(module, OneFormerTextTransformer): + proj_std = (module.width**-0.5) * ((2 * module.num_layers) ** -0.5) + attn_std = module.width**-0.5 + fc_std = (2 * module.width) ** -0.5 + for layer in module.layers: + init.normal_(layer.self_attn.in_proj_weight, std=attn_std) + init.normal_(layer.self_attn.out_proj.weight, std=proj_std) + init.normal_(layer.mlp.fc1.weight, std=fc_std) + init.normal_(layer.mlp.fc2.weight, std=proj_std) + elif isinstance(module, OneFormerTextEncoder): + init.normal_(module.token_embedding.weight, std=0.02) + init.normal_(module.positional_embedding, std=0.01) + if hasattr(module, "reference_points"): + init.xavier_uniform_(module.reference_points.weight, gain=1.0) + init.constant_(module.reference_points.bias, 0.0) + elif isinstance(module, OneFormerMLPPredictionHead): + for submodule in module.modules(): + if isinstance(submodule, nn.Linear): + init.xavier_uniform_(submodule.weight, gain=xavier_std) + init.constant_(submodule.bias, 0) + elif isinstance(module, nn.MultiheadAttention): + init.normal_(module.in_proj_weight, mean=0.0, std=std) + init.zeros_(module.in_proj_bias) + elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + init.normal_(module.weight, mean=0.0, std=std) + if module.bias is not None: + init.zeros_(module.bias) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)): + init.ones_(module.weight) + init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=std) + # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag + if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): + init.zeros_(module.weight[module.padding_idx]) + elif isinstance(module, OneFormerLoss): + init.constant_(module.logit_scale, np.log(1 / self.config.contrastive_temperature)) + empty_weight = torch.ones(module.num_classes + 1) + empty_weight[-1] = module.eos_coef + init.copy_(module.empty_weight, empty_weight) + + +@auto_docstring +class OneFormerModel(OneFormerPreTrainedModel): + main_input_name = ["pixel_values", "task_inputs"] + + def __init__(self, config: OneFormerConfig): + super().__init__(config) + self.pixel_level_module = OneFormerPixelLevelModule(config) + self.transformer_module = OneFormerTransformerModule(in_features=config.conv_dim, config=config) + self.task_encoder = OneFormerTaskModel(config) + self.is_training = config.is_training + + if self.is_training: + self.text_mapper = OneFormerTextMapper(config) + else: + self.text_mapper = None + + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: Tensor, + task_inputs: Tensor, + text_inputs: Tensor | None = None, + pixel_mask: Tensor | None = None, + output_hidden_states: bool | None = None, + output_attentions: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> OneFormerModelOutput: + r""" + task_inputs (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Task inputs. Task inputs can be obtained using [`AutoImageProcessor`]. See [`OneFormerProcessor.__call__`] + for details. + text_inputs (`list[torch.Tensor]`, *optional*): + Tensor of shape `(num_queries, sequence_length)` to be fed to a model + + Example: + + ```python + >>> import torch + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import OneFormerProcessor, OneFormerModel + + >>> # download texting image + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> # load processor for preprocessing the inputs + >>> processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny") + >>> model = OneFormerModel.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny") + >>> inputs = processor(image, ["semantic"], return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> mask_predictions = outputs.transformer_decoder_mask_predictions + >>> class_predictions = outputs.transformer_decoder_class_predictions + + >>> f"👉 Mask Predictions Shape: {list(mask_predictions.shape)}, Class Predictions Shape: {list(class_predictions.shape)}" + '👉 Mask Predictions Shape: [1, 150, 128, 171], Class Predictions Shape: [1, 150, 151]' + ```""" + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + batch_size, _, height, width = pixel_values.shape + + if pixel_mask is None: + pixel_mask = torch.ones((batch_size, height, width), device=pixel_values.device) + + pixel_level_module_output = self.pixel_level_module(pixel_values, output_hidden_states) + + multi_scale_features = pixel_level_module_output.decoder_features + mask_features = pixel_level_module_output.decoder_last_feature + + task_token = self.task_encoder(task_inputs.to(self.dtype)) + + if self.is_training: + text_queries = self.text_mapper(text_inputs) + else: + text_queries = None + + transformer_module_output = self.transformer_module( + multi_scale_features=multi_scale_features, + mask_features=mask_features, + task_token=task_token, + output_attentions=output_attentions, + ) + + queries = transformer_module_output.object_queries + + encoder_hidden_states = None + pixel_decoder_hidden_states = None + transformer_decoder_hidden_states = None + + if output_hidden_states: + encoder_hidden_states = pixel_level_module_output.encoder_features + pixel_decoder_hidden_states = (pixel_level_module_output.decoder_last_feature,) + for f in pixel_level_module_output.decoder_features: + pixel_decoder_hidden_states += (f,) + transformer_decoder_hidden_states = transformer_module_output.auxiliary_predictions + + output = OneFormerModelOutput( + encoder_hidden_states=encoder_hidden_states, + pixel_decoder_hidden_states=pixel_decoder_hidden_states, + transformer_decoder_hidden_states=transformer_decoder_hidden_states, + transformer_decoder_object_queries=queries, + transformer_decoder_contrastive_queries=transformer_module_output.contrastive_logits, + transformer_decoder_mask_predictions=transformer_module_output.prediction_masks, + transformer_decoder_class_predictions=transformer_module_output.prediction_class, + transformer_decoder_auxiliary_predictions=transformer_module_output.auxiliary_predictions, + text_queries=text_queries, + task_token=task_token, + attentions=transformer_module_output.attentions, + ) + + if not return_dict: + output = tuple(v for v in output.values()) + + return output + + +@auto_docstring( + custom_intro=""" + OneFormer Model for instance, semantic and panoptic image segmentation. + """ +) +class OneFormerForUniversalSegmentation(OneFormerPreTrainedModel): + main_input_name = ["pixel_values", "task_inputs"] + + def __init__(self, config: OneFormerConfig): + super().__init__(config) + self.model = OneFormerModel(config) + + self.matcher = OneFormerHungarianMatcher( + cost_class=config.class_weight, + cost_dice=config.dice_weight, + cost_mask=config.mask_weight, + num_points=config.train_num_points, + ) + + self.weight_dict: dict[str, float] = { + "loss_cross_entropy": config.class_weight, + "loss_mask": config.mask_weight, + "loss_dice": config.dice_weight, + "loss_contrastive": config.contrastive_weight, + } + + self.criterion = OneFormerLoss( + num_classes=config.num_labels, + matcher=self.matcher, + weight_dict=self.weight_dict, + eos_coef=config.no_object_weight, + num_points=config.train_num_points, + oversample_ratio=config.oversample_ratio, + importance_sample_ratio=config.importance_sample_ratio, + contrastive_temperature=config.contrastive_temperature, + ) + + self.post_init() + + def get_loss_dict( + self, + masks_queries_logits: Tensor, + class_queries_logits: Tensor, + contrastive_queries_logits: Tensor, + mask_labels: Tensor, + class_labels: Tensor, + text_queries: Tensor, + auxiliary_predictions: dict[str, Tensor], + calculate_contrastive_loss: bool, + ) -> dict[str, Tensor]: + loss_dict: dict[str, Tensor] = self.criterion( + masks_queries_logits=masks_queries_logits, + class_queries_logits=class_queries_logits, + contrastive_queries_logits=contrastive_queries_logits, + mask_labels=mask_labels, + class_labels=class_labels, + text_queries=text_queries, + auxiliary_predictions=auxiliary_predictions, + calculate_contrastive_loss=calculate_contrastive_loss, + ) + + # weight each loss by `self.weight_dict[]` including auxiliary losses + for key, weight in self.weight_dict.items(): + for loss_key, loss in loss_dict.items(): + if key in loss_key: + loss *= weight + + return loss_dict + + def get_loss(self, loss_dict: dict[str, Tensor]) -> Tensor: + return sum(loss_dict.values()) + + @auto_docstring + def forward( + self, + pixel_values: Tensor, + task_inputs: Tensor, + text_inputs: Tensor | None = None, + mask_labels: list[Tensor] | None = None, + class_labels: list[Tensor] | None = None, + pixel_mask: Tensor | None = None, + output_auxiliary_logits: bool | None = None, + output_hidden_states: bool | None = None, + output_attentions: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> OneFormerForUniversalSegmentationOutput: + r""" + task_inputs (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Task inputs. Task inputs can be obtained using [`AutoImageProcessor`]. See [`OneFormerProcessor.__call__`] + for details. + text_inputs (`list[torch.Tensor]`, *optional*): + Tensor of shape `(num_queries, sequence_length)` to be fed to a model + mask_labels (`list[torch.Tensor]`, *optional*): + List of mask labels of shape `(num_labels, height, width)` to be fed to a model + class_labels (`list[torch.LongTensor]`, *optional*): + list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They identify the + labels of `mask_labels`, e.g. the label of `mask_labels[i][j]` if `class_labels[i][j]`. + output_auxiliary_logits (`bool`, *optional*): + Whether or not to output auxiliary logits. + + Example: + + Universal segmentation example: + + ```python + >>> from transformers import OneFormerProcessor, OneFormerForUniversalSegmentation + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> import torch + + >>> # load OneFormer fine-tuned on ADE20k for universal segmentation + >>> processor = OneFormerProcessor.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny") + >>> model = OneFormerForUniversalSegmentation.from_pretrained("shi-labs/oneformer_ade20k_swin_tiny") + + >>> url = ( + ... "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" + ... ) + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> # Semantic Segmentation + >>> inputs = processor(image, ["semantic"], return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)` + >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` + >>> class_queries_logits = outputs.class_queries_logits + >>> masks_queries_logits = outputs.masks_queries_logits + + >>> # you can pass them to processor for semantic postprocessing + >>> predicted_semantic_map = processor.post_process_semantic_segmentation( + ... outputs, target_sizes=[(image.height, image.width)] + ... )[0] + >>> f"👉 Semantic Predictions Shape: {list(predicted_semantic_map.shape)}" + '👉 Semantic Predictions Shape: [512, 683]' + + >>> # Instance Segmentation + >>> inputs = processor(image, ["instance"], return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)` + >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` + >>> class_queries_logits = outputs.class_queries_logits + >>> masks_queries_logits = outputs.masks_queries_logits + + >>> # you can pass them to processor for instance postprocessing + >>> predicted_instance_map = processor.post_process_instance_segmentation( + ... outputs, target_sizes=[(image.height, image.width)] + ... )[0]["segmentation"] + >>> f"👉 Instance Predictions Shape: {list(predicted_instance_map.shape)}" + '👉 Instance Predictions Shape: [512, 683]' + + >>> # Panoptic Segmentation + >>> inputs = processor(image, ["panoptic"], return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)` + >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` + >>> class_queries_logits = outputs.class_queries_logits + >>> masks_queries_logits = outputs.masks_queries_logits + + >>> # you can pass them to processor for panoptic postprocessing + >>> predicted_panoptic_map = processor.post_process_panoptic_segmentation( + ... outputs, target_sizes=[(image.height, image.width)] + ... )[0]["segmentation"] + >>> f"👉 Panoptic Predictions Shape: {list(predicted_panoptic_map.shape)}" + '👉 Panoptic Predictions Shape: [512, 683]' + ``` + """ + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.model( + pixel_values=pixel_values, + task_inputs=task_inputs, + text_inputs=text_inputs, + pixel_mask=pixel_mask, + output_hidden_states=output_hidden_states or self.config.use_auxiliary_loss, + output_attentions=output_attentions, + return_dict=True, + ) + + loss, loss_dict, auxiliary_predictions = None, None, None + + class_queries_logits = outputs.transformer_decoder_class_predictions + masks_queries_logits = outputs.transformer_decoder_mask_predictions + contrastive_queries_logits = outputs.transformer_decoder_contrastive_queries + auxiliary_predictions = outputs.transformer_decoder_auxiliary_predictions + text_queries = outputs.text_queries + + if mask_labels is not None and class_labels is not None: + loss_dict: dict[str, Tensor] = self.get_loss_dict( + masks_queries_logits=masks_queries_logits, + class_queries_logits=class_queries_logits, + contrastive_queries_logits=contrastive_queries_logits, + mask_labels=mask_labels, + class_labels=class_labels, + text_queries=text_queries, + auxiliary_predictions=auxiliary_predictions, + calculate_contrastive_loss=self.config.contrastive_temperature is not None, + ) + loss = self.get_loss(loss_dict) + + output_auxiliary_logits = ( + self.config.output_auxiliary_logits if output_auxiliary_logits is None else output_auxiliary_logits + ) + if not output_auxiliary_logits: + auxiliary_predictions = None + + output = OneFormerForUniversalSegmentationOutput( + class_queries_logits=class_queries_logits, + masks_queries_logits=masks_queries_logits, + auxiliary_predictions=auxiliary_predictions, + loss=loss, + **outputs, + ) + + if not return_dict: + output = tuple(v for v in output.values()) + if loss is not None: + output = (loss) + output + return output + + +__all__ = ["OneFormerForUniversalSegmentation", "OneFormerModel", "OneFormerPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/processing_oneformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/processing_oneformer.py new file mode 100644 index 0000000000000000000000000000000000000000..fef0b2c3b6803f728b9a952713afd96e0591dbef --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/oneformer/processing_oneformer.py @@ -0,0 +1,181 @@ +# Copyright 2022 SHI Labs and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Image/Text processor class for OneFormer +""" + +from ...processing_utils import ProcessorMixin +from ...utils import auto_docstring, is_torch_available + + +if is_torch_available(): + import torch + + +@auto_docstring +class OneFormerProcessor(ProcessorMixin): + def __init__( + self, image_processor=None, tokenizer=None, max_seq_length: int = 77, task_seq_length: int = 77, **kwargs + ): + r""" + max_seq_length (`int`, *optional*, defaults to `77`): + Maximum sequence length for encoding class names and text inputs. This parameter controls the + maximum number of tokens used when tokenizing class names and other text inputs for the model. + task_seq_length (`int`, *optional*, defaults to `77`): + Maximum sequence length specifically for encoding task descriptions. Task descriptions (e.g., + "the task is semantic") are tokenized with this length limit, which may differ from the general + text sequence length. + """ + self.max_seq_length = max_seq_length + self.task_seq_length = task_seq_length + + super().__init__(image_processor, tokenizer) + + def _preprocess_text(self, text_list=None, max_length=77): + if text_list is None: + raise ValueError("tokens cannot be None.") + + tokens = self.tokenizer(text_list, padding="max_length", max_length=max_length, truncation=True) + + attention_masks, input_ids = tokens["attention_mask"], tokens["input_ids"] + + token_inputs = [] + for attn_mask, input_id in zip(attention_masks, input_ids): + token = torch.tensor(attn_mask) * torch.tensor(input_id) + token_inputs.append(token.unsqueeze(0)) + + token_inputs = torch.cat(token_inputs, dim=0) + return token_inputs + + @auto_docstring + def __call__(self, images=None, task_inputs=None, segmentation_maps=None, **kwargs): + r""" + task_inputs (`str` or `list[str]`, *required*): + The task type(s) for segmentation. Must be one or more of `"semantic"`, `"instance"`, or `"panoptic"`. + Can be a single string for a single image, or a list of strings (one per image) for batch processing. + The task type determines which type of segmentation the model will perform on the input images. + segmentation_maps (`ImageInput`, *optional*): + The corresponding semantic segmentation maps with the pixel-wise annotations. + + (`bool`, *optional*, defaults to `True`): + Whether or not to pad images up to the largest image in a batch and create a pixel mask. + + If left to the default, will return a pixel mask that is: + + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + - **task_inputs** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + """ + + if task_inputs is None: + raise ValueError("You have to specify the task_input. Found None.") + elif images is None: + raise ValueError("You have to specify the image. Found None.") + + if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs): + raise ValueError("task_inputs must be semantic, instance, or panoptic.") + + encoded_inputs = self.image_processor(images, task_inputs, segmentation_maps, **kwargs) + + if isinstance(task_inputs, str): + task_inputs = [task_inputs] + + if isinstance(task_inputs, list) and all(isinstance(task_input, str) for task_input in task_inputs): + task_token_inputs = [] + for task in task_inputs: + task_input = f"the task is {task}" + task_token_inputs.append(task_input) + encoded_inputs["task_inputs"] = self._preprocess_text(task_token_inputs, max_length=self.task_seq_length) + else: + raise TypeError("Task Inputs should be a string or a list of strings.") + + if hasattr(encoded_inputs, "text_inputs"): + texts_list = encoded_inputs.text_inputs + + text_inputs = [] + for texts in texts_list: + text_input_list = self._preprocess_text(texts, max_length=self.max_seq_length) + text_inputs.append(text_input_list.unsqueeze(0)) + + encoded_inputs["text_inputs"] = torch.cat(text_inputs, dim=0) + + return encoded_inputs + + def encode_inputs(self, images=None, task_inputs=None, segmentation_maps=None, **kwargs): + """ + This method forwards all its arguments to [`OneFormerImageProcessor.encode_inputs`] and then tokenizes the + task_inputs. Please refer to the docstring of this method for more information. + """ + + if task_inputs is None: + raise ValueError("You have to specify the task_input. Found None.") + elif images is None: + raise ValueError("You have to specify the image. Found None.") + + if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs): + raise ValueError("task_inputs must be semantic, instance, or panoptic.") + + encoded_inputs = self.image_processor.encode_inputs(images, task_inputs, segmentation_maps, **kwargs) + + if isinstance(task_inputs, str): + task_inputs = [task_inputs] + + if isinstance(task_inputs, list) and all(isinstance(task_input, str) for task_input in task_inputs): + task_token_inputs = [] + for task in task_inputs: + task_input = f"the task is {task}" + task_token_inputs.append(task_input) + encoded_inputs["task_inputs"] = self._preprocess_text(task_token_inputs, max_length=self.task_seq_length) + else: + raise TypeError("Task Inputs should be a string or a list of strings.") + + if hasattr(encoded_inputs, "text_inputs"): + texts_list = encoded_inputs.text_inputs + + text_inputs = [] + for texts in texts_list: + text_input_list = self._preprocess_text(texts, max_length=self.max_seq_length) + text_inputs.append(text_input_list.unsqueeze(0)) + + encoded_inputs["text_inputs"] = torch.cat(text_inputs, dim=0) + + return encoded_inputs + + def post_process_semantic_segmentation(self, *args, **kwargs): + """ + This method forwards all its arguments to [`OneFormerImageProcessor.post_process_semantic_segmentation`]. + Please refer to the docstring of this method for more information. + """ + return self.image_processor.post_process_semantic_segmentation(*args, **kwargs) + + def post_process_instance_segmentation(self, *args, **kwargs): + """ + This method forwards all its arguments to [`OneFormerImageProcessor.post_process_instance_segmentation`]. + Please refer to the docstring of this method for more information. + """ + return self.image_processor.post_process_instance_segmentation(*args, **kwargs) + + def post_process_panoptic_segmentation(self, *args, **kwargs): + """ + This method forwards all its arguments to [`OneFormerImageProcessor.post_process_panoptic_segmentation`]. + Please refer to the docstring of this method for more information. + """ + return self.image_processor.post_process_panoptic_segmentation(*args, **kwargs) + + +__all__ = ["OneFormerProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..16248677beaef3fd253b4946298eaa1aeb2ce7f7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_openai import * + from .modeling_openai import * + from .tokenization_openai import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/configuration_openai.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/configuration_openai.py new file mode 100644 index 0000000000000000000000000000000000000000..b00b7171e4e52038b9a56845ceea40d9e82a85c6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/configuration_openai.py @@ -0,0 +1,103 @@ +# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""OpenAI GPT configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="openai-community/openai-gpt") +@strict +class OpenAIGPTConfig(PreTrainedConfig): + r""" + afn (`str` or `Callable`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): + The epsilon to use in the layer normalization layers + summary_type (`str`, *optional*, defaults to `"cls_index"`): + Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and + [`OpenAIGPTDoubleHeadsModel`]. + Has to be one of the following options: + - `"last"`: Take the last token hidden state (like XLNet). + - `"first"`: Take the first token hidden state (like BERT). + - `"mean"`: Take the mean of all tokens hidden states. + - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2). + - `"attn"`: Not implemented now, use multi-head attention. + summary_use_proj (`bool`, *optional*, defaults to `True`): + Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and + [`OpenAIGPTDoubleHeadsModel`]. + Whether or not to add a projection after the vector extraction. + summary_activation (`str`, *optional*): + Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and + [`OpenAIGPTDoubleHeadsModel`]. + Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. + summary_proj_to_labels (`bool`, *optional*, defaults to `True`): + Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and + [`OpenAIGPTDoubleHeadsModel`]. + Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. + summary_first_dropout (`float`, *optional*, defaults to 0.1): + Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and + [`OpenAIGPTDoubleHeadsModel`]. + The dropout ratio to be used after the projection and activation. + + Examples: + + ```python + >>> from transformers import OpenAIGPTConfig, OpenAIGPTModel + + >>> # Initializing a GPT configuration + >>> configuration = OpenAIGPTConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = OpenAIGPTModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "openai-gpt" + attribute_map = { + "max_position_embeddings": "n_positions", + "hidden_size": "n_embd", + "num_attention_heads": "n_head", + "num_hidden_layers": "n_layer", + } + + vocab_size: int = 40478 + n_positions: int = 512 + n_embd: int = 768 + n_layer: int = 12 + n_head: int = 12 + afn: str = "gelu" + resid_pdrop: float | int = 0.1 + embd_pdrop: float | int = 0.1 + attn_pdrop: float | int = 0.1 + layer_norm_epsilon: float = 1e-5 + initializer_range: float = 0.02 + summary_type: str = "cls_index" + summary_use_proj: bool = True + summary_activation: str | None = None + summary_proj_to_labels: bool = True + summary_first_dropout: float | int = 0.1 + pad_token_id: int | None = None + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = None + tie_word_embeddings: bool = True + + +__all__ = ["OpenAIGPTConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/modeling_openai.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/modeling_openai.py new file mode 100644 index 0000000000000000000000000000000000000000..3c5eba0f5006a2715723fe2ec079d63d7584a9ef --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/modeling_openai.py @@ -0,0 +1,728 @@ +# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch OpenAI GPT model.""" + +import math +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import gelu_new, get_activation, silu +from ...generation import GenerationMixin +from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import Conv1D +from ...utils import ( + ModelOutput, + auto_docstring, + logging, +) +from .configuration_openai import OpenAIGPTConfig + + +logger = logging.get_logger(__name__) + + +ACT_FNS = {"relu": nn.ReLU(), "silu": silu, "gelu": gelu_new, "swish": silu} + + +class Attention(nn.Module): + def __init__(self, nx, n_positions, config, scale=False): + super().__init__() + self.n_positions = n_positions + n_state = nx # in Attention: n_state=768 (nx=n_embd) + if n_state % config.n_head != 0: + raise ValueError(f"Attention n_state shape: {n_state} must be divisible by config.n_head {config.n_head}") + self.register_buffer( + "bias", + torch.tril(torch.ones(n_positions, n_positions)).view(1, 1, n_positions, n_positions), + persistent=False, + ) + self.n_head = config.n_head + self.split_size = n_state + self.scale = scale + + self.c_attn = Conv1D(n_state * 3, nx) + self.c_proj = Conv1D(n_state, nx) + self.attn_dropout = nn.Dropout(config.attn_pdrop) + self.resid_dropout = nn.Dropout(config.resid_pdrop) + + def _attn(self, q, k, v, attention_mask=None, output_attentions=False): + w = torch.matmul(q, k) + if self.scale: + w = w / math.sqrt(v.size(-1)) + # XD: self.b may be larger than w, so we need to crop it + b = self.bias[:, :, : w.size(-2), : w.size(-1)] + w = w * b + -1e4 * (1 - b) + + if attention_mask is not None: + # Apply the attention mask + w = w + attention_mask + + w = nn.functional.softmax(w, dim=-1) + w = self.attn_dropout(w) + + outputs = [torch.matmul(w, v)] + if output_attentions: + outputs.append(w) + return outputs + + def merge_heads(self, x): + x = x.permute(0, 2, 1, 3).contiguous() + new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),) + return x.view(*new_x_shape) + + def split_heads(self, x, k=False): + new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head) + x = x.view(*new_x_shape) + if k: + return x.permute(0, 2, 3, 1) + else: + return x.permute(0, 2, 1, 3) + + def forward(self, x, attention_mask=None, output_attentions=False): + x = self.c_attn(x) + query, key, value = x.split(self.split_size, dim=2) + query = self.split_heads(query) + key = self.split_heads(key, k=True) + value = self.split_heads(value) + + attn_outputs = self._attn(query, key, value, attention_mask, output_attentions) + a = attn_outputs[0] + + a = self.merge_heads(a) + a = self.c_proj(a) + a = self.resid_dropout(a) + + outputs = [a] + attn_outputs[1:] + return outputs # a, (attentions) + + +class MLP(nn.Module): + def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd) + super().__init__() + nx = config.n_embd + self.c_fc = Conv1D(n_state, nx) + self.c_proj = Conv1D(nx, n_state) + self.act = ACT_FNS[config.afn] + self.dropout = nn.Dropout(config.resid_pdrop) + + def forward(self, x): + h = self.act(self.c_fc(x)) + h2 = self.c_proj(h) + return self.dropout(h2) + + +class Block(nn.Module): + def __init__(self, n_positions, config, scale=False): + super().__init__() + nx = config.n_embd + self.attn = Attention(nx, n_positions, config, scale) + self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon) + self.mlp = MLP(4 * nx, config) + self.ln_2 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon) + + def forward(self, x, attention_mask=None, output_attentions=False): + attn_outputs = self.attn( + x, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + a = attn_outputs[0] + + n = self.ln_1(x + a) + m = self.mlp(n) + h = self.ln_2(n + m) + + outputs = [h] + attn_outputs[1:] + return outputs + + +# Copied from transformers.models.xlm.modeling_xlm.XLMSequenceSummary with XLM->OpenAIGPT +class OpenAIGPTSequenceSummary(nn.Module): + r""" + Compute a single vector summary of a sequence hidden states. + + Args: + config ([`OpenAIGPTConfig`]): + The config used by the model. Relevant arguments in the config class of the model are (refer to the actual + config class of your model for the default values it uses): + + - **summary_type** (`str`) -- The method to use to make this summary. Accepted values are: + + - `"last"` -- Take the last token hidden state (like XLNet) + - `"first"` -- Take the first token hidden state (like Bert) + - `"mean"` -- Take the mean of all tokens hidden states + - `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2) + - `"attn"` -- Not implemented now, use multi-head attention + + - **summary_use_proj** (`bool`) -- Add a projection after the vector extraction. + - **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes + (otherwise to `config.hidden_size`). + - **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output, + another string or `None` will add no activation. + - **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation. + - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation. + """ + + def __init__(self, config: OpenAIGPTConfig): + super().__init__() + + self.summary_type = getattr(config, "summary_type", "last") + if self.summary_type == "attn": + # We should use a standard multi-head attention module with absolute positional embedding for that. + # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276 + # We can probably just use the multi-head attention module of PyTorch >=1.1.0 + raise NotImplementedError + + self.summary = nn.Identity() + if hasattr(config, "summary_use_proj") and config.summary_use_proj: + if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0: + num_classes = config.num_labels + else: + num_classes = config.hidden_size + self.summary = nn.Linear(config.hidden_size, num_classes) + + activation_string = getattr(config, "summary_activation", None) + self.activation: Callable = get_activation(activation_string) if activation_string else nn.Identity() + + self.first_dropout = nn.Identity() + if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0: + self.first_dropout = nn.Dropout(config.summary_first_dropout) + + self.last_dropout = nn.Identity() + if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0: + self.last_dropout = nn.Dropout(config.summary_last_dropout) + + def forward( + self, hidden_states: torch.FloatTensor, cls_index: torch.LongTensor | None = None + ) -> torch.FloatTensor: + """ + Compute a single vector summary of a sequence hidden states. + + Args: + hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`): + The hidden states of the last layer. + cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*): + Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token. + + Returns: + `torch.FloatTensor`: The summary of the sequence hidden states. + """ + if self.summary_type == "last": + output = hidden_states[:, -1] + elif self.summary_type == "first": + output = hidden_states[:, 0] + elif self.summary_type == "mean": + output = hidden_states.mean(dim=1) + elif self.summary_type == "cls_index": + if cls_index is None: + cls_index = torch.full_like( + hidden_states[..., :1, :], + hidden_states.shape[-2] - 1, + dtype=torch.long, + ) + else: + cls_index = cls_index.unsqueeze(-1).unsqueeze(-1) + cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),)) + # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states + output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size) + elif self.summary_type == "attn": + raise NotImplementedError + + output = self.first_dropout(output) + output = self.summary(output) + output = self.activation(output) + output = self.last_dropout(output) + + return output + + +@auto_docstring +class OpenAIGPTPreTrainedModel(PreTrainedModel): + config: OpenAIGPTConfig + base_model_prefix = "transformer" + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Attention): + n_positions = module.n_positions + init.copy_( + module.bias, torch.tril(torch.ones(n_positions, n_positions)).view(1, 1, n_positions, n_positions) + ) + elif isinstance(module, OpenAIGPTModel): + init.copy_(module.position_ids, torch.arange(module.config.n_positions)) + + +@auto_docstring( + custom_intro=""" + Base class for outputs of models predicting if two sentences are consecutive or not. + """ +) +@dataclass +class OpenAIGPTDoubleHeadsModelOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss. + mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mc_labels` is provided): + Multiple choice classification loss. + logits (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + mc_logits (`torch.FloatTensor` of shape `(batch_size, num_choices)`): + Prediction scores of the multiple choice classification head (scores for each choice before SoftMax). + """ + + loss: torch.FloatTensor | None = None + mc_loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + mc_logits: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring +class OpenAIGPTModel(OpenAIGPTPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd) + self.positions_embed = nn.Embedding(config.n_positions, config.n_embd) + self.drop = nn.Dropout(config.embd_pdrop) + self.h = nn.ModuleList([Block(config.n_positions, config, scale=True) for _ in range(config.n_layer)]) + + self.register_buffer("position_ids", torch.arange(config.n_positions), persistent=False) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.tokens_embed + + def set_input_embeddings(self, new_embeddings): + self.tokens_embed = new_embeddings + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | BaseModelOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if position_ids is None: + # Code is different from when we had a single embedding matrix from position and token embeddings + position_ids = self.position_ids[None, : input_shape[-1]] + + # Attention mask. + if attention_mask is not None: + # We create a 3D attention mask from a 2D tensor mask. + # Sizes are [batch_size, 1, 1, to_seq_length] + # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] + # this attention mask is more simple than the triangular masking of causal attention + # used in OpenAI GPT, we just need to prepare the broadcast dimension here. + attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and the dtype's smallest value for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + attention_mask = attention_mask.to(dtype=next(self.parameters()).dtype) # fp16 compatibility + attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min + + if inputs_embeds is None: + inputs_embeds = self.tokens_embed(input_ids) + position_embeds = self.positions_embed(position_ids) + if token_type_ids is not None: + token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) + token_type_embeds = self.tokens_embed(token_type_ids) + else: + token_type_embeds = 0 + hidden_states = inputs_embeds + position_embeds + token_type_embeds + hidden_states = self.drop(hidden_states) + + output_shape = input_shape + (hidden_states.size(-1),) + + all_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + for i, block in enumerate(self.h): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + outputs = block(hidden_states, attention_mask, output_attentions=output_attentions) + hidden_states = outputs[0] + if output_attentions: + all_attentions = all_attentions + (outputs[1],) + + hidden_states = hidden_states.view(*output_shape) + # Add last layer + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None) + + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_attentions, + ) + + +@auto_docstring( + custom_intro=""" + OpenAI GPT Model transformer with a language modeling head on top (linear layer with weights tied to the input + embeddings). + """ +) +class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "transformer.tokens_embed.weight"} + + def __init__(self, config): + super().__init__(config) + self.transformer = OpenAIGPTModel(config) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple[torch.Tensor] | CausalLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + transformer_outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + if not return_dict: + output = (logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutput( + loss=loss, + logits=logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs) -> dict[str, Any]: + # Overwritten -- old model with reduced inputs + model_inputs = {"input_ids": input_ids} + + # Forward ALL kwargs that are uninitialized (e.g. `use_cache`). + for key, value in kwargs.items(): + if key not in model_inputs: + model_inputs[key] = value + + return model_inputs + + +@auto_docstring( + custom_intro=""" + OpenAI GPT Model transformer with a language modeling and a multiple-choice classification head on top e.g. for + RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the + input embeddings, the classification head takes as input the input of a specified classification token index in the + input sequence). + """ +) +class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel): + _tied_weights_keys = {"transformer.tokens_embed.weight": "lm_head.weight"} + + def __init__(self, config): + super().__init__(config) + + config.num_labels = 1 + self.transformer = OpenAIGPTModel(config) + self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) + self.multiple_choice_head = OpenAIGPTSequenceSummary(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + mc_token_ids: torch.LongTensor | None = None, + labels: torch.LongTensor | None = None, + mc_labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | OpenAIGPTDoubleHeadsModelOutput: + r""" + mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input): + Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) - + 1]`. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-1, 0, ..., config.vocab_size]` All labels set to `-100` are + ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + mc_labels (`torch.LongTensor` of shape `(batch_size)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` + where *num_choices* is the size of the second dimension of the input tensors. (see *input_ids* above) + + Examples: + + ```python + >>> from transformers import AutoTokenizer, OpenAIGPTDoubleHeadsModel + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt") + >>> model = OpenAIGPTDoubleHeadsModel.from_pretrained("openai-community/openai-gpt") + >>> tokenizer.add_special_tokens( + ... {"cls_token": "[CLS]"} + ... ) # Add a [CLS] to the vocabulary (we should train it also!) + >>> model.resize_token_embeddings(len(tokenizer)) + + >>> choices = ["Hello, my dog is cute [CLS]", "Hello, my cat is cute [CLS]"] + >>> input_ids = torch.tensor([tokenizer.encode(s) for s in choices]).unsqueeze(0) # Batch size 1, 2 choices + >>> mc_token_ids = torch.tensor([input_ids.size(-1) - 1, input_ids.size(-1) - 1]).unsqueeze(0) # Batch size 1 + + >>> outputs = model(input_ids, mc_token_ids=mc_token_ids) + >>> lm_logits = outputs.logits + >>> mc_logits = outputs.mc_logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + transformer_outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + lm_logits = self.lm_head(hidden_states) + mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1) + + lm_loss, mc_loss = None, None + if mc_labels is not None: + loss_fct = CrossEntropyLoss() + mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1)) + if labels is not None: + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + loss_fct = CrossEntropyLoss() + lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + if not return_dict: + output = (lm_logits, mc_logits) + transformer_outputs[1:] + if mc_loss is not None: + output = (mc_loss,) + output + return ((lm_loss,) + output) if lm_loss is not None else output + + return OpenAIGPTDoubleHeadsModelOutput( + loss=lm_loss, + mc_loss=mc_loss, + logits=lm_logits, + mc_logits=mc_logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The Original OpenAI GPT Model transformer with a sequence classification head on top (linear layer). + [`OpenAIGPTForSequenceClassification`] uses the last token in order to do the classification, as other causal + models (e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the + last token. If a `pad_token_id` is defined in the configuration, it finds the last token that is not a padding + token in each row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since + it cannot guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take + the last value in each row of the batch). + """ +) +class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.transformer = OpenAIGPTModel(config) + self.score = nn.Linear(config.n_embd, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | SequenceClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + transformer_outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size, sequence_length = input_ids.shape[:2] + else: + batch_size, sequence_length = inputs_embeds.shape[:2] + + # Ensure the batch size is > 1 if there is no padding. + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + last_non_pad_token = -1 + elif input_ids is not None: + # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id + non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32) + token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32) + last_non_pad_token = (token_indices * non_pad_mask).argmax(-1) + else: + last_non_pad_token = -1 + logger.warning_once( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`" + ) + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=pooled_logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + +__all__ = [ + "OpenAIGPTDoubleHeadsModel", + "OpenAIGPTForSequenceClassification", + "OpenAIGPTLMHeadModel", + "OpenAIGPTModel", + "OpenAIGPTPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/tokenization_openai.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/tokenization_openai.py new file mode 100644 index 0000000000000000000000000000000000000000..143c7aedabbdb9fcea18fe7560485ee02d38c743 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai/tokenization_openai.py @@ -0,0 +1,98 @@ +# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for OpenAI GPT.""" + +from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers +from tokenizers.models import BPE + +from ...tokenization_utils_tokenizers import TokenizersBackend +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} + + +class OpenAIGPTTokenizer(TokenizersBackend): + """ + Construct a GPT Tokenizer (backed by HuggingFace's *tokenizers* library). Based on Byte-Pair-Encoding with + the following peculiarities: + + - lower case all inputs + - uses BERT's BasicTokenizer for pre-BPE tokenization + + This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + Args: + vocab_file (`str`, *optional*): + Path to the vocabulary file. + merges_file (`str`, *optional*): + Path to the merges file. + tokenizer_file (`str`, *optional*): + Path to a tokenizers JSON file containing the serialization of a tokenizer. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + vocab (`str` or `dict[str, int]`, *optional*): + Custom vocabulary dictionary. If not provided, a blank vocabulary is initialized. + merges (`str` or `list[str]`, *optional*): + Custom merges list. If not provided, an empty list is used. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + model = BPE + + def __init__( + self, + vocab: str | dict[str, int] | None = None, + merges: str | list[str] | None = None, + unk_token: str = "", + **kwargs, + ): + self._vocab = vocab if vocab is not None else {str(unk_token): 0} + self._merges = merges or [] + + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + dropout=None, + continuing_subword_prefix="", + end_of_word_suffix="", + fuse_unk=False, + unk_token=str(unk_token), + ) + ) + + # Set normalizer and pre-tokenizer to mimic OpenAI GPT behavior + # OpenAI GPT uses BERT BasicTokenizer with lower_case=True + self._tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True) + + self._tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer() + self._tokenizer.decoder = decoders.BPEDecoder(suffix="") + + super().__init__( + unk_token=unk_token, + **kwargs, + ) + + @property + def do_lower_case(self): + return True + + +__all__ = ["OpenAIGPTTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e6dd80219edcfbd162d72d32b859839f0e967045 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_openai_privacy_filter import * + from .modeling_openai_privacy_filter import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/configuration_openai_privacy_filter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/configuration_openai_privacy_filter.py new file mode 100644 index 0000000000000000000000000000000000000000..e7aaefde4bcaf23f5ce8fb18da5d961196e19ea1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/configuration_openai_privacy_filter.py @@ -0,0 +1,118 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/openai_privacy_filter/modular_openai_privacy_filter.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_openai_privacy_filter.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +OPENAI_PRIVACY_FILTER_SPAN_LABELS = ( + "O", + "account_number", + "private_address", + "private_date", + "private_email", + "private_person", + "private_phone", + "private_url", + "secret", +) + +OPENAI_PRIVACY_FILTER_NER_LABELS = ("O",) + tuple( + f"{prefix}-{label}" + for label in OPENAI_PRIVACY_FILTER_SPAN_LABELS + if label != "O" + for prefix in ("B", "I", "E", "S") +) + + +@auto_docstring(checkpoint="openai/privacy-filter") +@strict +class OpenAIPrivacyFilterConfig(PreTrainedConfig): + model_type = "openai_privacy_filter" + attribute_map = { + "num_experts": "num_local_experts", + } + default_theta = 150000.0 + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + base_model_ep_plan = { + "layers.*.mlp.router": "ep_router", + "layers.*.mlp.experts.gate_up_proj": "grouped_gemm", + "layers.*.mlp.experts.gate_up_proj_bias": "grouped_gemm", + "layers.*.mlp.experts.down_proj": "grouped_gemm", + "layers.*.mlp.experts.down_proj_bias": "grouped_gemm", + "layers.*.mlp.experts": "moe_tp_experts", + } + num_hidden_layers: int = 8 + num_local_experts: int = 128 + vocab_size: int = 200064 + hidden_size: int = 640 + intermediate_size: int = 640 + head_dim: int = 64 + num_attention_heads: int = 14 + num_key_value_heads: int = 2 + sliding_window: int = 128 + tie_word_embeddings: bool = False + initializer_range: float = 0.02 + max_position_embeddings: int = 131072 + rms_norm_eps: float = 1e-5 + rope_parameters: dict | None = None + attention_dropout: float | int = 0.0 + num_experts_per_tok: int = 4 + router_aux_loss_coef: float = 0.001 + output_router_logits: bool = False + use_cache: bool = True + pad_token_id: int | None = 199999 + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = 199999 + attention_bias: bool = True + classifier_dropout: float = 0.0 + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + self.head_dim = self.head_dim if self.head_dim is not None else self.hidden_size // self.num_attention_heads + + if self.rope_parameters is None: + self.rope_parameters = { + "rope_type": "yarn", + "factor": 32.0, + "beta_fast": 32.0, + "beta_slow": 1.0, + "truncate": False, + "original_max_position_embeddings": 4096, + } + + requested_num_labels = kwargs.pop("num_labels", len(OPENAI_PRIVACY_FILTER_NER_LABELS)) + if self.id2label is None and requested_num_labels == len(OPENAI_PRIVACY_FILTER_NER_LABELS): + self.id2label = dict(enumerate(OPENAI_PRIVACY_FILTER_NER_LABELS)) + elif self.id2label is None: + self.num_labels = requested_num_labels + if self.label2id is None and self.id2label is not None: + self.label2id = {label: idx for idx, label in self.id2label.items()} + + super().__post_init__(**kwargs) + + +__all__ = ["OpenAIPrivacyFilterConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/modeling_openai_privacy_filter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/modeling_openai_privacy_filter.py new file mode 100644 index 0000000000000000000000000000000000000000..4082a3ee9bf48b57447d2efd490dc25b970d0b6b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/modeling_openai_privacy_filter.py @@ -0,0 +1,506 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/openai_privacy_filter/modular_openai_privacy_filter.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_openai_privacy_filter.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable +from typing import Optional + +import torch +from torch import nn +from torch.nn import functional as F + +from ... import initialization as init +from ...integrations import use_experts_implementation, use_kernel_forward_from_hub, use_kernelized_func +from ...masking_utils import create_bidirectional_sliding_window_mask +from ...modeling_layers import GenericForTokenClassification, GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_openai_privacy_filter import OpenAIPrivacyFilterConfig + + +@use_kernel_forward_from_hub("RMSNorm") +class OpenAIPrivacyFilterRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + OpenAIPrivacyFilterRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return (self.weight * hidden_states).to(input_dtype) # main diff with Llama + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class OpenAIPrivacyFilterRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: OpenAIPrivacyFilterConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: OpenAIPrivacyFilterConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = freqs + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(x.dtype), sin.to(x.dtype) + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = _apply_rotary_emb(q, cos, sin) + k_embed = _apply_rotary_emb(k, cos, sin) + return q_embed, k_embed + + +def _apply_rotary_emb( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> torch.Tensor: + # Interleaving layout instead of concatenated + first_half, second_half = x[..., ::2], x[..., 1::2] + first_ = first_half * cos - second_half * sin + second_ = second_half * cos + first_half * sin + return torch.stack((first_, second_), dim=-1).flatten(-2) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float | int = 0.0, + **kwargs, +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + sinks = module.sinks.reshape(1, -1, 1, 1).expand(query.shape[0], -1, query.shape[-2], -1) + combined_logits = torch.cat([attn_weights, sinks], dim=-1) + + # This was not in the original implementation and slightly affect results; it prevents overflow in BF16/FP16 + # when training with bsz>1 we clamp max values. + + combined_logits = combined_logits - combined_logits.max(dim=-1, keepdim=True).values + probs = nn.functional.softmax(combined_logits, dim=-1, dtype=torch.float32) # Softmax in fp32 + scores = probs[..., :-1] # we drop the sink here + attn_weights = nn.functional.dropout(scores, p=dropout, training=module.training).to(value_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class OpenAIPrivacyFilterAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: OpenAIPrivacyFilterConfig): + super().__init__() + self.config = config + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = config.head_dim**-0.25 + self.attention_dropout = config.attention_dropout + self.is_causal = False + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + self.sliding_window = config.sliding_window + 1 # Account for FA symmetry using -1 + self.sinks = nn.Parameter(torch.empty(config.num_attention_heads)) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + # Unique: applying scale individually to each Q and K + query_states = query_states * self.scaling + key_states = key_states * self.scaling + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=1.0, # scaling applied before + sliding_window=self.sliding_window, + s_aux=self.sinks, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +@use_experts_implementation(is_transposed=True, has_bias=True) +class OpenAIPrivacyFilterExperts(nn.Module): + def __init__(self, config): + super().__init__() + self.intermediate_size = config.intermediate_size + self.num_experts = config.num_local_experts + self.hidden_size = config.hidden_size + self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_size, 2 * self.intermediate_size)) + self.gate_up_proj_bias = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_size)) + self.down_proj = nn.Parameter(torch.empty((self.num_experts, self.intermediate_size, self.hidden_size))) + self.down_proj_bias = nn.Parameter(torch.empty(self.num_experts, self.hidden_size)) + self.alpha = 1.702 + self.limit = 7.0 + + def _apply_gate(self, gate_up: torch.Tensor) -> torch.Tensor: + # Concatenated layout instead of interleaving + gate, up = gate_up.chunk(2, dim=-1) + gate = gate.clamp(min=None, max=self.limit) + up = up.clamp(min=-self.limit, max=self.limit) + glu = gate * torch.sigmoid(gate * self.alpha) + gated_output = (up + 1) * glu + return gated_output + + def forward(self, hidden_states: torch.Tensor, router_indices=None, routing_weights=None) -> torch.Tensor: + original_dtype = hidden_states.dtype + + # Accumulate over fp32 + next_states = torch.zeros_like(hidden_states, dtype=torch.float32, device=hidden_states.device) + with torch.no_grad(): + expert_mask = torch.nn.functional.one_hot( + router_indices, num_classes=self.num_experts + ) # masking is also a class + expert_mask = expert_mask.permute(2, 1, 0) + # we sum on the top_k and on the sequence length to get which experts + # are hit this time around + expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero() + + # Key change to original gpt oss is to stay in fp32 precision for all linear projections / muls + for expert_idx in expert_hit: + # expert_idx only have 1 element, so we can use scale for fast indexing + expert_idx = expert_idx[0] + # skip masking index + if expert_idx == self.num_experts: + continue + top_k_pos, token_idx = torch.where(expert_mask[expert_idx]) + current_state = hidden_states[token_idx] + gate_up = ( + current_state.float() @ self.gate_up_proj[expert_idx].float() + + self.gate_up_proj_bias[expert_idx].float() + ) + gated_output = self._apply_gate(gate_up).float() + out = gated_output.float() @ self.down_proj[expert_idx].float() + self.down_proj_bias[expert_idx].float() + weighted_output = out * routing_weights[token_idx, top_k_pos, None].float() + next_states.index_add_(0, token_idx, weighted_output) + + return next_states.to(original_dtype) + + +class OpenAIPrivacyFilterTopKRouter(nn.Module): + def __init__(self, config): + super().__init__() + self.top_k = config.num_experts_per_tok + self.num_experts = config.num_local_experts + self.hidden_dim = config.hidden_size + self.weight = nn.Parameter(torch.zeros(self.num_experts, self.hidden_dim)) + self.bias = nn.Parameter(torch.zeros(self.num_experts)) + + def forward(self, hidden_states): + # Force fp32 + router_logits = F.linear( + hidden_states.float(), self.weight.float(), self.bias.float() + ) # (num_tokens, num_experts) + router_top_value, router_indices = torch.topk(router_logits, self.top_k, dim=-1) # (num_tokens, top_k) + router_scores = torch.nn.functional.softmax(router_top_value, dim=1, dtype=router_top_value.dtype) + # Additional scaling + router_scores = router_scores / self.top_k + return router_logits, router_scores, router_indices + + +class OpenAIPrivacyFilterMLP(nn.Module): + """Similar to GPT Oss but with FP32 focus + added experts scaling""" + + def __init__(self, config): + super().__init__() + self.router = OpenAIPrivacyFilterTopKRouter(config) + self.num_experts = config.num_experts_per_tok + self.experts = OpenAIPrivacyFilterExperts(config) + + def forward(self, hidden_states): + batch_size, sequence_length, hidden_dim = hidden_states.shape + hidden_states = hidden_states.reshape(-1, hidden_dim) + _, router_scores, router_indices = self.router(hidden_states) + hidden_states = self.experts(hidden_states, router_indices, router_scores) + # Additional scaling + hidden_states = hidden_states * self.num_experts + hidden_states = hidden_states.reshape(batch_size, sequence_length, hidden_dim) + return hidden_states, router_scores + + +class OpenAIPrivacyFilterEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: OpenAIPrivacyFilterConfig): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = OpenAIPrivacyFilterAttention(config) + self.mlp = OpenAIPrivacyFilterMLP(config) + self.input_layernorm = OpenAIPrivacyFilterRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = OpenAIPrivacyFilterRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + position_embeddings=position_embeddings, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states, _ = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class OpenAIPrivacyFilterPreTrainedModel(PreTrainedModel): + config: OpenAIPrivacyFilterConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["OpenAIPrivacyFilterEncoderLayer"] + _skip_keys_device_placement = None # No cache + _supports_flash_attn = True + _supports_sdpa = False + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + + _can_record_outputs = { + "router_logits": OutputRecorder(OpenAIPrivacyFilterTopKRouter, index=0), + "hidden_states": OpenAIPrivacyFilterEncoderLayer, + "attentions": OpenAIPrivacyFilterAttention, + } + _keep_in_fp32_modules = [] + _compatible_flash_implementations = ["kernels-community/vllm-flash-attn3", "flash_attention_4"] + _keep_in_fp32_modules_strict = ["sinks"] + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + std = self.config.initializer_range + if isinstance(module, OpenAIPrivacyFilterExperts): + init.normal_(module.gate_up_proj, mean=0.0, std=std) + init.zeros_(module.gate_up_proj_bias) + init.normal_(module.down_proj, mean=0.0, std=std) + init.zeros_(module.down_proj_bias) + elif isinstance(module, OpenAIPrivacyFilterAttention): + init.normal_(module.sinks, mean=0.0, std=std) + elif isinstance(module, OpenAIPrivacyFilterTopKRouter): + init.normal_(module.weight, mean=0.0, std=std) + init.normal_(module.bias, mean=0.0, std=std) + + def get_correct_experts_implementation(self, requested_experts: str | None) -> str: + """The model is very sensitive to accumulation orders, hence we default to `eager` instead""" + requested_experts = "eager" if requested_experts is None else requested_experts + return super().get_correct_experts_implementation(requested_experts) + + +@auto_docstring +class OpenAIPrivacyFilterModel(OpenAIPrivacyFilterPreTrainedModel): + def __init__(self, config: OpenAIPrivacyFilterConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList([OpenAIPrivacyFilterEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.norm = OpenAIPrivacyFilterRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = OpenAIPrivacyFilterRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + hidden_states = inputs_embeds + + if position_ids is None: + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + position_ids = position_ids.unsqueeze(0) + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + attention_mask = create_bidirectional_sliding_window_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + ) + + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + position_embeddings=position_embeddings, + attention_mask=attention_mask, + position_ids=position_ids, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +class OpenAIPrivacyFilterForTokenClassification(GenericForTokenClassification, OpenAIPrivacyFilterPreTrainedModel): ... + + +__all__ = [ + "OpenAIPrivacyFilterForTokenClassification", + "OpenAIPrivacyFilterModel", + "OpenAIPrivacyFilterPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/modular_openai_privacy_filter.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/modular_openai_privacy_filter.py new file mode 100644 index 0000000000000000000000000000000000000000..422235d9da91042955f865986eee40bbff9852ee --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/openai_privacy_filter/modular_openai_privacy_filter.py @@ -0,0 +1,404 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Privacy Filter model.""" + +from collections.abc import Callable + +import torch +from huggingface_hub.dataclasses import strict +from torch import nn +from torch.nn import functional as F + +from ...configuration_utils import PreTrainedConfig +from ...integrations import use_experts_implementation +from ...masking_utils import create_bidirectional_sliding_window_mask +from ...modeling_layers import GenericForTokenClassification +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from ..gpt_oss.configuration_gpt_oss import GptOssConfig +from ..gpt_oss.modeling_gpt_oss import ( + GptOssAttention, + GptOssDecoderLayer, + GptOssExperts, + GptOssModel, + GptOssPreTrainedModel, + GptOssRMSNorm, + GptOssRotaryEmbedding, + GptOssTopKRouter, + apply_rotary_pos_emb, + repeat_kv, +) + + +logger = logging.get_logger(__name__) + + +OPENAI_PRIVACY_FILTER_SPAN_LABELS = ( + "O", + "account_number", + "private_address", + "private_date", + "private_email", + "private_person", + "private_phone", + "private_url", + "secret", +) + +OPENAI_PRIVACY_FILTER_NER_LABELS = ("O",) + tuple( + f"{prefix}-{label}" + for label in OPENAI_PRIVACY_FILTER_SPAN_LABELS + if label != "O" + for prefix in ("B", "I", "E", "S") +) + + +@auto_docstring(checkpoint="openai/privacy-filter") +@strict +class OpenAIPrivacyFilterConfig(GptOssConfig): + model_type = "openai_privacy_filter" + vocab_size: int = 200064 + hidden_size: int = 640 + intermediate_size: int = 640 + num_hidden_layers: int = 8 + num_attention_heads: int = 14 + num_key_value_heads: int = 2 + sliding_window: int = 128 + classifier_dropout: float = 0.0 + pad_token_id: int | None = 199999 + eos_token_id: int | list[int] | None = 199999 + layer_types = AttributeError() # SWA only + hidden_act = AttributeError() # Not used as it's expert MLPs only + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + self.head_dim = self.head_dim if self.head_dim is not None else self.hidden_size // self.num_attention_heads + + if self.rope_parameters is None: + self.rope_parameters = { + "rope_type": "yarn", + "factor": 32.0, + "beta_fast": 32.0, + "beta_slow": 1.0, + "truncate": False, + "original_max_position_embeddings": 4096, + } + + requested_num_labels = kwargs.pop("num_labels", len(OPENAI_PRIVACY_FILTER_NER_LABELS)) + if self.id2label is None and requested_num_labels == len(OPENAI_PRIVACY_FILTER_NER_LABELS): + self.id2label = dict(enumerate(OPENAI_PRIVACY_FILTER_NER_LABELS)) + elif self.id2label is None: + self.num_labels = requested_num_labels + if self.label2id is None and self.id2label is not None: + self.label2id = {label: idx for idx, label in self.id2label.items()} + + PreTrainedConfig.__post_init__(self, **kwargs) + + +class OpenAIPrivacyFilterRMSNorm(GptOssRMSNorm): + pass + + +class OpenAIPrivacyFilterRotaryEmbedding(GptOssRotaryEmbedding): + pass + + +def _apply_rotary_emb( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> torch.Tensor: + # Interleaving layout instead of concatenated + first_half, second_half = x[..., ::2], x[..., 1::2] + first_ = first_half * cos - second_half * sin + second_ = second_half * cos + first_half * sin + return torch.stack((first_, second_), dim=-1).flatten(-2) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float | int = 0.0, + **kwargs, +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + sinks = module.sinks.reshape(1, -1, 1, 1).expand(query.shape[0], -1, query.shape[-2], -1) + combined_logits = torch.cat([attn_weights, sinks], dim=-1) + + # This was not in the original implementation and slightly affect results; it prevents overflow in BF16/FP16 + # when training with bsz>1 we clamp max values. + + combined_logits = combined_logits - combined_logits.max(dim=-1, keepdim=True).values + probs = nn.functional.softmax(combined_logits, dim=-1, dtype=torch.float32) # Softmax in fp32 + scores = probs[..., :-1] # we drop the sink here + attn_weights = nn.functional.dropout(scores, p=dropout, training=module.training).to(value_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + return attn_output, attn_weights + + +class OpenAIPrivacyFilterAttention(GptOssAttention): + def __init__(self, config: OpenAIPrivacyFilterConfig): + super().__init__(config) + del self.layer_idx # Only for caching + del self.layer_type # SWA only + self.is_causal = False + self.sliding_window = config.sliding_window + 1 # Account for FA symmetry using -1 + self.scaling = config.head_dim**-0.25 + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + # Unique: applying scale individually to each Q and K + query_states = query_states * self.scaling + key_states = key_states * self.scaling + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=1.0, # scaling applied before + sliding_window=self.sliding_window, + s_aux=self.sinks, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +@use_experts_implementation(is_transposed=True, has_bias=True) +class OpenAIPrivacyFilterExperts(GptOssExperts): + def _apply_gate(self, gate_up: torch.Tensor) -> torch.Tensor: + # Concatenated layout instead of interleaving + gate, up = gate_up.chunk(2, dim=-1) + gate = gate.clamp(min=None, max=self.limit) + up = up.clamp(min=-self.limit, max=self.limit) + glu = gate * torch.sigmoid(gate * self.alpha) + gated_output = (up + 1) * glu + return gated_output + + def forward(self, hidden_states: torch.Tensor, router_indices=None, routing_weights=None) -> torch.Tensor: + original_dtype = hidden_states.dtype + + # Accumulate over fp32 + next_states = torch.zeros_like(hidden_states, dtype=torch.float32, device=hidden_states.device) + with torch.no_grad(): + expert_mask = torch.nn.functional.one_hot( + router_indices, num_classes=self.num_experts + ) # masking is also a class + expert_mask = expert_mask.permute(2, 1, 0) + # we sum on the top_k and on the sequence length to get which experts + # are hit this time around + expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero() + + # Key change to original gpt oss is to stay in fp32 precision for all linear projections / muls + for expert_idx in expert_hit: + # expert_idx only have 1 element, so we can use scale for fast indexing + expert_idx = expert_idx[0] + # skip masking index + if expert_idx == self.num_experts: + continue + top_k_pos, token_idx = torch.where(expert_mask[expert_idx]) + current_state = hidden_states[token_idx] + gate_up = ( + current_state.float() @ self.gate_up_proj[expert_idx].float() + + self.gate_up_proj_bias[expert_idx].float() + ) + gated_output = self._apply_gate(gate_up).float() + out = gated_output.float() @ self.down_proj[expert_idx].float() + self.down_proj_bias[expert_idx].float() + weighted_output = out * routing_weights[token_idx, top_k_pos, None].float() + next_states.index_add_(0, token_idx, weighted_output) + + return next_states.to(original_dtype) + + +class OpenAIPrivacyFilterTopKRouter(GptOssTopKRouter): + def forward(self, hidden_states): + # Force fp32 + router_logits = F.linear( + hidden_states.float(), self.weight.float(), self.bias.float() + ) # (num_tokens, num_experts) + router_top_value, router_indices = torch.topk(router_logits, self.top_k, dim=-1) # (num_tokens, top_k) + router_scores = torch.nn.functional.softmax(router_top_value, dim=1, dtype=router_top_value.dtype) + # Additional scaling + router_scores = router_scores / self.top_k + return router_logits, router_scores, router_indices + + +class OpenAIPrivacyFilterMLP(nn.Module): + """Similar to GPT Oss but with FP32 focus + added experts scaling""" + + def __init__(self, config): + super().__init__() + self.router = OpenAIPrivacyFilterTopKRouter(config) + self.num_experts = config.num_experts_per_tok + self.experts = OpenAIPrivacyFilterExperts(config) + + def forward(self, hidden_states): + batch_size, sequence_length, hidden_dim = hidden_states.shape + hidden_states = hidden_states.reshape(-1, hidden_dim) + _, router_scores, router_indices = self.router(hidden_states) + hidden_states = self.experts(hidden_states, router_indices, router_scores) + # Additional scaling + hidden_states = hidden_states * self.num_experts + hidden_states = hidden_states.reshape(batch_size, sequence_length, hidden_dim) + return hidden_states, router_scores + + +class OpenAIPrivacyFilterEncoderLayer(GptOssDecoderLayer): + def __init__(self, config: OpenAIPrivacyFilterConfig): + super().__init__(config) + self.self_attn = OpenAIPrivacyFilterAttention(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + position_embeddings=position_embeddings, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states, _ = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class OpenAIPrivacyFilterPreTrainedModel(GptOssPreTrainedModel): + config: OpenAIPrivacyFilterConfig + _no_split_modules = ["OpenAIPrivacyFilterEncoderLayer"] + _skip_keys_device_placement = None # No cache + _keep_in_fp32_modules = [] + _keep_in_fp32_modules_strict = ["sinks"] + + _can_record_outputs = { + "router_logits": OutputRecorder(OpenAIPrivacyFilterTopKRouter, index=0), + "hidden_states": OpenAIPrivacyFilterEncoderLayer, + "attentions": OpenAIPrivacyFilterAttention, + } + + def get_correct_experts_implementation(self, requested_experts: str | None) -> str: + """The model is very sensitive to accumulation orders, hence we default to `eager` instead""" + requested_experts = "eager" if requested_experts is None else requested_experts + return PreTrainedModel.get_correct_experts_implementation(self, requested_experts) + + +@auto_docstring +class OpenAIPrivacyFilterModel(GptOssModel): + def __init__(self, config: OpenAIPrivacyFilterConfig): + super().__init__(config) + self.layers = nn.ModuleList([OpenAIPrivacyFilterEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + hidden_states = inputs_embeds + + if position_ids is None: + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + position_ids = position_ids.unsqueeze(0) + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + attention_mask = create_bidirectional_sliding_window_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + ) + + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + position_embeddings=position_embeddings, + attention_mask=attention_mask, + position_ids=position_ids, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +class OpenAIPrivacyFilterForTokenClassification(GenericForTokenClassification, OpenAIPrivacyFilterPreTrainedModel): ... + + +__all__ = [ + "OpenAIPrivacyFilterForTokenClassification", + "OpenAIPrivacyFilterModel", + "OpenAIPrivacyFilterPreTrainedModel", + "OpenAIPrivacyFilterConfig", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ecf8f8dee94501c6c63d92443944a407b0ad6e24 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_opt import * + from .modeling_opt import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/configuration_opt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/configuration_opt.py new file mode 100644 index 0000000000000000000000000000000000000000..a4816a9305ba411806bedca92fc58f127fe620bf --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/configuration_opt.py @@ -0,0 +1,83 @@ +# Copyright 2022 The Metaseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""OPT model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="facebook/opt-350m") +@strict +class OPTConfig(PreTrainedConfig): + r""" + do_layer_norm_before (`bool`, *optional*, defaults to `True`): + Whether to perform layer normalization before the attention block. + word_embed_proj_dim (`int`, *optional*): + `word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to + `hidden_size`. + enable_bias (`bool`, *optional*, defaults to `True`): + Whether or not if the linear layers in the attention blocks should use the bias term. + layer_norm_elementwise_affine (`bool`, *optional*, defaults to `True`): + Whether or not if the layer norms should have learnable parameters. + + Example: + + ```python + >>> from transformers import OPTConfig, OPTModel + + >>> # Initializing a OPT facebook/opt-large style configuration + >>> configuration = OPTConfig() + + >>> # Initializing a model (with random weights) from the facebook/opt-large style configuration + >>> model = OPTModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "opt" + keys_to_ignore_at_inference = ["past_key_values"] + + vocab_size: int = 50272 + hidden_size: int = 768 + num_hidden_layers: int = 12 + ffn_dim: int = 3072 + max_position_embeddings: int = 2048 + do_layer_norm_before: bool = True + _remove_final_layer_norm: bool = False + word_embed_proj_dim: int | None = None + dropout: float | int = 0.1 + attention_dropout: float | int = 0.0 + num_attention_heads: int = 12 + activation_function: str = "relu" + layerdrop: float | int = 0.0 + init_std: float = 0.02 + use_cache: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = 2 + eos_token_id: int | list[int] | None = 2 + enable_bias: bool = True + layer_norm_elementwise_affine: bool = True + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + self.word_embed_proj_dim = ( + self.word_embed_proj_dim if self.word_embed_proj_dim is not None else self.hidden_size + ) + super().__post_init__(**kwargs) + + +__all__ = ["OPTConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/modeling_opt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/modeling_opt.py new file mode 100644 index 0000000000000000000000000000000000000000..b19889759e1432206f397dc1433f68224daa1ca9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/opt/modeling_opt.py @@ -0,0 +1,751 @@ +# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch OPT model.""" + +from collections.abc import Callable + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...masking_utils import create_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_opt import OPTConfig + + +logger = logging.get_logger(__name__) + + +class OPTLearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int): + # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2 + # and adjust num_embeddings appropriately. Other models don't have this hack + self.offset = 2 + super().__init__(num_embeddings + self.offset, embedding_dim) + + def forward( + self, + attention_mask: torch.LongTensor, + past_key_values_length: int = 0, + position_ids: torch.LongTensor | None = None, + ): + """`input_ids_shape` is expected to be [bsz x seqlen].""" + + if position_ids is None: + position_ids = torch.cumsum(attention_mask, dim=1) + position_ids = (position_ids * attention_mask - 1).long() + # cut positions if `past_key_values_length` is > 0 + position_ids = position_ids[:, past_key_values_length:] + + return super().forward(position_ids + self.offset) + + +# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class OPTAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + config: OPTConfig, + layer_idx: int | None = None, + **kwargs, + ): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.dropout = config.attention_dropout + self.enable_bias = config.enable_bias + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.head_dim = self.embed_dim // self.num_heads + self.is_causal = True + + if (self.head_dim * self.num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {self.num_heads})." + ) + self.scaling = self.head_dim**-0.5 + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias) + + def forward( + self, + hidden_states: torch.Tensor, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]: + """Input shape: Batch x Time x Channel""" + bsz, tgt_len, _ = hidden_states.size() + + # Scaling is susceptible to floating point arithmetics' inprecisions + # which can lead to different results (this is dependent from model + # to model, e.g. whisper is one such case). We therefore keep the + # original order of scaling to follow the original implementation + # and enforce no scaling (1.0) in the attention call below. + query_states = self.q_proj(hidden_states) * self.scaling + query_states = query_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) + + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if past_key_values is not None: + # save all key/value_states to cache to be re-used for fast auto-regressive generation + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=1.0, + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class OPTDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: OPTConfig, layer_idx: int | None = None): + super().__init__() + self.embed_dim = config.hidden_size + + self.self_attn = OPTAttention(config=config, layer_idx=layer_idx) + + self.do_layer_norm_before = config.do_layer_norm_before + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + + self.self_attn_layer_norm = nn.LayerNorm( + self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine + ) + self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias) + self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias) + self.final_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_ids: torch.LongTensor | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> torch.Tensor: + residual = hidden_states + + # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention + if self.do_layer_norm_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + position_ids=position_ids, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # 350m applies layer norm AFTER attention + if not self.do_layer_norm_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Fully Connected + hidden_states_shape = hidden_states.shape + hidden_states = hidden_states.reshape(-1, hidden_states.size(-1)) + residual = hidden_states + + # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention + if self.do_layer_norm_before: + hidden_states = self.final_layer_norm(hidden_states) + + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + hidden_states = (residual + hidden_states).view(hidden_states_shape) + + # 350m applies layer norm AFTER attention + if not self.do_layer_norm_before: + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states + + +@auto_docstring +class OPTPreTrainedModel(PreTrainedModel): + config: OPTConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["OPTDecoderLayer"] + _supports_attention_backend = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _can_compile_fullgraph = True + _can_record_outputs = { + "hidden_states": OPTDecoderLayer, + "attentions": OPTAttention, + } + + +class OPTDecoder(OPTPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`] + + Args: + config: OPTConfig + """ + + def __init__(self, config: OPTConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx) + self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size) + + if config.word_embed_proj_dim != config.hidden_size: + self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False) + else: + self.project_out = None + + if config.word_embed_proj_dim != config.hidden_size: + self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False) + else: + self.project_in = None + + # Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility + # with checkpoints that have been fine-tuned before transformers v4.20.1 + # see https://github.com/facebookresearch/metaseq/pull/164 + if config.do_layer_norm_before and not config._remove_final_layer_norm: + self.final_layer_norm = nn.LayerNorm( + config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine + ) + else: + self.final_layer_norm = None + + self.layers = nn.ModuleList([OPTDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + position_ids: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if input_ids is not None: + input_ids = input_ids.view(-1, input_ids.shape[-1]) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + + if attention_mask is None: + seq_length = past_seen_tokens + inputs_embeds.shape[1] + attention_mask = torch.ones(inputs_embeds.shape[0], seq_length, device=inputs_embeds.device) + + # embed positions + if position_ids is None: + position_ids = torch.cumsum(attention_mask, dim=1) + position_ids = (position_ids * attention_mask - 1).long() + # cut positions if `past_seen_tokens` is > 0 + position_ids = position_ids[:, past_seen_tokens:] + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + + pos_embeds = self.embed_positions(attention_mask, past_seen_tokens, position_ids=position_ids) + + if self.project_in is not None: + inputs_embeds = self.project_in(inputs_embeds) + + hidden_states = inputs_embeds + pos_embeds.to(inputs_embeds.device) + + # decoder layers + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + if self.final_layer_norm is not None: + hidden_states = self.final_layer_norm(hidden_states) + + if self.project_out is not None: + hidden_states = self.project_out(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class OPTModel(OPTPreTrainedModel): + def __init__(self, config: OPTConfig): + super().__init__(config) + self.decoder = OPTDecoder(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.decoder.embed_tokens = value + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + position_ids: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + decoder_outputs: BaseModelOutputWithPast = self.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return BaseModelOutputWithPast( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + hidden_states=decoder_outputs.hidden_states, + attentions=decoder_outputs.attentions, + ) + + +class OPTForCausalLM(OPTPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.decoder.embed_tokens.weight"} + + def __init__(self, config): + super().__init__(config) + self.model = OPTModel(config) + + # the lm_head weight is automatically tied to the embed tokens weight + self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + position_ids: torch.LongTensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, OPTForCausalLM + + >>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m") + >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo." + ```""" + + outputs: BaseModelOutputWithPast = self.model.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]).contiguous() + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The OPT Model transformer with a sequence classification head on top (linear layer). + + [`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models + (e.g. GPT-2) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If + no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the + padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in + each row of the batch). + """ +) +class OPTForSequenceClassification(OPTPreTrainedModel): + def __init__(self, config: OPTConfig): + super().__init__(config) + self.num_labels = config.num_labels + self.model = OPTModel(config) + self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + position_ids: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | SequenceClassifierOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + transformer_outputs: BaseModelOutputWithPast = self.model( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + hidden_states = transformer_outputs.last_hidden_state + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size, sequence_length = input_ids.shape[:2] + else: + batch_size, sequence_length = inputs_embeds.shape[:2] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + last_non_pad_token = -1 + elif input_ids is not None: + # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id + non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32) + token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32) + last_non_pad_token = (token_indices * non_pad_mask).argmax(-1) + else: + last_non_pad_token = -1 + logger.warning_once( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`" + ) + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + +@auto_docstring +class OPTForQuestionAnswering(OPTPreTrainedModel): + def __init__(self, config: OPTConfig): + super().__init__(config) + self.model = OPTModel(config) + self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + start_positions: torch.LongTensor | None = None, + end_positions: torch.LongTensor | None = None, + use_cache: bool | None = None, + position_ids: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | QuestionAnsweringModelOutput: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, OPTForQuestionAnswering + >>> import torch + + >>> torch.manual_seed(4) # doctest: +IGNORE_RESULT + >>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m") + + >>> # note: we are loading a OPTForQuestionAnswering from the hub here, + >>> # so the head will be randomly initialized, hence the predictions will be random + >>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m") + + >>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" + + >>> inputs = tokenizer(question, text, return_tensors="pt") + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> answer_start_index = outputs.start_logits.argmax() + >>> answer_end_index = outputs.end_logits.argmax() + + >>> answer_offset = len(tokenizer(question)[0]) + + >>> predict_answer_tokens = inputs.input_ids[ + ... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1 + ... ] + >>> predicted = tokenizer.decode(predict_answer_tokens) + >>> predicted + ' a nice puppet' + ```""" + transformer_outputs: BaseModelOutputWithPast = self.model( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + hidden_states = transformer_outputs.last_hidden_state + + logits = self.qa_outputs(hidden_states) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index).to(logits.device) + end_positions = end_positions.clamp(0, ignored_index).to(logits.device) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + +__all__ = [ + "OPTForCausalLM", + "OPTModel", + "OPTPreTrainedModel", + "OPTForSequenceClassification", + "OPTForQuestionAnswering", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2ed043a216a2a1b7a6647a59dba1aab72f230f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/__init__.py @@ -0,0 +1,31 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_ovis2 import * + from .image_processing_ovis2 import * + from .image_processing_pil_ovis2 import * + from .modeling_ovis2 import * + from .processing_ovis2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/configuration_ovis2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/configuration_ovis2.py new file mode 100644 index 0000000000000000000000000000000000000000..dc7e829de3614eb68a40b9065bf5c321af2eb212 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/configuration_ovis2.py @@ -0,0 +1,102 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..qwen2.configuration_qwen2 import Qwen2Config + + +@auto_docstring(checkpoint="thisisiron/Ovis2-1B-hf") +@strict +class Ovis2VisionConfig(PreTrainedConfig): + r""" + hidden_stride (`int`, *optional*, defaults to 1): + The stride of the hidden layer in the Vision Transformer. + num_visual_indicator_tokens (`int`, *optional*, defaults to 5): + Number of visual indicator tokens. + tokenize_function (`str`, *optional*, defaults to `"softmax"`): + The function used to tokenize the visual indicator tokens. + """ + + base_config_key = "vision_config" + + hidden_size: int = 1024 + intermediate_size: int = 2816 + num_hidden_layers: int = 24 + num_attention_heads: int = 8 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 224 + patch_size: int | list[int] | tuple[int, int] = 14 + rms_norm_eps: float = 1e-5 + attention_dropout: float | int = 0.0 + qkv_bias: bool = False + mlp_bias: bool = False + hidden_act: str = "silu" + vocab_size: int = 16384 + hidden_stride: int = 1 + num_visual_indicator_tokens: int = 5 + initializer_range: float = 0.02 + tokenize_function: str = "softmax" + + +@auto_docstring(checkpoint="thisisiron/Ovis2-1B-hf") +@strict +class Ovis2Config(PreTrainedConfig): + r""" + visual_indicator_token_ids (`List[int]`, *optional*, defaults to `[151666, 151667, 151668, 151669, 151670]`): + The visual indicator token ids to encode the image prompt. + + ```python + >>> from transformers import Ovis2ForConditionalGeneration, Ovis2Config + + >>> # Initializing a Ovis2 style configuration + >>> configuration = Ovis2Config() + + >>> # Initializing a model from the Ovis2-2B style configuration + >>> model = Ovis2ForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "ovis2" + sub_configs = {"text_config": Qwen2Config, "vision_config": Ovis2VisionConfig} + + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + image_token_id: int = 151665 + visual_indicator_token_ids: list[int] | tuple[int, ...] = (151666, 151667, 151668, 151669, 151670) + vocab_size: int = 151643 + hidden_size: int = 1536 + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = Ovis2VisionConfig(**self.vision_config) + if self.vision_config is None: + self.vision_config = Ovis2VisionConfig(num_visual_indicator_tokens=len(self.visual_indicator_token_ids)) + + if isinstance(self.text_config, dict): + self.text_config = Qwen2Config(**self.text_config) + elif self.text_config is None: + self.text_config = Qwen2Config() + + super().__post_init__(**kwargs) + + +__all__ = ["Ovis2VisionConfig", "Ovis2Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/image_processing_ovis2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/image_processing_ovis2.py new file mode 100644 index 0000000000000000000000000000000000000000..e15f35b2d4f10e64405c0b680e5994645d5fc05e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/image_processing_ovis2.py @@ -0,0 +1,327 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for OVIS2.""" + +from functools import lru_cache + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +# Helper functions for patch/tile calculations +@lru_cache(maxsize=10) +def get_all_supported_aspect_ratios(min_image_tiles: int, max_image_tiles: int) -> list[tuple[int, int]]: + """Computes all allowed aspect ratios for a given minimum and maximum number of input tiles.""" + aspect_ratios = [] + for width in range(1, max_image_tiles + 1): + for height in range(1, max_image_tiles + 1): + if width * height <= max_image_tiles and width * height >= min_image_tiles: + aspect_ratios.append((width, height)) + return sorted(aspect_ratios, key=lambda x: x[0] * x[1]) + + +@lru_cache(maxsize=100) +def get_optimal_tiled_canvas( + original_image_size: tuple[int, int], + target_tile_size: tuple[int, int], + min_image_tiles: int, + max_image_tiles: int, +) -> tuple[int, int]: + """Find the canvas with the closest aspect ratio to the original image aspect ratio.""" + possible_tile_arrangements = get_all_supported_aspect_ratios(min_image_tiles, max_image_tiles) + original_height, original_width = original_image_size + target_tile_height, target_tile_width = target_tile_size + aspect_ratio = original_width / original_height + area = original_width * original_height + + best_ratio_diff = float("inf") + best_grid = (1, 1) + for grid in possible_tile_arrangements: + grid_aspect_ratio = grid[0] / grid[1] + ratio_diff = abs(aspect_ratio - grid_aspect_ratio) + if ratio_diff < best_ratio_diff: + best_ratio_diff = ratio_diff + best_grid = grid + elif ratio_diff == best_ratio_diff: + if area > 0.5 * target_tile_height * target_tile_width * grid[0] * grid[1]: + best_grid = grid + return best_grid + + +def compute_patch_covering_area(left: int, upper: int, right: int, lower: int, side: int) -> float: + w = right - left + h = lower - upper + w, h = max(w, h), min(w, h) + if w > side: + h = h / w * side + w = side + return w * h + + +def split_image_into_grid(h: int, w: int, grid: tuple[int, int]) -> list[tuple[int, int, int, int]]: + row_height = h // grid[0] + col_width = w // grid[1] + return [ + ( + col * col_width, + row * row_height, + w if col == grid[1] - 1 else (col + 1) * col_width, + h if row == grid[0] - 1 else (row + 1) * row_height, + ) + for row in range(grid[0]) + for col in range(grid[1]) + ] + + +@lru_cache(maxsize=100) +def get_min_tile_covering_grid( + image_size: tuple[int, int], + target_patch_size: int, + max_image_tiles: int, + covering_threshold: float = 0.9, +) -> tuple[int, int]: + image_height, image_width = image_size + image_area = image_width * image_height + candidate_tile_grids = get_all_supported_aspect_ratios(1, max_image_tiles) + evaluated_grids = [] + sufficient_covering_grids = [] + + for tile_grid in candidate_tile_grids: + tile_regions = split_image_into_grid(image_height, image_width, tile_grid) + tile_covering_ratio = ( + sum(compute_patch_covering_area(*region, target_patch_size) for region in tile_regions) / image_area + ) + evaluated_grids.append((tile_grid, tile_covering_ratio)) + if tile_covering_ratio > covering_threshold: + sufficient_covering_grids.append((tile_grid, tile_covering_ratio)) + + if sufficient_covering_grids: + return min(sufficient_covering_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0] + return min(evaluated_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0] + + +class Ovis2ImageProcessorKwargs(ImagesKwargs, total=False): + """ + crop_to_patches (`bool`, *optional*, defaults to `False`): + Whether to crop the image to patches. Can be overridden by the `crop_to_patches` parameter in the + `preprocess` method. + min_patches (`int`, *optional*, defaults to 1): + The minimum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is + set to `True`. Can be overridden by the `min_patches` parameter in the `preprocess` method. + max_patches (`int`, *optional*, defaults to 12): + The maximum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is + set to `True`. Can be overridden by the `max_patches` parameter in the `preprocess` method. + use_covering_area_grid (`bool`, *optional*, defaults to `True`): + Whether to use the covering area grid to determine the number of patches. Only has an effect if + `crop_to_patches` is set to `True`. Can be overridden by the `use_covering_area_grid` parameter in the + `preprocess` method. + """ + + crop_to_patches: bool + min_patches: int + max_patches: int + use_covering_area_grid: bool + + +@auto_docstring +class Ovis2ImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 384, "width": 384} + default_to_square = True + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + crop_to_patches = False + min_patches = 1 + max_patches = 12 + use_covering_area_grid = True + valid_kwargs = Ovis2ImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[Ovis2ImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[Ovis2ImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def crop_image_to_patches( + self, + images: "torch.Tensor", + min_patches: int, + max_patches: int, + use_covering_area_grid: bool = True, + covering_threshold: float = 0.9, + patch_size: SizeDict | None = None, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + ): + """ + Crop the images to patches and return a list of cropped images. + The number of patches and their grid arrangement are determined by the original image size, + the target patch size and the minimum and maximum number of patches. + The aspect ratio of the patches grid is chosen to be the closest to the original image aspect ratio. + + Args: + images (`torch.Tensor`): + The images to be cropped. + min_patches (`int`): + The minimum number of patches to be extracted from the image. + max_patches (`int`): + The maximum number of patches to be extracted from the image. + use_covering_area_grid (`bool`, *optional*, defaults to `True`): + Whether to use the original OVIS2 approach: compute the minimal number of tiles that cover at least 90% + of the image area. If `False`, the closest aspect ratio to the target is used. + covering_threshold (`float`, *optional*, defaults to `0.9`): + The threshold for the covering area. Only has an effect if `use_covering_area_grid` is set to `True`. + patch_size (`SizeDict`, *optional*): + The size of the output patches. + resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): + Resampling filter to use if resizing the image. + + Returns: + Tuple[`torch.Tensor`, `list`]: A tuple containing the processed images tensor and the grid information. + """ + num_image = images.shape[0] + patch_size_height, patch_size_width = patch_size.height, patch_size.width + original_height, original_width = images.shape[-2:] + + if use_covering_area_grid: + # Use the original OVIS2 approach: compute the minimal number of tiles that cover at least 90% of the image area + num_columns, num_rows = get_min_tile_covering_grid( + (original_height, original_width), + target_patch_size=patch_size_height, # square patch size + max_image_tiles=max_patches, + covering_threshold=covering_threshold, + ) + else: + # find the closest aspect ratio to the target + num_columns, num_rows = get_optimal_tiled_canvas( + (original_height, original_width), (patch_size_height, patch_size_width), min_patches, max_patches + ) + + # calculate the target width and height + target_width = patch_size_width * num_columns + target_height = patch_size_height * num_rows + num_blocks = num_columns * num_rows + + # resize the image so that each patch is of patch_size + resized_image = self.resize(images, SizeDict(height=target_height, width=target_width), resample=resample) + # split the image into patches + processed_images = [] + for i in range(num_blocks): + column = i % num_columns + row = i // num_columns + box = ( + column * patch_size_width, + row * patch_size_height, + (column + 1) * patch_size_width, + (row + 1) * patch_size_height, + ) + # split the image + patch_image = resized_image[..., box[1] : box[3], box[0] : box[2]] + processed_images.append(patch_image) + + if len(processed_images) != 1: + thumbnail_img = self.resize(images, patch_size, resample=resample) + processed_images.insert(0, thumbnail_img) + + processed_images = torch.stack(processed_images, dim=0).transpose(0, 1).contiguous() + grid = [[num_rows, num_columns] for _ in range(num_image)] + + return processed_images, grid + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + crop_to_patches: bool = False, + min_patches: int = 1, + max_patches: int = 12, + use_covering_area_grid: bool = True, + **kwargs, + ) -> BatchFeature: + if crop_to_patches and max_patches > 1: + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + processed_images_grouped = {} + grids = {} + for shape, stacked_images in grouped_images.items(): + stacked_images, grid = self.crop_image_to_patches( + stacked_images, + min_patches, + max_patches, + patch_size=size, + use_covering_area_grid=use_covering_area_grid, + resample=resample, + ) + processed_images_grouped[shape] = stacked_images + grids[shape] = grid + images = reorder_images(processed_images_grouped, grouped_images_index) + images = [image for images_list in images for image in images_list] + grids = reorder_images(grids, grouped_images_index) + else: + grids = [[1, 1] for _ in range(len(images))] + + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + return BatchFeature(data={"pixel_values": processed_images, "grids": grids}, tensor_type=return_tensors) + + +__all__ = ["Ovis2ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/image_processing_pil_ovis2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/image_processing_pil_ovis2.py new file mode 100644 index 0000000000000000000000000000000000000000..73772ec65846a5a0862f0b0125b5df3cf1bc65ae --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/image_processing_pil_ovis2.py @@ -0,0 +1,297 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PIL Image processor class for OVIS2.""" + +from functools import lru_cache + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import to_channel_dimension_format +from ...image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + infer_channel_dimension_format, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +# Adapted from transformers.models.ovis2.image_processing_ovis2.Ovis2ImageProcessorKwargs +class Ovis2ImageProcessorKwargs(ImagesKwargs, total=False): + """ + crop_to_patches (`bool`, *optional*, defaults to `False`): + Whether to crop the image to patches. Can be overridden by the `crop_to_patches` parameter in the + `preprocess` method. + min_patches (`int`, *optional*, defaults to 1): + The minimum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is + set to `True`. Can be overridden by the `min_patches` parameter in the `preprocess` method. + max_patches (`int`, *optional*, defaults to 12): + The maximum number of patches to be extracted from the image. Only has an effect if `crop_to_patches` is + set to `True`. Can be overridden by the `max_patches` parameter in the `preprocess` method. + use_covering_area_grid (`bool`, *optional*, defaults to `True`): + Whether to use the covering area grid to determine the number of patches. Only has an effect if + `crop_to_patches` is set to `True`. Can be overridden by the `use_covering_area_grid` parameter in the + `preprocess` method. + """ + + crop_to_patches: bool + min_patches: int + max_patches: int + use_covering_area_grid: bool + + +# Adapted from transformers.models.ovis2.image_processing_ovis2.get_all_supported_aspect_ratios +@lru_cache(maxsize=10) +def get_all_supported_aspect_ratios(min_image_tiles: int, max_image_tiles: int) -> list[tuple[int, int]]: + """Computes all allowed aspect ratios for a given minimum and maximum number of input tiles.""" + aspect_ratios = [] + for width in range(1, max_image_tiles + 1): + for height in range(1, max_image_tiles + 1): + if width * height <= max_image_tiles and width * height >= min_image_tiles: + aspect_ratios.append((width, height)) + return sorted(aspect_ratios, key=lambda x: x[0] * x[1]) + + +# Adapted from transformers.models.ovis2.image_processing_ovis2.compute_patch_covering_area +def compute_patch_covering_area(left: int, upper: int, right: int, lower: int, side: int) -> float: + w = right - left + h = lower - upper + w, h = max(w, h), min(w, h) + if w > side: + h = h / w * side + w = side + return w * h + + +# Adapted from transformers.models.ovis2.image_processing_ovis2.split_image_into_grid +def split_image_into_grid(h: int, w: int, grid: tuple[int, int]) -> list[tuple[int, int, int, int]]: + row_height = h // grid[0] + col_width = w // grid[1] + return [ + ( + col * col_width, + row * row_height, + w if col == grid[1] - 1 else (col + 1) * col_width, + h if row == grid[0] - 1 else (row + 1) * row_height, + ) + for row in range(grid[0]) + for col in range(grid[1]) + ] + + +# Adapted from transformers.models.ovis2.image_processing_ovis2.get_min_tile_covering_grid +@lru_cache(maxsize=100) +def get_min_tile_covering_grid( + image_size: tuple[int, int], + target_patch_size: int, + max_image_tiles: int, + covering_threshold: float = 0.9, +) -> tuple[int, int]: + image_height, image_width = image_size + image_area = image_width * image_height + candidate_tile_grids = get_all_supported_aspect_ratios(1, max_image_tiles) + evaluated_grids = [] + sufficient_covering_grids = [] + + for tile_grid in candidate_tile_grids: + tile_regions = split_image_into_grid(image_height, image_width, tile_grid) + tile_covering_ratio = ( + sum(compute_patch_covering_area(*region, target_patch_size) for region in tile_regions) / image_area + ) + evaluated_grids.append((tile_grid, tile_covering_ratio)) + if tile_covering_ratio > covering_threshold: + sufficient_covering_grids.append((tile_grid, tile_covering_ratio)) + + if sufficient_covering_grids: + return min(sufficient_covering_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0] + return min(evaluated_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0] + + +# Adapted from transformers.models.ovis2.image_processing_ovis2.get_optimal_tiled_canvas +@lru_cache(maxsize=100) +def get_optimal_tiled_canvas( + original_image_size: tuple[int, int], + target_tile_size: tuple[int, int], + min_image_tiles: int, + max_image_tiles: int, +) -> tuple[int, int]: + """Find the canvas with the closest aspect ratio to the original image aspect ratio.""" + possible_tile_arrangements = get_all_supported_aspect_ratios(min_image_tiles, max_image_tiles) + original_height, original_width = original_image_size + target_tile_height, target_tile_width = target_tile_size + aspect_ratio = original_width / original_height + area = original_width * original_height + + best_ratio_diff = float("inf") + best_grid = (1, 1) + for grid in possible_tile_arrangements: + grid_aspect_ratio = grid[0] / grid[1] + ratio_diff = abs(aspect_ratio - grid_aspect_ratio) + if ratio_diff < best_ratio_diff: + best_ratio_diff = ratio_diff + best_grid = grid + elif ratio_diff == best_ratio_diff: + if area > 0.5 * target_tile_height * target_tile_width * grid[0] * grid[1]: + best_grid = grid + return best_grid + + +@auto_docstring +class Ovis2ImageProcessorPil(PilBackend): + resample = PILImageResampling.BICUBIC + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 384, "width": 384} + default_to_square = True + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + crop_to_patches = False + min_patches = 1 + max_patches = 12 + use_covering_area_grid = True + valid_kwargs = Ovis2ImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[Ovis2ImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[Ovis2ImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def crop_image_to_patches( + self, + image: np.ndarray, + min_patches: int, + max_patches: int, + use_covering_area_grid: bool = True, + covering_threshold: float = 0.9, + patch_size: SizeDict | None = None, + resample: "PILImageResampling | None" = None, + ): + """ + Crop the image to patches and return a list of cropped images. + Mirrors TorchvisionBackend.crop_image_to_patches. + """ + # Normalize to CHW when called directly (e.g. from tests); _preprocess already receives CHW + input_data_format = infer_channel_dimension_format(image) + image = to_channel_dimension_format(image, ChannelDimension.FIRST, input_data_format) + + patch_size_height, patch_size_width = patch_size.height, patch_size.width + original_height, original_width = image.shape[-2:] + + if use_covering_area_grid: + # Use the original OVIS2 approach: compute the minimal number of tiles that cover at least 90% of the image area + num_columns, num_rows = get_min_tile_covering_grid( + (original_height, original_width), + target_patch_size=patch_size_height, # square patch size + max_image_tiles=max_patches, + covering_threshold=covering_threshold, + ) + else: + # find the closest aspect ratio to the target + num_columns, num_rows = get_optimal_tiled_canvas( + (original_height, original_width), (patch_size_height, patch_size_width), min_patches, max_patches + ) + + # calculate the target width and height + target_width = patch_size_width * num_columns + target_height = patch_size_height * num_rows + num_blocks = num_columns * num_rows + + # resize the image so that each patch is of patch_size + resized_image = self.resize(image, SizeDict(height=target_height, width=target_width), resample=resample) + # split the image into patches + processed_images = [] + for i in range(num_blocks): + column = i % num_columns + row = i // num_columns + box = ( + column * patch_size_width, + row * patch_size_height, + (column + 1) * patch_size_width, + (row + 1) * patch_size_height, + ) + patch_image = resized_image[:, box[1] : box[3], box[0] : box[2]] + processed_images.append(patch_image) + + if len(processed_images) != 1: + thumbnail_img = self.resize(image, patch_size, resample=resample) + processed_images.insert(0, thumbnail_img) + + return processed_images, [num_rows, num_columns] + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + crop_to_patches: bool = False, + min_patches: int = 1, + max_patches: int = 12, + use_covering_area_grid: bool = True, + **kwargs, + ) -> BatchFeature: + if crop_to_patches and max_patches > 1: + # Crop to patches first + processed_images = [] + grids = [] + for image in images: + patches, grid = self.crop_image_to_patches( + image, + min_patches, + max_patches, + patch_size=size, + use_covering_area_grid=use_covering_area_grid, + resample=resample, + ) + processed_images.extend(patches) + grids.append(grid) + images = processed_images + else: + grids = [[1, 1] for _ in range(len(images))] + + # Process all images (including patches if any) through the standard pipeline + processed_images = [] + for image in images: + if do_resize: + image = self.resize(image, size=size, resample=resample) + if do_center_crop: + image = self.center_crop(image, crop_size) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + + return BatchFeature(data={"pixel_values": processed_images, "grids": grids}, tensor_type=return_tensors) + + +__all__ = ["Ovis2ImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/modeling_ovis2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/modeling_ovis2.py new file mode 100644 index 0000000000000000000000000000000000000000..7cd52b248fa62a3ca7834ed5803143ba29f93321 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/modeling_ovis2.py @@ -0,0 +1,742 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/ovis2/modular_ovis2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_ovis2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from .configuration_ovis2 import Ovis2Config, Ovis2VisionConfig + + +@auto_docstring +@dataclass +class BaseModelOutputWithVisualIndicatorFeatures(BaseModelOutputWithPooling): + r""" + visual_indicator_features (`torch.FloatTensor` of shape `(batch_size, visual_indicator_size)`): + Visual indicator features extracted from the model, which can be used for auxiliary tasks or further processing. + """ + + visual_indicator_features: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for Llava outputs, with hidden states and attentions. + """ +) +@dataclass +class Ovis2ModelOutputWithPast(BaseModelOutputWithPast): + r""" + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + image_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for Ovis2 causal language model (or autoregressive) outputs. + """ +) +@dataclass +class Ovis2CausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + image_hidden_states: torch.FloatTensor | None = None + + +@use_kernel_forward_from_hub("RMSNorm") +class Ovis2RMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + Ovis2RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class Ovis2VisionMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class Ovis2VisionEmbeddings(nn.Module): + def __init__(self, config: Ovis2VisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches = (self.image_size // self.patch_size) ** 2 + self.num_positions = self.num_patches + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) + self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps) + + def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) + embeddings = patch_embeds.flatten(2).transpose(1, 2) + embeddings = self.rms_norm(embeddings) + + embeddings = embeddings + self.position_embedding(self.position_ids) + + return embeddings + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Ovis2VisionAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + self.is_causal = False + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + queries = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + keys = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + values = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + queries, + keys, + values, + attention_mask, + is_causal=self.is_causal, + scaling=self.scale, + dropout=0.0 if not self.training else self.dropout, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class Ovis2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class Ovis2VisionEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Ovis2VisionConfig): + super().__init__() + self.attention = Ovis2VisionAttention(config) + self.ffn = Ovis2MLP(config) + self.rms_norm1 = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps) + self.rms_norm2 = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + norm_hidden_states = self.rms_norm1(hidden_states) + attn_output, _ = self.attention(hidden_states=norm_hidden_states, attention_mask=attention_mask, **kwargs) + + hidden_states = hidden_states + attn_output + norm_hidden_states = self.rms_norm2(hidden_states) + mlp_output = self.ffn(norm_hidden_states) + + hidden_states = hidden_states + mlp_output + return hidden_states + + +class Ovis2VisionEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`Ovis2VisionEncoderLayer`]. + + Args: + config: Ovis2VisionConfig + """ + + def __init__(self, config: Ovis2VisionConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([Ovis2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + # Ignore copy + @can_return_tuple + @auto_docstring + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer(hidden_states, attention_mask, **kwargs) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +class Ovis2VisionTransformer(nn.Module): + def __init__(self, config: Ovis2VisionConfig): + super().__init__() + self.config = config + self.embeddings = Ovis2VisionEmbeddings(config) + self.encoder = Ovis2VisionEncoder(config) + self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps) + self.gradient_checkpointing = False + + @can_return_tuple + def forward( + self, + pixel_values, + attention_mask: torch.Tensor | None = None, + **kwargs, + ): + hidden_states = self.embeddings(pixel_values) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.rms_norm(last_hidden_state) + + return BaseModelOutput(last_hidden_state=last_hidden_state) + + +class Ovis2VisualEmbeddingTable(nn.Embedding): + def forward(self, visual_tokens: torch.Tensor) -> torch.Tensor: + if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]: + return super().forward(visual_tokens) + return torch.matmul(visual_tokens, self.weight) + + +class Ovis2PreTrainedModel(PreTrainedModel): + config: Ovis2Config + base_model_prefix = "model" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + _no_split_modules = ["Ovis2VisionAttention"] + _skip_keys_device_placement = "past_key_values" + _supports_cache_class = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_sdpa = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Ovis2VisionEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + + +def hard_softmax(logits: torch.Tensor, dim: int): + y_soft = logits.softmax(dim) + # Straight through. + index = y_soft.max(dim, keepdim=True)[1] + y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0) + ret = y_hard - y_soft.detach() + y_soft + + return ret + + +class Ovis2VisionModel(Ovis2PreTrainedModel): + config: Ovis2VisionConfig + _can_record_outputs = { + "hidden_states": Ovis2VisionEncoderLayer, + "attentions": Ovis2VisionAttention, + } + + def __init__(self, config: Ovis2VisionConfig): + super().__init__(config) + self.config = config + self.transformer = Ovis2VisionTransformer(config) + self.num_visual_indicator_tokens = config.num_visual_indicator_tokens + self.vocab_size = config.vocab_size + self.head_linear = nn.Linear( + config.hidden_size * config.hidden_stride * config.hidden_stride, + self.vocab_size - self.num_visual_indicator_tokens, + bias=False, + ) + self.head_norm = nn.LayerNorm(self.vocab_size - self.num_visual_indicator_tokens) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithVisualIndicatorFeatures: + outputs = self.transformer(pixel_values, **kwargs) + last_hidden_state = outputs[0] + if self.config.hidden_stride > 1: + num_images, seq_len, hidden_dim = last_hidden_state.shape + hidden_stride = self.config.hidden_stride + + sqrt_l = int(math.sqrt(seq_len)) + if sqrt_l * sqrt_l != seq_len: + raise ValueError("Token sequence length must be a perfect square") + + pad_size = (hidden_stride - (sqrt_l % hidden_stride)) % hidden_stride + last_hidden_state = nn.functional.pad(last_hidden_state, (0, 0, 0, pad_size, 0, pad_size), "constant", 0) + sqrt_l += pad_size + + last_hidden_state = last_hidden_state.reshape( + num_images, sqrt_l // hidden_stride, hidden_stride, sqrt_l // hidden_stride, hidden_stride, hidden_dim + ) + last_hidden_state = last_hidden_state.permute(0, 1, 3, 2, 4, 5) + last_hidden_state = last_hidden_state.reshape( + num_images, -1, hidden_stride * hidden_stride * hidden_dim + ) # (n, (sqrt_l//hs)^2, hs^2*d) + + logits = self.head_linear(last_hidden_state) + logits = self.head_norm(logits) + + if self.config.tokenize_function == "gumbel_argmax": + prob_token = nn.functional.gumbel_softmax(logits, dim=-1, hard=True) + elif self.config.tokenize_function == "st_argmax": + prob_token = hard_softmax(logits, dim=-1) + elif self.config.tokenize_function == "softmax": + prob_token = nn.functional.softmax(logits, dim=-1) + + return BaseModelOutputWithVisualIndicatorFeatures( + last_hidden_state=last_hidden_state, + pooler_output=prob_token, + ) + + +@auto_docstring( + custom_intro=""" + The Ovis2 model which consists of a vision backbone and a language model, without a language modeling head. + """ +) +class Ovis2Model(Ovis2PreTrainedModel): + def __init__(self, config: Ovis2Config): + super().__init__(config) + self.vision_tower = Ovis2VisionModel(config.vision_config) + self.language_model = AutoModel.from_config(config.text_config) + self.visual_embeddings_table = Ovis2VisualEmbeddingTable(config.vision_config.vocab_size, config.hidden_size) + + self.visual_vocab_size = config.vision_config.vocab_size + self.vocab_size = config.vocab_size + self.visual_indicator_token_ids = config.visual_indicator_token_ids + self.post_init() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + @can_return_tuple + @auto_docstring( + custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection." + ) + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithVisualIndicatorFeatures: + image_outputs = self.vision_tower(pixel_values, return_dict=True, **kwargs) + image_features = image_outputs.pooler_output + batch_size, img_seq_len, _ = image_features.shape + padding_tensor = torch.zeros( + (batch_size, img_seq_len, self.vision_tower.num_visual_indicator_tokens), + dtype=image_features.dtype, + device=image_features.device, + requires_grad=False, + layout=image_features.layout, + ) + image_features = torch.cat([image_features, padding_tensor], dim=2) + image_features = self.visual_embeddings_table(image_features) + + visual_indicator = torch.arange( + self.visual_vocab_size - self.vision_tower.num_visual_indicator_tokens, + self.visual_vocab_size, + dtype=torch.long, + ).to(image_features.device) + image_outputs.pooler_output = image_features + image_outputs.visual_indicator_features = self.visual_embeddings_table(visual_indicator) + + return image_outputs + + def get_placeholder_mask( + self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + + n_image_tokens = special_image_mask.sum() + n_image_features = image_features.shape[0] * image_features.shape[1] + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}", + ) + return special_image_mask + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | Ovis2ModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + if pixel_values is not None: + image_outputs = self.get_image_features(pixel_values=pixel_values, return_dict=True) + image_features = image_outputs.pooler_output + visual_indicator_features = image_outputs.visual_indicator_features + + special_image_mask = self.get_placeholder_mask( + input_ids, + inputs_embeds=inputs_embeds, + image_features=image_features, + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + for i, visual_indicator_id in enumerate(self.visual_indicator_token_ids): + if input_ids is None: + mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(visual_indicator_id, dtype=torch.long, device=inputs_embeds.device) + ) + mask = mask.all(-1) + else: + mask = (input_ids == visual_indicator_id).to(inputs_embeds.device) + + if mask.any(): + inputs_embeds[mask] = ( + visual_indicator_features[i] + .expand_as(inputs_embeds[mask]) + .to(inputs_embeds.device, inputs_embeds.dtype) + ) + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **kwargs, + ) + + return Ovis2ModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=image_features if pixel_values is not None else None, + ) + + +@auto_docstring +class Ovis2ForConditionalGeneration(Ovis2PreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"} + + def __init__(self, config: Ovis2Config): + super().__init__(config) + self.model = Ovis2Model(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + def get_output_embeddings(self) -> nn.Module: + return self.lm_head + + @auto_docstring + def get_image_features( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithVisualIndicatorFeatures: + return self.model.get_image_features(pixel_values=pixel_values, **kwargs) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | Ovis2CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, Ovis2ForConditionalGeneration + + >>> model = Ovis2ForConditionalGeneration.from_pretrained("thisisiron/Ovis2-2B-hf") + >>> processor = AutoProcessor.from_pretrained("thisisiron/Ovis2-2B-hf") + + >>> prompt = "<|im_start|>user\n\nDescribe the image.<|im_end|>\n<|im_start|>assistant\n" + >>> url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, text=prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(**inputs, max_new_tokens=15) + >>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0] + "user\n\nDescribe the image.\nassistant\nThe image features a brown dog standing on a wooden floor, looking up with" + ```""" + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return Ovis2CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + inputs_embeds=None, + pixel_values=None, + attention_mask=None, + logits_to_keep=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + logits_to_keep=logits_to_keep, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + if is_first_iteration or not kwargs.get("use_cache", True): + # Pixel values are used only in the first iteration if available + # In subsequent iterations, they are already merged with text and cached + # NOTE: first iteration doesn't have to be prefill, it can be the first + # iteration with a question and cached system prompt (continue generate from cache) + model_inputs["pixel_values"] = pixel_values + + return model_inputs + + +__all__ = ["Ovis2PreTrainedModel", "Ovis2Model", "Ovis2ForConditionalGeneration"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/modular_ovis2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/modular_ovis2.py new file mode 100644 index 0000000000000000000000000000000000000000..b77e66d921de517495afe8c57d15b4fb68b512d7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/modular_ovis2.py @@ -0,0 +1,448 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass + +import torch +from torch import nn + +from ... import initialization as init +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..aimv2.modeling_aimv2 import Aimv2Attention, Aimv2EncoderLayer +from ..auto import AutoModel +from ..llama.modeling_llama import LlamaMLP, LlamaRMSNorm +from ..llava.modeling_llava import LlavaForConditionalGeneration, LlavaModel +from ..llava_next.modeling_llava_next import LlavaNextCausalLMOutputWithPast, LlavaNextModelOutputWithPast +from ..siglip.modeling_siglip import SiglipEncoder, SiglipVisionEmbeddings +from .configuration_ovis2 import Ovis2Config, Ovis2VisionConfig + + +def hard_softmax(logits: torch.Tensor, dim: int): + y_soft = logits.softmax(dim) + # Straight through. + index = y_soft.max(dim, keepdim=True)[1] + y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0) + ret = y_hard - y_soft.detach() + y_soft + + return ret + + +@auto_docstring +@dataclass +class BaseModelOutputWithVisualIndicatorFeatures(BaseModelOutputWithPooling): + r""" + visual_indicator_features (`torch.FloatTensor` of shape `(batch_size, visual_indicator_size)`): + Visual indicator features extracted from the model, which can be used for auxiliary tasks or further processing. + """ + + visual_indicator_features: torch.FloatTensor | None = None + + +class Ovis2ModelOutputWithPast(LlavaNextModelOutputWithPast): + pass + + +class Ovis2CausalLMOutputWithPast(LlavaNextCausalLMOutputWithPast): + pass + + +class Ovis2RMSNorm(LlamaRMSNorm): + pass + + +class Ovis2VisionMLP(LlamaMLP): + pass + + +class Ovis2VisionEmbeddings(SiglipVisionEmbeddings): + def __init__(self, config: Ovis2VisionConfig): + super().__init__(config) + self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps) + + def interpolate_pos_encoding(self): + raise NotImplementedError("Not needed for Ovis2") + + def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) + embeddings = patch_embeds.flatten(2).transpose(1, 2) + embeddings = self.rms_norm(embeddings) + + embeddings = embeddings + self.position_embedding(self.position_ids) + + return embeddings + + +class Ovis2VisionAttention(Aimv2Attention): + pass + + +class Ovis2VisionEncoderLayer(Aimv2EncoderLayer): + def __init__(self, config: Ovis2VisionConfig): + super().__init__() + self.attention = Ovis2VisionAttention(config) + + +class Ovis2VisionEncoder(SiglipEncoder): + def __init__(self, config: Ovis2VisionConfig): + super().__init__(config) + self.layers = nn.ModuleList([Ovis2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + + @can_return_tuple + @auto_docstring + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer(hidden_states, attention_mask, **kwargs) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +class Ovis2VisionTransformer(nn.Module): + def __init__(self, config: Ovis2VisionConfig): + super().__init__() + self.config = config + self.embeddings = Ovis2VisionEmbeddings(config) + self.encoder = Ovis2VisionEncoder(config) + self.rms_norm = Ovis2RMSNorm(config.hidden_size, config.rms_norm_eps) + self.gradient_checkpointing = False + + @can_return_tuple + def forward( + self, + pixel_values, + attention_mask: torch.Tensor | None = None, + **kwargs, + ): + hidden_states = self.embeddings(pixel_values) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.rms_norm(last_hidden_state) + + return BaseModelOutput(last_hidden_state=last_hidden_state) + + +class Ovis2VisualEmbeddingTable(nn.Embedding): + def forward(self, visual_tokens: torch.Tensor) -> torch.Tensor: + if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]: + return super().forward(visual_tokens) + return torch.matmul(visual_tokens, self.weight) + + +class Ovis2PreTrainedModel(PreTrainedModel): + config: Ovis2Config + base_model_prefix = "model" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + _no_split_modules = ["Ovis2VisionAttention"] + _skip_keys_device_placement = "past_key_values" + _supports_cache_class = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_sdpa = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Ovis2VisionEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + + +class Ovis2VisionModel(Ovis2PreTrainedModel): + config: Ovis2VisionConfig + _can_record_outputs = { + "hidden_states": Ovis2VisionEncoderLayer, + "attentions": Ovis2VisionAttention, + } + + def __init__(self, config: Ovis2VisionConfig): + super().__init__(config) + self.config = config + self.transformer = Ovis2VisionTransformer(config) + self.num_visual_indicator_tokens = config.num_visual_indicator_tokens + self.vocab_size = config.vocab_size + self.head_linear = nn.Linear( + config.hidden_size * config.hidden_stride * config.hidden_stride, + self.vocab_size - self.num_visual_indicator_tokens, + bias=False, + ) + self.head_norm = nn.LayerNorm(self.vocab_size - self.num_visual_indicator_tokens) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithVisualIndicatorFeatures: + outputs = self.transformer(pixel_values, **kwargs) + last_hidden_state = outputs[0] + if self.config.hidden_stride > 1: + num_images, seq_len, hidden_dim = last_hidden_state.shape + hidden_stride = self.config.hidden_stride + + sqrt_l = int(math.sqrt(seq_len)) + if sqrt_l * sqrt_l != seq_len: + raise ValueError("Token sequence length must be a perfect square") + + pad_size = (hidden_stride - (sqrt_l % hidden_stride)) % hidden_stride + last_hidden_state = nn.functional.pad(last_hidden_state, (0, 0, 0, pad_size, 0, pad_size), "constant", 0) + sqrt_l += pad_size + + last_hidden_state = last_hidden_state.reshape( + num_images, sqrt_l // hidden_stride, hidden_stride, sqrt_l // hidden_stride, hidden_stride, hidden_dim + ) + last_hidden_state = last_hidden_state.permute(0, 1, 3, 2, 4, 5) + last_hidden_state = last_hidden_state.reshape( + num_images, -1, hidden_stride * hidden_stride * hidden_dim + ) # (n, (sqrt_l//hs)^2, hs^2*d) + + logits = self.head_linear(last_hidden_state) + logits = self.head_norm(logits) + + if self.config.tokenize_function == "gumbel_argmax": + prob_token = nn.functional.gumbel_softmax(logits, dim=-1, hard=True) + elif self.config.tokenize_function == "st_argmax": + prob_token = hard_softmax(logits, dim=-1) + elif self.config.tokenize_function == "softmax": + prob_token = nn.functional.softmax(logits, dim=-1) + + return BaseModelOutputWithVisualIndicatorFeatures( + last_hidden_state=last_hidden_state, + pooler_output=prob_token, + ) + + +class Ovis2Model(LlavaModel): + def __init__(self, config: Ovis2Config): + super().__init__(config) + self.vision_tower = Ovis2VisionModel(config.vision_config) + self.visual_embeddings_table = Ovis2VisualEmbeddingTable(config.vision_config.vocab_size, config.hidden_size) + + self.visual_vocab_size = config.vision_config.vocab_size + self.vocab_size = config.vocab_size + self.visual_indicator_token_ids = config.visual_indicator_token_ids + self.language_model = AutoModel.from_config(config.text_config) + del self.multi_modal_projector + + @can_return_tuple + @auto_docstring( + custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection." + ) + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithVisualIndicatorFeatures: + image_outputs = self.vision_tower(pixel_values, return_dict=True, **kwargs) + image_features = image_outputs.pooler_output + batch_size, img_seq_len, _ = image_features.shape + padding_tensor = torch.zeros( + (batch_size, img_seq_len, self.vision_tower.num_visual_indicator_tokens), + dtype=image_features.dtype, + device=image_features.device, + requires_grad=False, + layout=image_features.layout, + ) + image_features = torch.cat([image_features, padding_tensor], dim=2) + image_features = self.visual_embeddings_table(image_features) + + visual_indicator = torch.arange( + self.visual_vocab_size - self.vision_tower.num_visual_indicator_tokens, + self.visual_vocab_size, + dtype=torch.long, + ).to(image_features.device) + image_outputs.pooler_output = image_features + image_outputs.visual_indicator_features = self.visual_embeddings_table(visual_indicator) + + return image_outputs + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | Ovis2ModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + if pixel_values is not None: + image_outputs = self.get_image_features(pixel_values=pixel_values, return_dict=True) + image_features = image_outputs.pooler_output + visual_indicator_features = image_outputs.visual_indicator_features + + special_image_mask = self.get_placeholder_mask( + input_ids, + inputs_embeds=inputs_embeds, + image_features=image_features, + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + for i, visual_indicator_id in enumerate(self.visual_indicator_token_ids): + if input_ids is None: + mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(visual_indicator_id, dtype=torch.long, device=inputs_embeds.device) + ) + mask = mask.all(-1) + else: + mask = (input_ids == visual_indicator_id).to(inputs_embeds.device) + + if mask.any(): + inputs_embeds[mask] = ( + visual_indicator_features[i] + .expand_as(inputs_embeds[mask]) + .to(inputs_embeds.device, inputs_embeds.dtype) + ) + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **kwargs, + ) + + return Ovis2ModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=image_features if pixel_values is not None else None, + ) + + +@auto_docstring +class Ovis2ForConditionalGeneration(LlavaForConditionalGeneration, GenerationMixin): + def __init__(self, config: Ovis2Config): + super().__init__(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + @auto_docstring + def get_image_features( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithVisualIndicatorFeatures: + return self.model.get_image_features(pixel_values=pixel_values, **kwargs) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | Ovis2CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, Ovis2ForConditionalGeneration + + >>> model = Ovis2ForConditionalGeneration.from_pretrained("thisisiron/Ovis2-2B-hf") + >>> processor = AutoProcessor.from_pretrained("thisisiron/Ovis2-2B-hf") + + >>> prompt = "<|im_start|>user\n\nDescribe the image.<|im_end|>\n<|im_start|>assistant\n" + >>> url = "http://images.cocodataset.org/val2014/COCO_val2014_000000537955.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, text=prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(**inputs, max_new_tokens=15) + >>> processor.batch_decode(generate_ids, skip_special_tokens=True)[0] + "user\n\nDescribe the image.\nassistant\nThe image features a brown dog standing on a wooden floor, looking up with" + ```""" + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return Ovis2CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + ) + + +__all__ = ["Ovis2PreTrainedModel", "Ovis2Model", "Ovis2ForConditionalGeneration"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/processing_ovis2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/processing_ovis2.py new file mode 100644 index 0000000000000000000000000000000000000000..acebbb4b2f84139ad5437477f81f35ae9702e7ff --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/ovis2/processing_ovis2.py @@ -0,0 +1,149 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +class Ovis2ProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": False, + }, + "image_kwargs": {}, + } + + +@auto_docstring +class Ovis2Processor(ProcessorMixin): + def __init__( + self, + image_processor=None, + tokenizer=None, + chat_template=None, + image_token="", + image_seq_length=256, + **kwargs, + ): + r""" + image_token (`str`, *optional*, defaults to `""`): + Special token used to denote image location. + image_seq_length (`int`, *optional*, defaults to 256): + The number of image tokens to be used for each image in the input. + """ + self.image_seq_length = image_seq_length + self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token + self.image_token_id = ( + tokenizer.image_token_id + if getattr(tokenizer, "image_token_id", None) + else tokenizer.convert_tokens_to_ids(self.image_token) + ) + super().__init__(image_processor, tokenizer, chat_template=chat_template, **kwargs) + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[Ovis2ProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **image_sizes** -- Size of each image that will be used to unpad an image. Returned when `images` is not `None`. + """ + + output_kwargs = self._merge_kwargs( + Ovis2ProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + if isinstance(text, str): + text = [text] + elif not isinstance(text, list) and not isinstance(text[0], str): + raise TypeError("Invalid input text. Please provide a string, or a list of strings") + + image_inputs = {} + + if images is not None: + image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"]) + image_grids = image_inputs.pop("grids").tolist() + text = self._expand_image_tokens(text, image_grids) + + text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) + return BatchFeature(data={**text_inputs, **image_inputs}) + + def _expand_image_tokens( + self, + text: list[TextInput], + grids: list[list[int]], + ): + processed_text = [] + grid_index = 0 + for sample in text: + while "" in sample: + grid = grids[grid_index] + row, col = grid[0], grid[1] + placeholder = f"{'' * self.image_seq_length}" + if row * col > 1: + for r in range(row): + for c in range(col): + placeholder += f"{'' * self.image_seq_length}" + if c < col - 1: + placeholder += "" + if r < row - 1: + placeholder += "" + placeholder += "" + + sample = sample.replace("", placeholder, 1) + grid_index += 1 + processed_text.append(sample) + return processed_text + + def batch_decode(self, *args, **kwargs): + """ + This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please + refer to the docstring of this method for more information. + """ + return self.tokenizer.batch_decode(*args, **kwargs) + + def decode(self, *args, **kwargs): + """ + This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to + the docstring of this method for more information. + """ + return self.tokenizer.decode(*args, **kwargs) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return list(tokenizer_input_names) + list(image_processor_input_names) + + +__all__ = ["Ovis2Processor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..607f3f70442a3fb824117b90cd2e2e66a3b85e8d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_owlv2 import * + from .image_processing_owlv2 import * + from .image_processing_pil_owlv2 import * + from .modeling_owlv2 import * + from .processing_owlv2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/configuration_owlv2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/configuration_owlv2.py new file mode 100644 index 0000000000000000000000000000000000000000..9eb9c0a8b7f7f9d2e14437a6fe117e0bf25979b3 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/configuration_owlv2.py @@ -0,0 +1,131 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""OWLv2 model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@strict +@auto_docstring(checkpoint="google/owlv2-base-patch16") +# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTTextConfig with OwlViT->Owlv2, owlvit-base-patch32->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2 +class Owlv2TextConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Owlv2TextConfig, Owlv2TextModel + + >>> # Initializing a Owlv2TextModel with google/owlv2-base-patch16 style configuration + >>> configuration = Owlv2TextConfig() + + >>> # Initializing a Owlv2TextConfig from the google/owlv2-base-patch16 style configuration + >>> model = Owlv2TextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "owlv2_text_model" + base_config_key = "text_config" + + vocab_size: int = 49408 + hidden_size: int = 512 + intermediate_size: int = 2048 + num_hidden_layers: int = 12 + num_attention_heads: int = 8 + max_position_embeddings: int = 16 + hidden_act: str = "quick_gelu" + layer_norm_eps: float = 1e-5 + attention_dropout: float | int = 0.0 + initializer_range: float = 0.02 + initializer_factor: float = 1.0 + pad_token_id: int | None = 0 + bos_token_id: int | None = 49406 + eos_token_id: int | list[int] | None = 49407 + + +@strict +@auto_docstring(checkpoint="google/owlv2-base-patch16") +# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTVisionConfig with OwlViT->Owlv2, owlvit-base-patch32->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2, 32->16 +class Owlv2VisionConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Owlv2VisionConfig, Owlv2VisionModel + + >>> # Initializing a Owlv2VisionModel with google/owlv2-base-patch16 style configuration + >>> configuration = Owlv2VisionConfig() + + >>> # Initializing a Owlv2VisionModel model from the google/owlv2-base-patch16 style configuration + >>> model = Owlv2VisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "owlv2_vision_model" + base_config_key = "vision_config" + + hidden_size: int = 768 + intermediate_size: int = 3072 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 768 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "quick_gelu" + layer_norm_eps: float = 1e-5 + attention_dropout: float | int = 0.0 + initializer_range: float = 0.02 + initializer_factor: float = 1.0 + + +@strict +@auto_docstring(checkpoint="google/owlv2-base-patch16") +# Copied from transformers.models.owlvit.configuration_owlvit.OwlViTConfig with OwlViT->Owlv2, owlvit-base-patch32->owlv2-base-patch16, owlvit->owlv2, OWL-ViT->OWLv2 +class Owlv2Config(PreTrainedConfig): + model_type = "owlv2" + sub_configs = {"text_config": Owlv2TextConfig, "vision_config": Owlv2VisionConfig} + + text_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + projection_dim: int = 512 + logit_scale_init_value: float = 2.6592 + return_dict: bool = True + initializer_factor: float = 1.0 + + def __post_init__(self, **kwargs): + if self.text_config is None: + self.text_config = Owlv2TextConfig() + logger.info("`text_config` is `None`. initializing the `Owlv2TextConfig` with default values.") + elif isinstance(self.text_config, dict): + self.text_config = Owlv2TextConfig(**self.text_config) + + if self.vision_config is None: + self.vision_config = Owlv2VisionConfig() + logger.info("`vision_config` is `None`. initializing the `Owlv2VisionConfig` with default values.") + elif isinstance(self.vision_config, dict): + self.vision_config = Owlv2VisionConfig(**self.vision_config) + + super().__post_init__(**kwargs) + + +__all__ = ["Owlv2Config", "Owlv2TextConfig", "Owlv2VisionConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/image_processing_owlv2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/image_processing_owlv2.py new file mode 100644 index 0000000000000000000000000000000000000000..ecf947519755cf32b091801bbc7e4edbcdba62d0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/image_processing_owlv2.py @@ -0,0 +1,415 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/owlv2/modular_owlv2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_owlv2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import warnings +from typing import TYPE_CHECKING + +import torch +import torchvision.transforms.v2.functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import center_to_corners_format, group_images_by_shape, reorder_images +from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +if TYPE_CHECKING: + from .modeling_owlv2 import Owlv2ObjectDetectionOutput + + +def _upcast(t): + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +def box_area(boxes): + """ + Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. + + Args: + boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): + Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 + < x2` and `0 <= y1 < y2`. + Returns: + `torch.FloatTensor`: a tensor containing the area for each box. + """ + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] + inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +def _scale_boxes(boxes, target_sizes): + """ + Scale batch of bounding boxes to the target sizes. + + Args: + boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): + Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. + target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): + Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. + + Returns: + `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. + """ + + if isinstance(target_sizes, (list, tuple)): + image_height = torch.tensor([i[0] for i in target_sizes]) + image_width = torch.tensor([i[1] for i in target_sizes]) + elif isinstance(target_sizes, torch.Tensor): + image_height, image_width = target_sizes.unbind(1) + else: + raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor") + + # for owlv2 image is padded to max size unlike owlvit, that's why we have to scale boxes to max size + max_size = torch.max(image_height, image_width) + + scale_factor = torch.stack([max_size, max_size, max_size, max_size], dim=1) + scale_factor = scale_factor.unsqueeze(1).to(boxes.device) + boxes = boxes * scale_factor + return boxes + + +@auto_docstring +class Owlv2ImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BILINEAR + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 960, "width": 960} + default_to_square = True + crop_size = None + do_resize = True + do_center_crop = None + do_rescale = True + do_normalize = True + do_convert_rgb = True + model_input_names = ["pixel_values"] + rescale_factor = 1 / 255 + do_pad = True + + def __init__(self, **kwargs: Unpack[ImagesKwargs]): + # Early versions of the OWL-ViT config on the hub had "rescale" as a flag. This clashes with the + # vision image processor method `rescale` as it would be set as an attribute during the super().__init__ + # call. This is for backwards compatibility. + if "rescale" in kwargs: + rescale_val = kwargs.pop("rescale") + kwargs["do_rescale"] = rescale_val + + super().__init__(**kwargs) + + def post_process_object_detection( + self, + outputs: "Owlv2ObjectDetectionOutput", + threshold: float = 0.1, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the raw output of [`Owlv2ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. + + Args: + outputs ([`Owlv2ObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.1): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: + - "scores": The confidence scores for each predicted box on the image. + - "labels": Indexes of the classes predicted by the model on the image. + - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. + """ + batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes + batch_size = len(batch_logits) + + if target_sizes is not None and len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as images") + + # batch_logits of shape (batch_size, num_queries, num_classes) + batch_class_logits = torch.max(batch_logits, dim=-1) + batch_scores = torch.sigmoid(batch_class_logits.values) + batch_labels = batch_class_logits.indices + + # Convert to [x0, y0, x1, y1] format + batch_boxes = center_to_corners_format(batch_boxes) + + # Convert from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + batch_boxes = _scale_boxes(batch_boxes, target_sizes) + + results = [] + for scores, labels, boxes in zip(batch_scores, batch_labels, batch_boxes): + keep = scores > threshold + scores = scores[keep] + labels = labels[keep] + boxes = boxes[keep] + results.append({"scores": scores, "labels": labels, "boxes": boxes}) + + return results + + def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_threshold=0.3, target_sizes=None): + """ + Converts the output of [`Owlv2ForObjectDetection.image_guided_detection`] into the format expected by the COCO + api. + + Args: + outputs ([`Owlv2ImageGuidedObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.0): + Minimum confidence threshold to use to filter out predicted boxes. + nms_threshold (`float`, *optional*, defaults to 0.3): + IoU threshold for non-maximum suppression of overlapping boxes. + target_sizes (`torch.Tensor`, *optional*): + Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in + the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to + None, predictions will not be unnormalized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. All labels are set to None as + `Owlv2ForObjectDetection.image_guided_detection` perform one-shot object detection. + """ + logits, target_boxes = outputs.logits, outputs.target_pred_boxes + + if target_sizes is not None and len(logits) != len(target_sizes): + raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits") + if target_sizes is not None and target_sizes.shape[1] != 2: + raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch") + + probs = torch.max(logits, dim=-1) + scores = torch.sigmoid(probs.values) + + # Convert to [x0, y0, x1, y1] format + target_boxes = center_to_corners_format(target_boxes) + + # Apply non-maximum suppression (NMS) + if nms_threshold < 1.0: + for idx in range(target_boxes.shape[0]): + for i in torch.argsort(-scores[idx]): + if not scores[idx][i]: + continue + + ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0] + ious[i] = -1.0 # Mask self-IoU. + scores[idx][ious > nms_threshold] = 0.0 + + # Convert from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + target_boxes = _scale_boxes(target_boxes, target_sizes) + + # Compute box display alphas based on prediction scores + results = [] + alphas = torch.zeros_like(scores) + + for idx in range(target_boxes.shape[0]): + # Select scores for boxes matching the current query: + query_scores = scores[idx] + if not query_scores.nonzero().numel(): + continue + + # Apply threshold on scores before scaling + query_scores[query_scores < threshold] = 0.0 + + # Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1. + # All other boxes will either belong to a different query, or will not be shown. + max_score = torch.max(query_scores) + 1e-6 + query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9) + query_alphas = torch.clip(query_alphas, 0.0, 1.0) + alphas[idx] = query_alphas + + mask = alphas[idx] > 0 + box_scores = alphas[idx][mask] + boxes = target_boxes[idx][mask] + results.append({"scores": box_scores, "labels": None, "boxes": boxes}) + + return results + + def _pad_images(self, images: "torch.Tensor", constant_value: float = 0.0) -> "torch.Tensor": + """ + Pad an image with zeros to the given size. + """ + height, width = images.shape[-2:] + size = max(height, width) + pad_bottom = size - height + pad_right = size - width + + padding = (0, 0, pad_right, pad_bottom) + padded_image = tvF.pad(images, padding, fill=constant_value) + return padded_image + + def pad( + self, + images: list["torch.Tensor"], + disable_grouping: bool | None, + constant_value: float = 0.0, + **kwargs, + ) -> list["torch.Tensor"]: + """ + Unlike the Base class `self.pad` where all images are padded to the maximum image size, + Owlv2 pads an image to square. + """ + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + stacked_images = self._pad_images( + stacked_images, + constant_value=constant_value, + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return processed_images + + def resize( + self, + image: "torch.Tensor", + size: SizeDict, + anti_aliasing: bool = True, + anti_aliasing_sigma=None, + **kwargs, + ) -> "torch.Tensor": + """ + Resize an image as per the original implementation. + + Args: + image (`Tensor`): + Image to resize. + size (`dict[str, int]`): + Dictionary containing the height and width to resize the image to. + anti_aliasing (`bool`, *optional*, defaults to `True`): + Whether to apply anti-aliasing when downsampling the image. + anti_aliasing_sigma (`float`, *optional*, defaults to `None`): + Standard deviation for Gaussian kernel when downsampling the image. If `None`, it will be calculated + automatically. + """ + output_shape = (size.height, size.width) + + input_shape = image.shape + + # select height and width from input tensor + factors = torch.tensor(input_shape[2:]).to(image.device) / torch.tensor(output_shape).to(image.device) + + if anti_aliasing: + if anti_aliasing_sigma is None: + anti_aliasing_sigma = ((factors - 1) / 2).clamp(min=0) + else: + anti_aliasing_sigma = torch.atleast_1d(anti_aliasing_sigma) * torch.ones_like(factors) + if torch.any(anti_aliasing_sigma < 0): + raise ValueError("Anti-aliasing standard deviation must be greater than or equal to zero") + elif torch.any((anti_aliasing_sigma > 0) & (factors <= 1)): + warnings.warn( + "Anti-aliasing standard deviation greater than zero but not down-sampling along all axes" + ) + if torch.any(anti_aliasing_sigma == 0): + filtered = image + else: + kernel_sizes = 2 * torch.ceil(3 * anti_aliasing_sigma).int() + 1 + + filtered = tvF.gaussian_blur( + image, (kernel_sizes[0], kernel_sizes[1]), sigma=anti_aliasing_sigma.tolist() + ) + + else: + filtered = image + + return super().resize(filtered, size=size, antialias=False) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_pad: bool, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + processed_images_grouped = {} + + for shape, stacked_images in grouped_images.items(): + # Rescale images before other operations as done in original implementation + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, False, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, constant_value=0.0, disable_grouping=disable_grouping) + + grouped_images, grouped_images_index = group_images_by_shape( + processed_images, disable_grouping=disable_grouping + ) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + resized_stack = self.resize(image=stacked_images, size=size, resample=resample) + resized_images_grouped[shape] = resized_stack + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, False, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["Owlv2ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/image_processing_pil_owlv2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/image_processing_pil_owlv2.py new file mode 100644 index 0000000000000000000000000000000000000000..b61db3c0484e3ee21627af410cd45cb7860cb21f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/image_processing_pil_owlv2.py @@ -0,0 +1,454 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/owlv2/modular_owlv2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_owlv2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import warnings +from typing import TYPE_CHECKING + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import center_to_corners_format, pad, to_channel_dimension_format +from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_scipy_available, is_torch_available, requires_backends +from ...utils.import_utils import requires + + +if is_scipy_available(): + from scipy import ndimage as ndi + + +if TYPE_CHECKING: + from .modeling_owlv2 import Owlv2ObjectDetectionOutput +if is_torch_available(): + import torch + + +def _upcast(t): + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + import torch + + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +def box_area(boxes): + """ + Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. + + Args: + boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): + Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 + < x2` and `0 <= y1 < y2`. + Returns: + `torch.FloatTensor`: a tensor containing the area for each box. + """ + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# Adapted from transformers.models.owlv2.image_processing_owlv2.box_iou +def box_iou(boxes1, boxes2): + import torch + + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] + inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +def _preprocess_resize_output_shape(image, output_shape): + """Validate resize output shape according to input image. + + Args: + image (`np.ndarray`): + Image to be resized. + output_shape (`iterable`): + Size of the generated output image `(rows, cols[, ...][, dim])`. If `dim` is not provided, the number of + channels is preserved. + + Returns + image (`np.ndarray`): + The input image, but with additional singleton dimensions appended in the case where `len(output_shape) > + input.ndim`. + output_shape (`Tuple`): + The output shape converted to tuple. + + Raises ------ ValueError: + If output_shape length is smaller than the image number of dimensions. + + Notes ----- The input image is reshaped if its number of dimensions is not equal to output_shape_length. + + """ + output_shape = tuple(output_shape) + output_ndim = len(output_shape) + input_shape = image.shape + if output_ndim > image.ndim: + # append dimensions to input_shape + input_shape += (1,) * (output_ndim - image.ndim) + image = np.reshape(image, input_shape) + elif output_ndim == image.ndim - 1: + # multichannel case: append shape of last axis + output_shape = output_shape + (image.shape[-1],) + elif output_ndim < image.ndim: + raise ValueError("output_shape length cannot be smaller than the image number of dimensions") + + return image, output_shape + + +def _clip_warp_output(input_image, output_image): + """Clip output image to range of values of input image. + + Note that this function modifies the values of *output_image* in-place. + + Taken from: + https://github.com/scikit-image/scikit-image/blob/b4b521d6f0a105aabeaa31699949f78453ca3511/skimage/transform/_warps.py#L640. + + Args: + input_image : ndarray + Input image. + output_image : ndarray + Output image, which is modified in-place. + """ + min_val = np.min(input_image) + if np.isnan(min_val): + # NaNs detected, use NaN-safe min/max + min_func = np.nanmin + max_func = np.nanmax + min_val = min_func(input_image) + else: + min_func = np.min + max_func = np.max + max_val = max_func(input_image) + + output_image = np.clip(output_image, min_val, max_val) + + return output_image + + +def _scale_boxes(boxes, target_sizes): + """ + Scale batch of bounding boxes to the target sizes. + + Args: + boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): + Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. + target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): + Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. + + Returns: + `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. + """ + + if isinstance(target_sizes, (list, tuple)): + image_height = torch.tensor([i[0] for i in target_sizes]) + image_width = torch.tensor([i[1] for i in target_sizes]) + elif isinstance(target_sizes, torch.Tensor): + image_height, image_width = target_sizes.unbind(1) + else: + raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor") + + # for owlv2 image is padded to max size unlike owlvit, that's why we have to scale boxes to max size + max_size = torch.max(image_height, image_width) + + scale_factor = torch.stack([max_size, max_size, max_size, max_size], dim=1) + scale_factor = scale_factor.unsqueeze(1).to(boxes.device) + boxes = boxes * scale_factor + return boxes + + +@auto_docstring +@requires(backends=("torch",)) +class Owlv2ImageProcessorPil(PilBackend): + resample = PILImageResampling.BILINEAR + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 960, "width": 960} + default_to_square = True + crop_size = None + do_resize = True + do_center_crop = None + do_rescale = True + do_normalize = True + do_convert_rgb = True + model_input_names = ["pixel_values"] + rescale_factor = 1 / 255 + do_pad = True + + def __init__(self, **kwargs: Unpack[ImagesKwargs]): + # Early versions of the OWL-ViT config on the hub had "rescale" as a flag. This clashes with the + # vision image processor method `rescale` as it would be set as an attribute during the super().__init__ + # call. This is for backwards compatibility. + if "rescale" in kwargs: + rescale_val = kwargs.pop("rescale") + kwargs["do_rescale"] = rescale_val + + super().__init__(**kwargs) + + @requires(backends=("torch",)) + def post_process_object_detection( + self, + outputs: "Owlv2ObjectDetectionOutput", + threshold: float = 0.1, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the raw output of [`Owlv2ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. + + Args: + outputs ([`Owlv2ObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.1): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: + - "scores": The confidence scores for each predicted box on the image. + - "labels": Indexes of the classes predicted by the model on the image. + - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. + """ + import torch + + batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes + batch_size = len(batch_logits) + + if target_sizes is not None and len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as images") + + # batch_logits of shape (batch_size, num_queries, num_classes) + batch_class_logits = torch.max(batch_logits, dim=-1) + batch_scores = torch.sigmoid(batch_class_logits.values) + batch_labels = batch_class_logits.indices + + # Convert to [x0, y0, x1, y1] format + batch_boxes = center_to_corners_format(batch_boxes) + + # Convert from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + batch_boxes = _scale_boxes(batch_boxes, target_sizes) + + results = [] + for scores, labels, boxes in zip(batch_scores, batch_labels, batch_boxes): + keep = scores > threshold + scores = scores[keep] + labels = labels[keep] + boxes = boxes[keep] + results.append({"scores": scores, "labels": labels, "boxes": boxes}) + + return results + + @requires(backends=("torch",)) + def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_threshold=0.3, target_sizes=None): + """ + Converts the output of [`Owlv2ForObjectDetection.image_guided_detection`] into the format expected by the COCO + api. + + Args: + outputs ([`Owlv2ImageGuidedObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.0): + Minimum confidence threshold to use to filter out predicted boxes. + nms_threshold (`float`, *optional*, defaults to 0.3): + IoU threshold for non-maximum suppression of overlapping boxes. + target_sizes (`torch.Tensor`, *optional*): + Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in + the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to + None, predictions will not be unnormalized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. All labels are set to None as + `Owlv2ForObjectDetection.image_guided_detection` perform one-shot object detection. + """ + import torch + + logits, target_boxes = outputs.logits, outputs.target_pred_boxes + + if target_sizes is not None and len(logits) != len(target_sizes): + raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits") + if target_sizes is not None and target_sizes.shape[1] != 2: + raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch") + + probs = torch.max(logits, dim=-1) + scores = torch.sigmoid(probs.values) + + # Convert to [x0, y0, x1, y1] format + target_boxes = center_to_corners_format(target_boxes) + + # Apply non-maximum suppression (NMS) + if nms_threshold < 1.0: + for idx in range(target_boxes.shape[0]): + for i in torch.argsort(-scores[idx]): + if not scores[idx][i]: + continue + + ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0] + ious[i] = -1.0 # Mask self-IoU. + scores[idx][ious > nms_threshold] = 0.0 + + # Convert from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + target_boxes = _scale_boxes(target_boxes, target_sizes) + + # Compute box display alphas based on prediction scores + results = [] + alphas = torch.zeros_like(scores) + + for idx in range(target_boxes.shape[0]): + # Select scores for boxes matching the current query: + query_scores = scores[idx] + if not query_scores.nonzero().numel(): + continue + + # Apply threshold on scores before scaling + query_scores[query_scores < threshold] = 0.0 + + # Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1. + # All other boxes will either belong to a different query, or will not be shown. + max_score = torch.max(query_scores) + 1e-6 + query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9) + query_alphas = torch.clip(query_alphas, 0.0, 1.0) + alphas[idx] = query_alphas + + mask = alphas[idx] > 0 + box_scores = alphas[idx][mask] + boxes = target_boxes[idx][mask] + results.append({"scores": box_scores, "labels": None, "boxes": boxes}) + + return results + + def pad(self, image: np.ndarray, constant_value: float = 0.0) -> np.ndarray: + """ + Pad an image with zeros to the given size. + """ + height, width = image.shape[-2:] + size = max(height, width) + pad_bottom = size - height + pad_right = size - width + image = pad( + image=image, + padding=((0, pad_bottom), (0, pad_right)), + constant_values=constant_value, + ) + return image + + def resize( + self, + image: np.ndarray, + size: dict[str, int], + anti_aliasing: bool = True, + anti_aliasing_sigma=None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image as per the original implementation. + + Args: + image (`np.ndarray`): + Image to resize. + size (`dict[str, int]`): + Dictionary containing the height and width to resize the image to. + anti_aliasing (`bool`, *optional*, defaults to `True`): + Whether to apply anti-aliasing when downsampling the image. + anti_aliasing_sigma (`float`, *optional*, defaults to `None`): + Standard deviation for Gaussian kernel when downsampling the image. If `None`, it will be calculated + automatically. + """ + requires_backends(self, "scipy") + + output_shape = (size["height"], size["width"]) + image = to_channel_dimension_format(image, ChannelDimension.LAST) + image, output_shape = _preprocess_resize_output_shape(image, output_shape) + input_shape = image.shape + factors = np.divide(input_shape, output_shape) + + # Translate modes used by np.pad to those used by scipy.ndimage + ndi_mode = "mirror" + cval = 0 + order = 1 + if anti_aliasing: + if anti_aliasing_sigma is None: + anti_aliasing_sigma = np.maximum(0, (factors - 1) / 2) + else: + anti_aliasing_sigma = np.atleast_1d(anti_aliasing_sigma) * np.ones_like(factors) + if np.any(anti_aliasing_sigma < 0): + raise ValueError("Anti-aliasing standard deviation must be greater than or equal to zero") + elif np.any((anti_aliasing_sigma > 0) & (factors <= 1)): + warnings.warn( + "Anti-aliasing standard deviation greater than zero but not down-sampling along all axes" + ) + filtered = ndi.gaussian_filter(image, anti_aliasing_sigma, cval=cval, mode=ndi_mode) + else: + filtered = image + + zoom_factors = [1 / f for f in factors] + out = ndi.zoom(filtered, zoom_factors, order=order, mode=ndi_mode, cval=cval, grid_mode=True) + + image = _clip_warp_output(image, out) + + image = to_channel_dimension_format(image, ChannelDimension.FIRST) + return image + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_pad: bool, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + processed_images = [] + for image in images: + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_pad: + image = self.pad(image) + if do_resize: + image = self.resize(image, size, resample) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["Owlv2ImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/modeling_owlv2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/modeling_owlv2.py new file mode 100644 index 0000000000000000000000000000000000000000..b44970ef12998661a9f04c8e9e1cf002813343bb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/modeling_owlv2.py @@ -0,0 +1,1536 @@ +# Copyright 2023 Google AI and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch OWLv2 model.""" + +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import torch +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + is_vision_available, + logging, + torch_int, +) +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_owlv2 import Owlv2Config, Owlv2TextConfig, Owlv2VisionConfig + + +if is_vision_available(): + from transformers.image_transforms import center_to_corners_format + + +logger = logging.get_logger(__name__) + + +# See all Owlv2 models at https://huggingface.co/models?filter=owlv2 + + +# Copied from transformers.models.clip.modeling_clip.contrastive_loss with clip->owlv2 +def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: + return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) + + +# Copied from transformers.models.clip.modeling_clip.image_text_contrastive_loss +def image_text_contrastive_loss(similarity: torch.Tensor) -> torch.Tensor: + caption_loss = contrastive_loss(similarity) + image_loss = contrastive_loss(similarity.T) + return (caption_loss + image_loss) / 2.0 + + +@dataclass +@auto_docstring +class Owlv2Output(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): + Contrastive loss for image-text similarity. + logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): + The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text + similarity scores. + logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): + The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image + similarity scores. + text_embeds (`torch.FloatTensor` of shape `(batch_size * num_max_text_queries, output_dim`): + The text embeddings obtained by applying the projection layer to the pooled output of [`Owlv2TextModel`]. + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`): + The image embeddings obtained by applying the projection layer to the pooled output of + [`Owlv2VisionModel`]. + text_model_output (tuple[`BaseModelOutputWithPooling`]): + The output of the [`Owlv2TextModel`]. + vision_model_output (`BaseModelOutputWithPooling`): + The output of the [`Owlv2VisionModel`]. + """ + + loss: torch.FloatTensor | None = None + logits_per_image: torch.FloatTensor | None = None + logits_per_text: torch.FloatTensor | None = None + text_embeds: torch.FloatTensor | None = None + image_embeds: torch.FloatTensor | None = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() + for k in self.keys() + ) + + +# Copied from transformers.loss.loss_for_object_detection._upcast +def _upcast(t: Tensor) -> Tensor: + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +# Copied from transformers.loss.loss_for_object_detection.box_area +def box_area(boxes: Tensor) -> Tensor: + """ + Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. + + Args: + boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): + Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 + < x2` and `0 <= y1 < y2`. + + Returns: + `torch.FloatTensor`: a tensor containing the area for each box. + """ + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# Copied from transformers.loss.loss_for_object_detection.box_iou +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] + inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +# Copied from transformers.loss.loss_for_object_detection.generalized_box_iou +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. + + Returns: + `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): + raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") + if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): + raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") + iou, union = box_iou(boxes1, boxes2) + + top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] + area = width_height[:, :, 0] * width_height[:, :, 1] + + return iou - (area - union) / area + + +@auto_docstring( + custom_intro=""" + Output type of [`Owlv2ForObjectDetection`]. + """ +) +@dataclass +class Owlv2ObjectDetectionOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): + Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a + bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized + scale-invariant IoU loss. + loss_dict (`Dict`, *optional*): + A dictionary containing the individual losses. Useful for logging. + logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`): + Classification logits (including no-object) for all queries. + objectness_logits (`torch.FloatTensor` of shape `(batch_size, num_patches, 1)`): + The objectness logits of all image patches. OWL-ViT represents images as a set of image patches where the + total number of patches is (image_size / patch_size)**2. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to retrieve the + unnormalized bounding boxes. + text_embeds (`torch.FloatTensor` of shape `(batch_size, num_max_text_queries, output_dim`): + The text embeddings obtained by applying the projection layer to the pooled output of [`Owlv2TextModel`]. + image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): + Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes image + embeddings for each patch. + class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`): + Class embeddings of all image patches. OWLv2 represents images as a set of image patches where the total + number of patches is (image_size / patch_size)**2. + text_model_output (tuple[`BaseModelOutputWithPooling`]): + The output of the [`Owlv2TextModel`]. + vision_model_output (`BaseModelOutputWithPooling`): + The output of the [`Owlv2VisionModel`]. + """ + + loss: torch.FloatTensor | None = None + loss_dict: dict | None = None + logits: torch.FloatTensor | None = None + objectness_logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + text_embeds: torch.FloatTensor | None = None + image_embeds: torch.FloatTensor | None = None + class_embeds: torch.FloatTensor | None = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() + for k in self.keys() + ) + + +@dataclass +@auto_docstring( + custom_intro=""" + Output type of [`Owlv2ForObjectDetection.image_guided_detection`]. + """ +) +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTImageGuidedObjectDetectionOutput with OwlViT->Owlv2,OWL-ViT->OWLv2 +class Owlv2ImageGuidedObjectDetectionOutput(ModelOutput): + r""" + logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`): + Classification logits (including no-object) for all queries. + image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): + Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes + image embeddings for each patch. + query_image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): + Pooled output of [`Owlv2VisionModel`]. OWLv2 represents images as a set of image patches and computes + image embeddings for each patch. + target_pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual target image in the batch + (disregarding possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to + retrieve the unnormalized bounding boxes. + query_pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual query image in the batch + (disregarding possible padding). You can use [`~Owlv2ImageProcessor.post_process_object_detection`] to + retrieve the unnormalized bounding boxes. + class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`): + Class embeddings of all image patches. OWLv2 represents images as a set of image patches where the total + number of patches is (image_size / patch_size)**2. + text_model_output (tuple[`BaseModelOutputWithPooling`]): + The output of the [`Owlv2TextModel`]. + vision_model_output (`BaseModelOutputWithPooling`): + The output of the [`Owlv2VisionModel`]. + """ + + logits: torch.FloatTensor | None = None + image_embeds: torch.FloatTensor | None = None + query_image_embeds: torch.FloatTensor | None = None + target_pred_boxes: torch.FloatTensor | None = None + query_pred_boxes: torch.FloatTensor | None = None + class_embeds: torch.FloatTensor | None = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() + for k in self.keys() + ) + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionEmbeddings with OwlViT->Owlv2 +class Owlv2VisionEmbeddings(nn.Module): + def __init__(self, config: Owlv2VisionConfig): + super().__init__() + self.patch_size = config.patch_size + self.config = config + self.embed_dim = config.hidden_size + self.class_embedding = nn.Parameter(torch.randn(config.hidden_size)) + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=config.patch_size, + stride=config.patch_size, + bias=False, + ) + + self.num_patches = (config.image_size // config.patch_size) ** 2 + self.num_positions = self.num_patches + 1 + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) + + # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings.interpolate_pos_encoding + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support torch.jit tracing. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + + num_patches = embeddings.shape[1] - 1 + position_embedding = self.position_embedding.weight.unsqueeze(0) + num_positions = position_embedding.shape[1] - 1 + + # always interpolate when tracing to ensure the exported model works for dynamic input shapes + if not torch.jit.is_tracing() and num_patches == num_positions and height == width: + return self.position_embedding(self.position_ids) + + class_pos_embed = position_embedding[:, :1] + patch_pos_embed = position_embedding[:, 1:] + + dim = embeddings.shape[-1] + + new_height = height // self.patch_size + new_width = width // self.patch_size + + sqrt_num_positions = torch_int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(new_height, new_width), + mode="bicubic", + align_corners=False, + ) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + + return torch.cat((class_pos_embed, patch_pos_embed), dim=1) + + def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: + batch_size, _, height, width = pixel_values.shape + patch_embeds = self.patch_embedding(pixel_values) # shape = [batch_size, num_channels, height, width] + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + + class_embeds = self.class_embedding.expand(batch_size, 1, -1) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + else: + embeddings = embeddings + self.position_embedding(self.position_ids) + return embeddings + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTTextEmbeddings with OwlViT->Owlv2 +class Owlv2TextEmbeddings(nn.Module): + def __init__(self, config: Owlv2TextConfig): + super().__init__() + self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size) + self.position_embedding = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + ) -> torch.Tensor: + seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + if inputs_embeds is None: + inputs_embeds = self.token_embedding(input_ids) + + position_embeddings = self.position_embedding(position_ids) + embeddings = inputs_embeds + position_embeddings + + return embeddings + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTAttention with OwlViT->Owlv2 +class Owlv2Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(*hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(*hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(*hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + scaling=self.scale, + dropout=0.0 if not self.training else self.dropout, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Owlv2 +class Owlv2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Owlv2 +class Owlv2EncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Owlv2VisionConfig | Owlv2TextConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = Owlv2Attention(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = Owlv2MLP(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTPreTrainedModel with OwlViT->Owlv2,owlvit->owlv2 +class Owlv2PreTrainedModel(PreTrainedModel): + config: Owlv2Config + base_model_prefix = "owlv2" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + _supports_sdpa = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_attention_backend = True + _no_split_modules = ["Owlv2EncoderLayer"] + _can_record_outputs = { + "hidden_states": Owlv2EncoderLayer, + "attentions": Owlv2Attention, + } + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + """Initialize the weights""" + factor = self.config.initializer_factor + if isinstance(module, Owlv2TextEmbeddings): + init.normal_(module.token_embedding.weight, mean=0.0, std=factor * 0.02) + init.normal_(module.position_embedding.weight, mean=0.0, std=factor * 0.02) + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, Owlv2VisionEmbeddings): + init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) + init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) + init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, Owlv2Attention): + in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor + out_proj_std = (module.embed_dim**-0.5) * factor + init.normal_(module.q_proj.weight, std=in_proj_std) + init.normal_(module.k_proj.weight, std=in_proj_std) + init.normal_(module.v_proj.weight, std=in_proj_std) + init.normal_(module.out_proj.weight, std=out_proj_std) + elif isinstance(module, Owlv2MLP): + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor + fc_std = (2 * module.config.hidden_size) ** -0.5 * factor + init.normal_(module.fc1.weight, std=fc_std) + init.normal_(module.fc2.weight, std=in_proj_std) + elif isinstance(module, Owlv2Model): + init.normal_( + module.text_projection.weight, + std=module.text_embed_dim**-0.5 * factor, + ) + init.normal_( + module.visual_projection.weight, + std=module.vision_embed_dim**-0.5 * factor, + ) + init.constant_(module.logit_scale, self.config.logit_scale_init_value) + elif isinstance(module, Owlv2ForObjectDetection): + init.copy_(module.box_bias, module.compute_box_bias(module.num_patches_height, module.num_patches_width)) + if isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + if isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=factor) + if module.bias is not None: + init.zeros_(module.bias) + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTEncoder with OwlViT->Owlv2 +class Owlv2Encoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`Owlv2EncoderLayer`]. + + Args: + config: Owlv2Config + """ + + def __init__(self, config: Owlv2Config): + super().__init__() + self.config = config + self.layers = nn.ModuleList([Owlv2EncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + **kwargs, + ) + + return BaseModelOutput( + last_hidden_state=hidden_states, + ) + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTTextTransformer with OWLVIT->OWLV2,OwlViT->Owlv2 +class Owlv2TextTransformer(Owlv2PreTrainedModel): + def __init__(self, config: Owlv2TextConfig): + super().__init__(config) + + embed_dim = config.hidden_size + self.embeddings = Owlv2TextEmbeddings(config) + self.encoder = Owlv2Encoder(config) + self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids) + """ + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) + + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + past_key_values=None, + ) + + kwargs.pop("is_causal", None) + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + is_causal=True, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.final_layer_norm(last_hidden_state) + + # take features from the end of tokens embedding (end of token is the highest number in each sequence) + # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 + pooled_output = last_hidden_state[ + torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), + input_ids.to(torch.int).argmax(dim=-1).to(last_hidden_state.device), + ] + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + ) + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTTextModel with google/owlvit-base-patch32->google/owlv2-base-patch16, OWLVIT->OWLV2,OwlViT->Owlv2 +class Owlv2TextModel(Owlv2PreTrainedModel): + config: Owlv2TextConfig + input_modalities = ("text",) + + def __init__(self, config: Owlv2TextConfig): + super().__init__(config) + self.text_model = Owlv2TextTransformer(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, value): + self.text_model.embeddings.token_embedding = value + + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids) + + Examples: + ```python + >>> from transformers import AutoProcessor, Owlv2TextModel + + >>> model = Owlv2TextModel.from_pretrained("google/owlv2-base-patch16") + >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16") + >>> inputs = processor( + ... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt" + ... ) + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled (EOS token) states + ```""" + + return self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + **kwargs, + ) + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionTransformer with OWLVIT->OWLV2,OwlViT->Owlv2 +class Owlv2VisionTransformer(Owlv2PreTrainedModel): + def __init__(self, config: Owlv2VisionConfig): + super().__init__(config) + + self.embeddings = Owlv2VisionEmbeddings(config) + self.pre_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.encoder = Owlv2Encoder(config) + self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + interpolate_pos_encoding: bool | None = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + # Cast the input to the expected `dtype` + expected_input_dtype = self.embeddings.patch_embedding.weight.dtype + pixel_values = pixel_values.to(expected_input_dtype) + + hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) + hidden_states = self.pre_layernorm(hidden_states) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + pooled_output = last_hidden_state[:, 0, :] + pooled_output = self.post_layernorm(pooled_output) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + ) + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTVisionModel with OWLVIT->OWLV2,OwlViT->Owlv2,google/owlvit-base-patch32->google/owlv2-base-patch16 +class Owlv2VisionModel(Owlv2PreTrainedModel): + config: Owlv2VisionConfig + main_input_name = "pixel_values" + input_modalities = ("image",) + + def __init__(self, config: Owlv2VisionConfig): + super().__init__(config) + self.vision_model = Owlv2VisionTransformer(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_model.embeddings.patch_embedding + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + r""" + Examples: + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, Owlv2VisionModel + + >>> model = Owlv2VisionModel.from_pretrained("google/owlv2-base-patch16") + >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled CLS states + ```""" + + return self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + +@auto_docstring +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTModel with google/owlvit-base-patch32->google/owlv2-base-patch16-ensemble, OWLVIT->OWLV2,OwlViT->Owlv2,owlvit->owlv2,OWL-ViT->OWLv2 +class Owlv2Model(Owlv2PreTrainedModel): + config: Owlv2Config + + def __init__(self, config: Owlv2Config): + super().__init__(config) + + text_config = config.text_config + vision_config = config.vision_config + + self.projection_dim = config.projection_dim + self.text_embed_dim = text_config.hidden_size + self.vision_embed_dim = vision_config.hidden_size + + self.text_model = Owlv2TextTransformer(text_config) + self.vision_model = Owlv2VisionTransformer(vision_config) + + self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) + self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) + self.logit_scale = nn.Parameter(torch.tensor(config.logit_scale_init_value)) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def get_text_features( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids) + + Examples: + ```python + >>> import torch + >>> from transformers import AutoProcessor, Owlv2Model + + >>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble") + >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") + >>> inputs = processor( + ... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt" + ... ) + >>> with torch.inference_mode(): + ... text_features = model.get_text_features(**inputs) + ```""" + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + **kwargs, + ) + pooled_output = text_outputs.pooler_output + text_outputs.pooler_output = self.text_projection(pooled_output) + + return text_outputs + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.Tensor, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + Examples: + ```python + >>> import torch + >>> from transformers.image_utils import load_image + >>> from transformers import AutoProcessor, Owlv2Model + + >>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble") + >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = load_image(url) + + >>> inputs = processor(images=image, return_tensors="pt") + >>> with torch.inference_mode(): + ... image_features = model.get_image_features(**inputs) + ```""" + vision_outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + vision_outputs.pooler_output = self.visual_projection(vision_outputs.pooler_output) + + return vision_outputs + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + return_loss: bool | None = None, + interpolate_pos_encoding: bool = False, + return_base_image_embeds: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Owlv2Output: + r""" + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. + return_base_image_embeds (`bool`, *optional*): + Whether or not to return the base image embeddings. + + Examples: + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, Owlv2Model + + >>> model = Owlv2Model.from_pretrained("google/owlv2-base-patch16-ensemble") + >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> inputs = processor(text=[["a photo of a cat", "a photo of a dog"]], images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score + >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities + ```""" + vision_outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + # Get embeddings for all text queries in all batch samples + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + **kwargs, + ) + + text_embeds = text_outputs.pooler_output + text_embeds = self.text_projection(text_embeds) + image_embeds = vision_outputs.pooler_output + image_embeds = self.visual_projection(image_embeds) + + # normalized features + image_embeds = image_embeds / torch.linalg.norm(image_embeds, ord=2, dim=-1, keepdim=True) + text_embeds_norm = text_embeds / torch.linalg.norm(text_embeds, ord=2, dim=-1, keepdim=True) + + # cosine similarity as logits and set it on the correct device + logit_scale = self.logit_scale.exp().to(image_embeds.device) + + logits_per_text = torch.matmul(text_embeds_norm, image_embeds.t()) * logit_scale + logits_per_image = logits_per_text.t() + + loss = None + if return_loss: + loss = image_text_contrastive_loss(logits_per_text) + + text_embeds = text_embeds_norm + + return Owlv2Output( + loss=loss, + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTBoxPredictionHead with OwlViT->Owlv2 +class Owlv2BoxPredictionHead(nn.Module): + def __init__(self, config: Owlv2Config, out_dim: int = 4): + super().__init__() + + width = config.vision_config.hidden_size + self.dense0 = nn.Linear(width, width) + self.dense1 = nn.Linear(width, width) + self.gelu = nn.GELU() + self.dense2 = nn.Linear(width, out_dim) + + def forward(self, image_features: torch.Tensor) -> torch.FloatTensor: + output = self.dense0(image_features) + output = self.gelu(output) + output = self.dense1(output) + output = self.gelu(output) + output = self.dense2(output) + return output + + +# Copied from transformers.models.owlvit.modeling_owlvit.OwlViTClassPredictionHead with OwlViT->Owlv2 +class Owlv2ClassPredictionHead(nn.Module): + def __init__(self, config: Owlv2Config): + super().__init__() + + out_dim = config.text_config.hidden_size + self.query_dim = config.vision_config.hidden_size + + self.dense0 = nn.Linear(self.query_dim, out_dim) + self.logit_shift = nn.Linear(self.query_dim, 1) + self.logit_scale = nn.Linear(self.query_dim, 1) + self.elu = nn.ELU() + + def forward( + self, + image_embeds: torch.FloatTensor, + query_embeds: torch.FloatTensor | None, + query_mask: torch.Tensor | None, + ) -> tuple[torch.FloatTensor]: + image_class_embeds = self.dense0(image_embeds) + if query_embeds is None: + device = image_class_embeds.device + batch_size, num_patches = image_class_embeds.shape[:2] + pred_logits = torch.zeros((batch_size, num_patches, self.query_dim)).to(device) + return (pred_logits, image_class_embeds) + + # Normalize image and text features + image_class_embeds = image_class_embeds / (torch.linalg.norm(image_class_embeds, dim=-1, keepdim=True) + 1e-6) + query_embeds = query_embeds / (torch.linalg.norm(query_embeds, dim=-1, keepdim=True) + 1e-6) + + # Get class predictions + pred_logits = torch.einsum("...pd,...qd->...pq", image_class_embeds, query_embeds) + + # Apply a learnable shift and scale to logits + logit_shift = self.logit_shift(image_embeds) + logit_scale = self.logit_scale(image_embeds) + logit_scale = self.elu(logit_scale) + 1 + pred_logits = (pred_logits + logit_shift) * logit_scale + + if query_mask is not None: + if query_mask.ndim > 1: + query_mask = torch.unsqueeze(query_mask, dim=-2) + + pred_logits = torch.where(query_mask == 0, torch.finfo(pred_logits.dtype).min, pred_logits) + pred_logits = pred_logits.to(torch.float32) + + return (pred_logits, image_class_embeds) + + +class Owlv2ForObjectDetection(Owlv2PreTrainedModel): + config: Owlv2Config + + def __init__(self, config: Owlv2Config): + super().__init__(config) + + self.owlv2 = Owlv2Model(config) + self.class_head = Owlv2ClassPredictionHead(config) + self.box_head = Owlv2BoxPredictionHead(config) + self.objectness_head = Owlv2BoxPredictionHead(config, out_dim=1) + + self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps) + self.sigmoid = nn.Sigmoid() + self.config = config + self.num_patches_height = self.config.vision_config.image_size // self.config.vision_config.patch_size + self.num_patches_width = self.config.vision_config.image_size // self.config.vision_config.patch_size + self.register_buffer( + "box_bias", self.compute_box_bias(self.num_patches_height, self.num_patches_width), persistent=False + ) + + # Initialize weights and apply final processing + self.post_init() + + @staticmethod + # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.normalize_grid_corner_coordinates + def normalize_grid_corner_coordinates(num_patches_height: int, num_patches_width: int) -> torch.Tensor: + # Create grid coordinates using torch + x_coordinates = torch.arange(1, num_patches_width + 1, dtype=torch.float32) + y_coordinates = torch.arange(1, num_patches_height + 1, dtype=torch.float32) + xx, yy = torch.meshgrid(x_coordinates, y_coordinates, indexing="xy") + + # Stack the coordinates and divide by their respective patch counts + box_coordinates = torch.stack((xx, yy), dim=-1) + box_coordinates[..., 0] /= num_patches_width + box_coordinates[..., 1] /= num_patches_height + + # Flatten (h, w, 2) -> (h*w, 2) + box_coordinates = box_coordinates.view(-1, 2) + + return box_coordinates + + def objectness_predictor(self, image_features: torch.FloatTensor) -> torch.FloatTensor: + """Predicts the probability that each image feature token is an object. + + Args: + image_features (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_dim)`)): + Features extracted from the image. + Returns: + Objectness scores. + """ + image_features = image_features.detach() + objectness_logits = self.objectness_head(image_features) + objectness_logits = objectness_logits[..., 0] + return objectness_logits + + # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.compute_box_bias + def compute_box_bias(self, num_patches_height: int, num_patches_width: int) -> torch.Tensor: + # The box center is biased to its position on the feature grid + box_coordinates = self.normalize_grid_corner_coordinates(num_patches_height, num_patches_width) + box_coordinates = torch.clip(box_coordinates, 0.0, 1.0) + + # Unnormalize xy + box_coord_bias = torch.log(box_coordinates + 1e-4) - torch.log1p(-box_coordinates + 1e-4) + + # The box size is biased to the patch size + box_size = torch.full_like(box_coord_bias, 1.0) + box_size[..., 0] /= num_patches_width + box_size[..., 1] /= num_patches_height + box_size_bias = torch.log(box_size + 1e-4) - torch.log1p(-box_size + 1e-4) + + # Compute box bias + box_bias = torch.cat([box_coord_bias, box_size_bias], dim=-1) + return box_bias + + # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.box_predictor + def box_predictor( + self, + image_feats: torch.FloatTensor, + feature_map: torch.FloatTensor, + interpolate_pos_encoding: bool = False, + ) -> torch.FloatTensor: + """ + Args: + image_feats: + Features extracted from the image, returned by the `image_text_embedder` method. + feature_map: + A spatial re-arrangement of image_features, also returned by the `image_text_embedder` method. + interpolate_pos_encoding: + Whether to interpolate the pre-trained position encodings. + Returns: + pred_boxes: + List of predicted boxes (cxcywh normalized to 0, 1) nested within a dictionary. + """ + # Bounding box detection head [batch_size, num_boxes, 4]. + pred_boxes = self.box_head(image_feats) + + # Compute the location of each token on the grid and use it to compute a bias for the bbox prediction + if interpolate_pos_encoding: + _, num_patches_height, num_patches_width, _ = feature_map.shape + box_bias = self.compute_box_bias(num_patches_height, num_patches_width) + else: + box_bias = self.box_bias + + box_bias = box_bias.to(feature_map.device) + pred_boxes += box_bias + pred_boxes = self.sigmoid(pred_boxes) + return pred_boxes + + # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.class_predictor + def class_predictor( + self, + image_feats: torch.FloatTensor, + query_embeds: torch.FloatTensor | None = None, + query_mask: torch.Tensor | None = None, + ) -> tuple[torch.FloatTensor]: + """ + Args: + image_feats: + Features extracted from the `image_text_embedder`. + query_embeds: + Text query embeddings. + query_mask: + Must be provided with query_embeddings. A mask indicating which query embeddings are valid. + """ + (pred_logits, image_class_embeds) = self.class_head(image_feats, query_embeds, query_mask) + + return (pred_logits, image_class_embeds) + + # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.image_text_embedder with owlvit->owlv2 + def image_text_embedder( + self, + input_ids: torch.Tensor, + pixel_values: torch.FloatTensor, + attention_mask: torch.Tensor, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor]: + outputs = self.owlv2( + pixel_values=pixel_values, + input_ids=input_ids, + attention_mask=attention_mask, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + if interpolate_pos_encoding: + _, _, height, width = pixel_values.shape + num_patches_height = height // self.config.vision_config.patch_size + num_patches_width = width // self.config.vision_config.patch_size + else: + num_patches_height = self.num_patches_height + num_patches_width = self.num_patches_width + + # Get image embeddings + last_hidden_state = outputs.vision_model_output[0] + image_embeds = self.owlv2.vision_model.post_layernorm(last_hidden_state) + + # Resize class token + class_token_out = torch.broadcast_to(image_embeds[:, :1, :], image_embeds[:, :-1].shape) + + # Merge image embedding with class tokens + image_embeds = image_embeds[:, 1:, :] * class_token_out + image_embeds = self.layer_norm(image_embeds) + + # Resize to [batch_size, num_patches_height, num_patches_width, hidden_size] + new_size = ( + image_embeds.shape[0], + num_patches_height, + num_patches_width, + image_embeds.shape[-1], + ) + image_embeds = image_embeds.reshape(new_size) + text_embeds = outputs[-4] + + return (text_embeds, image_embeds, outputs) + + # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.image_embedder with owlvit->owlv2, OwlViTModel->Owlv2Model + def image_embedder( + self, + pixel_values: torch.FloatTensor, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor]: + # Get Owlv2Model vision embeddings (same as CLIP) + vision_outputs: BaseModelOutputWithPooling = self.owlv2.vision_model( + pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs + ) + + if interpolate_pos_encoding: + _, _, height, width = pixel_values.shape + num_patches_height = height // self.config.vision_config.patch_size + num_patches_width = width // self.config.vision_config.patch_size + else: + num_patches_height = self.num_patches_height + num_patches_width = self.num_patches_width + + # Apply post_layernorm to last_hidden_state, return non-projected output + last_hidden_state = vision_outputs[0] + image_embeds = self.owlv2.vision_model.post_layernorm(last_hidden_state) + + # Resize class token + class_token_out = torch.broadcast_to(image_embeds[:, :1, :], image_embeds[:, :-1].shape) + + # Merge image embedding with class tokens + image_embeds = image_embeds[:, 1:, :] * class_token_out + image_embeds = self.layer_norm(image_embeds) + + # Resize to [batch_size, num_patches_height, num_patches_width, hidden_size] + new_size = ( + image_embeds.shape[0], + num_patches_height, + num_patches_width, + image_embeds.shape[-1], + ) + image_embeds = image_embeds.reshape(new_size) + + return (image_embeds, vision_outputs) + + # Copied from transformers.models.owlvit.modeling_owlvit.OwlViTForObjectDetection.embed_image_query + def embed_image_query( + self, + query_image_features: torch.FloatTensor, + query_feature_map: torch.FloatTensor, + interpolate_pos_encoding: bool = False, + ) -> torch.FloatTensor: + _, class_embeds = self.class_predictor(query_image_features) + pred_boxes = self.box_predictor(query_image_features, query_feature_map, interpolate_pos_encoding) + pred_boxes_as_corners = center_to_corners_format(pred_boxes) + + # Loop over query images + best_class_embeds = [] + best_box_indices = [] + pred_boxes_device = pred_boxes_as_corners.device + + for i in range(query_image_features.shape[0]): + each_query_box = torch.tensor([[0, 0, 1, 1]], device=pred_boxes_device) + each_query_pred_boxes = pred_boxes_as_corners[i] + ious, _ = box_iou(each_query_box, each_query_pred_boxes) + + # If there are no overlapping boxes, fall back to generalized IoU + if torch.all(ious[0] == 0.0): + ious = generalized_box_iou(each_query_box, each_query_pred_boxes) + + # Use an adaptive threshold to include all boxes within 80% of the best IoU + iou_threshold = torch.max(ious) * 0.8 + + selected_inds = (ious[0] >= iou_threshold).nonzero() + if selected_inds.numel(): + selected_embeddings = class_embeds[i][selected_inds.squeeze(1)] + mean_embeds = torch.mean(class_embeds[i], axis=0) + mean_sim = torch.einsum("d,id->i", mean_embeds, selected_embeddings) + best_box_ind = selected_inds[torch.argmin(mean_sim)] + best_class_embeds.append(class_embeds[i][best_box_ind]) + best_box_indices.append(best_box_ind) + + if best_class_embeds: + query_embeds = torch.stack(best_class_embeds) + box_indices = torch.stack(best_box_indices) + else: + query_embeds, box_indices = None, None + + return query_embeds, box_indices, pred_boxes + + @can_return_tuple + @auto_docstring + def image_guided_detection( + self, + pixel_values: torch.FloatTensor, + query_pixel_values: torch.FloatTensor | None = None, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> Owlv2ImageGuidedObjectDetectionOutput: + r""" + query_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values of query image(s) to be detected. Pass in one query image per target image. + + Examples: + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + >>> import torch + >>> from transformers import AutoProcessor, Owlv2ForObjectDetection + + >>> processor = AutoProcessor.from_pretrained("google/owlv2-base-patch16-ensemble") + >>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> query_url = "http://images.cocodataset.org/val2017/000000001675.jpg" + >>> with httpx.stream("GET", query_url) as response: + ... query_image = Image.open(BytesIO(response.read())) + >>> inputs = processor(images=image, query_images=query_image, return_tensors="pt") + + >>> # forward pass + >>> with torch.no_grad(): + ... outputs = model.image_guided_detection(**inputs) + + >>> target_sizes = torch.Tensor([image.size[::-1]]) + + >>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> results = processor.post_process_image_guided_detection( + ... outputs=outputs, threshold=0.9, nms_threshold=0.3, target_sizes=target_sizes + ... ) + >>> i = 0 # Retrieve predictions for the first image + >>> boxes, scores = results[i]["boxes"], results[i]["scores"] + >>> for box, score in zip(boxes, scores): + ... box = [round(i, 2) for i in box.tolist()] + ... print(f"Detected similar object with confidence {round(score.item(), 3)} at location {box}") + Detected similar object with confidence 0.938 at location [327.31, 54.94, 547.39, 268.06] + Detected similar object with confidence 0.959 at location [5.78, 360.65, 619.12, 366.39] + Detected similar object with confidence 0.902 at location [2.85, 360.01, 627.63, 380.8] + Detected similar object with confidence 0.985 at location [176.98, -29.45, 672.69, 182.83] + Detected similar object with confidence 1.0 at location [6.53, 14.35, 624.87, 470.82] + Detected similar object with confidence 0.998 at location [579.98, 29.14, 615.49, 489.05] + Detected similar object with confidence 0.985 at location [206.15, 10.53, 247.74, 466.01] + Detected similar object with confidence 0.947 at location [18.62, 429.72, 646.5, 457.72] + Detected similar object with confidence 0.996 at location [523.88, 20.69, 586.84, 483.18] + Detected similar object with confidence 0.998 at location [3.39, 360.59, 617.29, 499.21] + Detected similar object with confidence 0.969 at location [4.47, 449.05, 614.5, 474.76] + Detected similar object with confidence 0.966 at location [31.44, 463.65, 654.66, 471.07] + Detected similar object with confidence 0.924 at location [30.93, 468.07, 635.35, 475.39] + ```""" + # Compute feature maps for the input and query images + query_feature_map = self.image_embedder( + pixel_values=query_pixel_values, interpolate_pos_encoding=interpolate_pos_encoding + )[0] + feature_map, vision_outputs = self.image_embedder( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + batch_size, num_patches_height, num_patches_width, hidden_dim = feature_map.shape + image_feats = torch.reshape(feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim)) + + batch_size, num_patches_height, num_patches_width, hidden_dim = query_feature_map.shape + query_image_feats = torch.reshape( + query_feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim) + ) + # Get top class embedding and best box index for each query image in batch + query_embeds, best_box_indices, query_pred_boxes = self.embed_image_query( + query_image_feats, query_feature_map, interpolate_pos_encoding + ) + + # Predict object classes [batch_size, num_patches, num_queries+1] + (pred_logits, class_embeds) = self.class_predictor(image_feats=image_feats, query_embeds=query_embeds) + + # Predict object boxes + target_pred_boxes = self.box_predictor(image_feats, feature_map, interpolate_pos_encoding) + + return Owlv2ImageGuidedObjectDetectionOutput( + image_embeds=feature_map, + query_image_embeds=query_feature_map, + target_pred_boxes=target_pred_boxes, + query_pred_boxes=query_pred_boxes, + logits=pred_logits, + class_embeds=class_embeds, + text_model_output=None, + vision_model_output=vision_outputs, + ) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor, + pixel_values: torch.FloatTensor, + attention_mask: torch.Tensor | None = None, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> Owlv2ObjectDetectionOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`, *optional*): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids). + + Examples: + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + >>> import torch + + >>> from transformers import Owlv2Processor, Owlv2ForObjectDetection + + >>> processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble") + >>> model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> text_labels = [["a photo of a cat", "a photo of a dog"]] + >>> inputs = processor(text=text_labels, images=image, return_tensors="pt") + >>> outputs = model(**inputs) + + >>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2] + >>> target_sizes = torch.tensor([(image.height, image.width)]) + >>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> results = processor.post_process_grounded_object_detection( + ... outputs=outputs, target_sizes=target_sizes, threshold=0.1, text_labels=text_labels + ... ) + >>> # Retrieve predictions for the first image for the corresponding text queries + >>> result = results[0] + >>> boxes, scores, text_labels = result["boxes"], result["scores"], result["text_labels"] + >>> for box, score, text_label in zip(boxes, scores, text_labels): + ... box = [round(i, 2) for i in box.tolist()] + ... print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}") + Detected a photo of a cat with confidence 0.614 at location [341.67, 23.39, 642.32, 371.35] + Detected a photo of a cat with confidence 0.665 at location [6.75, 51.96, 326.62, 473.13] + ```""" + # Embed images and text queries + query_embeds, feature_map, outputs = self.image_text_embedder( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + # Text and vision model outputs + text_outputs = outputs.text_model_output + vision_outputs = outputs.vision_model_output + + batch_size, num_patches_height, num_patches_width, hidden_dim = feature_map.shape + image_feats = torch.reshape(feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim)) + + # Reshape from [batch_size * max_text_queries, hidden_dim] -> [batch_size, max_text_queries, hidden_dim] + max_text_queries = input_ids.shape[0] // batch_size + query_embeds = query_embeds.reshape(batch_size, max_text_queries, query_embeds.shape[-1]) + + # If first token is 0, then this is a padded query [batch_size, num_queries]. + input_ids = input_ids.reshape(batch_size, max_text_queries, input_ids.shape[-1]) + query_mask = input_ids[..., 0] > 0 + + # Predict object classes [batch_size, num_patches, num_queries+1] + (pred_logits, class_embeds) = self.class_predictor(image_feats, query_embeds, query_mask) + + # Predict objectness + objectness_logits = self.objectness_predictor(image_feats) + + # Predict object boxes + pred_boxes = self.box_predictor(image_feats, feature_map, interpolate_pos_encoding) + + return Owlv2ObjectDetectionOutput( + image_embeds=feature_map, + text_embeds=query_embeds, + pred_boxes=pred_boxes, + logits=pred_logits, + objectness_logits=objectness_logits, + class_embeds=class_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +__all__ = ["Owlv2Model", "Owlv2PreTrainedModel", "Owlv2TextModel", "Owlv2VisionModel", "Owlv2ForObjectDetection"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/modular_owlv2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/modular_owlv2.py new file mode 100644 index 0000000000000000000000000000000000000000..6a7729afd7c373c0eaa1ded8f2828ac175d5da80 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/modular_owlv2.py @@ -0,0 +1,430 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import warnings + +import numpy as np +import torch +import torchvision.transforms.v2.functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import ( + group_images_by_shape, + pad, + reorder_images, + to_channel_dimension_format, +) +from ...image_utils import ( + OPENAI_CLIP_MEAN, + OPENAI_CLIP_STD, + ChannelDimension, + PILImageResampling, + SizeDict, +) +from ...utils import ( + TensorType, + auto_docstring, + is_scipy_available, + requires_backends, +) +from ...utils.import_utils import requires +from ..owlvit.image_processing_owlvit import OwlViTImageProcessor +from ..owlvit.image_processing_pil_owlvit import OwlViTImageProcessorPil + + +if is_scipy_available(): + from scipy import ndimage as ndi + + +def _preprocess_resize_output_shape(image, output_shape): + """Validate resize output shape according to input image. + + Args: + image (`np.ndarray`): + Image to be resized. + output_shape (`iterable`): + Size of the generated output image `(rows, cols[, ...][, dim])`. If `dim` is not provided, the number of + channels is preserved. + + Returns + image (`np.ndarray`): + The input image, but with additional singleton dimensions appended in the case where `len(output_shape) > + input.ndim`. + output_shape (`Tuple`): + The output shape converted to tuple. + + Raises ------ ValueError: + If output_shape length is smaller than the image number of dimensions. + + Notes ----- The input image is reshaped if its number of dimensions is not equal to output_shape_length. + + """ + output_shape = tuple(output_shape) + output_ndim = len(output_shape) + input_shape = image.shape + if output_ndim > image.ndim: + # append dimensions to input_shape + input_shape += (1,) * (output_ndim - image.ndim) + image = np.reshape(image, input_shape) + elif output_ndim == image.ndim - 1: + # multichannel case: append shape of last axis + output_shape = output_shape + (image.shape[-1],) + elif output_ndim < image.ndim: + raise ValueError("output_shape length cannot be smaller than the image number of dimensions") + + return image, output_shape + + +def _clip_warp_output(input_image, output_image): + """Clip output image to range of values of input image. + + Note that this function modifies the values of *output_image* in-place. + + Taken from: + https://github.com/scikit-image/scikit-image/blob/b4b521d6f0a105aabeaa31699949f78453ca3511/skimage/transform/_warps.py#L640. + + Args: + input_image : ndarray + Input image. + output_image : ndarray + Output image, which is modified in-place. + """ + min_val = np.min(input_image) + if np.isnan(min_val): + # NaNs detected, use NaN-safe min/max + min_func = np.nanmin + max_func = np.nanmax + min_val = min_func(input_image) + else: + min_func = np.min + max_func = np.max + max_val = max_func(input_image) + + output_image = np.clip(output_image, min_val, max_val) + + return output_image + + +def _scale_boxes(boxes, target_sizes): + """ + Scale batch of bounding boxes to the target sizes. + + Args: + boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): + Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. + target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): + Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. + + Returns: + `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. + """ + + if isinstance(target_sizes, (list, tuple)): + image_height = torch.tensor([i[0] for i in target_sizes]) + image_width = torch.tensor([i[1] for i in target_sizes]) + elif isinstance(target_sizes, torch.Tensor): + image_height, image_width = target_sizes.unbind(1) + else: + raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor") + + # for owlv2 image is padded to max size unlike owlvit, that's why we have to scale boxes to max size + max_size = torch.max(image_height, image_width) + + scale_factor = torch.stack([max_size, max_size, max_size, max_size], dim=1) + scale_factor = scale_factor.unsqueeze(1).to(boxes.device) + boxes = boxes * scale_factor + return boxes + + +@auto_docstring +class Owlv2ImageProcessor(OwlViTImageProcessor): + resample = PILImageResampling.BILINEAR + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 960, "width": 960} + rescale_factor = 1 / 255 + do_resize = True + do_rescale = True + do_normalize = True + do_pad = True + crop_size = None + do_center_crop = None + + def _pad_images(self, images: "torch.Tensor", constant_value: float = 0.0) -> "torch.Tensor": + """ + Pad an image with zeros to the given size. + """ + height, width = images.shape[-2:] + size = max(height, width) + pad_bottom = size - height + pad_right = size - width + + padding = (0, 0, pad_right, pad_bottom) + padded_image = tvF.pad(images, padding, fill=constant_value) + return padded_image + + def pad( + self, + images: list["torch.Tensor"], + disable_grouping: bool | None, + constant_value: float = 0.0, + **kwargs, + ) -> list["torch.Tensor"]: + """ + Unlike the Base class `self.pad` where all images are padded to the maximum image size, + Owlv2 pads an image to square. + """ + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + stacked_images = self._pad_images( + stacked_images, + constant_value=constant_value, + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return processed_images + + def resize( + self, + image: "torch.Tensor", + size: SizeDict, + anti_aliasing: bool = True, + anti_aliasing_sigma=None, + **kwargs, + ) -> "torch.Tensor": + """ + Resize an image as per the original implementation. + + Args: + image (`Tensor`): + Image to resize. + size (`dict[str, int]`): + Dictionary containing the height and width to resize the image to. + anti_aliasing (`bool`, *optional*, defaults to `True`): + Whether to apply anti-aliasing when downsampling the image. + anti_aliasing_sigma (`float`, *optional*, defaults to `None`): + Standard deviation for Gaussian kernel when downsampling the image. If `None`, it will be calculated + automatically. + """ + output_shape = (size.height, size.width) + + input_shape = image.shape + + # select height and width from input tensor + factors = torch.tensor(input_shape[2:]).to(image.device) / torch.tensor(output_shape).to(image.device) + + if anti_aliasing: + if anti_aliasing_sigma is None: + anti_aliasing_sigma = ((factors - 1) / 2).clamp(min=0) + else: + anti_aliasing_sigma = torch.atleast_1d(anti_aliasing_sigma) * torch.ones_like(factors) + if torch.any(anti_aliasing_sigma < 0): + raise ValueError("Anti-aliasing standard deviation must be greater than or equal to zero") + elif torch.any((anti_aliasing_sigma > 0) & (factors <= 1)): + warnings.warn( + "Anti-aliasing standard deviation greater than zero but not down-sampling along all axes" + ) + if torch.any(anti_aliasing_sigma == 0): + filtered = image + else: + kernel_sizes = 2 * torch.ceil(3 * anti_aliasing_sigma).int() + 1 + + filtered = tvF.gaussian_blur( + image, (kernel_sizes[0], kernel_sizes[1]), sigma=anti_aliasing_sigma.tolist() + ) + + else: + filtered = image + + return TorchvisionBackend.resize(filtered, size=size, antialias=False) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_pad: bool, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + processed_images_grouped = {} + + for shape, stacked_images in grouped_images.items(): + # Rescale images before other operations as done in original implementation + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, False, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, constant_value=0.0, disable_grouping=disable_grouping) + + grouped_images, grouped_images_index = group_images_by_shape( + processed_images, disable_grouping=disable_grouping + ) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + resized_stack = self.resize(image=stacked_images, size=size, resample=resample) + resized_images_grouped[shape] = resized_stack + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, False, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +@auto_docstring +@requires(backends=("torch",)) +class Owlv2ImageProcessorPil(OwlViTImageProcessorPil): + resample = PILImageResampling.BILINEAR + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 960, "width": 960} + rescale_factor = 1 / 255 + do_resize = True + do_rescale = True + do_normalize = True + do_pad = True + crop_size = None + do_center_crop = None + + def pad(self, image: np.ndarray, constant_value: float = 0.0) -> np.ndarray: + """ + Pad an image with zeros to the given size. + """ + height, width = image.shape[-2:] + size = max(height, width) + pad_bottom = size - height + pad_right = size - width + image = pad( + image=image, + padding=((0, pad_bottom), (0, pad_right)), + constant_values=constant_value, + ) + return image + + def resize( + self, + image: np.ndarray, + size: dict[str, int], + anti_aliasing: bool = True, + anti_aliasing_sigma=None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image as per the original implementation. + + Args: + image (`np.ndarray`): + Image to resize. + size (`dict[str, int]`): + Dictionary containing the height and width to resize the image to. + anti_aliasing (`bool`, *optional*, defaults to `True`): + Whether to apply anti-aliasing when downsampling the image. + anti_aliasing_sigma (`float`, *optional*, defaults to `None`): + Standard deviation for Gaussian kernel when downsampling the image. If `None`, it will be calculated + automatically. + """ + requires_backends(self, "scipy") + + output_shape = (size["height"], size["width"]) + image = to_channel_dimension_format(image, ChannelDimension.LAST) + image, output_shape = _preprocess_resize_output_shape(image, output_shape) + input_shape = image.shape + factors = np.divide(input_shape, output_shape) + + # Translate modes used by np.pad to those used by scipy.ndimage + ndi_mode = "mirror" + cval = 0 + order = 1 + if anti_aliasing: + if anti_aliasing_sigma is None: + anti_aliasing_sigma = np.maximum(0, (factors - 1) / 2) + else: + anti_aliasing_sigma = np.atleast_1d(anti_aliasing_sigma) * np.ones_like(factors) + if np.any(anti_aliasing_sigma < 0): + raise ValueError("Anti-aliasing standard deviation must be greater than or equal to zero") + elif np.any((anti_aliasing_sigma > 0) & (factors <= 1)): + warnings.warn( + "Anti-aliasing standard deviation greater than zero but not down-sampling along all axes" + ) + filtered = ndi.gaussian_filter(image, anti_aliasing_sigma, cval=cval, mode=ndi_mode) + else: + filtered = image + + zoom_factors = [1 / f for f in factors] + out = ndi.zoom(filtered, zoom_factors, order=order, mode=ndi_mode, cval=cval, grid_mode=True) + + image = _clip_warp_output(image, out) + + image = to_channel_dimension_format(image, ChannelDimension.FIRST) + return image + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_pad: bool, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + processed_images = [] + for image in images: + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_pad: + image = self.pad(image) + if do_resize: + image = self.resize(image, size, resample) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["Owlv2ImageProcessor", "Owlv2ImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/processing_owlv2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/processing_owlv2.py new file mode 100644 index 0000000000000000000000000000000000000000..39c8623937ea6abe9f5a67ccac119a805fbbfd2c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlv2/processing_owlv2.py @@ -0,0 +1,227 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Image/Text processor class for OWLv2 +""" + +from typing import TYPE_CHECKING + +import numpy as np + +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ( + ImagesKwargs, + ProcessingKwargs, + ProcessorMixin, + Unpack, +) +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import TensorType, auto_docstring, is_torch_available + + +if TYPE_CHECKING: + from .modeling_owlv2 import Owlv2ImageGuidedObjectDetectionOutput, Owlv2ObjectDetectionOutput + + +class Owlv2ImagesKwargs(ImagesKwargs, total=False): + """ + query_images (`ImageInput`, *optional*): + Query images to use for image-guided object detection. When provided, these images serve as visual queries + to find similar objects in the main `images`. The query images override any text prompts, and the model + performs image-to-image matching instead of text-to-image matching. + """ + + query_images: ImageInput | None + + +class Owlv2ProcessorKwargs(ProcessingKwargs, total=False): + images_kwargs: Owlv2ImagesKwargs + _defaults = { + "text_kwargs": { + "padding": "max_length", + }, + "common_kwargs": { + "return_tensors": "np", + }, + } + + +@auto_docstring +class Owlv2Processor(ProcessorMixin): + def __init__(self, image_processor, tokenizer, **kwargs): + super().__init__(image_processor, tokenizer) + + @auto_docstring + # Copied from transformers.models.owlvit.processing_owlvit.OwlViTProcessor.__call__ with OwlViT->Owlv2 + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[Owlv2ProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **query_pixel_values** -- Pixel values of the query images to be fed to a model. Returned when `query_images` is not `None`. + """ + output_kwargs = self._merge_kwargs( + Owlv2ProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + query_images = output_kwargs["images_kwargs"].pop("query_images", None) + return_tensors = output_kwargs["text_kwargs"]["return_tensors"] + + if text is None and query_images is None and images is None: + raise ValueError( + "You have to specify at least one text or query image or image. All three cannot be none." + ) + + data = {} + if text is not None: + if isinstance(text, str) or (isinstance(text, list) and not isinstance(text[0], list)): + encodings = [self.tokenizer(text, **output_kwargs["text_kwargs"])] + + elif isinstance(text, list) and isinstance(text[0], list): + encodings = [] + + # Maximum number of queries across batch + max_num_queries = max(len(text_single) for text_single in text) + + # Pad all batch samples to max number of text queries + for text_single in text: + if len(text_single) != max_num_queries: + text_single = text_single + [" "] * (max_num_queries - len(text_single)) + + encoding = self.tokenizer(text_single, **output_kwargs["text_kwargs"]) + encodings.append(encoding) + else: + raise TypeError("Input text should be a string, a list of strings or a nested list of strings") + + if return_tensors == "np": + input_ids = np.concatenate([encoding["input_ids"] for encoding in encodings], axis=0) + attention_mask = np.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0) + elif return_tensors == "pt" and is_torch_available(): + import torch + + input_ids = torch.cat([encoding["input_ids"] for encoding in encodings], dim=0) + attention_mask = torch.cat([encoding["attention_mask"] for encoding in encodings], dim=0) + else: + raise ValueError("Target return tensor type could not be returned") + + data["input_ids"] = input_ids + data["attention_mask"] = attention_mask + + if query_images is not None: + query_pixel_values = self.image_processor(query_images, **output_kwargs["images_kwargs"]).pixel_values + # Query images always override the text prompt + data = {"query_pixel_values": query_pixel_values} + + if images is not None: + image_features = self.image_processor(images, **output_kwargs["images_kwargs"]) + data["pixel_values"] = image_features.pixel_values + + return BatchFeature(data=data, tensor_type=return_tensors) + + # Copied from transformers.models.owlvit.processing_owlvit.OwlViTProcessor.post_process_grounded_object_detection with OwlViT->Owlv2 + def post_process_grounded_object_detection( + self, + outputs: "Owlv2ObjectDetectionOutput", + threshold: float = 0.1, + target_sizes: TensorType | list[tuple] | None = None, + text_labels: list[list[str]] | None = None, + ): + """ + Converts the raw output of [`Owlv2ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. + + Args: + outputs ([`Owlv2ObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.1): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + text_labels (`list[list[str]]`, *optional*): + List of lists of text labels for each image in the batch. If unset, "text_labels" in output will be + set to `None`. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: + - "scores": The confidence scores for each predicted box on the image. + - "labels": Indexes of the classes predicted by the model on the image. + - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. + - "text_labels": The text labels for each predicted bounding box on the image. + """ + output = self.image_processor.post_process_object_detection( + outputs=outputs, threshold=threshold, target_sizes=target_sizes + ) + + if text_labels is not None and len(text_labels) != len(output): + raise ValueError("Make sure that you pass in as many lists of text labels as images") + + # adding text labels to the output + if text_labels is not None: + for image_output, image_text_labels in zip(output, text_labels): + object_text_labels = [image_text_labels[i] for i in image_output["labels"]] + image_output["text_labels"] = object_text_labels + else: + for image_output in output: + image_output["text_labels"] = None + + return output + + # Copied from transformers.models.owlvit.processing_owlvit.OwlViTProcessor.post_process_image_guided_detection with OwlViT->Owlv2 + def post_process_image_guided_detection( + self, + outputs: "Owlv2ImageGuidedObjectDetectionOutput", + threshold: float = 0.0, + nms_threshold: float = 0.3, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the output of [`Owlv2ForObjectDetection.image_guided_detection`] into the format expected by the COCO + api. + + Args: + outputs ([`Owlv2ImageGuidedObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.0): + Minimum confidence threshold to use to filter out predicted boxes. + nms_threshold (`float`, *optional*, defaults to 0.3): + IoU threshold for non-maximum suppression of overlapping boxes. + target_sizes (`torch.Tensor`, *optional*): + Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in + the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to + None, predictions will not be unnormalized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: + - "scores": The confidence scores for each predicted box on the image. + - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. + - "labels": Set to `None`. + """ + return self.image_processor.post_process_image_guided_detection( + outputs=outputs, threshold=threshold, nms_threshold=nms_threshold, target_sizes=target_sizes + ) + + +__all__ = ["Owlv2Processor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6eab614d1787c7cfe0720d94e28bf3fcd01078f1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/__init__.py @@ -0,0 +1,31 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_owlvit import * + from .feature_extraction_owlvit import * + from .image_processing_owlvit import * + from .image_processing_pil_owlvit import * + from .modeling_owlvit import * + from .processing_owlvit import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/configuration_owlvit.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/configuration_owlvit.py new file mode 100644 index 0000000000000000000000000000000000000000..fc5f6c2504b9e07573c3312b7f8460b4701f3ff5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/configuration_owlvit.py @@ -0,0 +1,128 @@ +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""OWL-ViT model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="google/owlvit-base-patch16") +@strict +class OwlViTTextConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import OwlViTTextConfig, OwlViTTextModel + + >>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration + >>> configuration = OwlViTTextConfig() + + >>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration + >>> model = OwlViTTextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "owlvit_text_model" + base_config_key = "text_config" + + vocab_size: int = 49408 + hidden_size: int = 512 + intermediate_size: int = 2048 + num_hidden_layers: int = 12 + num_attention_heads: int = 8 + max_position_embeddings: int = 16 + hidden_act: str = "quick_gelu" + layer_norm_eps: float = 1e-5 + attention_dropout: float | int = 0.0 + initializer_range: float = 0.02 + initializer_factor: float = 1.0 + pad_token_id: int | None = 0 + bos_token_id: int | None = 49406 + eos_token_id: int | list[int] | None = 49407 + + +@auto_docstring(checkpoint="google/owlvit-base-patch16") +@strict +class OwlViTVisionConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import OwlViTVisionConfig, OwlViTVisionModel + + >>> # Initializing a OwlViTVisionModel with google/owlvit-base-patch32 style configuration + >>> configuration = OwlViTVisionConfig() + + >>> # Initializing a OwlViTVisionModel model from the google/owlvit-base-patch32 style configuration + >>> model = OwlViTVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "owlvit_vision_model" + base_config_key = "vision_config" + + hidden_size: int = 768 + intermediate_size: int = 3072 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 768 + patch_size: int | list[int] | tuple[int, int] = 32 + hidden_act: str = "quick_gelu" + layer_norm_eps: float = 1e-5 + attention_dropout: float | int = 0.0 + initializer_range: float = 0.02 + initializer_factor: float = 1.0 + + +@auto_docstring(checkpoint="google/owlvit-base-patch16") +@strict +class OwlViTConfig(PreTrainedConfig): + model_type = "owlvit" + sub_configs = {"text_config": OwlViTTextConfig, "vision_config": OwlViTVisionConfig} + + text_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + projection_dim: int = 512 + logit_scale_init_value: float = 2.6592 + return_dict: bool = True + initializer_factor: float = 1.0 + + def __post_init__(self, **kwargs): + if self.text_config is None: + self.text_config = OwlViTTextConfig() + logger.info("`text_config` is `None`. initializing the `OwlViTTextConfig` with default values.") + elif isinstance(self.text_config, dict): + self.text_config = OwlViTTextConfig(**self.text_config) + + if self.vision_config is None: + self.vision_config = OwlViTVisionConfig() + logger.info("`vision_config` is `None`. initializing the `OwlViTVisionConfig` with default values.") + elif isinstance(self.vision_config, dict): + self.vision_config = OwlViTVisionConfig(**self.vision_config) + + super().__post_init__(**kwargs) + + +__all__ = ["OwlViTConfig", "OwlViTTextConfig", "OwlViTVisionConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/image_processing_owlvit.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/image_processing_owlvit.py new file mode 100644 index 0000000000000000000000000000000000000000..0f477c62724e205223fb452162d3ca8837edd9a4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/image_processing_owlvit.py @@ -0,0 +1,257 @@ +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for OwlViT""" + +from typing import TYPE_CHECKING + +import torch + +from ...image_processing_backends import TorchvisionBackend +from ...image_transforms import center_to_corners_format +from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, logging + + +if TYPE_CHECKING: + from .modeling_owlvit import OwlViTObjectDetectionOutput + + +logger = logging.get_logger(__name__) + + +def _upcast(t): + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +def _scale_boxes(boxes, target_sizes): + """ + Scale batch of bounding boxes to the target sizes. + + Args: + boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): + Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. + target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): + Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. + + Returns: + `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. + """ + + if isinstance(target_sizes, (list, tuple)): + image_height = torch.tensor([i[0] for i in target_sizes]) + image_width = torch.tensor([i[1] for i in target_sizes]) + elif isinstance(target_sizes, torch.Tensor): + image_height, image_width = target_sizes.unbind(1) + else: + raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor") + + scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1) + scale_factor = scale_factor.unsqueeze(1).to(boxes.device) + boxes = boxes * scale_factor + return boxes + + +def box_area(boxes): + """ + Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. + + Args: + boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): + Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 + < x2` and `0 <= y1 < y2`. + Returns: + `torch.FloatTensor`: a tensor containing the area for each box. + """ + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] + inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +@auto_docstring +class OwlViTImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 768, "width": 768} + default_to_square = True + crop_size = {"height": 768, "width": 768} + do_resize = True + do_center_crop = False + do_rescale = True + do_normalize = True + do_convert_rgb = True + model_input_names = ["pixel_values"] + + def __init__(self, **kwargs: Unpack[ImagesKwargs]): + # Early versions of the OWL-ViT config on the hub had "rescale" as a flag. This clashes with the + # vision image processor method `rescale` as it would be set as an attribute during the super().__init__ + # call. This is for backwards compatibility. + if "rescale" in kwargs: + rescale_val = kwargs.pop("rescale") + kwargs["do_rescale"] = rescale_val + + super().__init__(**kwargs) + + def post_process_object_detection( + self, + outputs: "OwlViTObjectDetectionOutput", + threshold: float = 0.1, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the raw output of [`OwlViTForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. + + Args: + outputs ([`OwlViTObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.1): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: + - "scores": The confidence scores for each predicted box on the image. + - "labels": Indexes of the classes predicted by the model on the image. + - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. + """ + batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes + batch_size = len(batch_logits) + + if target_sizes is not None and len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as images") + + # batch_logits of shape (batch_size, num_queries, num_classes) + batch_class_logits = torch.max(batch_logits, dim=-1) + batch_scores = torch.sigmoid(batch_class_logits.values) + batch_labels = batch_class_logits.indices + + # Convert to [x0, y0, x1, y1] format + batch_boxes = center_to_corners_format(batch_boxes) + + # Convert from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + batch_boxes = _scale_boxes(batch_boxes, target_sizes) + + results = [] + for scores, labels, boxes in zip(batch_scores, batch_labels, batch_boxes): + keep = scores > threshold + scores = scores[keep] + labels = labels[keep] + boxes = boxes[keep] + results.append({"scores": scores, "labels": labels, "boxes": boxes}) + + return results + + def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_threshold=0.3, target_sizes=None): + """ + Converts the output of [`OwlViTForObjectDetection.image_guided_detection`] into the format expected by the COCO + api. + + Args: + outputs ([`OwlViTImageGuidedObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.0): + Minimum confidence threshold to use to filter out predicted boxes. + nms_threshold (`float`, *optional*, defaults to 0.3): + IoU threshold for non-maximum suppression of overlapping boxes. + target_sizes (`torch.Tensor`, *optional*): + Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in + the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to + None, predictions will not be unnormalized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. All labels are set to None as + `OwlViTForObjectDetection.image_guided_detection` perform one-shot object detection. + """ + logits, target_boxes = outputs.logits, outputs.target_pred_boxes + + if target_sizes is not None and len(logits) != len(target_sizes): + raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits") + if target_sizes is not None and target_sizes.shape[1] != 2: + raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch") + + probs = torch.max(logits, dim=-1) + scores = torch.sigmoid(probs.values) + + # Convert to [x0, y0, x1, y1] format + target_boxes = center_to_corners_format(target_boxes) + + # Apply non-maximum suppression (NMS) + if nms_threshold < 1.0: + for idx in range(target_boxes.shape[0]): + for i in torch.argsort(-scores[idx]): + if not scores[idx][i]: + continue + + ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0] + ious[i] = -1.0 # Mask self-IoU. + scores[idx][ious > nms_threshold] = 0.0 + + # Convert from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + target_boxes = _scale_boxes(target_boxes, target_sizes) + + # Compute box display alphas based on prediction scores + results = [] + alphas = torch.zeros_like(scores) + + for idx in range(target_boxes.shape[0]): + # Select scores for boxes matching the current query: + query_scores = scores[idx] + if not query_scores.nonzero().numel(): + continue + + # Apply threshold on scores before scaling + query_scores[query_scores < threshold] = 0.0 + + # Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1. + # All other boxes will either belong to a different query, or will not be shown. + max_score = torch.max(query_scores) + 1e-6 + query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9) + query_alphas = torch.clip(query_alphas, 0.0, 1.0) + alphas[idx] = query_alphas + + mask = alphas[idx] > 0 + box_scores = alphas[idx][mask] + boxes = target_boxes[idx][mask] + results.append({"scores": box_scores, "labels": None, "boxes": boxes}) + + return results + + +__all__ = ["OwlViTImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/image_processing_pil_owlvit.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/image_processing_pil_owlvit.py new file mode 100644 index 0000000000000000000000000000000000000000..d688aa8d4f6ae11e312b788eaf8a49a9fd23be0b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/image_processing_pil_owlvit.py @@ -0,0 +1,269 @@ +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for OwlViT""" + +from typing import TYPE_CHECKING + +from ...image_processing_backends import PilBackend +from ...image_transforms import center_to_corners_format +from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, logging +from ...utils.import_utils import requires + + +def _upcast(t): + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + import torch + + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +# Adapted from transformers.models.owlvit.image_processing_owlvit._scale_boxes +def _scale_boxes(boxes, target_sizes): + """ + Scale batch of bounding boxes to the target sizes. + + Args: + boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): + Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. + target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): + Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. + + Returns: + `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. + """ + import torch + + if isinstance(target_sizes, (list, tuple)): + image_height = torch.tensor([i[0] for i in target_sizes]) + image_width = torch.tensor([i[1] for i in target_sizes]) + elif isinstance(target_sizes, torch.Tensor): + image_height, image_width = target_sizes.unbind(1) + else: + raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor") + + scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1) + scale_factor = scale_factor.unsqueeze(1).to(boxes.device) + boxes = boxes * scale_factor + return boxes + + +def box_area(boxes): + """ + Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. + + Args: + boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): + Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 + < x2` and `0 <= y1 < y2`. + Returns: + `torch.FloatTensor`: a tensor containing the area for each box. + """ + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# Adapted from transformers.models.owlvit.image_processing_owlvit.box_iou +def box_iou(boxes1, boxes2): + import torch + + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] + inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +if TYPE_CHECKING: + from .modeling_owlvit import OwlViTObjectDetectionOutput + + +logger = logging.get_logger(__name__) + + +@auto_docstring +class OwlViTImageProcessorPil(PilBackend): + resample = PILImageResampling.BICUBIC + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + size = {"height": 768, "width": 768} + default_to_square = True + crop_size = {"height": 768, "width": 768} + do_resize = True + do_center_crop = False + do_rescale = True + do_normalize = True + do_convert_rgb = True + model_input_names = ["pixel_values"] + + def __init__(self, **kwargs: Unpack[ImagesKwargs]): + # Early versions of the OWL-ViT config on the hub had "rescale" as a flag. This clashes with the + # vision image processor method `rescale` as it would be set as an attribute during the super().__init__ + # call. This is for backwards compatibility. + if "rescale" in kwargs: + rescale_val = kwargs.pop("rescale") + kwargs["do_rescale"] = rescale_val + + super().__init__(**kwargs) + + @requires(backends=("torch",)) + def post_process_object_detection( + self, + outputs: "OwlViTObjectDetectionOutput", + threshold: float = 0.1, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the raw output of [`OwlViTForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. + + Args: + outputs ([`OwlViTObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.1): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: + - "scores": The confidence scores for each predicted box on the image. + - "labels": Indexes of the classes predicted by the model on the image. + - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. + """ + import torch + + batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes + batch_size = len(batch_logits) + + if target_sizes is not None and len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as images") + + # batch_logits of shape (batch_size, num_queries, num_classes) + batch_class_logits = torch.max(batch_logits, dim=-1) + batch_scores = torch.sigmoid(batch_class_logits.values) + batch_labels = batch_class_logits.indices + + # Convert to [x0, y0, x1, y1] format + batch_boxes = center_to_corners_format(batch_boxes) + + # Convert from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + batch_boxes = _scale_boxes(batch_boxes, target_sizes) + + results = [] + for scores, labels, boxes in zip(batch_scores, batch_labels, batch_boxes): + keep = scores > threshold + scores = scores[keep] + labels = labels[keep] + boxes = boxes[keep] + results.append({"scores": scores, "labels": labels, "boxes": boxes}) + + return results + + @requires(backends=("torch",)) + def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_threshold=0.3, target_sizes=None): + """ + Converts the output of [`OwlViTForObjectDetection.image_guided_detection`] into the format expected by the COCO + api. + + Args: + outputs ([`OwlViTImageGuidedObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.0): + Minimum confidence threshold to use to filter out predicted boxes. + nms_threshold (`float`, *optional*, defaults to 0.3): + IoU threshold for non-maximum suppression of overlapping boxes. + target_sizes (`torch.Tensor`, *optional*): + Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in + the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to + None, predictions will not be unnormalized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. All labels are set to None as + `OwlViTForObjectDetection.image_guided_detection` perform one-shot object detection. + """ + import torch + + logits, target_boxes = outputs.logits, outputs.target_pred_boxes + + if target_sizes is not None and len(logits) != len(target_sizes): + raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits") + if target_sizes is not None and target_sizes.shape[1] != 2: + raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch") + + probs = torch.max(logits, dim=-1) + scores = torch.sigmoid(probs.values) + + # Convert to [x0, y0, x1, y1] format + target_boxes = center_to_corners_format(target_boxes) + + # Apply non-maximum suppression (NMS) + if nms_threshold < 1.0: + for idx in range(target_boxes.shape[0]): + for i in torch.argsort(-scores[idx]): + if not scores[idx][i]: + continue + + ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0] + ious[i] = -1.0 # Mask self-IoU. + scores[idx][ious > nms_threshold] = 0.0 + + # Convert from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + target_boxes = _scale_boxes(target_boxes, target_sizes) + + # Compute box display alphas based on prediction scores + results = [] + alphas = torch.zeros_like(scores) + + for idx in range(target_boxes.shape[0]): + # Select scores for boxes matching the current query: + query_scores = scores[idx] + if not query_scores.nonzero().numel(): + continue + + # Apply threshold on scores before scaling + query_scores[query_scores < threshold] = 0.0 + + # Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1. + # All other boxes will either belong to a different query, or will not be shown. + max_score = torch.max(query_scores) + 1e-6 + query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9) + query_alphas = torch.clip(query_alphas, 0.0, 1.0) + alphas[idx] = query_alphas + + mask = alphas[idx] > 0 + box_scores = alphas[idx][mask] + boxes = target_boxes[idx][mask] + results.append({"scores": box_scores, "labels": None, "boxes": boxes}) + + return results + + +__all__ = ["OwlViTImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/modeling_owlvit.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/modeling_owlvit.py new file mode 100644 index 0000000000000000000000000000000000000000..85b0578dd5c94a6a79e13db7501ec303b395f6cb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/modeling_owlvit.py @@ -0,0 +1,1474 @@ +# Copyright 2022 Google AI and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch OWL-ViT model.""" + +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import torch +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + is_vision_available, + logging, + torch_int, +) +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_owlvit import OwlViTConfig, OwlViTTextConfig, OwlViTVisionConfig + + +if is_vision_available(): + from transformers.image_transforms import center_to_corners_format + + +logger = logging.get_logger(__name__) + + +# See all OwlViT models at https://huggingface.co/models?filter=owlvit + + +# Copied from transformers.models.clip.modeling_clip.contrastive_loss with clip->owlvit +def contrastive_loss(logits: torch.Tensor) -> torch.Tensor: + return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device)) + + +# Copied from transformers.models.clip.modeling_clip.image_text_contrastive_loss +def image_text_contrastive_loss(similarity: torch.Tensor) -> torch.Tensor: + caption_loss = contrastive_loss(similarity) + image_loss = contrastive_loss(similarity.T) + return (caption_loss + image_loss) / 2.0 + + +@dataclass +@auto_docstring +class OwlViTOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): + Contrastive loss for image-text similarity. + logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): + The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text + similarity scores. + logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): + The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image + similarity scores. + text_embeds (`torch.FloatTensor` of shape `(batch_size * num_max_text_queries, output_dim`): + The text embeddings obtained by applying the projection layer to the pooled output of [`OwlViTTextModel`]. + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`): + The image embeddings obtained by applying the projection layer to the pooled output of + [`OwlViTVisionModel`]. + text_model_output (tuple[`BaseModelOutputWithPooling`]): + The output of the [`OwlViTTextModel`]. + vision_model_output (`BaseModelOutputWithPooling`): + The output of the [`OwlViTVisionModel`]. + """ + + loss: torch.FloatTensor | None = None + logits_per_image: torch.FloatTensor | None = None + logits_per_text: torch.FloatTensor | None = None + text_embeds: torch.FloatTensor | None = None + image_embeds: torch.FloatTensor | None = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() + for k in self.keys() + ) + + +# Copied from transformers.loss.loss_for_object_detection._upcast +def _upcast(t: Tensor) -> Tensor: + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +# Copied from transformers.loss.loss_for_object_detection.box_area +def box_area(boxes: Tensor) -> Tensor: + """ + Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. + + Args: + boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): + Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 + < x2` and `0 <= y1 < y2`. + + Returns: + `torch.FloatTensor`: a tensor containing the area for each box. + """ + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# Copied from transformers.loss.loss_for_object_detection.box_iou +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] + inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +# Copied from transformers.loss.loss_for_object_detection.generalized_box_iou +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. + + Returns: + `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): + raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") + if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): + raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") + iou, union = box_iou(boxes1, boxes2) + + top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] + area = width_height[:, :, 0] * width_height[:, :, 1] + + return iou - (area - union) / area + + +@auto_docstring( + custom_intro=""" + Output type of [`OwlViTForObjectDetection`]. + """ +) +@dataclass +class OwlViTObjectDetectionOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): + Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a + bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized + scale-invariant IoU loss. + loss_dict (`Dict`, *optional*): + A dictionary containing the individual losses. Useful for logging. + logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~OwlViTImageProcessor.post_process_object_detection`] to retrieve the + unnormalized bounding boxes. + text_embeds (`torch.FloatTensor` of shape `(batch_size, num_max_text_queries, output_dim`): + The text embeddings obtained by applying the projection layer to the pooled output of [`OwlViTTextModel`]. + image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): + Pooled output of [`OwlViTVisionModel`]. OWL-ViT represents images as a set of image patches and computes + image embeddings for each patch. + class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`): + Class embeddings of all image patches. OWL-ViT represents images as a set of image patches where the total + number of patches is (image_size / patch_size)**2. + text_model_output (tuple[`BaseModelOutputWithPooling`]): + The output of the [`OwlViTTextModel`]. + vision_model_output (`BaseModelOutputWithPooling`): + The output of the [`OwlViTVisionModel`]. + """ + + loss: torch.FloatTensor | None = None + loss_dict: dict | None = None + logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + text_embeds: torch.FloatTensor | None = None + image_embeds: torch.FloatTensor | None = None + class_embeds: torch.FloatTensor | None = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() + for k in self.keys() + ) + + +@auto_docstring( + custom_intro=""" + Output type of [`OwlViTForObjectDetection.image_guided_detection`]. + """ +) +@dataclass +class OwlViTImageGuidedObjectDetectionOutput(ModelOutput): + r""" + logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`): + Classification logits (including no-object) for all queries. + image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): + Pooled output of [`OwlViTVisionModel`]. OWL-ViT represents images as a set of image patches and computes + image embeddings for each patch. + query_image_embeds (`torch.FloatTensor` of shape `(batch_size, patch_size, patch_size, output_dim`): + Pooled output of [`OwlViTVisionModel`]. OWL-ViT represents images as a set of image patches and computes + image embeddings for each patch. + target_pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual target image in the batch + (disregarding possible padding). You can use [`~OwlViTImageProcessor.post_process_object_detection`] to + retrieve the unnormalized bounding boxes. + query_pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_patches, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual query image in the batch + (disregarding possible padding). You can use [`~OwlViTImageProcessor.post_process_object_detection`] to + retrieve the unnormalized bounding boxes. + class_embeds (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`): + Class embeddings of all image patches. OWL-ViT represents images as a set of image patches where the total + number of patches is (image_size / patch_size)**2. + text_model_output (tuple[`BaseModelOutputWithPooling`]): + The output of the [`OwlViTTextModel`]. + vision_model_output (`BaseModelOutputWithPooling`): + The output of the [`OwlViTVisionModel`]. + """ + + logits: torch.FloatTensor | None = None + image_embeds: torch.FloatTensor | None = None + query_image_embeds: torch.FloatTensor | None = None + target_pred_boxes: torch.FloatTensor | None = None + query_pred_boxes: torch.FloatTensor | None = None + class_embeds: torch.FloatTensor | None = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() + for k in self.keys() + ) + + +class OwlViTVisionEmbeddings(nn.Module): + def __init__(self, config: OwlViTVisionConfig): + super().__init__() + self.patch_size = config.patch_size + self.config = config + self.embed_dim = config.hidden_size + self.class_embedding = nn.Parameter(torch.randn(config.hidden_size)) + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=config.patch_size, + stride=config.patch_size, + bias=False, + ) + + self.num_patches = (config.image_size // config.patch_size) ** 2 + self.num_positions = self.num_patches + 1 + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) + + # Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings.interpolate_pos_encoding + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support torch.jit tracing. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + + num_patches = embeddings.shape[1] - 1 + position_embedding = self.position_embedding.weight.unsqueeze(0) + num_positions = position_embedding.shape[1] - 1 + + # always interpolate when tracing to ensure the exported model works for dynamic input shapes + if not torch.jit.is_tracing() and num_patches == num_positions and height == width: + return self.position_embedding(self.position_ids) + + class_pos_embed = position_embedding[:, :1] + patch_pos_embed = position_embedding[:, 1:] + + dim = embeddings.shape[-1] + + new_height = height // self.patch_size + new_width = width // self.patch_size + + sqrt_num_positions = torch_int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(new_height, new_width), + mode="bicubic", + align_corners=False, + ) + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return torch.cat((class_pos_embed, patch_pos_embed), dim=1) + + def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: + batch_size, _, height, width = pixel_values.shape + patch_embeds = self.patch_embedding(pixel_values) # shape = [batch_size, num_channels, height, width] + patch_embeds = patch_embeds.flatten(2).transpose(1, 2) + class_embeds = self.class_embedding.expand(batch_size, 1, -1) + embeddings = torch.cat([class_embeds, patch_embeds], dim=1) + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + else: + embeddings = embeddings + self.position_embedding(self.position_ids) + return embeddings + + +class OwlViTTextEmbeddings(nn.Module): + def __init__(self, config: OwlViTTextConfig): + super().__init__() + self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size) + self.position_embedding = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + ) -> torch.Tensor: + seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + if inputs_embeds is None: + inputs_embeds = self.token_embedding(input_ids) + + position_embeddings = self.position_embedding(position_ids) + embeddings = inputs_embeds + position_embeddings + + return embeddings + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class OwlViTAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(*hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(*hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(*hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + scaling=self.scale, + dropout=0.0 if not self.training else self.dropout, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->OwlViT +class OwlViTMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->OwlViT +class OwlViTEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: OwlViTVisionConfig | OwlViTTextConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = OwlViTAttention(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = OwlViTMLP(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class OwlViTPreTrainedModel(PreTrainedModel): + config: OwlViTConfig + base_model_prefix = "owlvit" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + _supports_sdpa = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_attention_backend = True + _no_split_modules = ["OwlViTEncoderLayer"] + _can_record_outputs = { + "hidden_states": OwlViTEncoderLayer, + "attentions": OwlViTAttention, + } + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + """Initialize the weights""" + factor = self.config.initializer_factor + if isinstance(module, OwlViTTextEmbeddings): + init.normal_(module.token_embedding.weight, mean=0.0, std=factor * 0.02) + init.normal_(module.position_embedding.weight, mean=0.0, std=factor * 0.02) + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, OwlViTVisionEmbeddings): + init.normal_(module.class_embedding, mean=0.0, std=module.embed_dim**-0.5 * factor) + init.normal_(module.patch_embedding.weight, std=module.config.initializer_range * factor) + init.normal_(module.position_embedding.weight, std=module.config.initializer_range * factor) + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, OwlViTAttention): + in_proj_std = (module.embed_dim**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor + out_proj_std = (module.embed_dim**-0.5) * factor + init.normal_(module.q_proj.weight, std=in_proj_std) + init.normal_(module.k_proj.weight, std=in_proj_std) + init.normal_(module.v_proj.weight, std=in_proj_std) + init.normal_(module.out_proj.weight, std=out_proj_std) + elif isinstance(module, OwlViTMLP): + in_proj_std = (module.config.hidden_size**-0.5) * ((2 * module.config.num_hidden_layers) ** -0.5) * factor + fc_std = (2 * module.config.hidden_size) ** -0.5 * factor + init.normal_(module.fc1.weight, std=fc_std) + init.normal_(module.fc2.weight, std=in_proj_std) + elif isinstance(module, OwlViTModel): + init.normal_( + module.text_projection.weight, + std=module.text_embed_dim**-0.5 * factor, + ) + init.normal_( + module.visual_projection.weight, + std=module.vision_embed_dim**-0.5 * factor, + ) + init.constant_(module.logit_scale, self.config.logit_scale_init_value) + elif isinstance(module, OwlViTForObjectDetection): + init.copy_(module.box_bias, module.compute_box_bias(module.num_patches_height, module.num_patches_width)) + if isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + if isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=factor) + if module.bias is not None: + init.zeros_(module.bias) + + +# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->OwlViT +class OwlViTEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`OwlViTEncoderLayer`]. + + Args: + config: OwlViTConfig + """ + + def __init__(self, config: OwlViTConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([OwlViTEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + **kwargs, + ) + + return BaseModelOutput( + last_hidden_state=hidden_states, + ) + + +class OwlViTTextTransformer(OwlViTPreTrainedModel): + def __init__(self, config: OwlViTTextConfig): + super().__init__(config) + + embed_dim = config.hidden_size + self.embeddings = OwlViTTextEmbeddings(config) + self.encoder = OwlViTEncoder(config) + self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids) + """ + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) + + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + past_key_values=None, + ) + + kwargs.pop("is_causal", None) + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + is_causal=True, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.final_layer_norm(last_hidden_state) + + # take features from the end of tokens embedding (end of token is the highest number in each sequence) + # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 + pooled_output = last_hidden_state[ + torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device), + input_ids.to(torch.int).argmax(dim=-1).to(last_hidden_state.device), + ] + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + ) + + +class OwlViTTextModel(OwlViTPreTrainedModel): + config: OwlViTTextConfig + input_modalities = ("text",) + + def __init__(self, config: OwlViTTextConfig): + super().__init__(config) + self.text_model = OwlViTTextTransformer(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, value): + self.text_model.embeddings.token_embedding = value + + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids) + + Examples: + ```python + >>> from transformers import AutoProcessor, OwlViTTextModel + + >>> model = OwlViTTextModel.from_pretrained("google/owlvit-base-patch32") + >>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32") + >>> inputs = processor( + ... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt" + ... ) + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled (EOS token) states + ```""" + + return self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + **kwargs, + ) + + +class OwlViTVisionTransformer(OwlViTPreTrainedModel): + def __init__(self, config: OwlViTVisionConfig): + super().__init__(config) + + self.embeddings = OwlViTVisionEmbeddings(config) + self.pre_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.encoder = OwlViTEncoder(config) + self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + interpolate_pos_encoding: bool | None = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + # Cast the input to the expected `dtype` + expected_input_dtype = self.embeddings.patch_embedding.weight.dtype + pixel_values = pixel_values.to(expected_input_dtype) + + hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) + hidden_states = self.pre_layernorm(hidden_states) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + pooled_output = last_hidden_state[:, 0, :] + pooled_output = self.post_layernorm(pooled_output) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + ) + + +class OwlViTVisionModel(OwlViTPreTrainedModel): + config: OwlViTVisionConfig + main_input_name = "pixel_values" + input_modalities = ("image",) + + def __init__(self, config: OwlViTVisionConfig): + super().__init__(config) + self.vision_model = OwlViTVisionTransformer(config) + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_model.embeddings.patch_embedding + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + r""" + Examples: + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, OwlViTVisionModel + + >>> model = OwlViTVisionModel.from_pretrained("google/owlvit-base-patch32") + >>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled CLS states + ```""" + + return self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + +@auto_docstring +class OwlViTModel(OwlViTPreTrainedModel): + config: OwlViTConfig + + def __init__(self, config: OwlViTConfig): + super().__init__(config) + + text_config = config.text_config + vision_config = config.vision_config + + self.projection_dim = config.projection_dim + self.text_embed_dim = text_config.hidden_size + self.vision_embed_dim = vision_config.hidden_size + + self.text_model = OwlViTTextTransformer(text_config) + self.vision_model = OwlViTVisionTransformer(vision_config) + + self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False) + self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False) + self.logit_scale = nn.Parameter(torch.tensor(config.logit_scale_init_value)) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def get_text_features( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids) + + Examples: + ```python + >>> import torch + >>> from transformers import AutoProcessor, OwlViTModel + + >>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32") + >>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32") + >>> inputs = processor( + ... text=[["a photo of a cat", "a photo of a dog"], ["photo of a astranaut"]], return_tensors="pt" + ... ) + >>> with torch.inference_mode(): + ... text_features = model.get_text_features(**inputs) + ```""" + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + **kwargs, + ) + pooled_output = text_outputs.pooler_output + text_outputs.pooler_output = self.text_projection(pooled_output) + + return text_outputs + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.Tensor, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + Examples: + ```python + >>> import torch + >>> from transformers.image_utils import load_image + >>> from transformers import AutoProcessor, OwlViTModel + + >>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32") + >>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = load_image(url) + + >>> inputs = processor(images=image, return_tensors="pt") + >>> with torch.inference_mode(): + ... image_features = model.get_image_features(**inputs) + ```""" + vision_outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + vision_outputs.pooler_output = self.visual_projection(vision_outputs.pooler_output) + + return vision_outputs + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + return_loss: bool | None = None, + interpolate_pos_encoding: bool = False, + return_base_image_embeds: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | OwlViTOutput: + r""" + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. + return_base_image_embeds (`bool`, *optional*): + Whether or not to return the base image embeddings. + + Examples: + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, OwlViTModel + + >>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32") + >>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> inputs = processor(text=[["a photo of a cat", "a photo of a dog"]], images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score + >>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities + ```""" + vision_outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + # Get embeddings for all text queries in all batch samples + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + **kwargs, + ) + + text_embeds = text_outputs.pooler_output + text_embeds = self.text_projection(text_embeds) + image_embeds = vision_outputs.pooler_output + image_embeds = self.visual_projection(image_embeds) + + # normalized features + image_embeds = image_embeds / torch.linalg.norm(image_embeds, ord=2, dim=-1, keepdim=True) + text_embeds_norm = text_embeds / torch.linalg.norm(text_embeds, ord=2, dim=-1, keepdim=True) + + # cosine similarity as logits and set it on the correct device + logit_scale = self.logit_scale.exp().to(image_embeds.device) + + logits_per_text = torch.matmul(text_embeds_norm, image_embeds.t()) * logit_scale + logits_per_image = logits_per_text.t() + + loss = None + if return_loss: + loss = image_text_contrastive_loss(logits_per_text) + + text_embeds = text_embeds_norm + + return OwlViTOutput( + loss=loss, + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +class OwlViTBoxPredictionHead(nn.Module): + def __init__(self, config: OwlViTConfig, out_dim: int = 4): + super().__init__() + + width = config.vision_config.hidden_size + self.dense0 = nn.Linear(width, width) + self.dense1 = nn.Linear(width, width) + self.gelu = nn.GELU() + self.dense2 = nn.Linear(width, out_dim) + + def forward(self, image_features: torch.Tensor) -> torch.FloatTensor: + output = self.dense0(image_features) + output = self.gelu(output) + output = self.dense1(output) + output = self.gelu(output) + output = self.dense2(output) + return output + + +class OwlViTClassPredictionHead(nn.Module): + def __init__(self, config: OwlViTConfig): + super().__init__() + + out_dim = config.text_config.hidden_size + self.query_dim = config.vision_config.hidden_size + + self.dense0 = nn.Linear(self.query_dim, out_dim) + self.logit_shift = nn.Linear(self.query_dim, 1) + self.logit_scale = nn.Linear(self.query_dim, 1) + self.elu = nn.ELU() + + def forward( + self, + image_embeds: torch.FloatTensor, + query_embeds: torch.FloatTensor | None, + query_mask: torch.Tensor | None, + ) -> tuple[torch.FloatTensor]: + image_class_embeds = self.dense0(image_embeds) + if query_embeds is None: + device = image_class_embeds.device + batch_size, num_patches = image_class_embeds.shape[:2] + pred_logits = torch.zeros((batch_size, num_patches, self.query_dim)).to(device) + return (pred_logits, image_class_embeds) + + # Normalize image and text features + image_class_embeds = image_class_embeds / (torch.linalg.norm(image_class_embeds, dim=-1, keepdim=True) + 1e-6) + query_embeds = query_embeds / (torch.linalg.norm(query_embeds, dim=-1, keepdim=True) + 1e-6) + + # Get class predictions + pred_logits = torch.einsum("...pd,...qd->...pq", image_class_embeds, query_embeds) + + # Apply a learnable shift and scale to logits + logit_shift = self.logit_shift(image_embeds) + logit_scale = self.logit_scale(image_embeds) + logit_scale = self.elu(logit_scale) + 1 + pred_logits = (pred_logits + logit_shift) * logit_scale + + if query_mask is not None: + if query_mask.ndim > 1: + query_mask = torch.unsqueeze(query_mask, dim=-2) + + pred_logits = torch.where(query_mask == 0, torch.finfo(pred_logits.dtype).min, pred_logits) + pred_logits = pred_logits.to(torch.float32) + + return (pred_logits, image_class_embeds) + + +class OwlViTForObjectDetection(OwlViTPreTrainedModel): + config: OwlViTConfig + + def __init__(self, config: OwlViTConfig): + super().__init__(config) + + self.owlvit = OwlViTModel(config) + self.class_head = OwlViTClassPredictionHead(config) + self.box_head = OwlViTBoxPredictionHead(config) + + self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size, eps=config.vision_config.layer_norm_eps) + self.sigmoid = nn.Sigmoid() + self.config = config + self.num_patches_height = self.config.vision_config.image_size // self.config.vision_config.patch_size + self.num_patches_width = self.config.vision_config.image_size // self.config.vision_config.patch_size + self.register_buffer( + "box_bias", self.compute_box_bias(self.num_patches_height, self.num_patches_width), persistent=False + ) + + self.post_init() + + @staticmethod + def normalize_grid_corner_coordinates(num_patches_height: int, num_patches_width: int) -> torch.Tensor: + # Create grid coordinates using torch + x_coordinates = torch.arange(1, num_patches_width + 1, dtype=torch.float32) + y_coordinates = torch.arange(1, num_patches_height + 1, dtype=torch.float32) + xx, yy = torch.meshgrid(x_coordinates, y_coordinates, indexing="xy") + + # Stack the coordinates and divide by their respective patch counts + box_coordinates = torch.stack((xx, yy), dim=-1) + box_coordinates[..., 0] /= num_patches_width + box_coordinates[..., 1] /= num_patches_height + + # Flatten (h, w, 2) -> (h*w, 2) + box_coordinates = box_coordinates.view(-1, 2) + + return box_coordinates + + def compute_box_bias(self, num_patches_height: int, num_patches_width: int) -> torch.Tensor: + # The box center is biased to its position on the feature grid + box_coordinates = self.normalize_grid_corner_coordinates(num_patches_height, num_patches_width) + box_coordinates = torch.clip(box_coordinates, 0.0, 1.0) + + # Unnormalize xy + box_coord_bias = torch.log(box_coordinates + 1e-4) - torch.log1p(-box_coordinates + 1e-4) + + # The box size is biased to the patch size + box_size = torch.full_like(box_coord_bias, 1.0) + box_size[..., 0] /= num_patches_width + box_size[..., 1] /= num_patches_height + box_size_bias = torch.log(box_size + 1e-4) - torch.log1p(-box_size + 1e-4) + + # Compute box bias + box_bias = torch.cat([box_coord_bias, box_size_bias], dim=-1) + return box_bias + + def box_predictor( + self, + image_feats: torch.FloatTensor, + feature_map: torch.FloatTensor, + interpolate_pos_encoding: bool = False, + ) -> torch.FloatTensor: + """ + Args: + image_feats: + Features extracted from the image, returned by the `image_text_embedder` method. + feature_map: + A spatial re-arrangement of image_features, also returned by the `image_text_embedder` method. + interpolate_pos_encoding: + Whether to interpolate the pre-trained position encodings. + Returns: + pred_boxes: + List of predicted boxes (cxcywh normalized to 0, 1) nested within a dictionary. + """ + # Bounding box detection head [batch_size, num_boxes, 4]. + pred_boxes = self.box_head(image_feats) + + # Compute the location of each token on the grid and use it to compute a bias for the bbox prediction + if interpolate_pos_encoding: + _, num_patches_height, num_patches_width, _ = feature_map.shape + box_bias = self.compute_box_bias(num_patches_height, num_patches_width) + else: + box_bias = self.box_bias + + box_bias = box_bias.to(feature_map.device) + pred_boxes += box_bias + pred_boxes = self.sigmoid(pred_boxes) + return pred_boxes + + def class_predictor( + self, + image_feats: torch.FloatTensor, + query_embeds: torch.FloatTensor | None = None, + query_mask: torch.Tensor | None = None, + ) -> tuple[torch.FloatTensor]: + """ + Args: + image_feats: + Features extracted from the `image_text_embedder`. + query_embeds: + Text query embeddings. + query_mask: + Must be provided with query_embeddings. A mask indicating which query embeddings are valid. + """ + (pred_logits, image_class_embeds) = self.class_head(image_feats, query_embeds, query_mask) + + return (pred_logits, image_class_embeds) + + def image_text_embedder( + self, + input_ids: torch.Tensor, + pixel_values: torch.FloatTensor, + attention_mask: torch.Tensor, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor]: + outputs = self.owlvit( + pixel_values=pixel_values, + input_ids=input_ids, + attention_mask=attention_mask, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + if interpolate_pos_encoding: + _, _, height, width = pixel_values.shape + num_patches_height = height // self.config.vision_config.patch_size + num_patches_width = width // self.config.vision_config.patch_size + else: + num_patches_height = self.num_patches_height + num_patches_width = self.num_patches_width + + # Get image embeddings + last_hidden_state = outputs.vision_model_output[0] + image_embeds = self.owlvit.vision_model.post_layernorm(last_hidden_state) + + # Resize class token + class_token_out = torch.broadcast_to(image_embeds[:, :1, :], image_embeds[:, :-1].shape) + + # Merge image embedding with class tokens + image_embeds = image_embeds[:, 1:, :] * class_token_out + image_embeds = self.layer_norm(image_embeds) + + # Resize to [batch_size, num_patches_height, num_patches_width, hidden_size] + new_size = ( + image_embeds.shape[0], + num_patches_height, + num_patches_width, + image_embeds.shape[-1], + ) + image_embeds = image_embeds.reshape(new_size) + text_embeds = outputs[-4] + + return (text_embeds, image_embeds, outputs) + + def image_embedder( + self, + pixel_values: torch.FloatTensor, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor]: + # Get OwlViTModel vision embeddings (same as CLIP) + vision_outputs: BaseModelOutputWithPooling = self.owlvit.vision_model( + pixel_values=pixel_values, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs + ) + + if interpolate_pos_encoding: + _, _, height, width = pixel_values.shape + num_patches_height = height // self.config.vision_config.patch_size + num_patches_width = width // self.config.vision_config.patch_size + else: + num_patches_height = self.num_patches_height + num_patches_width = self.num_patches_width + + # Apply post_layernorm to last_hidden_state, return non-projected output + last_hidden_state = vision_outputs[0] + image_embeds = self.owlvit.vision_model.post_layernorm(last_hidden_state) + + # Resize class token + class_token_out = torch.broadcast_to(image_embeds[:, :1, :], image_embeds[:, :-1].shape) + + # Merge image embedding with class tokens + image_embeds = image_embeds[:, 1:, :] * class_token_out + image_embeds = self.layer_norm(image_embeds) + + # Resize to [batch_size, num_patches_height, num_patches_width, hidden_size] + new_size = ( + image_embeds.shape[0], + num_patches_height, + num_patches_width, + image_embeds.shape[-1], + ) + image_embeds = image_embeds.reshape(new_size) + + return (image_embeds, vision_outputs) + + def embed_image_query( + self, + query_image_features: torch.FloatTensor, + query_feature_map: torch.FloatTensor, + interpolate_pos_encoding: bool = False, + ) -> torch.FloatTensor: + _, class_embeds = self.class_predictor(query_image_features) + pred_boxes = self.box_predictor(query_image_features, query_feature_map, interpolate_pos_encoding) + pred_boxes_as_corners = center_to_corners_format(pred_boxes) + + # Loop over query images + best_class_embeds = [] + best_box_indices = [] + pred_boxes_device = pred_boxes_as_corners.device + + for i in range(query_image_features.shape[0]): + each_query_box = torch.tensor([[0, 0, 1, 1]], device=pred_boxes_device) + each_query_pred_boxes = pred_boxes_as_corners[i] + ious, _ = box_iou(each_query_box, each_query_pred_boxes) + + # If there are no overlapping boxes, fall back to generalized IoU + if torch.all(ious[0] == 0.0): + ious = generalized_box_iou(each_query_box, each_query_pred_boxes) + + # Use an adaptive threshold to include all boxes within 80% of the best IoU + iou_threshold = torch.max(ious) * 0.8 + + selected_inds = (ious[0] >= iou_threshold).nonzero() + if selected_inds.numel(): + selected_embeddings = class_embeds[i][selected_inds.squeeze(1)] + mean_embeds = torch.mean(class_embeds[i], axis=0) + mean_sim = torch.einsum("d,id->i", mean_embeds, selected_embeddings) + best_box_ind = selected_inds[torch.argmin(mean_sim)] + best_class_embeds.append(class_embeds[i][best_box_ind]) + best_box_indices.append(best_box_ind) + + if best_class_embeds: + query_embeds = torch.stack(best_class_embeds) + box_indices = torch.stack(best_box_indices) + else: + query_embeds, box_indices = None, None + + return query_embeds, box_indices, pred_boxes + + @can_return_tuple + @auto_docstring + def image_guided_detection( + self, + pixel_values: torch.FloatTensor, + query_pixel_values: torch.FloatTensor | None = None, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> OwlViTImageGuidedObjectDetectionOutput: + r""" + query_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values of query image(s) to be detected. Pass in one query image per target image. + + Examples: + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + >>> import torch + >>> from transformers import AutoProcessor, OwlViTForObjectDetection + + >>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch16") + >>> model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch16") + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> query_url = "http://images.cocodataset.org/val2017/000000001675.jpg" + >>> with httpx.stream("GET", query_url) as response: + ... query_image = Image.open(BytesIO(response.read())) + >>> inputs = processor(images=image, query_images=query_image, return_tensors="pt") + >>> with torch.no_grad(): + ... outputs = model.image_guided_detection(**inputs) + >>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2] + >>> target_sizes = torch.Tensor([image.size[::-1]]) + >>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> results = processor.post_process_image_guided_detection( + ... outputs=outputs, threshold=0.6, nms_threshold=0.3, target_sizes=target_sizes + ... ) + >>> i = 0 # Retrieve predictions for the first image + >>> boxes, scores = results[i]["boxes"], results[i]["scores"] + >>> for box, score in zip(boxes, scores): + ... box = [round(i, 2) for i in box.tolist()] + ... print(f"Detected similar object with confidence {round(score.item(), 3)} at location {box}") + Detected similar object with confidence 0.856 at location [10.94, 50.4, 315.8, 471.39] + Detected similar object with confidence 1.0 at location [334.84, 25.33, 636.16, 374.71] + ```""" + # Compute feature maps for the input and query images + query_feature_map = self.image_embedder( + pixel_values=query_pixel_values, interpolate_pos_encoding=interpolate_pos_encoding + )[0] + feature_map, vision_outputs = self.image_embedder( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + batch_size, num_patches_height, num_patches_width, hidden_dim = feature_map.shape + image_feats = torch.reshape(feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim)) + + batch_size, num_patches_height, num_patches_width, hidden_dim = query_feature_map.shape + query_image_feats = torch.reshape( + query_feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim) + ) + # Get top class embedding and best box index for each query image in batch + query_embeds, best_box_indices, query_pred_boxes = self.embed_image_query( + query_image_feats, query_feature_map, interpolate_pos_encoding + ) + + # Predict object classes [batch_size, num_patches, num_queries+1] + (pred_logits, class_embeds) = self.class_predictor(image_feats=image_feats, query_embeds=query_embeds) + + # Predict object boxes + target_pred_boxes = self.box_predictor(image_feats, feature_map, interpolate_pos_encoding) + + return OwlViTImageGuidedObjectDetectionOutput( + image_embeds=feature_map, + query_image_embeds=query_feature_map, + target_pred_boxes=target_pred_boxes, + query_pred_boxes=query_pred_boxes, + logits=pred_logits, + class_embeds=class_embeds, + text_model_output=None, + vision_model_output=vision_outputs, + ) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor, + pixel_values: torch.FloatTensor, + attention_mask: torch.Tensor | None = None, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> OwlViTObjectDetectionOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size * num_max_text_queries, sequence_length)`, *optional*): + Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input + IDs?](../glossary#input-ids). + + Examples: + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + >>> import torch + + >>> from transformers import OwlViTProcessor, OwlViTForObjectDetection + + >>> processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32") + >>> model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> text_labels = [["a photo of a cat", "a photo of a dog"]] + >>> inputs = processor(text=text_labels, images=image, return_tensors="pt") + >>> outputs = model(**inputs) + + >>> # Target image sizes (height, width) to rescale box predictions [batch_size, 2] + >>> target_sizes = torch.tensor([(image.height, image.width)]) + >>> # Convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> results = processor.post_process_grounded_object_detection( + ... outputs=outputs, target_sizes=target_sizes, threshold=0.1, text_labels=text_labels + ... ) + >>> # Retrieve predictions for the first image for the corresponding text queries + >>> result = results[0] + >>> boxes, scores, text_labels = result["boxes"], result["scores"], result["text_labels"] + >>> for box, score, text_label in zip(boxes, scores, text_labels): + ... box = [round(i, 2) for i in box.tolist()] + ... print(f"Detected {text_label} with confidence {round(score.item(), 3)} at location {box}") + Detected a photo of a cat with confidence 0.707 at location [324.97, 20.44, 640.58, 373.29] + Detected a photo of a cat with confidence 0.717 at location [1.46, 55.26, 315.55, 472.17] + ```""" + # Embed images and text queries + query_embeds, feature_map, outputs = self.image_text_embedder( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + # Text and vision model outputs + text_outputs = outputs.text_model_output + vision_outputs = outputs.vision_model_output + + batch_size, num_patches_height, num_patches_width, hidden_dim = feature_map.shape + image_feats = torch.reshape(feature_map, (batch_size, num_patches_height * num_patches_width, hidden_dim)) + + # Reshape from [batch_size * max_text_queries, hidden_dim] -> [batch_size, max_text_queries, hidden_dim] + max_text_queries = input_ids.shape[0] // batch_size + query_embeds = query_embeds.reshape(batch_size, max_text_queries, query_embeds.shape[-1]) + + # If first token is 0, then this is a padded query [batch_size, num_queries]. + input_ids = input_ids.reshape(batch_size, max_text_queries, input_ids.shape[-1]) + query_mask = input_ids[..., 0] > 0 + + # Predict object classes [batch_size, num_patches, num_queries+1] + (pred_logits, class_embeds) = self.class_predictor(image_feats, query_embeds, query_mask) + + # Predict object boxes + pred_boxes = self.box_predictor(image_feats, feature_map, interpolate_pos_encoding) + + return OwlViTObjectDetectionOutput( + image_embeds=feature_map, + text_embeds=query_embeds, + pred_boxes=pred_boxes, + logits=pred_logits, + class_embeds=class_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +__all__ = ["OwlViTModel", "OwlViTPreTrainedModel", "OwlViTTextModel", "OwlViTVisionModel", "OwlViTForObjectDetection"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/processing_owlvit.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/processing_owlvit.py new file mode 100644 index 0000000000000000000000000000000000000000..0863c5775fdceff64a11a319ae2110772606f6ef --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/owlvit/processing_owlvit.py @@ -0,0 +1,231 @@ +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Image/Text processor class for OWL-ViT +""" + +from typing import TYPE_CHECKING + +import numpy as np + +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ( + ImagesKwargs, + ProcessingKwargs, + ProcessorMixin, + Unpack, +) +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import TensorType, auto_docstring, is_torch_available + + +if TYPE_CHECKING: + from .modeling_owlvit import OwlViTImageGuidedObjectDetectionOutput, OwlViTObjectDetectionOutput + + +class OwlViTImagesKwargs(ImagesKwargs, total=False): + """ + query_images (`ImageInput`, *optional*): + Query images to use for image-guided object detection. When provided, these images serve as visual queries + to find similar objects in the main `images`. The query images override any text prompts, and the model + performs image-to-image matching instead of text-to-image matching. + """ + + query_images: ImageInput | None + + +class OwlViTProcessorKwargs(ProcessingKwargs, total=False): + images_kwargs: OwlViTImagesKwargs + _defaults = { + "text_kwargs": { + "padding": "max_length", + }, + "common_kwargs": { + "return_tensors": "pt", + }, + } + + +@auto_docstring +class OwlViTProcessor(ProcessorMixin): + def __init__(self, image_processor=None, tokenizer=None, **kwargs): + super().__init__(image_processor, tokenizer) + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[OwlViTProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **query_pixel_values** -- Pixel values of the query images to be fed to a model. Returned when `query_images` is not `None`. + """ + output_kwargs = self._merge_kwargs( + OwlViTProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + query_images = output_kwargs["images_kwargs"].pop("query_images", None) + return_tensors = output_kwargs["text_kwargs"]["return_tensors"] + + if text is None and query_images is None and images is None: + raise ValueError( + "You have to specify at least one text or query image or image. All three cannot be none." + ) + + data = {} + if text is not None: + if isinstance(text, str) or (isinstance(text, list) and not isinstance(text[0], list)): + encodings = [self.tokenizer(text, **output_kwargs["text_kwargs"])] + + elif isinstance(text, list) and isinstance(text[0], list): + encodings = [] + + # Maximum number of queries across batch + max_num_queries = max(len(text_single) for text_single in text) + + # Pad all batch samples to max number of text queries + for text_single in text: + if len(text_single) != max_num_queries: + text_single = text_single + [" "] * (max_num_queries - len(text_single)) + + encoding = self.tokenizer(text_single, **output_kwargs["text_kwargs"]) + encodings.append(encoding) + else: + raise TypeError("Input text should be a string, a list of strings or a nested list of strings") + + if return_tensors == "np": + input_ids = np.concatenate([encoding["input_ids"] for encoding in encodings], axis=0) + attention_mask = np.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0) + elif return_tensors == "pt" and is_torch_available(): + import torch + + input_ids = torch.cat([encoding["input_ids"] for encoding in encodings], dim=0) + attention_mask = torch.cat([encoding["attention_mask"] for encoding in encodings], dim=0) + else: + raise ValueError("Target return tensor type could not be returned") + + data["input_ids"] = input_ids + data["attention_mask"] = attention_mask + + if query_images is not None: + query_pixel_values = self.image_processor(query_images, **output_kwargs["images_kwargs"]).pixel_values + # Query images always override the text prompt + data = {"query_pixel_values": query_pixel_values} + + if images is not None: + image_features = self.image_processor(images, **output_kwargs["images_kwargs"]) + data["pixel_values"] = image_features.pixel_values + + return BatchFeature(data=data, tensor_type=return_tensors) + + def post_process(self, *args, **kwargs): + """ + This method forwards all its arguments to [`OwlViTImageProcessor.post_process`]. Please refer to the docstring + of this method for more information. + """ + return self.image_processor.post_process(*args, **kwargs) + + def post_process_grounded_object_detection( + self, + outputs: "OwlViTObjectDetectionOutput", + threshold: float = 0.1, + target_sizes: TensorType | list[tuple] | None = None, + text_labels: list[list[str]] | None = None, + ): + """ + Converts the raw output of [`OwlViTForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. + + Args: + outputs ([`OwlViTObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.1): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + text_labels (`list[list[str]]`, *optional*): + List of lists of text labels for each image in the batch. If unset, "text_labels" in output will be + set to `None`. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: + - "scores": The confidence scores for each predicted box on the image. + - "labels": Indexes of the classes predicted by the model on the image. + - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. + - "text_labels": The text labels for each predicted bounding box on the image. + """ + output = self.image_processor.post_process_object_detection( + outputs=outputs, threshold=threshold, target_sizes=target_sizes + ) + + if text_labels is not None and len(text_labels) != len(output): + raise ValueError("Make sure that you pass in as many lists of text labels as images") + + # adding text labels to the output + if text_labels is not None: + for image_output, image_text_labels in zip(output, text_labels): + object_text_labels = [image_text_labels[i] for i in image_output["labels"]] + image_output["text_labels"] = object_text_labels + else: + for image_output in output: + image_output["text_labels"] = None + + return output + + def post_process_image_guided_detection( + self, + outputs: "OwlViTImageGuidedObjectDetectionOutput", + threshold: float = 0.0, + nms_threshold: float = 0.3, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the output of [`OwlViTForObjectDetection.image_guided_detection`] into the format expected by the COCO + api. + + Args: + outputs ([`OwlViTImageGuidedObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.0): + Minimum confidence threshold to use to filter out predicted boxes. + nms_threshold (`float`, *optional*, defaults to 0.3): + IoU threshold for non-maximum suppression of overlapping boxes. + target_sizes (`torch.Tensor`, *optional*): + Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in + the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to + None, predictions will not be unnormalized. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the following keys: + - "scores": The confidence scores for each predicted box on the image. + - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format. + - "labels": Set to `None`. + """ + return self.image_processor.post_process_image_guided_detection( + outputs=outputs, threshold=threshold, nms_threshold=nms_threshold, target_sizes=target_sizes + ) + + +__all__ = ["OwlViTProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1ddca6bff1eb2098b2d32e6822887a96b51da1cc --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/__init__.py @@ -0,0 +1,31 @@ +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_paddleocr_vl import * + from .image_processing_paddleocr_vl import * + from .image_processing_pil_paddleocr_vl import * + from .modeling_paddleocr_vl import * + from .processing_paddleocr_vl import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/configuration_paddleocr_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/configuration_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..343a22ade8142493e40b56934459a356d2344a2e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/configuration_paddleocr_vl.py @@ -0,0 +1,193 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_paddleocr_vl.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import inspect + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="PaddlePaddle/PaddleOCR-VL") +@strict +class PaddleOCRVisionConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PaddleOCRVisionConfig, PaddleOCRVisionModel + + >>> # Initializing a PaddleOCRVisionConfig with PaddlePaddle/PaddleOCR-VL style configuration + >>> configuration = PaddleOCRVisionConfig() + + >>> # Initializing a PaddleOCRVisionModel (with random weights) from the PaddlePaddle/PaddleOCR-VL style configuration + >>> model = PaddleOCRVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "paddleocr_vl_vision" + base_config_key = "vision_config" + + hidden_size: int = 1152 + intermediate_size: int = 4304 + num_hidden_layers: int = 27 + num_attention_heads: int = 16 + num_channels: int = 3 + image_size: int = 384 + patch_size: int = 14 + hidden_act: str = "gelu_pytorch_tanh" + layer_norm_eps: float = 1e-6 + attention_dropout: float | int = 0.0 + spatial_merge_size: int = 2 + + +@auto_docstring(checkpoint="PaddlePaddle/PaddleOCR-VL") +@strict +class PaddleOCRTextConfig(PreTrainedConfig): + r""" + use_bias (`bool`, *optional*, defaults to `False`): + Whether to use a bias in any of the projections including mlp and attention for example. + + Example: + + ```python + >>> from transformers import PaddleOCRTextModel, PaddleOCRTextConfig + + >>> # Initializing a PaddleOCRText 0.3B style configuration + >>> configuration = PaddleOCRTextConfig() + + >>> # Initializing a model from the 0.3B style configuration + >>> model = PaddleOCRTextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "paddleocr_vl_text" + keys_to_ignore_at_inference = ["past_key_values"] + default_theta = 500000.0 + # Default tensor parallel plan for base model `PaddleOCRTextModel` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 103424 + hidden_size: int = 1024 + intermediate_size: int = 3072 + num_hidden_layers: int = 18 + num_attention_heads: int = 16 + num_key_value_heads: int | None = 2 + hidden_act: str = "silu" + max_position_embeddings: int = 131072 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-05 + use_cache: bool | None = True + pad_token_id: int | None = 0 + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 2 + tie_word_embeddings: bool = True + rope_parameters: RopeParameters | dict | None = None + use_bias: bool | None = False + head_dim: int | None = 128 + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + self.head_dim = self.head_dim if self.head_dim is not None else self.hidden_size // self.num_attention_heads + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="PaddlePaddle/PaddleOCR-VL") +@strict +class PaddleOCRVLConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PaddleOCRVLForConditionalGeneration, PaddleOCRVLConfig + + >>> # Initializing a PaddleOCRVL style configuration + >>> configuration = PaddleOCRVLConfig() + + >>> # Initializing a model from the PaddleOCRVL style configuration + >>> model = PaddleOCRVLForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "paddleocr_vl" + + sub_configs = {"vision_config": PaddleOCRVisionConfig, "text_config": PaddleOCRTextConfig} + keys_to_ignore_at_inference = ["past_key_values"] + + text_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + + image_token_id: int = 100295 + video_token_id: int = 100296 + vision_start_token_id: int = 101305 + vision_end_token_id: int = 101306 + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = self.sub_configs["vision_config"](**self.vision_config) + elif self.vision_config is None: + self.vision_config = self.sub_configs["vision_config"]() + + # Hub configs are saved as flat dicts so we pop some of kwargs to init `TextConfig` + text_params = inspect.signature(self.sub_configs["text_config"].__init__).parameters.keys() + text_params = list(text_params) + ["rope_parameters", "rope_scaling", "rope_theta"] + text_kwargs = {key: kwargs.pop(key) for key in text_params if key in kwargs} + + if isinstance(self.text_config, dict): + self.text_config = self.sub_configs["text_config"](**self.text_config) + elif self.text_config is None: + # Hub configs are saved as flat dicts so we pop some of kwargs to init `TextConfig` + text_kwargs["dtype"] = kwargs.get("torch_dtype", kwargs.get("dtype")) # don't pop the dtype + self.text_config = self.sub_configs["text_config"](**text_kwargs) + + super().__post_init__(**kwargs) + + +__all__ = ["PaddleOCRVLConfig", "PaddleOCRVisionConfig", "PaddleOCRTextConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/image_processing_paddleocr_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/image_processing_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..14045b22d0f2463c799bfc86f21cf2665a15c8b1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/image_processing_paddleocr_vl.py @@ -0,0 +1,253 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_paddleocr_vl.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Iterable + +import torch + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ImageInput, PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +class PaddleOCRVLImageProcessorKwargs(ImagesKwargs, total=False): + r""" + patch_size (`int`, *optional*, defaults to 14): + The spatial patch size of the vision encoder. + temporal_patch_size (`int`, *optional*, defaults to 1): + The temporal patch size of the vision encoder. + merge_size (`int`, *optional*, defaults to 2): + The merge size of the vision encoder to llm encoder. + """ + + min_pixels: int + max_pixels: int + patch_size: int + temporal_patch_size: int + merge_size: int + + +def smart_resize( + height: int, + width: int, + factor: int = 28, + min_pixels: int = 384 * 384, + max_pixels: int = 1536 * 1536, +): + if height < factor: + width = round((width * factor) / height) + height = factor + + if width < factor: + height = round((height * factor) / width) + width = factor + + if max(height, width) / min(height, width) > 200: + raise ValueError( + f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" + ) + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = max(factor, math.floor(height / beta / factor) * factor) + w_bar = max(factor, math.floor(width / beta / factor) * factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + return h_bar, w_bar + + +@auto_docstring +class PaddleOCRVLImageProcessor(TorchvisionBackend): + do_resize = True + resample = PILImageResampling.BICUBIC + size = {"shortest_edge": 384 * 384, "longest_edge": 1536 * 1536} + default_to_square = False + do_rescale = True + do_normalize = True + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + do_convert_rgb = True + patch_size = 14 + temporal_patch_size = 1 + merge_size = 2 + valid_kwargs = PaddleOCRVLImageProcessorKwargs + model_input_names = ["pixel_values", "image_grid_thw"] + + def __init__(self, **kwargs: Unpack[PaddleOCRVLImageProcessorKwargs]): + size = kwargs.pop("size", None) + min_pixels = kwargs.pop("min_pixels", None) + max_pixels = kwargs.pop("max_pixels", None) + # backward compatibility: override size with min_pixels and max_pixels if they are provided + size = self.size if size is None else size + if min_pixels is not None: + size["shortest_edge"] = min_pixels + size.pop("min_pixels", None) + if max_pixels is not None: + size["longest_edge"] = max_pixels + size.pop("max_pixels", None) + if "shortest_edge" not in size or "longest_edge" not in size: + raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.") + + super().__init__(size=size, **kwargs) + + def _standardize_kwargs( + self, + size: int | Iterable[int] | dict[str, int] | SizeDict | None = None, + min_pixels: int | None = None, + max_pixels: int | None = None, + **kwargs, + ) -> dict: + if min_pixels is not None and max_pixels is not None: + size = SizeDict(shortest_edge=min_pixels, longest_edge=max_pixels) + kwargs = super()._standardize_kwargs(size=size, **kwargs) + size = kwargs.get("size", self.size) + if not size.shortest_edge or not size.longest_edge: + raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.") + return kwargs + + @auto_docstring + def preprocess( + self, + images: ImageInput, + **kwargs: Unpack[PaddleOCRVLImageProcessorKwargs], + ) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + patch_size: int, + temporal_patch_size: int, + merge_size: int, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + height, width = stacked_images.shape[-2:] + if do_resize: + resized_height, resized_width = smart_resize( + height, + width, + factor=patch_size * merge_size, + min_pixels=size.shortest_edge, + max_pixels=size.longest_edge, + ) + stacked_images = self.resize( + image=stacked_images, + size=SizeDict(height=resized_height, width=resized_width), + resample=resample, + ) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + processed_grids = {} + for shape, stacked_images in grouped_images.items(): + resized_height, resized_width = stacked_images.shape[-2:] + patches = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + if patches.ndim == 4: + patches = patches.unsqueeze(1) + if patches.shape[1] % temporal_patch_size != 0: + repeats = patches[:, -1:].repeat(1, temporal_patch_size - 1, 1, 1, 1) + patches = torch.cat([patches, repeats], dim=1) + + batch_size, grid_t, channel = patches.shape[:3] + grid_t = grid_t // temporal_patch_size + grid_h, grid_w = resized_height // patch_size, resized_width // patch_size + + patches = patches.view( + batch_size, + grid_t, + temporal_patch_size, + channel, + grid_h, + patch_size, + grid_w, + patch_size, + ) + patches = patches.permute(0, 1, 4, 6, 3, 2, 5, 7) + flatten_patches = patches.reshape(batch_size, grid_t * grid_h * grid_w, channel, patch_size, patch_size) + + processed_images_grouped[shape] = flatten_patches + processed_grids[shape] = [[grid_t, grid_h, grid_w]] * batch_size + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + processed_grids = reorder_images(processed_grids, grouped_images_index) + pixel_values = torch.cat(processed_images, dim=0) + image_grid_thw = torch.tensor(processed_grids) + + return BatchFeature( + data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors + ) + + def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None): + """ + A utility that returns number of image patches for a given image size. + + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + images_kwargs (`dict`, *optional*) + Any kwargs to override defaults of the image processor. + Returns: + `int`: Number of image patches per image. + """ + min_pixels = images_kwargs["min_pixels"] if "min_pixels" in images_kwargs else self.size["shortest_edge"] + max_pixels = images_kwargs["max_pixels"] if "max_pixels" in images_kwargs else self.size["longest_edge"] + patch_size = images_kwargs.get("patch_size", self.patch_size) + merge_size = images_kwargs.get("merge_size", self.merge_size) + + factor = patch_size * merge_size + resized_height, resized_width = smart_resize( + height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels + ) + grid_h, grid_w = resized_height // patch_size, resized_width // patch_size + return grid_h * grid_w + + +__all__ = ["PaddleOCRVLImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/image_processing_pil_paddleocr_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/image_processing_pil_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..ac639892640f5b50ed999202af621e33dce54023 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/image_processing_pil_paddleocr_vl.py @@ -0,0 +1,251 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_paddleocr_vl.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Iterable + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ImageInput, PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +class PaddleOCRVLImageProcessorKwargs(ImagesKwargs, total=False): + r""" + patch_size (`int`, *optional*, defaults to 14): + The spatial patch size of the vision encoder. + temporal_patch_size (`int`, *optional*, defaults to 1): + The temporal patch size of the vision encoder. + merge_size (`int`, *optional*, defaults to 2): + The merge size of the vision encoder to llm encoder. + """ + + min_pixels: int + max_pixels: int + patch_size: int + temporal_patch_size: int + merge_size: int + + +def smart_resize( + height: int, + width: int, + factor: int = 28, + min_pixels: int = 384 * 384, + max_pixels: int = 1536 * 1536, +): + if height < factor: + width = round((width * factor) / height) + height = factor + + if width < factor: + height = round((height * factor) / width) + width = factor + + if max(height, width) / min(height, width) > 200: + raise ValueError( + f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" + ) + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = max(factor, math.floor(height / beta / factor) * factor) + w_bar = max(factor, math.floor(width / beta / factor) * factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + return h_bar, w_bar + + +@auto_docstring +class PaddleOCRVLImageProcessorPil(PilBackend): + do_resize = True + resample = PILImageResampling.BICUBIC + size = {"shortest_edge": 384 * 384, "longest_edge": 1536 * 1536} + default_to_square = False + do_rescale = True + do_normalize = True + image_mean = OPENAI_CLIP_MEAN + image_std = OPENAI_CLIP_STD + do_convert_rgb = True + patch_size = 14 + temporal_patch_size = 1 + merge_size = 2 + valid_kwargs = PaddleOCRVLImageProcessorKwargs + model_input_names = ["pixel_values", "image_grid_thw"] + + def __init__(self, **kwargs: Unpack[PaddleOCRVLImageProcessorKwargs]): + size = kwargs.pop("size", None) + min_pixels = kwargs.pop("min_pixels", None) + max_pixels = kwargs.pop("max_pixels", None) + # backward compatibility: override size with min_pixels and max_pixels if they are provided + size = self.size if size is None else size + if min_pixels is not None: + size["shortest_edge"] = min_pixels + size.pop("min_pixels", None) + if max_pixels is not None: + size["longest_edge"] = max_pixels + size.pop("max_pixels", None) + if "shortest_edge" not in size or "longest_edge" not in size: + raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.") + + super().__init__(size=size, **kwargs) + + def _standardize_kwargs( + self, + size: int | Iterable[int] | dict[str, int] | SizeDict | None = None, + min_pixels: int | None = None, + max_pixels: int | None = None, + **kwargs, + ) -> dict: + if min_pixels is not None and max_pixels is not None: + size = SizeDict(shortest_edge=min_pixels, longest_edge=max_pixels) + kwargs = super()._standardize_kwargs(size=size, **kwargs) + size = kwargs.get("size", self.size) + if not size.shortest_edge or not size.longest_edge: + raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.") + return kwargs + + @auto_docstring + def preprocess( + self, + images: ImageInput, + **kwargs: Unpack[PaddleOCRVLImageProcessorKwargs], + ) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + patch_size: int, + temporal_patch_size: int, + merge_size: int, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + all_patches = [] + all_grids = [] + + for image in images: + height, width = image.shape[-2:] + if do_resize: + resized_height, resized_width = smart_resize( + height, + width, + factor=patch_size * merge_size, + min_pixels=size.shortest_edge, + max_pixels=size.longest_edge, + ) + image = self.resize( + image, + size=SizeDict(height=resized_height, width=resized_width), + resample=resample, + ) + else: + resized_height, resized_width = height, width + + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + + patches = np.expand_dims(image, axis=0) + if patches.ndim == 4: + patches = np.expand_dims(patches, axis=1) + if patches.shape[1] % temporal_patch_size != 0: + repeats = np.repeat( + patches[:, -1:], temporal_patch_size - (patches.shape[1] % temporal_patch_size), axis=1 + ) + patches = np.concatenate([patches, repeats], axis=1) + + batch_size = 1 + grid_t = patches.shape[1] // temporal_patch_size + channel = patches.shape[2] + grid_h, grid_w = resized_height // patch_size, resized_width // patch_size + + patches = patches.reshape( + batch_size, + grid_t, + temporal_patch_size, + channel, + grid_h, + patch_size, + grid_w, + patch_size, + ) + patches = patches.transpose(0, 1, 4, 6, 3, 2, 5, 7) + flatten_patches = patches.reshape(batch_size, grid_t * grid_h * grid_w, channel, patch_size, patch_size) + + all_patches.append(flatten_patches.squeeze(0)) + all_grids.append([grid_t, grid_h, grid_w]) + + pixel_values = np.concatenate(all_patches, axis=0) + image_grid_thw = np.array(all_grids, dtype=np.int64) + + return BatchFeature( + data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors + ) + + def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None): + """ + A utility that returns number of image patches for a given image size. + + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + images_kwargs (`dict`, *optional*) + Any kwargs to override defaults of the image processor. + Returns: + `int`: Number of image patches per image. + """ + min_pixels = images_kwargs["min_pixels"] if "min_pixels" in images_kwargs else self.size["shortest_edge"] + max_pixels = images_kwargs["max_pixels"] if "max_pixels" in images_kwargs else self.size["longest_edge"] + patch_size = images_kwargs.get("patch_size", self.patch_size) + merge_size = images_kwargs.get("merge_size", self.merge_size) + + factor = patch_size * merge_size + resized_height, resized_width = smart_resize( + height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels + ) + grid_h, grid_w = resized_height // patch_size, resized_width // patch_size + return grid_h * grid_w + + +__all__ = ["PaddleOCRVLImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/modeling_paddleocr_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/modeling_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..e8519ae0858cc4fc81364fb52b3875d28b616e34 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/modeling_paddleocr_vl.py @@ -0,0 +1,1737 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_paddleocr_vl.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN, GELUActivation +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check, torch_int +from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_paddleocr_vl import PaddleOCRTextConfig, PaddleOCRVisionConfig, PaddleOCRVLConfig + + +logger = logging.get_logger(__name__) + + +class PaddleOCRProjector(nn.Module): + def __init__(self, config: PaddleOCRVLConfig): + super().__init__() + self.merge_kernel_size = (config.vision_config.spatial_merge_size, config.vision_config.spatial_merge_size) + + hidden_size = config.vision_config.hidden_size * self.merge_kernel_size[0] * self.merge_kernel_size[1] + + self.pre_norm = torch.nn.LayerNorm(config.vision_config.hidden_size, eps=1e-05) + self.linear_1 = nn.Linear(hidden_size, hidden_size, bias=True) + self.act = GELUActivation() + self.linear_2 = nn.Linear(hidden_size, config.text_config.hidden_size, bias=True) + + def forward(self, image_features: torch.Tensor, image_grid_thw: torch.Tensor) -> torch.Tensor: + image_features_chunks = image_features.split(image_grid_thw.prod(dim=1).tolist(), dim=0) + m1, m2 = self.merge_kernel_size + + processed_features = [] + for image_feature, image_grid in zip(image_features_chunks, image_grid_thw): + image_feature = self.pre_norm(image_feature) + t, h, w = image_grid + d = image_feature.shape[-1] + h_block = h // m1 + w_block = w // m2 + + image_feature = image_feature.reshape(t, h_block, m1, w_block, m2, d) + image_feature = image_feature.transpose(2, 3) + image_feature = image_feature.reshape(t * h_block * w_block, m1 * m2 * d) + + hidden_states = self.linear_1(image_feature) + hidden_states = self.act(hidden_states) + hidden_states = self.linear_2(hidden_states) + processed_features.append(hidden_states) + + return torch.cat(processed_features, dim=0) + + +class PaddleOCRVisionRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, dim: int, theta: float = 10000.0) -> None: + super().__init__() + self.dim = dim + self.theta = theta + inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + def forward(self, seqlen: int) -> torch.Tensor: + seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + freqs = torch.outer(seq, self.inv_freq) + return freqs + + +class PaddleOCRRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: PaddleOCRVLConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PaddleOCRVLConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + # Ignore copy + def forward(self, x, position_ids): + # In contrast to other models, PaddleOCR has different position ids for the grids + # So we expand the inv_freq to shape (3, ...) + inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1) + position_ids_expanded = position_ids[:, :, None, :].float() # shape (3, bs, 1, positions) + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class PaddleOCRMLP(nn.Module): + def __init__(self, config: PaddleOCRTextConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1): + """Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors (https://qwenlm.github.io/blog/qwen2-vl/). + + Explanation: + Multimodal 3D rotary position embedding is an extension to 1D rotary position embedding. The input embedding + sequence contains vision (images / videos) embedding and text embedding or just contains text embedding. For + vision embedding part, we apply rotary position embedding on temporal, height and width dimension separately. + Here we split the channel dimension to 3 chunks for the temporal, height and width rotary position embedding. + For text embedding part, we just apply 1D rotary position embedding. The three rotary position index (temporal, + height and width) of text embedding is always the same, so the text embedding rotary position embedding has no + difference with modern LLMs. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + mrope_section(`List(int)`): + Multimodal rope section is for channel dimension of temporal, height and width in rope calculation. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + mrope_section = mrope_section * 2 + cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze( + unsqueeze_dim + ) + sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze( + unsqueeze_dim + ) + + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class PaddleOCRAttention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: PaddleOCRVLConfig, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads) + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.is_causal = True + + self.attention_dropout = 0.0 + self.rope_parameters = config.rope_parameters + self.scaling = self.head_dim**-0.5 + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.use_bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_bias) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.use_bias) + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_multimodal_rotary_pos_emb( + query_states, key_states, cos, sin, self.config.rope_parameters["mrope_section"] + ) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, + position_ids=position_ids, # pass positions for FA2 + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +@use_kernel_forward_from_hub("RMSNorm") +class PaddleOCRRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + PaddleOCRRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class PaddleOCRDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PaddleOCRTextConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = PaddleOCRAttention(config=config, layer_idx=layer_idx) + + self.mlp = PaddleOCRMLP(config) + self.input_layernorm = PaddleOCRRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = PaddleOCRRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class PaddleOCRVLPreTrainedModel(PreTrainedModel): + config: PaddleOCRVLConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PaddleOCRDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + + _can_record_outputs = { + "hidden_states": PaddleOCRDecoderLayer, + "attentions": PaddleOCRAttention, + } + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, PaddleOCRVisionEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, PaddleOCRVisionRotaryEmbedding): + inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim)) + init.copy_(module.inv_freq, inv_freq) + + +@auto_docstring +class PaddleOCRTextModel(PaddleOCRVLPreTrainedModel): + def __init__(self, config: PaddleOCRTextConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [PaddleOCRDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = PaddleOCRRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = PaddleOCRRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) + elif position_ids.ndim == 2: + position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) + + if position_ids.ndim == 3 and position_ids.shape[0] == 4: + text_position_ids = position_ids[0] + position_ids = position_ids[1:] + else: + text_position_ids = None + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=text_position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_embeddings=position_embeddings, + position_ids=text_position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class PaddleOCRVisionEmbeddings(nn.Module): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches = (self.image_size // self.patch_size) ** 2 + self.num_positions = self.num_patches + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support torch.jit tracing and no class embeddings. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + num_positions = self.position_embedding.weight.shape[0] + + patch_pos_embed = self.position_embedding.weight.unsqueeze(0) + + dim = embeddings.shape[-1] + + sqrt_num_positions = torch_int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(height, width), + mode="bilinear", + align_corners=False, + ) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return patch_pos_embed + + def forward( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]] | None = None, + ) -> torch.Tensor: + """ + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, image_channels, patch_size, patch_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + batch_size, squence_len, channel, height, width = pixel_values.shape + target_dtype = self.patch_embedding.weight.dtype + pixel_values = pixel_values.reshape(batch_size * squence_len, channel, height, width) + patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] + embeddings = patch_embeds.flatten(-2).squeeze(-1) + embeddings = embeddings.reshape(batch_size, squence_len, -1) + + start = 0 + embeddings = embeddings.squeeze(0) + tmp_embeddings = [] + for image_grid in image_grid_thw: + t, h, w = image_grid + end = start + t * h * w + image_embeddings = embeddings[start:end, :] + position_embedding = self.interpolate_pos_encoding(image_embeddings, h, w).squeeze(0).repeat(t, 1) + image_embeddings = image_embeddings + position_embedding + tmp_embeddings.append(image_embeddings) + start = end + embeddings = torch.concat(tmp_embeddings, dim=0) + + return embeddings + + +def apply_rotary_pos_emb_vision( + q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor]: + orig_q_dtype = q.dtype + orig_k_dtype = k.dtype + q, k = q.float(), k.float() + cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float() + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + q_embed = q_embed.to(orig_q_dtype) + k_embed = k_embed.to(orig_k_dtype) + return q_embed, k_embed + + +class PaddleOCRVisionAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.num_key_value_groups = 1 + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """ + Args: + hidden_states (`torch.Tensor`): + Input to the layer of shape `(seq_len, embed_dim)`. + cu_seqlens (`torch.Tensor` of shape `(num_images_or_videos + 1,)`): + The cumulative sequence lengths of each image or video feature. + position_embeddings (`tuple(torch.Tensor, torch.Tensor)` of shape `(num_patches, head_dim // 2)`): + The cosine and sine position embeddings for vision attention. + """ + seq_length = hidden_states.shape[0] + query_states = self.q_proj(hidden_states).view(seq_length, self.num_heads, self.head_dim) + key_states = self.k_proj(hidden_states).view(seq_length, self.num_heads, self.head_dim) + value_states = self.v_proj(hidden_states).view(seq_length, self.num_heads, self.head_dim) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin) + + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config): + # Flash Attention 2: Use cu_seqlens for variable length attention + max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + cu_seq_lens_q=cu_seqlens, + cu_seq_lens_k=cu_seqlens, + max_length_q=max_seqlen, + max_length_k=max_seqlen, + is_causal=False, + **kwargs, + ) + else: + # Other implementations: Process each chunk separately + lengths = cu_seqlens[1:] - cu_seqlens[:-1] + splits = [ + torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states) + ] + + attn_outputs, attn_weights = [], [] + for q, k, v in zip(*splits): + attn_output, attn_weight = attention_interface( + self, + q, + k, + v, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + is_causal=False, + **kwargs, + ) + attn_outputs.append(attn_output) + attn_weights.append(attn_weight) + + attn_output = torch.cat(attn_outputs, dim=1) + + attn_output = attn_output.reshape(seq_length, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class PaddleOCRVisionMLP(nn.Module): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class PaddleOCRVisionEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.self_attn = PaddleOCRVisionAttention(config=config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = PaddleOCRVisionMLP(config=config) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + r""" + cu_seqlens (`torch.Tensor` of shape `(num_images_or_videos + 1,)`): + The cumulative sequence lengths of each image or video feature. + position_embeddings (`tuple(torch.Tensor, torch.Tensor)` of shape `(num_patches, head_dim // 2)`): + The cosine and sine position embeddings for vision attention. + """ + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states, + cu_seqlens=cu_seqlens, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class PaddleOCRVisionEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`PaddleOCRVisionEncoderLayer`]. + + Args: + config: PaddleOCRVisionConfig + """ + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([PaddleOCRVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + embed_dim = config.hidden_size + num_heads = config.num_attention_heads + head_dim = embed_dim // num_heads + self.rotary_pos_emb = PaddleOCRVisionRotaryEmbedding(head_dim // 2) + + # Ignore copy + @can_return_tuple + @auto_docstring + def forward( + self, + inputs_embeds: torch.FloatTensor, + cu_seqlens: torch.Tensor, + attention_mask: torch.Tensor | None = None, + image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + cu_seqlens (`torch.Tensor` of shape `(num_images + 1,)`): + The cumulative sequence lengths of each image or video feature. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + The attention_mask used in forward function shape [batch_size X sequence_length] if not None. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + device = inputs_embeds.device + hidden_states = inputs_embeds + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + split_hids = [] + split_wids = [] + for t, h, w in image_grid_thw: + image_pids = torch.arange(t * h * w, device=device) % (h * w) + sample_hids = image_pids // w + sample_wids = image_pids % w + split_hids.append(sample_hids) + split_wids.append(sample_wids) + width_position_ids = torch.concat(split_wids, dim=0) + height_position_ids = torch.concat(split_hids, dim=0) + + pids = torch.stack([height_position_ids, width_position_ids], dim=-1) + max_grid_size = pids.max() + 1 + rotary_embeddings_max_grid = self.rotary_pos_emb(max_grid_size) + rotary_embeddings = rotary_embeddings_max_grid[pids].flatten(1) + rotary_embeddings = rotary_embeddings.repeat(1, 2) + position_embeddings = (rotary_embeddings.cos(), rotary_embeddings.sin()) + + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + cu_seqlens=cu_seqlens, + position_embeddings=position_embeddings, + **kwargs, + ) + + return BaseModelOutput( + last_hidden_state=hidden_states, + ) + + +class PaddleOCRVisionTransformer(PaddleOCRVLPreTrainedModel): + config: PaddleOCRVisionConfig + main_input_name = "pixel_values" + input_modalities = "image" + _can_record_outputs = { + "hidden_states": PaddleOCRVisionEncoderLayer, + "attentions": PaddleOCRVisionAttention, + } + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + + self.embeddings = PaddleOCRVisionEmbeddings(config) + self.encoder = PaddleOCRVisionEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + pixel_values: torch.FloatTensor, + cu_seqlens: torch.Tensor, + attention_mask: torch.Tensor | None = None, + image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + """ + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size * patch_size * image_channels)`): + The tensors corresponding to the input images. + cu_seqlens (`torch.Tensor` of shape `(num_images + 1,)`): + The cumulative sequence lengths of each image or video feature. + attention_mask (`torch.Tensor`, *optional*): + The attention_mask used in forward function shape [batch_size X sequence_length] if not None. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + hidden_states = self.embeddings(pixel_values, image_grid_thw=image_grid_thw) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + cu_seqlens=cu_seqlens, + attention_mask=attention_mask, + image_grid_thw=image_grid_thw, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=None, + ) + + +class PaddleOCRVisionModel(PaddleOCRVLPreTrainedModel): + config: PaddleOCRVisionConfig + main_input_name = "pixel_values" + input_modalities = "image" + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__(config) + + self.vision_model = PaddleOCRVisionTransformer(config) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + pixel_values: torch.FloatTensor, + cu_seqlens: torch.Tensor, + image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + """ + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, image_channels, patch_size, patch_size)`): + The tensors corresponding to the input images. + cu_seqlens (`torch.Tensor` of shape `(num_images + 1,)`): + The cumulative sequence lengths of each image or video feature. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + return self.vision_model( + pixel_values=pixel_values, + cu_seqlens=cu_seqlens, + image_grid_thw=image_grid_thw, + **kwargs, + ) + + +@auto_docstring( + custom_intro=""" + Base class for Llava outputs, with hidden states and attentions. + """ +) +@dataclass +class PaddleOCRVLModelOutputWithPast(ModelOutput): + r""" + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + last_hidden_state: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for PaddleOCRVL causal language model (or autoregressive) outputs. + """ +) +@dataclass +class PaddleOCRVLCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + + +@auto_docstring +class PaddleOCRVLModel(PaddleOCRVLPreTrainedModel): + base_model_prefix = "model" + # Reference: fix gemma3 grad acc #37208 + accepts_loss_kwargs = False + _keys_to_ignore_on_load_unexpected = ["packing_position_embedding", "vision_model.head"] + + def __init__(self, config: PaddleOCRVLConfig): + super().__init__(config) + self.visual = PaddleOCRVisionModel._from_config(config.vision_config) + self.language_model = PaddleOCRTextModel._from_config(config.text_config) + self.rope_deltas = None + self.projector = PaddleOCRProjector(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.language_model.embed_tokens + + def set_input_embeddings(self, value): + self.language_model.embed_tokens = value + + def get_vision_position_ids( + self, + start_position: int, + grid_thw: list[int, int, int] | torch.Tensor, + temp_merge_size: int = 1, + spatial_merge_size: int = 1, + time_interval: int = 1, + device: str | torch.device | None = None, + ): + """ + Compute 3D positional indices for vision tokens derived from a single image or video input. + + The positions are generated from the input grid defined by temporal (T), height (H), and + width (W) dimensions. Temporal and spatial dimensions can be downscaled according to the + merge sizes used in the vision backbone. The resulting positions are offset by `start_position`. + + Args: + start_position (`int`): + Offset added to all computed positional indices. + grid_thw (`Sequence[int]` or `torch.Tensor` of shape `(3,)`): + The (T, H, W) grid representing the feature layout of the current image or video after patch embedding. + temp_merge_size (`int`, *optional*): + Factor by which the temporal dimension is reduced in the backbone. The temporal grid size is divided + by this value. Defaults to 1. + spatial_merge_size (`int`, *optional*): + Factor by which the spatial dimensions (H and W) are reduced in the backbone. Both H and W are divided + by this value. Defaults to 1. + time_interval (`int`, *optional*): + Spacing factor applied between consecutive temporal position indices.Defaults to 1. + device (`str` or `torch.device`, *optional*): + Device on which the resulting tensor is allocated. If `None`, uses the current default device. + + Returns: + torch.LongTensor of shape (3, sequence_length): + Positional indices for temporal, height, and width dimensions, + flattened into sequence form and offset by `start_position`. + """ + llm_grid_t, llm_grid_h, llm_grid_w = ( + grid_thw[0].item() // temp_merge_size, + grid_thw[1].item() // spatial_merge_size, + grid_thw[2].item() // spatial_merge_size, + ) + + # Add `start_position` after arange for compile + position_temporal = torch.arange(llm_grid_t, device=device) * time_interval + position_width = torch.arange(llm_grid_w, device=device) + start_position + position_height = torch.arange(llm_grid_h, device=device) + start_position + + # Repeat the positions per each grid and per video frame. Repeat patterns are important + # do not modify without checking values! + position_width = position_width.repeat(llm_grid_h * llm_grid_t) + position_height = position_height.repeat_interleave(llm_grid_w).repeat(llm_grid_t) + # Important: add `start_positions` after applying `time_interval`, order matters + position_temporal = position_temporal.repeat_interleave(llm_grid_h * llm_grid_w) + start_position + vision_position_ids = torch.stack([position_temporal, position_height, position_width], dim=0) + + return vision_position_ids + + def get_rope_index( + self, + input_ids: torch.LongTensor, + mm_token_type_ids: torch.IntTensor, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Calculate the 3D rope index based on image and video's sizes. The utility expects a `vision + text` + sequence and will error out otherwise. For pure text sequence, please rely on model's auto-inferred + position ids. In a mixed vision + text sequence, vision tokens use 3D RoPE (temporal, height, width) + while text tokens use standard 1D RoPE. + + Example: + Temporal patches: 3; Height patches: 2; Width patches: 2 + Each vision input results in (temporal x height × width) positions. Here: 3 x 2 × 2 = 12 positions total. + + Temporal position IDs are spaced by: + `interval = tokens_per_second * temporal_patch_size / fps` + + If fps = 1; tokens_per_second = 25; temporal_patch_size = 2, temporal IDs increase by 50 for each temporal patch: + `[0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100]` + + Height IDs repeat per row: `[0, 0, 1, 1, ...]` + Width IDs alternate per column: `[0, 1, 0, 1, ...]` + Text tokens follow standard 1D RoPE and the position IDs grow consequently with a step of `1` + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + mm_token_type_ids (`torch.IntTensor` of shape `(batch_size, sequence_length)`): + Token type ids matching each modality to a different value in the input sequence, i.e. text (0), image (1), video (2). + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + Returns: + position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) + mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) + """ + spatial_merge_size = self.config.vision_config.spatial_merge_size + + mrope_position_deltas = [] + position_ids = torch.zeros( + 3, + input_ids.shape[0], + input_ids.shape[1], + dtype=input_ids.dtype, + device=input_ids.device, + ) + grid_iters = { + 1: iter(image_grid_thw) if image_grid_thw is not None else None, + 2: iter(video_grid_thw) if video_grid_thw is not None else None, + } + + for batch_idx, current_input_ids in enumerate(input_ids): + input_token_type = mm_token_type_ids[batch_idx] + if attention_mask is not None: + current_input_ids = current_input_ids[attention_mask[batch_idx].bool()] + input_token_type = input_token_type[attention_mask[batch_idx].bool()] + + input_type_group = [] + for key, group in itertools.groupby(enumerate(input_token_type.tolist()), lambda x: x[1]): + group = list(group) + start_index = group[0][0] + end_index = group[-1][0] + 1 + input_type_group.append((key, start_index, end_index)) + + current_pos = 0 + llm_pos_ids_list = [] + for modality_type, start_idx, end_idx in input_type_group: + # text == 0 + if modality_type == 0: + text_len = end_idx - start_idx + llm_pos_ids_list.append( + torch.arange(text_len, device=input_ids.device).view(1, -1).expand(3, -1) + current_pos + ) + current_pos += text_len + # image == 1, video == 2 + else: + grid_thw = next(grid_iters[modality_type]) + vision_position_ids = self.get_vision_position_ids( + current_pos, grid_thw, 1, spatial_merge_size, device=input_ids.device + ) + llm_pos_ids_list.append(vision_position_ids) + current_pos += max(grid_thw[1], grid_thw[2]) // spatial_merge_size + llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) + if attention_mask is not None: + position_ids[:, batch_idx, attention_mask[batch_idx].bool()] = llm_positions.to(position_ids.device) + else: + position_ids[:, batch_idx] = llm_positions.to(position_ids.device) + mrope_position_deltas.append(llm_positions.max() + 1 - len(current_input_ids)) + mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1) + return position_ids, mrope_position_deltas + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + pixel_values = pixel_values.type(self.visual.dtype).unsqueeze(0) + cu_seqlens = torch.repeat_interleave(image_grid_thw[:, 1] * image_grid_thw[:, 2], image_grid_thw[:, 0]).cumsum( + dim=0, + # Select dtype based on the following factors: + # - FA2 requires that cu_seqlens_q must have dtype int32 + # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw + # See https://github.com/huggingface/transformers/pull/34852 for more information + dtype=image_grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_seqlens = torch.nn.functional.pad(cu_seqlens, (1, 0), value=0) + vision_outputs = self.visual( + pixel_values=pixel_values, + image_grid_thw=image_grid_thw, + cu_seqlens=cu_seqlens, + return_dict=True, + **kwargs, + ) + image_embeds = vision_outputs.last_hidden_state + image_embeds = self.projector(image_embeds, image_grid_thw) + vision_outputs.pooler_output = image_embeds + + return vision_outputs + + def get_placeholder_mask( + self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + n_image_features = image_features.shape[0] * image_features.shape[1] + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}", + ) + return special_image_mask + + def compute_3d_position_ids( + self, + input_ids: torch.Tensor | None, + inputs_embeds: torch.Tensor | None, + image_grid_thw: torch.Tensor | None = None, + video_grid_thw: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: torch.Tensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + ) -> torch.Tensor | None: + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + has_multimodal = image_grid_thw is not None or video_grid_thw is not None + if has_multimodal and mm_token_type_ids is None and input_ids is not None: + raise ValueError( + "Multimodal data was passed (via `image_grid_thw` or `video_grid_thw`) but `mm_token_type_ids` is " + "missing. Please pass `mm_token_type_ids` to the model so that multimodal RoPE (M-RoPE) can be " + "computed correctly. `mm_token_type_ids` is returned by the processor alongside `input_ids`." + ) + can_compute_mrope = input_ids is not None and mm_token_type_ids is not None and has_multimodal + + if can_compute_mrope and (self.rope_deltas is None or past_key_values_length == 0): + position_ids, rope_deltas = self.get_rope_index( + input_ids, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + attention_mask=attention_mask, + mm_token_type_ids=mm_token_type_ids, + ) + self.rope_deltas = rope_deltas + # Use pre-calculated rope-deltas to infer correct 3D position ids during incremental + # generation (past_key_values_length > 0) or when only inputs_embeds is provided (no input_ids + # to recompute from). Skip when input_ids is provided without past_key_values to avoid shape + # mismatches from stale rope_deltas (e.g., training forward pass after generation). + elif self.rope_deltas is not None and (past_key_values_length > 0 or input_ids is None): + batch_size, seq_length, _ = inputs_embeds.shape + if attention_mask is not None: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids = position_ids.masked_fill(attention_mask == 0, 0) + position_ids = position_ids.view(1, batch_size, -1).repeat(3, 1, 1).to(inputs_embeds.device) + else: + position_ids = torch.arange(past_key_values_length, past_key_values_length + seq_length) + position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1).to(inputs_embeds.device) + delta = self.rope_deltas.repeat_interleave(batch_size // self.rope_deltas.shape[0], dim=0) + position_ids = position_ids + delta.to(device=inputs_embeds.device) + else: + # Can't build correct 3D positions. Let the model infer it + position_ids = None + return position_ids + + @can_return_tuple + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + pixel_values: torch.Tensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + **kwargs, + ) -> tuple | PaddleOCRVLModelOutputWithPast: + r""" + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + if inputs_embeds is None: + inputs_embeds = self.language_model.embed_tokens(input_ids) + + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw, return_dict=True).pooler_output + image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) + image_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) + + if position_ids is None: + position_ids = self.compute_3d_position_ids( + input_ids=input_ids, + image_grid_thw=image_grid_thw, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + mm_token_type_ids=mm_token_type_ids, + ) + + outputs = self.language_model( + input_ids=None, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + output = PaddleOCRVLModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + ) + + return output + + +class PaddleOCRVLForConditionalGeneration(PaddleOCRVLPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"} + _keys_to_ignore_on_load_unexpected = ["packing_position_embedding", "vision_model.head"] + + def __init__(self, config): + super().__init__(config) + self.model = PaddleOCRVLModel(config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + return self.model.get_image_features(pixel_values=pixel_values, image_grid_thw=image_grid_thw, **kwargs) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + pixel_values: torch.Tensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | PaddleOCRVLCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + + Example: + + ```python + >>> from transformers import AutoProcessor, PaddleOCRVLForConditionalGeneration + + >>> model = PaddleOCRVLForConditionalGeneration.from_pretrained("PaddlePaddle/PaddleOCR-VL", dtype="bfloat16") + >>> processor = AutoProcessor.from_pretrained("PaddlePaddle/PaddleOCR-VL") + + >>> messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/ocr_demo.jpg", + }, + {"type": "text", "text": "OCR:"}, + ], + } + ] + + >>> inputs = processor.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_dict=True, + return_tensors="pt" + ).to(model.device) + + >>> # Generate + >>> generated_ids = model.generate(**inputs, max_new_tokens=1024) + >>> generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)] + >>> output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + >>> print(output_text) + ``` + """ + outputs: PaddleOCRVLModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + image_grid_thw=image_grid_thw, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + pixel_values=pixel_values, + rope_deltas=rope_deltas, + mm_token_type_ids=mm_token_type_ids, + **kwargs, + ) + hidden_states = outputs.last_hidden_state + + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return PaddleOCRVLCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=outputs.rope_deltas, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + pixel_values=None, + pixel_values_videos=None, + image_grid_thw=None, + video_grid_thw=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + use_cache=use_cache, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + if not is_first_iteration and use_cache: + model_inputs["pixel_values"] = None + model_inputs["pixel_values_videos"] = None + + return model_inputs + + def _prepare_position_ids_for_generation(self, inputs_tensor, model_kwargs): + # Overwritten -- requires 3D position ids + + text_positions = super()._prepare_position_ids_for_generation(inputs_tensor, model_kwargs) + + # Early exit in case we are continuing generation from past kv + past_length = 0 + if (cache := model_kwargs.get("past_key_values")) is not None: + past_length = cache.get_seq_length() + if past_length != 0 and self.model.rope_deltas is not None: + position_ids = text_positions[None, ...] + self.model.rope_deltas + return position_ids + + # Otherwise compute 3d position ids for vision tokens and concat with text position ids + if "input_ids" in model_kwargs and model_kwargs["input_ids"].shape[1] > 0: + inputs_tensor = model_kwargs["input_ids"] + + is_input_ids = len(inputs_tensor.shape) == 2 and inputs_tensor.dtype in [torch.int, torch.long] + if ( + is_input_ids + and model_kwargs.get("mm_token_type_ids") is not None + and (model_kwargs.get("image_grid_thw") is not None or model_kwargs.get("video_grid_thw") is not None) + ): + model_kwargs = {k: v for k, v in model_kwargs.items() if k != "input_ids"} + vision_positions, rope_deltas = self.model.get_rope_index(inputs_tensor, **model_kwargs) + self.model.rope_deltas = rope_deltas + else: + vision_positions = text_positions.unsqueeze(0).expand(3, -1, -1) + self.model.rope_deltas = torch.zeros( + inputs_tensor.shape[0], 1, dtype=torch.long, device=inputs_tensor.device + ) + + # Concatenate "text + vision" positions into [4, bs, seq-len] + text_positions = text_positions[None, ...] + position_ids = torch.cat([text_positions, vision_positions], dim=0) + + return position_ids + + def _get_image_nums_and_video_nums( + self, + input_ids: torch.LongTensor | None, + inputs_embeds: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Get the number of images and videos for each sample to calculate the separation length of the sample tensor. + These parameters are not passed through the processor to avoid unpredictable impacts from interface modifications. + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Returns: + image_nums (`torch.LongTensor` of shape `(batch_size, num_images_sample)`) + video_nums (`torch.LongTensor` of shape `(batch_size, num_videos_sample)`) + """ + image_token_id = self.config.image_token_id + video_token_id = self.config.video_token_id + vision_start_token_id = self.config.vision_start_token_id + + if inputs_embeds is not None: + vision_start_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(vision_start_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + image_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + video_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + else: + vision_start_mask = input_ids == vision_start_token_id + image_mask = input_ids == image_token_id + video_mask = input_ids == video_token_id + + vision_first_mask = torch.roll(vision_start_mask, shifts=1, dims=1) + image_nums = torch.sum(vision_first_mask & image_mask, dim=1) + video_nums = torch.sum(vision_first_mask & video_mask, dim=1) + + return image_nums, video_nums + + def _expand_inputs_for_generation( + self, + expand_size: int = 1, + is_encoder_decoder: bool = False, + input_ids: torch.LongTensor | None = None, + **model_kwargs, + ) -> tuple[torch.LongTensor, dict[str, Any]]: + # Overwritten -- Support for expanding tensors without a batch size dimension + # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw, second_per_grid_t + # pixel_values.shape[0] is sum(seqlen_images for samples) + # image_grid_thw.shape[0] is sum(num_images for samples) + + if expand_size == 1: + return input_ids, model_kwargs + + visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw", "second_per_grid_ts"] + + def _expand_dict_for_generation_visual(dict_to_expand): + image_grid_thw = model_kwargs.get("image_grid_thw", None) + video_grid_thw = model_kwargs.get("video_grid_thw", None) + image_nums, video_nums = self._get_image_nums_and_video_nums( + input_ids, inputs_embeds=model_kwargs.get("inputs_embeds", None) + ) + + def _repeat_interleave_samples(x, lengths, repeat_times): + samples = torch.split(x, lengths) + repeat_args = [repeat_times] + [1] * (x.dim() - 1) + result = torch.cat([sample.repeat(*repeat_args) for sample in samples], dim=0) + return result + + for key in dict_to_expand: + if key == "pixel_values": + # split images into samples + samples = torch.split(image_grid_thw, list(image_nums)) + # compute the sequence length of images for each sample + lengths = [torch.prod(sample, dim=1).sum() for sample in samples] + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "image_grid_thw": + # get the num of images for each sample + lengths = list(image_nums) + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "pixel_values_videos": + samples = torch.split(video_grid_thw, list(video_nums)) + lengths = [torch.prod(sample, dim=1).sum() for sample in samples] + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "video_grid_thw": + lengths = list(video_nums) + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "second_per_grid_ts": + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=list(video_nums), repeat_times=expand_size + ) + return dict_to_expand + + def _expand_dict_for_generation(dict_to_expand): + for key in dict_to_expand: + if key == "position_ids" and dict_to_expand[key].ndim == 3: + dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=1) + elif ( + dict_to_expand[key] is not None + and isinstance(dict_to_expand[key], torch.Tensor) + and key not in visual_keys + ): + dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0) + return dict_to_expand + + model_kwargs = _expand_dict_for_generation_visual(model_kwargs) + + if input_ids is not None: + input_ids = input_ids.repeat_interleave(expand_size, dim=0) + + model_kwargs = _expand_dict_for_generation(model_kwargs) + + if is_encoder_decoder: + if model_kwargs.get("encoder_outputs") is None: + raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.") + model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"]) + + return input_ids, model_kwargs + + +__all__ = [ + "PaddleOCRVLForConditionalGeneration", + "PaddleOCRVLModel", + "PaddleOCRVLPreTrainedModel", + "PaddleOCRVisionTransformer", + "PaddleOCRTextModel", + "PaddleOCRVisionModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/modular_paddleocr_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/modular_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..02895d6e2576f2517aad3fda6301b361a75e60a2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/modular_paddleocr_vl.py @@ -0,0 +1,1203 @@ +# Copyright 2025 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import numpy as np +import torch +from huggingface_hub.dataclasses import strict +from torch import nn + +from ... import initialization as init +from ...activations import GELUActivation +from ...cache_utils import Cache, DynamicCache +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + ImageInput, + PILImageResampling, + SizeDict, +) +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPooling +from ...modeling_utils import PreTrainedModel +from ...models.qwen2_vl.image_processing_pil_qwen2_vl import Qwen2VLImageProcessorPil +from ...models.qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessor, Qwen2VLImageProcessorKwargs +from ...processing_utils import ( + ProcessingKwargs, + ProcessorMixin, + Unpack, +) +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import ( + TensorType, + TransformersKwargs, + auto_docstring, + can_return_tuple, + logging, + torch_compilable_check, + torch_int, +) +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..ernie4_5.configuration_ernie4_5 import Ernie4_5Config +from ..ernie4_5.modeling_ernie4_5 import ( + Ernie4_5DecoderLayer, + Ernie4_5MLP, + Ernie4_5Model, + Ernie4_5RMSNorm, +) +from ..qwen2_5_omni.modeling_qwen2_5_omni import ( + Qwen2_5OmniAttention, +) +from ..qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig +from ..qwen2_vl.modeling_qwen2_vl import ( + Qwen2VLCausalLMOutputWithPast, + Qwen2VLForConditionalGeneration, + Qwen2VLModel, + Qwen2VLModelOutputWithPast, + Qwen2VLRotaryEmbedding, + VisionRotaryEmbedding, +) +from ..siglip.configuration_siglip import SiglipVisionConfig +from ..siglip.modeling_siglip import ( + SiglipMLP, + SiglipVisionEmbeddings, +) +from ..video_llama_3.modeling_video_llama_3 import ( + VideoLlama3VisionAttention, + VideoLlama3VisionEncoder, + VideoLlama3VisionEncoderLayer, +) + + +logger = logging.get_logger(__name__) + + +def smart_resize( + height: int, + width: int, + factor: int = 28, + min_pixels: int = 384 * 384, + max_pixels: int = 1536 * 1536, +): + if height < factor: + width = round((width * factor) / height) + height = factor + + if width < factor: + height = round((height * factor) / width) + width = factor + + if max(height, width) / min(height, width) > 200: + raise ValueError( + f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}" + ) + h_bar = round(height / factor) * factor + w_bar = round(width / factor) * factor + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = max(factor, math.floor(height / beta / factor) * factor) + w_bar = max(factor, math.floor(width / beta / factor) * factor) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = math.ceil(height * beta / factor) * factor + w_bar = math.ceil(width * beta / factor) * factor + return h_bar, w_bar + + +class PaddleOCRVLImageProcessorKwargs(Qwen2VLImageProcessorKwargs): + r""" + patch_size (`int`, *optional*, defaults to 14): + The spatial patch size of the vision encoder. + temporal_patch_size (`int`, *optional*, defaults to 1): + The temporal patch size of the vision encoder. + merge_size (`int`, *optional*, defaults to 2): + The merge size of the vision encoder to llm encoder. + """ + + +class PaddleOCRVLImageProcessorPil(Qwen2VLImageProcessorPil): + size = {"shortest_edge": 384 * 384, "longest_edge": 1536 * 1536} + temporal_patch_size = 1 + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + patch_size: int, + temporal_patch_size: int, + merge_size: int, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + all_patches = [] + all_grids = [] + + for image in images: + height, width = image.shape[-2:] + if do_resize: + resized_height, resized_width = smart_resize( + height, + width, + factor=patch_size * merge_size, + min_pixels=size.shortest_edge, + max_pixels=size.longest_edge, + ) + image = self.resize( + image, + size=SizeDict(height=resized_height, width=resized_width), + resample=resample, + ) + else: + resized_height, resized_width = height, width + + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + + patches = np.expand_dims(image, axis=0) + if patches.ndim == 4: + patches = np.expand_dims(patches, axis=1) + if patches.shape[1] % temporal_patch_size != 0: + repeats = np.repeat( + patches[:, -1:], temporal_patch_size - (patches.shape[1] % temporal_patch_size), axis=1 + ) + patches = np.concatenate([patches, repeats], axis=1) + + batch_size = 1 + grid_t = patches.shape[1] // temporal_patch_size + channel = patches.shape[2] + grid_h, grid_w = resized_height // patch_size, resized_width // patch_size + + patches = patches.reshape( + batch_size, + grid_t, + temporal_patch_size, + channel, + grid_h, + patch_size, + grid_w, + patch_size, + ) + patches = patches.transpose(0, 1, 4, 6, 3, 2, 5, 7) + flatten_patches = patches.reshape(batch_size, grid_t * grid_h * grid_w, channel, patch_size, patch_size) + + all_patches.append(flatten_patches.squeeze(0)) + all_grids.append([grid_t, grid_h, grid_w]) + + pixel_values = np.concatenate(all_patches, axis=0) + image_grid_thw = np.array(all_grids, dtype=np.int64) + + return BatchFeature( + data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors + ) + + def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None): + """ + A utility that returns number of image patches for a given image size. + + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + images_kwargs (`dict`, *optional*) + Any kwargs to override defaults of the image processor. + Returns: + `int`: Number of image patches per image. + """ + min_pixels = images_kwargs["min_pixels"] if "min_pixels" in images_kwargs else self.size["shortest_edge"] + max_pixels = images_kwargs["max_pixels"] if "max_pixels" in images_kwargs else self.size["longest_edge"] + patch_size = images_kwargs.get("patch_size", self.patch_size) + merge_size = images_kwargs.get("merge_size", self.merge_size) + + factor = patch_size * merge_size + resized_height, resized_width = smart_resize( + height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels + ) + grid_h, grid_w = resized_height // patch_size, resized_width // patch_size + return grid_h * grid_w + + +class PaddleOCRVLImageProcessor(Qwen2VLImageProcessor): + size = {"shortest_edge": 384 * 384, "longest_edge": 1536 * 1536} + temporal_patch_size = 1 + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + patch_size: int, + temporal_patch_size: int, + merge_size: int, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + height, width = stacked_images.shape[-2:] + if do_resize: + resized_height, resized_width = smart_resize( + height, + width, + factor=patch_size * merge_size, + min_pixels=size.shortest_edge, + max_pixels=size.longest_edge, + ) + stacked_images = self.resize( + image=stacked_images, + size=SizeDict(height=resized_height, width=resized_width), + resample=resample, + ) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + processed_grids = {} + for shape, stacked_images in grouped_images.items(): + resized_height, resized_width = stacked_images.shape[-2:] + patches = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + if patches.ndim == 4: + patches = patches.unsqueeze(1) + if patches.shape[1] % temporal_patch_size != 0: + repeats = patches[:, -1:].repeat(1, temporal_patch_size - 1, 1, 1, 1) + patches = torch.cat([patches, repeats], dim=1) + + batch_size, grid_t, channel = patches.shape[:3] + grid_t = grid_t // temporal_patch_size + grid_h, grid_w = resized_height // patch_size, resized_width // patch_size + + patches = patches.view( + batch_size, + grid_t, + temporal_patch_size, + channel, + grid_h, + patch_size, + grid_w, + patch_size, + ) + patches = patches.permute(0, 1, 4, 6, 3, 2, 5, 7) + flatten_patches = patches.reshape(batch_size, grid_t * grid_h * grid_w, channel, patch_size, patch_size) + + processed_images_grouped[shape] = flatten_patches + processed_grids[shape] = [[grid_t, grid_h, grid_w]] * batch_size + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + processed_grids = reorder_images(processed_grids, grouped_images_index) + pixel_values = torch.cat(processed_images, dim=0) + image_grid_thw = torch.tensor(processed_grids) + + return BatchFeature( + data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors + ) + + def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None): + """ + A utility that returns number of image patches for a given image size. + + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + images_kwargs (`dict`, *optional*) + Any kwargs to override defaults of the image processor. + Returns: + `int`: Number of image patches per image. + """ + min_pixels = images_kwargs["min_pixels"] if "min_pixels" in images_kwargs else self.size["shortest_edge"] + max_pixels = images_kwargs["max_pixels"] if "max_pixels" in images_kwargs else self.size["longest_edge"] + patch_size = images_kwargs.get("patch_size", self.patch_size) + merge_size = images_kwargs.get("merge_size", self.merge_size) + + factor = patch_size * merge_size + resized_height, resized_width = smart_resize( + height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels + ) + grid_h, grid_w = resized_height // patch_size, resized_width // patch_size + return grid_h * grid_w + + +class PaddleOCRVLProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": False, + "return_mm_token_type_ids": True, + }, + } + + +class PaddleOCRVLProcessor(ProcessorMixin): + r""" + [`PaddleOCRVLProcessor`] offers all the functionalities of [`PaddleOCRVLImageProcessor`] and [`LLamaTokenizerFast`]. See the + [`~PaddleOCRVLProcessor.__call__`] and [`~PaddleOCRVLProcessor.decode`] for more information. + Args: + image_processor ([`PaddleOCRVLImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`LLamaTokenizerFast`], *optional*): + The tokenizer is a required input. + chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages + in a chat into a tokenizable string. + """ + + image_processor_class = "AutoImageProcessor" + tokenizer_class = "AutoTokenizer" + + def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): + self.image_token = tokenizer.image_token + self.image_token_id = tokenizer.image_token_id + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + def __call__( + self, + images: ImageInput = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[PaddleOCRVLProcessorKwargs], + ) -> BatchFeature: + """ + Args: + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. Both channels-first and channels-last formats are supported. + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. + """ + output_kwargs = self._merge_kwargs( + PaddleOCRVLProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + if images is not None: + image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + image_grid_thw = image_inputs["image_grid_thw"] + + else: + image_inputs = {} + image_grid_thw = None + + if not isinstance(text, list): + text = [text] + + text = text.copy() + + if image_grid_thw is not None: + index = 0 + for i in range(len(text)): + while self.image_token in text[i]: + text[i] = text[i].replace( + self.image_token, + "<|placeholder|>" + * ( + image_grid_thw[index].prod() + // self.image_processor.merge_size + // self.image_processor.merge_size + ), + 1, + ) + index += 1 + text[i] = text[i].replace("<|placeholder|>", self.image_token) + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False) + text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None) + + if return_mm_token_type_ids: + text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"]) + return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors) + + +@auto_docstring(checkpoint="PaddlePaddle/PaddleOCR-VL") +@strict +class PaddleOCRVisionConfig(SiglipVisionConfig): + r""" + Example: + + ```python + >>> from transformers import PaddleOCRVisionConfig, PaddleOCRVisionModel + + >>> # Initializing a PaddleOCRVisionConfig with PaddlePaddle/PaddleOCR-VL style configuration + >>> configuration = PaddleOCRVisionConfig() + + >>> # Initializing a PaddleOCRVisionModel (with random weights) from the PaddlePaddle/PaddleOCR-VL style configuration + >>> model = PaddleOCRVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "paddleocr_vl_vision" + base_config_key = "vision_config" + + hidden_size: int = 1152 + intermediate_size: int = 4304 + num_hidden_layers: int = 27 + num_attention_heads: int = 16 + image_size: int = 384 + patch_size: int = 14 + spatial_merge_size: int = 2 + + +@auto_docstring(checkpoint="PaddlePaddle/PaddleOCR-VL") +@strict +class PaddleOCRTextConfig(Ernie4_5Config): + model_type = "paddleocr_vl_text" + + +@auto_docstring(checkpoint="PaddlePaddle/PaddleOCR-VL") +@strict +class PaddleOCRVLConfig(Qwen2VLConfig): + r""" + Example: + + ```python + >>> from transformers import PaddleOCRVLForConditionalGeneration, PaddleOCRVLConfig + + >>> # Initializing a PaddleOCRVL style configuration + >>> configuration = PaddleOCRVLConfig() + + >>> # Initializing a model from the PaddleOCRVL style configuration + >>> model = PaddleOCRVLForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + sub_configs = {"vision_config": PaddleOCRVisionConfig, "text_config": PaddleOCRTextConfig} + + image_token_id: int = 100295 + video_token_id: int = 100296 + vision_start_token_id: int = 101305 + vision_end_token_id: int = 101306 + tie_word_embeddings: bool = True + + +class PaddleOCRProjector(nn.Module): + def __init__(self, config: PaddleOCRVLConfig): + super().__init__() + self.merge_kernel_size = (config.vision_config.spatial_merge_size, config.vision_config.spatial_merge_size) + + hidden_size = config.vision_config.hidden_size * self.merge_kernel_size[0] * self.merge_kernel_size[1] + + self.pre_norm = torch.nn.LayerNorm(config.vision_config.hidden_size, eps=1e-05) + self.linear_1 = nn.Linear(hidden_size, hidden_size, bias=True) + self.act = GELUActivation() + self.linear_2 = nn.Linear(hidden_size, config.text_config.hidden_size, bias=True) + + def forward(self, image_features: torch.Tensor, image_grid_thw: torch.Tensor) -> torch.Tensor: + image_features_chunks = image_features.split(image_grid_thw.prod(dim=1).tolist(), dim=0) + m1, m2 = self.merge_kernel_size + + processed_features = [] + for image_feature, image_grid in zip(image_features_chunks, image_grid_thw): + image_feature = self.pre_norm(image_feature) + t, h, w = image_grid + d = image_feature.shape[-1] + h_block = h // m1 + w_block = w // m2 + + image_feature = image_feature.reshape(t, h_block, m1, w_block, m2, d) + image_feature = image_feature.transpose(2, 3) + image_feature = image_feature.reshape(t * h_block * w_block, m1 * m2 * d) + + hidden_states = self.linear_1(image_feature) + hidden_states = self.act(hidden_states) + hidden_states = self.linear_2(hidden_states) + processed_features.append(hidden_states) + + return torch.cat(processed_features, dim=0) + + +class PaddleOCRVisionRotaryEmbedding(VisionRotaryEmbedding): + pass + + +class PaddleOCRRotaryEmbedding(Qwen2VLRotaryEmbedding): + pass + + +class PaddleOCRMLP(Ernie4_5MLP): + def __init__(self, config: PaddleOCRTextConfig): + super().__init__() + + +class PaddleOCRAttention(Qwen2_5OmniAttention): + def __init__(self, config: PaddleOCRVLConfig, layer_idx: int | None = None): + super().__init__() + + self.attention_dropout = 0.0 + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.use_bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.use_bias) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.use_bias) + + +class PaddleOCRRMSNorm(Ernie4_5RMSNorm): + pass + + +class PaddleOCRDecoderLayer(Ernie4_5DecoderLayer): + def __init__(self, config: PaddleOCRTextConfig, layer_idx: int): + super().__init__() + + +@auto_docstring +class PaddleOCRVLPreTrainedModel(PreTrainedModel): + config: PaddleOCRVLConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PaddleOCRDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + + _can_record_outputs = { + "hidden_states": PaddleOCRDecoderLayer, + "attentions": PaddleOCRAttention, + } + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, PaddleOCRVisionEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, PaddleOCRVisionRotaryEmbedding): + inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim)) + init.copy_(module.inv_freq, inv_freq) + + +class PaddleOCRTextModel(PaddleOCRVLPreTrainedModel, Ernie4_5Model): + def __init__(self, config: PaddleOCRTextConfig): + super().__init__(config) + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) + elif position_ids.ndim == 2: + position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) + + if position_ids.ndim == 3 and position_ids.shape[0] == 4: + text_position_ids = position_ids[0] + position_ids = position_ids[1:] + else: + text_position_ids = None + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=text_position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_embeddings=position_embeddings, + position_ids=text_position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class PaddleOCRVisionEmbeddings(SiglipVisionEmbeddings): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + num_positions = self.position_embedding.weight.shape[0] + + patch_pos_embed = self.position_embedding.weight.unsqueeze(0) + + dim = embeddings.shape[-1] + + sqrt_num_positions = torch_int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(height, width), + mode="bilinear", + align_corners=False, + ) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return patch_pos_embed + + def forward( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]] | None = None, + ) -> torch.Tensor: + """ + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, image_channels, patch_size, patch_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + batch_size, squence_len, channel, height, width = pixel_values.shape + target_dtype = self.patch_embedding.weight.dtype + pixel_values = pixel_values.reshape(batch_size * squence_len, channel, height, width) + patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] + embeddings = patch_embeds.flatten(-2).squeeze(-1) + embeddings = embeddings.reshape(batch_size, squence_len, -1) + + start = 0 + embeddings = embeddings.squeeze(0) + tmp_embeddings = [] + for image_grid in image_grid_thw: + t, h, w = image_grid + end = start + t * h * w + image_embeddings = embeddings[start:end, :] + position_embedding = self.interpolate_pos_encoding(image_embeddings, h, w).squeeze(0).repeat(t, 1) + image_embeddings = image_embeddings + position_embedding + tmp_embeddings.append(image_embeddings) + start = end + embeddings = torch.concat(tmp_embeddings, dim=0) + + return embeddings + + +class PaddleOCRVisionAttention(VideoLlama3VisionAttention): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + + +class PaddleOCRVisionMLP(SiglipMLP): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + + +class PaddleOCRVisionEncoderLayer(VideoLlama3VisionEncoderLayer): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + + +class PaddleOCRVisionEncoder(VideoLlama3VisionEncoder): + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__() + embed_dim = config.hidden_size + num_heads = config.num_attention_heads + head_dim = embed_dim // num_heads + self.rotary_pos_emb = PaddleOCRVisionRotaryEmbedding(head_dim // 2) + + def forward( + self, + inputs_embeds: torch.FloatTensor, + cu_seqlens: torch.Tensor, + attention_mask: torch.Tensor | None = None, + image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + cu_seqlens (`torch.Tensor` of shape `(num_images + 1,)`): + The cumulative sequence lengths of each image or video feature. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + The attention_mask used in forward function shape [batch_size X sequence_length] if not None. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + device = inputs_embeds.device + hidden_states = inputs_embeds + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + split_hids = [] + split_wids = [] + for t, h, w in image_grid_thw: + image_pids = torch.arange(t * h * w, device=device) % (h * w) + sample_hids = image_pids // w + sample_wids = image_pids % w + split_hids.append(sample_hids) + split_wids.append(sample_wids) + width_position_ids = torch.concat(split_wids, dim=0) + height_position_ids = torch.concat(split_hids, dim=0) + + pids = torch.stack([height_position_ids, width_position_ids], dim=-1) + max_grid_size = pids.max() + 1 + rotary_embeddings_max_grid = self.rotary_pos_emb(max_grid_size) + rotary_embeddings = rotary_embeddings_max_grid[pids].flatten(1) + rotary_embeddings = rotary_embeddings.repeat(1, 2) + position_embeddings = (rotary_embeddings.cos(), rotary_embeddings.sin()) + + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + cu_seqlens=cu_seqlens, + position_embeddings=position_embeddings, + **kwargs, + ) + + return BaseModelOutput( + last_hidden_state=hidden_states, + ) + + +class PaddleOCRVisionTransformer(PaddleOCRVLPreTrainedModel): + config: PaddleOCRVisionConfig + main_input_name = "pixel_values" + input_modalities = "image" + _can_record_outputs = { + "hidden_states": PaddleOCRVisionEncoderLayer, + "attentions": PaddleOCRVisionAttention, + } + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + + self.embeddings = PaddleOCRVisionEmbeddings(config) + self.encoder = PaddleOCRVisionEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + pixel_values: torch.FloatTensor, + cu_seqlens: torch.Tensor, + attention_mask: torch.Tensor | None = None, + image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + """ + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size * patch_size * image_channels)`): + The tensors corresponding to the input images. + cu_seqlens (`torch.Tensor` of shape `(num_images + 1,)`): + The cumulative sequence lengths of each image or video feature. + attention_mask (`torch.Tensor`, *optional*): + The attention_mask used in forward function shape [batch_size X sequence_length] if not None. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + hidden_states = self.embeddings(pixel_values, image_grid_thw=image_grid_thw) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + cu_seqlens=cu_seqlens, + attention_mask=attention_mask, + image_grid_thw=image_grid_thw, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=None, + ) + + +class PaddleOCRVisionModel(PaddleOCRVLPreTrainedModel): + config: PaddleOCRVisionConfig + main_input_name = "pixel_values" + input_modalities = "image" + + def __init__(self, config: PaddleOCRVisionConfig): + super().__init__(config) + + self.vision_model = PaddleOCRVisionTransformer(config) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + pixel_values: torch.FloatTensor, + cu_seqlens: torch.Tensor, + image_grid_thw: list[tuple[int, int, int] | list[tuple[int, int, int]]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + """ + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, image_channels, patch_size, patch_size)`): + The tensors corresponding to the input images. + cu_seqlens (`torch.Tensor` of shape `(num_images + 1,)`): + The cumulative sequence lengths of each image or video feature. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + return self.vision_model( + pixel_values=pixel_values, + cu_seqlens=cu_seqlens, + image_grid_thw=image_grid_thw, + **kwargs, + ) + + +class PaddleOCRVLModelOutputWithPast(Qwen2VLModelOutputWithPast): + pass + + +class PaddleOCRVLCausalLMOutputWithPast(Qwen2VLCausalLMOutputWithPast): + pass + + +class PaddleOCRVLModel(Qwen2VLModel): + _keys_to_ignore_on_load_unexpected = ["packing_position_embedding", "vision_model.head"] + + def __init__(self, config: PaddleOCRVLConfig): + super().__init__(config) + self.visual = PaddleOCRVisionModel._from_config(config.vision_config) + self.projector = PaddleOCRProjector(config) + self.language_model = PaddleOCRTextModel._from_config(config.text_config) + self.rope_deltas = None + + self.post_init() + + def get_input_embeddings(self): + return self.language_model.embed_tokens + + def set_input_embeddings(self, value): + self.language_model.embed_tokens = value + + def get_video_features(self): + raise AttributeError("PaddleOCRVLModel does not support video.") + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + pixel_values = pixel_values.type(self.visual.dtype).unsqueeze(0) + cu_seqlens = torch.repeat_interleave(image_grid_thw[:, 1] * image_grid_thw[:, 2], image_grid_thw[:, 0]).cumsum( + dim=0, + # Select dtype based on the following factors: + # - FA2 requires that cu_seqlens_q must have dtype int32 + # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw + # See https://github.com/huggingface/transformers/pull/34852 for more information + dtype=image_grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_seqlens = torch.nn.functional.pad(cu_seqlens, (1, 0), value=0) + vision_outputs = self.visual( + pixel_values=pixel_values, + image_grid_thw=image_grid_thw, + cu_seqlens=cu_seqlens, + return_dict=True, + **kwargs, + ) + image_embeds = vision_outputs.last_hidden_state + image_embeds = self.projector(image_embeds, image_grid_thw) + vision_outputs.pooler_output = image_embeds + + return vision_outputs + + def get_placeholder_mask( + self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + n_image_features = image_features.shape[0] * image_features.shape[1] + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}", + ) + return special_image_mask + + @can_return_tuple + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + pixel_values: torch.Tensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + **kwargs, + ) -> tuple | PaddleOCRVLModelOutputWithPast: + r""" + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + if inputs_embeds is None: + inputs_embeds = self.language_model.embed_tokens(input_ids) + + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw, return_dict=True).pooler_output + image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) + image_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) + + if position_ids is None: + position_ids = self.compute_3d_position_ids( + input_ids=input_ids, + image_grid_thw=image_grid_thw, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + mm_token_type_ids=mm_token_type_ids, + ) + + outputs = self.language_model( + input_ids=None, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + output = PaddleOCRVLModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + ) + + return output + + +class PaddleOCRVLForConditionalGeneration(Qwen2VLForConditionalGeneration): + _keys_to_ignore_on_load_unexpected = ["packing_position_embedding", "vision_model.head"] + + def get_video_features(self): + raise AttributeError("PaddleOCRVLForConditionalGeneration does not support video.") + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + pixel_values: torch.Tensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | PaddleOCRVLCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + + Example: + + ```python + >>> from transformers import AutoProcessor, PaddleOCRVLForConditionalGeneration + + >>> model = PaddleOCRVLForConditionalGeneration.from_pretrained("PaddlePaddle/PaddleOCR-VL", dtype="bfloat16") + >>> processor = AutoProcessor.from_pretrained("PaddlePaddle/PaddleOCR-VL") + + >>> messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/ocr_demo.jpg", + }, + {"type": "text", "text": "OCR:"}, + ], + } + ] + + >>> inputs = processor.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_dict=True, + return_tensors="pt" + ).to(model.device) + + >>> # Generate + >>> generated_ids = model.generate(**inputs, max_new_tokens=1024) + >>> generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)] + >>> output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + >>> print(output_text) + ``` + """ + outputs: PaddleOCRVLModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + image_grid_thw=image_grid_thw, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + pixel_values=pixel_values, + rope_deltas=rope_deltas, + mm_token_type_ids=mm_token_type_ids, + **kwargs, + ) + hidden_states = outputs.last_hidden_state + + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return PaddleOCRVLCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=outputs.rope_deltas, + ) + + +__all__ = [ + "PaddleOCRVLForConditionalGeneration", + "PaddleOCRVLModel", + "PaddleOCRVLPreTrainedModel", + "PaddleOCRVisionTransformer", + "PaddleOCRVLConfig", + "PaddleOCRTextModel", + "PaddleOCRVisionModel", + "PaddleOCRVisionConfig", + "PaddleOCRTextConfig", + "PaddleOCRVLImageProcessor", + "PaddleOCRVLImageProcessorPil", + "PaddleOCRVLProcessor", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/processing_paddleocr_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/processing_paddleocr_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..5a71289e0188df72cf5805e33315dd43123d183b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paddleocr_vl/processing_paddleocr_vl.py @@ -0,0 +1,139 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_paddleocr_vl.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput + + +class PaddleOCRVLProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": False, + "return_mm_token_type_ids": True, + }, + } + + +class PaddleOCRVLProcessor(ProcessorMixin): + r""" + [`PaddleOCRVLProcessor`] offers all the functionalities of [`PaddleOCRVLImageProcessor`] and [`LLamaTokenizerFast`]. See the + [`~PaddleOCRVLProcessor.__call__`] and [`~PaddleOCRVLProcessor.decode`] for more information. + Args: + image_processor ([`PaddleOCRVLImageProcessor`], *optional*): + The image processor is a required input. + tokenizer ([`LLamaTokenizerFast`], *optional*): + The tokenizer is a required input. + chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages + in a chat into a tokenizable string. + """ + + image_processor_class = "AutoImageProcessor" + tokenizer_class = "AutoTokenizer" + + def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): + self.image_token = tokenizer.image_token + self.image_token_id = tokenizer.image_token_id + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + def __call__( + self, + images: ImageInput = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[PaddleOCRVLProcessorKwargs], + ) -> BatchFeature: + """ + Args: + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. Both channels-first and channels-last formats are supported. + text (`str`, `List[str]`, `List[List[str]]`): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors of a particular framework. Acceptable values are: + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return NumPy `np.ndarray` objects. + - `'jax'`: Return JAX `jnp.ndarray` objects. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. + """ + output_kwargs = self._merge_kwargs( + PaddleOCRVLProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + if images is not None: + image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + image_grid_thw = image_inputs["image_grid_thw"] + + else: + image_inputs = {} + image_grid_thw = None + + if not isinstance(text, list): + text = [text] + + text = text.copy() + + if image_grid_thw is not None: + index = 0 + for i in range(len(text)): + while self.image_token in text[i]: + text[i] = text[i].replace( + self.image_token, + "<|placeholder|>" + * ( + image_grid_thw[index].prod() + // self.image_processor.merge_size + // self.image_processor.merge_size + ), + 1, + ) + index += 1 + text[i] = text[i].replace("<|placeholder|>", self.image_token) + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False) + text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None) + + if return_mm_token_type_ids: + text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"]) + return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors) + + +__all__ = ["PaddleOCRVLProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..9048afe6adbdc0ad36007e02f60e899cae677c55 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_paligemma import * + from .modeling_paligemma import * + from .processing_paligemma import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/configuration_paligemma.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/configuration_paligemma.py new file mode 100644 index 0000000000000000000000000000000000000000..9ed9465f51ff639ffbb943fb9fdf48636b764ded --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/configuration_paligemma.py @@ -0,0 +1,102 @@ +# Copyright 2024 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PaliGemmamodel configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="google/paligemma-3b-pt-224") +@strict +class PaliGemmaConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PaliGemmaForConditionalGeneration, PaliGemmaConfig, SiglipVisionConfig, GemmaConfig + + >>> # Initializing a Siglip-like vision config + >>> vision_config = SiglipVisionConfig() + + >>> # Initializing a PaliGemma config + >>> text_config = GemmaConfig() + + >>> # Initializing a PaliGemma paligemma-3b-224 style configuration + >>> configuration = PaliGemmaConfig(vision_config, text_config) + + >>> # Initializing a model from the paligemma-3b-224 style configuration + >>> model = PaliGemmaForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "paligemma" + attribute_map = { + "image_token_id": "image_token_index", + } + sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig} + keys_to_ignore_at_inference = ["past_key_values"] + + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + image_token_index: int = 256000 + vocab_size: int = 257152 + projection_dim: int = 2048 + hidden_size: int = 2048 + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "siglip_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["siglip_vision_model"]( + intermediate_size=4096, + hidden_size=1152, + patch_size=14, + image_size=224, + num_hidden_layers=27, + num_attention_heads=16, + vocab_size=257152, + vision_use_head=False, + ) + + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "gemma") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["gemma"]( + hidden_size=2048, + num_hidden_layers=18, + intermediate_size=16384, + num_attention_heads=8, + num_key_value_heads=1, + is_encoder_decoder=False, + vocab_size=self.vocab_size, + ) + + # BC: `use_bidirectional_attention` was originally unset in PaliGemma1 (backbone = Gemma1) AND PaliGemma2 + # (backbone = Gemma2). Both PaliGemmas want to default to True. + if self.text_config.use_bidirectional_attention is None: + self.text_config.use_bidirectional_attention = True + + self.text_config.num_image_tokens = (self.vision_config.image_size // self.vision_config.patch_size) ** 2 + self.vision_config.projection_dim = self.projection_dim + super().__post_init__(**kwargs) + + +__all__ = ["PaliGemmaConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/modeling_paligemma.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/modeling_paligemma.py new file mode 100644 index 0000000000000000000000000000000000000000..37665f7fcb1a0981d2d30390d44187cd681c8434 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/modeling_paligemma.py @@ -0,0 +1,551 @@ +# Copyright 2024 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PaliGemmamodel.""" + +from collections.abc import Callable +from dataclasses import dataclass + +import torch +from torch import nn + +from ...cache_utils import Cache +from ...configuration_utils import PreTrainedConfig +from ...generation import GenerationMixin +from ...masking_utils import create_masks_for_generate +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + can_return_tuple, + logging, + torch_compilable_check, +) +from ...utils.deprecation import deprecate_kwarg +from ..auto import AutoModel +from .configuration_paligemma import PaliGemmaConfig + + +logger = logging.get_logger(__name__) + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for Paligemma outputs, with hidden states and attentions. + """ +) +class PaligemmaModelOutputWithPast(BaseModelOutputWithPast): + r""" + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + image_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for PaliGemma causal language model (or autoregressive) outputs. + """ +) +@dataclass +class PaliGemmaCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.text_config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder after projecting last hidden state. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + image_hidden_states: torch.FloatTensor | None = None + + +class PaliGemmaMultiModalProjector(nn.Module): + def __init__(self, config: PaliGemmaConfig): + super().__init__() + self.linear = nn.Linear(config.vision_config.hidden_size, config.vision_config.projection_dim, bias=True) + + def forward(self, image_features): + hidden_states = self.linear(image_features) + + return hidden_states + + +def token_type_ids_mask_function(group_ids: torch.Tensor) -> Callable: + """ + This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths, + not start and end indices. + Args: + group_ids (`torch.Tensor`): + A tensor of shape `(bs, len)` assigning each token to a vision group. Tokens with the same group + come from the same input image. Text is denoted by `-1`. + """ + + def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool: + seq_length = group_ids.shape[-1] + + # clamp indices because with static cache they can go beyond `group_ids.shape[-1]` + q_idx_clamped = q_idx.clamp(max=seq_length - 1) + kv_idx_clamped = kv_idx.clamp(max=seq_length - 1) + + # Unmask if the q and kv come from same group which is not -1 (i.e. non-text) + q_group = group_ids[batch_idx, q_idx_clamped] + kv_group = group_ids[batch_idx, kv_idx_clamped] + q_group = torch.where(q_idx < seq_length, q_group, -1) + kv_group = torch.where(kv_idx < seq_length, kv_group, -1) + return (q_group == kv_group) & (q_group >= 0) + + return inner_mask + + +@deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds") +def create_causal_mask_mapping( + config: PreTrainedConfig, + inputs_embeds: torch.Tensor, + attention_mask: torch.Tensor | None, + past_key_values: Cache | None, + position_ids: torch.Tensor | None, + token_type_ids: torch.Tensor | None = None, + pixel_values: torch.FloatTensor | None = None, + is_training: bool | None = False, + is_first_iteration: bool | None = None, + **kwargs, +) -> dict: + """ + Overwrites the base `create_masks_for_generate` with `token_type_ids` masking to create the causal mask mapping + for all kinds of forward passes. Paligemma uses a bidirectional mask on the prompt tokens. + + Uses `pixel_values` as an optional input to disambiguate edge cases. + """ + if is_training and token_type_ids is None: + raise ValueError("`token_type_ids` is required as a model input when training") + + mask_kwargs = { + "config": config.get_text_config(), + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": position_ids, + } + # Infer if prefill or decoding stage, if the flag isn't passed. This happens only when the mask is constructed + # from `forward` call. If users run a `forward` call, we have no option to infer `is_first_iteration` because users may be + # running generation with custom loop. Thus we need to infer it in a `non-perfect` way + # NOTE: Determining prefill in that case requires checking data values, which is not compile-compatible. + is_first_iteration = ( + is_first_iteration + if is_first_iteration + else (past_key_values is None or not past_key_values.is_initialized or pixel_values is not None) + ) + + if is_first_iteration or not kwargs.get("use_cache", True): + if token_type_ids is not None: + # The logic bellow was originally written for Gemma3, where `token_type_ids` is reversed. Let's reverse + # it to then use exactly the same logic. + token_type_ids = 1 - token_type_ids + else: + logger.warning_once( + "It is a prefill stage but The `token_type_ids` is not provided. We recommend " + "passing `token_type_ids` to the model to prevent bad attention masking." + ) + # NOTE: this branch can't be reached when training because `token_type_ids` is required as a model input. + token_type_ids = torch.ones_like(inputs_embeds)[:, :, 0] + + # Logic originally copied from Gemma3. It holds up for Paligemma as well because Paligemma assumes up to one image + # per prompt AND we reverse `token_type_ids` above. Gemma3 uses a bidirectional mask for images, tagged through + # `token_type_ids` 1s. + if token_type_ids is not None and is_first_iteration: + # We need to pass an additional mask function to account for token type ids, and it needs to be an `or` (to + # undo the causal masking) + + # First find where a new image block starts: 1 if image and previous not image + # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally + is_image = (token_type_ids == 1).to(inputs_embeds.device) + is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1] + new_image_start = is_image & ~is_previous_image + group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1 + group_ids = torch.where(is_image, group_ids, torch.full_like(token_type_ids, -1)) + mask_kwargs["or_mask_function"] = token_type_ids_mask_function(group_ids) + + return create_masks_for_generate(**mask_kwargs) + + +@auto_docstring +class PaliGemmaPreTrainedModel(PreTrainedModel): + config: PaliGemmaConfig + base_model_prefix = "model" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + _no_split_modules = ["PaliGemmaMultiModalProjector"] + _skip_keys_device_placement = "past_key_values" + _can_compile_fullgraph = False + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + + +@auto_docstring( + custom_intro=""" + The Base Paligemma model which consists of a vision backbone and a language model without language modeling head., + """ +) +class PaliGemmaModel(PaliGemmaPreTrainedModel): + # we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch + accepts_loss_kwargs = False + + def __init__(self, config: PaliGemmaConfig): + super().__init__(config) + self.vision_tower = AutoModel.from_config(config=config.vision_config) + self.multi_modal_projector = PaliGemmaMultiModalProjector(config) + self.vocab_size = config.text_config.vocab_size + + language_model = AutoModel.from_config(config=config.text_config) + self.language_model = language_model + + self.text_config_dtype = self.config.get_text_config().dtype or self.dtype + self.post_init() + + # Copied from transformers.models.llava.modeling_llava.LlavaModel.get_input_embeddings with Llava->PaliGemma + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + # Copied from transformers.models.llava.modeling_llava.LlavaModel.set_input_embeddings with Llava->PaliGemma + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + @can_return_tuple + @auto_docstring( + custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection." + ) + def get_image_features( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithPooling: + image_outputs = self.vision_tower(pixel_values, **kwargs) + selected_image_feature = image_outputs.last_hidden_state + image_features = self.multi_modal_projector(selected_image_feature) + image_outputs.pooler_output = image_features + + return image_outputs + + def get_placeholder_mask( + self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + + n_image_tokens = special_image_mask.sum() + n_image_features = image_features.shape[0] * image_features.shape[1] + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}", + ) + return special_image_mask + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple | PaligemmaModelOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`. + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, PaliGemmaForConditionalGeneration + + >>> model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma2-3b-mix-224") + >>> processor = AutoProcessor.from_pretrained("google/paligemma2-3b-mix-224") + + >>> prompt = "Where is the cat standing?" + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, text=prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(**inputs,) + >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Where is the cat standing?\nsnow" + ```""" + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + # Replace image id with PAD if the image token if OOV, to avoid index-errors + if input_ids is not None and self.config.image_token_id >= self.vocab_size: + special_image_mask = input_ids == self.config.image_token_id + llm_input_ids = input_ids.clone() + llm_input_ids[special_image_mask] = 0 + else: + llm_input_ids = input_ids + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(llm_input_ids) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + 1 # Paligemma positions are 1-indexed + + # Merge text and images + if pixel_values is not None: + image_features = self.get_image_features(pixel_values).pooler_output + image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) + special_image_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + causal_mask_mapping = create_causal_mask_mapping( + self.config, + inputs_embeds, + attention_mask, + past_key_values, + position_ids, + token_type_ids, + pixel_values, + is_training=self.training, + ) + + outputs = self.language_model( + attention_mask=causal_mask_mapping, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return PaligemmaModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=image_features if pixel_values is not None else None, + ) + + +@auto_docstring( + custom_intro=""" + The Base Paligemma model which consists of a vision backbone and a language model without language modeling head., + """ +) +class PaliGemmaForConditionalGeneration(PaliGemmaPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"} + + def __init__(self, config: PaliGemmaConfig): + super().__init__(config) + self.model = PaliGemmaModel(config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.post_init() + + @auto_docstring + def get_image_features(self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]): + return self.model.get_image_features(pixel_values, **kwargs) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | PaliGemmaCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`. + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, PaliGemmaForConditionalGeneration + + >>> model = PaliGemmaForConditionalGeneration.from_pretrained("google/paligemma2-3b-mix-224") + >>> processor = AutoProcessor.from_pretrained("google/paligemma2-3b-mix-224") + + >>> prompt = "Where is the cat standing?" + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, text=prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(**inputs,) + >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Where is the cat standing?\nsnow" + ```""" + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + token_type_ids=token_type_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + labels=labels, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return PaliGemmaCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + inputs_embeds=None, + position_ids=None, + pixel_values=None, + attention_mask=None, + token_type_ids=None, + use_cache=True, + logits_to_keep=None, + labels=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- custom `position_ids` and `pixel_values` handling + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + token_type_ids=token_type_ids, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + # position_ids in Paligemma are 1-indexed + if model_inputs.get("position_ids") is not None: + # NOTE: we need this op out-of-place, otherwise it modifies the `model_kwargs` dict used in `generate` in-place! + model_inputs["position_ids"] = model_inputs["position_ids"] + 1 + + # Pixel values are used only in the first iteration if available + # In subsequent iterations, they are already merged with text and cached + # NOTE: first iteration doesn't have to be prefill, it can be the first + # iteration with a question and cached system prompt (continue generate from cache). NOTE: use_cache=False needs pixel_values always + if is_first_iteration or not use_cache: + model_inputs["pixel_values"] = pixel_values + + return model_inputs + + @staticmethod + @deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds") + def create_masks_for_generate( + config: PreTrainedConfig, + inputs_embeds: torch.Tensor, + attention_mask: torch.Tensor | None, + past_key_values: Cache | None, + position_ids: torch.Tensor | None, + token_type_ids: torch.Tensor | None = None, + is_first_iteration: bool | None = False, + **kwargs, + ) -> dict: + # Uses the overwritten `create_masks_for_generate` with `token_type_ids` masking + return create_causal_mask_mapping( + config, + inputs_embeds, + attention_mask, + past_key_values, + position_ids, + token_type_ids, + is_first_iteration=is_first_iteration, + **{k: v for k, v in kwargs.items() if k != "pixel_values"}, + ) + + +__all__ = ["PaliGemmaForConditionalGeneration", "PaliGemmaPreTrainedModel", "PaliGemmaModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/processing_paligemma.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/processing_paligemma.py new file mode 100644 index 0000000000000000000000000000000000000000..d72c67c99d4fe25ae4b88b60c3e3f6ec1baf9bf6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/paligemma/processing_paligemma.py @@ -0,0 +1,272 @@ +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for PaliGemma. +""" + +import numpy as np + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput, is_valid_image +from ...processing_utils import ( + MultiModalData, + ProcessingKwargs, + ProcessorMixin, + TextKwargs, + Unpack, +) +from ...tokenization_utils_base import AddedToken, PreTokenizedInput, TextInput +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + +IMAGE_TOKEN = "" +EXTRA_TOKENS = [f"4}>" for i in range(1024)] + [f"3}>" for i in range(128)] + + +class PaliGemmaTextKwargs(TextKwargs): + """ + suffix (`str`, `list[str]`, `list[list[str]]`): + The suffixes or batch of suffixes to be encoded. Only necessary for finetuning. See https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md + for more information. If your prompt is " What is on the image", the suffix corresponds to the expected prediction "a cow sitting on a bench". + """ + + suffix: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None + + +class PaliGemmaProcessorKwargs(ProcessingKwargs, total=False): + text_kwargs: PaliGemmaTextKwargs + _defaults = { + "text_kwargs": { + "padding": False, + "return_mm_token_type_ids": False, + }, + "images_kwargs": { + "data_format": "channels_first", + }, + } + + +# Copied from transformers.models.idefics2.processing_idefics2.is_url +def is_url(val) -> bool: + return isinstance(val, str) and val.startswith("http") + + +# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url +def is_image_or_image_url(elem): + return is_url(elem) or is_valid_image(elem) + + +def _is_str_or_image(elem): + return isinstance(elem, (str)) or is_image_or_image_url(elem) + + +def build_string_from_input(prompt, bos_token, image_seq_len, image_token, num_images): + """ + Builds a string from the input prompt and image tokens. + For example, for the call: + build_string_from_input( + prompt="Prefix str" + bos_token="", + image_seq_len=3, + image_token="", + ) + The output will be: + "Initial str" + Args: + prompt (`list[Union[str, ImageInput]]`): The input prompt. + bos_token (`str`): The beginning of sentence token. + image_seq_len (`int`): The length of the image sequence. + image_token (`str`): The image token. + num_images (`int`): Number of images in the prompt. + """ + return f"{image_token * image_seq_len * num_images}{bos_token}{prompt}\n" + + +@auto_docstring +class PaliGemmaProcessor(ProcessorMixin): + def __init__( + self, + image_processor=None, + tokenizer=None, + chat_template=None, + **kwargs, + ): + if not hasattr(image_processor, "image_seq_length"): + raise ValueError("Image processor is missing an `image_seq_length` attribute.") + + self.image_seq_length = image_processor.image_seq_length + + if not hasattr(tokenizer, "image_token"): + image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True) + tokens_to_add = {"additional_special_tokens": [image_token]} + tokenizer.add_special_tokens(tokens_to_add) + self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN) + self.image_token = IMAGE_TOKEN + else: + self.image_token_id = tokenizer.image_token_id + self.image_token = tokenizer.image_token + + tokenizer.add_tokens(EXTRA_TOKENS) + tokenizer.add_bos_token = False + tokenizer.add_eos_token = False + + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[PaliGemmaProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix` + is provided, the `input_ids` will also contain the suffix input ids. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **labels** -- Labels compatible with training if `suffix` is not None + """ + + output_kwargs = self._merge_kwargs( + PaliGemmaProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + suffix = output_kwargs["text_kwargs"].pop("suffix", None) + + return_token_type_ids = True + + if images is None: + raise ValueError("`images` are expected as arguments to a `PaliGemmaProcessor` instance.") + if text is None: + logger.warning_once( + "You are using PaliGemma without a text prefix. It will perform as a picture-captioning model." + ) + text = "" + + if _is_str_or_image(text): + text = [text] + elif isinstance(text, list) and _is_str_or_image(text[0]): + pass + + if text is not None and images is not None: + if not any(IMAGE_TOKEN in sample for sample in text): + logger.warning( + "You are passing both `text` and `images` to `PaliGemmaProcessor`. The processor expects special " + "image tokens in the text, as many tokens as there are images per each text. It is recommended to " + "add `` tokens in the very beginning of your text. For this call, we will infer how many images " + "each text has and add special tokens." + ) + + if isinstance(text, list) and isinstance(images, list): + if len(images) != len(text): + raise ValueError( + f"Received {len(images)} images for {len(text)} prompts. Each prompt should be associated with an image or list of images." + ) + + # make a nested list of lists to be able to iterate over the images and text below + if is_valid_image(images): + images = [[images]] + elif isinstance(images, (list, tuple)) and is_valid_image(images[0]): + images = [[image] for image in images] + elif not ( + isinstance(images, (list, tuple)) + and isinstance(images[0], (list, tuple)) + and is_valid_image(images[0][0]) + ): + raise ValueError("images must be an image, list of images or list of list of images") + + input_strings = [ + build_string_from_input( + prompt=prompt, + bos_token=self.tokenizer.bos_token, + image_seq_len=self.image_seq_length, + image_token=IMAGE_TOKEN, + num_images=len(image_list) if isinstance(image_list, list) else 1, + ) + for prompt, image_list in zip(text, images) + ] + else: + expanded_samples = [] + for sample in text: + expanded_sample = sample.replace(IMAGE_TOKEN, IMAGE_TOKEN * self.image_seq_length) + bos_rfind_index = expanded_sample.rfind(IMAGE_TOKEN) + bos_index = bos_rfind_index + len(IMAGE_TOKEN) if bos_rfind_index != -1 else 0 + expanded_sample = ( + expanded_sample[:bos_index] + self.tokenizer.bos_token + expanded_sample[bos_index:] + ) + expanded_samples.append(expanded_sample) + input_strings = [f"{sample}\n" for sample in expanded_samples] + + if suffix is not None and _is_str_or_image(suffix): + suffix = [suffix] + if suffix is not None: + suffix = [sfx + self.tokenizer.eos_token for sfx in suffix] + pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"] + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None) + inputs = self.tokenizer( + input_strings, + text_pair=suffix, + return_token_type_ids=return_token_type_ids, + **output_kwargs["text_kwargs"], + ) + self._check_special_mm_tokens(input_strings, inputs, modalities=["image"]) + + return_data = {**inputs, "pixel_values": pixel_values} + + # TODO: ideally we would control label generation separately, now that we always return token_type_ids. + if return_token_type_ids: + labels = np.array(inputs["input_ids"]) + labels[np.array(inputs["token_type_ids"]) == 0] = -100 + return_data.update({"labels": labels}) + + if return_mm_token_type_ids: + return_data["mm_token_type_ids"] = self.create_mm_token_type_ids(return_data["input_ids"]) + return BatchFeature(data=return_data, tensor_type=return_tensors) + + def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs): + """ + Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. + + Args: + image_sizes (list[list[str]], *optional*): + The input sizes formatted as (height, width) per each image. + Returns: + `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided + input modalities, along with other useful data. + """ + vision_data = {} + if image_sizes is not None: + num_image_tokens = [self.image_seq_length] * len(image_sizes) + num_image_patches = [1] * len(image_sizes) + vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) + return MultiModalData(**vision_data) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + ["token_type_ids", "labels"] + image_processor_input_names = self.image_processor.model_input_names + return list(tokenizer_input_names + image_processor_input_names) + + +__all__ = ["PaliGemmaProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5c54b2e2eadb2432844098e3c3ea75f129377a19 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_parakeet import * + from .feature_extraction_parakeet import * + from .modeling_parakeet import * + from .tokenization_parakeet_fast import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/configuration_parakeet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/configuration_parakeet.py new file mode 100644 index 0000000000000000000000000000000000000000..6f4622ea3b2f5aa5fad104c9abeddf2101c8484c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/configuration_parakeet.py @@ -0,0 +1,138 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Parakeet model configuration.""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="nvidia/parakeet-ctc-1.1b") +@strict +class ParakeetEncoderConfig(PreTrainedConfig): + r""" + convolution_bias (`bool`, *optional*, defaults to `True`): + Whether to use bias in convolutions of the conformer's convolution module. + conv_kernel_size (`int`, *optional*, defaults to 9): + The kernel size of the convolution layers in the Conformer block. + subsampling_factor (`int`, *optional*, defaults to 8): + The factor by which the input sequence is subsampled. + subsampling_conv_channels (`int`, *optional*, defaults to 256): + The number of channels in the subsampling convolution layers. + num_mel_bins (`int`, *optional*, defaults to 80): + Number of mel features. + subsampling_conv_kernel_size (`int`, *optional*, defaults to 3): + The kernel size of the subsampling convolution layers. + subsampling_conv_stride (`int`, *optional*, defaults to 2): + The stride of the subsampling convolution layers. + dropout_positions (`float`, *optional*, defaults to 0.0): + The dropout ratio for the positions in the input sequence. + scale_input (`bool`, *optional*, defaults to `True`): + Whether to scale the input embeddings. + + Example: + ```python + >>> from transformers import ParakeetEncoderModel, ParakeetEncoderConfig + + >>> # Initializing a `ParakeetEncoder` configuration + >>> configuration = ParakeetEncoderConfig() + + >>> # Initializing a model from the configuration + >>> model = ParakeetEncoderModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + + This configuration class is based on the ParakeetEncoder architecture from NVIDIA NeMo. You can find more details + and pre-trained models at [nvidia/parakeet-ctc-1.1b](https://huggingface.co/nvidia/parakeet-ctc-1.1b). + """ + + model_type = "parakeet_encoder" + keys_to_ignore_at_inference = ["past_key_values"] + + hidden_size: int = 1024 + num_hidden_layers: int = 24 + num_attention_heads: int = 8 + intermediate_size: int = 4096 + hidden_act: str = "silu" + attention_bias: bool = True + convolution_bias: bool = True + conv_kernel_size: int = 9 + subsampling_factor: int = 8 + subsampling_conv_channels: int = 256 + num_mel_bins: int = 80 + subsampling_conv_kernel_size: int = 3 + subsampling_conv_stride: int = 2 + dropout: float | int = 0.1 + dropout_positions: float | int = 0.0 + layerdrop: float | int = 0.1 + activation_dropout: float | int = 0.1 + attention_dropout: float | int = 0.1 + max_position_embeddings: int = 5000 + scale_input: bool = True + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + self.num_key_value_heads = self.num_attention_heads + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="nvidia/parakeet-ctc-1.1b") +@strict +class ParakeetCTCConfig(PreTrainedConfig): + r""" + ctc_loss_reduction (`str`, *optional*, defaults to `"mean"`): + Specifies the reduction to apply to the output of `torch.nn.CTCLoss`. Only relevant when training an + instance of [`ParakeetForCTC`]. + ctc_zero_infinity (`bool`, *optional*, defaults to `True`): + Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly + occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance + of [`ParakeetForCTC`]. + encoder_config (`Union[dict, ParakeetEncoderConfig]`, *optional*): + The config object or dictionary of the encoder. + + Example: + + ```python + >>> from transformers import ParakeetForCTC, ParakeetCTCConfig + >>> # Initializing a Parakeet configuration + >>> configuration = ParakeetCTCConfig() + >>> # Initializing a model from the configuration + >>> model = ParakeetForCTC(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "parakeet_ctc" + sub_configs = {"encoder_config": ParakeetEncoderConfig} + + vocab_size: int = 1025 + ctc_loss_reduction: str = "mean" + ctc_zero_infinity: bool = True + encoder_config: dict | PreTrainedConfig | None = None + pad_token_id: int | None = 1024 + + def __post_init__(self, **kwargs): + if isinstance(self.encoder_config, dict): + self.encoder_config = ParakeetEncoderConfig(**self.encoder_config) + elif self.encoder_config is None: + self.encoder_config = ParakeetEncoderConfig() + self.initializer_range = self.encoder_config.initializer_range + super().__post_init__(**kwargs) + + +__all__ = ["ParakeetCTCConfig", "ParakeetEncoderConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/feature_extraction_parakeet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/feature_extraction_parakeet.py new file mode 100644 index 0000000000000000000000000000000000000000..c745d02c9629b44f78a87ac478df99b887d6d974 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/feature_extraction_parakeet.py @@ -0,0 +1,285 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import torch + +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...feature_extraction_utils import BatchFeature +from ...utils import TensorType, is_librosa_available, logging +from ...utils.import_utils import requires + + +if is_librosa_available(): + import librosa + + +EPSILON = 1e-5 +LOG_ZERO_GUARD_VALUE = 2**-24 + + +logger = logging.get_logger(__name__) + + +@requires(backends=("torch", "librosa")) +class ParakeetFeatureExtractor(SequenceFeatureExtractor): + r""" + Constructs a Parakeet feature extractor. + + This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains + most of the main methods. Users should refer to this superclass for more information regarding those methods. + + This class extracts mel-filter bank features from raw speech using a custom numpy implementation of the `Short Time + Fourier Transform` which should match pytorch's `torch.stft` equivalent. + + Args: + feature_size (`int`, *optional*, defaults to 80): + The feature dimension of the extracted features. + sampling_rate (`int`, *optional*, defaults to 16000): + The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). + hop_length (`int`, *optional*, defaults to 160): + Length of the overlapping windows for the STFT used to obtain the Mel Frequency coefficients. + n_fft (`int`, *optional*, defaults to 512): + Size of the Fourier transform. + win_length (`int`, *optional*, defaults to 400): + The window length for the STFT computation. + preemphasis (`float`, *optional*, defaults to 0.97): + A preemphasis filter coefficient. 0.0 means no preemphasis filter. + padding_value (`float`, *optional*, defaults to 0.0): + Padding value used to pad the audio. Should correspond to silences. + """ + + model_input_names = ["input_features", "attention_mask"] + + def __init__( + self, + feature_size=80, + sampling_rate=16000, + hop_length=160, + n_fft=512, + win_length=400, + preemphasis=0.97, + padding_value=0.0, + **kwargs, + ): + super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) + + self.hop_length = hop_length + self.n_fft = n_fft + self.win_length = win_length + self.preemphasis = preemphasis + + # TODO: @eustlb, for now we use librosa to compute the mel filters + # indeed mel_filter_bank uses np.float64 (while librosa uses np.float32), giving numerical differences + # self.mel_filters = mel_filter_bank( + # num_frequency_bins=n_fft // 2 + 1, + # num_mel_filters=feature_size, + # min_frequency=0.0, + # max_frequency=sampling_rate / 2, + # sampling_rate=sampling_rate, + # norm="slaney", + # mel_scale="slaney", + # ) + mel_filters = librosa.filters.mel( + sr=sampling_rate, n_fft=n_fft, n_mels=feature_size, fmin=0.0, fmax=sampling_rate / 2, norm="slaney" + ) + self.mel_filters = torch.from_numpy(mel_filters).to(torch.float32) + + def _torch_extract_fbank_features(self, waveform, device="cpu"): + # spectrogram + window = torch.hann_window(self.win_length, periodic=False, device=device) + stft = torch.stft( + waveform, + self.n_fft, + hop_length=self.hop_length, + win_length=self.win_length, + window=window, + return_complex=True, + pad_mode="constant", + ) + # Let's math original implementation + # magnitudes = torch.abs(stft) ** 2 + magnitudes = torch.view_as_real(stft) + magnitudes = torch.sqrt(magnitudes.pow(2).sum(-1)) + magnitudes = magnitudes.pow(2) + + # log mel spectrogram + mel_filters = self.mel_filters.to(device) + mel_spec = mel_filters @ magnitudes + mel_spec = torch.log(mel_spec + LOG_ZERO_GUARD_VALUE) + + # (batch_size, num_mel_filters, num_frames) -> (batch_size, num_frames, num_mel_filters) + mel_spec = mel_spec.permute(0, 2, 1) + + return mel_spec + + def __call__( + self, + raw_speech: np.ndarray | list[float] | list[np.ndarray] | list[list[float]], + truncation: bool = False, + pad_to_multiple_of: int | None = None, + return_tensors: str | TensorType | None = None, + return_attention_mask: bool | None = None, + padding: str | None = "longest", + max_length: int | None = None, + sampling_rate: int | None = None, + do_normalize: bool | None = None, + device: str | None = "cpu", + return_token_timestamps: bool | None = None, + **kwargs, + ) -> BatchFeature: + """ + Main method to featurize and prepare for the model one or several sequence(s). Implementation uses PyTorch for + the STFT computation if available, otherwise a slower NumPy based one. + + Args: + raw_speech (`np.ndarray`, `list[float]`, `list[np.ndarray]`, `list[list[float]]`): + The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float + values, a list of numpy arrays or a list of list of float values. Must be mono channel audio, not + stereo, i.e. single float per timestep. + truncation (`bool`, *optional*, default to `True`): + Activates truncation to cut input sequences longer than *max_length* to *max_length*. + pad_to_multiple_of (`int`, *optional*, defaults to None): + If set will pad the sequence to a multiple of the provided value. + + This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability + `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific feature_extractor's default. + + [What are attention masks?](../glossary#attention-mask) + + + + For Parakeet models, `attention_mask` should always be passed for batched inference, to avoid subtle + bugs. + + + + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'tf'`: Return TensorFlow `tf.constant` objects. + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + sampling_rate (`int`, *optional*): + The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass + `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition + pipeline. + padding_value (`float`, *optional*, defaults to 0.0): + The value that is used to fill the padding values / vectors. + do_normalize (`bool`, *optional*, defaults to `False`): + Whether or not to zero-mean unit-variance normalize the input. Normalizing can help to significantly + improve the performance of the model. + device (`str`, *optional*, defaults to `'cpu'`): + Specifies the device for computation of the log-mel spectrogram of audio signals in the + `_torch_extract_fbank_features` method. (e.g., "cpu", "cuda") + return_token_timestamps (`bool`, *optional*, defaults to `None`): + Deprecated. Use `return_attention_mask` instead from which the number of frames can be inferred. + + Whether or not to return the number of frames of the input raw_speech. + These num_frames can be used by the model to compute word level timestamps. + """ + if sampling_rate is not None: + if sampling_rate != self.sampling_rate: + raise ValueError( + f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a" + f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input" + f" was sampled with {self.sampling_rate} and not {sampling_rate}." + ) + else: + logger.warning( + f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. " + "Failing to do so can result in silent errors that might be hard to debug." + ) + + # Convert to torch tensor + if isinstance(raw_speech, np.ndarray): + raw_speech = torch.tensor(raw_speech) + elif isinstance(raw_speech, (list, tuple)) and isinstance(raw_speech[0], np.ndarray): + raw_speech = [torch.tensor(speech) for speech in raw_speech] + + is_batched_torch = isinstance(raw_speech, torch.Tensor) and len(raw_speech.shape) > 1 + if is_batched_torch and len(raw_speech.shape) > 2: + logger.warning( + f"Only mono-channel audio is supported for input to {self.__class__.__name__}. " + "We will take the mean of the channels to convert to mono." + ) + raw_speech = raw_speech.mean(-1) + + is_batched_sequence = isinstance(raw_speech, (list, tuple)) + if is_batched_sequence: + for speech in raw_speech: + if len(speech.shape) > 1: + logger.warning( + f"Only mono-channel audio is supported for input to {self.__class__.__name__}. " + "We will take the mean of the channels to convert to mono." + ) + speech = speech.mean(-1) + + if is_batched_torch or is_batched_sequence: + raw_speech = [speech[:, None].to(torch.float32) for speech in raw_speech] + else: + raw_speech = [raw_speech[:, None].to(torch.float32)] + + audio_lengths = [len(speech) for speech in raw_speech] + batched_speech = BatchFeature({"input_features": raw_speech, "audio_lengths": audio_lengths}) + + padded_inputs = self.pad( + batched_speech, + padding=padding, + max_length=max_length, + truncation=truncation, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors="pt", + ) + input_features = padded_inputs.input_features.squeeze(-1) + + # preemphasis + if self.preemphasis is not None: + timemask = torch.arange(input_features.shape[1], device=input_features.device).unsqueeze( + 0 + ) < padded_inputs.audio_lengths.unsqueeze(1) + input_features = torch.cat( + [input_features[:, :1], input_features[:, 1:] - self.preemphasis * input_features[:, :-1]], dim=1 + ) + input_features = input_features.masked_fill(~timemask, 0.0) + + input_features = self._torch_extract_fbank_features(input_features, device) + features_lengths = torch.floor_divide( + padded_inputs.audio_lengths + self.n_fft // 2 * 2 - self.n_fft, self.hop_length + ) + attention_mask = torch.arange(input_features.shape[1], device=device)[None, :] < features_lengths[:, None] + + # normalize mel features, ignoring padding + mask = attention_mask.unsqueeze(-1) + input_features_masked = input_features * mask + mean = input_features_masked.sum(dim=1) / features_lengths.unsqueeze(-1) + mean = mean.unsqueeze(1) + variance = ((input_features_masked - mean) ** 2 * mask).sum(dim=1) / (features_lengths - 1).unsqueeze(-1) + std = torch.sqrt(variance).unsqueeze(1) + input_features = (input_features - mean) / (std + EPSILON) + input_features *= mask + + return BatchFeature( + data={ + "input_features": input_features, + "attention_mask": attention_mask, + }, + tensor_type=return_tensors, + ) + + +__all__ = ["ParakeetFeatureExtractor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/modeling_parakeet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/modeling_parakeet.py new file mode 100644 index 0000000000000000000000000000000000000000..501a573f849431ef65c003e456629915cd7ae516 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/modeling_parakeet.py @@ -0,0 +1,815 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/parakeet/modular_parakeet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_parakeet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...integrations import use_kernel_func_from_hub, use_kernelized_func +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, CausalLMOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_parakeet import ParakeetCTCConfig, ParakeetEncoderConfig + + +@dataclass +@auto_docstring( + custom_intro=""" + Extends [~modeling_outputs.BaseModelOutput] to include the output attention mask since sequence length is not preserved in the model's forward. + """ +) +class ParakeetEncoderModelOutput(BaseModelOutput): + attention_mask: torch.Tensor | None = None + + +class ParakeetEncoderRelPositionalEncoding(nn.Module): + """Relative positional encoding for Parakeet.""" + + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: ParakeetEncoderConfig, device=None): + super().__init__() + self.max_position_embeddings = config.max_position_embeddings + base = 10000.0 + inv_freq = 1.0 / ( + base + ** ( + torch.arange(0, config.hidden_size, 2, dtype=torch.int64).to(device=device, dtype=torch.float) + / config.hidden_size + ) + ) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + + @torch.no_grad() + def forward(self, hidden_states: torch.Tensor): + seq_length = hidden_states.shape[1] + if seq_length > self.max_position_embeddings: + raise ValueError( + f"Sequence Length: {seq_length} has to be less or equal than " + f"config.max_position_embeddings {self.max_position_embeddings}." + ) + + position_ids = torch.arange(seq_length - 1, -seq_length, -1, device=hidden_states.device) + inv_freq_expanded = ( + self.inv_freq[None, :, None].float().expand(hidden_states.shape[0], -1, 1).to(hidden_states.device) + ) + position_ids_expanded = position_ids[None, None, :].float() + + device_type = ( + hidden_states.device.type + if isinstance(hidden_states.device.type, str) and hidden_states.device.type != "mps" + else "cpu" + ) + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + sin = freqs.sin() + cos = freqs.cos() + # interleave sin and cos + pos_embed = torch.stack([sin, cos], dim=-1) + pos_embed = pos_embed.reshape(*pos_embed.shape[:-2], -1) + + return pos_embed.to(dtype=hidden_states.dtype) + + +class ParakeetEncoderFeedForward(nn.Module): + def __init__(self, config: ParakeetEncoderConfig): + super().__init__() + self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.attention_bias) + self.activation = ACT2FN[config.hidden_act] + self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.attention_bias) + self.activation_dropout = config.activation_dropout + + def forward(self, hidden_states): + hidden_states = self.activation(self.linear1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.linear2(hidden_states) + return hidden_states + + +class ParakeetEncoderConvolutionModule(nn.Module): + def __init__(self, config: ParakeetEncoderConfig, module_config=None): + """ + Args: + config (ParakeetEncoderConfig): Configuration for the model. + module_config (dict): Configuration for the module (e.g., encoder or decoder). + """ + super().__init__() + channels = config.hidden_size + # kernel_size should be an odd number for 'SAME' padding + if module_config is None: + # e.g. using `ParakeetEncoderEncoderConfig` in src/transformers/models/parakeet_encoder/configuration_parakeet_encoder.py + kernel_size = config.conv_kernel_size + self.activation = ACT2FN[getattr(config, "hidden_act", "silu")] + else: + kernel_size = module_config["kernel_size"] + self.activation = ACT2FN[module_config.get("activation", "silu")] + self.padding = (kernel_size - 1) // 2 + self.pointwise_conv1 = nn.Conv1d( + channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=config.convolution_bias + ) + self.depthwise_conv = nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + padding=self.padding, + groups=channels, + bias=config.convolution_bias, + ) + self.norm = nn.BatchNorm1d(channels) + self.pointwise_conv2 = nn.Conv1d( + channels, channels, kernel_size=1, stride=1, padding=0, bias=config.convolution_bias + ) + + def forward(self, hidden_states, attention_mask=None): + """ + Compute convolution module. + + Args: + hidden_states (`torch.Tensor` of shape `(batch, time, channels)`): Input tensor. + attention_mask (`torch.Tensor` of shape `(batch, 1, time, time)`): Attention mask. + + Returns: + `torch.Tensor`: Output tensor of shape `(batch, time, channels)`. + + """ + # exchange the temporal dimension and the feature dimension + hidden_states = hidden_states.transpose(1, 2) + + # GLU mechanism, (batch_size, 2*channel, dim) + hidden_states = self.pointwise_conv1(hidden_states) + # (batch_size, channel, dim) + hidden_states = nn.functional.glu(hidden_states, dim=1) + + # Apply padding mask before convolution + if attention_mask is not None: + if attention_mask.dtype == torch.bool: + all_masked_rows = torch.all(~attention_mask, dim=2) + else: + all_masked_rows = torch.all(~(attention_mask == 0.0), dim=2) + hidden_states = hidden_states.masked_fill(all_masked_rows, 0.0) + + # 1D Depthwise Conv + hidden_states = self.depthwise_conv(hidden_states) + hidden_states = self.norm(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.pointwise_conv2(hidden_states) + + return hidden_states.transpose(1, 2) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class ParakeetEncoderAttention(nn.Module): + """Multi-head attention with relative positional encoding. See section 3.3 of https://huggingface.co/papers/1901.02860.""" + + def __init__(self, config: ParakeetEncoderConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = False + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + # W_{k,R} projection + self.relative_k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + # global content bias + self.bias_u = nn.Parameter(torch.zeros(config.num_attention_heads, self.head_dim)) + # global positional bias + self.bias_v = nn.Parameter(torch.zeros(config.num_attention_heads, self.head_dim)) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + batch_size, seq_length = input_shape + hidden_shape = (batch_size, seq_length, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + query_states_with_bias_u = query_states + self.bias_u.view( + 1, self.config.num_attention_heads, 1, self.head_dim + ) + query_states_with_bias_v = query_states + self.bias_v.view( + 1, self.config.num_attention_heads, 1, self.head_dim + ) + + relative_key_states = self.relative_k_proj(position_embeddings) + relative_key_states = relative_key_states.view(batch_size, -1, self.config.num_attention_heads, self.head_dim) + + # terms (b) and (d) + matrix_bd = query_states_with_bias_v @ relative_key_states.permute(0, 2, 3, 1) + matrix_bd = self._rel_shift(matrix_bd) + matrix_bd = matrix_bd[..., :seq_length] + matrix_bd = matrix_bd * self.scaling + + if attention_mask is not None: + # here the original codebase uses -10000.0 rather than float("-inf") and then manual masked fill with 0.0s + # see: https://github.com/NVIDIA-NeMo/NeMo/blob/8cfedd7203462cb251a914e700e5605444277561/nemo/collections/asr/parts/submodules/multi_head_attention.py#L320-L340 + # we rather went for a straight-forward approach with float("-inf") + matrix_bd = matrix_bd.masked_fill_(attention_mask.logical_not(), float("-inf")) + + # will compute matrix_ac - terms (a) and (c) - and add matrix_bd + attn_output, attn_weights = attention_interface( + self, + query=query_states_with_bias_u, + key=key_states, + value=value_states, + attention_mask=matrix_bd, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + def _rel_shift(self, attention_scores): + """Relative position shift for Shaw et al. style attention. See appendix B of https://huggingface.co/papers/1901.02860.""" + batch_size, num_heads, query_length, position_length = attention_scores.shape + attention_scores = nn.functional.pad(attention_scores, pad=(1, 0)) + attention_scores = attention_scores.view(batch_size, num_heads, -1, query_length) + attention_scores = attention_scores[:, :, 1:].view(batch_size, num_heads, query_length, position_length) + return attention_scores + + +class ParakeetEncoderSubsamplingConv2D(nn.Module): + def __init__(self, config: ParakeetEncoderConfig): + super().__init__() + + self.kernel_size = config.subsampling_conv_kernel_size + self.stride = config.subsampling_conv_stride + self.channels = config.subsampling_conv_channels + self.padding = (self.kernel_size - 1) // 2 + self.num_layers = int(math.log2(config.subsampling_factor)) + + # define layers + self.layers = nn.ModuleList() + self.layers.append( + nn.Conv2d(1, self.channels, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding) + ) + self.layers.append(nn.ReLU()) + for i in range(self.num_layers - 1): + # depthwise conv + self.layers.append( + nn.Conv2d( + self.channels, + self.channels, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + groups=self.channels, + ) + ) + # pointwise conv + self.layers.append(nn.Conv2d(self.channels, self.channels, kernel_size=1)) + # activation + self.layers.append(nn.ReLU()) + + out_length = config.num_mel_bins // (self.stride**self.num_layers) + self.linear = nn.Linear(config.subsampling_conv_channels * out_length, config.hidden_size, bias=True) + + def _get_output_length(self, input_lengths: torch.Tensor, conv_layer: nn.Conv2d): + if hasattr(conv_layer, "stride") and conv_layer.stride != (1, 1): + padding = conv_layer.padding + kernel_size = conv_layer.kernel_size[0] + stride = conv_layer.stride[0] + + output_lengths = (input_lengths + padding[0] + padding[1] - kernel_size) // stride + 1 + return output_lengths + + return input_lengths + + def forward(self, input_features: torch.Tensor, attention_mask: torch.Tensor = None): + hidden_states = input_features.unsqueeze(1) + current_lengths = attention_mask.sum(-1) if attention_mask is not None else None + + for layer in self.layers: + hidden_states = layer(hidden_states) + + # mask the hidden states + if isinstance(layer, nn.Conv2d) and attention_mask is not None: + current_lengths = self._get_output_length(current_lengths, layer) + current_seq_length = hidden_states.shape[2] + channel_mask = ( + torch.arange(current_seq_length, device=attention_mask.device) < current_lengths[:, None] + ) + hidden_states *= channel_mask[:, None, :, None] + + hidden_states = hidden_states.transpose(1, 2).reshape(hidden_states.shape[0], hidden_states.shape[2], -1) + hidden_states = self.linear(hidden_states) + + return hidden_states + + +class ParakeetEncoderBlock(GradientCheckpointingLayer): + def __init__(self, config: ParakeetEncoderConfig, layer_idx: int | None = None): + super().__init__() + self.gradient_checkpointing = False + + self.feed_forward1 = ParakeetEncoderFeedForward(config) + self.self_attn = ParakeetEncoderAttention(config, layer_idx) + self.conv = ParakeetEncoderConvolutionModule(config) + self.feed_forward2 = ParakeetEncoderFeedForward(config) + + self.norm_feed_forward1 = nn.LayerNorm(config.hidden_size) + self.norm_self_att = nn.LayerNorm(config.hidden_size) + self.norm_conv = nn.LayerNorm(config.hidden_size) + self.norm_feed_forward2 = nn.LayerNorm(config.hidden_size) + self.norm_out = nn.LayerNorm(config.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.feed_forward1(self.norm_feed_forward1(hidden_states)) + hidden_states = residual + 0.5 * hidden_states # the conformer architecture uses a factor of 0.5 + + normalized_hidden_states = self.norm_self_att(hidden_states) + attn_output, _ = self.self_attn( + hidden_states=normalized_hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = hidden_states + attn_output + + conv_output = self.conv(self.norm_conv(hidden_states), attention_mask=attention_mask) + hidden_states = hidden_states + conv_output + + ff2_output = self.feed_forward2(self.norm_feed_forward2(hidden_states)) + hidden_states = hidden_states + 0.5 * ff2_output # the conformer architecture uses a factor of 0.5 + + hidden_states = self.norm_out(hidden_states) + + return hidden_states + + +@auto_docstring +class ParakeetPreTrainedModel(PreTrainedModel): + config: ParakeetCTCConfig + base_model_prefix = "model" + main_input_name = "input_features" + input_modalities = "audio" + supports_gradient_checkpointing = True + _no_split_modules = ["ParakeetEncoderBlock"] + _supports_flat_attention_mask = True + _supports_sdpa = True + _supports_flex_attn = True + + # TODO: @eustlb, add support when flash attention supports custom attention bias + _supports_flash_attn = False + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": ParakeetEncoderBlock, + "attentions": ParakeetEncoderAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + + if hasattr(self.config, "initializer_range"): + std = self.config.initializer_range + else: + # 0.02 is the standard default value across the library + std = getattr(self.config.get_text_config(), "initializer_range", 0.02) + + if isinstance(module, ParakeetEncoderAttention): + # Initialize positional bias parameters + init.normal_(module.bias_u, mean=0.0, std=std) + init.normal_(module.bias_v, mean=0.0, std=std) + elif isinstance(module, ParakeetEncoderRelPositionalEncoding): + inv_freq = 1.0 / ( + 10000.0 ** (torch.arange(0, self.config.hidden_size, 2, dtype=torch.int64) / self.config.hidden_size) + ) + init.copy_(module.inv_freq, inv_freq) + + def _get_subsampling_output_length(self, input_lengths: torch.Tensor): + encoder_config = self.config.encoder_config if isinstance(self.config, ParakeetCTCConfig) else self.config + + kernel_size = encoder_config.subsampling_conv_kernel_size + stride = encoder_config.subsampling_conv_stride + num_layers = int(math.log2(encoder_config.subsampling_factor)) + + all_paddings = (kernel_size - 1) // 2 * 2 + add_pad = all_paddings - kernel_size + lengths = input_lengths + + for _ in range(num_layers): + lengths = torch.div(lengths.to(dtype=torch.float) + add_pad, stride) + 1.0 + lengths = torch.floor(lengths) + + return lengths.to(dtype=torch.int) + + def _get_output_attention_mask(self, attention_mask: torch.Tensor, target_length: int | None = None): + """ + Convert the input attention mask to its subsampled form. `target_length` sets the desired output length, useful + when the attention mask length differs from `sum(-1).max()` (i.e., when the longest sequence in the batch is padded) + """ + output_lengths = self._get_subsampling_output_length(attention_mask.sum(-1)) + # Use target_length if provided, otherwise use max length in batch + max_length = target_length if target_length is not None else output_lengths.max() + attention_mask = torch.arange(max_length, device=attention_mask.device) < output_lengths[:, None] + return attention_mask + + +@auto_docstring( + custom_intro=""" + The Parakeet Encoder model, based on the [Fast Conformer architecture](https://huggingface.co/papers/2305.05084). + """ +) +class ParakeetEncoder(ParakeetPreTrainedModel): + config: ParakeetEncoderConfig + base_model_prefix = "encoder" + + def __init__(self, config: ParakeetEncoderConfig): + super().__init__(config) + self.config = config + self.gradient_checkpointing = False + + self.dropout = config.dropout + self.dropout_positions = config.dropout_positions + self.layerdrop = config.layerdrop + + self.input_scale = math.sqrt(config.hidden_size) if config.scale_input else 1.0 + self.subsampling = ParakeetEncoderSubsamplingConv2D(config) + self.encode_positions = ParakeetEncoderRelPositionalEncoding(config) + + self.layers = nn.ModuleList( + [ParakeetEncoderBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + + self.post_init() + + @auto_docstring + @merge_with_config_defaults + @capture_outputs + @can_return_tuple + def forward( + self, + input_features: torch.Tensor, + attention_mask: torch.Tensor | None = None, + output_attention_mask: bool = True, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + output_attention_mask (`bool`, *optional*, defaults to `True`): + Whether to return the output attention mask. Only effective when `attention_mask` is provided. + + Example: + + ```python + >>> from transformers import AutoProcessor, ParakeetEncoder + >>> from datasets import load_dataset, Audio + + >>> model_id = "nvidia/parakeet-ctc-1.1b" + >>> processor = AutoProcessor.from_pretrained(model_id) + >>> encoder = ParakeetEncoder.from_pretrained(model_id) + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate)) + + >>> inputs = processor(ds[0]["audio"]["array"]) + >>> encoder_outputs = encoder(**inputs) + + >>> print(encoder_outputs.last_hidden_state.shape) + ``` + """ + + hidden_states = self.subsampling(input_features, attention_mask) + hidden_states = hidden_states * self.input_scale + position_embeddings = self.encode_positions(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + position_embeddings = nn.functional.dropout( + position_embeddings, p=self.dropout_positions, training=self.training + ) + + if attention_mask is not None: + output_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1]) + attention_mask = output_mask.unsqueeze(1).expand(-1, hidden_states.shape[1], -1) + attention_mask = attention_mask & attention_mask.transpose(1, 2) + attention_mask = attention_mask.unsqueeze(1) + + for encoder_layer in self.layers: + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if not to_drop: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + return ParakeetEncoderModelOutput( + last_hidden_state=hidden_states, + attention_mask=output_mask.int() if attention_mask is not None and output_attention_mask else None, + ) + + +@dataclass +class ParakeetGenerateOutput(ModelOutput): + """ + Outputs of Parakeet models. + + Args: + sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter + if all batches finished early due to the `eos_token_id`. + logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`): + Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) + at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for + each generated token), with each tensor of shape `(batch_size, config.vocab_size)`. + attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`): + Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of + `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`. + hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`): + Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of + `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`. + """ + + sequences: torch.LongTensor + logits: tuple[torch.FloatTensor] | None = None + attentions: tuple[tuple[torch.FloatTensor]] | None = None + hidden_states: tuple[tuple[torch.FloatTensor]] | None = None + + +@auto_docstring( + custom_intro=""" + Parakeet Encoder with a Connectionist Temporal Classification (CTC) head. + """ +) +class ParakeetForCTC(ParakeetPreTrainedModel): + config: ParakeetCTCConfig + + def __init__(self, config: ParakeetCTCConfig): + super().__init__(config) + self.encoder = ParakeetEncoder(config.encoder_config) + # Conv rather than linear to be consistent with NeMO decoding layer + self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1) + + self.post_init() + + @auto_docstring + @can_return_tuple + def forward( + self, + input_features: torch.Tensor, + attention_mask: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutput: + r""" + Example: + + ```python + >>> from transformers import AutoProcessor, ParakeetForCTC + >>> from datasets import load_dataset, Audio + + >>> model_id = "nvidia/parakeet-ctc-1.1b" + >>> processor = AutoProcessor.from_pretrained(model_id) + >>> model = ParakeetForCTC.from_pretrained(model_id) + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate)) + + >>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"]) + >>> outputs = model(**inputs) + + >>> print(outputs.loss) + ```""" + + encoder_outputs = self.encoder( + input_features=input_features, + attention_mask=attention_mask, + **kwargs, + ) + + hidden_states = encoder_outputs.last_hidden_state + logits = self.ctc_head(hidden_states.transpose(1, 2)).transpose(1, 2) + + loss = None + if labels is not None: + # retrieve loss input_lengths from attention_mask + attention_mask = ( + attention_mask if attention_mask is not None else torch.ones_like(input_features, dtype=torch.long) + ) + input_lengths = self._get_subsampling_output_length(attention_mask.sum(-1)) + + # assuming that padded tokens are filled with -100 + # when not being attended to + labels_mask = labels != self.config.pad_token_id + target_lengths = labels_mask.sum(-1) + flattened_targets = labels.masked_select(labels_mask) + + # ctc_loss doesn't support fp16 + log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1) + + with torch.backends.cudnn.flags(enabled=False): + loss = nn.functional.ctc_loss( + log_probs, + flattened_targets, + input_lengths, + target_lengths, + blank=self.config.pad_token_id, + reduction=self.config.ctc_loss_reduction, + zero_infinity=self.config.ctc_zero_infinity, + ) + + return CausalLMOutput( + loss=loss, + logits=logits, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_features: torch.Tensor, + attention_mask: torch.Tensor | None = None, + return_dict_in_generate: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> ParakeetGenerateOutput | torch.LongTensor: + r""" + Example: + + ```python + >>> from transformers import AutoProcessor, ParakeetForCTC + >>> from datasets import load_dataset, Audio + + >>> model_id = "nvidia/parakeet-ctc-1.1b" + >>> processor = AutoProcessor.from_pretrained(model_id) + >>> model = ParakeetForCTC.from_pretrained(model_id) + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate)) + + >>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"]) + >>> predicted_ids = model.generate(**inputs) + >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) + + >>> print(transcription) + ``` + """ + kwargs["return_dict"] = True + outputs: CausalLMOutput = self.forward( + input_features=input_features, + attention_mask=attention_mask, + **kwargs, + ) + + # greedy decoding + sequences = outputs.logits.argmax(dim=-1) + + # mask out padded tokens + if attention_mask is not None: + attention_mask = self._get_output_attention_mask(attention_mask, target_length=sequences.shape[1]) + sequences[~attention_mask] = self.config.pad_token_id + + if return_dict_in_generate: + return ParakeetGenerateOutput( + sequences=sequences, + logits=outputs.logits, + attentions=outputs.attentions, + hidden_states=outputs.hidden_states, + ) + + return sequences + + +__all__ = ["ParakeetForCTC", "ParakeetEncoder", "ParakeetPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/modular_parakeet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/modular_parakeet.py new file mode 100644 index 0000000000000000000000000000000000000000..b53d61a0c22d483fd04476b677cd496853d664e5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/modular_parakeet.py @@ -0,0 +1,654 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Parakeet model.""" + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, CausalLMOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..fastspeech2_conformer.modeling_fastspeech2_conformer import FastSpeech2ConformerConvolutionModule +from ..llama.modeling_llama import LlamaAttention, eager_attention_forward +from .configuration_parakeet import ParakeetCTCConfig, ParakeetEncoderConfig + + +@dataclass +@auto_docstring( + custom_intro=""" + Extends [~modeling_outputs.BaseModelOutput] to include the output attention mask since sequence length is not preserved in the model's forward. + """ +) +class ParakeetEncoderModelOutput(BaseModelOutput): + attention_mask: torch.Tensor | None = None + + +class ParakeetEncoderRelPositionalEncoding(nn.Module): + """Relative positional encoding for Parakeet.""" + + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: ParakeetEncoderConfig, device=None): + super().__init__() + self.max_position_embeddings = config.max_position_embeddings + base = 10000.0 + inv_freq = 1.0 / ( + base + ** ( + torch.arange(0, config.hidden_size, 2, dtype=torch.int64).to(device=device, dtype=torch.float) + / config.hidden_size + ) + ) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + + @torch.no_grad() + def forward(self, hidden_states: torch.Tensor): + seq_length = hidden_states.shape[1] + if seq_length > self.max_position_embeddings: + raise ValueError( + f"Sequence Length: {seq_length} has to be less or equal than " + f"config.max_position_embeddings {self.max_position_embeddings}." + ) + + position_ids = torch.arange(seq_length - 1, -seq_length, -1, device=hidden_states.device) + inv_freq_expanded = ( + self.inv_freq[None, :, None].float().expand(hidden_states.shape[0], -1, 1).to(hidden_states.device) + ) + position_ids_expanded = position_ids[None, None, :].float() + + device_type = ( + hidden_states.device.type + if isinstance(hidden_states.device.type, str) and hidden_states.device.type != "mps" + else "cpu" + ) + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + sin = freqs.sin() + cos = freqs.cos() + # interleave sin and cos + pos_embed = torch.stack([sin, cos], dim=-1) + pos_embed = pos_embed.reshape(*pos_embed.shape[:-2], -1) + + return pos_embed.to(dtype=hidden_states.dtype) + + +class ParakeetEncoderFeedForward(nn.Module): + def __init__(self, config: ParakeetEncoderConfig): + super().__init__() + self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.attention_bias) + self.activation = ACT2FN[config.hidden_act] + self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.attention_bias) + self.activation_dropout = config.activation_dropout + + def forward(self, hidden_states): + hidden_states = self.activation(self.linear1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.linear2(hidden_states) + return hidden_states + + +class ParakeetEncoderConvolutionModule(FastSpeech2ConformerConvolutionModule): + def __init__(self, config: ParakeetEncoderConfig, module_config=None): + super().__init__(config, module_config) + + +class ParakeetEncoderAttention(LlamaAttention): + """Multi-head attention with relative positional encoding. See section 3.3 of https://huggingface.co/papers/1901.02860.""" + + def __init__(self, config: ParakeetEncoderConfig, layer_idx: int): + super().__init__(config, layer_idx=layer_idx) + self.is_causal = False + # W_{k,R} projection + self.relative_k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False) + # global content bias + self.bias_u = nn.Parameter(torch.zeros(config.num_attention_heads, self.head_dim)) + # global positional bias + self.bias_v = nn.Parameter(torch.zeros(config.num_attention_heads, self.head_dim)) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + batch_size, seq_length = input_shape + hidden_shape = (batch_size, seq_length, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + query_states_with_bias_u = query_states + self.bias_u.view( + 1, self.config.num_attention_heads, 1, self.head_dim + ) + query_states_with_bias_v = query_states + self.bias_v.view( + 1, self.config.num_attention_heads, 1, self.head_dim + ) + + relative_key_states = self.relative_k_proj(position_embeddings) + relative_key_states = relative_key_states.view(batch_size, -1, self.config.num_attention_heads, self.head_dim) + + # terms (b) and (d) + matrix_bd = query_states_with_bias_v @ relative_key_states.permute(0, 2, 3, 1) + matrix_bd = self._rel_shift(matrix_bd) + matrix_bd = matrix_bd[..., :seq_length] + matrix_bd = matrix_bd * self.scaling + + if attention_mask is not None: + # here the original codebase uses -10000.0 rather than float("-inf") and then manual masked fill with 0.0s + # see: https://github.com/NVIDIA-NeMo/NeMo/blob/8cfedd7203462cb251a914e700e5605444277561/nemo/collections/asr/parts/submodules/multi_head_attention.py#L320-L340 + # we rather went for a straight-forward approach with float("-inf") + matrix_bd = matrix_bd.masked_fill_(attention_mask.logical_not(), float("-inf")) + + # will compute matrix_ac - terms (a) and (c) - and add matrix_bd + attn_output, attn_weights = attention_interface( + self, + query=query_states_with_bias_u, + key=key_states, + value=value_states, + attention_mask=matrix_bd, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + def _rel_shift(self, attention_scores): + """Relative position shift for Shaw et al. style attention. See appendix B of https://huggingface.co/papers/1901.02860.""" + batch_size, num_heads, query_length, position_length = attention_scores.shape + attention_scores = nn.functional.pad(attention_scores, pad=(1, 0)) + attention_scores = attention_scores.view(batch_size, num_heads, -1, query_length) + attention_scores = attention_scores[:, :, 1:].view(batch_size, num_heads, query_length, position_length) + return attention_scores + + +class ParakeetEncoderSubsamplingConv2D(nn.Module): + def __init__(self, config: ParakeetEncoderConfig): + super().__init__() + + self.kernel_size = config.subsampling_conv_kernel_size + self.stride = config.subsampling_conv_stride + self.channels = config.subsampling_conv_channels + self.padding = (self.kernel_size - 1) // 2 + self.num_layers = int(math.log2(config.subsampling_factor)) + + # define layers + self.layers = nn.ModuleList() + self.layers.append( + nn.Conv2d(1, self.channels, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding) + ) + self.layers.append(nn.ReLU()) + for i in range(self.num_layers - 1): + # depthwise conv + self.layers.append( + nn.Conv2d( + self.channels, + self.channels, + kernel_size=self.kernel_size, + stride=self.stride, + padding=self.padding, + groups=self.channels, + ) + ) + # pointwise conv + self.layers.append(nn.Conv2d(self.channels, self.channels, kernel_size=1)) + # activation + self.layers.append(nn.ReLU()) + + out_length = config.num_mel_bins // (self.stride**self.num_layers) + self.linear = nn.Linear(config.subsampling_conv_channels * out_length, config.hidden_size, bias=True) + + def _get_output_length(self, input_lengths: torch.Tensor, conv_layer: nn.Conv2d): + if hasattr(conv_layer, "stride") and conv_layer.stride != (1, 1): + padding = conv_layer.padding + kernel_size = conv_layer.kernel_size[0] + stride = conv_layer.stride[0] + + output_lengths = (input_lengths + padding[0] + padding[1] - kernel_size) // stride + 1 + return output_lengths + + return input_lengths + + def forward(self, input_features: torch.Tensor, attention_mask: torch.Tensor = None): + hidden_states = input_features.unsqueeze(1) + current_lengths = attention_mask.sum(-1) if attention_mask is not None else None + + for layer in self.layers: + hidden_states = layer(hidden_states) + + # mask the hidden states + if isinstance(layer, nn.Conv2d) and attention_mask is not None: + current_lengths = self._get_output_length(current_lengths, layer) + current_seq_length = hidden_states.shape[2] + channel_mask = ( + torch.arange(current_seq_length, device=attention_mask.device) < current_lengths[:, None] + ) + hidden_states *= channel_mask[:, None, :, None] + + hidden_states = hidden_states.transpose(1, 2).reshape(hidden_states.shape[0], hidden_states.shape[2], -1) + hidden_states = self.linear(hidden_states) + + return hidden_states + + +class ParakeetEncoderBlock(GradientCheckpointingLayer): + def __init__(self, config: ParakeetEncoderConfig, layer_idx: int | None = None): + super().__init__() + self.gradient_checkpointing = False + + self.feed_forward1 = ParakeetEncoderFeedForward(config) + self.self_attn = ParakeetEncoderAttention(config, layer_idx) + self.conv = ParakeetEncoderConvolutionModule(config) + self.feed_forward2 = ParakeetEncoderFeedForward(config) + + self.norm_feed_forward1 = nn.LayerNorm(config.hidden_size) + self.norm_self_att = nn.LayerNorm(config.hidden_size) + self.norm_conv = nn.LayerNorm(config.hidden_size) + self.norm_feed_forward2 = nn.LayerNorm(config.hidden_size) + self.norm_out = nn.LayerNorm(config.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.feed_forward1(self.norm_feed_forward1(hidden_states)) + hidden_states = residual + 0.5 * hidden_states # the conformer architecture uses a factor of 0.5 + + normalized_hidden_states = self.norm_self_att(hidden_states) + attn_output, _ = self.self_attn( + hidden_states=normalized_hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = hidden_states + attn_output + + conv_output = self.conv(self.norm_conv(hidden_states), attention_mask=attention_mask) + hidden_states = hidden_states + conv_output + + ff2_output = self.feed_forward2(self.norm_feed_forward2(hidden_states)) + hidden_states = hidden_states + 0.5 * ff2_output # the conformer architecture uses a factor of 0.5 + + hidden_states = self.norm_out(hidden_states) + + return hidden_states + + +@auto_docstring +class ParakeetPreTrainedModel(PreTrainedModel): + config: ParakeetCTCConfig + base_model_prefix = "model" + main_input_name = "input_features" + input_modalities = "audio" + supports_gradient_checkpointing = True + _no_split_modules = ["ParakeetEncoderBlock"] + _supports_flat_attention_mask = True + _supports_sdpa = True + _supports_flex_attn = True + + # TODO: @eustlb, add support when flash attention supports custom attention bias + _supports_flash_attn = False + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": ParakeetEncoderBlock, + "attentions": ParakeetEncoderAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + + if hasattr(self.config, "initializer_range"): + std = self.config.initializer_range + else: + # 0.02 is the standard default value across the library + std = getattr(self.config.get_text_config(), "initializer_range", 0.02) + + if isinstance(module, ParakeetEncoderAttention): + # Initialize positional bias parameters + init.normal_(module.bias_u, mean=0.0, std=std) + init.normal_(module.bias_v, mean=0.0, std=std) + elif isinstance(module, ParakeetEncoderRelPositionalEncoding): + inv_freq = 1.0 / ( + 10000.0 ** (torch.arange(0, self.config.hidden_size, 2, dtype=torch.int64) / self.config.hidden_size) + ) + init.copy_(module.inv_freq, inv_freq) + + def _get_subsampling_output_length(self, input_lengths: torch.Tensor): + encoder_config = self.config.encoder_config if isinstance(self.config, ParakeetCTCConfig) else self.config + + kernel_size = encoder_config.subsampling_conv_kernel_size + stride = encoder_config.subsampling_conv_stride + num_layers = int(math.log2(encoder_config.subsampling_factor)) + + all_paddings = (kernel_size - 1) // 2 * 2 + add_pad = all_paddings - kernel_size + lengths = input_lengths + + for _ in range(num_layers): + lengths = torch.div(lengths.to(dtype=torch.float) + add_pad, stride) + 1.0 + lengths = torch.floor(lengths) + + return lengths.to(dtype=torch.int) + + def _get_output_attention_mask(self, attention_mask: torch.Tensor, target_length: int | None = None): + """ + Convert the input attention mask to its subsampled form. `target_length` sets the desired output length, useful + when the attention mask length differs from `sum(-1).max()` (i.e., when the longest sequence in the batch is padded) + """ + output_lengths = self._get_subsampling_output_length(attention_mask.sum(-1)) + # Use target_length if provided, otherwise use max length in batch + max_length = target_length if target_length is not None else output_lengths.max() + attention_mask = torch.arange(max_length, device=attention_mask.device) < output_lengths[:, None] + return attention_mask + + +@auto_docstring( + custom_intro=""" + The Parakeet Encoder model, based on the [Fast Conformer architecture](https://huggingface.co/papers/2305.05084). + """ +) +class ParakeetEncoder(ParakeetPreTrainedModel): + config: ParakeetEncoderConfig + base_model_prefix = "encoder" + + def __init__(self, config: ParakeetEncoderConfig): + super().__init__(config) + self.config = config + self.gradient_checkpointing = False + + self.dropout = config.dropout + self.dropout_positions = config.dropout_positions + self.layerdrop = config.layerdrop + + self.input_scale = math.sqrt(config.hidden_size) if config.scale_input else 1.0 + self.subsampling = ParakeetEncoderSubsamplingConv2D(config) + self.encode_positions = ParakeetEncoderRelPositionalEncoding(config) + + self.layers = nn.ModuleList( + [ParakeetEncoderBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + + self.post_init() + + @auto_docstring + @merge_with_config_defaults + @capture_outputs + @can_return_tuple + def forward( + self, + input_features: torch.Tensor, + attention_mask: torch.Tensor | None = None, + output_attention_mask: bool = True, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + output_attention_mask (`bool`, *optional*, defaults to `True`): + Whether to return the output attention mask. Only effective when `attention_mask` is provided. + + Example: + + ```python + >>> from transformers import AutoProcessor, ParakeetEncoder + >>> from datasets import load_dataset, Audio + + >>> model_id = "nvidia/parakeet-ctc-1.1b" + >>> processor = AutoProcessor.from_pretrained(model_id) + >>> encoder = ParakeetEncoder.from_pretrained(model_id) + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate)) + + >>> inputs = processor(ds[0]["audio"]["array"]) + >>> encoder_outputs = encoder(**inputs) + + >>> print(encoder_outputs.last_hidden_state.shape) + ``` + """ + + hidden_states = self.subsampling(input_features, attention_mask) + hidden_states = hidden_states * self.input_scale + position_embeddings = self.encode_positions(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + position_embeddings = nn.functional.dropout( + position_embeddings, p=self.dropout_positions, training=self.training + ) + + if attention_mask is not None: + output_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1]) + attention_mask = output_mask.unsqueeze(1).expand(-1, hidden_states.shape[1], -1) + attention_mask = attention_mask & attention_mask.transpose(1, 2) + attention_mask = attention_mask.unsqueeze(1) + + for encoder_layer in self.layers: + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if not to_drop: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + return ParakeetEncoderModelOutput( + last_hidden_state=hidden_states, + attention_mask=output_mask.int() if attention_mask is not None and output_attention_mask else None, + ) + + +@dataclass +class ParakeetGenerateOutput(ModelOutput): + """ + Outputs of Parakeet models. + + Args: + sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter + if all batches finished early due to the `eos_token_id`. + logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`): + Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax) + at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for + each generated token), with each tensor of shape `(batch_size, config.vocab_size)`. + attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`): + Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of + `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`. + hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`): + Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of + `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`. + """ + + sequences: torch.LongTensor + logits: tuple[torch.FloatTensor] | None = None + attentions: tuple[tuple[torch.FloatTensor]] | None = None + hidden_states: tuple[tuple[torch.FloatTensor]] | None = None + + +@auto_docstring( + custom_intro=""" + Parakeet Encoder with a Connectionist Temporal Classification (CTC) head. + """ +) +class ParakeetForCTC(ParakeetPreTrainedModel): + config: ParakeetCTCConfig + + def __init__(self, config: ParakeetCTCConfig): + super().__init__(config) + self.encoder = ParakeetEncoder(config.encoder_config) + # Conv rather than linear to be consistent with NeMO decoding layer + self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1) + + self.post_init() + + @auto_docstring + @can_return_tuple + def forward( + self, + input_features: torch.Tensor, + attention_mask: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutput: + r""" + Example: + + ```python + >>> from transformers import AutoProcessor, ParakeetForCTC + >>> from datasets import load_dataset, Audio + + >>> model_id = "nvidia/parakeet-ctc-1.1b" + >>> processor = AutoProcessor.from_pretrained(model_id) + >>> model = ParakeetForCTC.from_pretrained(model_id) + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate)) + + >>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"]) + >>> outputs = model(**inputs) + + >>> print(outputs.loss) + ```""" + + encoder_outputs = self.encoder( + input_features=input_features, + attention_mask=attention_mask, + **kwargs, + ) + + hidden_states = encoder_outputs.last_hidden_state + logits = self.ctc_head(hidden_states.transpose(1, 2)).transpose(1, 2) + + loss = None + if labels is not None: + # retrieve loss input_lengths from attention_mask + attention_mask = ( + attention_mask if attention_mask is not None else torch.ones_like(input_features, dtype=torch.long) + ) + input_lengths = self._get_subsampling_output_length(attention_mask.sum(-1)) + + # assuming that padded tokens are filled with -100 + # when not being attended to + labels_mask = labels != self.config.pad_token_id + target_lengths = labels_mask.sum(-1) + flattened_targets = labels.masked_select(labels_mask) + + # ctc_loss doesn't support fp16 + log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1) + + with torch.backends.cudnn.flags(enabled=False): + loss = nn.functional.ctc_loss( + log_probs, + flattened_targets, + input_lengths, + target_lengths, + blank=self.config.pad_token_id, + reduction=self.config.ctc_loss_reduction, + zero_infinity=self.config.ctc_zero_infinity, + ) + + return CausalLMOutput( + loss=loss, + logits=logits, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_features: torch.Tensor, + attention_mask: torch.Tensor | None = None, + return_dict_in_generate: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> ParakeetGenerateOutput | torch.LongTensor: + r""" + Example: + + ```python + >>> from transformers import AutoProcessor, ParakeetForCTC + >>> from datasets import load_dataset, Audio + + >>> model_id = "nvidia/parakeet-ctc-1.1b" + >>> processor = AutoProcessor.from_pretrained(model_id) + >>> model = ParakeetForCTC.from_pretrained(model_id) + + >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") + >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate)) + + >>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"]) + >>> predicted_ids = model.generate(**inputs) + >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) + + >>> print(transcription) + ``` + """ + kwargs["return_dict"] = True + outputs: CausalLMOutput = self.forward( + input_features=input_features, + attention_mask=attention_mask, + **kwargs, + ) + + # greedy decoding + sequences = outputs.logits.argmax(dim=-1) + + # mask out padded tokens + if attention_mask is not None: + attention_mask = self._get_output_attention_mask(attention_mask, target_length=sequences.shape[1]) + sequences[~attention_mask] = self.config.pad_token_id + + if return_dict_in_generate: + return ParakeetGenerateOutput( + sequences=sequences, + logits=outputs.logits, + attentions=outputs.attentions, + hidden_states=outputs.hidden_states, + ) + + return sequences + + +__all__ = ["ParakeetForCTC", "ParakeetEncoder", "ParakeetPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/processing_parakeet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/processing_parakeet.py new file mode 100644 index 0000000000000000000000000000000000000000..69734fb055afd0c72bbcd366dd61410d6cba37f4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/processing_parakeet.py @@ -0,0 +1,94 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ...audio_utils import AudioInput, make_list_of_audio +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +class ParakeetProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "audio_kwargs": { + "sampling_rate": 16000, + "padding": "longest", + "return_attention_mask": True, + }, + "text_kwargs": { + "padding": True, + "padding_side": "right", + "add_special_tokens": False, + }, + "common_kwargs": {"return_tensors": "pt"}, + } + + +@auto_docstring +class ParakeetProcessor(ProcessorMixin): + def __init__(self, feature_extractor, tokenizer): + super().__init__(feature_extractor, tokenizer) + + @auto_docstring + def __call__( + self, + audio: AudioInput, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + sampling_rate: int | None = None, + **kwargs: Unpack[ParakeetProcessorKwargs], + ): + r""" + sampling_rate (`int`, *optional*): + The sampling rate of the input audio in Hz. This should match the sampling rate expected by the feature + extractor (defaults to 16000 Hz). If provided, it will be validated against the processor's expected + sampling rate, and an error will be raised if they don't match. If not provided, a warning will be + issued and the default sampling rate will be assumed. + """ + audio = make_list_of_audio(audio) + + output_kwargs = self._merge_kwargs( + ParakeetProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + if sampling_rate is None: + logger.warning_once( + f"You've provided audio without specifying the sampling rate. It will be assumed to be {output_kwargs['audio_kwargs']['sampling_rate']}, which can result in silent errors." + ) + elif sampling_rate != output_kwargs["audio_kwargs"]["sampling_rate"]: + raise ValueError( + f"The sampling rate of the audio ({sampling_rate}) does not match the sampling rate of the processor ({output_kwargs['audio_kwargs']['sampling_rate']}). Please provide resampled the audio to the expected sampling rate." + ) + + if audio is not None: + inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"]) + if text is not None: + encodings = self.tokenizer(text, **output_kwargs["text_kwargs"]) + + if text is None: + return inputs + else: + inputs["labels"] = encodings["input_ids"] + return inputs + + @property + def model_input_names(self): + feature_extractor_input_names = self.feature_extractor.model_input_names + return feature_extractor_input_names + ["labels"] + + +__all__ = ["ParakeetProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/tokenization_parakeet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/tokenization_parakeet.py new file mode 100644 index 0000000000000000000000000000000000000000..7b51885aef06818d63c47b7c1166c91046153793 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/parakeet/tokenization_parakeet.py @@ -0,0 +1,52 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools + +from ...tokenization_utils_tokenizers import TokenizersBackend + + +class ParakeetTokenizer(TokenizersBackend): + """ + Inherits all methods from [`PreTrainedTokenizerFast`]. Users should refer to this superclass for more information regarding those methods, + except for `_decode` which is overridden to adapt it to CTC decoding: + 1. Group consecutive tokens + 2. Filter out the blank token + """ + + def _decode( + self, + token_ids: int | list[int], + skip_special_tokens: bool = False, + clean_up_tokenization_spaces: bool | None = None, + group_tokens: bool = True, + **kwargs, + ) -> str: + if isinstance(token_ids, int): + token_ids = [token_ids] + if group_tokens: + token_ids = [token_group[0] for token_group in itertools.groupby(token_ids)] + + # for CTC we filter out the blank token, which is the pad token + token_ids = [token for token in token_ids if token != self.pad_token_id] + + return super()._decode( + token_ids=token_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + +__all__ = ["ParakeetTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..285c1970308a47827806fca349d130703f40a2c8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_patchtsmixer import * + from .modeling_patchtsmixer import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/configuration_patchtsmixer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/configuration_patchtsmixer.py new file mode 100644 index 0000000000000000000000000000000000000000..92dd5a3d3247ccafac2a598e94c14ebc60413764 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/configuration_patchtsmixer.py @@ -0,0 +1,166 @@ +# Copyright 2023 IBM and HuggingFace Inc. team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PatchTSMixer model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="ibm/patchtsmixer-etth1-pretrain") +@strict +class PatchTSMixerConfig(PreTrainedConfig): + r""" + context_length (`int`, *optional*, defaults to 32): + The context/history length for the input sequence. + patch_length (`int`, *optional*, defaults to 8): + The patch length for the input sequence. + patch_stride (`int`, *optional*, defaults to 8): + Determines the overlap between two consecutive patches. Set it to patch_length (or greater), if we want + non-overlapping patches. + num_parallel_samples (`int`, *optional*, defaults to 100): + The number of samples to generate in parallel for probabilistic forecast. + expansion_factor (`int`, *optional*, defaults to 2): + Expansion factor to use inside MLP. Recommended range is 2-5. Larger value indicates more complex model. + mode (`str`, *optional*, defaults to `"common_channel"`): + Mixer Mode. Determines how to process the channels. Allowed values: "common_channel", "mix_channel". In + "common_channel" mode, we follow Channel-independent modelling with no explicit channel-mixing. Channel + mixing happens in an implicit manner via shared weights across channels. (preferred first approach) In + "mix_channel" mode, we follow explicit channel-mixing in addition to patch and feature mixer. (preferred + approach when channel correlations are very important to model) + gated_attn (`bool`, *optional*, defaults to `True`): + Enable Gated Attention. + norm_mlp (`str`, *optional*, defaults to `"LayerNorm"`): + Normalization layer (BatchNorm or LayerNorm). + self_attn (`bool`, *optional*, defaults to `False`): + Enable Tiny self attention across patches. This can be enabled when the output of Vanilla PatchTSMixer with + gated attention is not satisfactory. Enabling this leads to explicit pair-wise attention and modelling + across patches. + self_attn_heads (`int`, *optional*, defaults to 1): + Number of self-attention heads. Works only when `self_attn` is set to `True`. + use_positional_encoding (`bool`, *optional*, defaults to `False`): + Enable the use of positional embedding for the tiny self-attention layers. Works only when `self_attn` is + set to `True`. + positional_encoding_type (`str`, *optional*, defaults to `"sincos"`): + Positional encodings. Options `"random"` and `"sincos"` are supported. Works only when + `use_positional_encoding` is set to `True` + scaling (`string` or `bool`, *optional*, defaults to `"std"`): + Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the + scaler is set to "mean". + loss (`string`, *optional*, defaults to `"mse"`): + The loss function for the model corresponding to the `distribution_output` head. For parametric + distributions it is the negative log likelihood ("nll") and for point estimates it is the mean squared + error "mse". + norm_eps (`float`, *optional*, defaults to 1e-05): + A value added to the denominator for numerical stability of normalization. + mask_type (`str`, *optional*, defaults to `"random"`): + Type of masking to use for Masked Pretraining mode. Allowed values are "random", "forecast". In Random + masking, points are masked randomly. In Forecast masking, points are masked towards the end. + random_mask_ratio (`float`, *optional*, defaults to 0.5): + Masking ratio to use when `mask_type` is `random`. Higher value indicates more masking. + num_forecast_mask_patches (`int` or `list`, *optional*, defaults to `[2]`): + Number of patches to be masked at the end of each batch sample. If it is an integer, all the samples in the + batch will have the same number of masked patches. If it is a list, samples in the batch will be randomly + masked by numbers defined in the list. This argument is only used for forecast pretraining. + mask_value (`float`, *optional*, defaults to `0.0`): + Mask value to use. + masked_loss (`bool`, *optional*, defaults to `True`): + Whether to compute pretraining loss only at the masked portions, or on the entire output. + channel_consistent_masking (`bool`, *optional*, defaults to `True`): + When true, masking will be same across all channels of a timeseries. Otherwise, masking positions will vary + across channels. + unmasked_channel_indices (`list`, *optional*): + Channels that are not masked during pretraining. + head_dropout (`float`, *optional*, defaults to 0.2): + The dropout probability the `PatchTSMixer` head. + distribution_output (`string`, *optional*, defaults to `"student_t"`): + The distribution emission head for the model when loss is "nll". Could be either "student_t", "normal" or + "negative_binomial". + prediction_length (`int`, *optional*, defaults to 16): + Number of time steps to forecast for a forecasting task. Also known as the Forecast Horizon. + prediction_channel_indices (`list`, *optional*): + List of channel indices to forecast. If None, forecast all channels. Target data is expected to have all + channels and we explicitly filter the channels in prediction and target before loss computation. + num_targets (`int`, *optional*, defaults to 3): + Number of targets (dimensionality of the regressed variable) for a regression task. + output_range (`list`, *optional*): + Output range to restrict for the regression task. Defaults to None. + head_aggregation (`str`, *optional*, defaults to `"max_pool"`): + Aggregation mode to enable for classification or regression task. Allowed values are `None`, "use_last", + "max_pool", "avg_pool". + + Example: + + ```python + >>> from transformers import PatchTSMixerConfig, PatchTSMixerModel + + >>> # Initializing a default PatchTSMixer configuration + >>> configuration = PatchTSMixerConfig() + + >>> # Randomly initializing a model (with random weights) from the configuration + >>> model = PatchTSMixerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "patchtsmixer" + attribute_map = { + "hidden_size": "d_model", + "num_hidden_layers": "num_layers", + } + + context_length: int = 32 + patch_length: int = 8 + num_input_channels: int = 1 + patch_stride: int = 8 + num_parallel_samples: int = 100 + d_model: int = 8 + expansion_factor: int = 2 + num_layers: int = 3 + dropout: float | int = 0.2 + mode: str = "common_channel" + gated_attn: bool = True + norm_mlp: str = "LayerNorm" + self_attn: bool = False + self_attn_heads: int = 1 + use_positional_encoding: bool = False + positional_encoding_type: str = "sincos" + scaling: str | bool | None = "std" + loss: str = "mse" + init_std: float = 0.02 + norm_eps: float = 1e-5 + mask_type: str = "random" + random_mask_ratio: float = 0.5 + num_forecast_mask_patches: list[int] | tuple[int, ...] | int | None = (2,) + mask_value: int = 0 + masked_loss: bool = True + channel_consistent_masking: bool = True + unmasked_channel_indices: list[int] | None = None + head_dropout: float | int = 0.2 + distribution_output: str = "student_t" + prediction_length: int = 16 + prediction_channel_indices: list | None = None + num_targets: int = 3 + output_range: list | None = None + head_aggregation: str | None = "max_pool" + + def __post_init__(self, **kwargs): + self.num_patches = (max(self.context_length, self.patch_length) - self.patch_length) // self.patch_stride + 1 + self.patch_last = True + super().__post_init__(**kwargs) + + +__all__ = ["PatchTSMixerConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/modeling_patchtsmixer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/modeling_patchtsmixer.py new file mode 100644 index 0000000000000000000000000000000000000000..3146261e3204916fbeca067be90fcbd25de7d044 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtsmixer/modeling_patchtsmixer.py @@ -0,0 +1,2121 @@ +# Copyright 2023 IBM and HuggingFace Inc. team. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PatchTSMixer model.""" + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import torch +import torch.nn as nn + +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ModelOutput + +from ... import initialization as init +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...processing_utils import Unpack +from ...time_series_utils import NegativeBinomialOutput, NormalOutput, StudentTOutput +from ...utils import TransformersKwargs, auto_docstring, logging +from .configuration_patchtsmixer import PatchTSMixerConfig + + +logger = logging.get_logger(__name__) + + +class PatchTSMixerGatedAttention(nn.Module): + """ + Module that applies gated attention to input data. + + Args: + in_size (`int`): The input size. + out_size (`int`): The output size. + """ + + def __init__(self, in_size: int, out_size: int): + super().__init__() + self.attn_layer = nn.Linear(in_size, out_size) + self.attn_softmax = nn.Softmax(dim=-1) + + def forward(self, inputs): + attn_weight = self.attn_softmax(self.attn_layer(inputs)) + inputs = inputs * attn_weight + return inputs + + +# Copied from transformers.models.patchtst.modeling_patchtst.PatchTSTBatchNorm with PatchTST->PatchTSMixer +class PatchTSMixerBatchNorm(nn.Module): + """ + Compute batch normalization over the sequence length (time) dimension. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + self.batchnorm = nn.BatchNorm1d(config.d_model, eps=config.norm_eps) + + def forward(self, inputs: torch.Tensor): + """ + Parameters: + inputs (`torch.Tensor` of shape `(batch_size, sequence_length, d_model)`): + input for Batch norm calculation + Returns: + `torch.Tensor` of shape `(batch_size, sequence_length, d_model)` + """ + output = inputs.transpose(1, 2) # output: (batch_size, d_model, sequence_length) + output = self.batchnorm(output) + return output.transpose(1, 2) + + +class PatchTSMixerPositionalEncoding(nn.Module): + """ + Class for positional encoding + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + # positional encoding: [num_patches x d_model] + if config.use_positional_encoding: + self.position_enc = self._init_pe(config) + else: + self.position_enc = nn.Parameter(torch.zeros(config.num_patches, config.d_model)) + + @staticmethod + def _init_pe(config: PatchTSMixerConfig) -> nn.Parameter: + # Positional encoding + if config.positional_encoding_type == "random": + position_enc = nn.Parameter(torch.randn(config.num_patches, config.d_model), requires_grad=True) + elif config.positional_encoding_type == "sincos": + position_enc = torch.zeros(config.num_patches, config.d_model) + position = torch.arange(0, config.num_patches).unsqueeze(1) + div_term = torch.exp(torch.arange(0, config.d_model, 2) * -(math.log(10000.0) / config.d_model)) + position_enc[:, 0::2] = torch.sin(position * div_term) + position_enc[:, 1::2] = torch.cos(position * div_term) + position_enc = position_enc - position_enc.mean() + position_enc = position_enc / (position_enc.std() * 10) + position_enc = nn.Parameter(position_enc, requires_grad=False) + else: + raise ValueError( + f"{config.positional_encoding_type} is not a valid positional encoder. Available types are 'random' and 'sincos'." + ) + return position_enc + + def forward(self, patch_input: torch.Tensor): + # hidden_state: [bs x num_channels x num_patches x d_model] + hidden_state = patch_input + self.position_enc + return hidden_state + + +class PatchTSMixerNormLayer(nn.Module): + """Normalization block + + Args: + config (`PatchTSMixerConfig`): + Configuration. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + + self.norm_mlp = config.norm_mlp + + if "batch" in config.norm_mlp.lower(): + self.norm = PatchTSMixerBatchNorm(config) + else: + self.norm = nn.LayerNorm(config.d_model, eps=config.norm_eps) + + def forward(self, inputs: torch.Tensor): + """ + Args: + inputs (`torch.Tensor` of shape `((batch_size, num_channels, num_patches, d_model))`): + Input to the normalization layer. + Returns: + `torch.Tensor` of shape `((batch_size, num_channels, num_patches, d_model))` + """ + if "batch" in self.norm_mlp.lower(): + # reshape the data + inputs_reshaped = torch.reshape( + inputs, + ( + inputs.shape[0] * inputs.shape[1], + inputs.shape[2], + inputs.shape[3], + ), + ) # inputs_reshaped: [batch_size*num_channels, num_patches, d_model] + + # inputs_reshaped: [batch_size*num_channels, num_patches, d_model] + inputs_reshaped = self.norm(inputs_reshaped) + + # put back data to the original shape + inputs = torch.reshape(inputs_reshaped, inputs.shape) + + else: + inputs = self.norm(inputs) + + return inputs + + +class PatchTSMixerMLP(nn.Module): + def __init__(self, in_features, out_features, config): + super().__init__() + num_hidden = in_features * config.expansion_factor + self.fc1 = nn.Linear(in_features, num_hidden) + self.dropout1 = nn.Dropout(config.dropout) + self.fc2 = nn.Linear(num_hidden, out_features) + self.dropout2 = nn.Dropout(config.dropout) + + def forward(self, inputs: torch.Tensor): + """ + Args: + inputs (`torch.Tensor` of shape `((batch_size, num_channels, num_patches, d_model))`): + Input to the MLP layer. + Returns: + `torch.Tensor` of the same shape as `inputs` + """ + inputs = self.dropout1(nn.functional.gelu(self.fc1(inputs))) + inputs = self.fc2(inputs) + inputs = self.dropout2(inputs) + return inputs + + +class PatchTSMixerChannelFeatureMixerBlock(nn.Module): + """This module mixes the features in the channel dimension. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + + self.norm = PatchTSMixerNormLayer(config) + self.gated_attn = config.gated_attn + self.mlp = PatchTSMixerMLP( + in_features=config.num_input_channels, + out_features=config.num_input_channels, + config=config, + ) + + if config.gated_attn: + self.gating_block = PatchTSMixerGatedAttention( + in_size=config.num_input_channels, out_size=config.num_input_channels + ) + + def forward(self, inputs: torch.Tensor): + """ + Args: + inputs (`torch.Tensor` of shape `((batch_size, num_channels, num_patches, d_model))`): + input to the MLP layer + Returns: + `torch.Tensor` of the same shape as `inputs` + """ + residual = inputs + inputs = self.norm(inputs) + + inputs = inputs.permute(0, 3, 2, 1) + + if self.gated_attn: + inputs = self.gating_block(inputs) + + inputs = self.mlp(inputs) + + inputs = inputs.permute(0, 3, 2, 1) + + out = inputs + residual + return out + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Attention with Wav2Vec2->PatchTSMixer +class PatchTSMixerAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: PatchTSMixerConfig | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = False, + # TODO: we need a refactor so that the different attention modules can get their specific kwargs + # ATM, we have mixed things encoder, decoder, and encoder-decoder attn + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + + # get query proj + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + current_states = key_value_states if is_cross_attention else hidden_states + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = self.k_proj(current_states).view(kv_shape).transpose(1, 2) + value_states = self.v_proj(current_states).view(kv_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + output_attentions=output_attentions, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights, None + + +class PatchMixerBlock(nn.Module): + """This module mixes the patch dimension. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + + self.norm = PatchTSMixerNormLayer(config) + + self.self_attn = config.self_attn + self.gated_attn = config.gated_attn + + self.mlp = PatchTSMixerMLP( + in_features=config.num_patches, + out_features=config.num_patches, + config=config, + ) + + if config.gated_attn: + self.gating_block = PatchTSMixerGatedAttention(in_size=config.num_patches, out_size=config.num_patches) + + if config.self_attn: + self.self_attn_layer = PatchTSMixerAttention( + embed_dim=config.d_model, + num_heads=config.self_attn_heads, + dropout=config.dropout, + config=config, + ) + self.norm_attn = PatchTSMixerNormLayer(config) + + def forward(self, hidden_state): + """ + Args: + hidden_state (`torch.Tensor`): Input tensor. + + Returns: + `torch.Tensor`: Transformed tensor. + """ + residual = hidden_state + + hidden_state = self.norm(hidden_state) + + if self.self_attn: + batch_size, n_vars, num_patches, d_model = hidden_state.shape + hidden_state_reshaped = hidden_state.reshape(batch_size * n_vars, num_patches, d_model) + + x_attn, _, _ = self.self_attn_layer(hidden_state_reshaped, output_attentions=False) + x_attn = x_attn.reshape(batch_size, n_vars, num_patches, d_model) + + # Transpose so that num_patches is the last dimension + hidden_state = hidden_state.transpose(2, 3) + hidden_state = self.mlp(hidden_state) + + if self.gated_attn: + hidden_state = self.gating_block(hidden_state) + + # Transpose back + hidden_state = hidden_state.transpose(2, 3) + + if self.self_attn: + hidden_state = self.norm_attn(hidden_state + x_attn) + + out = hidden_state + residual + return out + + +class FeatureMixerBlock(nn.Module): + """This module mixes the hidden feature dimension. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + + self.norm = PatchTSMixerNormLayer(config) + + self.gated_attn = config.gated_attn + + self.mlp = PatchTSMixerMLP( + in_features=config.d_model, + out_features=config.d_model, + config=config, + ) + + if config.gated_attn: + self.gating_block = PatchTSMixerGatedAttention(in_size=config.d_model, out_size=config.d_model) + + def forward(self, hidden: torch.Tensor): + """ + Args: + hidden (`torch.Tensor` of shape `(batch_size, num_patches, d_model)`): + Input tensor to the layer. + + Returns: + `torch.Tensor`: Transformed tensor. + """ + residual = hidden + hidden = self.norm(hidden) + hidden = self.mlp(hidden) + + if self.gated_attn: + hidden = self.gating_block(hidden) + + out = hidden + residual + return out + + +class PatchTSMixerLayer(nn.Module): + """ + The `PatchTSMixer` layer that does all three kinds of mixing. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + + self.patch_mixer = PatchMixerBlock(config=config) + self.feature_mixer = FeatureMixerBlock(config=config) + + self.mode = config.mode + + if config.mode == "mix_channel": + self.channel_feature_mixer = PatchTSMixerChannelFeatureMixerBlock(config=config) + + def forward(self, hidden: torch.Tensor): + """ + Args: + hidden (`torch.Tensor` of shape `(batch_size, num_patches, d_model)`): + Input tensor to the layer. + + Returns: + `torch.Tensor`: Transformed tensor. + """ + if self.mode == "mix_channel": + hidden = self.channel_feature_mixer(hidden) + + hidden = self.patch_mixer(hidden) + hidden = self.feature_mixer(hidden) # hidden: (batch_size x num_patches x d_model) + return hidden + + +class PatchTSMixerBlock(nn.Module): + """The main computing framework of the `PatchTSMixer` model. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + + num_layers = config.num_layers + + self.mixers = nn.ModuleList([PatchTSMixerLayer(config=config) for _ in range(num_layers)]) + + def forward(self, hidden_state, output_hidden_states: bool = False): + """ + Args: + hidden_state (`torch.Tensor`): The input tensor. + output_hidden_states (`bool`, *optional*, defaults to False.): + Whether to output the hidden states as well. + + Returns: + `torch.Tensor`: The embedding. `list`: List of all hidden states if `output_hidden_states` is set to + `True`. + """ + all_hidden_states = [] + + embedding = hidden_state + + for mod in self.mixers: + embedding = mod(embedding) + if output_hidden_states: + all_hidden_states.append(embedding) + + if output_hidden_states: + return embedding, all_hidden_states + else: + return embedding, None + + +class PatchTSMixerForPredictionHead(nn.Module): + """Prediction Head for Forecasting + + Args: + config (`PatchTSMixerConfig`): + Configuration. + """ + + def __init__(self, config: PatchTSMixerConfig, distribution_output=None): + super().__init__() + + self.prediction_channel_indices = config.prediction_channel_indices + + if self.prediction_channel_indices is not None: + self.prediction_channel_indices.sort() + + self.dropout_layer = nn.Dropout(config.head_dropout) + if distribution_output is None: + self.base_forecast_block = nn.Linear((config.num_patches * config.d_model), config.prediction_length) + else: + self.base_forecast_block = distribution_output.get_parameter_projection( + config.num_patches * config.d_model + ) + + self.flatten = nn.Flatten(start_dim=-2) + + def forward(self, hidden_features): + """ + + Args: + hidden_features (`torch.Tensor` of shape `(batch_size, num_patch, d_model)` in `flatten` mode + or `(batch_size, n_vars, num_patch, d_model)` in `common_channel`/`mix_channel` mode.): Input hidden + features. + + Returns: + `torch.Tensor` of shape `(batch_size, prediction_length, nvars)`. + + """ + + hidden_features = self.flatten(hidden_features) # [batch_size x n_vars x num_patch * d_model] + hidden_features = self.dropout_layer(hidden_features) # [batch_size x n_vars x num_patch * d_model] + forecast = self.base_forecast_block(hidden_features) # [batch_size x n_vars x prediction_length] + if isinstance(forecast, tuple): + forecast = tuple(z.transpose(-1, -2) for z in forecast) + else: + forecast = forecast.transpose(-1, -2) # [batch_size x prediction_length x n_vars] + + if self.prediction_channel_indices is not None: + if isinstance(forecast, tuple): + forecast = tuple(z[..., self.prediction_channel_indices] for z in forecast) + else: + forecast = forecast[..., self.prediction_channel_indices] # [batch_size x prediction_length x n_vars] + + return forecast + + +class PatchTSMixerLinearHead(nn.Module): + """Linear head for Classification and Regression. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + """ + + def __init__(self, config: PatchTSMixerConfig, distribution_output=None): + super().__init__() + + self.head_aggregation = config.head_aggregation + self.output_range = config.output_range + + if config.head_aggregation is None: + mul_factor = config.num_patches + else: + mul_factor = 1 + self.distribution_output = distribution_output + if distribution_output is None: + self.projection = nn.Linear( + config.d_model * config.num_input_channels * mul_factor, + config.num_targets, + ) + else: + self.projection = distribution_output.get_parameter_projection( + config.d_model * config.num_input_channels * mul_factor + ) + + if config.head_aggregation is None: + self.flatten = nn.Flatten(start_dim=-3) + else: + self.flatten = nn.Flatten(start_dim=-2) + + self.dropout = nn.Dropout(config.head_dropout) + + def forward(self, hidden_features): + """ + Args: + hidden_features (`torch.Tensor` of shape `(batch_size x num_patch x d_model)` in `flatten` mode + or `(batch_size x n_vars x num_patch x d_model)` in `common_channel`/`mix_channel` mode.): Input hidden + features. + + Returns: + `torch.Tensor` of shape `(batch_size x num_targets)`. + """ + + # batch_size x d_model x num_patch or batch_size x n_vars x d_model x num_patch + hidden_features = hidden_features.transpose(-1, -2) + if self.head_aggregation == "use_last": + # batch_size x d_model (flatten) or # batch_size x n_vars x d_model (common_channel) + hidden_features = hidden_features[..., -1] + elif self.head_aggregation == "max_pool": + # batch_size x n_vars x d_model or batch_size x d_model + hidden_features = hidden_features.max(dim=-1).values + elif self.head_aggregation == "avg_pool": + # batch_size x n_vars x d_model or batch_size x d_model + hidden_features = hidden_features.mean(dim=-1) + + if self.flatten: + hidden_features = self.flatten(hidden_features) + hidden_features = self.dropout(hidden_features) + hidden_features = self.projection(hidden_features) # batch_size x num_targets + + if (self.distribution_output is None) and (self.output_range is not None): + hidden_features = ( + torch.sigmoid(hidden_features) * (self.output_range[1] - self.output_range[0]) + self.output_range[0] + ) + return hidden_features + + +@auto_docstring +class PatchTSMixerPreTrainedModel(PreTrainedModel): + # Weight initialization + config: PatchTSMixerConfig + base_model_prefix = "model" + main_input_name = "past_values" + input_modalities = ("time",) + supports_gradient_checkpointing = False + + @torch.no_grad() + def _init_weights(self, module): + """Initialize weights""" + if isinstance(module, PatchTSMixerPositionalEncoding): + # initialize positional encoding + if self.config.positional_encoding_type == "random": + init.normal_(module.position_enc, mean=0.0, std=0.1) + elif isinstance(module, (nn.LayerNorm, nn.BatchNorm1d)): + init.zeros_(module.bias) + init.ones_(module.weight) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + elif isinstance(module, PatchTSMixerBatchNorm): + init.zeros_(module.batchnorm.bias) + init.ones_(module.batchnorm.weight) + elif isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=self.config.init_std) + if module.bias is not None: + init.zeros_(module.bias) + + +class PatchTSMixerPretrainHead(nn.Module): + """Pretraining head. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + + self.dropout_layer = nn.Dropout(config.head_dropout) + self.base_pt_block = nn.Linear(config.d_model, config.patch_length) + + def forward(self, hidden_features): + """ + Args: + hidden_features (`torch.Tensor` of shape `(batch_size x num_patch x d_model)` in `flatten` mode + or `(batch_size x n_vars x num_patch x d_model)` in `common_channel`/`mix_channel` mode.): Input hidden + features. + + Returns: + `torch.Tensor` of shape `(batch_size x n_vars x num_patch x patch_length)`. + """ + + hidden_features = self.dropout_layer(hidden_features) + forecast = self.base_pt_block(hidden_features) # [batch_size x n_vars x num_patch x patch_length] + return forecast + + +# Copied from transformers.models.patchtst.modeling_patchtst.random_masking +def random_masking( + inputs: torch.Tensor, + mask_ratio: float, + unmasked_channel_indices: list | None = None, + channel_consistent_masking: bool = False, + mask_value: int = 0, +): + """random_masking: Mask the input considering the control variables. + + Args: + inputs (`torch.Tensor` of shape `(batch_size, num_channels, sequence_length, num_features)`): + The input tensor to mask. + mask_ratio (`float`): + Masking ratio applied to mask the input data during random pretraining. It is the number between 0 and 1. + unmasked_channel_indices (list, *optional*): + Indices of channels that will not be masked. + channel_consistent_masking (bool, *optional*, defaults to `False`): + When true, masking will be same across all channels of a timeseries. Otherwise, masking positions will vary + across channels. + mask_value (int, *optional*, defaults to 0): + Define the value of masked patches for pretraining. + + Returns: + `tuple(torch.Tensor)`: inputs_mask, masked input, same shape as input Tensor and mask tensor of shape [bs x c x + n] + """ + if mask_ratio < 0 or mask_ratio >= 1: + raise ValueError(f"Mask ratio {mask_ratio} has to be between 0 and 1.") + + batch_size, num_channels, sequence_length, num_features = inputs.shape + device = inputs.device + + len_keep = int(sequence_length * (1 - mask_ratio)) + + if channel_consistent_masking: + noise = torch.rand(batch_size, 1, sequence_length, device=device) # noise in [0, 1], bs x 1 x L + noise = noise.repeat(1, num_channels, 1) # bs x num_channels x time + else: + # noise in [0, 1], bs x num_channels x L + noise = torch.rand(batch_size, num_channels, sequence_length, device=device) + + # mask: [bs x num_channels x num_patch] + mask = torch.ones(batch_size, num_channels, sequence_length, device=device) + mask[:, :, :len_keep] = 0 + + # sort noise for each sample + ids_shuffle = torch.argsort(noise, dim=-1) # ascend: small is keep, large is remove + ids_restore = torch.argsort(ids_shuffle, dim=-1) # ids_restore: [bs x num_channels x L] + + mask = torch.gather(mask, dim=-1, index=ids_restore) + mask = mask.unsqueeze(-1).repeat(1, 1, 1, num_features) # mask: [bs x num_channels x num_patches x patch_length] + if unmasked_channel_indices is not None: + mask[:, unmasked_channel_indices, :, :] = 0 + + inputs_mask = inputs.masked_fill(mask.bool(), mask_value) + return inputs_mask, mask[..., 0] + + +# Copied from transformers.models.patchtst.modeling_patchtst.forecast_masking +def forecast_masking( + inputs: torch.Tensor, + num_forecast_mask_patches: list | int, + unmasked_channel_indices: list | None = None, + mask_value: int = 0, +): + """Forecast masking that masks the last K patches where K is from the num_forecast_mask_patches. + If num_forecast_mask_patches is a list, samples in the batch will be randomly masked by numbers defined in the list. + + Parameters: + inputs (`torch.Tensor`): + Input of shape `(bs, num_channels, num_patch, patch_length)` + num_forecast_mask_patches (`list`): + Number of patches to be masked at the end of each batch sample. e.g. 4 or [3, 5]. + unmasked_channel_indices (`list`, *optional*): + Indices of channels that are not masked. + mask_value (`int`, *optional*, defaults to 0): + Values in the masked patches will be filled by `mask_value`. + + Returns: + `tuple(torch.Tensor)`: inputs_mask, masked input, same shape as inputs Tensor and Mask tensor of shape `(bs, + num_channels , num_patch)` or `(bs, tsg1, tsg2, num_channels, num_patch)` + """ + + if isinstance(num_forecast_mask_patches, int): + num_forecast_mask_patches = [num_forecast_mask_patches] + forecast_mask_ratios = [1 for _ in num_forecast_mask_patches] + + batch_size, num_channels, sequence_length, num_features = inputs.shape + mask = torch.zeros(batch_size, num_channels, sequence_length, device=inputs.device) + + t_list = [] + total_length = 0 + total_ratio = sum(forecast_mask_ratios) + + for patch_length, ratio in zip(num_forecast_mask_patches, forecast_mask_ratios): + if patch_length <= 0 or patch_length >= sequence_length: + raise ValueError( + f"num_forecast_mask_patches {patch_length} should be greater than 0 and less than total patches." + ) + temp_len = int(batch_size * ratio / total_ratio) + t_list.append([patch_length, ratio, temp_len]) + total_length += temp_len + + t_list = sorted(t_list, key=lambda x: x[2]) + + if total_length < batch_size: + t_list[0][2] = t_list[0][2] + (batch_size - total_length) + elif total_length > batch_size: + t_list[-1][2] = t_list[-1][2] + (total_length - batch_size) + + batch1 = 0 + for patch_len, _, temp_len in t_list: + batch2 = batch1 + temp_len + mask[batch1:batch2, :, -patch_len:] = 1 + batch1 = batch2 + + perm = torch.randperm(mask.shape[0]) + mask = mask[perm] + + mask = mask.unsqueeze(-1).repeat(1, 1, 1, num_features) # mask: [bs x num_channels x num_patch x patch_len] + if unmasked_channel_indices is not None: + mask[:, unmasked_channel_indices, :, :] = 0 + + inputs_mask = inputs.masked_fill(mask.bool(), mask_value) + return inputs_mask, mask[..., 0] + + +# Copied from transformers.models.patchtst.modeling_patchtst.PatchTSTPatchify with PatchTST->PatchTSMixer +class PatchTSMixerPatchify(nn.Module): + """ + A class to patchify the time series sequence into different patches + + Returns: + `torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)` + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + + self.sequence_length = config.context_length + self.patch_length = config.patch_length + self.patch_stride = config.patch_stride + + if self.sequence_length <= self.patch_length: + raise ValueError( + f"Sequence length ({self.sequence_length}) has to be greater than the patch length ({self.patch_length})" + ) + + # get the number of patches + self.num_patches = (max(self.sequence_length, self.patch_length) - self.patch_length) // self.patch_stride + 1 + new_sequence_length = self.patch_length + self.patch_stride * (self.num_patches - 1) + self.sequence_start = self.sequence_length - new_sequence_length + + def forward(self, past_values: torch.Tensor): + """ + Parameters: + past_values (`torch.Tensor` of shape `(batch_size, sequence_length, num_channels)`, *required*): + Input for patchification + + Returns: + `torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)` + """ + sequence_length = past_values.shape[-2] + if sequence_length != self.sequence_length: + raise ValueError( + f"Input sequence length ({sequence_length}) doesn't match model configuration ({self.sequence_length})." + ) + # output: [bs x new_sequence_length x num_channels] + output = past_values[:, self.sequence_start :, :] + # output: [bs x num_patches x num_input_channels x patch_length] + output = output.unfold(dimension=-2, size=self.patch_length, step=self.patch_stride) + # output: [bs x num_input_channels x num_patches x patch_length] + output = output.transpose(-2, -3).contiguous() + return output + + +# Copied from transformers.models.patchtst.modeling_patchtst.PatchTSTMasking with PatchTST->PatchTSMixer +class PatchTSMixerMasking(nn.Module): + """ + Class to perform random or forecast masking. + + Parameters: + config (`PatchTSMixerConfig`): model config + Returns: + x_mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`) + Masked patched input + mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches)`) + Bool tensor indicating True on masked points + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + self.random_mask_ratio = config.random_mask_ratio + self.channel_consistent_masking = config.channel_consistent_masking + self.mask_type = config.mask_type + self.num_forecast_mask_patches = config.num_forecast_mask_patches + self.unmasked_channel_indices = config.unmasked_channel_indices + self.mask_value = config.mask_value + if self.unmasked_channel_indices is not None: + self.unmasked_channel_indices = sorted(self.unmasked_channel_indices) + + def forward(self, patch_input: torch.Tensor): + """ + Parameters: + patch_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`, *required*): + Patch input + + Return: + masked_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`) + Masked patched input + mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches)`) + Bool tensor indicating True on masked points + + """ + if self.mask_type == "random": + masked_input, mask = random_masking( + inputs=patch_input, + mask_ratio=self.random_mask_ratio, + unmasked_channel_indices=self.unmasked_channel_indices, + channel_consistent_masking=self.channel_consistent_masking, + mask_value=self.mask_value, + ) + elif self.mask_type == "forecast": + masked_input, mask = forecast_masking( + inputs=patch_input, + num_forecast_mask_patches=self.num_forecast_mask_patches, + unmasked_channel_indices=self.unmasked_channel_indices, + mask_value=self.mask_value, + ) + else: + raise ValueError(f"Invalid mask type {self.mask_type}.") + + # mask: [bs x num_input_channels x num_patch] + mask = mask.bool() + return masked_input, mask + + +# Copied from transformers.models.patchtst.modeling_patchtst.PatchTSTStdScaler with PatchTST->PatchTSMixer +class PatchTSMixerStdScaler(nn.Module): + """ + Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by + subtracting from the mean and dividing by the standard deviation. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1 + self.keepdim = config.keepdim if hasattr(config, "keepdim") else True + self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-5 + + def forward( + self, data: torch.Tensor, observed_indicator: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Parameters: + data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`): + input for Batch norm calculation + observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Calculating the scale on the observed indicator. + Returns: + tuple of `torch.Tensor` of shapes + (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`, + `(batch_size, 1, num_input_channels)`) + """ + denominator = observed_indicator.sum(self.dim, keepdim=self.keepdim) + denominator = denominator.clamp_min(1.0) + loc = (data * observed_indicator).sum(self.dim, keepdim=self.keepdim) / denominator + + variance = (((data - loc) * observed_indicator) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator + scale = torch.sqrt(variance + self.minimum_scale) + return (data - loc) / scale, loc, scale + + +# Copied from transformers.models.patchtst.modeling_patchtst.PatchTSTMeanScaler with PatchTST->PatchTSMixer +class PatchTSMixerMeanScaler(nn.Module): + """ + Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data + accordingly. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1 + self.keepdim = config.keepdim if hasattr(config, "keepdim") else True + self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10 + self.default_scale = config.default_scale if hasattr(config, "default_scale") else None + + def forward( + self, data: torch.Tensor, observed_indicator: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Parameters: + data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`): + input for Batch norm calculation + observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Calculating the scale on the observed indicator. + Returns: + tuple of `torch.Tensor` of shapes + (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`, + `(batch_size, 1, num_input_channels)`) + """ + ts_sum = (data * observed_indicator).abs().sum(self.dim, keepdim=True) + num_observed = observed_indicator.sum(self.dim, keepdim=True) + + scale = ts_sum / torch.clamp(num_observed, min=1) + + # If `default_scale` is provided, we use it, otherwise we use the scale + # of the batch. + if self.default_scale is None: + batch_sum = ts_sum.sum(dim=0) + batch_observations = torch.clamp(num_observed.sum(0), min=1) + default_scale = torch.squeeze(batch_sum / batch_observations) + else: + default_scale = self.default_scale * torch.ones_like(scale) + + # apply default scale where there are no observations + scale = torch.where(num_observed > 0, scale, default_scale) + + # ensure the scale is at least `self.minimum_scale` + scale = torch.clamp(scale, min=self.minimum_scale) + scaled_data = data / scale + + if not self.keepdim: + scale = scale.squeeze(dim=self.dim) + + return scaled_data, torch.zeros_like(scale), scale + + +# Copied from transformers.models.patchtst.modeling_patchtst.PatchTSTNOPScaler with PatchTST->PatchTSMixer +class PatchTSMixerNOPScaler(nn.Module): + """ + Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__() + self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1 + self.keepdim = config.keepdim if hasattr(config, "keepdim") else True + + def forward( + self, data: torch.Tensor, observed_indicator: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Parameters: + data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`): + input for Batch norm calculation + Returns: + tuple of `torch.Tensor` of shapes + (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`, + `(batch_size, 1, num_input_channels)`) + """ + scale = torch.ones_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim) + loc = torch.zeros_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim) + return data, loc, scale + + +@auto_docstring( + custom_intro=""" + Base class for `PatchTSMixerEncoderOutput`, with potential hidden states. + """ +) +@dataclass +class PatchTSMixerEncoderOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, d_model)`): + Hidden-state at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Hidden-states of the model at the output of each layer. + """ + + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +class PatchTSMixerEncoder(PatchTSMixerPreTrainedModel): + """ + Encoder for PatchTSMixer which inputs patched time-series and outputs patched embeddings. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__(config) + + self.return_dict = config.return_dict + + self.patcher = nn.Linear(config.patch_length, config.d_model) + if config.use_positional_encoding: + self.positional_encoder = PatchTSMixerPositionalEncoding(config=config) + else: + self.positional_encoder = None + self.mlp_mixer_encoder = PatchTSMixerBlock(config=config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + past_values: torch.Tensor, + output_hidden_states: bool | None = False, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PatchTSMixerEncoderOutput: + r""" + past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`): + Context values of the time series. For a pretraining task, this denotes the input time series to + predict the masked portion. For a forecasting task, this denotes the history/past time series values. + Similarly, for classification or regression tasks, it denotes the appropriate context values of the + time series. + + For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, + it is greater than 1. + + Returns: + `torch.FloatTensor` of shape `(batch_size, n_vars, num_patches, d_model)` + """ + + return_dict = return_dict if return_dict is not None else self.return_dict + + # flatten [bs x num_patch x d_model]. common_channel/mix_channel: [bs x n_vars x num_patch x d_model] + patches = self.patcher(past_values) + + # add positional encoder + if self.positional_encoder is not None: + patches = self.positional_encoder(patches) + + last_hidden_state, hidden_states = self.mlp_mixer_encoder(patches, output_hidden_states=output_hidden_states) + + if not return_dict: + return tuple( + v + for v in [ + last_hidden_state, + hidden_states, + ] + ) + + return PatchTSMixerEncoderOutput(last_hidden_state=last_hidden_state, hidden_states=hidden_states) + + +@auto_docstring( + custom_intro=""" + Base class for model's outputs, with potential hidden states. + """ +) +@dataclass +class PatchTSMixerModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, d_model)`): + Hidden-state at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Hidden-states of the model at the output of each layer. + patch_input (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, patch_length)`): + Patched input data to the model. + mask (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches)`, *optional*): + Bool Tensor indicating True in masked patches and False otherwise. + loc (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`, *optional*): + Gives the mean of the context window per channel. Used for revin denorm outside the model, if revin + enabled. + scale (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`, *optional*): + Gives the std dev of the context window per channel. Used for revin denorm outside the model, if revin + enabled. + """ + + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + patch_input: torch.FloatTensor | None = None + mask: torch.FloatTensor | None = None + loc: torch.FloatTensor | None = None + scale: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + The PatchTSMixer Model for time-series forecasting. + """ +) +class PatchTSMixerModel(PatchTSMixerPreTrainedModel): + def __init__(self, config: PatchTSMixerConfig, mask_input: bool = False): + r""" + mask_input (bool, *optional*, defaults to `False`): + Whether to mask the input using the [`PatchTSMixerMasking`] module. + """ + super().__init__(config) + + self.return_dict = config.return_dict + self.encoder = PatchTSMixerEncoder(config) + self.patching = PatchTSMixerPatchify(config) + + if mask_input is True: + self.masking = PatchTSMixerMasking(config) + else: + self.masking = None + + if config.scaling == "mean": + self.scaler = PatchTSMixerMeanScaler(config) + elif config.scaling == "std" or config.scaling is True: + self.scaler = PatchTSMixerStdScaler(config) + else: + self.scaler = PatchTSMixerNOPScaler(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + past_values: torch.Tensor, + observed_mask: torch.Tensor | None = None, + output_hidden_states: bool | None = False, + return_dict: bool | None = None, + **kwargs, + ) -> PatchTSMixerModelOutput: + r""" + past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`): + Context values of the time series. For a pretraining task, this denotes the input time series to predict + the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, + for classification or regression tasks, it denotes the appropriate context values of the time series. + + For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is + greater than 1. + observed_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + """ + return_dict = return_dict if return_dict is not None else self.return_dict + + mask = None + if observed_mask is None: + observed_mask = torch.ones_like(past_values) + scaled_past_values, loc, scale = self.scaler(past_values, observed_mask) + + patched_x = self.patching(scaled_past_values) # [batch_size x num_input_channels x num_patch x patch_length + + enc_input = patched_x + if self.masking is not None: + enc_input, mask = self.masking(patched_x) + # enc_input: [batch_size x num_input_channels x num_patch x patch_length] + # mask: [batch_size x num_input_channels x num_patch] + + encoder_output = self.encoder( + enc_input, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if isinstance(encoder_output, tuple): + encoder_output = PatchTSMixerEncoderOutput(*encoder_output) + + if not return_dict: + return tuple( + v + for v in [ + encoder_output.last_hidden_state, + encoder_output.hidden_states, + patched_x, + mask, + loc, + scale, + ] + ) + + return PatchTSMixerModelOutput( + last_hidden_state=encoder_output.last_hidden_state, + hidden_states=encoder_output.hidden_states, + patch_input=patched_x, + mask=mask, + loc=loc, + scale=scale, + ) + + +@auto_docstring( + custom_intro=""" + Output type of [`PatchTSMixerForPreTrainingOutput`]. + """ +) +@dataclass +class PatchTSMixerForPreTrainingOutput(ModelOutput): + r""" + loss (*optional*, returned when `y` is provided, `torch.FloatTensor` of shape `()`): + Total loss + prediction_outputs (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, patch_length)`): + Prediction output from the pretrain head. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, d_model)`): + Backbone embeddings before passing through the head. + hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Hidden-states of the model at the output of each layer. + """ + + loss: torch.FloatTensor | None = None + prediction_outputs: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + `PatchTSMixer` for mask pretraining. + """ +) +class PatchTSMixerForPretraining(PatchTSMixerPreTrainedModel): + def __init__(self, config: PatchTSMixerConfig): + super().__init__(config) + self.model = PatchTSMixerModel(config, mask_input=True) + self.head = PatchTSMixerPretrainHead(config=config) + self.masked_loss = config.masked_loss + self.return_dict = config.return_dict + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + past_values: torch.Tensor, + observed_mask: torch.Tensor | None = None, + output_hidden_states: bool | None = False, + return_loss: bool = True, + return_dict: bool | None = None, + **kwargs, + ) -> PatchTSMixerForPreTrainingOutput: + r""" + past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`): + Context values of the time series. For a pretraining task, this denotes the input time series to predict + the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, + for classification or regression tasks, it denotes the appropriate context values of the time series. + + For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is + greater than 1. + observed_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + return_loss (`bool`, *optional*): + Whether to return the loss in the `forward` call. + """ + return_dict = return_dict if return_dict is not None else self.return_dict + + if self.masked_loss is True: + loss = torch.nn.MSELoss(reduction="none") + else: + loss = torch.nn.MSELoss(reduction="mean") + + # past_values: tensor [batch_size x context_length x num_input_channels] + model_output = self.model( + past_values, + observed_mask=observed_mask, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) # x.last_hidden_state: [batch_size x nvars x num_patch x d_model] + if isinstance(model_output, tuple): + model_output = PatchTSMixerModelOutput(*model_output) + + x_hat = self.head(model_output.last_hidden_state) # tensor [batch_size x nvars x num_patch x patch_length] + + if return_loss is True: + loss_val = loss(x_hat, model_output.patch_input) + else: + loss_val = None + + # calculate masked_loss + if self.masked_loss is True and loss_val is not None: + loss_val = (loss_val.mean(dim=-1) * model_output.mask).sum() / (model_output.mask.sum() + 1e-10) + + if not return_dict: + return tuple( + v + for v in [ + loss_val, + x_hat, + model_output.last_hidden_state, + model_output.hidden_states, + ] + ) + + return PatchTSMixerForPreTrainingOutput( + loss=loss_val, + prediction_outputs=x_hat, # tensor [batch_size x nvars x num_patch x patch_length] + last_hidden_state=model_output.last_hidden_state, # x: [batch_size x nvars x num_patch x d_model] + hidden_states=model_output.hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + Output type of [`PatchTSMixerForPredictionOutput`]. + """ +) +@dataclass +class PatchTSMixerForPredictionOutput(ModelOutput): + r""" + loss (*optional*, returned when `y` is provided, `torch.FloatTensor` of shape `()`): + Total loss. + prediction_outputs (`torch.FloatTensor` of shape `(batch_size, prediction_length, num_input_channels)`): + Prediction output from the forecast head. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, d_model)`): + Backbone embeddings before passing through the head. + hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + loc (`torch.FloatTensor`, *optional* of shape `(batch_size, 1, num_input_channels)`): + Input mean + scale (`torch.FloatTensor`, *optional* of shape `(batch_size, 1, num_input_channels)`): + Input std dev + """ + + loss: torch.FloatTensor | None = None + prediction_outputs: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + loc: torch.FloatTensor | None = None + scale: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for time series model's predictions outputs that contains the sampled values from the chosen + distribution. + """ +) +@dataclass +class SamplePatchTSMixerPredictionOutput(ModelOutput): + r""" + sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length, number_channels)`): + Sampled values from the chosen distribution. + """ + + sequences: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for time series model's predictions outputs that contains the sampled values from the chosen + distribution. + """ +) +@dataclass +class SamplePatchTSMixerRegressionOutput(ModelOutput): + r""" + sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length, number_channels)`): + Sampled values from the chosen distribution. + """ + + sequences: torch.FloatTensor | None = None + + +# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.nll +def nll(input: torch.distributions.Distribution, target: torch.Tensor) -> torch.Tensor: + """ + Computes the negative log likelihood loss from input distribution with respect to target. + """ + return -input.log_prob(target) + + +# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.weighted_average +def weighted_average(input_tensor: torch.Tensor, weights: torch.Tensor | None = None, dim=None) -> torch.Tensor: + """ + Computes the weighted average of a given tensor across a given `dim`, masking values associated with weight zero, + meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`. + + Args: + input_tensor (`torch.FloatTensor`): + Input tensor, of which the average must be computed. + weights (`torch.FloatTensor`, *optional*): + Weights tensor, of the same shape as `input_tensor`. + dim (`int`, *optional*): + The dim along which to average `input_tensor`. + + Returns: + `torch.FloatTensor`: The tensor with values averaged along the specified `dim`. + """ + if weights is not None: + weighted_tensor = torch.where(weights != 0, input_tensor * weights, torch.zeros_like(input_tensor)) + sum_weights = torch.clamp(weights.sum(dim=dim) if dim else weights.sum(), min=1.0) + return (weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()) / sum_weights + else: + return input_tensor.mean(dim=dim) + + +class PatchTSMixerForPrediction(PatchTSMixerPreTrainedModel): + r""" + `PatchTSMixer` for forecasting application. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + + Returns: + `None`. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__(config) + self.loss = config.loss + self.return_dict = config.return_dict + self.prediction_channel_indices = config.prediction_channel_indices + self.num_parallel_samples = config.num_parallel_samples + + if config.loss == "mse": + self.distribution_output = None + else: + dim = config.prediction_length + distribution_output_map = { + "student_t": StudentTOutput, + "normal": NormalOutput, + "negative_binomial": NegativeBinomialOutput, + } + output_class = distribution_output_map.get(config.distribution_output) + if output_class is not None: + self.distribution_output = output_class(dim=dim) + else: + raise ValueError(f"Unknown distribution output {config.distribution_output}") + + self.model = PatchTSMixerModel(config) + self.head = PatchTSMixerForPredictionHead( + config=config, + distribution_output=self.distribution_output, + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + past_values: torch.Tensor, + observed_mask: torch.Tensor | None = None, + future_values: torch.Tensor | None = None, + output_hidden_states: bool | None = False, + return_loss: bool = True, + return_dict: bool | None = None, + **kwargs, + ) -> PatchTSMixerForPredictionOutput: + r""" + past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`): + Context values of the time series. For a pretraining task, this denotes the input time series to predict + the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, + for classification or regression tasks, it denotes the appropriate context values of the time series. + + For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is + greater than 1. + observed_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + future_values (`torch.FloatTensor` of shape `(batch_size, target_len, num_input_channels)` for forecasting,: + `(batch_size, num_targets)` for regression, or `(batch_size,)` for classification, *optional*): + Target values of the time series, that serve as labels for the model. The `future_values` is what the + Transformer needs during training to learn to output, given the `past_values`. Note that, this is NOT + required for a pretraining task. + + For a forecasting task, the shape is be `(batch_size, target_len, num_input_channels)`. Even if we want + to forecast only specific channels by setting the indices in `prediction_channel_indices` parameter, + pass the target data with all channels, as channel Filtering for both prediction and target will be + manually applied before the loss computation. + return_loss (`bool`, *optional*): + Whether to return the loss in the `forward` call. + """ + if self.loss == "mse": + loss = nn.MSELoss(reduction="mean") + elif self.loss == "nll": + loss = nll + else: + raise ValueError("Invalid loss function: Allowed values: mse and nll") + + return_dict = return_dict if return_dict is not None else self.return_dict + + # past_values: tensor [batch_size x context_length x num_input_channels] + model_output = self.model( + past_values, + observed_mask=observed_mask, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) # model_output: [batch_size x nvars x num_patch x d_model] + if isinstance(model_output, tuple): + model_output = PatchTSMixerModelOutput(*model_output) + + # tensor [batch_size x prediction_length x num_input_channels] + y_hat = self.head(model_output.last_hidden_state) + + loss_val = None + if self.prediction_channel_indices is not None: + if self.distribution_output: + distribution = self.distribution_output.distribution( + y_hat, + loc=model_output.loc[..., self.prediction_channel_indices], + scale=model_output.scale[..., self.prediction_channel_indices], + ) + if future_values is not None and return_loss is True: + loss_val = loss( + distribution, + future_values[..., self.prediction_channel_indices], + ) + # take average of the loss + loss_val = weighted_average(loss_val) + else: + y_hat = ( + y_hat * model_output.scale[..., self.prediction_channel_indices] + + model_output.loc[..., self.prediction_channel_indices] + ) + if future_values is not None and return_loss is True: + loss_val = loss(y_hat, future_values[..., self.prediction_channel_indices]) + else: + if self.distribution_output: + distribution = self.distribution_output.distribution( + y_hat, loc=model_output.loc, scale=model_output.scale + ) + if future_values is not None and return_loss is True: + loss_val = loss(distribution, future_values) + loss_val = weighted_average(loss_val) + else: + y_hat = y_hat * model_output.scale + model_output.loc + if future_values is not None and return_loss is True: + loss_val = loss(y_hat, future_values) + + if self.prediction_channel_indices is not None: + loc = model_output.loc[..., self.prediction_channel_indices] + scale = model_output.scale[..., self.prediction_channel_indices] + else: + loc = model_output.loc + scale = model_output.scale + + if not return_dict: + return tuple( + v + for v in [ + loss_val, + y_hat, + model_output.last_hidden_state, + model_output.hidden_states, + loc, + scale, + ] + ) + + return PatchTSMixerForPredictionOutput( + loss=loss_val, + prediction_outputs=y_hat, # tensor [batch_size x prediction_length x num_input_channels] + last_hidden_state=model_output.last_hidden_state, # x: [batch_size x nvars x num_patch x d_model] + hidden_states=model_output.hidden_states, + loc=loc, + scale=scale, + ) + + @torch.no_grad() + def generate( + self, + past_values: torch.Tensor, + observed_mask: torch.Tensor | None = None, + ) -> SamplePatchTSMixerPredictionOutput: + """ + Generate sequences of sample predictions from a model with a probability distribution head. + + Args: + past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Past values of the time series that serves as context in order to predict the future. + + observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + + Return: + [`SamplePatchTSMixerPredictionOutput`] where the outputs `sequences` tensor will have shape `(batch_size, + number of samples, prediction_length, num_input_channels)`. + """ + # get number of samples + num_parallel_samples = self.num_parallel_samples + + # get model output + outputs = self( + past_values=past_values, + future_values=None, + observed_mask=observed_mask, + output_hidden_states=False, + ) + + # get distribution + + distribution = self.distribution_output.distribution( + outputs.prediction_outputs, loc=outputs.loc, scale=outputs.scale + ) + + # get samples: list of [batch_size x prediction_length x num_channels] + samples = [distribution.sample() for _ in range(num_parallel_samples)] + + # stack tensors + samples = torch.stack(samples, dim=1) # [batch_size x num_samples x prediction_length x num_channels] + return SamplePatchTSMixerPredictionOutput(sequences=samples) + + +@auto_docstring( + custom_intro=""" + Output type of [`PatchTSMixerForTimeSeriesClassificationOutput`]. + """ +) +@dataclass +class PatchTSMixerForTimeSeriesClassificationOutput(ModelOutput): + r""" + loss (*optional*, returned when `y` is provided, `torch.FloatTensor` of shape `()`): + Total loss. + prediction_outputs (`torch.FloatTensor` of shape `(batch_size, num_labels)`): + Prediction output from the classification head. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, d_model)`): + Backbone embeddings before passing through the head. + hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + """ + + loss: torch.FloatTensor | None = None + prediction_outputs: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +class PatchTSMixerForTimeSeriesClassification(PatchTSMixerPreTrainedModel): + r""" + `PatchTSMixer` for classification application. + + Args: + config (`PatchTSMixerConfig`): + Configuration. + + Returns: + `None`. + """ + + def __init__(self, config: PatchTSMixerConfig): + super().__init__(config) + + self.model = PatchTSMixerModel(config) + self.head = PatchTSMixerLinearHead( + config=config, + ) + self.return_dict = config.return_dict + if config.scaling in ["std", "mean", True]: + self.inject_scale = InjectScalerStatistics4D(d_model=config.d_model, num_patches=config.num_patches) + else: + self.inject_scale = None + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + past_values: torch.Tensor, + target_values: torch.Tensor | None = None, + output_hidden_states: bool | None = False, + return_loss: bool = True, + return_dict: bool | None = None, + **kwargs, + ) -> PatchTSMixerForTimeSeriesClassificationOutput: + r""" + past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`): + Context values of the time series. For a pretraining task, this denotes the input time series to predict + the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, + for classification or regression tasks, it denotes the appropriate context values of the time series. + + For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is + greater than 1. + target_values (`torch.FloatTensor` of shape `(batch_size, target_len, num_input_channels)` for forecasting, + `(batch_size, num_targets)` for regression, or `(batch_size,)` for classification, *optional*): + Target + values of the time series, that serve as labels for the model. The `target_values` is what the + Transformer needs during training to learn to output, given the `past_values`. Note that, this is NOT + required for a pretraining task. + + For a forecasting task, the shape is be `(batch_size, target_len, num_input_channels)`. Even if we want + to forecast only specific channels by setting the indices in `prediction_channel_indices` parameter, + pass the target data with all channels, as channel Filtering for both prediction and target will be + manually applied before the loss computation. + + For a classification task, it has a shape of `(batch_size,)`. + + For a regression task, it has a shape of `(batch_size, num_targets)`. + return_loss (`bool`, *optional*): + Whether to return the loss in the `forward` call. + """ + + loss = torch.nn.CrossEntropyLoss() + + return_dict = return_dict if return_dict is not None else self.return_dict + + model_output = self.model( + past_values, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) # x: [batch_size x nvars x num_patch x d_model] + if isinstance(model_output, tuple): + model_output = PatchTSMixerModelOutput(*model_output) + + if self.inject_scale is not None: + model_output.last_hidden_state = self.inject_scale( + model_output.last_hidden_state, + loc=model_output.loc, + scale=model_output.scale, + ) # x: [batch_size x nvars x num_patch x d_model] + + y_hat = self.head(model_output.last_hidden_state) # tensor [batch_size x n_labels] + + if target_values is not None and return_loss is True: + loss_val = loss(y_hat, target_values) + else: + loss_val = None + + if not return_dict: + return tuple( + v + for v in [ + loss_val, + y_hat, + model_output.last_hidden_state, + model_output.hidden_states, + ] + ) + + return PatchTSMixerForTimeSeriesClassificationOutput( + loss=loss_val, + prediction_outputs=y_hat, # tensor [batch_size x n_labels] + last_hidden_state=model_output.last_hidden_state, # x: [batch_size x nvars x num_patch x d_model] + hidden_states=model_output.hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + Output type of [`PatchTSMixerForRegressionOutput`]. + """ +) +@dataclass +class PatchTSMixerForRegressionOutput(ModelOutput): + r""" + loss (*optional*, returned when `y` is provided, `torch.FloatTensor` of shape `()`): + Total loss. + regression_outputs (`torch.FloatTensor` of shape `(batch_size, num_targets)`): + Prediction output from the regression head. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_input_channels, num_patches, d_model)`): + Backbone embeddings before passing through the head. + hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. + """ + + loss: torch.FloatTensor | None = None + regression_outputs: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + + +class InjectScalerStatistics4D(nn.Module): + def __init__(self, d_model: int, num_patches: int, expansion: int = 2): + super().__init__() + + self.inverse_trans_expansion = nn.Linear(d_model + 2, expansion * d_model) + self.inverse_trans_compression = nn.Linear(expansion * d_model, d_model) + self.map_scale_expansion = nn.Linear(2, 2 * expansion) + self.map_scale_compression = nn.Linear(2 * expansion, 2) + self.num_patches = num_patches + + def forward(self, inputs: torch.Tensor, loc: torch.Tensor, scale: torch.Tensor): + """ + Args: + inputs (`torch.Tensor` of shape `(batch_size, num_input_channels, num_patch, d_model)`) + loc (`torch.Tensor` of shape `(batch_size, 1, num_input_channels)`) + scale (`torch.Tensor` of shape `(batch_size, 1, num_input_channels)`) + Returns: + `torch.Tensor` of shape `(batch_size, num_input_channels, num_patch, d_model)` + """ + + mean = loc.transpose(-1, -2) # [batch_size x n_channels x 1 ] + mean = mean.unsqueeze(-2) # [batch_size x n_channels x 1 x 1] + mean = mean.repeat(1, 1, self.num_patches, 1) # [batch_size x n_channels x num_patch x 1] + + stdev = scale.transpose(-1, -2) # [batch_size x n_channels x 1 ] + stdev = stdev.unsqueeze(-2) # [batch_size x n_channels x 1 x 1] + stdev = stdev.repeat(1, 1, self.num_patches, 1) # [batch_size x n_channels x num_patch x 1] + + concat_stats = torch.cat([mean, stdev], dim=-1) # [batch_size x n_channels x num_patch x 2] + + concat_stats = self.map_scale_expansion(concat_stats) # [batch_size x n_channels x num_patch x (2*expansion)] + concat_stats = self.map_scale_compression(concat_stats) # [batch_size x n_channels x num_patch x 2] + + inputs = torch.cat([inputs, concat_stats], dim=-1) # [batch_size x channels x num_patch x d_model+2] + inputs = self.inverse_trans_expansion(inputs) # [batch_size x channels x num_patch x (expansion*d_model)] + inputs = self.inverse_trans_compression(inputs) # [batch_size x channels x num_patch x d_model] + + return inputs + + +@auto_docstring( + custom_intro=""" + `PatchTSMixer` for regression application. + """ +) +class PatchTSMixerForRegression(PatchTSMixerPreTrainedModel): + def __init__(self, config: PatchTSMixerConfig): + super().__init__(config) + + self.model = PatchTSMixerModel(config) + + self.loss = config.loss + self.distribution_output = config.distribution_output + + self.return_dict = config.return_dict + self.num_parallel_samples = config.num_parallel_samples + + if config.loss == "mse": + self.distribution_output = None + else: + distribution_output_map = { + "student_t": StudentTOutput, + "normal": NormalOutput, + "negative_binomial": NegativeBinomialOutput, + } + output_class = distribution_output_map.get(config.distribution_output) + if output_class is not None: + self.distribution_output = output_class(dim=config.num_targets) + else: + raise ValueError(f"Unknown distribution output {config.distribution_output}") + + if config.scaling in ["std", "mean", True]: + self.inject_scale = InjectScalerStatistics4D(d_model=config.d_model, num_patches=config.num_patches) + else: + self.inject_scale = None + + self.head = PatchTSMixerLinearHead( + config=config, + distribution_output=self.distribution_output, + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + past_values: torch.Tensor, + target_values: torch.Tensor | None = None, + output_hidden_states: bool | None = False, + return_loss: bool = True, + return_dict: bool | None = None, + **kwargs, + ) -> PatchTSMixerForRegressionOutput: + r""" + past_values (`torch.FloatTensor` of shape `(batch_size, seq_length, num_input_channels)`): + Context values of the time series. For a pretraining task, this denotes the input time series to predict + the masked portion. For a forecasting task, this denotes the history/past time series values. Similarly, + for classification or regression tasks, it denotes the appropriate context values of the time series. + + For univariate time series, `num_input_channels` dimension should be 1. For multivariate time series, it is + greater than 1. + target_values (`torch.FloatTensor` of shape `(batch_size, target_len, num_input_channels)` for forecasting, + `(batch_size, num_targets)` for regression, or `(batch_size,)` for classification, *optional*): + Target values of the time series, that serve as labels for the model. The `target_values` is what the + Transformer needs during training to learn to output, given the `past_values`. Note that, this is NOT + required for a pretraining task. + + For a forecasting task, the shape is be `(batch_size, target_len, num_input_channels)`. Even if we want + to forecast only specific channels by setting the indices in `prediction_channel_indices` parameter, + pass the target data with all channels, as channel Filtering for both prediction and target will be + manually applied before the loss computation. + + For a classification task, it has a shape of `(batch_size,)`. + + For a regression task, it has a shape of `(batch_size, num_targets)`. + return_loss (`bool`, *optional*): + Whether to return the loss in the `forward` call. + """ + + if self.loss == "mse": + loss = nn.MSELoss(reduction="mean") + elif self.loss == "nll": + loss = nll + else: + raise ValueError("Invalid loss function: Allowed values: mse and nll") + + return_dict = return_dict if return_dict is not None else self.return_dict + model_output = self.model( + past_values, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) # model_output: [batch_size x nvars x num_patch x d_model] + if isinstance(model_output, tuple): + model_output = PatchTSMixerModelOutput(*model_output) + + if self.inject_scale is not None: + model_output.last_hidden_state = self.inject_scale( + model_output.last_hidden_state, + loc=model_output.loc, + scale=model_output.scale, + ) # x: [batch_size x nvars x num_patch x d_model] + + y_hat = self.head(model_output.last_hidden_state) # [batch_size x num_targets] + + if target_values is not None and return_loss is True: + if self.distribution_output: + if self.distribution_output == "negative_binomial" and torch.any(target_values < 0): + raise Exception("target_values cannot be negative for negative_binomial distribution.") + distribution = self.distribution_output.distribution(y_hat) + # y_hat should be a 2-tuple, each with dimension [bs, num_targets] + y_hat = tuple(item.view(-1, self.config.num_targets) for item in y_hat) + loss_val = loss(distribution, target_values) + # take average of the loss + loss_val = weighted_average(loss_val) + else: + loss_val = loss(y_hat, target_values) + else: + loss_val = None + + if not return_dict: + return tuple( + v + for v in [ + loss_val, + y_hat, + model_output.last_hidden_state, + model_output.hidden_states, + ] + ) + + return PatchTSMixerForRegressionOutput( + loss=loss_val, + regression_outputs=y_hat, # tensor [batch_size x num_targets] + last_hidden_state=model_output.last_hidden_state, # [batch_size x nvars x num_patch x d_model] + hidden_states=model_output.hidden_states, + ) + + @torch.no_grad() + def generate( + self, + past_values: torch.Tensor, + ) -> SamplePatchTSMixerRegressionOutput: + """ + Generate sequences of sample predictions from a model with a probability distribution head. + + Args: + past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Past values of the time series that serves as context in order to predict the target values. + + Return: + [`SamplePatchTSMixerRegressionOutput`] where the outputs `sequences` tensor will have shape `(batch_size, + number of samples, num_targets)`. + """ + # get number of samples + num_parallel_samples = self.num_parallel_samples + + # get model output + outputs = self( + past_values=past_values, + target_values=None, + output_hidden_states=False, + ) + + # get distribution + distribution = self.distribution_output.distribution(outputs.regression_outputs) + + # get samples + samples = [ + distribution.sample() for _ in range(num_parallel_samples) + ] # samples: list of [batch_size x num_targets] + # stack tensors + # [batch_size x num_samples x num_targets] + samples = torch.stack(samples, dim=1).view(-1, num_parallel_samples, self.config.num_targets) + return SamplePatchTSMixerRegressionOutput(sequences=samples) + + +__all__ = [ + "PatchTSMixerPreTrainedModel", + "PatchTSMixerModel", + "PatchTSMixerForPretraining", + "PatchTSMixerForPrediction", + "PatchTSMixerForTimeSeriesClassification", + "PatchTSMixerForRegression", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f1a045bfb1820424f77c9b798a3c027569eed3eb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_patchtst import * + from .modeling_patchtst import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/configuration_patchtst.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/configuration_patchtst.py new file mode 100644 index 0000000000000000000000000000000000000000..21e12276c89739f915105ca86aba523032df6b2b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/configuration_patchtst.py @@ -0,0 +1,170 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PatchTST model configuration""" + +from huggingface_hub.dataclasses import strict + +from transformers.configuration_utils import PreTrainedConfig +from transformers.utils import auto_docstring + + +@auto_docstring(checkpoint="ibm-granite/granite-timeseries-patchtst") +@strict +class PatchTSTConfig(PreTrainedConfig): + r""" + context_length (`int`, *optional*, defaults to 32): + The context length of the input sequence. + distribution_output (`str`, *optional*, defaults to `"student_t"`): + The distribution emission head for the model when loss is "nll". Could be either "student_t", "normal" or + "negative_binomial". + loss (`str`, *optional*, defaults to `"mse"`): + The loss function for the model corresponding to the `distribution_output` head. For parametric + distributions it is the negative log likelihood ("nll") and for point estimates it is the mean squared + error "mse". + patch_length (`int`, *optional*, defaults to 1): + Define the patch length of the patchification process. + patch_stride (`int`, *optional*, defaults to 1): + Define the stride of the patchification process. + num_attention_heads (`int`, *optional*, defaults to 4): + Number of attention heads for each attention layer in the Transformer encoder. + share_embedding (`bool`, *optional*, defaults to `True`): + Sharing the input embedding across all channels. + channel_attention (`bool`, *optional*, defaults to `False`): + Activate channel attention block in the Transformer to allow channels to attend each other. + ffn_dim (`int`, *optional*, defaults to 512): + Dimension of the "intermediate" (often named feed-forward) layer in the Transformer encoder. + norm_type (`str` , *optional*, defaults to `"batchnorm"`): + Normalization at each Transformer layer. Can be `"batchnorm"` or `"layernorm"`. + norm_eps (`float`, *optional*, defaults to 1e-05): + A value added to the denominator for numerical stability of normalization. + positional_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability in the positional embedding layer. + path_dropout (`float`, *optional*, defaults to 0.0): + The dropout path in the residual block. + ff_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability used between the two layers of the feed-forward networks. + bias (`bool`, *optional*, defaults to `True`): + Whether to add bias in the feed-forward networks. + activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (string) in the Transformer.`"gelu"` and `"relu"` are supported. + pre_norm (`bool`, *optional*, defaults to `True`): + Normalization is applied before self-attention if pre_norm is set to `True`. Otherwise, normalization is + applied after residual block. + positional_encoding_type (`str`, *optional*, defaults to `"sincos"`): + Positional encodings. Options `"random"` and `"sincos"` are supported. + use_cls_token (`bool`, *optional*, defaults to `False`): + Whether cls token is used. + init_std (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated normal weight initialization distribution. + share_projection (`bool`, *optional*, defaults to `True`): + Sharing the projection layer across different channels in the forecast head. + scaling (`Union`, *optional*, defaults to `"std"`): + Whether to scale the input targets via "mean" scaler, "std" scaler or no scaler if `None`. If `True`, the + scaler is set to "mean". + do_mask_input (`bool`, *optional*): + Apply masking during the pretraining. + mask_type (`str`, *optional*, defaults to `"random"`): + Masking type. Only `"random"` and `"forecast"` are currently supported. + random_mask_ratio (`float`, *optional*, defaults to 0.5): + Masking ratio applied to mask the input data during random pretraining. + num_forecast_mask_patches (`int` or `list`, *optional*, defaults to `[2]`): + Number of patches to be masked at the end of each batch sample. If it is an integer, + all the samples in the batch will have the same number of masked patches. If it is a list, + samples in the batch will be randomly masked by numbers defined in the list. This argument is only used + for forecast pretraining. + channel_consistent_masking (`bool`, *optional*, defaults to `False`): + If channel consistent masking is True, all the channels will have the same masking pattern. + unmasked_channel_indices (`list`, *optional*): + Indices of channels that are not masked during pretraining. Values in the list are number between 1 and + `num_input_channels` + mask_value (`int`, *optional*, defaults to 0): + Values in the masked patches will be filled by `mask_value`. + pooling_type (`str`, *optional*, defaults to `"mean"`): + Pooling of the embedding. `"mean"`, `"max"` and `None` are supported. + head_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for head. + prediction_length (`int`, *optional*, defaults to 24): + The prediction horizon that the model will output. + num_targets (`int`, *optional*, defaults to 1): + Number of targets for regression and classification tasks. For classification, it is the number of + classes. + output_range (`list`, *optional*): + Output range for regression task. The range of output values can be set to enforce the model to produce + values within a range. + num_parallel_samples (`int`, *optional*, defaults to 100): + The number of samples is generated in parallel for probabilistic prediction. + + ```python + >>> from transformers import PatchTSTConfig, PatchTSTModel + + >>> # Initializing an PatchTST configuration with 12 time steps for prediction + >>> configuration = PatchTSTConfig(prediction_length=12) + + >>> # Randomly initializing a model (with random weights) from the configuration + >>> model = PatchTSTModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "patchtst" + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "num_attention_heads", + "num_hidden_layers": "num_hidden_layers", + } + + num_input_channels: int = 1 + context_length: int = 32 + distribution_output: str = "student_t" + loss: str | None = "mse" + patch_length: int = 1 + patch_stride: int = 1 + num_hidden_layers: int = 3 + d_model: int = 128 + num_attention_heads: int = 4 + share_embedding: bool = True + channel_attention: bool = False + ffn_dim: int = 512 + norm_type: str = "batchnorm" + norm_eps: float = 1e-05 + attention_dropout: float | int = 0.0 + positional_dropout: float | int = 0.0 + path_dropout: float | int = 0.0 + ff_dropout: float | int = 0.0 + bias: bool = True + activation_function: str = "gelu" + pre_norm: bool = True + positional_encoding_type: str = "sincos" + use_cls_token: bool = False + init_std: float = 0.02 + share_projection: bool = True + scaling: str | bool | None = "std" + do_mask_input: bool | None = None + mask_type: str = "random" + random_mask_ratio: float = 0.5 + num_forecast_mask_patches: list[int] | tuple[int, ...] | int | None = (2,) + channel_consistent_masking: bool | None = False + unmasked_channel_indices: list[int] | None = None + mask_value: int = 0 + pooling_type: str | None = "mean" + head_dropout: float | int = 0.0 + prediction_length: int = 24 + num_targets: int = 1 + output_range: list | None = None + num_parallel_samples: int = 100 + + +__all__ = ["PatchTSTConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/modeling_patchtst.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/modeling_patchtst.py new file mode 100644 index 0000000000000000000000000000000000000000..1cdd3f381b58ada447343e077ef17a07465338fb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/patchtst/modeling_patchtst.py @@ -0,0 +1,1973 @@ +# Copyright 2023 IBM & Hugging Face. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PatchTST model.""" + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2CLS +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...time_series_utils import NegativeBinomialOutput, NormalOutput, StudentTOutput +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, logging +from .configuration_patchtst import PatchTSTConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Attention with Wav2Vec2->PatchTST +class PatchTSTAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: PatchTSTConfig | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = False, + # TODO: we need a refactor so that the different attention modules can get their specific kwargs + # ATM, we have mixed things encoder, decoder, and encoder-decoder attn + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + + # get query proj + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + current_states = key_value_states if is_cross_attention else hidden_states + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = self.k_proj(current_states).view(kv_shape).transpose(1, 2) + value_states = self.v_proj(current_states).view(kv_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + output_attentions=output_attentions, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights, None + + +class PatchTSTBatchNorm(nn.Module): + """ + Compute batch normalization over the sequence length (time) dimension. + """ + + def __init__(self, config: PatchTSTConfig): + super().__init__() + self.batchnorm = nn.BatchNorm1d(config.d_model, eps=config.norm_eps) + + def forward(self, inputs: torch.Tensor): + """ + Parameters: + inputs (`torch.Tensor` of shape `(batch_size, sequence_length, d_model)`): + input for Batch norm calculation + Returns: + `torch.Tensor` of shape `(batch_size, sequence_length, d_model)` + """ + output = inputs.transpose(1, 2) # output: (batch_size, d_model, sequence_length) + output = self.batchnorm(output) + return output.transpose(1, 2) + + +def random_masking( + inputs: torch.Tensor, + mask_ratio: float, + unmasked_channel_indices: list | None = None, + channel_consistent_masking: bool = False, + mask_value: int = 0, +): + """random_masking: Mask the input considering the control variables. + + Args: + inputs (`torch.Tensor` of shape `(batch_size, num_channels, sequence_length, num_features)`): + The input tensor to mask. + mask_ratio (`float`): + Masking ratio applied to mask the input data during random pretraining. It is the number between 0 and 1. + unmasked_channel_indices (list, *optional*): + Indices of channels that will not be masked. + channel_consistent_masking (bool, *optional*, defaults to `False`): + When true, masking will be same across all channels of a timeseries. Otherwise, masking positions will vary + across channels. + mask_value (int, *optional*, defaults to 0): + Define the value of masked patches for pretraining. + + Returns: + `tuple(torch.Tensor)`: inputs_mask, masked input, same shape as input Tensor and mask tensor of shape [bs x c x + n] + """ + if mask_ratio < 0 or mask_ratio >= 1: + raise ValueError(f"Mask ratio {mask_ratio} has to be between 0 and 1.") + + batch_size, num_channels, sequence_length, num_features = inputs.shape + device = inputs.device + + len_keep = int(sequence_length * (1 - mask_ratio)) + + if channel_consistent_masking: + noise = torch.rand(batch_size, 1, sequence_length, device=device) # noise in [0, 1], bs x 1 x L + noise = noise.repeat(1, num_channels, 1) # bs x num_channels x time + else: + # noise in [0, 1], bs x num_channels x L + noise = torch.rand(batch_size, num_channels, sequence_length, device=device) + + # mask: [bs x num_channels x num_patch] + mask = torch.ones(batch_size, num_channels, sequence_length, device=device) + mask[:, :, :len_keep] = 0 + + # sort noise for each sample + ids_shuffle = torch.argsort(noise, dim=-1) # ascend: small is keep, large is remove + ids_restore = torch.argsort(ids_shuffle, dim=-1) # ids_restore: [bs x num_channels x L] + + mask = torch.gather(mask, dim=-1, index=ids_restore) + mask = mask.unsqueeze(-1).repeat(1, 1, 1, num_features) # mask: [bs x num_channels x num_patches x patch_length] + if unmasked_channel_indices is not None: + mask[:, unmasked_channel_indices, :, :] = 0 + + inputs_mask = inputs.masked_fill(mask.bool(), mask_value) + return inputs_mask, mask[..., 0] + + +def forecast_masking( + inputs: torch.Tensor, + num_forecast_mask_patches: list | int, + unmasked_channel_indices: list | None = None, + mask_value: int = 0, +): + """Forecast masking that masks the last K patches where K is from the num_forecast_mask_patches. + If num_forecast_mask_patches is a list, samples in the batch will be randomly masked by numbers defined in the list. + + Parameters: + inputs (`torch.Tensor`): + Input of shape `(bs, num_channels, num_patch, patch_length)` + num_forecast_mask_patches (`list`): + Number of patches to be masked at the end of each batch sample. e.g. 4 or [3, 5]. + unmasked_channel_indices (`list`, *optional*): + Indices of channels that are not masked. + mask_value (`int`, *optional*, defaults to 0): + Values in the masked patches will be filled by `mask_value`. + + Returns: + `tuple(torch.Tensor)`: inputs_mask, masked input, same shape as inputs Tensor and Mask tensor of shape `(bs, + num_channels , num_patch)` or `(bs, tsg1, tsg2, num_channels, num_patch)` + """ + + if isinstance(num_forecast_mask_patches, int): + num_forecast_mask_patches = [num_forecast_mask_patches] + forecast_mask_ratios = [1 for _ in num_forecast_mask_patches] + + batch_size, num_channels, sequence_length, num_features = inputs.shape + mask = torch.zeros(batch_size, num_channels, sequence_length, device=inputs.device) + + t_list = [] + total_length = 0 + total_ratio = sum(forecast_mask_ratios) + + for patch_length, ratio in zip(num_forecast_mask_patches, forecast_mask_ratios): + if patch_length <= 0 or patch_length >= sequence_length: + raise ValueError( + f"num_forecast_mask_patches {patch_length} should be greater than 0 and less than total patches." + ) + temp_len = int(batch_size * ratio / total_ratio) + t_list.append([patch_length, ratio, temp_len]) + total_length += temp_len + + t_list = sorted(t_list, key=lambda x: x[2]) + + if total_length < batch_size: + t_list[0][2] = t_list[0][2] + (batch_size - total_length) + elif total_length > batch_size: + t_list[-1][2] = t_list[-1][2] + (total_length - batch_size) + + batch1 = 0 + for patch_len, _, temp_len in t_list: + batch2 = batch1 + temp_len + mask[batch1:batch2, :, -patch_len:] = 1 + batch1 = batch2 + + perm = torch.randperm(mask.shape[0]) + mask = mask[perm] + + mask = mask.unsqueeze(-1).repeat(1, 1, 1, num_features) # mask: [bs x num_channels x num_patch x patch_len] + if unmasked_channel_indices is not None: + mask[:, unmasked_channel_indices, :, :] = 0 + + inputs_mask = inputs.masked_fill(mask.bool(), mask_value) + return inputs_mask, mask[..., 0] + + +class PatchTSTPatchify(nn.Module): + """ + A class to patchify the time series sequence into different patches + + Returns: + `torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)` + """ + + def __init__(self, config: PatchTSTConfig): + super().__init__() + + self.sequence_length = config.context_length + self.patch_length = config.patch_length + self.patch_stride = config.patch_stride + + if self.sequence_length <= self.patch_length: + raise ValueError( + f"Sequence length ({self.sequence_length}) has to be greater than the patch length ({self.patch_length})" + ) + + # get the number of patches + self.num_patches = (max(self.sequence_length, self.patch_length) - self.patch_length) // self.patch_stride + 1 + new_sequence_length = self.patch_length + self.patch_stride * (self.num_patches - 1) + self.sequence_start = self.sequence_length - new_sequence_length + + def forward(self, past_values: torch.Tensor): + """ + Parameters: + past_values (`torch.Tensor` of shape `(batch_size, sequence_length, num_channels)`, *required*): + Input for patchification + + Returns: + `torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)` + """ + sequence_length = past_values.shape[-2] + if sequence_length != self.sequence_length: + raise ValueError( + f"Input sequence length ({sequence_length}) doesn't match model configuration ({self.sequence_length})." + ) + # output: [bs x new_sequence_length x num_channels] + output = past_values[:, self.sequence_start :, :] + # output: [bs x num_patches x num_input_channels x patch_length] + output = output.unfold(dimension=-2, size=self.patch_length, step=self.patch_stride) + # output: [bs x num_input_channels x num_patches x patch_length] + output = output.transpose(-2, -3).contiguous() + return output + + +class PatchTSTMasking(nn.Module): + """ + Class to perform random or forecast masking. + + Parameters: + config (`PatchTSTConfig`): model config + Returns: + x_mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`) + Masked patched input + mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches)`) + Bool tensor indicating True on masked points + """ + + def __init__(self, config: PatchTSTConfig): + super().__init__() + self.random_mask_ratio = config.random_mask_ratio + self.channel_consistent_masking = config.channel_consistent_masking + self.mask_type = config.mask_type + self.num_forecast_mask_patches = config.num_forecast_mask_patches + self.unmasked_channel_indices = config.unmasked_channel_indices + self.mask_value = config.mask_value + if self.unmasked_channel_indices is not None: + self.unmasked_channel_indices = sorted(self.unmasked_channel_indices) + + def forward(self, patch_input: torch.Tensor): + """ + Parameters: + patch_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`, *required*): + Patch input + + Return: + masked_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`) + Masked patched input + mask (`torch.Tensor` of shape `(batch_size, num_channels, num_patches)`) + Bool tensor indicating True on masked points + + """ + if self.mask_type == "random": + masked_input, mask = random_masking( + inputs=patch_input, + mask_ratio=self.random_mask_ratio, + unmasked_channel_indices=self.unmasked_channel_indices, + channel_consistent_masking=self.channel_consistent_masking, + mask_value=self.mask_value, + ) + elif self.mask_type == "forecast": + masked_input, mask = forecast_masking( + inputs=patch_input, + num_forecast_mask_patches=self.num_forecast_mask_patches, + unmasked_channel_indices=self.unmasked_channel_indices, + mask_value=self.mask_value, + ) + else: + raise ValueError(f"Invalid mask type {self.mask_type}.") + + # mask: [bs x num_input_channels x num_patch] + mask = mask.bool() + return masked_input, mask + + +class PatchTSTEncoderLayer(nn.Module): + """ + PatchTST encoder layer + """ + + def __init__(self, config: PatchTSTConfig): + super().__init__() + + self.channel_attention = config.channel_attention + + self.self_attn = PatchTSTAttention( + embed_dim=config.d_model, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + config=config, + ) + + # Add & Norm of the sublayer 1 + self.dropout_path1 = nn.Dropout(config.path_dropout) if config.path_dropout > 0 else nn.Identity() + if config.norm_type == "batchnorm": + self.norm_sublayer1 = PatchTSTBatchNorm(config) + elif config.norm_type == "layernorm": + self.norm_sublayer1 = nn.LayerNorm(config.d_model, eps=config.norm_eps) + else: + raise ValueError(f"{config.norm_type} is not a supported norm layer type.") + + # Add & Norm of the sublayer 2 + if self.channel_attention: + self.dropout_path2 = nn.Dropout(config.path_dropout) if config.path_dropout > 0 else nn.Identity() + if config.norm_type == "batchnorm": + self.norm_sublayer2 = PatchTSTBatchNorm(config) + elif config.norm_type == "layernorm": + self.norm_sublayer2 = nn.LayerNorm(config.d_model, eps=config.norm_eps) + else: + raise ValueError(f"{config.norm_type} is not a supported norm layer type.") + + # Position-wise Feed-Forward + self.ff = nn.Sequential( + nn.Linear(config.d_model, config.ffn_dim, bias=config.bias), + ACT2CLS[config.activation_function](), + nn.Dropout(config.ff_dropout) if config.ff_dropout > 0 else nn.Identity(), + nn.Linear(config.ffn_dim, config.d_model, bias=config.bias), + ) + + # Add & Norm of sublayer 3 + self.dropout_path3 = nn.Dropout(config.path_dropout) if config.path_dropout > 0 else nn.Identity() + if config.norm_type == "batchnorm": + self.norm_sublayer3 = PatchTSTBatchNorm(config) + elif config.norm_type == "layernorm": + self.norm_sublayer3 = nn.LayerNorm(config.d_model, eps=config.norm_eps) + else: + raise ValueError(f"{config.norm_type} is not a supported norm layer type.") + + self.pre_norm = config.pre_norm + + def forward(self, hidden_state: torch.Tensor, output_attentions: bool | None = None): + """ + Parameters: + hidden_state (`torch.Tensor` of shape `(batch_size, num_channels, sequence_length, d_model)`, *required*): + Past values of the time series + output_attentions (`bool`, *optional*): + Whether or not to return the output attention of all layers + Return: + `torch.Tensor` of shape `(batch_size, num_channels, sequence_length, d_model)` + + """ + batch_size, num_input_channels, sequence_length, d_model = hidden_state.shape + + # First sublayer: attention across time + # hidden_states: [(bs*num_channels) x sequence_length x d_model] + hidden_state = hidden_state.view(batch_size * num_input_channels, sequence_length, d_model) + + if self.pre_norm: + ## Norm and Multi-Head attention and Add residual connection + attn_output, attn_weights, _ = self.self_attn( + hidden_states=self.norm_sublayer1(hidden_state), output_attentions=output_attentions + ) + # Add: residual connection with residual dropout + hidden_state = hidden_state + self.dropout_path1(attn_output) + else: + ## Multi-Head attention and Add residual connection and Norm - Standard Transformer from BERT + attn_output, attn_weights, _ = self.self_attn( + hidden_states=hidden_state, output_attentions=output_attentions + ) + # hidden_states: [(bs*num_channels) x sequence_length x d_model] + hidden_state = self.norm_sublayer1(hidden_state + self.dropout_path1(attn_output)) + + # hidden_state: [bs x num_channels x sequence_length x d_model] + hidden_state = hidden_state.reshape(batch_size, num_input_channels, sequence_length, d_model) + + # second sublayer: attention across variable at any given time + if self.channel_attention: + # hidden_state: [bs x sequence_length x num_channels x d_model] + hidden_state = hidden_state.transpose(2, 1).contiguous() + # hidden_state: [(bs*sequence_length) x num_channels x d_model] + hidden_state = hidden_state.view(batch_size * sequence_length, num_input_channels, d_model) + if self.pre_norm: + ## Norm and Multi-Head attention and Add residual connection + attn_output, channel_attn_weights, _ = self.self_attn( + hidden_states=self.norm_sublayer2(hidden_state), output_attentions=output_attentions + ) + # Add: residual connection with residual dropout + hidden_state = hidden_state + self.dropout_path2(attn_output) + else: + ## Multi-Head attention and Add residual connection and Norm + attn_output, channel_attn_weights, _ = self.self_attn( + hidden_states=hidden_state, output_attentions=output_attentions + ) + # hidden_states: [(bs*sequence_length) x num_channels x d_model] + hidden_state = self.norm_sublayer2(hidden_state + self.dropout_path2(attn_output)) + + # Reshape hidden state + # hidden_state: [bs x sequence_length x num_channels x d_model] + hidden_state = hidden_state.reshape(batch_size, sequence_length, num_input_channels, d_model) + # hidden_state: [bs x num_channels x sequence_length x d_model] + hidden_state = hidden_state.transpose(1, 2).contiguous() + + # Third sublayer: mixing across hidden + # hidden_state: [(batch_size*num_channels) x sequence_length x d_model] + hidden_state = hidden_state.view(batch_size * num_input_channels, sequence_length, d_model) + if self.pre_norm: + ## Norm and Position-wise Feed-Forward and Add residual connection + # Add: residual connection with residual dropout + hidden_state = hidden_state + self.dropout_path3(self.ff(self.norm_sublayer3(hidden_state))) + else: + ## Position-wise Feed-Forward and Add residual connection and Norm + # Add: residual connection with residual dropout + hidden_state = self.norm_sublayer3(hidden_state + self.dropout_path3(self.ff(hidden_state))) + + # [bs x num_channels x sequence_length x d_model] + hidden_state = hidden_state.reshape(batch_size, num_input_channels, sequence_length, d_model) + + outputs = (hidden_state,) + if output_attentions: + outputs += (attn_weights, channel_attn_weights) if self.channel_attention else (attn_weights,) + + return outputs + + +@auto_docstring +class PatchTSTPreTrainedModel(PreTrainedModel): + config: PatchTSTConfig + base_model_prefix = "model" + main_input_name = "past_values" + input_modalities = ("time",) + supports_gradient_checkpointing = False + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + """ + Initialize weights + """ + if isinstance(module, PatchTSTPositionalEncoding): + # get the number of patches + num_patches = ( + max(self.config.context_length, self.config.patch_length) - self.config.patch_length + ) // self.config.patch_stride + 1 + # initialize cls_token + if self.config.use_cls_token: + init.normal_(module.cls_token, std=0.02) + num_patches += 1 + # initialize positional encoding + position_enc = module._init_pe(self.config, num_patches) + if is_deepspeed_zero3_enabled(): + import deepspeed + + with deepspeed.zero.GatheredParameters(module.position_enc, modifier_rank=None): + if module.position_enc.numel() > 0: + init.copy_(module.position_enc, position_enc) + else: + init.copy_(module.position_enc, position_enc) + elif isinstance(module, (nn.LayerNorm, nn.BatchNorm1d)): + init.zeros_(module.bias) + init.ones_(module.weight) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + elif isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=self.config.init_std) + if module.bias is not None: + init.zeros_(module.bias) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (PatchTSTEncoder)): + module.gradient_checkpointing = value + + +class PatchTSTEmbedding(nn.Module): + def __init__(self, config: PatchTSTConfig): + super().__init__() + self.num_input_channels = config.num_input_channels + self.share_embedding = config.share_embedding + # Input encoding: projection of feature vectors onto a d-dim vector space + if self.share_embedding: + self.input_embedding = nn.Linear(config.patch_length, config.d_model) + else: + self.input_embedding = nn.ModuleList() + for _ in range(config.num_input_channels): + self.input_embedding.append(nn.Linear(config.patch_length, config.d_model)) + + def forward(self, patch_input: torch.Tensor): + """ + Parameters: + patch_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`, *required*): + Patch input for embedding + return: + `torch.Tensor` of shape `(batch_size, num_channels, num_patches, d_model)` + """ + # Input encoding + num_input_channels = patch_input.shape[1] + if num_input_channels != self.num_input_channels: + raise ValueError( + f"The defined number of input channels ({self.num_input_channels}) in the config " + f"has to be the same as the number of channels in the batch input ({num_input_channels})" + ) + if self.share_embedding: + embeddings = self.input_embedding(patch_input) # x: [bs x num_channels x num_patches x d_model] + else: + embeddings = [self.input_embedding[i](patch_input[:, i, :, :]) for i in range(num_input_channels)] + embeddings = torch.stack(embeddings, dim=1) + return embeddings + + +class PatchTSTPositionalEncoding(nn.Module): + """ + Class for positional encoding + """ + + def __init__(self, config: PatchTSTConfig, num_patches: int): + super().__init__() + self.use_cls_token = config.use_cls_token + self.num_input_channels = config.num_input_channels + if config.use_cls_token: + # cls_token: [1 x num_input_channels x 1 x d_model] + self.cls_token = nn.Parameter(torch.zeros(1, 1, 1, config.d_model)) + num_patches += 1 + # positional encoding: [num_patches x d_model] + self.position_enc = self._init_pe(config, num_patches) + # Positional dropout + self.positional_dropout = ( + nn.Dropout(config.positional_dropout) if config.positional_dropout > 0 else nn.Identity() + ) + + @staticmethod + def _init_pe(config: PatchTSTConfig, num_patches: int) -> nn.Parameter: + # Positional encoding + if config.positional_encoding_type == "random": + position_enc = nn.Parameter(torch.randn(num_patches, config.d_model), requires_grad=True) + elif config.positional_encoding_type == "sincos": + position_enc = torch.zeros(num_patches, config.d_model) + position = torch.arange(0, num_patches).unsqueeze(1) + div_term = torch.exp(torch.arange(0, config.d_model, 2) * -(math.log(10000.0) / config.d_model)) + position_enc[:, 0::2] = torch.sin(position * div_term) + position_enc[:, 1::2] = torch.cos(position * div_term) + position_enc = position_enc - position_enc.mean() + position_enc = position_enc / (position_enc.std() * 10) + position_enc = nn.Parameter(position_enc, requires_grad=False) + else: + raise ValueError( + f"{config.positional_encoding_type} is not a valid positional encoder. Available types are 'random' and 'sincos'." + ) + return position_enc + + def forward(self, patch_input: torch.Tensor): + if self.use_cls_token: + # patch_input: [bs x num_channels x num_patches x d_model] + patch_input = self.positional_dropout(patch_input + self.position_enc[1:, :]) + # append cls token where cls_token: [1 x num_channels x 1 x d_model] + cls_token = self.cls_token + self.position_enc[:1, :] + # get the same copy of cls_token for all the samples in batch: [bs x num_channels x 1 x d_model] + cls_tokens = cls_token.expand(patch_input.shape[0], self.num_input_channels, -1, -1) + # hidden_state: [bs x num_channels x (num_patches+1) x d_model] + hidden_state = torch.cat((cls_tokens, patch_input), dim=2) + else: + # hidden_state: [bs x num_channels x num_patches x d_model] + hidden_state = self.positional_dropout(patch_input + self.position_enc) + return hidden_state + + +class PatchTSTEncoder(PatchTSTPreTrainedModel): + """ + PatchTST Encoder + """ + + def __init__(self, config: PatchTSTConfig, num_patches: int): + super().__init__(config) + self.gradient_checkpointing = False + + # Input embedding: projection of feature vectors onto a d-dim vector space + self.embedder = PatchTSTEmbedding(config) + # Positional encoding + self.positional_encoder = PatchTSTPositionalEncoding(config, num_patches) + # Encoder + self.layers = nn.ModuleList([PatchTSTEncoderLayer(config) for i in range(config.num_hidden_layers)]) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + patch_input: torch.Tensor, + output_hidden_states: bool | None = None, + output_attentions: bool | None = None, + **kwargs, + ) -> BaseModelOutput: + """ + Parameters: + patch_input (`torch.Tensor` of shape `(batch_size, num_channels, num_patches, patch_length)`, *required*): + Past values of the time series + output_hidden_states (bool, optional): Indicates if hidden states should be outputted. + output_attentions (bool, optional): Indicates if attentions should be outputted. + + return: + `BaseModelOutput` + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + # Input embedding + patch_input = self.embedder(patch_input) + # Positional encoding + hidden_state = self.positional_encoder(patch_input) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + for encoder_layer in self.layers: + if output_hidden_states: + encoder_states = encoder_states + (hidden_state,) + + layer_outputs = encoder_layer(hidden_state=hidden_state, output_attentions=output_attentions) + # get hidden state. hidden_state shape is [bs x num_channels x num_patches x d_model] + # or [bs x num_channels x (num_patches+1) x d_model] if use cls_token + hidden_state = layer_outputs[0] + # append attention matrix at each layer + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + # return past_values, hidden_states + return BaseModelOutput(last_hidden_state=hidden_state, hidden_states=encoder_states, attentions=all_attentions) + + +@auto_docstring( + custom_intro=""" + Base class for model's outputs, with potential hidden states. + """ +) +@dataclass +class PatchTSTModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, patch_length)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, num_channels, height, width)`. Hidden-states of + the model at the output of each layer plus the optional initial embedding outputs. + mask (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches)`, *optional*): + Bool masked tensor indicating which patches are masked + loc (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`, *optional*): + Mean of the input data (batch_size, sequence_length, num_channels) over the sequence_length + scale (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`, *optional*): + Std of the input data (batch_size, sequence_length, num_channels) over the sequence_length + patch_input (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, patch_length)`): + Patched input to the Transformer + """ + + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + mask: torch.FloatTensor | None = None + loc: torch.FloatTensor | None = None + scale: torch.FloatTensor | None = None + patch_input: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Output type of [`PatchTSTForPretraining`]. + """ +) +@dataclass +class PatchTSTForPretrainingOutput(ModelOutput): + r""" + loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): + MSE loss. + prediction_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction outputs of the time series modeling heads. + """ + + loss: torch.FloatTensor | None = None + prediction_output: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Output type of [`PatchTSTForRegression`]. + """ +) +@dataclass +class PatchTSTForRegressionOutput(ModelOutput): + r""" + loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): + MSE loss. + regression_outputs (`torch.FloatTensor` of shape `(batch_size, num_targets)`): + Regression outputs of the time series modeling heads. + """ + + loss: torch.FloatTensor | None = None + regression_outputs: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Output type of [`PatchTSTForPrediction`]. + """ +) +@dataclass +class PatchTSTForPredictionOutput(ModelOutput): + r""" + loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): + MSE loss. + prediction_outputs (`torch.FloatTensor` of shape `(batch_size, prediction_length, -1)`): + Prediction outputs of the time series modeling heads. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + loc: (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`, *optional*) + Mean of the input data (batch_size, sequence_length, num_channels) over the sequence_length + scale: (`torch.FloatTensor` of shape `(batch_size, 1, num_channels)`, *optional*) + Std of the input data (batch_size, sequence_length, num_channels) over the sequence_length + """ + + loss: torch.FloatTensor | None = None + prediction_outputs: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + loc: torch.FloatTensor | None = None + scale: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Output type of [`PatchTSTForClassification`]. + """ +) +@dataclass +class PatchTSTForClassificationOutput(ModelOutput): + r""" + loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`): + Total loss as the sum of the masked language modeling loss and the next sequence prediction + (classification) loss. + prediction_logits (`torch.FloatTensor` of shape `(batch_size, num_targets)`): + Prediction scores of the PatchTST modeling head (scores before SoftMax). + """ + + loss: torch.FloatTensor | None = None + prediction_logits: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for time series model's predictions outputs that contains the sampled values from the chosen + distribution. + """ +) +@dataclass +class SamplePatchTSTOutput(ModelOutput): + r""" + sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length, num_targets)`): + Sampled values from the chosen distribution. + """ + + sequences: torch.FloatTensor | None = None + + +# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.nll +def nll(input: torch.distributions.Distribution, target: torch.Tensor) -> torch.Tensor: + """ + Computes the negative log likelihood loss from input distribution with respect to target. + """ + return -input.log_prob(target) + + +# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.weighted_average +def weighted_average(input_tensor: torch.Tensor, weights: torch.Tensor | None = None, dim=None) -> torch.Tensor: + """ + Computes the weighted average of a given tensor across a given `dim`, masking values associated with weight zero, + meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`. + + Args: + input_tensor (`torch.FloatTensor`): + Input tensor, of which the average must be computed. + weights (`torch.FloatTensor`, *optional*): + Weights tensor, of the same shape as `input_tensor`. + dim (`int`, *optional*): + The dim along which to average `input_tensor`. + + Returns: + `torch.FloatTensor`: The tensor with values averaged along the specified `dim`. + """ + if weights is not None: + weighted_tensor = torch.where(weights != 0, input_tensor * weights, torch.zeros_like(input_tensor)) + sum_weights = torch.clamp(weights.sum(dim=dim) if dim else weights.sum(), min=1.0) + return (weighted_tensor.sum(dim=dim) if dim else weighted_tensor.sum()) / sum_weights + else: + return input_tensor.mean(dim=dim) + + +# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesStdScaler with TimeSeriesTransformer->PatchTST,TimeSeries->PatchTST +class PatchTSTStdScaler(nn.Module): + """ + Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by + subtracting from the mean and dividing by the standard deviation. + """ + + def __init__(self, config: PatchTSTConfig): + super().__init__() + self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1 + self.keepdim = config.keepdim if hasattr(config, "keepdim") else True + self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-5 + + def forward( + self, data: torch.Tensor, observed_indicator: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Parameters: + data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`): + input for Batch norm calculation + observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Calculating the scale on the observed indicator. + Returns: + tuple of `torch.Tensor` of shapes + (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`, + `(batch_size, 1, num_input_channels)`) + """ + denominator = observed_indicator.sum(self.dim, keepdim=self.keepdim) + denominator = denominator.clamp_min(1.0) + loc = (data * observed_indicator).sum(self.dim, keepdim=self.keepdim) / denominator + + variance = (((data - loc) * observed_indicator) ** 2).sum(self.dim, keepdim=self.keepdim) / denominator + scale = torch.sqrt(variance + self.minimum_scale) + return (data - loc) / scale, loc, scale + + +# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesMeanScaler with TimeSeriesTransformer->PatchTST,TimeSeries->PatchTST +class PatchTSTMeanScaler(nn.Module): + """ + Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data + accordingly. + """ + + def __init__(self, config: PatchTSTConfig): + super().__init__() + self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1 + self.keepdim = config.keepdim if hasattr(config, "keepdim") else True + self.minimum_scale = config.minimum_scale if hasattr(config, "minimum_scale") else 1e-10 + self.default_scale = config.default_scale if hasattr(config, "default_scale") else None + + def forward( + self, data: torch.Tensor, observed_indicator: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Parameters: + data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`): + input for Batch norm calculation + observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Calculating the scale on the observed indicator. + Returns: + tuple of `torch.Tensor` of shapes + (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`, + `(batch_size, 1, num_input_channels)`) + """ + ts_sum = (data * observed_indicator).abs().sum(self.dim, keepdim=True) + num_observed = observed_indicator.sum(self.dim, keepdim=True) + + scale = ts_sum / torch.clamp(num_observed, min=1) + + # If `default_scale` is provided, we use it, otherwise we use the scale + # of the batch. + if self.default_scale is None: + batch_sum = ts_sum.sum(dim=0) + batch_observations = torch.clamp(num_observed.sum(0), min=1) + default_scale = torch.squeeze(batch_sum / batch_observations) + else: + default_scale = self.default_scale * torch.ones_like(scale) + + # apply default scale where there are no observations + scale = torch.where(num_observed > 0, scale, default_scale) + + # ensure the scale is at least `self.minimum_scale` + scale = torch.clamp(scale, min=self.minimum_scale) + scaled_data = data / scale + + if not self.keepdim: + scale = scale.squeeze(dim=self.dim) + + return scaled_data, torch.zeros_like(scale), scale + + +# Copied from transformers.models.time_series_transformer.modeling_time_series_transformer.TimeSeriesNOPScaler with TimeSeriesTransformer->PatchTST,TimeSeries->PatchTST +class PatchTSTNOPScaler(nn.Module): + """ + Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data. + """ + + def __init__(self, config: PatchTSTConfig): + super().__init__() + self.dim = config.scaling_dim if hasattr(config, "scaling_dim") else 1 + self.keepdim = config.keepdim if hasattr(config, "keepdim") else True + + def forward( + self, data: torch.Tensor, observed_indicator: torch.Tensor | None = None + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Parameters: + data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`): + input for Batch norm calculation + Returns: + tuple of `torch.Tensor` of shapes + (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`, + `(batch_size, 1, num_input_channels)`) + """ + scale = torch.ones_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim) + loc = torch.zeros_like(data, requires_grad=False).mean(dim=self.dim, keepdim=self.keepdim) + return data, loc, scale + + +class PatchTSTScaler(nn.Module): + def __init__(self, config: PatchTSTConfig): + super().__init__() + if config.scaling == "mean" or config.scaling is True: + self.scaler = PatchTSTMeanScaler(config) + elif config.scaling == "std": + self.scaler = PatchTSTStdScaler(config) + else: + self.scaler = PatchTSTNOPScaler(config) + + def forward( + self, data: torch.Tensor, observed_indicator: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Parameters: + data (`torch.Tensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Input for scaler calculation + observed_indicator (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Calculating the scale on the observed indicator. + Returns: + tuple of `torch.Tensor` of shapes + (`(batch_size, sequence_length, num_input_channels)`,`(batch_size, 1, num_input_channels)`, + `(batch_size, 1, um_input_channels)`) + """ + data, loc, scale = self.scaler(data, observed_indicator) + return data, loc, scale + + +@auto_docstring +class PatchTSTModel(PatchTSTPreTrainedModel): + def __init__(self, config: PatchTSTConfig): + super().__init__(config) + + self.scaler = PatchTSTScaler(config) + self.patchifier = PatchTSTPatchify(config) + self.do_mask_input = config.do_mask_input + # get num_patches information from PatchTSTPatchify + num_patches = self.patchifier.num_patches + + if self.do_mask_input: + self.masking = PatchTSTMasking(config) + else: + self.masking = nn.Identity() + self.encoder = PatchTSTEncoder(config, num_patches=num_patches) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + past_values: torch.Tensor, + past_observed_mask: torch.Tensor | None = None, + future_values: torch.Tensor | None = None, + output_hidden_states: bool | None = None, + output_attentions: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PatchTSTModelOutput: + r""" + Parameters: + past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*): + Input sequence to the model + past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + future_values (`torch.BoolTensor` of shape `(batch_size, prediction_length, num_input_channels)`, *optional*): + Future target values associated with the `past_values` + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers + output_attentions (`bool`, *optional*): + Whether or not to return the output attention of all layers + return_dict (`bool`, *optional*): + Whether or not to return a `ModelOutput` instead of a plain tuple. + + Returns: + `PatchTSTModelOutput` or tuple of `torch.Tensor` (if `return_dict`=False or `config.return_dict`=False) + + Examples: + + ```python + >>> from huggingface_hub import hf_hub_download + >>> import torch + >>> from transformers import PatchTSTModel + + >>> file = hf_hub_download( + ... repo_id="hf-internal-testing/etth1-hourly-batch", filename="train-batch.pt", repo_type="dataset" + ... ) + >>> batch = torch.load(file) + + >>> model = PatchTSTModel.from_pretrained("namctin/patchtst_etth1_pretrain") + + >>> # during training, one provides both past and future values + >>> outputs = model( + ... past_values=batch["past_values"], + ... future_values=batch["future_values"], + ... ) + + >>> last_hidden_state = outputs.last_hidden_state + ```""" + + return_dict = return_dict if return_dict is not None else self.config.return_dict + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + if past_observed_mask is None: + past_observed_mask = torch.ones_like(past_values) + + # x: tensor [bs x sequence_length x num_input_channels] + scaled_past_values, loc, scale = self.scaler(past_values, past_observed_mask) + + # patched_values: [bs x num_input_channels x num_patches x patch_length] for pretrain + patched_values = self.patchifier(scaled_past_values) + if self.do_mask_input: + masked_values, mask = self.masking(patched_values) + else: + masked_values, mask = self.masking(patched_values), None + + encoder_output = self.encoder( + patch_input=masked_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions + ) + + if not return_dict: + outputs = (encoder_output.last_hidden_state, encoder_output.hidden_states, encoder_output.attentions) + outputs = outputs + (mask, loc, scale, patched_values) + return tuple(v for v in outputs if v is not None) + + return PatchTSTModelOutput( + last_hidden_state=encoder_output.last_hidden_state, + hidden_states=encoder_output.hidden_states, + attentions=encoder_output.attentions, + mask=mask, + loc=loc, + scale=scale, + patch_input=patched_values, + ) + + +class PatchTSTMaskPretrainHead(nn.Module): + """ + Pretraining head for mask modelling + """ + + def __init__(self, config: PatchTSTConfig): + super().__init__() + self.dropout = nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity() + self.linear = nn.Linear(config.d_model, config.patch_length) + self.use_cls_token = config.use_cls_token + + def forward(self, embedding: torch.Tensor) -> torch.Tensor: + """ + Parameters: + embedding (`torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)` or + `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True, *required*): + Embedding from the model + Returns: + `torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)` or + `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True + + """ + embedding = self.linear(self.dropout(embedding)) # [bs x num_channels x num_patches x patch_length] + if self.use_cls_token: + embedding = embedding[:, :, 1:, :] # remove the first cls token + return embedding + + +@auto_docstring( + custom_intro=""" + The PatchTST for pretrain model. + """ +) +class PatchTSTForPretraining(PatchTSTPreTrainedModel): + def __init__(self, config: PatchTSTConfig): + super().__init__(config) + + config.do_mask_input = True + self.model = PatchTSTModel(config=config) + self.head = PatchTSTMaskPretrainHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + past_values: torch.Tensor, + past_observed_mask: torch.Tensor | None = None, + output_hidden_states: bool | None = None, + output_attentions: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PatchTSTForPretrainingOutput: + r""" + Parameters: + past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*): + Input sequence to the model + past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers + output_attentions (`bool`, *optional*): + Whether or not to return the output attention of all layers + return_dict (`bool`, *optional*): Whether or not to return a `ModelOutput` instead of a plain tuple. + + Returns: + `PatchTSTForPretrainingOutput` or tuple of `torch.Tensor` (if `return_dict`=False or + `config.return_dict`=False) + + Examples: + + ```python + >>> from huggingface_hub import hf_hub_download + >>> import torch + >>> from transformers import PatchTSTConfig, PatchTSTForPretraining + + >>> file = hf_hub_download( + ... repo_id="hf-internal-testing/etth1-hourly-batch", filename="train-batch.pt", repo_type="dataset" + ... ) + >>> batch = torch.load(file) + + >>> # Config for random mask pretraining + >>> config = PatchTSTConfig( + ... num_input_channels=7, + ... context_length=512, + ... patch_length=12, + ... stride=12, + ... mask_type='random', + ... random_mask_ratio=0.4, + ... use_cls_token=True, + ... ) + >>> # Config for forecast mask pretraining + >>> config = PatchTSTConfig( + ... num_input_channels=7, + ... context_length=512, + ... patch_length=12, + ... stride=12, + ... mask_type='forecast', + ... num_forecast_mask_patches=5, + ... use_cls_token=True, + ... ) + >>> model = PatchTSTForPretraining(config) + + >>> # during training, one provides both past and future values + >>> outputs = model(past_values=batch["past_values"]) + + >>> loss = outputs.loss + >>> loss.backward() + ```""" + + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # past_values: [bs x num_channels x num_patches x d_model] or + # [bs x num_channels x (num_patches+1) x d_model] if use cls_token + model_output = self.model( + past_values=past_values, + past_observed_mask=past_observed_mask, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + return_dict=True, + ) + + # last_hidden_state: [bs x num_channels x num_patches x patch_length] or + # [bs x num_channels x (num_patches+1) x patch_length] if use cls_token + x_hat = self.head(model_output.last_hidden_state) + + # calculate masked_loss + loss = nn.MSELoss(reduction="none") + loss_val = loss(x_hat, model_output.patch_input) + masked_loss = (loss_val.mean(dim=-1) * model_output.mask).sum() / (model_output.mask.sum() + 1e-10) + + encoder_states = model_output.hidden_states + if not return_dict: + outputs = (x_hat,) + model_output[1:-4] + outputs = (masked_loss,) + outputs if masked_loss is not None else outputs + return outputs + return PatchTSTForPretrainingOutput( + loss=masked_loss, prediction_output=x_hat, hidden_states=encoder_states, attentions=model_output.attentions + ) + + +class PatchTSTClassificationHead(nn.Module): + def __init__(self, config: PatchTSTConfig): + super().__init__() + self.use_cls_token = config.use_cls_token + self.pooling_type = config.pooling_type + self.flatten = nn.Flatten(start_dim=1) + self.dropout = nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity() + self.linear = nn.Linear(config.num_input_channels * config.d_model, config.num_targets) + + def forward(self, embedding: torch.Tensor): + """ + Parameters: + embedding (`torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)` or + `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True, *required*): + Embedding from the model + Returns: + `torch.Tensor` of shape `(bs, num_targets)` + + """ + if self.use_cls_token: + # use the first output token, pooled_embedding: bs x num_channels x d_model + pooled_embedding = embedding[:, :, 0, :] + elif self.pooling_type == "mean": + # pooled_embedding: [bs x num_channels x d_model] + pooled_embedding = embedding.mean(dim=2) + elif self.pooling_type == "max": + # pooled_embedding: [bs x num_channels x d_model] + pooled_embedding = embedding.max(dim=2).values + else: + raise ValueError(f"pooling operator {self.pooling_type} is not implemented yet") + # pooled_embedding: bs x num_channels * d_model + pooled_embedding = self.flatten(pooled_embedding) + # output: bs x n_classes + output = self.linear(self.dropout(pooled_embedding)) + return output + + +@auto_docstring( + custom_intro=""" + The PatchTST for classification model. + """ +) +class PatchTSTForClassification(PatchTSTPreTrainedModel): + def __init__(self, config: PatchTSTConfig): + super().__init__(config) + + # Turn off masking + if config.do_mask_input: + logger.warning("Setting `do_mask_input` parameter to False.") + config.do_mask_input = False + + self.model = PatchTSTModel(config) + self.head = PatchTSTClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + past_values: torch.Tensor, + target_values: torch.Tensor | None = None, + past_observed_mask: bool | None = None, + output_hidden_states: bool | None = None, + output_attentions: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PatchTSTForClassificationOutput: + r""" + past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*): + Input sequence to the model + target_values (`torch.Tensor`, *optional*): + Labels associates with the `past_values` + past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + + Examples: + + ```python + >>> from transformers import PatchTSTConfig, PatchTSTForClassification + + >>> # classification task with two input channel2 and 3 classes + >>> config = PatchTSTConfig( + ... num_input_channels=2, + ... num_targets=3, + ... context_length=512, + ... patch_length=12, + ... stride=12, + ... use_cls_token=True, + ... ) + >>> model = PatchTSTForClassification(config=config) + + >>> # during inference, one only provides past values + >>> past_values = torch.randn(20, 512, 2) + >>> outputs = model(past_values=past_values) + >>> labels = outputs.prediction_logits + ```""" + + return_dict = return_dict if return_dict is not None else self.config.return_dict + + model_output = self.model( + past_values=past_values, + past_observed_mask=past_observed_mask, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + return_dict=True, + ) + y_hat = self.head(model_output.last_hidden_state) + + loss_val = None + if target_values is not None: + loss = nn.CrossEntropyLoss() + loss_val = loss(y_hat, target_values) + + if not return_dict: + outputs = (y_hat,) + model_output[1:-3] + outputs = (loss_val,) + outputs if loss_val is not None else outputs + return outputs + return PatchTSTForClassificationOutput( + loss=loss_val, + prediction_logits=y_hat, + hidden_states=model_output.hidden_states, + attentions=model_output.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The PatchTST for regression Model. + """ +) +class PatchTSTPredictionHead(nn.Module): + def __init__(self, config: PatchTSTConfig, num_patches: int, distribution_output=None): + r""" + num_patches (`int`): + The number of patches in the input sequence. + distribution_output (`DistributionOutput`, *optional*): + The distribution output layer for probabilistic forecasting. If None, a linear output layer is used. + """ + super().__init__() + + self.share_projection = config.share_projection + self.num_input_channels = config.num_input_channels + self.use_cls_token = config.use_cls_token + self.pooling_type = config.pooling_type + if self.pooling_type or self.use_cls_token: + head_dim = config.d_model + else: + head_dim = config.d_model * num_patches + + if not self.share_projection: + # if each channel has its own head + self.projections = nn.ModuleList() + self.dropouts = nn.ModuleList() + self.flattens = nn.ModuleList() + for i in range(self.num_input_channels): + self.flattens.append(nn.Flatten(start_dim=2)) + if distribution_output is None: + # use linear head + self.projections.append(nn.Linear(head_dim, config.prediction_length)) + else: + # use distribution head + self.projections.append(distribution_output.get_parameter_projection(head_dim)) + self.dropouts.append(nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity()) + else: + # all the channels share the same head + self.flatten = nn.Flatten(start_dim=2) + if distribution_output is None: + # use linear head + self.projection = nn.Linear(head_dim, config.prediction_length) + else: + # use distribution head + self.projection = distribution_output.get_parameter_projection(head_dim) + self.dropout = nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity() + + def forward(self, embedding: torch.Tensor): + """ + Parameters: + embedding (`torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)` or + `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True, *required*): + Embedding from the model + Returns: + `torch.Tensor` of shape `(bs, forecast_len, num_channels)` + + """ + if self.use_cls_token: + # pooled_embedding: [bs x num_channels x d_model] + pooled_embedding = embedding[:, :, 0, :] + else: + if self.pooling_type == "mean": + # pooled_embedding: [bs x num_channels x d_model] + pooled_embedding = embedding.mean(dim=2) + elif self.pooling_type == "max": + # pooled_embedding: [bs x num_channels x d_model] + pooled_embedding = embedding.max(dim=2).values + else: + # pooled_embedding: [bs x num_channels x num_patches x d_model] + pooled_embedding = embedding + + if not self.share_projection: + output = [] + for i in range(self.num_input_channels): + # pooled_embedding: [bs x (d_model * num_patches)] or [bs x d_model)] + pooled_embedding = self.flattens[i](pooled_embedding[:, i, :]) + pooled_embedding = self.dropouts[i](pooled_embedding) + # pooled_embedding: [bs x forecast_len] + # or tuple ([bs x forecast_len], [bs x forecast_len]) if using distribution head + pooled_embedding = self.projections[i](pooled_embedding) + output.append(pooled_embedding) + # output: [bs x num_channels x forecast_len] + output = torch.stack(output, dim=1) + else: + # pooled_embedding: [bs x num_channels x (d_model * num_patches)] or [bs x num_channels x d_model)] + pooled_embedding = self.flatten(pooled_embedding) + pooled_embedding = self.dropout(pooled_embedding) + # output: [bs x num_channels x forecast_len] or + # tuple ([bs x num_channels x forecast_len], [bs x num_channels x forecast_len]) if using distribution head + output = self.projection(pooled_embedding) + + if isinstance(output, tuple): + # output: ([bs x forecast_len x num_channels], [bs x forecast_len x num_channels]) + output = tuple(z.transpose(2, 1) for z in output) + else: + output = output.transpose(2, 1) # [bs x forecast_len x num_channels] + return output + + +@auto_docstring( + custom_intro=""" + The PatchTST for prediction model. + """ +) +class PatchTSTForPrediction(PatchTSTPreTrainedModel): + def __init__(self, config: PatchTSTConfig): + super().__init__(config) + + # Turn off masking + if config.do_mask_input: + logger.warning("Setting `do_mask_input` parameter to False.") + config.do_mask_input = False + + self.model = PatchTSTModel(config) + + if config.loss == "mse": + self.distribution_output = None + else: + if config.distribution_output == "student_t": + self.distribution_output = StudentTOutput(dim=config.prediction_length) + elif config.distribution_output == "normal": + self.distribution_output = NormalOutput(dim=config.prediction_length) + elif config.distribution_output == "negative_binomial": + self.distribution_output = NegativeBinomialOutput(dim=config.prediction_length) + else: + raise ValueError(f"Unknown distribution output {config.distribution_output}") + + self.head = PatchTSTPredictionHead( + config, self.model.patchifier.num_patches, distribution_output=self.distribution_output + ) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + past_values: torch.Tensor, + past_observed_mask: torch.Tensor | None = None, + future_values: torch.Tensor | None = None, + output_hidden_states: bool | None = None, + output_attentions: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PatchTSTForPredictionOutput: + r""" + Parameters: + past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*): + Input sequence to the model + past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + future_values (`torch.Tensor` of shape `(bs, forecast_len, num_input_channels)`, *optional*): + Future target values associated with the `past_values` + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers + output_attentions (`bool`, *optional*): + Whether or not to return the output attention of all layers + return_dict (`bool`, *optional*): + Whether or not to return a `ModelOutput` instead of a plain tuple. + + Returns: + `PatchTSTForPredictionOutput` or tuple of `torch.Tensor` (if `return_dict`=False or + `config.return_dict`=False) + + Examples: + + ```python + >>> from huggingface_hub import hf_hub_download + >>> import torch + >>> from transformers import PatchTSTConfig, PatchTSTForPrediction + + >>> file = hf_hub_download( + ... repo_id="hf-internal-testing/etth1-hourly-batch", filename="train-batch.pt", repo_type="dataset" + ... ) + >>> batch = torch.load(file) + + >>> # Prediction task with 7 input channels and prediction length is 96 + >>> model = PatchTSTForPrediction.from_pretrained("namctin/patchtst_etth1_forecast") + + >>> # during training, one provides both past and future values + >>> outputs = model( + ... past_values=batch["past_values"], + ... future_values=batch["future_values"], + ... ) + + >>> loss = outputs.loss + >>> loss.backward() + + >>> # during inference, one only provides past values, the model outputs future values + >>> outputs = model(past_values=batch["past_values"]) + >>> prediction_outputs = outputs.prediction_outputs + ```""" + + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # get model output + model_output = self.model( + past_values=past_values, + past_observed_mask=past_observed_mask, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + return_dict=True, + ) + # get output head + y_hat = self.head(model_output.last_hidden_state) + + loss_val = None + + if self.distribution_output: + y_hat_out = y_hat + else: + y_hat_out = y_hat * model_output.scale + model_output.loc + + if future_values is not None: + if self.distribution_output: + distribution = self.distribution_output.distribution( + y_hat, loc=model_output.loc, scale=model_output.scale + ) + loss_val = nll(distribution, future_values) + # take average of the loss + loss_val = weighted_average(loss_val) + else: + loss = nn.MSELoss(reduction="mean") + loss_val = loss(y_hat_out, future_values) + + loc = model_output.loc + scale = model_output.scale + + if not return_dict: + outputs = (y_hat_out,) + model_output[1:-1] + outputs = (loss_val,) + outputs if loss_val is not None else outputs + return outputs + return PatchTSTForPredictionOutput( + loss=loss_val, + prediction_outputs=y_hat_out, + hidden_states=model_output.hidden_states, + attentions=model_output.attentions, + loc=loc, + scale=scale, + ) + + @torch.no_grad() + def generate( + self, + past_values: torch.Tensor, + past_observed_mask: torch.Tensor | None = None, + ) -> SamplePatchTSTOutput: + """ + Generate sequences of sample predictions from a model with a probability distribution head. + + Parameters: + past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Past values of the time series that serves as context in order to predict the future. + past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + + Return: + [`SamplePatchTSTOutput`] where the outputs `sequences` tensor will have shape `(batch_size, number of + samples, prediction_length, 1)` or `(batch_size, number of samples, prediction_length, num_input_channels)` + for multivariate predictions. + """ + # get number of samples + num_parallel_samples = self.config.num_parallel_samples + + # get model output + outputs = self( + past_values=past_values, + future_values=None, + past_observed_mask=past_observed_mask, + output_hidden_states=False, + ) + if self.distribution_output: + # get distribution + distribution = self.distribution_output.distribution( + outputs.prediction_outputs, loc=outputs.loc, scale=outputs.scale + ) + # get samples: list of [bs x forecast_len x num_channels] + samples = [distribution.sample() for _ in range(num_parallel_samples)] + # samples: [bs x num_samples x forecast_len x num_channels] + samples = torch.stack(samples, dim=1) + else: + samples = outputs.prediction_outputs.unsqueeze(1) + + return SamplePatchTSTOutput(sequences=samples) + + +class PatchTSTRegressionHead(nn.Module): + """ + Regression head + """ + + def __init__(self, config: PatchTSTConfig, distribution_output=None): + super().__init__() + self.y_range = config.output_range + self.use_cls_token = config.use_cls_token + self.pooling_type = config.pooling_type + self.distribution_output = distribution_output + + head_dim = config.num_input_channels * config.d_model + + self.flatten = nn.Flatten(start_dim=1) + self.dropout = nn.Dropout(config.head_dropout) if config.head_dropout > 0 else nn.Identity() + + if distribution_output is None: + self.projection = nn.Linear(head_dim, config.num_targets) + else: + self.projection = distribution_output.get_parameter_projection(head_dim) + + def forward(self, embedding: torch.Tensor): + """ + Parameters: + embedding (`torch.Tensor` of shape `(bs, num_channels, num_patches, d_model)` or + `(bs, num_channels, num_patches+1, d_model)` if `cls_token` is set to True, *required*): + Embedding from the model + Returns: + `torch.Tensor` of shape `(bs, output_dim)` + + """ + if self.use_cls_token: + # use the first output token, pooled_embedding: [bs x num_channels x d_model] + pooled_embedding = embedding[:, :, 0, :] + elif self.pooling_type == "mean": + # pooled_embedding: [bs x num_channels x d_model] + pooled_embedding = embedding.mean(dim=2) + elif self.pooling_type == "max": + # pooled_embedding: [bs x num_channels x d_model] + pooled_embedding = embedding.max(dim=2).values + else: + raise ValueError(f"pooling operator {self.pooling_type} is not implemented yet") + # flatten the input + # pooled_embedding: bs x (num_channels * d_model) + pooled_embedding = self.dropout(self.flatten(pooled_embedding)) + # projection + # output: bs x output_dim or a tuple of this shape for distribution head + output = self.projection(pooled_embedding) + # apply sigmoid to bound the output if required + if (self.distribution_output is None) & (self.y_range is not None): # linear head + output = torch.sigmoid(output) * (self.y_range[1] - self.y_range[0]) + self.y_range[0] + return output + + +@auto_docstring( + custom_intro=""" + The PatchTST for regression model. + """ +) +class PatchTSTForRegression(PatchTSTPreTrainedModel): + def __init__(self, config: PatchTSTConfig): + super().__init__(config) + + # Turn off masking + if config.do_mask_input: + logger.warning("Setting `do_mask_input` parameter to False.") + config.do_mask_input = False + + self.model = PatchTSTModel(config) + if config.loss == "mse": + self.distribution_output = None + else: + if config.distribution_output == "student_t": + self.distribution_output = StudentTOutput(dim=config.num_targets) + elif config.distribution_output == "normal": + self.distribution_output = NormalOutput(dim=config.num_targets) + elif config.distribution_output == "negative_binomial": + self.distribution_output = NegativeBinomialOutput(dim=config.num_targets) + else: + raise ValueError(f"Unknown distribution output {config.distribution_output}") + + self.head = PatchTSTRegressionHead(config, self.distribution_output) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + past_values: torch.Tensor, + target_values: torch.Tensor | None = None, + past_observed_mask: torch.Tensor | None = None, + output_hidden_states: bool | None = None, + output_attentions: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PatchTSTForRegressionOutput: + r""" + past_values (`torch.Tensor` of shape `(bs, sequence_length, num_input_channels)`, *required*): + Input sequence to the model + target_values (`torch.Tensor` of shape `(bs, num_input_channels)`): + Target values associates with the `past_values` + past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + Whether or not to return a `ModelOutput` instead of a plain tuple. + + Examples: + + ```python + >>> from transformers import PatchTSTConfig, PatchTSTForRegression + + >>> # Regression task with 6 input channels and regress 2 targets + >>> model = PatchTSTForRegression.from_pretrained("namctin/patchtst_etth1_regression") + + >>> # during inference, one only provides past values, the model outputs future values + >>> past_values = torch.randn(20, 512, 6) + >>> outputs = model(past_values=past_values) + >>> regression_outputs = outputs.regression_outputs + ```""" + + return_dict = return_dict if return_dict is not None else self.config.return_dict + + model_output = self.model( + past_values=past_values, + past_observed_mask=past_observed_mask, + output_hidden_states=output_hidden_states, + output_attentions=output_attentions, + return_dict=True, + ) + # get output head. y_hat is of shape [bs x num_targets] or tuple of this shape + y_hat = self.head(model_output.last_hidden_state) + + loss = None + if target_values is not None: + if self.distribution_output: + distribution = self.distribution_output.distribution(y_hat) + # y_hat should be a 2-tuple, each with dimension [bs, num_targets] + y_hat = tuple(item.view(-1, self.config.num_targets) for item in y_hat) + loss = nll(distribution, target_values) + # take average of the loss + loss = weighted_average(loss) + else: + loss = nn.MSELoss(reduction="mean") + loss = loss(y_hat, target_values) + + if not return_dict: + # hidden_states, attentions, mask + outputs = (y_hat,) + model_output[1:-3] + outputs = (loss,) + outputs if loss is not None else outputs + return outputs + return PatchTSTForRegressionOutput( + loss=loss, + regression_outputs=y_hat, + hidden_states=model_output.hidden_states, + attentions=model_output.attentions, + ) + + @torch.no_grad() + def generate( + self, + past_values: torch.Tensor, + past_observed_mask: torch.Tensor | None = None, + ) -> SamplePatchTSTOutput: + """ + Generate sequences of sample predictions from a model with a probability distribution head. + + Parameters: + past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_input_channels)`): + Past values of the time series that serves as context in order to predict the future. + past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length, num_input_channels)`, *optional*): + Boolean mask to indicate which `past_values` were observed and which were missing. Mask values selected + in `[0, 1]`: + + - 1 for values that are **observed**, + - 0 for values that are **missing** (i.e. NaNs that were replaced by zeros). + + Return: + [`SamplePatchTSTOutput`] where the outputs `sequences` tensor will have shape `(batch_size, number of + samples, num_targets)`. + """ + # get number of samples + num_parallel_samples = self.config.num_parallel_samples + + # get model output + outputs = self( + past_values=past_values, + target_values=None, + past_observed_mask=past_observed_mask, + output_hidden_states=False, + ) + + # get distribution + distribution = self.distribution_output.distribution(outputs.regression_outputs) + # get samples: list of [bs x num_targets] + samples = [distribution.sample() for _ in range(num_parallel_samples)] + # samples: [bs x num_samples x num_targets] + samples = torch.stack(samples, dim=1).view(-1, num_parallel_samples, self.config.num_targets) + return SamplePatchTSTOutput(sequences=samples) + + +__all__ = [ + "PatchTSTModel", + "PatchTSTPreTrainedModel", + "PatchTSTForPrediction", + "PatchTSTForPretraining", + "PatchTSTForRegression", + "PatchTSTForClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dead10e3f5de486207f4e2f513a50ca51f877ba9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pe_audio import * + from .feature_extraction_pe_audio import * + from .modeling_pe_audio import * + from .processing_pe_audio import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/configuration_pe_audio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/configuration_pe_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..c7555d15b20ced3d80ba3c4e4d4e380f2d3a69c7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/configuration_pe_audio.py @@ -0,0 +1,138 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="facebook/pe-av-large") +@strict +class PeAudioEncoderConfig(PreTrainedConfig): + r""" + dac_config (`Union[PreTrainedConfig, dict]`, *optional*): + Configuration for the DAC audio encoder used to tokenize the raw audio inputs. If a dictionary is passed, it + will be used to instantiate a [`~transformers.DacConfig`] with default DAC hyperparameters. + + ```python + >>> from transformers import PeAudioEncoder, PeAudioEncoderConfig + + >>> # Initializing a PeAudioEncoder style configuration + >>> configuration = PeAudioEncoderConfig() + + >>> # Initializing a model from the pe-av-large style configuration + >>> model = PeAudioEncoder(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "pe_audio_encoder" + sub_configs = {"dac_config": AutoConfig} + base_config_key = "audio_video_config" + + _default_dac_config_kwargs = { + "downsampling_ratios": [2, 8, 10, 12], + "encoder_hidden_size": 64, + "codebook_dim": 128, + } + dac_config: dict | PreTrainedConfig | None = None + hidden_size: int = 1792 + intermediate_size: int = 4800 + num_hidden_layers: int = 6 + num_attention_heads: int = 14 + num_key_value_heads: int | None = None + head_dim: int = 128 + hidden_act: str = "silu" + max_position_embeddings: int = 10000 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-5 + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.rope_parameters is None: + self.rope_parameters = {"rope_theta": 20000, "rope_type": "default"} + + if isinstance(self.dac_config, dict): + self.dac_config["model_type"] = self.dac_config.get("model_type", "dac") + self.dac_config = CONFIG_MAPPING[self.dac_config["model_type"]]( + **{**self._default_dac_config_kwargs, **self.dac_config} + ) + elif self.dac_config is None: + self.dac_config = CONFIG_MAPPING["dac"](**self._default_dac_config_kwargs) + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="facebook/pe-av-large") +@strict +class PeAudioConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PeAudioModel, PeAudioConfig + + >>> # Initializing a PeAudioModel style configuration + >>> configuration = PeAudioConfig() + + >>> # Initializing a model from the pe-av-large style configuration + >>> model = PeAudioModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pe_audio" + sub_configs = {"text_config": AutoConfig, "audio_config": PeAudioEncoderConfig} + base_config_key = "audio_video_config" + + _default_text_config_kwargs = { + "model_type": "modernbert", + "hidden_size": 1024, + "intermediate_size": 2624, + "num_hidden_layers": 22, + "num_attention_heads": 16, + } + + text_config: dict | PreTrainedConfig | None = None + audio_config: dict | PreTrainedConfig | None = None + + def __post_init__(self, **kwargs): + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "modernbert") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]]( + **{**self._default_text_config_kwargs, **self.text_config} + ) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["modernbert"](**self._default_text_config_kwargs) + + if isinstance(self.audio_config, dict): + self.audio_config = PeAudioEncoderConfig(**self.audio_config) + elif self.audio_config is None: + self.audio_config = PeAudioEncoderConfig() + super().__post_init__(**kwargs) + + +__all__ = ["PeAudioEncoderConfig", "PeAudioConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/feature_extraction_pe_audio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/feature_extraction_pe_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..a7738d3089ac601acf746f992c84595ff9e2847a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/feature_extraction_pe_audio.py @@ -0,0 +1,160 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np + +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...feature_extraction_utils import BatchFeature +from ...processing_utils import load_audio +from ...utils import PaddingStrategy, TensorType, logging + + +logger = logging.get_logger(__name__) + + +class PeAudioFeatureExtractor(SequenceFeatureExtractor): + r""" + Constructs a PeAudioFeatureExtractor feature extractor. + + This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains + most of the main methods. Users should refer to this superclass for more information regarding those methods. + + Args: + feature_size (`int`, *optional*, defaults to 1): + The feature dimension of the extracted features. Use 1 for mono, 2 for stereo. + sampling_rate (`int`, *optional*, defaults to 48000): + The sampling rate at which the audio waveform should be digitalized, expressed in hertz (Hz). + padding_value (`float`, *optional*, defaults to 0.0): + The value that is used for padding. + hop_length (`int`, *optional*, defaults to 1920): + Overlap length between successive windows. + """ + + model_input_names = ["input_values"] + + def __init__( + self, + feature_size: int = 1, + sampling_rate: int = 48_000, + padding_value: float = 0.0, + hop_length: int = 1920, + **kwargs, + ): + super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) + self.hop_length = hop_length + + def _reflect_pad(self, wav): + if len(wav) % self.hop_length == 0: + return wav + p1d = (0, self.hop_length - (len(wav) % self.hop_length)) + return np.pad(wav, p1d, "reflect") + + def __call__( + self, + raw_audio: np.ndarray | list[float] | list[np.ndarray] | list[list[float]] | str | list[str], + padding: bool | str | PaddingStrategy | None = None, + truncation: bool | None = False, + max_length: int | None = None, + return_tensors: str | TensorType | None = None, + sampling_rate: int | None = None, + ) -> BatchFeature: + from_file = False + if isinstance(raw_audio, str): + raw_audio = [raw_audio] + + if isinstance(raw_audio, (list, tuple)) and isinstance(raw_audio[0], str): + loaded = [] + for audio_file in raw_audio: + loaded.append(load_audio(audio_file, self.sampling_rate)) + raw_audio = loaded + from_file = True + + if sampling_rate is not None: + if sampling_rate != self.sampling_rate: + raise ValueError( + f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of" + f" {self.sampling_rate}. Please make sure that the provided audio input was sampled with" + f" {self.sampling_rate} and not {sampling_rate}." + ) + elif not from_file: + logger.warning( + f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. " + "Failing to do so can result in silent errors that might be hard to debug." + ) + + if padding and truncation: + raise ValueError("Both padding and truncation were set. Make sure you only set one.") + elif padding is None: + # by default let's pad the inputs + padding = True + + is_batched = bool( + isinstance(raw_audio, (list, tuple)) and (isinstance(raw_audio[0], (np.ndarray, tuple, list))) + ) + + if is_batched: + raw_audio = [np.asarray(audio, dtype=np.float32).T for audio in raw_audio] + elif not is_batched and not isinstance(raw_audio, np.ndarray): + raw_audio = np.asarray(raw_audio, dtype=np.float32) + elif isinstance(raw_audio, np.ndarray) and raw_audio.dtype is np.dtype(np.float64): + raw_audio = raw_audio.astype(np.float32) + + # always return batch + if not is_batched: + raw_audio = [np.asarray(raw_audio).T] + + if isinstance(raw_audio, list): + raw_audio = [self._reflect_pad(x) for x in raw_audio] + else: + raw_audio = self._reflect_pad(raw_audio) + + # verify inputs are valid + for example in raw_audio: + if example.ndim > 2: + raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}") + if self.feature_size == 1 and example.ndim != 1: + raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels") + if self.feature_size == 2: + raise ValueError("Stereo audio isn't supported for now") + + input_values = BatchFeature({"input_values": raw_audio}) + + # normal padding on batch + padded_inputs = self.pad( + input_values, + max_length=max_length, + truncation=truncation, + padding=padding, + return_attention_mask=padding, + pad_to_multiple_of=self.hop_length, + ) + if padding: + padded_inputs["padding_mask"] = padded_inputs.pop("attention_mask") + if padding: + padded_inputs.input_values = padded_inputs.input_values[:, np.newaxis, :] + + input_values = [] + for example in padded_inputs.pop("input_values"): + if self.feature_size == 1: + example = example[..., None] + input_values.append(example.T) + + padded_inputs["input_values"] = input_values + if return_tensors is not None: + padded_inputs = padded_inputs.convert_to_tensors(return_tensors) + + return padded_inputs + + +__all__ = ["PeAudioFeatureExtractor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/modeling_pe_audio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/modeling_pe_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..5597b101dd8168975c6588c61fd1844d234a55d0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/modeling_pe_audio.py @@ -0,0 +1,824 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pe_audio/modular_pe_audio.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pe_audio.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...configuration_utils import PreTrainedConfig +from ...integrations import use_kernel_forward_from_hub, use_kernelized_func +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPooling, MaskedLMOutput +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from .configuration_pe_audio import PeAudioConfig, PeAudioEncoderConfig + + +class Snake1d(nn.Module): + """ + A 1-dimensional Snake activation function module. + """ + + def __init__(self, hidden_dim): + super().__init__() + self.alpha = nn.Parameter(torch.ones(1, hidden_dim, 1)) + + def forward(self, hidden_states): + shape = hidden_states.shape + hidden_states = hidden_states.reshape(shape[0], shape[1], -1) + hidden_states = hidden_states + (self.alpha + 1e-9).reciprocal() * torch.sin(self.alpha * hidden_states).pow(2) + hidden_states = hidden_states.reshape(shape) + return hidden_states + + +class PeAudioDacResidualUnit(nn.Module): + """ + A residual unit composed of Snake1d and weight-normalized Conv1d layers with dilations. + """ + + def __init__(self, dimension: int = 16, dilation: int = 1): + super().__init__() + pad = ((7 - 1) * dilation) // 2 + + self.snake1 = Snake1d(dimension) + self.conv1 = nn.Conv1d(dimension, dimension, kernel_size=7, dilation=dilation, padding=pad) + self.snake2 = Snake1d(dimension) + self.conv2 = nn.Conv1d(dimension, dimension, kernel_size=1) + + def forward(self, hidden_state): + """ + Forward pass through the residual unit. + + Args: + hidden_state (`torch.Tensor` of shape `(batch_size, channels, time_steps)`): + Input tensor . + + Returns: + output_tensor (`torch.Tensor` of shape `(batch_size, channels, time_steps)`): + Input tensor after passing through the residual unit. + """ + output_tensor = hidden_state + output_tensor = self.conv1(self.snake1(output_tensor)) + output_tensor = self.conv2(self.snake2(output_tensor)) + + padding = (hidden_state.shape[-1] - output_tensor.shape[-1]) // 2 + if padding > 0: + hidden_state = hidden_state[..., padding:-padding] + output_tensor = hidden_state + output_tensor + return output_tensor + + +class PeAudioDacEncoderBlock(nn.Module): + """Encoder block used in PE_AUDIO_DAC encoder.""" + + def __init__(self, config: PreTrainedConfig, stride: int = 1, stride_index: int = 1): + super().__init__() + + dimension = config.encoder_hidden_size * 2**stride_index + self.res_unit1 = PeAudioDacResidualUnit(dimension // 2, dilation=1) + self.res_unit2 = PeAudioDacResidualUnit(dimension // 2, dilation=3) + self.res_unit3 = PeAudioDacResidualUnit(dimension // 2, dilation=9) + self.snake1 = Snake1d(dimension // 2) + self.conv1 = nn.Conv1d( + dimension // 2, dimension, kernel_size=2 * stride, stride=stride, padding=math.ceil(stride / 2) + ) + + def forward(self, hidden_state): + hidden_state = self.res_unit1(hidden_state) + hidden_state = self.res_unit2(hidden_state) + hidden_state = self.snake1(self.res_unit3(hidden_state)) + hidden_state = self.conv1(hidden_state) + + return hidden_state + + +class PeAudioDacEncoder(nn.Module): + """PE_AUDIO_DAC Encoder""" + + def __init__(self, config: PreTrainedConfig): + super().__init__() + + strides = config.downsampling_ratios + # Create first convolution + self.conv1 = nn.Conv1d(1, config.encoder_hidden_size, kernel_size=7, padding=3) + + self.block = [] + # Create EncoderBlocks that double channels as they downsample by `stride` + for stride_index, stride in enumerate(strides): + stride_index = stride_index + 1 + self.block += [PeAudioDacEncoderBlock(config, stride=stride, stride_index=stride_index)] + + self.block = nn.ModuleList(self.block) + d_model = config.encoder_hidden_size * 2**stride_index + self.snake1 = Snake1d(d_model) + self.conv2 = nn.Conv1d(d_model, config.hidden_size, kernel_size=3, padding=1) + + def forward(self, hidden_state): + hidden_state = self.conv1(hidden_state) + + for module in self.block: + hidden_state = module(hidden_state) + + hidden_state = self.snake1(hidden_state) + hidden_state = self.conv2(hidden_state) + + return hidden_state + + +class PeAudioEncoderEmbedder(nn.Module): + def __init__(self, config: PeAudioEncoderConfig): + super().__init__() + self.dac_encoder = PeAudioDacEncoder(config.dac_config) + self.bottleneck = nn.Conv1d(config.dac_config.hidden_size, config.dac_config.codebook_dim, 1) + self.data_proj = nn.Linear(config.dac_config.codebook_dim, config.hidden_size) + self.config = config + + def forward( + self, + input_values: torch.Tensor, + padding_mask: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + with torch.no_grad(), torch.backends.cudnn.flags(enabled=False): + hidden_states = self.dac_encoder(input_values) + hidden_states = self.bottleneck(hidden_states) + + codec_features = hidden_states.transpose(1, 2) + inputs_embeds = self.data_proj(codec_features) + + if padding_mask is not None: + padding_mask = padding_mask[:, :: self.config.dac_config.hop_length] + + return inputs_embeds, padding_mask + + +class PeAudioContrastiveHead(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + ) -> None: + super().__init__() + self.layer_norm = nn.LayerNorm(normalized_shape=in_dim, eps=1e-6) + self.proj = nn.Linear(in_dim, out_dim, bias=False) + + def forward(self, x: torch.Tensor) -> torch.FloatTensor: + return self.proj(self.layer_norm(x)) + + +class PeAudioMaskedGroupNorm(nn.GroupNorm): + def forward(self, x, padding_mask=None): + if padding_mask is None: + return super().forward(x) + + batch_size, hidden_size, seq_len = x.shape + group_size = hidden_size // self.num_groups + grouped_shape = (batch_size, -1, group_size, seq_len) + + x_grouped = x.view(grouped_shape) + padding_mask_grouped = padding_mask.reshape(grouped_shape).bool() + + mean = torch.masked.mean(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True) + var = torch.masked.var(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True, unbiased=False) + + x_norm = (x_grouped - mean) / torch.sqrt(var + self.eps) + x_norm = x_norm.view(x.shape) + + if self.affine: + x_norm = x_norm * self.weight.view(1, -1, 1) + self.bias.view(1, -1, 1) + + return x_norm * padding_mask + + +class PeAudioConvBlock1d(nn.Module): + def __init__(self, config): + super().__init__() + self.groupnorm = PeAudioMaskedGroupNorm(num_groups=1, num_channels=config.hidden_size) + self.activation = nn.SiLU() + self.project = nn.Conv1d( + in_channels=config.hidden_size, + out_channels=config.hidden_size, + kernel_size=3, + padding="same", + ) + + def forward(self, x, padding_mask=None): + x = self.groupnorm(x, padding_mask=padding_mask) + x = self.activation(x) + return self.project(x) + + +class PeAudioResnetBlock1d(nn.Module): + def __init__(self, config): + super().__init__() + self.block1 = PeAudioConvBlock1d(config) + self.block2 = PeAudioConvBlock1d(config) + + def forward(self, hidden_states, padding_mask=None): + """ + Args: + hidden_states: (batch_size, seq_len, hidden_size) + padding_mask: (batch_size, seq_len) + Returns: + hidden_states: (batch_size, seq_len, hidden_size) + """ + # transpose for convolutions + # (batch_size, seq_len, hidden_size) -> (batch_size, hidden_size, seq_len) + hidden_states = hidden_states.transpose(1, 2) + + if padding_mask is not None: + padding_mask = padding_mask.unsqueeze(1).expand_as(hidden_states) + + residual = hidden_states + hidden_states = self.block1(hidden_states, padding_mask=padding_mask) + hidden_states = self.block2(hidden_states, padding_mask=padding_mask) + hidden_states = residual + hidden_states + + return hidden_states.transpose(1, 2) + + +class PeAudioEncoderPatchEmbedder(nn.Module): + def __init__(self, config): + super().__init__() + self.resnet_block = PeAudioResnetBlock1d(config) + self.class_embedding = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + + def forward(self, inputs_embeds, padding_mask=None): + # Embedding step: prepend class token and run the ResNet block. + hidden_states = torch.cat( + [self.class_embedding.expand(inputs_embeds.size(0), -1, -1), inputs_embeds], + dim=1, + ) + + if padding_mask is not None: + # TODO: any reason why we take padding_mask[0] and not just 1? + padding_mask = torch.cat([padding_mask[:, [0]], padding_mask], dim=1) + + hidden_states = self.resnet_block(hidden_states, padding_mask=padding_mask) + return hidden_states, padding_mask + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def stack_freqs(cos: torch.Tensor, sin: torch.Tensor): + dim = cos.size(-1) + cos = cos.narrow(-1, 0, dim // 2) + sin = sin.narrow(-1, 0, dim // 2) + freqs_cis = torch.stack((cos, -sin, sin, cos), dim=-1).view(*cos.size(), 2, 2) + return freqs_cis + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + freqs_cis = stack_freqs(cos, sin) + freqs_cis = freqs_cis.unsqueeze(unsqueeze_dim) + q_ = q.reshape(*q.shape[:-1], -1, 1, 2) + k_ = k.reshape(*k.shape[:-1], -1, 1, 2) + return (q_ * freqs_cis).sum(5).flatten(3), (k_ * freqs_cis).sum(5).flatten(3) + + +@use_kernel_forward_from_hub("RMSNorm") +class PeAudioEncoderRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + PeAudioEncoderRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +@use_kernelized_func(apply_rotary_pos_emb) +class PeAudioEncoderAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config, layer_idx): + super().__init__() + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = False + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + self.q_norm = PeAudioEncoderRMSNorm( + self.head_dim, eps=config.rms_norm_eps + ) # unlike olmo, only on the head dim! + self.k_norm = PeAudioEncoderRMSNorm( + self.head_dim, eps=config.rms_norm_eps + ) # thus post q_norm does not need reshape + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2) + key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class PeAudioEncoderMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class PeAudioEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_idx): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = PeAudioEncoderAttention(config=config, layer_idx=layer_idx) + + self.mlp = PeAudioEncoderMLP(config) + self.input_layernorm = PeAudioEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = PeAudioEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class PeAudioPreTrainedModel(PreTrainedModel): + config: PeAudioConfig + base_model_prefix = "audio_model" + supports_gradient_checkpointing = True + _no_split_modules = ["PeAudioEncoderLayer", "TimmWrapperForImageClassification"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PeAudioEncoderLayer, + "attentions": PeAudioEncoderAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + + if hasattr(self.config, "initializer_range"): + std = self.config.initializer_range + else: + # 0.02 is the standard default value across the library + std = getattr(self.config.get_text_config(), "initializer_range", 0.02) + + if isinstance(module, PeAudioEncoderPatchEmbedder): + embed_dim = module.class_embedding.shape[-1] + init.normal_(module.class_embedding, mean=0.0, std=embed_dim**-0.5 * std) + if isinstance(module, nn.Conv1d): + init.trunc_normal_(module.weight, std=0.02) + init.constant_(module.bias, 0) + elif isinstance(module, Snake1d): + init.ones_(module.alpha) + elif isinstance(module, nn.ConvTranspose1d): + module.reset_parameters() + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=0.02) + + +@dataclass +@auto_docstring( + custom_intro=""" + Class for outputs of [`PeAudioEncoder`]. + """ +) +class PeAudioEncoderOutput(BaseModelOutputWithPooling): + codec_features: torch.FloatTensor | None = None + output_mask: tuple[torch.FloatTensor] | None = None + + +class PeAudioEncoderRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: PeAudioEncoderConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PeAudioEncoderConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +@auto_docstring( + custom_intro=""" + The PeAudio Encoder model. + """ +) +class PeAudioEncoder(PeAudioPreTrainedModel): + config: PeAudioEncoderConfig + main_input_name = "input_values" + base_model_prefix = "audio_model.audio_encoder" + + def __init__(self, config: PeAudioEncoderConfig): + super().__init__(config) + self.embedder = PeAudioEncoderEmbedder(config) + self.patch_embedder = PeAudioEncoderPatchEmbedder(config) + self.layers = nn.ModuleList( + [PeAudioEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = PeAudioEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = PeAudioEncoderRotaryEmbedding(config=config) + self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=False) + self.gradient_checkpointing = False + + self.post_init() + + @can_return_tuple + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_values: torch.Tensor, + padding_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPooling: + inputs_embeds, padding_mask = self.embedder(input_values, padding_mask=padding_mask) + inputs_embeds, attention_mask = self.patch_embedder(inputs_embeds, padding_mask=padding_mask) + + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) + position_embeddings = self.rotary_emb(inputs_embeds, position_ids) + + hidden_states = inputs_embeds + for encoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + hidden_states = self.output(hidden_states) + + return PeAudioEncoderOutput( + last_hidden_state=hidden_states[:, 1:], + pooler_output=hidden_states[:, 0], + output_mask=padding_mask, + ) + + +# TODO: not sure about the typing for text_model_output +@dataclass +# @auto_docstring +class PeAudioOutput(ModelOutput): + loss: torch.FloatTensor | None = None + logits_audio_text: torch.FloatTensor | None = None + text_audio_embeds: torch.FloatTensor | None = None + audio_embeds: torch.FloatTensor | None = None + text_outputs: BaseModelOutputWithPooling = None + audio_outputs: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_outputs", "audio_outputs"] else getattr(self, k).to_tuple() for k in self.keys() + ) + + +class PeAudioModel(PeAudioPreTrainedModel): + def __init__(self, config: PeAudioConfig): + super().__init__(config) + self.text_model = AutoModel.from_config(config.text_config) + self.audio_encoder = PeAudioEncoder(config.audio_config) + + self.text_audio_head = PeAudioContrastiveHead(config.text_config.hidden_size, config.text_config.hidden_size) + self.audio_head = PeAudioContrastiveHead(config.audio_config.hidden_size, config.text_config.hidden_size) + + self.text_audio_logit_scale = nn.Parameter(torch.zeros(1)) + self.text_audio_logit_bias = nn.Parameter(torch.zeros(1)) + + self.post_init() + + def get_text_audio_embeds(self, input_ids, attention_mask=None): + # TODO: naming can be improved here... + text_outputs: MaskedLMOutput = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + ) + text_audio_embeds = text_outputs.hidden_states[-1][:, 0] + return self.text_audio_head(text_audio_embeds) + + def get_audio_embeds(self, input_values, padding_mask=None): + audio_outputs: BaseModelOutputWithPooling = self.audio_encoder( + input_values=input_values, + padding_mask=padding_mask, + return_dict=True, + ) + audio_embeds = audio_outputs.pooler_output + return self.audio_head(audio_embeds) + + @can_return_tuple + def forward( + self, + input_ids: torch.Tensor, + input_values: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + return_loss: bool | None = None, + **kwargs, + ) -> PeAudioOutput: + audio_outputs: BaseModelOutputWithPooling = self.audio_encoder( + input_values=input_values, padding_mask=padding_mask, **kwargs + ) + + kwargs["output_hidden_states"] = True + text_outputs: MaskedLMOutput = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) + + audio_embeds = audio_outputs.pooler_output + audio_embeds = self.audio_head(audio_embeds) + + text_audio_embeds = text_outputs.hidden_states[-1][:, 0] + text_audio_embeds = self.text_audio_head(text_audio_embeds) + + logits_audio_text = audio_embeds @ text_audio_embeds.T + logits_audio_text = logits_audio_text * self.text_audio_logit_scale.to( + logits_audio_text.device + ) + self.text_audio_logit_bias.to(logits_audio_text.device) + + loss = None + if return_loss: + labels = torch.eye(logits_audio_text.shape[0], device=logits_audio_text.device) + loss = -F.logsigmoid(labels * logits_audio_text).sum() / logits_audio_text.shape[0] + + return PeAudioOutput( + logits_audio_text=logits_audio_text, + text_audio_embeds=text_audio_embeds, + audio_embeds=audio_embeds, + text_outputs=text_outputs, + audio_outputs=audio_outputs, + loss=loss, + ) + + +# TODO: underline in documentation that logits output shape is +# 1. Model: (n_audio, n_text) +# 2. Frame-level: (n_audio, n_text, n_frames) +class PeAudioFrameLevelModel(PeAudioModel): + def get_audio_embeds(self, input_values, padding_mask=None): + audio_outputs: BaseModelOutputWithPooling = self.audio_encoder( + input_values=input_values, + padding_mask=padding_mask, + return_dict=True, + ) + audio_embeds = audio_outputs.last_hidden_state + audio_embeds = self.audio_head(audio_embeds) + return audio_embeds + + @can_return_tuple + def forward( + self, + input_ids: torch.Tensor, + input_values: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + return_loss: bool | None = None, + **kwargs, + ) -> PeAudioOutput: + audio_outputs: BaseModelOutputWithPooling = self.audio_encoder( + input_values=input_values, padding_mask=padding_mask, **kwargs + ) + kwargs["output_hidden_states"] = True + text_outputs: MaskedLMOutput = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) + + audio_embeds = audio_outputs.last_hidden_state + audio_embeds = self.audio_head(audio_embeds) + + text_audio_embeds = text_outputs.hidden_states[-1][:, 0] + text_audio_embeds = self.text_audio_head(text_audio_embeds) + + logits_audio_text = (audio_embeds @ text_audio_embeds.T).transpose(1, 2) + logits_audio_text = logits_audio_text * self.text_audio_logit_scale + self.text_audio_logit_bias + + loss = None + if return_loss: + labels = torch.eye(logits_audio_text.shape[0], device=logits_audio_text.device) + loss = -F.logsigmoid(labels * logits_audio_text).sum() / logits_audio_text.shape[0] + + return PeAudioOutput( + logits_audio_text=logits_audio_text, + text_audio_embeds=text_audio_embeds, + audio_embeds=audio_embeds, + text_outputs=text_outputs, + audio_outputs=audio_outputs, + loss=loss, + ) + + +__all__ = ["PeAudioFrameLevelModel", "PeAudioModel", "PeAudioEncoder"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/modular_pe_audio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/modular_pe_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..2ad12650e55efd7aa2fbf35722b7d6233afe0c16 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/modular_pe_audio.py @@ -0,0 +1,307 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...configuration_utils import PreTrainedConfig +from ...masking_utils import create_bidirectional_mask +from ...modeling_outputs import BaseModelOutputWithPooling, MaskedLMOutput +from ...utils import ModelOutput, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from ..dac.modeling_dac import DacEncoder, DacEncoderBlock, Snake1d +from ..pe_audio_video.modeling_pe_audio_video import ( + PeAudioVideoContrastiveHead, + PeAudioVideoEncoder, + PeAudioVideoPreTrainedModel, +) +from .configuration_pe_audio import PeAudioConfig, PeAudioEncoderConfig + + +class PeAudioDacEncoderBlock(DacEncoderBlock): + def __init__(self, config: PreTrainedConfig, stride: int = 1, stride_index: int = 1): + super().__init__(config, stride=stride, stride_index=stride_index) + + +class PeAudioDacEncoder(DacEncoder): + def __init__(self, config: PreTrainedConfig): + super().__init__(config) + + +class PeAudioEncoderEmbedder(nn.Module): + def __init__(self, config: PeAudioEncoderConfig): + super().__init__() + self.dac_encoder = PeAudioDacEncoder(config.dac_config) + self.bottleneck = nn.Conv1d(config.dac_config.hidden_size, config.dac_config.codebook_dim, 1) + self.data_proj = nn.Linear(config.dac_config.codebook_dim, config.hidden_size) + self.config = config + + def forward( + self, + input_values: torch.Tensor, + padding_mask: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + with torch.no_grad(), torch.backends.cudnn.flags(enabled=False): + hidden_states = self.dac_encoder(input_values) + hidden_states = self.bottleneck(hidden_states) + + codec_features = hidden_states.transpose(1, 2) + inputs_embeds = self.data_proj(codec_features) + + if padding_mask is not None: + padding_mask = padding_mask[:, :: self.config.dac_config.hop_length] + + return inputs_embeds, padding_mask + + +class PeAudioContrastiveHead(PeAudioVideoContrastiveHead): ... + + +class PeAudioPreTrainedModel(PeAudioVideoPreTrainedModel): + base_model_prefix = "audio_model" + _no_split_modules = ["PeAudioEncoderLayer", "TimmWrapperForImageClassification"] + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, nn.Conv1d): + init.trunc_normal_(module.weight, std=0.02) + init.constant_(module.bias, 0) + elif isinstance(module, Snake1d): + init.ones_(module.alpha) + elif isinstance(module, nn.ConvTranspose1d): + module.reset_parameters() + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=0.02) + + +@dataclass +@auto_docstring( + custom_intro=""" + Class for outputs of [`PeAudioEncoder`]. + """ +) +class PeAudioEncoderOutput(BaseModelOutputWithPooling): + codec_features: torch.FloatTensor | None = None + output_mask: tuple[torch.FloatTensor] | None = None + + +# TODO: add the capture of codec features? +@auto_docstring( + custom_intro=""" + The PeAudio Encoder model. + """ +) +class PeAudioEncoder(PeAudioVideoEncoder): + base_model_prefix = "audio_model.audio_encoder" + + @can_return_tuple + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_values: torch.Tensor, + padding_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPooling: + inputs_embeds, padding_mask = self.embedder(input_values, padding_mask=padding_mask) + inputs_embeds, attention_mask = self.patch_embedder(inputs_embeds, padding_mask=padding_mask) + + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) + position_embeddings = self.rotary_emb(inputs_embeds, position_ids) + + hidden_states = inputs_embeds + for encoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + hidden_states = self.output(hidden_states) + + return PeAudioEncoderOutput( + last_hidden_state=hidden_states[:, 1:], + pooler_output=hidden_states[:, 0], + output_mask=padding_mask, + ) + + +# TODO: not sure about the typing for text_model_output +@dataclass +# @auto_docstring +class PeAudioOutput(ModelOutput): + loss: torch.FloatTensor | None = None + logits_audio_text: torch.FloatTensor | None = None + text_audio_embeds: torch.FloatTensor | None = None + audio_embeds: torch.FloatTensor | None = None + text_outputs: BaseModelOutputWithPooling = None + audio_outputs: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_outputs", "audio_outputs"] else getattr(self, k).to_tuple() for k in self.keys() + ) + + +class PeAudioModel(PeAudioPreTrainedModel): + def __init__(self, config: PeAudioConfig): + super().__init__(config) + self.text_model = AutoModel.from_config(config.text_config) + self.audio_encoder = PeAudioEncoder(config.audio_config) + + self.text_audio_head = PeAudioContrastiveHead(config.text_config.hidden_size, config.text_config.hidden_size) + self.audio_head = PeAudioContrastiveHead(config.audio_config.hidden_size, config.text_config.hidden_size) + + self.text_audio_logit_scale = nn.Parameter(torch.zeros(1)) + self.text_audio_logit_bias = nn.Parameter(torch.zeros(1)) + + self.post_init() + + def get_text_audio_embeds(self, input_ids, attention_mask=None): + # TODO: naming can be improved here... + text_outputs: MaskedLMOutput = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + ) + text_audio_embeds = text_outputs.hidden_states[-1][:, 0] + return self.text_audio_head(text_audio_embeds) + + def get_audio_embeds(self, input_values, padding_mask=None): + audio_outputs: BaseModelOutputWithPooling = self.audio_encoder( + input_values=input_values, + padding_mask=padding_mask, + return_dict=True, + ) + audio_embeds = audio_outputs.pooler_output + return self.audio_head(audio_embeds) + + @can_return_tuple + def forward( + self, + input_ids: torch.Tensor, + input_values: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + return_loss: bool | None = None, + **kwargs, + ) -> PeAudioOutput: + audio_outputs: BaseModelOutputWithPooling = self.audio_encoder( + input_values=input_values, padding_mask=padding_mask, **kwargs + ) + + kwargs["output_hidden_states"] = True + text_outputs: MaskedLMOutput = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) + + audio_embeds = audio_outputs.pooler_output + audio_embeds = self.audio_head(audio_embeds) + + text_audio_embeds = text_outputs.hidden_states[-1][:, 0] + text_audio_embeds = self.text_audio_head(text_audio_embeds) + + logits_audio_text = audio_embeds @ text_audio_embeds.T + logits_audio_text = logits_audio_text * self.text_audio_logit_scale.to( + logits_audio_text.device + ) + self.text_audio_logit_bias.to(logits_audio_text.device) + + loss = None + if return_loss: + labels = torch.eye(logits_audio_text.shape[0], device=logits_audio_text.device) + loss = -F.logsigmoid(labels * logits_audio_text).sum() / logits_audio_text.shape[0] + + return PeAudioOutput( + logits_audio_text=logits_audio_text, + text_audio_embeds=text_audio_embeds, + audio_embeds=audio_embeds, + text_outputs=text_outputs, + audio_outputs=audio_outputs, + loss=loss, + ) + + +# TODO: underline in documentation that logits output shape is +# 1. Model: (n_audio, n_text) +# 2. Frame-level: (n_audio, n_text, n_frames) +class PeAudioFrameLevelModel(PeAudioModel): + def get_audio_embeds(self, input_values, padding_mask=None): + audio_outputs: BaseModelOutputWithPooling = self.audio_encoder( + input_values=input_values, + padding_mask=padding_mask, + return_dict=True, + ) + audio_embeds = audio_outputs.last_hidden_state + audio_embeds = self.audio_head(audio_embeds) + return audio_embeds + + @can_return_tuple + def forward( + self, + input_ids: torch.Tensor, + input_values: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + return_loss: bool | None = None, + **kwargs, + ) -> PeAudioOutput: + audio_outputs: BaseModelOutputWithPooling = self.audio_encoder( + input_values=input_values, padding_mask=padding_mask, **kwargs + ) + kwargs["output_hidden_states"] = True + text_outputs: MaskedLMOutput = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) + + audio_embeds = audio_outputs.last_hidden_state + audio_embeds = self.audio_head(audio_embeds) + + text_audio_embeds = text_outputs.hidden_states[-1][:, 0] + text_audio_embeds = self.text_audio_head(text_audio_embeds) + + logits_audio_text = (audio_embeds @ text_audio_embeds.T).transpose(1, 2) + logits_audio_text = logits_audio_text * self.text_audio_logit_scale + self.text_audio_logit_bias + + loss = None + if return_loss: + labels = torch.eye(logits_audio_text.shape[0], device=logits_audio_text.device) + loss = -F.logsigmoid(labels * logits_audio_text).sum() / logits_audio_text.shape[0] + + return PeAudioOutput( + logits_audio_text=logits_audio_text, + text_audio_embeds=text_audio_embeds, + audio_embeds=audio_embeds, + text_outputs=text_outputs, + audio_outputs=audio_outputs, + loss=loss, + ) + + +__all__ = [ + "PeAudioFrameLevelModel", + "PeAudioModel", + "PeAudioEncoder", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/processing_pe_audio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/processing_pe_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..f12c462aa9bcb487b8f8205673a0f4c5083ce656 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio/processing_pe_audio.py @@ -0,0 +1,23 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from ...processing_utils import ProcessorMixin + + +class PeAudioProcessor(ProcessorMixin): + attributes = ["feature_extractor", "tokenizer"] + feature_extractor_class = "PeAudioFeatureExtractor" + tokenizer_class = "AutoTokenizer" + + +__all__ = ["PeAudioProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c8f0b6d9565d457606de3e5bfa9b7c378c13cb70 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pe_audio_video import * + from .modeling_pe_audio_video import * + from .processing_pe_audio_video import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/configuration_pe_audio_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/configuration_pe_audio_video.py new file mode 100644 index 0000000000000000000000000000000000000000..7a23cb4193fbbc2bdd3c9d0a15887a7451a199fb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/configuration_pe_audio_video.py @@ -0,0 +1,153 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="facebook/pe-av-large") +@strict +class PeAudioVideoEncoderConfig(PreTrainedConfig): + r""" + video_config (`Union[PreTrainedConfig, dict]`, *optional*): + Configuration for the video encoder. If a dictionary is provided, it is used to instantiate + [`~transformers.PeVideoEncoderConfig`]. + + ```python + >>> from transformers import PeAudioVideoEncoder, PeAudioVideoEncoderConfig + + >>> # Initializing a PeAudioVideoEncoder style configuration + >>> configuration = PeAudioVideoEncoderConfig() + + >>> # Initializing a model from the pe-av-large style configuration + >>> model = PeAudioVideoEncoder(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "pe_audio_video_encoder" + base_config_key = "audio_video_config" + sub_configs = {"audio_config": AutoConfig, "video_config": AutoConfig} + + audio_config: dict | PreTrainedConfig | None = None + video_config: dict | PreTrainedConfig | None = None + hidden_size: int = 1792 + intermediate_size: int = 4800 + num_hidden_layers: int = 6 + num_attention_heads: int = 14 + num_key_value_heads: int | None = None + head_dim: int = 128 + hidden_act: str = "silu" + max_position_embeddings: int = 10000 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-5 + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if isinstance(self.audio_config, dict): + self.audio_config["model_type"] = self.audio_config.get("model_type", "pe_audio_encoder") + self.audio_config = CONFIG_MAPPING[self.audio_config["model_type"]](**self.audio_config) + elif self.audio_config is None: + self.audio_config = CONFIG_MAPPING["pe_audio_encoder"]() + + if isinstance(self.video_config, dict): + self.video_config["model_type"] = self.video_config.get("model_type", "pe_video_encoder") + self.video_config = CONFIG_MAPPING[self.video_config["model_type"]](**self.video_config) + elif self.video_config is None: + self.video_config = CONFIG_MAPPING["pe_video_encoder"]() + + if self.rope_parameters is None: + self.rope_parameters = {"rope_theta": 20000} + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="facebook/pe-av-large") +@strict +class PeAudioVideoConfig(PreTrainedConfig): + r""" + audio_video_config (`dict` or `PreTrainedConfig`, *optional*): + Configuration for the audio-video encoder component. + + ```python + >>> from transformers import PeAudioVideoModel, PeAudioVideoConfig + + >>> # Initializing a PeAudioVideoModel style configuration + >>> configuration = PeAudioVideoConfig() + + >>> # Initializing a model from the pe-av-large style configuration + >>> model = PeAudioModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pe_audio_video" + sub_configs = {"text_config": AutoConfig, "audio_video_config": PeAudioVideoEncoderConfig} + + _default_text_config_kwargs = { + "model_type": "modernbert", + "hidden_size": 1024, + "intermediate_size": 2624, + "num_hidden_layers": 22, + "num_attention_heads": 16, + } + + text_config: dict | PreTrainedConfig | None = None + audio_video_config: dict | PreTrainedConfig | None = None + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "modernbert") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]]( + **{**self._default_text_config_kwargs, **self.text_config} + ) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["modernbert"](**self._default_text_config_kwargs) + + if isinstance(self.audio_video_config, dict): + self.audio_video_config = PeAudioVideoEncoderConfig(**self.audio_video_config) + elif self.audio_video_config is None: + self.audio_video_config = PeAudioVideoEncoderConfig() + + super().__post_init__(**kwargs) + + @property + def audio_config(self): + return CONFIG_MAPPING["pe_audio"]( + text_config=self.text_config, + audio_config=self.audio_video_config.audio_config, + ) + + @property + def video_config(self): + return CONFIG_MAPPING["pe_video"]( + text_config=self.text_config, + video_config=self.audio_video_config.video_config, + ) + + +__all__ = ["PeAudioVideoEncoderConfig", "PeAudioVideoConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/modeling_pe_audio_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/modeling_pe_audio_video.py new file mode 100644 index 0000000000000000000000000000000000000000..16ff654b146e6164f4b9078e22f2df9653b22950 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/modeling_pe_audio_video.py @@ -0,0 +1,1029 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pe_audio_video/modular_pe_audio_video.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pe_audio_video.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional + +import torch +import torch.nn as nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...integrations import use_kernel_forward_from_hub, use_kernelized_func +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPooling, MaskedLMOutput +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from .configuration_pe_audio_video import PeAudioVideoConfig, PeAudioVideoEncoderConfig + + +class PeAudioVideoMaskedGroupNorm(nn.GroupNorm): + def forward(self, x, padding_mask=None): + if padding_mask is None: + return super().forward(x) + + batch_size, hidden_size, seq_len = x.shape + group_size = hidden_size // self.num_groups + grouped_shape = (batch_size, -1, group_size, seq_len) + + x_grouped = x.view(grouped_shape) + padding_mask_grouped = padding_mask.reshape(grouped_shape).bool() + + mean = torch.masked.mean(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True) + var = torch.masked.var(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True, unbiased=False) + + x_norm = (x_grouped - mean) / torch.sqrt(var + self.eps) + x_norm = x_norm.view(x.shape) + + if self.affine: + x_norm = x_norm * self.weight.view(1, -1, 1) + self.bias.view(1, -1, 1) + + return x_norm * padding_mask + + +class PeAudioVideoConvBlock1d(nn.Module): + def __init__(self, config): + super().__init__() + self.groupnorm = PeAudioVideoMaskedGroupNorm(num_groups=1, num_channels=config.hidden_size) + self.activation = nn.SiLU() + self.project = nn.Conv1d( + in_channels=config.hidden_size, + out_channels=config.hidden_size, + kernel_size=3, + padding="same", + ) + + def forward(self, x, padding_mask=None): + x = self.groupnorm(x, padding_mask=padding_mask) + x = self.activation(x) + return self.project(x) + + +class PeAudioVideoResnetBlock1d(nn.Module): + def __init__(self, config): + super().__init__() + self.block1 = PeAudioVideoConvBlock1d(config) + self.block2 = PeAudioVideoConvBlock1d(config) + + def forward(self, hidden_states, padding_mask=None): + """ + Args: + hidden_states: (batch_size, seq_len, hidden_size) + padding_mask: (batch_size, seq_len) + Returns: + hidden_states: (batch_size, seq_len, hidden_size) + """ + # transpose for convolutions + # (batch_size, seq_len, hidden_size) -> (batch_size, hidden_size, seq_len) + hidden_states = hidden_states.transpose(1, 2) + + if padding_mask is not None: + padding_mask = padding_mask.unsqueeze(1).expand_as(hidden_states) + + residual = hidden_states + hidden_states = self.block1(hidden_states, padding_mask=padding_mask) + hidden_states = self.block2(hidden_states, padding_mask=padding_mask) + hidden_states = residual + hidden_states + + return hidden_states.transpose(1, 2) + + +class PeAudioVideoEncoderPatchEmbedder(nn.Module): + def __init__(self, config): + super().__init__() + self.resnet_block = PeAudioVideoResnetBlock1d(config) + self.class_embedding = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + + def forward(self, inputs_embeds, padding_mask=None): + # Embedding step: prepend class token and run the ResNet block. + hidden_states = torch.cat( + [self.class_embedding.expand(inputs_embeds.size(0), -1, -1), inputs_embeds], + dim=1, + ) + + if padding_mask is not None: + # TODO: any reason why we take padding_mask[0] and not just 1? + padding_mask = torch.cat([padding_mask[:, [0]], padding_mask], dim=1) + + hidden_states = self.resnet_block(hidden_states, padding_mask=padding_mask) + return hidden_states, padding_mask + + +class PeAudioVideoContrastiveHead(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + ) -> None: + super().__init__() + self.layer_norm = nn.LayerNorm(normalized_shape=in_dim, eps=1e-6) + self.proj = nn.Linear(in_dim, out_dim, bias=False) + + def forward(self, x: torch.Tensor) -> torch.FloatTensor: + return self.proj(self.layer_norm(x)) + + +class PeAudioVideoEncoderEmbedder(nn.Module): + def __init__(self, config: PeAudioVideoEncoderConfig): + super().__init__() + self.audio_encoder = AutoModel.from_config(config.audio_config) + self.video_encoder = AutoModel.from_config(config.video_config) + + self.video_proj = nn.Conv1d(config.video_config.hidden_size, config.audio_config.hidden_size, 1) + self.video_norm = nn.LayerNorm(config.audio_config.hidden_size) + + self.concat_modality_proj = nn.Linear( + config.audio_config.hidden_size + config.video_config.hidden_size, + config.hidden_size, + ) + self.data_proj = nn.Linear(config.hidden_size, config.hidden_size) + + def _align_video_hidden_state( + self, + video_hidden_state: torch.Tensor, + audio_hidden_state: torch.Tensor, + padding_mask_videos: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + ) -> torch.Tensor: + """ + Align video_hidden_state to audio_hidden_state by nearest neighbor interpolation. + """ + if video_hidden_state.shape[1] == audio_hidden_state.shape[1]: + return video_hidden_state + + if padding_mask_videos is not None: + video_lengths = padding_mask_videos.sum(dim=-1) + else: + video_lengths = video_hidden_state.shape[1] * video_hidden_state.new_ones( + video_hidden_state.shape[0], dtype=torch.long + ) + + if padding_mask is not None: + audio_lengths = padding_mask.sum(dim=-1) + else: + audio_lengths = audio_hidden_state.shape[1] * audio_hidden_state.new_ones( + audio_hidden_state.shape[0], dtype=torch.long + ) + + if (audio_lengths == video_hidden_state.shape[1]).all() or ( + video_lengths == audio_hidden_state.shape[1] + ).all(): + # no need to align taking into account the padding masks + # note: when one of the above is true, we can expect the other to be true as there is no reason + # to have masked audio without masked video and vice versa + + return nn.functional.interpolate( + video_hidden_state.transpose(1, 2), size=audio_hidden_state.shape[1], mode="nearest" + ).transpose(1, 2) + + aligned_shape = (*audio_hidden_state.shape[:2], video_hidden_state.shape[-1]) + aligned_hidden_state = audio_hidden_state.new_zeros(aligned_shape) + + for i, (hidden_state, video_length, audio_length) in enumerate( + zip(video_hidden_state, video_lengths, audio_lengths) + ): + hidden_state = hidden_state[:video_length] + if hidden_state.numel() > 0 and audio_length > 0: + interpolated_hidden_state = nn.functional.interpolate( + hidden_state[None].transpose(1, 2), size=audio_length, mode="nearest" + ).transpose(1, 2)[0] + aligned_hidden_state[i, :audio_length, :] = interpolated_hidden_state + + return aligned_hidden_state + + def forward( + self, + input_values: torch.Tensor, + pixel_values_videos: torch.Tensor, + padding_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + ): + audio_output = self.audio_encoder(input_values, padding_mask=padding_mask) + video_output = self.video_encoder(pixel_values_videos, padding_mask_videos=padding_mask_videos) + + audio_hidden_state = audio_output.last_hidden_state + video_hidden_state = video_output.last_hidden_state + padding_mask = audio_output.output_mask + + video_hidden_state = self.video_proj(video_hidden_state.transpose(1, 2)).transpose(1, 2) + video_hidden_state = self._align_video_hidden_state( + video_hidden_state=video_hidden_state, + audio_hidden_state=audio_hidden_state, + padding_mask_videos=padding_mask_videos, + padding_mask=padding_mask, + ) + video_hidden_state = self.video_norm(video_hidden_state) + inputs_embeds = torch.cat([audio_hidden_state, video_hidden_state], dim=-1) + inputs_embeds = self.concat_modality_proj(inputs_embeds) + inputs_embeds = self.data_proj(inputs_embeds) + + return inputs_embeds, padding_mask, audio_output, video_output + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def stack_freqs(cos: torch.Tensor, sin: torch.Tensor): + dim = cos.size(-1) + cos = cos.narrow(-1, 0, dim // 2) + sin = sin.narrow(-1, 0, dim // 2) + freqs_cis = torch.stack((cos, -sin, sin, cos), dim=-1).view(*cos.size(), 2, 2) + return freqs_cis + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + freqs_cis = stack_freqs(cos, sin) + freqs_cis = freqs_cis.unsqueeze(unsqueeze_dim) + q_ = q.reshape(*q.shape[:-1], -1, 1, 2) + k_ = k.reshape(*k.shape[:-1], -1, 1, 2) + return (q_ * freqs_cis).sum(5).flatten(3), (k_ * freqs_cis).sum(5).flatten(3) + + +@use_kernelized_func(apply_rotary_pos_emb) +class PeAudioVideoEncoderAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config, layer_idx): + super().__init__() + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = False + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + self.q_norm = PeAudioVideoEncoderRMSNorm( + self.head_dim, eps=config.rms_norm_eps + ) # unlike olmo, only on the head dim! + self.k_norm = PeAudioVideoEncoderRMSNorm( + self.head_dim, eps=config.rms_norm_eps + ) # thus post q_norm does not need reshape + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2) + key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class PeAudioVideoEncoderMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class PeAudioVideoEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_idx): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = PeAudioVideoEncoderAttention(config=config, layer_idx=layer_idx) + + self.mlp = PeAudioVideoEncoderMLP(config) + self.input_layernorm = PeAudioVideoEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = PeAudioVideoEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@use_kernel_forward_from_hub("RMSNorm") +class PeAudioVideoEncoderRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + PeAudioVideoEncoderRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class PeAudioVideoEncoderRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: PeAudioVideoEncoderConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PeAudioVideoEncoderConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +@auto_docstring +class PeAudioVideoPreTrainedModel(PreTrainedModel): + config: PeAudioVideoConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PeAudioVideoEncoderLayer", "TimmWrapperForImageClassification"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PeAudioVideoEncoderLayer, + "attentions": PeAudioVideoEncoderAttention, + } + + def _init_weights(self, module): + super()._init_weights(module) + + if hasattr(self.config, "initializer_range"): + std = self.config.initializer_range + else: + # 0.02 is the standard default value across the library + std = getattr(self.config.get_text_config(), "initializer_range", 0.02) + + if isinstance(module, PeAudioVideoEncoderPatchEmbedder): + embed_dim = module.class_embedding.shape[-1] + init.normal_(module.class_embedding, mean=0.0, std=embed_dim**-0.5 * std) + + +@dataclass +@auto_docstring( + custom_intro=""" + Class for outputs of [`PeAudioVideoEncoder`]. + """ +) +class PeAudioVideoEncoderOutput(BaseModelOutputWithPooling): + audio_model_output: BaseModelOutputWithPooling | None = None + video_model_output: BaseModelOutputWithPooling | None = None + + +@auto_docstring( + custom_intro=""" + The PeAudioVideo Encoder model. + """ +) +class PeAudioVideoEncoder(PeAudioVideoPreTrainedModel): + config: PeAudioVideoEncoderConfig + main_input_name = "input_values" + base_model_prefix = "audio_video_encoder" + + def __init__(self, config: PeAudioVideoEncoderConfig): + super().__init__(config) + self.embedder = PeAudioVideoEncoderEmbedder(config) + self.patch_embedder = PeAudioVideoEncoderPatchEmbedder(config) + self.layers = nn.ModuleList( + [PeAudioVideoEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = PeAudioVideoEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = PeAudioVideoEncoderRotaryEmbedding(config=config) + self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=False) + self.gradient_checkpointing = False + + self.post_init() + + @can_return_tuple + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_values: torch.Tensor | None = None, + pixel_values_videos: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + **kwargs, + ) -> tuple | PeAudioVideoEncoderOutput: + inputs_embeds, padding_mask, audio_output, video_output = self.embedder( + input_values, + pixel_values_videos, + padding_mask=padding_mask, + padding_mask_videos=padding_mask_videos, + ) + inputs_embeds, attention_mask = self.patch_embedder(inputs_embeds, padding_mask=padding_mask) + + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) + position_embeddings = self.rotary_emb(inputs_embeds, position_ids) + + hidden_states = inputs_embeds + for encoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + hidden_states = self.output(hidden_states) + + return PeAudioVideoEncoderOutput( + last_hidden_state=hidden_states[:, 1:], + pooler_output=hidden_states[:, 0], + audio_model_output=audio_output, + video_model_output=video_output, + ) + + +@auto_docstring( + custom_intro=""" + Class for outputs of [`PeAudioVideoModel`] when using text, audio, and/or video. + """ +) +@dataclass +class PeAudioVideoOutput(ModelOutput): + r""" + audio_embeds (`torch.FloatTensor`, *optional*): + Audio modality embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + video_embeds (`torch.FloatTensor`, *optional*): + Video modality embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + audio_video_embeds (`torch.FloatTensor`, *optional*): + Joint audio-video embeddings produced by a fusion module. Shape `(batch_size, sequence_length, hidden_size)`. + text_audio_embeds (`torch.FloatTensor`, *optional*): + Joint text-audio embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + text_video_embeds (`torch.FloatTensor`, *optional*): + Joint text-video embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + text_audio_video_embeds (`torch.FloatTensor`, *optional*): + Joint text-audio-video embeddings combining all three modalities. Shape `(batch_size, sequence_length, hidden_size)`. + audio_plus_text_embeds (`torch.FloatTensor`, *optional*): + Combined audio and text embeddings (e.g., concatenation or additive fusion). Shape `(batch_size, sequence_length, hidden_size)`. + video_plus_text_embeds (`torch.FloatTensor`, *optional*): + Combined video and text embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + text_outputs (`MaskedLMOutput`, *optional*): + Model outputs for the text encoder. Includes hidden states, attentions, and optionally loss. + audio_outputs (`BaseModelOutputWithPooling`, *optional*): + Model outputs for the audio encoder, including last hidden state and pooled output. + video_outputs (`BaseModelOutputWithPooling`, *optional*): + Model outputs for the video encoder, including last hidden state and pooled output. + audio_video_outputs (`BaseModelOutputWithPooling`, *optional*): + Model outputs for the joint audio-video encoder. + logits_audio_text (`torch.FloatTensor`, *optional*): + Similarity logits between audio and text embeddings. Shape `(batch_size, batch_size)`. + logits_video_text (`torch.FloatTensor`, *optional*): + Similarity logits between video and text embeddings. Shape `(batch_size, batch_size)`. + logits_audio_video (`torch.FloatTensor`, *optional*): + Similarity logits between audio and video embeddings. Shape `(batch_size, batch_size)`. + logits_audio_video_text (`torch.FloatTensor`, *optional*): + Similarity logits across audio, video, and text modalities. + logits_audio_plus_text_video (`torch.FloatTensor`, *optional*): + Similarity logits between fused (audio + text) embeddings and video embeddings. + logits_video_plus_text_audio (`torch.FloatTensor`, *optional*): + Similarity logits between fused (video + text) embeddings and audio embeddings. + audio_text_loss (`torch.FloatTensor`, *optional*): + Contrastive loss computed between audio and text representations. + video_text_loss (`torch.FloatTensor`, *optional*): + Contrastive loss computed between video and text representations. + audio_video_loss (`torch.FloatTensor`, *optional*): + Contrastive loss computed between audio and video representations. + audio_video_text_loss (`torch.FloatTensor`, *optional*): + Joint loss over audio, video, and text modalities. + audio_plus_text_video_loss (`torch.FloatTensor`, *optional*): + Loss between fused (audio + text) representations and video. + video_plus_text_audio_loss (`torch.FloatTensor`, *optional*): + Loss between fused (video + text) representations and audio. + loss (`torch.FloatTensor`, *optional*): + Combined loss for all modality-wise losses. + """ + + # embeddings + audio_embeds: torch.FloatTensor | None = None + video_embeds: torch.FloatTensor | None = None + audio_video_embeds: torch.FloatTensor | None = None + text_audio_embeds: torch.FloatTensor | None = None + text_video_embeds: torch.FloatTensor | None = None + text_audio_video_embeds: torch.FloatTensor | None = None + audio_plus_text_embeds: torch.FloatTensor | None = None + video_plus_text_embeds: torch.FloatTensor | None = None + + # model outputs + # TODO: update types to the correct ones + text_outputs: MaskedLMOutput | None = None + audio_outputs: BaseModelOutputWithPooling | None = None + video_outputs: BaseModelOutputWithPooling | None = None + audio_video_outputs: BaseModelOutputWithPooling | None = None + + # logits + logits_audio_text: torch.FloatTensor | None = None + logits_video_text: torch.FloatTensor | None = None + logits_audio_video: torch.FloatTensor | None = None + logits_audio_video_text: torch.FloatTensor | None = None + logits_audio_plus_text_video: torch.FloatTensor | None = None + logits_video_plus_text_audio: torch.FloatTensor | None = None + + audio_text_loss: torch.FloatTensor | None = None + video_text_loss: torch.FloatTensor | None = None + audio_video_loss: torch.FloatTensor | None = None + audio_video_text_loss: torch.FloatTensor | None = None + audio_plus_text_video_loss: torch.FloatTensor | None = None + video_plus_text_audio_loss: torch.FloatTensor | None = None + loss: torch.FloatTensor | None = None + + def to_tuple(self) -> tuple[Any]: + return tuple(self[k] if not k.endswith("model_output") else getattr(self, k).to_tuple() for k in self.keys()) + + +@dataclass +class AudioVideoEmbeddings(ModelOutput): + audio_embeds: torch.FloatTensor | None = None + video_embeds: torch.FloatTensor | None = None + audio_video_embeds: torch.FloatTensor | None = None + + +class PeAudioVideoModel(PeAudioVideoPreTrainedModel): + _tied_weights_keys = { + r"audio_model\.text_model\.(?!rotary_emb)": r"^text_model\.(?!rotary_emb)", + r"video_model\.text_model\.(?!rotary_emb)": r"^text_model\.(?!rotary_emb)", + r"audio_video_encoder\.embedder\.audio_encoder\.(?!rotary_emb)": r"audio_model\.audio_encoder\.(?!rotary_emb)", + r"audio_video_encoder\.embedder\.video_encoder\.(?!rotary_emb|.*\.rope\.pos_embed)": r"video_model\.video_encoder\.(?!rotary_emb|.*\.rope\.pos_embed)", + } + + def __init__(self, config: PeAudioVideoConfig): + super().__init__(config) + self.text_model = AutoModel.from_config(config.text_config) + self.audio_model = AutoModel.from_config(config.audio_config) + self.video_model = AutoModel.from_config(config.video_config) + self.audio_video_encoder = PeAudioVideoEncoder(config.audio_video_config) + + text_hidden_size = config.text_config.hidden_size + audio_hidden_size = config.audio_video_config.audio_config.hidden_size + video_hidden_size = config.audio_video_config.video_config.hidden_size + + # audio-video + self.audio_video_head = PeAudioVideoContrastiveHead(config.audio_video_config.hidden_size, text_hidden_size) + self.text_audio_video_head = PeAudioVideoContrastiveHead(text_hidden_size, text_hidden_size) + self.audio_video_logit_scale = nn.Parameter(torch.zeros(1)) + self.audio_video_logit_bias = nn.Parameter(torch.zeros(1)) + self.text_audio_video_logit_scale = nn.Parameter(torch.zeros(1)) + self.text_audio_video_logit_bias = nn.Parameter(torch.zeros(1)) + + # text-audio + self.audio_plus_text_head = PeAudioVideoContrastiveHead(text_hidden_size + audio_hidden_size, text_hidden_size) + self.audio_plus_text_logit_scale = nn.Parameter(torch.zeros(1)) + self.audio_plus_text_logit_bias = nn.Parameter(torch.zeros(1)) + + # text-video + self.video_plus_text_head = PeAudioVideoContrastiveHead(text_hidden_size + video_hidden_size, text_hidden_size) + self.video_plus_text_logit_scale = nn.Parameter(torch.zeros(1)) + self.video_plus_text_logit_bias = nn.Parameter(torch.zeros(1)) + + self.post_init() + + def _contrastive_loss(self, logits: torch.Tensor) -> torch.Tensor: + labels = torch.eye(logits.shape[0], device=logits.device) + loss = -nn.functional.logsigmoid(labels * logits).sum() / logits.shape[0] + return loss + + def get_text_audio_embeds(self, input_ids, attention_mask=None): + return self.audio_model.get_text_embeds(input_ids, attention_mask) + + def get_text_video_embeds(self, input_ids, attention_mask=None): + return self.video_model.get_text_embeds(input_ids, attention_mask) + + def get_text_audio_video_embeds(self, input_ids, attention_mask=None): + text_outputs: MaskedLMOutput = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + ) + text_embeds = text_outputs.hidden_states[-1][:, 0] + return self.text_audio_video_head(text_embeds) + + def get_audio_embeds(self, input_values, padding_mask=None): + return self.audio_model.get_audio_embeds(input_values, padding_mask) + + def get_video_embeds(self, pixel_values_videos, padding_mask_videos=None): + return self.video_model.get_video_embeds(pixel_values_videos, padding_mask_videos) + + def get_audio_video_embeds( + self, + input_values: torch.Tensor, + pixel_values_videos: torch.Tensor, + padding_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + return_audio_embeds: bool = False, + return_video_embeds: bool = False, + **kwargs, + ) -> AudioVideoEmbeddings: + audio_video_outputs = self.audio_video_encoder( + input_values=input_values, + pixel_values_videos=pixel_values_videos, + padding_mask=padding_mask, + padding_mask_videos=padding_mask_videos, + **kwargs, + ) + if return_audio_embeds: + audio_embeds = self.audio_model.audio_head(audio_video_outputs.audio_model_output.pooler_output) + if return_video_embeds: + video_embeds = self.video_model.video_head(audio_video_outputs.video_model_output.pooler_output) + + audio_video_embeds = self.audio_video_head(audio_video_outputs.pooler_output) + return AudioVideoEmbeddings( + audio_embeds=audio_embeds if return_audio_embeds else None, + video_embeds=video_embeds if return_video_embeds else None, + audio_video_embeds=audio_video_embeds, + ) + + def get_audio_plus_text_embeds( + self, + input_ids: torch.Tensor, + input_values: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + ) -> torch.Tensor: + audio_embeds = self.audio_model.audio_encoder( + input_values=input_values, + padding_mask=padding_mask, + return_dict=True, + ) + text_outputs: MaskedLMOutput = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + ) + text_embeds = text_outputs.hidden_states[-1][:, 0] + + audio_plus_text_embeds = torch.cat([text_embeds, audio_embeds], dim=-1) + return self.audio_plus_text_head(audio_plus_text_embeds) + + def get_video_plus_text_embeds( + self, + input_ids: torch.Tensor, + pixel_values_videos: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + ) -> torch.Tensor: + video_embeds = self.video_model.video_encoder( + pixel_values_videos=pixel_values_videos, + padding_mask_videos=padding_mask_videos, + return_dict=True, + ) + text_outputs: MaskedLMOutput = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + ) + text_embeds = text_outputs.hidden_states[-1][:, 0] + + video_plus_text_embeds = torch.cat([text_embeds, video_embeds], dim=-1) + return self.video_plus_text_head(video_plus_text_embeds) + + @can_return_tuple + def forward( + self, + input_ids: torch.Tensor | None = None, + pixel_values_videos: torch.Tensor | None = None, + input_values: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + return_loss=False, + **kwargs, + ) -> PeAudioVideoOutput: + if sum([input_ids is not None, pixel_values_videos is not None, input_values is not None]) < 2: + raise ValueError("At least two of input_ids, pixel_values_videos, or input_values must be provided") + + if pixel_values_videos is None: + outputs = self.audio_model( + input_ids=input_ids, + input_values=input_values, + attention_mask=attention_mask, + padding_mask=padding_mask, + return_dict=True, + ) + audio_plus_text_embeds = torch.cat( + [outputs.audio_outputs.pooler_output, outputs.text_outputs.hidden_states[-1][:, 0]], dim=-1 + ) + audio_plus_text_embeds = self.audio_plus_text_head(audio_plus_text_embeds) + return PeAudioVideoOutput(audio_plus_text_embeds=audio_plus_text_embeds, **outputs) + + if input_values is None: + outputs = self.video_model( + input_ids=input_ids, + pixel_values_videos=pixel_values_videos, + attention_mask=attention_mask, + padding_mask_videos=padding_mask_videos, + return_dict=True, + ) + video_plus_text_embeds = torch.cat( + [outputs.video_outputs.pooler_output, outputs.text_outputs.hidden_states[-1][:, 0]], dim=-1 + ) + video_plus_text_embeds = self.video_plus_text_head(video_plus_text_embeds) + return PeAudioVideoOutput(video_plus_text_embeds=video_plus_text_embeds, **outputs) + + audio_video_outputs = self.audio_video_encoder( + input_values=input_values, + pixel_values_videos=pixel_values_videos, + padding_mask=padding_mask, + padding_mask_videos=padding_mask_videos, + **kwargs, + ) + audio_embeds = audio_video_outputs.audio_model_output.pooler_output + video_embeds = audio_video_outputs.video_model_output.pooler_output + audio_video_embeds = audio_video_outputs.pooler_output + + audio_embeds = self.audio_model.audio_head(audio_embeds) + video_embeds = self.video_model.video_head(video_embeds) + audio_video_embeds = self.audio_video_head(audio_video_embeds) + logits_audio_video = audio_embeds @ video_embeds.T + logits_audio_video = logits_audio_video * self.audio_video_logit_scale + self.audio_video_logit_bias + audio_video_loss = self._contrastive_loss(logits_audio_video) if return_loss else None + + if input_ids is None: + return PeAudioVideoOutput( + logits_audio_video=logits_audio_video, + audio_embeds=audio_embeds, + video_embeds=video_embeds, + audio_video_embeds=audio_video_embeds, + loss=audio_video_loss, + audio_video_loss=audio_video_loss, + ) + + kwargs["output_hidden_states"] = True + text_outputs = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) + text_embeds = text_outputs.hidden_states[-1][:, 0] + audio_plus_text_embeds = torch.cat([audio_video_outputs.audio_model_output.pooler_output, text_embeds], dim=-1) + video_plus_text_embeds = torch.cat([audio_video_outputs.video_model_output.pooler_output, text_embeds], dim=-1) + + text_audio_embeds = self.audio_model.text_audio_head(text_embeds) + text_video_embeds = self.video_model.text_video_head(text_embeds) + text_audio_video_embeds = self.text_audio_video_head(text_embeds) + audio_plus_text_embeds = self.audio_plus_text_head(audio_plus_text_embeds) + video_plus_text_embeds = self.video_plus_text_head(video_plus_text_embeds) + + logits_audio_text = audio_embeds @ text_audio_embeds.T + logits_video_text = video_embeds @ text_video_embeds.T + logits_audio_video_text = audio_video_embeds @ text_audio_video_embeds.T + + logits_audio_plus_text_video = audio_plus_text_embeds @ video_embeds.T + logits_video_plus_text_audio = video_plus_text_embeds @ audio_embeds.T + + logits_audio_text = ( + logits_audio_text * self.audio_model.text_audio_logit_scale + self.audio_model.text_audio_logit_bias + ) + logits_video_text = ( + logits_video_text * self.video_model.text_video_logit_scale + self.video_model.text_video_logit_bias + ) + logits_audio_video_text = ( + logits_audio_video_text * self.text_audio_video_logit_scale + self.text_audio_video_logit_bias + ) + + logits_audio_plus_text_video = ( + logits_audio_plus_text_video * self.audio_plus_text_logit_scale + self.audio_plus_text_logit_bias + ) + logits_video_plus_text_audio = ( + logits_video_plus_text_audio * self.video_plus_text_logit_scale + self.video_plus_text_logit_bias + ) + + if return_loss: + audio_text_loss = self._contrastive_loss(logits_audio_text) + video_text_loss = self._contrastive_loss(logits_video_text) + audio_video_text_loss = self._contrastive_loss(logits_audio_video_text) + audio_plus_text_video_loss = self._contrastive_loss(logits_audio_plus_text_video) + video_plus_text_audio_loss = self._contrastive_loss(logits_video_plus_text_audio) + loss = ( + audio_video_text_loss + + audio_text_loss + + video_text_loss + + audio_video_loss + + audio_plus_text_video_loss + + video_plus_text_audio_loss + ) + + return PeAudioVideoOutput( + # embeddings + audio_embeds=audio_embeds, + video_embeds=video_embeds, + audio_video_embeds=audio_video_embeds, + text_audio_embeds=text_audio_embeds, + text_video_embeds=text_video_embeds, + text_audio_video_embeds=text_audio_video_embeds, + audio_plus_text_embeds=audio_plus_text_embeds, + video_plus_text_embeds=video_plus_text_embeds, + # model outputs + text_outputs=text_outputs, + audio_outputs=audio_video_outputs.audio_model_output, + video_outputs=audio_video_outputs.video_model_output, + audio_video_outputs=audio_video_outputs, + # logits + logits_audio_text=logits_audio_text, + logits_video_text=logits_video_text, + logits_audio_video=logits_audio_video, + logits_audio_video_text=logits_audio_video_text, + logits_audio_plus_text_video=logits_audio_plus_text_video, + logits_video_plus_text_audio=logits_video_plus_text_audio, + # losses + audio_text_loss=audio_text_loss if return_loss else None, + video_text_loss=video_text_loss if return_loss else None, + audio_video_loss=audio_video_loss if return_loss else None, + audio_video_text_loss=audio_video_text_loss if return_loss else None, + audio_plus_text_video_loss=audio_plus_text_video_loss if return_loss else None, + video_plus_text_audio_loss=video_plus_text_audio_loss if return_loss else None, + loss=loss if return_loss else None, + ) + + +__all__ = ["PeAudioVideoModel", "PeAudioVideoEncoder"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/modular_pe_audio_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/modular_pe_audio_video.py new file mode 100644 index 0000000000000000000000000000000000000000..4c49a22b20d0c24d27d13f7301c14393b3702e4a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/modular_pe_audio_video.py @@ -0,0 +1,824 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import torch +import torch.nn as nn + +from ... import initialization as init +from ...masking_utils import create_bidirectional_mask +from ...modeling_outputs import BaseModelOutputWithPooling, MaskedLMOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel, eager_attention_forward +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from ..qwen3.modeling_qwen3 import Qwen3Attention, Qwen3DecoderLayer, Qwen3RMSNorm, Qwen3RotaryEmbedding +from .configuration_pe_audio_video import PeAudioVideoConfig, PeAudioVideoEncoderConfig + + +class PeAudioVideoMaskedGroupNorm(nn.GroupNorm): + def forward(self, x, padding_mask=None): + if padding_mask is None: + return super().forward(x) + + batch_size, hidden_size, seq_len = x.shape + group_size = hidden_size // self.num_groups + grouped_shape = (batch_size, -1, group_size, seq_len) + + x_grouped = x.view(grouped_shape) + padding_mask_grouped = padding_mask.reshape(grouped_shape).bool() + + mean = torch.masked.mean(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True) + var = torch.masked.var(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True, unbiased=False) + + x_norm = (x_grouped - mean) / torch.sqrt(var + self.eps) + x_norm = x_norm.view(x.shape) + + if self.affine: + x_norm = x_norm * self.weight.view(1, -1, 1) + self.bias.view(1, -1, 1) + + return x_norm * padding_mask + + +class PeAudioVideoConvBlock1d(nn.Module): + def __init__(self, config): + super().__init__() + self.groupnorm = PeAudioVideoMaskedGroupNorm(num_groups=1, num_channels=config.hidden_size) + self.activation = nn.SiLU() + self.project = nn.Conv1d( + in_channels=config.hidden_size, + out_channels=config.hidden_size, + kernel_size=3, + padding="same", + ) + + def forward(self, x, padding_mask=None): + x = self.groupnorm(x, padding_mask=padding_mask) + x = self.activation(x) + return self.project(x) + + +class PeAudioVideoResnetBlock1d(nn.Module): + def __init__(self, config): + super().__init__() + self.block1 = PeAudioVideoConvBlock1d(config) + self.block2 = PeAudioVideoConvBlock1d(config) + + def forward(self, hidden_states, padding_mask=None): + """ + Args: + hidden_states: (batch_size, seq_len, hidden_size) + padding_mask: (batch_size, seq_len) + Returns: + hidden_states: (batch_size, seq_len, hidden_size) + """ + # transpose for convolutions + # (batch_size, seq_len, hidden_size) -> (batch_size, hidden_size, seq_len) + hidden_states = hidden_states.transpose(1, 2) + + if padding_mask is not None: + padding_mask = padding_mask.unsqueeze(1).expand_as(hidden_states) + + residual = hidden_states + hidden_states = self.block1(hidden_states, padding_mask=padding_mask) + hidden_states = self.block2(hidden_states, padding_mask=padding_mask) + hidden_states = residual + hidden_states + + return hidden_states.transpose(1, 2) + + +class PeAudioVideoEncoderPatchEmbedder(nn.Module): + def __init__(self, config): + super().__init__() + self.resnet_block = PeAudioVideoResnetBlock1d(config) + self.class_embedding = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + + def forward(self, inputs_embeds, padding_mask=None): + # Embedding step: prepend class token and run the ResNet block. + hidden_states = torch.cat( + [self.class_embedding.expand(inputs_embeds.size(0), -1, -1), inputs_embeds], + dim=1, + ) + + if padding_mask is not None: + # TODO: any reason why we take padding_mask[0] and not just 1? + padding_mask = torch.cat([padding_mask[:, [0]], padding_mask], dim=1) + + hidden_states = self.resnet_block(hidden_states, padding_mask=padding_mask) + return hidden_states, padding_mask + + +class PeAudioVideoContrastiveHead(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + ) -> None: + super().__init__() + self.layer_norm = nn.LayerNorm(normalized_shape=in_dim, eps=1e-6) + self.proj = nn.Linear(in_dim, out_dim, bias=False) + + def forward(self, x: torch.Tensor) -> torch.FloatTensor: + return self.proj(self.layer_norm(x)) + + +class PeAudioVideoEncoderEmbedder(nn.Module): + def __init__(self, config: PeAudioVideoEncoderConfig): + super().__init__() + self.audio_encoder = AutoModel.from_config(config.audio_config) + self.video_encoder = AutoModel.from_config(config.video_config) + + self.video_proj = nn.Conv1d(config.video_config.hidden_size, config.audio_config.hidden_size, 1) + self.video_norm = nn.LayerNorm(config.audio_config.hidden_size) + + self.concat_modality_proj = nn.Linear( + config.audio_config.hidden_size + config.video_config.hidden_size, + config.hidden_size, + ) + self.data_proj = nn.Linear(config.hidden_size, config.hidden_size) + + def _align_video_hidden_state( + self, + video_hidden_state: torch.Tensor, + audio_hidden_state: torch.Tensor, + padding_mask_videos: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + ) -> torch.Tensor: + """ + Align video_hidden_state to audio_hidden_state by nearest neighbor interpolation. + """ + if video_hidden_state.shape[1] == audio_hidden_state.shape[1]: + return video_hidden_state + + if padding_mask_videos is not None: + video_lengths = padding_mask_videos.sum(dim=-1) + else: + video_lengths = video_hidden_state.shape[1] * video_hidden_state.new_ones( + video_hidden_state.shape[0], dtype=torch.long + ) + + if padding_mask is not None: + audio_lengths = padding_mask.sum(dim=-1) + else: + audio_lengths = audio_hidden_state.shape[1] * audio_hidden_state.new_ones( + audio_hidden_state.shape[0], dtype=torch.long + ) + + if (audio_lengths == video_hidden_state.shape[1]).all() or ( + video_lengths == audio_hidden_state.shape[1] + ).all(): + # no need to align taking into account the padding masks + # note: when one of the above is true, we can expect the other to be true as there is no reason + # to have masked audio without masked video and vice versa + + return nn.functional.interpolate( + video_hidden_state.transpose(1, 2), size=audio_hidden_state.shape[1], mode="nearest" + ).transpose(1, 2) + + aligned_shape = (*audio_hidden_state.shape[:2], video_hidden_state.shape[-1]) + aligned_hidden_state = audio_hidden_state.new_zeros(aligned_shape) + + for i, (hidden_state, video_length, audio_length) in enumerate( + zip(video_hidden_state, video_lengths, audio_lengths) + ): + hidden_state = hidden_state[:video_length] + if hidden_state.numel() > 0 and audio_length > 0: + interpolated_hidden_state = nn.functional.interpolate( + hidden_state[None].transpose(1, 2), size=audio_length, mode="nearest" + ).transpose(1, 2)[0] + aligned_hidden_state[i, :audio_length, :] = interpolated_hidden_state + + return aligned_hidden_state + + def forward( + self, + input_values: torch.Tensor, + pixel_values_videos: torch.Tensor, + padding_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + ): + audio_output = self.audio_encoder(input_values, padding_mask=padding_mask) + video_output = self.video_encoder(pixel_values_videos, padding_mask_videos=padding_mask_videos) + + audio_hidden_state = audio_output.last_hidden_state + video_hidden_state = video_output.last_hidden_state + padding_mask = audio_output.output_mask + + video_hidden_state = self.video_proj(video_hidden_state.transpose(1, 2)).transpose(1, 2) + video_hidden_state = self._align_video_hidden_state( + video_hidden_state=video_hidden_state, + audio_hidden_state=audio_hidden_state, + padding_mask_videos=padding_mask_videos, + padding_mask=padding_mask, + ) + video_hidden_state = self.video_norm(video_hidden_state) + inputs_embeds = torch.cat([audio_hidden_state, video_hidden_state], dim=-1) + inputs_embeds = self.concat_modality_proj(inputs_embeds) + inputs_embeds = self.data_proj(inputs_embeds) + + return inputs_embeds, padding_mask, audio_output, video_output + + +class PeAudioVideoEncoderAttention(Qwen3Attention): + def __init__(self, config, layer_idx): + super().__init__(config, layer_idx) + self.is_causal = False + del self.sliding_window + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2) + key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class PeAudioVideoEncoderLayer(Qwen3DecoderLayer): + def __init__(self, config, layer_idx): + super().__init__(config, layer_idx) + del self.attention_type + + +class PeAudioVideoEncoderRMSNorm(Qwen3RMSNorm): ... + + +def stack_freqs(cos: torch.Tensor, sin: torch.Tensor): + dim = cos.size(-1) + cos = cos.narrow(-1, 0, dim // 2) + sin = sin.narrow(-1, 0, dim // 2) + freqs_cis = torch.stack((cos, -sin, sin, cos), dim=-1).view(*cos.size(), 2, 2) + return freqs_cis + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + freqs_cis = stack_freqs(cos, sin) + freqs_cis = freqs_cis.unsqueeze(unsqueeze_dim) + q_ = q.reshape(*q.shape[:-1], -1, 1, 2) + k_ = k.reshape(*k.shape[:-1], -1, 1, 2) + return (q_ * freqs_cis).sum(5).flatten(3), (k_ * freqs_cis).sum(5).flatten(3) + + +class PeAudioVideoEncoderRotaryEmbedding(Qwen3RotaryEmbedding): ... + + +@auto_docstring +class PeAudioVideoPreTrainedModel(PreTrainedModel): + config: PeAudioVideoConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PeAudioVideoEncoderLayer", "TimmWrapperForImageClassification"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PeAudioVideoEncoderLayer, + "attentions": PeAudioVideoEncoderAttention, + } + + def _init_weights(self, module): + super()._init_weights(module) + + if hasattr(self.config, "initializer_range"): + std = self.config.initializer_range + else: + # 0.02 is the standard default value across the library + std = getattr(self.config.get_text_config(), "initializer_range", 0.02) + + if isinstance(module, PeAudioVideoEncoderPatchEmbedder): + embed_dim = module.class_embedding.shape[-1] + init.normal_(module.class_embedding, mean=0.0, std=embed_dim**-0.5 * std) + + +@dataclass +@auto_docstring( + custom_intro=""" + Class for outputs of [`PeAudioVideoEncoder`]. + """ +) +class PeAudioVideoEncoderOutput(BaseModelOutputWithPooling): + audio_model_output: BaseModelOutputWithPooling | None = None + video_model_output: BaseModelOutputWithPooling | None = None + + +@auto_docstring( + custom_intro=""" + The PeAudioVideo Encoder model. + """ +) +class PeAudioVideoEncoder(PeAudioVideoPreTrainedModel): + config: PeAudioVideoEncoderConfig + main_input_name = "input_values" + base_model_prefix = "audio_video_encoder" + + def __init__(self, config: PeAudioVideoEncoderConfig): + super().__init__(config) + self.embedder = PeAudioVideoEncoderEmbedder(config) + self.patch_embedder = PeAudioVideoEncoderPatchEmbedder(config) + self.layers = nn.ModuleList( + [PeAudioVideoEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = PeAudioVideoEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = PeAudioVideoEncoderRotaryEmbedding(config=config) + self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=False) + self.gradient_checkpointing = False + + self.post_init() + + @can_return_tuple + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_values: torch.Tensor | None = None, + pixel_values_videos: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + **kwargs, + ) -> tuple | PeAudioVideoEncoderOutput: + inputs_embeds, padding_mask, audio_output, video_output = self.embedder( + input_values, + pixel_values_videos, + padding_mask=padding_mask, + padding_mask_videos=padding_mask_videos, + ) + inputs_embeds, attention_mask = self.patch_embedder(inputs_embeds, padding_mask=padding_mask) + + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) + position_embeddings = self.rotary_emb(inputs_embeds, position_ids) + + hidden_states = inputs_embeds + for encoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + hidden_states = self.output(hidden_states) + + return PeAudioVideoEncoderOutput( + last_hidden_state=hidden_states[:, 1:], + pooler_output=hidden_states[:, 0], + audio_model_output=audio_output, + video_model_output=video_output, + ) + + +@auto_docstring( + custom_intro=""" + Class for outputs of [`PeAudioVideoModel`] when using text, audio, and/or video. + """ +) +@dataclass +class PeAudioVideoOutput(ModelOutput): + r""" + audio_embeds (`torch.FloatTensor`, *optional*): + Audio modality embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + video_embeds (`torch.FloatTensor`, *optional*): + Video modality embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + audio_video_embeds (`torch.FloatTensor`, *optional*): + Joint audio-video embeddings produced by a fusion module. Shape `(batch_size, sequence_length, hidden_size)`. + text_audio_embeds (`torch.FloatTensor`, *optional*): + Joint text-audio embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + text_video_embeds (`torch.FloatTensor`, *optional*): + Joint text-video embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + text_audio_video_embeds (`torch.FloatTensor`, *optional*): + Joint text-audio-video embeddings combining all three modalities. Shape `(batch_size, sequence_length, hidden_size)`. + audio_plus_text_embeds (`torch.FloatTensor`, *optional*): + Combined audio and text embeddings (e.g., concatenation or additive fusion). Shape `(batch_size, sequence_length, hidden_size)`. + video_plus_text_embeds (`torch.FloatTensor`, *optional*): + Combined video and text embeddings. Shape `(batch_size, sequence_length, hidden_size)`. + text_outputs (`MaskedLMOutput`, *optional*): + Model outputs for the text encoder. Includes hidden states, attentions, and optionally loss. + audio_outputs (`BaseModelOutputWithPooling`, *optional*): + Model outputs for the audio encoder, including last hidden state and pooled output. + video_outputs (`BaseModelOutputWithPooling`, *optional*): + Model outputs for the video encoder, including last hidden state and pooled output. + audio_video_outputs (`BaseModelOutputWithPooling`, *optional*): + Model outputs for the joint audio-video encoder. + logits_audio_text (`torch.FloatTensor`, *optional*): + Similarity logits between audio and text embeddings. Shape `(batch_size, batch_size)`. + logits_video_text (`torch.FloatTensor`, *optional*): + Similarity logits between video and text embeddings. Shape `(batch_size, batch_size)`. + logits_audio_video (`torch.FloatTensor`, *optional*): + Similarity logits between audio and video embeddings. Shape `(batch_size, batch_size)`. + logits_audio_video_text (`torch.FloatTensor`, *optional*): + Similarity logits across audio, video, and text modalities. + logits_audio_plus_text_video (`torch.FloatTensor`, *optional*): + Similarity logits between fused (audio + text) embeddings and video embeddings. + logits_video_plus_text_audio (`torch.FloatTensor`, *optional*): + Similarity logits between fused (video + text) embeddings and audio embeddings. + audio_text_loss (`torch.FloatTensor`, *optional*): + Contrastive loss computed between audio and text representations. + video_text_loss (`torch.FloatTensor`, *optional*): + Contrastive loss computed between video and text representations. + audio_video_loss (`torch.FloatTensor`, *optional*): + Contrastive loss computed between audio and video representations. + audio_video_text_loss (`torch.FloatTensor`, *optional*): + Joint loss over audio, video, and text modalities. + audio_plus_text_video_loss (`torch.FloatTensor`, *optional*): + Loss between fused (audio + text) representations and video. + video_plus_text_audio_loss (`torch.FloatTensor`, *optional*): + Loss between fused (video + text) representations and audio. + loss (`torch.FloatTensor`, *optional*): + Combined loss for all modality-wise losses. + """ + + # embeddings + audio_embeds: torch.FloatTensor | None = None + video_embeds: torch.FloatTensor | None = None + audio_video_embeds: torch.FloatTensor | None = None + text_audio_embeds: torch.FloatTensor | None = None + text_video_embeds: torch.FloatTensor | None = None + text_audio_video_embeds: torch.FloatTensor | None = None + audio_plus_text_embeds: torch.FloatTensor | None = None + video_plus_text_embeds: torch.FloatTensor | None = None + + # model outputs + # TODO: update types to the correct ones + text_outputs: MaskedLMOutput | None = None + audio_outputs: BaseModelOutputWithPooling | None = None + video_outputs: BaseModelOutputWithPooling | None = None + audio_video_outputs: BaseModelOutputWithPooling | None = None + + # logits + logits_audio_text: torch.FloatTensor | None = None + logits_video_text: torch.FloatTensor | None = None + logits_audio_video: torch.FloatTensor | None = None + logits_audio_video_text: torch.FloatTensor | None = None + logits_audio_plus_text_video: torch.FloatTensor | None = None + logits_video_plus_text_audio: torch.FloatTensor | None = None + + audio_text_loss: torch.FloatTensor | None = None + video_text_loss: torch.FloatTensor | None = None + audio_video_loss: torch.FloatTensor | None = None + audio_video_text_loss: torch.FloatTensor | None = None + audio_plus_text_video_loss: torch.FloatTensor | None = None + video_plus_text_audio_loss: torch.FloatTensor | None = None + loss: torch.FloatTensor | None = None + + def to_tuple(self) -> tuple[Any]: + return tuple(self[k] if not k.endswith("model_output") else getattr(self, k).to_tuple() for k in self.keys()) + + +@dataclass +class AudioVideoEmbeddings(ModelOutput): + audio_embeds: torch.FloatTensor | None = None + video_embeds: torch.FloatTensor | None = None + audio_video_embeds: torch.FloatTensor | None = None + + +class PeAudioVideoModel(PeAudioVideoPreTrainedModel): + _tied_weights_keys = { + r"audio_model\.text_model\.(?!rotary_emb)": r"^text_model\.(?!rotary_emb)", + r"video_model\.text_model\.(?!rotary_emb)": r"^text_model\.(?!rotary_emb)", + r"audio_video_encoder\.embedder\.audio_encoder\.(?!rotary_emb)": r"audio_model\.audio_encoder\.(?!rotary_emb)", + r"audio_video_encoder\.embedder\.video_encoder\.(?!rotary_emb|.*\.rope\.pos_embed)": r"video_model\.video_encoder\.(?!rotary_emb|.*\.rope\.pos_embed)", + } + + def __init__(self, config: PeAudioVideoConfig): + super().__init__(config) + self.text_model = AutoModel.from_config(config.text_config) + self.audio_model = AutoModel.from_config(config.audio_config) + self.video_model = AutoModel.from_config(config.video_config) + self.audio_video_encoder = PeAudioVideoEncoder(config.audio_video_config) + + text_hidden_size = config.text_config.hidden_size + audio_hidden_size = config.audio_video_config.audio_config.hidden_size + video_hidden_size = config.audio_video_config.video_config.hidden_size + + # audio-video + self.audio_video_head = PeAudioVideoContrastiveHead(config.audio_video_config.hidden_size, text_hidden_size) + self.text_audio_video_head = PeAudioVideoContrastiveHead(text_hidden_size, text_hidden_size) + self.audio_video_logit_scale = nn.Parameter(torch.zeros(1)) + self.audio_video_logit_bias = nn.Parameter(torch.zeros(1)) + self.text_audio_video_logit_scale = nn.Parameter(torch.zeros(1)) + self.text_audio_video_logit_bias = nn.Parameter(torch.zeros(1)) + + # text-audio + self.audio_plus_text_head = PeAudioVideoContrastiveHead(text_hidden_size + audio_hidden_size, text_hidden_size) + self.audio_plus_text_logit_scale = nn.Parameter(torch.zeros(1)) + self.audio_plus_text_logit_bias = nn.Parameter(torch.zeros(1)) + + # text-video + self.video_plus_text_head = PeAudioVideoContrastiveHead(text_hidden_size + video_hidden_size, text_hidden_size) + self.video_plus_text_logit_scale = nn.Parameter(torch.zeros(1)) + self.video_plus_text_logit_bias = nn.Parameter(torch.zeros(1)) + + self.post_init() + + def _contrastive_loss(self, logits: torch.Tensor) -> torch.Tensor: + labels = torch.eye(logits.shape[0], device=logits.device) + loss = -nn.functional.logsigmoid(labels * logits).sum() / logits.shape[0] + return loss + + def get_text_audio_embeds(self, input_ids, attention_mask=None): + return self.audio_model.get_text_embeds(input_ids, attention_mask) + + def get_text_video_embeds(self, input_ids, attention_mask=None): + return self.video_model.get_text_embeds(input_ids, attention_mask) + + def get_text_audio_video_embeds(self, input_ids, attention_mask=None): + text_outputs: MaskedLMOutput = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + ) + text_embeds = text_outputs.hidden_states[-1][:, 0] + return self.text_audio_video_head(text_embeds) + + def get_audio_embeds(self, input_values, padding_mask=None): + return self.audio_model.get_audio_embeds(input_values, padding_mask) + + def get_video_embeds(self, pixel_values_videos, padding_mask_videos=None): + return self.video_model.get_video_embeds(pixel_values_videos, padding_mask_videos) + + def get_audio_video_embeds( + self, + input_values: torch.Tensor, + pixel_values_videos: torch.Tensor, + padding_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + return_audio_embeds: bool = False, + return_video_embeds: bool = False, + **kwargs, + ) -> AudioVideoEmbeddings: + audio_video_outputs = self.audio_video_encoder( + input_values=input_values, + pixel_values_videos=pixel_values_videos, + padding_mask=padding_mask, + padding_mask_videos=padding_mask_videos, + **kwargs, + ) + if return_audio_embeds: + audio_embeds = self.audio_model.audio_head(audio_video_outputs.audio_model_output.pooler_output) + if return_video_embeds: + video_embeds = self.video_model.video_head(audio_video_outputs.video_model_output.pooler_output) + + audio_video_embeds = self.audio_video_head(audio_video_outputs.pooler_output) + return AudioVideoEmbeddings( + audio_embeds=audio_embeds if return_audio_embeds else None, + video_embeds=video_embeds if return_video_embeds else None, + audio_video_embeds=audio_video_embeds, + ) + + def get_audio_plus_text_embeds( + self, + input_ids: torch.Tensor, + input_values: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + ) -> torch.Tensor: + audio_embeds = self.audio_model.audio_encoder( + input_values=input_values, + padding_mask=padding_mask, + return_dict=True, + ) + text_outputs: MaskedLMOutput = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + ) + text_embeds = text_outputs.hidden_states[-1][:, 0] + + audio_plus_text_embeds = torch.cat([text_embeds, audio_embeds], dim=-1) + return self.audio_plus_text_head(audio_plus_text_embeds) + + def get_video_plus_text_embeds( + self, + input_ids: torch.Tensor, + pixel_values_videos: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + ) -> torch.Tensor: + video_embeds = self.video_model.video_encoder( + pixel_values_videos=pixel_values_videos, + padding_mask_videos=padding_mask_videos, + return_dict=True, + ) + text_outputs: MaskedLMOutput = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + ) + text_embeds = text_outputs.hidden_states[-1][:, 0] + + video_plus_text_embeds = torch.cat([text_embeds, video_embeds], dim=-1) + return self.video_plus_text_head(video_plus_text_embeds) + + @can_return_tuple + def forward( + self, + input_ids: torch.Tensor | None = None, + pixel_values_videos: torch.Tensor | None = None, + input_values: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + return_loss=False, + **kwargs, + ) -> PeAudioVideoOutput: + if sum([input_ids is not None, pixel_values_videos is not None, input_values is not None]) < 2: + raise ValueError("At least two of input_ids, pixel_values_videos, or input_values must be provided") + + if pixel_values_videos is None: + outputs = self.audio_model( + input_ids=input_ids, + input_values=input_values, + attention_mask=attention_mask, + padding_mask=padding_mask, + return_dict=True, + ) + audio_plus_text_embeds = torch.cat( + [outputs.audio_outputs.pooler_output, outputs.text_outputs.hidden_states[-1][:, 0]], dim=-1 + ) + audio_plus_text_embeds = self.audio_plus_text_head(audio_plus_text_embeds) + return PeAudioVideoOutput(audio_plus_text_embeds=audio_plus_text_embeds, **outputs) + + if input_values is None: + outputs = self.video_model( + input_ids=input_ids, + pixel_values_videos=pixel_values_videos, + attention_mask=attention_mask, + padding_mask_videos=padding_mask_videos, + return_dict=True, + ) + video_plus_text_embeds = torch.cat( + [outputs.video_outputs.pooler_output, outputs.text_outputs.hidden_states[-1][:, 0]], dim=-1 + ) + video_plus_text_embeds = self.video_plus_text_head(video_plus_text_embeds) + return PeAudioVideoOutput(video_plus_text_embeds=video_plus_text_embeds, **outputs) + + audio_video_outputs = self.audio_video_encoder( + input_values=input_values, + pixel_values_videos=pixel_values_videos, + padding_mask=padding_mask, + padding_mask_videos=padding_mask_videos, + **kwargs, + ) + audio_embeds = audio_video_outputs.audio_model_output.pooler_output + video_embeds = audio_video_outputs.video_model_output.pooler_output + audio_video_embeds = audio_video_outputs.pooler_output + + audio_embeds = self.audio_model.audio_head(audio_embeds) + video_embeds = self.video_model.video_head(video_embeds) + audio_video_embeds = self.audio_video_head(audio_video_embeds) + logits_audio_video = audio_embeds @ video_embeds.T + logits_audio_video = logits_audio_video * self.audio_video_logit_scale + self.audio_video_logit_bias + audio_video_loss = self._contrastive_loss(logits_audio_video) if return_loss else None + + if input_ids is None: + return PeAudioVideoOutput( + logits_audio_video=logits_audio_video, + audio_embeds=audio_embeds, + video_embeds=video_embeds, + audio_video_embeds=audio_video_embeds, + loss=audio_video_loss, + audio_video_loss=audio_video_loss, + ) + + kwargs["output_hidden_states"] = True + text_outputs = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) + text_embeds = text_outputs.hidden_states[-1][:, 0] + audio_plus_text_embeds = torch.cat([audio_video_outputs.audio_model_output.pooler_output, text_embeds], dim=-1) + video_plus_text_embeds = torch.cat([audio_video_outputs.video_model_output.pooler_output, text_embeds], dim=-1) + + text_audio_embeds = self.audio_model.text_audio_head(text_embeds) + text_video_embeds = self.video_model.text_video_head(text_embeds) + text_audio_video_embeds = self.text_audio_video_head(text_embeds) + audio_plus_text_embeds = self.audio_plus_text_head(audio_plus_text_embeds) + video_plus_text_embeds = self.video_plus_text_head(video_plus_text_embeds) + + logits_audio_text = audio_embeds @ text_audio_embeds.T + logits_video_text = video_embeds @ text_video_embeds.T + logits_audio_video_text = audio_video_embeds @ text_audio_video_embeds.T + + logits_audio_plus_text_video = audio_plus_text_embeds @ video_embeds.T + logits_video_plus_text_audio = video_plus_text_embeds @ audio_embeds.T + + logits_audio_text = ( + logits_audio_text * self.audio_model.text_audio_logit_scale + self.audio_model.text_audio_logit_bias + ) + logits_video_text = ( + logits_video_text * self.video_model.text_video_logit_scale + self.video_model.text_video_logit_bias + ) + logits_audio_video_text = ( + logits_audio_video_text * self.text_audio_video_logit_scale + self.text_audio_video_logit_bias + ) + + logits_audio_plus_text_video = ( + logits_audio_plus_text_video * self.audio_plus_text_logit_scale + self.audio_plus_text_logit_bias + ) + logits_video_plus_text_audio = ( + logits_video_plus_text_audio * self.video_plus_text_logit_scale + self.video_plus_text_logit_bias + ) + + if return_loss: + audio_text_loss = self._contrastive_loss(logits_audio_text) + video_text_loss = self._contrastive_loss(logits_video_text) + audio_video_text_loss = self._contrastive_loss(logits_audio_video_text) + audio_plus_text_video_loss = self._contrastive_loss(logits_audio_plus_text_video) + video_plus_text_audio_loss = self._contrastive_loss(logits_video_plus_text_audio) + loss = ( + audio_video_text_loss + + audio_text_loss + + video_text_loss + + audio_video_loss + + audio_plus_text_video_loss + + video_plus_text_audio_loss + ) + + return PeAudioVideoOutput( + # embeddings + audio_embeds=audio_embeds, + video_embeds=video_embeds, + audio_video_embeds=audio_video_embeds, + text_audio_embeds=text_audio_embeds, + text_video_embeds=text_video_embeds, + text_audio_video_embeds=text_audio_video_embeds, + audio_plus_text_embeds=audio_plus_text_embeds, + video_plus_text_embeds=video_plus_text_embeds, + # model outputs + text_outputs=text_outputs, + audio_outputs=audio_video_outputs.audio_model_output, + video_outputs=audio_video_outputs.video_model_output, + audio_video_outputs=audio_video_outputs, + # logits + logits_audio_text=logits_audio_text, + logits_video_text=logits_video_text, + logits_audio_video=logits_audio_video, + logits_audio_video_text=logits_audio_video_text, + logits_audio_plus_text_video=logits_audio_plus_text_video, + logits_video_plus_text_audio=logits_video_plus_text_audio, + # losses + audio_text_loss=audio_text_loss if return_loss else None, + video_text_loss=video_text_loss if return_loss else None, + audio_video_loss=audio_video_loss if return_loss else None, + audio_video_text_loss=audio_video_text_loss if return_loss else None, + audio_plus_text_video_loss=audio_plus_text_video_loss if return_loss else None, + video_plus_text_audio_loss=video_plus_text_audio_loss if return_loss else None, + loss=loss if return_loss else None, + ) + + +__all__ = [ + "PeAudioVideoModel", + "PeAudioVideoEncoder", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/processing_pe_audio_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/processing_pe_audio_video.py new file mode 100644 index 0000000000000000000000000000000000000000..a03b213b10c540bdb74603ef86e7af10d2935ef6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_audio_video/processing_pe_audio_video.py @@ -0,0 +1,24 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from ...processing_utils import ProcessorMixin + + +class PeAudioVideoProcessor(ProcessorMixin): + attributes = ["feature_extractor", "video_processor", "tokenizer"] + feature_extractor_class = "PeAudioFeatureExtractor" + tokenizer_class = "AutoTokenizer" + video_processor_class = "PeVideoVideoProcessor" + + +__all__ = ["PeAudioVideoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b3ac2f050915f88e8b1593c704e8b6fed8a25653 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pe_video import * + from .modeling_pe_video import * + from .processing_pe_video import * + from .video_processing_pe_video import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/configuration_pe_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/configuration_pe_video.py new file mode 100644 index 0000000000000000000000000000000000000000..d6e260d73389fe679e4b0095f6d195678a5de048 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/configuration_pe_video.py @@ -0,0 +1,141 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig +from ..timm_wrapper import TimmWrapperConfig + + +@auto_docstring(checkpoint="facebook/pe-av-large") +@strict +class PeVideoEncoderConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PeAudioEncoder, PeAudioEncoderConfig + + >>> # Initializing a PeAudioEncoder style configuration + >>> configuration = PeAudioEncoderConfig() + + >>> # Initializing a model from the pe-av-large style configuration + >>> model = PeAudioEncoder(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pe_video_encoder" + sub_configs = {"vision_config": TimmWrapperConfig} + base_config_key = "audio_video_config" + + _default_vision_config_kwargs = { + "architecture": "vit_pe_core_large_patch14_336", + "do_pooling": True, + "num_classes": 1024, + "global_pool": "map", + "initializer_range": 0.02, + } + + vision_config: dict | PreTrainedConfig | None = None + hidden_size: int = 1792 + intermediate_size: int = 4800 + num_hidden_layers: int = 6 + num_attention_heads: int = 14 + num_key_value_heads: int | None = None + head_dim: int = 128 + hidden_act: str = "silu" + max_position_embeddings: int = 10000 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-5 + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.rope_parameters is None: + self.rope_parameters = {"rope_theta": 20000} + + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "timm_wrapper") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]].from_dict( + {**self._default_vision_config_kwargs, **self.vision_config} + ) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["timm_wrapper"].from_dict(self._default_vision_config_kwargs) + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="facebook/pe-av-large") +@strict +class PeVideoConfig(PreTrainedConfig): + r""" + video_config (`dict` or `PreTrainedConfig`, *optional*): + Configuration for the video encoder component. + + ```python + >>> from transformers import PeVideoModel, PeVideoConfig + + >>> # Initializing a PeVideoModel style configuration + >>> configuration = PeVideoConfig() + + >>> # Initializing a model from the pe-av-large style configuration + >>> model = PeVideoModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pe_video" + sub_configs = {"text_config": AutoConfig, "video_config": PeVideoEncoderConfig} + base_config_key = "audio_video_config" + + _default_text_config_kwargs = { + "model_type": "modernbert", + "hidden_size": 1024, + "intermediate_size": 2624, + "num_hidden_layers": 22, + "num_attention_heads": 16, + } + + text_config: dict | PreTrainedConfig | None = None + video_config: dict | PreTrainedConfig | None = None + + def __post_init__(self, **kwargs): + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "modernbert") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]]( + **{**self._default_text_config_kwargs, **self.text_config} + ) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["modernbert"](**self._default_text_config_kwargs) + + if isinstance(self.video_config, dict): + self.video_config = PeVideoEncoderConfig(**self.video_config) + elif self.video_config is None: + self.video_config = PeVideoEncoderConfig() + + super().__post_init__(**kwargs) + + +__all__ = ["PeVideoEncoderConfig", "PeVideoConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/modeling_pe_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/modeling_pe_video.py new file mode 100644 index 0000000000000000000000000000000000000000..5a712aa060280f080594dc77a5397735ec120c1c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/modeling_pe_video.py @@ -0,0 +1,650 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pe_video/modular_pe_video.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pe_video.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...integrations import use_kernel_forward_from_hub, use_kernelized_func +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPooling, MaskedLMOutput +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel, AutoModelForImageClassification +from .configuration_pe_video import PeVideoConfig, PeVideoEncoderConfig + + +# TODO: not sure about the typing for text_model_output +@dataclass +# @auto_docstring +class PeVideoOutput(ModelOutput): + loss: torch.FloatTensor | None = None + logits_video_text: torch.FloatTensor | None = None + text_video_embeds: torch.FloatTensor | None = None + video_embeds: torch.FloatTensor | None = None + text_outputs: BaseModelOutputWithPooling = None + video_outputs: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_outputs", "video_outputs"] else getattr(self, k).to_tuple() for k in self.keys() + ) + + +class PeVideoContrastiveHead(nn.Module): + def __init__( + self, + in_dim: int, + out_dim: int, + ) -> None: + super().__init__() + self.layer_norm = nn.LayerNorm(normalized_shape=in_dim, eps=1e-6) + self.proj = nn.Linear(in_dim, out_dim, bias=False) + + def forward(self, x: torch.Tensor) -> torch.FloatTensor: + return self.proj(self.layer_norm(x)) + + +class PeVideoMaskedGroupNorm(nn.GroupNorm): + def forward(self, x, padding_mask=None): + if padding_mask is None: + return super().forward(x) + + batch_size, hidden_size, seq_len = x.shape + group_size = hidden_size // self.num_groups + grouped_shape = (batch_size, -1, group_size, seq_len) + + x_grouped = x.view(grouped_shape) + padding_mask_grouped = padding_mask.reshape(grouped_shape).bool() + + mean = torch.masked.mean(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True) + var = torch.masked.var(x_grouped, mask=padding_mask_grouped, dim=(2, 3), keepdim=True, unbiased=False) + + x_norm = (x_grouped - mean) / torch.sqrt(var + self.eps) + x_norm = x_norm.view(x.shape) + + if self.affine: + x_norm = x_norm * self.weight.view(1, -1, 1) + self.bias.view(1, -1, 1) + + return x_norm * padding_mask + + +class PeVideoConvBlock1d(nn.Module): + def __init__(self, config): + super().__init__() + self.groupnorm = PeVideoMaskedGroupNorm(num_groups=1, num_channels=config.hidden_size) + self.activation = nn.SiLU() + self.project = nn.Conv1d( + in_channels=config.hidden_size, + out_channels=config.hidden_size, + kernel_size=3, + padding="same", + ) + + def forward(self, x, padding_mask=None): + x = self.groupnorm(x, padding_mask=padding_mask) + x = self.activation(x) + return self.project(x) + + +class PeVideoResnetBlock1d(nn.Module): + def __init__(self, config): + super().__init__() + self.block1 = PeVideoConvBlock1d(config) + self.block2 = PeVideoConvBlock1d(config) + + def forward(self, hidden_states, padding_mask=None): + """ + Args: + hidden_states: (batch_size, seq_len, hidden_size) + padding_mask: (batch_size, seq_len) + Returns: + hidden_states: (batch_size, seq_len, hidden_size) + """ + # transpose for convolutions + # (batch_size, seq_len, hidden_size) -> (batch_size, hidden_size, seq_len) + hidden_states = hidden_states.transpose(1, 2) + + if padding_mask is not None: + padding_mask = padding_mask.unsqueeze(1).expand_as(hidden_states) + + residual = hidden_states + hidden_states = self.block1(hidden_states, padding_mask=padding_mask) + hidden_states = self.block2(hidden_states, padding_mask=padding_mask) + hidden_states = residual + hidden_states + + return hidden_states.transpose(1, 2) + + +class PeVideoEncoderPatchEmbedder(nn.Module): + def __init__(self, config): + super().__init__() + self.resnet_block = PeVideoResnetBlock1d(config) + self.class_embedding = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + + def forward(self, inputs_embeds, padding_mask=None): + # Embedding step: prepend class token and run the ResNet block. + hidden_states = torch.cat( + [self.class_embedding.expand(inputs_embeds.size(0), -1, -1), inputs_embeds], + dim=1, + ) + + if padding_mask is not None: + # TODO: any reason why we take padding_mask[0] and not just 1? + padding_mask = torch.cat([padding_mask[:, [0]], padding_mask], dim=1) + + hidden_states = self.resnet_block(hidden_states, padding_mask=padding_mask) + return hidden_states, padding_mask + + +class PeVideoEncoderEmbedder(nn.Module): + def __init__(self, config: PeVideoEncoderConfig): + super().__init__() + self.vision_model = AutoModelForImageClassification.from_config(config.vision_config) + self.proj = nn.Linear(config.vision_config.num_labels, config.hidden_size, bias=False) + self.data_proj = nn.Linear(config.hidden_size, config.hidden_size) + + def forward( + self, + pixel_values_videos: torch.Tensor, + padding_mask: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = pixel_values_videos.shape + + pixel_values_videos = pixel_values_videos.view(-1, *input_shape[2:]) + vision_encoder_outputs = self.vision_model(pixel_values_videos) + + logits = vision_encoder_outputs.logits.view(*input_shape[:2], -1) + logits = F.normalize(logits, dim=-1) + + vision_features = self.proj(logits) + inputs_embeds = self.data_proj(vision_features) + + return inputs_embeds, padding_mask + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def stack_freqs(cos: torch.Tensor, sin: torch.Tensor): + dim = cos.size(-1) + cos = cos.narrow(-1, 0, dim // 2) + sin = sin.narrow(-1, 0, dim // 2) + freqs_cis = torch.stack((cos, -sin, sin, cos), dim=-1).view(*cos.size(), 2, 2) + return freqs_cis + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + freqs_cis = stack_freqs(cos, sin) + freqs_cis = freqs_cis.unsqueeze(unsqueeze_dim) + q_ = q.reshape(*q.shape[:-1], -1, 1, 2) + k_ = k.reshape(*k.shape[:-1], -1, 1, 2) + return (q_ * freqs_cis).sum(5).flatten(3), (k_ * freqs_cis).sum(5).flatten(3) + + +@use_kernel_forward_from_hub("RMSNorm") +class PeVideoEncoderRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + PeVideoEncoderRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +@use_kernelized_func(apply_rotary_pos_emb) +class PeVideoEncoderAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config, layer_idx): + super().__init__() + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = False + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + self.q_norm = PeVideoEncoderRMSNorm( + self.head_dim, eps=config.rms_norm_eps + ) # unlike olmo, only on the head dim! + self.k_norm = PeVideoEncoderRMSNorm( + self.head_dim, eps=config.rms_norm_eps + ) # thus post q_norm does not need reshape + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2) + key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class PeVideoEncoderMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class PeVideoEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_idx): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = PeVideoEncoderAttention(config=config, layer_idx=layer_idx) + + self.mlp = PeVideoEncoderMLP(config) + self.input_layernorm = PeVideoEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = PeVideoEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class PeVideoPreTrainedModel(PreTrainedModel): + config: PeVideoConfig + base_model_prefix = "video_model" + supports_gradient_checkpointing = True + _no_split_modules = ["PeVideoEncoderLayer", "TimmWrapperForImageClassification"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PeVideoEncoderLayer, + "attentions": PeVideoEncoderAttention, + } + main_input_name = "pixel_values_videos" + + def _init_weights(self, module): + super()._init_weights(module) + + if hasattr(self.config, "initializer_range"): + std = self.config.initializer_range + else: + # 0.02 is the standard default value across the library + std = getattr(self.config.get_text_config(), "initializer_range", 0.02) + + if isinstance(module, PeVideoEncoderPatchEmbedder): + embed_dim = module.class_embedding.shape[-1] + init.normal_(module.class_embedding, mean=0.0, std=embed_dim**-0.5 * std) + + +class PeVideoEncoderRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: PeVideoEncoderConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PeVideoEncoderConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +@auto_docstring( + custom_intro=""" + The PeVideo Encoder model. + """ +) +class PeVideoEncoder(PeVideoPreTrainedModel): + config: PeVideoEncoderConfig + main_input_name = "pixel_values_videos" + base_model_prefix = "video_model.video_encoder" + + def __init__(self, config: PeVideoEncoderConfig): + super().__init__(config) + self.embedder = PeVideoEncoderEmbedder(config) + self.patch_embedder = PeVideoEncoderPatchEmbedder(config) + self.layers = nn.ModuleList( + [PeVideoEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = PeVideoEncoderRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = PeVideoEncoderRotaryEmbedding(config=config) + self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=False) + self.gradient_checkpointing = False + + self.post_init() + + @can_return_tuple + @merge_with_config_defaults + @capture_outputs + def forward( + self, + pixel_values_videos: torch.Tensor, + padding_mask_videos: torch.Tensor | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPooling: + inputs_embeds, padding_mask = self.embedder(pixel_values_videos, padding_mask=padding_mask_videos) + inputs_embeds, attention_mask = self.patch_embedder(inputs_embeds, padding_mask=padding_mask) + + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) + position_embeddings = self.rotary_emb(inputs_embeds, position_ids) + + hidden_states = inputs_embeds + for encoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + hidden_states = self.output(hidden_states) + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states[:, 1:], + pooler_output=hidden_states[:, 0], + ) + + +class PeVideoModel(PeVideoPreTrainedModel): + main_input_name = "input_ids" + + def __init__(self, config: PeVideoConfig): + super().__init__(config) + self.text_model = AutoModel.from_config(config.text_config) + self.video_encoder = PeVideoEncoder(config.video_config) + + self.text_video_head = PeVideoContrastiveHead(config.text_config.hidden_size, config.text_config.hidden_size) + self.video_head = PeVideoContrastiveHead(config.video_config.hidden_size, config.text_config.hidden_size) + + self.text_video_logit_scale = nn.Parameter(torch.zeros(1)) + self.text_video_logit_bias = nn.Parameter(torch.zeros(1)) + + self.post_init() + + @can_return_tuple + @auto_docstring + def get_text_features( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + **kwargs, + ) + text_outputs.pooler_output = self.text_video_head(text_outputs.last_hidden_state) + return text_outputs + + @can_return_tuple + @auto_docstring + def get_video_features( + self, + pixel_values_videos: torch.Tensor, + padding_mask_videos: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + video_outputs: BaseModelOutputWithPooling = self.video_encoder( + pixel_values_videos=pixel_values_videos, + padding_mask_videos=padding_mask_videos, + return_dict=True, + **kwargs, + ) + video_outputs.pooler_output = self.video_head(video_outputs.pooler_output) + return video_outputs + + @can_return_tuple + def forward( + self, + input_ids: torch.Tensor, + pixel_values_videos: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + return_loss: bool | None = None, + **kwargs, + ) -> PeVideoOutput: + video_outputs: BaseModelOutputWithPooling = self.video_encoder( + pixel_values_videos=pixel_values_videos, padding_mask_videos=padding_mask_videos, **kwargs + ) + kwargs["output_hidden_states"] = True + text_outputs: MaskedLMOutput = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) + + video_embeds = video_outputs.pooler_output + video_embeds = self.video_head(video_embeds) + + text_video_embeds = text_outputs.hidden_states[-1][:, 0] + text_video_embeds = self.text_video_head(text_video_embeds) + + logits_video_text = video_embeds @ text_video_embeds.T + logits_video_text = logits_video_text * self.text_video_logit_scale + self.text_video_logit_bias + + loss = None + if return_loss: + labels = torch.eye(logits_video_text.shape[0], device=logits_video_text.device) + loss = -F.logsigmoid(labels * logits_video_text).sum() / logits_video_text.shape[0] + + return PeVideoOutput( + logits_video_text=logits_video_text, + text_video_embeds=text_video_embeds, + video_embeds=video_embeds, + text_outputs=text_outputs, + video_outputs=video_outputs, + loss=loss, + ) + + +__all__ = ["PeVideoEncoder", "PeVideoModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/modular_pe_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/modular_pe_video.py new file mode 100644 index 0000000000000000000000000000000000000000..e09be108bd2532ddfde8939388e5731a3d1f4bf6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/modular_pe_video.py @@ -0,0 +1,238 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass +from typing import Any + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...masking_utils import create_bidirectional_mask +from ...modeling_outputs import BaseModelOutputWithPooling, MaskedLMOutput +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel, AutoModelForImageClassification +from ..pe_audio_video.modeling_pe_audio_video import ( + PeAudioVideoContrastiveHead, + PeAudioVideoEncoder, + PeAudioVideoEncoderPatchEmbedder, + PeAudioVideoPreTrainedModel, +) +from .configuration_pe_video import PeVideoConfig, PeVideoEncoderConfig + + +# TODO: not sure about the typing for text_model_output +@dataclass +# @auto_docstring +class PeVideoOutput(ModelOutput): + loss: torch.FloatTensor | None = None + logits_video_text: torch.FloatTensor | None = None + text_video_embeds: torch.FloatTensor | None = None + video_embeds: torch.FloatTensor | None = None + text_outputs: BaseModelOutputWithPooling = None + video_outputs: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple( + self[k] if k not in ["text_outputs", "video_outputs"] else getattr(self, k).to_tuple() for k in self.keys() + ) + + +class PeVideoContrastiveHead(PeAudioVideoContrastiveHead): ... + + +class PeVideoEncoderPatchEmbedder(PeAudioVideoEncoderPatchEmbedder): ... + + +class PeVideoEncoderEmbedder(nn.Module): + def __init__(self, config: PeVideoEncoderConfig): + super().__init__() + self.vision_model = AutoModelForImageClassification.from_config(config.vision_config) + self.proj = nn.Linear(config.vision_config.num_labels, config.hidden_size, bias=False) + self.data_proj = nn.Linear(config.hidden_size, config.hidden_size) + + def forward( + self, + pixel_values_videos: torch.Tensor, + padding_mask: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = pixel_values_videos.shape + + pixel_values_videos = pixel_values_videos.view(-1, *input_shape[2:]) + vision_encoder_outputs = self.vision_model(pixel_values_videos) + + logits = vision_encoder_outputs.logits.view(*input_shape[:2], -1) + logits = F.normalize(logits, dim=-1) + + vision_features = self.proj(logits) + inputs_embeds = self.data_proj(vision_features) + + return inputs_embeds, padding_mask + + +class PeVideoPreTrainedModel(PeAudioVideoPreTrainedModel): + base_model_prefix = "video_model" + main_input_name = "pixel_values_videos" + _no_split_modules = ["PeVideoEncoderLayer", "TimmWrapperForImageClassification"] + + +@auto_docstring( + custom_intro=""" + The PeVideo Encoder model. + """ +) +class PeVideoEncoder(PeAudioVideoEncoder): + base_model_prefix = "video_model.video_encoder" + main_input_name = "pixel_values_videos" + + def __init__(self, config: PeVideoEncoderConfig): + super().__init__(config) + self.embedder = PeVideoEncoderEmbedder(config) + + @can_return_tuple + @merge_with_config_defaults + @capture_outputs + def forward( + self, + pixel_values_videos: torch.Tensor, + padding_mask_videos: torch.Tensor | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPooling: + inputs_embeds, padding_mask = self.embedder(pixel_values_videos, padding_mask=padding_mask_videos) + inputs_embeds, attention_mask = self.patch_embedder(inputs_embeds, padding_mask=padding_mask) + + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0) + position_embeddings = self.rotary_emb(inputs_embeds, position_ids) + + hidden_states = inputs_embeds + for encoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = encoder_layer( + hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + hidden_states = self.output(hidden_states) + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states[:, 1:], + pooler_output=hidden_states[:, 0], + ) + + +class PeVideoModel(PeVideoPreTrainedModel): + main_input_name = "input_ids" + + def __init__(self, config: PeVideoConfig): + super().__init__(config) + self.text_model = AutoModel.from_config(config.text_config) + self.video_encoder = PeVideoEncoder(config.video_config) + + self.text_video_head = PeVideoContrastiveHead(config.text_config.hidden_size, config.text_config.hidden_size) + self.video_head = PeVideoContrastiveHead(config.video_config.hidden_size, config.text_config.hidden_size) + + self.text_video_logit_scale = nn.Parameter(torch.zeros(1)) + self.text_video_logit_bias = nn.Parameter(torch.zeros(1)) + + self.post_init() + + @can_return_tuple + @auto_docstring + def get_text_features( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + return_dict=True, + **kwargs, + ) + text_outputs.pooler_output = self.text_video_head(text_outputs.last_hidden_state) + return text_outputs + + @can_return_tuple + @auto_docstring + def get_video_features( + self, + pixel_values_videos: torch.Tensor, + padding_mask_videos: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + video_outputs: BaseModelOutputWithPooling = self.video_encoder( + pixel_values_videos=pixel_values_videos, + padding_mask_videos=padding_mask_videos, + return_dict=True, + **kwargs, + ) + video_outputs.pooler_output = self.video_head(video_outputs.pooler_output) + return video_outputs + + @can_return_tuple + def forward( + self, + input_ids: torch.Tensor, + pixel_values_videos: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask_videos: torch.Tensor | None = None, + return_loss: bool | None = None, + **kwargs, + ) -> PeVideoOutput: + video_outputs: BaseModelOutputWithPooling = self.video_encoder( + pixel_values_videos=pixel_values_videos, padding_mask_videos=padding_mask_videos, **kwargs + ) + kwargs["output_hidden_states"] = True + text_outputs: MaskedLMOutput = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs) + + video_embeds = video_outputs.pooler_output + video_embeds = self.video_head(video_embeds) + + text_video_embeds = text_outputs.hidden_states[-1][:, 0] + text_video_embeds = self.text_video_head(text_video_embeds) + + logits_video_text = video_embeds @ text_video_embeds.T + logits_video_text = logits_video_text * self.text_video_logit_scale + self.text_video_logit_bias + + loss = None + if return_loss: + labels = torch.eye(logits_video_text.shape[0], device=logits_video_text.device) + loss = -F.logsigmoid(labels * logits_video_text).sum() / logits_video_text.shape[0] + + return PeVideoOutput( + logits_video_text=logits_video_text, + text_video_embeds=text_video_embeds, + video_embeds=video_embeds, + text_outputs=text_outputs, + video_outputs=video_outputs, + loss=loss, + ) + + +__all__ = [ + "PeVideoEncoder", + "PeVideoModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/processing_pe_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/processing_pe_video.py new file mode 100644 index 0000000000000000000000000000000000000000..515311ca99413991eeef735a7784c768d297d65c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/processing_pe_video.py @@ -0,0 +1,10 @@ +from ...processing_utils import ProcessorMixin + + +class PeVideoProcessor(ProcessorMixin): + attributes = ["video_processor", "tokenizer"] + video_processor_class = "PeVideoVideoProcessor" + tokenizer_class = "AutoTokenizer" + + +__all__ = ["PeVideoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/video_processing_pe_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/video_processing_pe_video.py new file mode 100644 index 0000000000000000000000000000000000000000..82a6d0e1121e0b1c5051fed6ffb940ff1a2e18ad --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pe_video/video_processing_pe_video.py @@ -0,0 +1,64 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch + +from ...image_processing_utils import BatchFeature +from ...image_utils import PILImageResampling +from ...processing_utils import Unpack, VideosKwargs +from ...video_processing_utils import BaseVideoProcessor, VideoMetadata +from ...video_utils import VideoInput + + +class PeVideoVideoProcessor(BaseVideoProcessor): + resample = PILImageResampling.BILINEAR + + def sample_frames( + self, + metadata: VideoMetadata, + num_frames: int | None = None, + fps: int | float | None = None, + **kwargs, + ): + if num_frames: + total_frames = metadata.total_num_frames + num_frames = num_frames if num_frames is not None else self.num_frames + assert num_frames is not None, "`num_frames` must be specified if `fixed_len_video == True`" + frame_idxs = [int(i * (total_frames - 1) / (num_frames - 1)) for i in range(num_frames)] + return torch.tensor(frame_idxs) + else: + return super().sample_frames(metadata, num_frames, fps, **kwargs) + + def _preprocess( + self, + videos: VideoInput, + **kwargs: Unpack[VideosKwargs], + ) -> BatchFeature: + # Always set `return_tensors` to `None` since it won't pad variable length videos + # We'll handle this after we call the parent' method + return_tensors = kwargs.pop("return_tensors", None) + result = super()._preprocess(videos, **kwargs) + pixels = result.pixel_values_videos + data = {"pixel_values_videos": pixels} + if return_tensors: + lengths = torch.tensor([video.size(0) for video in pixels]) + pixels = torch.nn.utils.rnn.pad_sequence(pixels, batch_first=True, padding_value=0.0) + data["pixel_values_videos"] = pixels + if lengths.unique().size(0) > 1: + mask = torch.arange(lengths.max())[None] < lengths[:, None] + data["padding_mask_videos"] = mask + return BatchFeature(data=data, tensor_type=return_tensors) + + +__all__ = ["PeVideoVideoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..288776550986876700ee1702a34c11b388738d1f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pegasus import * + from .modeling_pegasus import * + from .tokenization_pegasus import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/configuration_pegasus.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/configuration_pegasus.py new file mode 100644 index 0000000000000000000000000000000000000000..b4a3c87b54ab05fc4a6d51f3dc7b60030718f4bb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/configuration_pegasus.py @@ -0,0 +1,76 @@ +# Copyright 2021, Google and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PEGASUS model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="google/pegasus-large") +@strict +class PegasusConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PegasusConfig, PegasusModel + + >>> # Initializing a PEGASUS google/pegasus-large style configuration + >>> configuration = PegasusConfig() + + >>> # Initializing a model (with random weights) from the google/pegasus-large style configuration + >>> model = PegasusModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pegasus" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "num_attention_heads": "encoder_attention_heads", + "hidden_size": "d_model", + "num_hidden_layers": "encoder_layers", + } + + vocab_size: int = 50265 + max_position_embeddings: int = 1024 + encoder_layers: int = 12 + encoder_ffn_dim: int = 4096 + encoder_attention_heads: int = 16 + decoder_layers: int = 12 + decoder_ffn_dim: int = 4096 + decoder_attention_heads: int = 16 + encoder_layerdrop: float | int = 0.0 + decoder_layerdrop: float | int = 0.0 + use_cache: bool = True + is_encoder_decoder: bool = True + activation_function: str = "gelu" + d_model: int = 1024 + dropout: float | int = 0.1 + attention_dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + init_std: float = 0.02 + decoder_start_token_id: int | None = 0 + scale_embedding: bool = False + pad_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 1 + forced_eos_token_id: int | list[int] | None = 1 + is_decoder: bool = False + tie_word_embeddings: bool = True + + +__all__ = ["PegasusConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/modeling_pegasus.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/modeling_pegasus.py new file mode 100644 index 0000000000000000000000000000000000000000..1eed3f26e4e1c798632217c05f009cb7703c26cc --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/modeling_pegasus.py @@ -0,0 +1,1132 @@ +# Copyright 2021, Google and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PEGASUS model.""" + +import copy +import math +from collections.abc import Callable + +import numpy as np +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + is_torchdynamo_compiling, + logging, +) +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_pegasus import PegasusConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.bart.modeling_bart.shift_tokens_right +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +# Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->Pegasus +class PegasusSinusoidalPositionalEmbedding(nn.Embedding): + """This module produces sinusoidal positional embeddings of any length.""" + + def __init__(self, num_positions: int, embedding_dim: int, padding_idx: int | None = None) -> None: + super().__init__(num_positions, embedding_dim, _freeze=True) + + def create_weight(self): + """ + Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in + the 2nd half of the vector. [dim // 2:] + """ + n_pos, dim = self.weight.shape + position_enc = np.array( + [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)] + ) + out = torch.empty(n_pos, dim, dtype=self.weight.dtype, requires_grad=False) + sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1 + out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2])) + out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2])) + return out + + @torch.no_grad() + def forward( + self, input_ids_shape: torch.Size, past_key_values_length: int = 0, position_ids: torch.Tensor | None = None + ) -> torch.Tensor: + """`input_ids_shape` is expected to be [bsz x seqlen].""" + if position_ids is None: + bsz, seq_len = input_ids_shape[:2] + position_ids = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device + ) + return super().forward(position_ids) + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->Pegasus +class PegasusAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: PegasusConfig | None = None, + layer_idx: int | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + if layer_idx is None and self.is_decoder: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + # TODO: we need a refactor so that the different attention modules can get their specific kwargs + # ATM, we have mixed things encoder, decoder, and encoder-decoder attn + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + + # get query proj + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.k_proj(current_states) + value_states = self.v_proj(current_states) + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = key_states.view(kv_shape).transpose(1, 2) + value_states = value_states.view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +# Copied from transformers.models.mbart.modeling_mbart.MBartEncoderLayer with MBart->Pegasus, MBART->PEGASUS +class PegasusEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PegasusConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = PegasusAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + if hidden_states.dtype == torch.float16: + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer with MBart->Pegasus, MBART->PEGASUS +class PegasusDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PegasusConfig, layer_idx: int | None = None): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = PegasusAttention( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + is_causal=True, + config=config, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = PegasusAttention( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + config=config, + layer_idx=layer_idx, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = True, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + past_key_values (`Cache`): cached past key and value projection states + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Cross-Attention Block + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + hidden_states, _ = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class PegasusPreTrainedModel(PreTrainedModel): + config: PegasusConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _can_compile_fullgraph = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, PegasusSinusoidalPositionalEmbedding): + init.copy_(module.weight, module.create_weight()) + elif isinstance(module, PegasusForConditionalGeneration): + init.zeros_(module.final_logits_bias) + + +class PegasusEncoder(PegasusPreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`PegasusEncoderLayer`]. + + Args: + config: PegasusConfig + embed_tokens (nn.Embedding): output embedding + """ + + _can_record_outputs = { + "hidden_states": PegasusEncoderLayer, + "attentions": PegasusAttention, + } + + def __init__(self, config: PegasusConfig): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + self.padding_idx = config.pad_token_id + self.max_source_positions = config.max_position_embeddings + self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim, self.padding_idx) + + self.embed_positions = PegasusSinusoidalPositionalEmbedding( + config.max_position_embeddings, + embed_dim, + self.padding_idx, + ) + self.layers = nn.ModuleList([PegasusEncoderLayer(config) for _ in range(config.encoder_layers)]) + self.layer_norm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def resize_position_embeddings(self, new_num_position_embeddings: int): + """ + Resizes position embeddings matrix of the model if `new_num_position_embeddings != + config.max_position_embeddings`. + + Arguments: + new_num_position_embeddings (`int`): + The number of new position embeddings. If position embeddings are learned, increasing the size will add + newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If + position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will + add correct vectors at the end following the position encoding algorithm, whereas reducing the size + will remove vectors from the end. + """ + logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...") + self.config.max_position_embeddings = new_num_position_embeddings + + self.embed_positions = PegasusSinusoidalPositionalEmbedding( + self.config.max_position_embeddings, + self.config.d_model, + self.padding_idx, + ) + init.copy_(self.embed_positions.weight, self.embed_positions.create_weight()) + self.embed_positions.to(self.device) + + def get_position_embeddings(self) -> nn.Embedding: + """ + Returns the position embeddings matrix + """ + return self.embed_positions + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale + + input_shape = inputs_embeds.shape[:-1] + embed_pos = self.embed_positions(input_shape) + + hidden_states = inputs_embeds + embed_pos + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + for idx, encoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if not to_drop: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + **kwargs, + ) + + hidden_states = self.layer_norm(hidden_states) + + return BaseModelOutput( + last_hidden_state=hidden_states, + ) + + +class PegasusDecoder(PegasusPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PegasusDecoderLayer`] + + Args: + config: PegasusConfig + embed_tokens (nn.Embedding): output embedding + """ + + _can_record_outputs = { + "hidden_states": PegasusDecoderLayer, + "attentions": OutputRecorder(PegasusAttention, index=1, layer_name="self_attn"), + "cross_attentions": OutputRecorder(PegasusAttention, index=1, layer_name="encoder_attn"), + } + + def __init__(self, config: PegasusConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model, self.padding_idx) + + self.embed_positions = PegasusSinusoidalPositionalEmbedding( + config.max_position_embeddings, + config.d_model, + self.padding_idx, + ) + self.layers = nn.ModuleList([PegasusDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)]) + self.layer_norm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def resize_position_embeddings(self, new_num_position_embeddings: int): + """ + Resizes position embeddings matrix of the model if `new_num_position_embeddings != + config.max_position_embeddings`. + + Arguments: + new_num_position_embeddings (`int`): + The number of new position embeddings. If position embeddings are learned, increasing the size will add + newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If + position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will + add correct vectors at the end following the position encoding algorithm, whereas reducing the size + will remove vectors from the end. + """ + logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...") + self.config.max_position_embeddings = new_num_position_embeddings + + self.embed_positions = PegasusSinusoidalPositionalEmbedding( + self.config.max_position_embeddings, + self.config.d_model, + self.padding_idx, + ) + init.copy_(self.embed_positions.weight, self.embed_positions.create_weight()) + self.embed_positions.to(self.device) + + def get_position_embeddings(self) -> nn.Embedding: + """ + Returns the position embeddings matrix + """ + return self.embed_positions + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids=None, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + inputs_embeds=None, + use_cache=None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPastAndCrossAttentions: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # important to apply scale outside of `if` in case users pass `embeds` + inputs_embeds = inputs_embeds * self.embed_scale + + # initialize `past_key_values` + if use_cache and past_key_values is None: + past_key_values = ( + EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + if encoder_hidden_states is not None or self.config.is_encoder_decoder + else DynamicCache(config=self.config) + ) + + batch_size, seq_length = inputs_embeds.size()[:-1] + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(seq_length, device=inputs_embeds.device) + past_key_values_length + + if attention_mask is None and not is_torchdynamo_compiling(): + # required mask seq length can be calculated via length of past cache + mask_seq_length = past_key_values_length + seq_length + attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) + + self_attn_cache = ( + past_key_values.self_attention_cache + if isinstance(past_key_values, EncoderDecoderCache) + else past_key_values + ) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=self_attn_cache, + ) + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + # embed positions + positions = self.embed_positions((batch_size, seq_length), past_key_values_length, position_ids=position_ids) + hidden_states = inputs_embeds + positions + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + hidden_states = decoder_layer( + hidden_states, + causal_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.layer_norm(hidden_states) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class PegasusModel(PegasusPreTrainedModel): + _tied_weights_keys = { + "decoder.embed_tokens.weight": "shared.weight", + "encoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: PegasusConfig): + super().__init__(config) + + padding_idx, vocab_size = config.pad_token_id, config.vocab_size + self.shared = nn.Embedding(vocab_size, config.d_model, padding_idx) + + self.encoder = PegasusEncoder(config) + self.decoder = PegasusDecoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, value): + self.shared = value + self.encoder.embed_tokens = self.shared + self.decoder.embed_tokens = self.shared + + def resize_position_embeddings(self, new_num_position_embeddings: int): + """ + Resizes position embeddings matrix of the model if `new_num_position_embeddings != + config.max_position_embeddings`. + + Arguments: + new_num_position_embeddings (`int`): + The number of new position embeddings. If position embeddings are learned, increasing the size will add + newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If + position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will + add correct vectors at the end following the position encoding algorithm, whereas reducing the size + will remove vectors from the end. + """ + self.config.max_position_embeddings = new_num_position_embeddings + self.encoder.resize_position_embeddings(new_num_position_embeddings) + self.decoder.resize_position_embeddings(new_num_position_embeddings) + + def get_position_embeddings(self) -> tuple[nn.Embedding]: + """ + Returns the position embeddings matrix + """ + return (self.encoder.get_position_embeddings(), self.decoder.get_position_embeddings()) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.Tensor | None = None, + decoder_attention_mask: torch.Tensor | None = None, + encoder_outputs: tuple[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + decoder_inputs_embeds: torch.Tensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Seq2SeqModelOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Pegasus uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + Example: + + ```python + >>> from transformers import AutoTokenizer, PegasusModel + + >>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-large") + >>> model = PegasusModel.from_pretrained("google/pegasus-large") + + >>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt") + >>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt") + >>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 4, 1024] + ```""" + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + **kwargs, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput + elif not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The PEGASUS Model with a language modeling head. Can be used for summarization. + """ +) +class PegasusForConditionalGeneration(PegasusPreTrainedModel, GenerationMixin): + base_model_prefix = "model" + _keys_to_ignore_on_load_missing = ["final_logits_bias"] + _tied_weights_keys = { + "lm_head.weight": "model.shared.weight", + } + + def __init__(self, config: PegasusConfig): + super().__init__(config) + self.model = PegasusModel(config) + self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) + self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def resize_token_embeddings( + self, new_num_tokens: int, pad_to_multiple_of: int | None = None, mean_resizing: bool = True + ) -> nn.Embedding: + new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing) + self._resize_final_logits_bias(new_embeddings.weight.shape[0]) + return new_embeddings + + def _resize_final_logits_bias(self, new_num_tokens: int) -> None: + old_num_tokens = self.final_logits_bias.shape[-1] + if new_num_tokens <= old_num_tokens: + new_bias = self.final_logits_bias[:, :new_num_tokens] + else: + extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) + new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) + self.register_buffer("final_logits_bias", new_bias) + + def resize_position_embeddings(self, new_num_position_embeddings: int): + """ + Resizes position embeddings matrix of the model if `new_num_position_embeddings != + config.max_position_embeddings`. + + Arguments: + new_num_position_embeddings (`int`): + The number of new position embeddings. If position embeddings are learned, increasing the size will add + newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If + position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will + add correct vectors at the end following the position encoding algorithm, whereas reducing the size + will remove vectors from the end. + """ + self.config.max_position_embeddings = new_num_position_embeddings + self.model.encoder.resize_position_embeddings(new_num_position_embeddings) + self.model.decoder.resize_position_embeddings(new_num_position_embeddings) + + def get_position_embeddings(self) -> tuple[nn.Embedding]: + """ + Returns the position embeddings matrix + """ + return (self.model.encoder.get_position_embeddings(), self.model.decoder.get_position_embeddings()) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.Tensor | None = None, + decoder_attention_mask: torch.Tensor | None = None, + encoder_outputs: tuple[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + decoder_inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Seq2SeqLMOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Pegasus uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example Summarization: + + ```python + >>> from transformers import AutoTokenizer, PegasusForConditionalGeneration + + >>> model = PegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum") + >>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum") + + >>> ARTICLE_TO_SUMMARIZE = ( + ... "PG&E stated it scheduled the blackouts in response to forecasts for high winds " + ... "amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were " + ... "scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow." + ... ) + >>> inputs = tokenizer(ARTICLE_TO_SUMMARIZE, max_length=1024, return_tensors="pt") + + >>> # Generate Summary + >>> summary_ids = model.generate(inputs["input_ids"]) + >>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "California's largest electricity provider has turned off power to hundreds of thousands of customers." + ``` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + outputs: Seq2SeqModelOutput = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + lm_logits = self.lm_head(outputs.last_hidden_state) + self.final_logits_bias + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) + + +# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->Pegasus +class PegasusDecoderWrapper(PegasusPreTrainedModel): + """ + This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is + used in combination with the [`EncoderDecoderModel`] framework. + """ + + def __init__(self, config): + super().__init__(config) + self.decoder = PegasusDecoder(config) + self.post_init() + + def forward(self, *args, **kwargs): + return self.decoder(*args, **kwargs) + + +class PegasusForCausalLM(PegasusPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "lm_head.weight": "model.decoder.embed_tokens.weight", + } + + def __init__(self, config): + config = copy.deepcopy(config) + config.is_decoder = True + config.is_encoder_decoder = False + super().__init__(config) + self.model = PegasusDecoderWrapper(config) + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + def get_position_embeddings(self) -> nn.Embedding: + """ + Returns the position embeddings matrix + """ + return self.model.decoder.get_position_embeddings() + + def resize_position_embeddings(self, new_num_position_embeddings: int): + """ + Resizes position embeddings matrix of the model if `new_num_position_embeddings != + config.max_position_embeddings`. + + Arguments: + new_num_position_embeddings (`int`): + The number of new position embeddings. If position embeddings are learned, increasing the size will add + newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If + position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will + add correct vectors at the end following the position encoding algorithm, whereas reducing the size + will remove vectors from the end. + """ + self.config.max_position_embeddings = new_num_position_embeddings + self.model.decoder.resize_position_embeddings(new_num_position_embeddings) + + @can_return_tuple + @auto_docstring + # Copied from transformers.models.bart.modeling_bart.BartForCausalLM.forward with Bart->Pegasus, facebook/bart-base->google/pegasus-large + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | CausalLMOutputWithCrossAttentions: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, PegasusForCausalLM + + >>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-large") + >>> model = PegasusForCausalLM.from_pretrained("google/pegasus-large") + >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size] + >>> list(logits.shape) == expected_shape + True + ```""" + + outputs: BaseModelOutputWithPastAndCrossAttentions = self.model.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +__all__ = ["PegasusForCausalLM", "PegasusForConditionalGeneration", "PegasusModel", "PegasusPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/tokenization_pegasus.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/tokenization_pegasus.py new file mode 100644 index 0000000000000000000000000000000000000000..f9fd47da41bab9964eff7deb3f2d10291d885f4a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus/tokenization_pegasus.py @@ -0,0 +1,135 @@ +# Copyright 2020 Google and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for model PEGASUS.""" + +from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors +from tokenizers.models import Unigram + +from ...tokenization_utils_tokenizers import TokenizersBackend +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"} + + +class PegasusTokenizer(TokenizersBackend): + r""" + Construct a PEGASUS tokenizer (backed by HuggingFace's *tokenizers* library). Based on + [Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). + + This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + Args: + vocab_file (`str`, *optional*): + [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that + contains the vocabulary necessary to instantiate a tokenizer. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + mask_token (`str`, *optional*, defaults to `""`): + The token used for masking single token values. This is the token used when training this model with masked + language modeling (MLM). This is the token that the PEGASUS encoder will try to predict during pretraining. + It corresponds to *[MASK2]* in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive + Summarization](https://huggingface.co/papers/1912.08777). + mask_token_sent (`str`, *optional*, defaults to `""`): + The token used for masking whole target sentences. This is the token used when training this model with gap + sentences generation (GSG). This is the sentence that the PEGASUS decoder will try to predict during + pretraining. It corresponds to *[MASK1]* in [PEGASUS: Pre-training with Extracted Gap-sentences for + Abstractive Summarization](https://huggingface.co/papers/1912.08777). + additional_special_tokens (`List[str]`, *optional*): + Additional special tokens used by the tokenizer. If no additional_special_tokens are provided and + are used as additional special tokens corresponding to the [original PEGASUS + tokenizer](https://github.com/google-research/pegasus/blob/939830367bcf411193d2b5eca2f2f90f3f9260ca/pegasus/ops/pretrain_parsing_ops.cc#L66) + that uses the tokens 2 - 104 only for pretraining + offset (`int`, *optional*, defaults to 103): + Offset for additional special tokens. + vocab (`str` or `list[tuple[str, float]]`, *optional*): + Custom vocabulary with `(token, score)` tuples. If not provided, a blank vocabulary is initialized. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + model = Unigram + + def __init__( + self, + vocab: str | list[tuple[str, float]] | None = None, + pad_token="", + eos_token="", + unk_token="", + mask_token="", + mask_token_sent="", + _spm_precompiled_charsmap=None, + additional_special_tokens=None, + offset=103, + **kwargs, + ): + self.offset = offset + + if additional_special_tokens is None or mask_token_sent not in additional_special_tokens: + additional_special_tokens = [mask_token_sent] if mask_token_sent is not None else [] + else: + additional_special_tokens = [] + additional_special_tokens += [f"" for i in range(2, self.offset)] + + if vocab is None: + vocab = [(str(unk_token), 0.0), (str(pad_token), 0.0), (str(eos_token), 0.0), (str(mask_token), 0.0)] + + self._vocab = vocab + self._tokenizer = Tokenizer(Unigram(vocab=vocab, unk_id=self._vocab.index((str(unk_token), 0.0), 1))) + if _spm_precompiled_charsmap is not None: + self._tokenizer.normalizer = normalizers.Sequence( + [normalizers.Precompiled(_spm_precompiled_charsmap), normalizers.Replace(Regex(r" {2,}"), " ")] + ) + else: + self._tokenizer.normalizer = normalizers.Sequence( + [normalizers.Replace(Regex(r"\n"), " "), normalizers.Replace(Regex(r" {2,}"), " ")] + ) + + self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="always", split=True) + self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="always", split=True) + + super().__init__( + pad_token=pad_token, + eos_token=eos_token, + unk_token=unk_token, + mask_token=mask_token, + mask_token_sent=mask_token_sent, + offset=offset, + additional_special_tokens=additional_special_tokens, + **kwargs, + ) + self._tokenizer.post_processor = processors.TemplateProcessing( + single=f"$A {eos_token}", + pair=f"$A $B {eos_token}", + special_tokens=[(str(eos_token), self.convert_tokens_to_ids(str(eos_token)))], + ) + + +__all__ = ["PegasusTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3d362bacf491ea32dcbc89e7b8bba4d9ae1a9261 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pegasus_x import * + from .modeling_pegasus_x import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/configuration_pegasus_x.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/configuration_pegasus_x.py new file mode 100644 index 0000000000000000000000000000000000000000..1191b6ee227beb973cab335fc93d02a4af49597f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/configuration_pegasus_x.py @@ -0,0 +1,86 @@ +# Copyright 2022, Google and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PEGASUS-X model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="google/pegasus-x-large") +@strict +class PegasusXConfig(PreTrainedConfig): + r""" + num_global_tokens (`int`, *optional*, defaults to 128): + Number of global tokens to use for the encoder + block_size (`int`, *optional*, defaults to 512): + Block size for encoder local attention. Sequence length should be an exact multiple of block size. + block_size must be a multiple of 2 if stagger_local_block is True + stagger_local_blocks (`bool`, *optional*, defaults to `True`): + Whether to stagger every other local attention by half a block + + Example: + + ```python + >>> from transformers import PegasusXConfig, PegasusXModel + + >>> # Initializing a PEGASUS google/pegasus-x-large style configuration + >>> configuration = PegasusXConfig() + + >>> # Initializing a model (with random weights) from the google/pegasus-x-large style configuration + >>> model = PegasusXModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pegasus_x" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "num_attention_heads": "encoder_attention_heads", + "hidden_size": "d_model", + "num_hidden_layers": "encoder_layers", + } + + vocab_size: int = 96103 + max_position_embeddings: int = 16384 + encoder_layers: int = 16 + encoder_ffn_dim: int = 4096 + encoder_attention_heads: int = 16 + decoder_layers: int = 16 + decoder_ffn_dim: int = 4096 + decoder_attention_heads: int = 16 + encoder_layerdrop: float | int = 0.0 + decoder_layerdrop: float | int = 0.0 + use_cache: bool = True + is_encoder_decoder: bool = True + activation_function: str = "gelu" + d_model: int = 1024 + dropout: float | int = 0.1 + attention_dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + init_std: float = 0.02 + decoder_start_token_id: int | None = 0 + scale_embedding: bool = True + pad_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 1 + forced_eos_token_id: int | list[int] | None = 1 + num_global_tokens: int = 32 + block_size: int = 512 + stagger_local_blocks: bool = True + tie_word_embeddings: bool = True + + +__all__ = ["PegasusXConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/modeling_pegasus_x.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/modeling_pegasus_x.py new file mode 100644 index 0000000000000000000000000000000000000000..9fcc043fefad134c8206946e8178a3f51276b836 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pegasus_x/modeling_pegasus_x.py @@ -0,0 +1,1352 @@ +# Copyright 2022, Google and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PEGASUS-X model.""" + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging +from ...utils.deprecation import deprecate_kwarg +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_pegasus_x import PegasusXConfig + + +logger = logging.get_logger(__name__) + + +@dataclass +class DimensionInfo: + """Wrapper for dimension info.""" + + batch_size: int # batch size + seq_len: int # token length + block_size: int # block size + num_heads: int # num heads + hidden_dim: int # hidden dim + dim_per_head: int # dim per head + num_blocks: int # num blocks + global_len: int # global length + padded_seq_len: int # padded token seq length + + +# Copied from transformers.models.bart.modeling_bart.shift_tokens_right +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +# Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->PegasusX +class PegasusXScaledWordEmbedding(nn.Embedding): + """ + This module overrides nn.Embeddings' forward by multiplying with embeddings scale. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float | None = 1.0): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.embed_scale = embed_scale + + def forward(self, input_ids: torch.Tensor): + return super().forward(input_ids) * self.embed_scale + + +class PegasusXSinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length.""" + + def __init__(self, embed_dim, max_scale: int = 10000.0): + super().__init__() + self.embed_dim = embed_dim + self.max_scale = max_scale + + @deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds") + @torch.no_grad() + def forward( + self, inputs_embeds: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor | None = None + ) -> torch.Tensor: + """`input_ids_shape` is expected to be [bsz x seqlen].""" + batch_size, seq_len = inputs_embeds.shape[:2] + if position_ids is None: + position_ids = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=inputs_embeds.device + )[:, None] + + pe = torch.zeros((seq_len, self.embed_dim), device=inputs_embeds.device, dtype=inputs_embeds.dtype) + half_d_feature = self.embed_dim // 2 + div_term = torch.exp( + torch.arange(half_d_feature, device=inputs_embeds.device, dtype=torch.int64).type_as(inputs_embeds) + * -(np.log(float(self.max_scale)) / (half_d_feature - 1)) + ) + pe[:, :half_d_feature] = torch.sin(position_ids * div_term) + pe[:, half_d_feature:] = torch.cos(position_ids * div_term) + return pe[None].expand(batch_size, -1, -1) + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->PegasusX +class PegasusXAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: PegasusXConfig | None = None, + layer_idx: int | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + if layer_idx is None and self.is_decoder: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + # TODO: we need a refactor so that the different attention modules can get their specific kwargs + # ATM, we have mixed things encoder, decoder, and encoder-decoder attn + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + + # get query proj + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.k_proj(current_states) + value_states = self.v_proj(current_states) + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = key_states.view(kv_shape).transpose(1, 2) + value_states = value_states.view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class PegasusXGlobalLocalAttention(nn.Module): + """Global + Local attention. For use with Encoder only.""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + block_size: int, + dropout: float = 0.0, + is_decoder: bool = False, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.block_size = block_size + self.dropout = dropout + self.head_dim = embed_dim // num_heads + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + token_hidden_states: torch.Tensor, + global_hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + dim = DimensionInfo( + batch_size=token_hidden_states.shape[0], + seq_len=token_hidden_states.shape[1], + block_size=self.block_size, + num_heads=self.num_heads, + hidden_dim=token_hidden_states.shape[2], + dim_per_head=self.head_dim, + num_blocks=token_hidden_states.shape[1] // self.block_size, + global_len=global_hidden_states.shape[1], + padded_seq_len=token_hidden_states.shape[1], + ) + + # [batch_size, num_heads, padded_seq_len, dim_per_head] + local_q = self._shape( + self.q_proj(token_hidden_states) * self.scaling, + seq_len=dim.padded_seq_len, + bsz=dim.batch_size, + ) + local_k = self._shape( + self.k_proj(token_hidden_states), + seq_len=dim.padded_seq_len, + bsz=dim.batch_size, + ) + local_v = self._shape( + self.v_proj(token_hidden_states), + seq_len=dim.padded_seq_len, + bsz=dim.batch_size, + ) + + # [batch_size, num_heads, global_len, dim_per_head] + global_q = self._shape( + self.q_proj(global_hidden_states) * self.scaling, + seq_len=dim.global_len, + bsz=dim.batch_size, + ) + global_k = self._shape( + self.k_proj(global_hidden_states), + seq_len=dim.global_len, + bsz=dim.batch_size, + ) + global_v = self._shape( + self.v_proj(global_hidden_states), + seq_len=dim.global_len, + bsz=dim.batch_size, + ) + + global_attn_output, global_attn_probs = self.compute_global_attention_representations( + global_q=global_q, + global_k=global_k, + global_v=global_v, + local_k=local_k, + local_v=local_v, + mask=attention_mask, + dim=dim, + ) + local_attn_output, local_attn_probs = self.compute_local_attention_representations( + global_k=global_k, + global_v=global_v, + local_q=local_q, + local_k=local_k, + local_v=local_v, + mask=attention_mask, + dim=dim, + ) + + # [batch_size, global_len, hidden_dim] + global_attn_output = ( + global_attn_output.transpose(1, 2).contiguous().view(dim.batch_size, dim.global_len, dim.hidden_dim) + ) + # [batch_size, global_len, hidden_dim] + global_attn_output = self.out_proj(global_attn_output) + # [batch_size, num_heads, block_size, num_heads, dim_per_head] + local_attn_output = local_attn_output.permute(0, 2, 3, 1, 4).contiguous() + # [batch_size, padded_seq_len, hidden_dim] + local_attn_output = local_attn_output.view(dim.batch_size, dim.padded_seq_len, dim.hidden_dim) + # [batch_size, padded_seq_len, hidden_dim] + local_attn_output = self.out_proj(local_attn_output) + + attn_probs = {"global": global_attn_probs, "local": local_attn_probs} + + return local_attn_output, global_attn_output, attn_probs + + def compute_global_attention_representations( + self, global_q, global_k, global_v, local_k, local_v, mask, dim: DimensionInfo + ): + """Compute attention representations for global tokens. + + Global tokens will attend to both global tokens as well as all input sequence tokens. Because the input + sequence tokens are arranged in blocks for local attention, we unblock them and compute attention. + + Args: + global_q (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]: + query vectors from global tokens + global_k (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]: + key vectors from global tokens + global_v (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]: + value vectors from global tokens + local_k (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]: + key vectors from local tokens + local_v (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]: + value vectors from local tokens + mask (`torch.FloatTensor`) of shape [batch_size, padded_seq_len]: attention mask + dim (DimensionInfo): DimensionInfo wrapper for dimensions + + Returns: + output of shape `[batch_sizes, length, features]`. where length will be padded to a multiple of block_size + """ + # [batch_size, num_heads, global_len+padded_seq_len, dim_per_head] + global_and_local_k = torch.cat([global_k, local_k], dim=2) + # [batch_size, num_heads, global_len+padded_seq_len, dim_per_head] + global_and_local_v = torch.cat([global_v, local_v], dim=2) + + # [batch_size, global_len+padded_seq_len] + extended_mask = nn.functional.pad(mask, pad=(dim.global_len, 0), value=0) + + # [batch_size, num_heads, global_len, global_len+padded_seq_len] + attn_weights = torch.einsum("BHGF,BHXF->BHGX", global_q, global_and_local_k) + attn_weights = attn_weights + extended_mask[:, None, None, :] + attn_probs = nn.functional.softmax(attn_weights, dim=-1) + attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training) + + # [batch_size, num_heads, global_len, F] + attn_output = torch.einsum("BHGX,BHXF->BHGF", attn_probs, global_and_local_v) + return attn_output, attn_probs + + def compute_local_attention_representations( + self, global_k, global_v, local_q, local_k, local_v, mask, dim: DimensionInfo + ): + """Compute attention representations for local tokens. + + Local tokens will attend to both global tokens as well as all other tokens within the same local block. Hence, + we need to tile and concatenate the global tokens to every local block + + Args: + global_k (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]: + key vectors from global tokens + global_v (`torch.FloatTensor`) of shape [batch_size, num_heads, global_len, dim_per_head]: + value vectors from global tokens + local_q (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]: + query vectors from local tokens + local_k (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]: + key vectors from local tokens + local_v (`torch.FloatTensor`) of shape [batch_size, num_heads, padded_seq_len, dim_per_head]: + value vectors from local tokens + mask (`torch.FloatTensor`) of shape [batch_size, padded_seq_len]: attention mask + dim (DimensionInfo): DimensionInfo wrapper for dimensions + + Returns: + output of shape `[batch_sizes, length, features]`. where length will be padded to a multiple of block_size + """ + # [batch_size, num_heads, num_blocks, block_size, dim_per_head] + blocked_local_q = local_q.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head) + # [batch_size, num_heads, num_blocks, block_size, dim_per_head] + blocked_local_k = local_k.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head) + # [batch_size, num_heads, num_blocks, block_size, dim_per_head] + blocked_local_v = local_v.view(dim.batch_size, dim.num_heads, dim.num_blocks, dim.block_size, dim.dim_per_head) + + # [batch_size, num_blocks, global_len+block_size] + extended_mask = nn.functional.pad( + mask.view(dim.batch_size, dim.num_blocks, dim.block_size), + pad=(dim.global_len, 0), + value=0, + ) + + # [batch_size, num_heads, num_blocks, block_size, global_len] + blocked_local2global = torch.einsum("BHNKF,BHGF->BHNKG", blocked_local_q, global_k) + # [batch_size, num_heads, num_blocks, block_size, block_size] + blocked_local2local = torch.einsum("BHNKF,BHNXF->BHNKX", blocked_local_q, blocked_local_k) + + # [batch_size, num_heads, num_blocks, block_size, global_len+block_size] + attn_weights = torch.cat([blocked_local2global, blocked_local2local], dim=-1) + attn_weights = attn_weights + extended_mask[:, None, :, None, :] + attn_probs = nn.functional.softmax(attn_weights, dim=-1) + attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training) + + # [batch_size, num_heads, num_blocks, block_size, global_len] + local2global_attn_probs = attn_probs[:, :, :, :, : dim.global_len] + # [batch_size, num_heads, num_blocks, block_size, block_size] + local2local_attn_probs = attn_probs[:, :, :, :, dim.global_len :] + + # [batch_size, num_heads, num_blocks, block_size, dim_per_head] + local2global_attn_output = torch.einsum("BHNKG,BHGF->BHNKF", local2global_attn_probs, global_v) + # [batch_size, num_heads, num_blocks, block_size, dim_per_head] + local2local_attn_output = torch.einsum("BHNKX,BHNXF->BHNKF", local2local_attn_probs, blocked_local_v) + # [batch_size, num_heads, num_blocks, block_size, dim_per_head] + attn_output = local2global_attn_output + local2local_attn_output + return attn_output, attn_probs + + +class PegasusXEncoderLayer(GradientCheckpointingLayer): + def __init__(self, stagger_blocks_this_layer: bool, config: PegasusXConfig): + super().__init__() + self.embed_dim = config.d_model + self.self_attn = PegasusXGlobalLocalAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + block_size=config.block_size, + dropout=config.attention_dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.global_self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + self.stagger_blocks_this_layer = stagger_blocks_this_layer + self.block_size = config.block_size + + def forward( + self, + hidden_states: torch.Tensor, + global_hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)* + global_hidden_states (`torch.FloatTensor`): global token hidden states + *(seq_len, num_global_tokens, embed_dim)* + attention_mask (`torch.FloatTensor`): attention mask of size + *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + global_residual = global_hidden_states + + hidden_states = self.self_attn_layer_norm(hidden_states) + global_hidden_states = self.global_self_attn_layer_norm(global_hidden_states) + + if self.stagger_blocks_this_layer: + # Pad the blocks to simulate staggering + hidden_states, attention_mask = self.pad_local_tokens( + hidden_states=hidden_states, attention_mask=attention_mask, block_size=self.block_size + ) + + hidden_states, global_hidden_states, _ = self.self_attn( + token_hidden_states=hidden_states, + global_hidden_states=global_hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + + if self.stagger_blocks_this_layer: + # Undo the padding + hidden_states = self.unpad_local_tokens(padded_hidden_states=hidden_states, block_size=self.block_size) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + global_hidden_states = nn.functional.dropout(global_hidden_states, p=self.dropout, training=self.training) + global_hidden_states = global_residual + global_hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + global_residual = global_hidden_states + global_hidden_states = self.final_layer_norm(global_hidden_states) + global_hidden_states = self.activation_fn(self.fc1(global_hidden_states)) + global_hidden_states = nn.functional.dropout( + global_hidden_states, p=self.activation_dropout, training=self.training + ) + global_hidden_states = self.fc2(global_hidden_states) + global_hidden_states = nn.functional.dropout(global_hidden_states, p=self.dropout, training=self.training) + global_hidden_states = global_residual + global_hidden_states + + return hidden_states, global_hidden_states + + @classmethod + def pad_local_tokens(cls, hidden_states, attention_mask, block_size): + # hidden_states: [batch_size, seq_len, hidden_dim] + pad_size = block_size // 2 + mask_min_value = torch.finfo(hidden_states.dtype).min + padded_hidden_states = torch.nn.functional.pad( + hidden_states, + pad=(0, 0, pad_size, pad_size), + ) + padded_mask = torch.nn.functional.pad( + attention_mask, + pad=(pad_size, pad_size), + value=mask_min_value, + ) + return padded_hidden_states, padded_mask + + @classmethod + def unpad_local_tokens(cls, padded_hidden_states, block_size): + # padded_hidden_states: [batch_size, padded seq_len, hidden_dim] + pad_size = block_size // 2 + return padded_hidden_states[:, pad_size:-pad_size, :] + + +class PegasusXDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PegasusXConfig, layer_idx: int | None = None): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = PegasusXAttention( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + bias=False, + config=config, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = PegasusXAttention( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + bias=False, + config=config, + layer_idx=layer_idx, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = True, + **kwargs, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)* + attention_mask (`torch.FloatTensor`): attention mask of size + *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape *(seq_len, batch, embed_dim)* + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + *(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. + past_key_values (`Cache`): cached past key and value projection states + use_cache: Whether to us KV cache for decoding + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Cross-Attention Block + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + hidden_states, _ = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class PegasusXPreTrainedModel(PreTrainedModel): + config: PegasusXConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = [r"PegasusXEncoderLayer", r"PegasusXDecoderLayer"] + _supports_flash_attn = True + # Flaky logits + _supports_sdpa = False + _supports_flex_attn = True + _can_compile_fullgraph = True + + +class PegasusXEncoder(PegasusXPreTrainedModel): + _can_record_outputs = {"attentions": OutputRecorder(PegasusXGlobalLocalAttention, index=2)} + + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`PegasusXEncoderLayer`]. + + Args: + config: PegasusXConfig + embed_tokens (nn.Embedding): output embedding + """ + + def __init__(self, config: PegasusXConfig): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + padding_idx = config.pad_token_id + self.max_source_positions = config.max_position_embeddings + embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = PegasusXScaledWordEmbedding( + config.vocab_size, embed_dim, padding_idx, embed_scale=embed_scale + ) + + self.embed_global = nn.Embedding(config.num_global_tokens, embed_dim) + self.embed_positions = PegasusXSinusoidalPositionalEmbedding(embed_dim) + self.layers = nn.ModuleList( + [ + PegasusXEncoderLayer( + stagger_blocks_this_layer=i % 2 == 1 and config.stagger_local_blocks, config=config + ) + for i in range(config.encoder_layers) + ] + ) + self.layer_norm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def resize_position_embeddings(self, new_num_position_embeddings: int): + """ + Resizes position embeddings matrix of the model if `new_num_position_embeddings != + config.max_position_embeddings`. + + Arguments: + new_num_position_embeddings (`int`): + The number of new position embeddings. If position embeddings are learned, increasing the size will add + newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If + position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will + add correct vectors at the end following the position encoding algorithm, whereas reducing the size + will remove vectors from the end. + """ + logger.info(f"Setting `config.max_position_embeddings={new_num_position_embeddings}`...") + self.config.max_position_embeddings = new_num_position_embeddings + + self.embed_positions = PegasusXSinusoidalPositionalEmbedding(self.config.d_model) + self.embed_positions.to(self.device) + + def get_position_embeddings(self) -> nn.Embedding: + """ + Returns the position embeddings matrix + """ + return self.embed_positions + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids=None, + attention_mask=None, + inputs_embeds=None, + output_hidden_states=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + """ + # We need to treat this special because it only adds the last global state which is unique + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + embed_pos = self.embed_positions(inputs_embeds) + + hidden_states = inputs_embeds + embed_pos + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + batch_size, seq_len, _ = hidden_states.shape + + # Setup mask + if attention_mask is None: + attention_mask = torch.ones( + *inputs_embeds.shape[:-1], dtype=inputs_embeds.dtype, device=inputs_embeds.device + ) + attention_mask = attention_mask.to(dtype=hidden_states.dtype) + mask_min_value = torch.finfo(hidden_states.dtype).min + inverted_mask = 1.0 - attention_mask + attention_mask = inverted_mask.masked_fill( + inverted_mask.to(torch.bool), + mask_min_value, + ) + + # padding to block_size + if seq_len % self.config.block_size != 0: + pad_len = self.config.block_size - seq_len % self.config.block_size + hidden_states = nn.functional.pad(hidden_states, pad=(0, 0, 0, pad_len), value=0) + attention_mask = nn.functional.pad(attention_mask, pad=(0, pad_len), value=mask_min_value) + + # Global tokens + global_hidden_states = self.embed_global( + torch.arange(self.config.num_global_tokens, device=hidden_states.device)[None].expand(batch_size, -1) + ) + + encoder_states = () if output_hidden_states else None + for encoder_layer in self.layers: + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + hidden_states, global_hidden_states = (None, None) + else: + hidden_states, global_hidden_states = encoder_layer( + hidden_states, + global_hidden_states, + attention_mask, + **kwargs, + ) + + # Undo padding-to-block-size + hidden_states = hidden_states[:, :seq_len] + + hidden_states = self.layer_norm(hidden_states) + + if output_hidden_states: + encoder_states = encoder_states + ((hidden_states, global_hidden_states),) + + return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states) + + +class PegasusXDecoder(PegasusXPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PegasusDecoderLayer`] + + Args: + config: PegasusXConfig + embed_tokens (nn.Embedding): output embedding + """ + + _can_record_outputs = { + "hidden_states": PegasusXDecoderLayer, + "attentions": OutputRecorder(PegasusXAttention, index=1, layer_name="self_attn"), + "cross_attentions": OutputRecorder(PegasusXAttention, index=1, layer_name="encoder_attn"), + } + + def __init__(self, config: PegasusXConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.max_target_positions = config.max_position_embeddings + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + padding_idx = config.pad_token_id + + self.embed_tokens = PegasusXScaledWordEmbedding( + config.vocab_size, config.d_model, padding_idx=padding_idx, embed_scale=embed_scale + ) + + self.embed_positions = PegasusXSinusoidalPositionalEmbedding(config.d_model) + self.layers = nn.ModuleList([PegasusXDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)]) + self.layer_norm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @capture_outputs + @merge_with_config_defaults + def forward( + self, + input_ids=None, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + inputs_embeds=None, + use_cache=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of + shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing + `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more + control over how to convert `input_ids` indices into associated vectors than the model's internal + embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # initialize `past_key_values` + if use_cache and past_key_values is None: + past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + + batch_size, seq_length = inputs_embeds.size()[:-1] + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + if attention_mask is None and not is_torchdynamo_compiling(): + # required mask seq length can be calculated via length of past cache + mask_seq_length = past_key_values_length + seq_length + attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) + + self_attn_cache = ( + past_key_values.self_attention_cache + if isinstance(past_key_values, EncoderDecoderCache) + else past_key_values + ) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=self_attn_cache, + ) + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + # embed positions + position_ids = torch.arange(seq_length, device=inputs_embeds.device) + past_key_values_length + position_ids = position_ids.unsqueeze(1) + position_ids = self.embed_positions(inputs_embeds, past_key_values_length, position_ids) + position_ids = position_ids.to(inputs_embeds.device) + hidden_states = inputs_embeds + position_ids + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # decoder layers + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + hidden_states = decoder_layer( + hidden_states, + causal_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.layer_norm(hidden_states) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class PegasusXModel(PegasusXPreTrainedModel): + _tied_weights_keys = { + "encoder.embed_tokens.weight": "shared.weight", + "decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: PegasusXConfig): + super().__init__(config) + + vocab_size = config.vocab_size + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + padding_idx = config.pad_token_id + self.shared = PegasusXScaledWordEmbedding( + vocab_size, config.d_model, padding_idx=padding_idx, embed_scale=embed_scale + ) + + self.encoder = PegasusXEncoder(config) + self.decoder = PegasusXDecoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, value): + self.shared = value + self.encoder.embed_tokens = self.shared + self.decoder.embed_tokens = self.shared + + def resize_position_embeddings(self, new_num_position_embeddings: int): + """ + Resizes position embeddings matrix of the model if `new_num_position_embeddings != + config.max_position_embeddings`. + + Arguments: + new_num_position_embeddings (`int`): + The number of new position embeddings. If position embeddings are learned, increasing the size will add + newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If + position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will + add correct vectors at the end following the position encoding algorithm, whereas reducing the size + will remove vectors from the end. + """ + self.config.max_position_embeddings = new_num_position_embeddings + self.encoder.resize_position_embeddings(new_num_position_embeddings) + self.decoder.resize_position_embeddings(new_num_position_embeddings) + + def get_position_embeddings(self) -> tuple[nn.Embedding]: + """ + Returns the position embeddings matrix + """ + return (self.encoder.get_position_embeddings(), self.decoder.get_position_embeddings()) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.Tensor | None = None, + decoder_attention_mask: torch.Tensor | None = None, + encoder_outputs: tuple[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + decoder_inputs_embeds: torch.Tensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Seq2SeqModelOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + PEGASUS-X uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + Example: + + ```python + >>> from transformers import AutoTokenizer, PegasusModel + + >>> tokenizer = AutoTokenizer.from_pretrained("google/pegasus-x-large") + >>> model = PegasusModel.from_pretrained("google/pegasus-x-large") + + >>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt") + >>> decoder_inputs = tokenizer("Studies show that", return_tensors="pt") + >>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_inputs.input_ids) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 4, 1024] + ```""" + + if encoder_outputs is None: + encoder_outputs: BaseModelOutput = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + **kwargs, + ) + elif not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + decoder_outputs: BaseModelOutputWithPastAndCrossAttentions = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The PEGASUS-X for conditional generation (e.g. summarization). + """ +) +class PegasusXForConditionalGeneration(PegasusXPreTrainedModel, GenerationMixin): + base_model_prefix = "model" + _tied_weights_keys = { + "lm_head.weight": "model.shared.weight", + } + + def __init__(self, config: PegasusXConfig): + super().__init__(config) + self.model = PegasusXModel(config) + self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def resize_position_embeddings(self, new_num_position_embeddings: int): + """ + Resizes position embeddings matrix of the model if `new_num_position_embeddings != + config.max_position_embeddings`. + + Arguments: + new_num_position_embeddings (`int`): + The number of new position embeddings. If position embeddings are learned, increasing the size will add + newly initialized vectors at the end, whereas reducing the size will remove vectors from the end. If + position embeddings are not learned (*e.g.* sinusoidal position embeddings), increasing the size will + add correct vectors at the end following the position encoding algorithm, whereas reducing the size + will remove vectors from the end. + """ + self.config.max_position_embeddings = new_num_position_embeddings + self.model.encoder.resize_position_embeddings(new_num_position_embeddings) + self.model.decoder.resize_position_embeddings(new_num_position_embeddings) + + def get_position_embeddings(self) -> tuple[nn.Embedding]: + """ + Returns the position embeddings matrix + """ + return (self.model.encoder.get_position_embeddings(), self.model.decoder.get_position_embeddings()) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.Tensor | None = None, + decoder_attention_mask: torch.Tensor | None = None, + encoder_outputs: tuple[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + decoder_inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Seq2SeqLMOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + PEGASUS-X uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + outputs: Seq2SeqModelOutput = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + lm_logits = self.lm_head(outputs.last_hidden_state) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id) + + +# Copied from transformers.models.bart.modeling_bart.BartDecoderWrapper with Bart->PegasusX +class PegasusXDecoderWrapper(PegasusXPreTrainedModel): + """ + This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is + used in combination with the [`EncoderDecoderModel`] framework. + """ + + def __init__(self, config): + super().__init__(config) + self.decoder = PegasusXDecoder(config) + self.post_init() + + def forward(self, *args, **kwargs): + return self.decoder(*args, **kwargs) + + +__all__ = ["PegasusXForConditionalGeneration", "PegasusXModel", "PegasusXPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ca337eb4c7b363ffe043bd0dad050fd5a8f9e15c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_perceiver import * + from .image_processing_perceiver import * + from .image_processing_pil_perceiver import * + from .modeling_perceiver import * + from .tokenization_perceiver import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/configuration_perceiver.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/configuration_perceiver.py new file mode 100644 index 0000000000000000000000000000000000000000..02d7547af9ad4bd68a67d9d48ba1409cda5fba53 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/configuration_perceiver.py @@ -0,0 +1,114 @@ +# Copyright Deepmind and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Perceiver model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="deepmind/language-perceiver") +@strict +class PerceiverConfig(PreTrainedConfig): + r""" + num_latents (`int`, *optional*, defaults to 256): + The number of latents. + d_latents (`int`, *optional*, defaults to 1280): + Dimension of the latent embeddings. + num_blocks (`int`, *optional*, defaults to 1): + Number of blocks in the Transformer encoder. + num_self_attends_per_block (`int`, *optional*, defaults to 26): + The number of self-attention layers per block. + num_self_attention_heads (`int`, *optional*, defaults to 8): + Number of attention heads for each self-attention layer in the Transformer encoder. + num_cross_attention_heads (`int`, *optional*, defaults to 8): + Number of attention heads for each cross-attention layer in the Transformer encoder. + qk_channels (`int`, *optional*): + Dimension to project the queries + keys before applying attention in the cross-attention and self-attention + layers of the encoder. Will default to preserving the dimension of the queries if not specified. + v_channels (`int`, *optional*): + Dimension to project the values before applying attention in the cross-attention and self-attention layers + of the encoder. Will default to preserving the dimension of the queries if not specified. + cross_attention_shape_for_attention (`str`, *optional*, defaults to `"kv"`): + Dimension to use when downsampling the queries and keys in the cross-attention layer of the encoder. + self_attention_widening_factor (`int`, *optional*, defaults to 1): + Dimension of the feed-forward layer in the cross-attention layer of the Transformer encoder. + cross_attention_widening_factor (`int`, *optional*, defaults to 1): + Dimension of the feed-forward layer in the self-attention layers of the Transformer encoder. + use_query_residual (`float`, *optional*, defaults to `True`): + Whether to add a query residual in the cross-attention layer of the encoder. + image_size (`int`, *optional*, defaults to 56): + Size of the images after preprocessing, for [`PerceiverForImageClassificationLearned`]. + train_size (`list[int]`, *optional*, defaults to `[368, 496]`): + Training size of the images for the optical flow model. + num_frames (`int`, *optional*, defaults to 16): + Number of video frames used for the multimodal autoencoding model. + audio_samples_per_frame (`int`, *optional*, defaults to 1920): + Number of audio samples per frame for the multimodal autoencoding model. + samples_per_patch (`int`, *optional*, defaults to 16): + Number of audio samples per patch when preprocessing the audio for the multimodal autoencoding model. + output_shape (`list[int]`, *optional*, defaults to `[1, 16, 224, 224]`): + Shape of the output (batch_size, num_frames, height, width) for the video decoder queries of the multimodal + autoencoding model. This excludes the channel dimension. + output_num_channels (`int`, *optional*, defaults to 512): + Number of output channels for each modalitiy decoder. + + Example: + + ```python + >>> from transformers import PerceiverModel, PerceiverConfig + + >>> # Initializing a Perceiver deepmind/language-perceiver style configuration + >>> configuration = PerceiverConfig() + + >>> # Initializing a model from the deepmind/language-perceiver style configuration + >>> model = PerceiverModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "perceiver" + + num_latents: int = 256 + d_latents: int = 1280 + d_model: int = 768 + num_blocks: int = 1 + num_self_attends_per_block: int = 26 + num_self_attention_heads: int = 8 + num_cross_attention_heads: int = 8 + qk_channels: int | None = None + v_channels: int | None = None + cross_attention_shape_for_attention: str = "kv" + self_attention_widening_factor: int = 1 + cross_attention_widening_factor: int = 1 + hidden_act: str = "gelu" + attention_probs_dropout_prob: float | int = 0.1 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-12 + use_query_residual: bool = True + vocab_size: int = 262 + max_position_embeddings: int = 2048 + image_size: int | list[int] | tuple[int, int] = 56 + train_size: list[int] | tuple[int, ...] = (368, 496) + num_frames: int = 16 + audio_samples_per_frame: int = 1920 + samples_per_patch: int = 16 + output_shape: list[int] | tuple[int, ...] = (1, 16, 224, 224) + output_num_channels: int = 512 + _label_trainable_num_channels: int = 1024 + + +__all__ = ["PerceiverConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/image_processing_perceiver.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/image_processing_perceiver.py new file mode 100644 index 0000000000000000000000000000000000000000..a6b8e5896d341a7b30b01bd17e938381236686e2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/image_processing_perceiver.py @@ -0,0 +1,124 @@ +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Perceiver.""" + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +@auto_docstring +class PerceiverImageProcessor(TorchvisionBackend): + """Torchvision backend for Perceiver with custom center crop.""" + + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 224, "width": 224} + crop_size = {"height": 256, "width": 256} + do_resize = True + do_center_crop = True + do_rescale = True + do_normalize = True + + def __init__(self, **kwargs: Unpack[ImagesKwargs]): + super().__init__(**kwargs) + + def center_crop( + self, + image: "torch.Tensor", + size: SizeDict, + crop_size: SizeDict, + **kwargs, + ) -> "torch.Tensor": + """ + Center crop an image to ((size.height / crop_size.height) * min_dim, (size.width / crop_size.width) * min_dim), + where min_dim is the minimum of the image height and width. + If the requested crop size exceeds the image dimensions along any edge, the image is padded with zeros before + center cropping. + """ + if size.height is None or size.width is None: + raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}") + if crop_size.height is None or crop_size.width is None: + raise ValueError(f"The crop_size dictionary must have keys 'height' and 'width'. Got {crop_size.keys()}") + height, width = image.shape[-2:] + min_dim = min(height, width) + cropped_height = int((size.height / crop_size.height) * min_dim) + cropped_width = int((size.width / crop_size.width) * min_dim) + return super().center_crop( + image, + SizeDict(height=cropped_height, width=cropped_width), + **kwargs, + ) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """Custom preprocessing for Perceiver: center_crop -> resize -> rescale and normalize.""" + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + cropped_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, size=size, crop_size=crop_size) + cropped_images_grouped[shape] = stacked_images + cropped_images = reorder_images(cropped_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(cropped_images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["PerceiverImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/image_processing_pil_perceiver.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/image_processing_pil_perceiver.py new file mode 100644 index 0000000000000000000000000000000000000000..d1ab681929ddfdf67e824cca5505b6c3d16ce7ed --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/image_processing_pil_perceiver.py @@ -0,0 +1,107 @@ +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Perceiver.""" + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +@auto_docstring +class PerceiverImageProcessorPil(PilBackend): + """PIL backend for Perceiver with custom center crop.""" + + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 224, "width": 224} + crop_size = {"height": 256, "width": 256} + do_resize = True + do_center_crop = True + do_rescale = True + do_normalize = True + + def __init__(self, **kwargs: Unpack[ImagesKwargs]): + super().__init__(**kwargs) + + def center_crop( + self, + image: np.ndarray, + size: SizeDict, + crop_size: SizeDict, + **kwargs, + ) -> np.ndarray: + """ + Center crop an image to ((size.height / crop_size.height) * min_dim, (size.width / crop_size.width) * min_dim), + where min_dim is the minimum of the image height and width. + If the requested crop size exceeds the image dimensions along any edge, the image is padded with zeros before + center cropping. + """ + if size.height is None or size.width is None: + raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}") + if crop_size.height is None or crop_size.width is None: + raise ValueError(f"The crop_size dictionary must have keys 'height' and 'width'. Got {crop_size.keys()}") + height, width = image.shape[-2:] + min_dim = min(height, width) + cropped_height = int((size.height / crop_size.height) * min_dim) + cropped_width = int((size.width / crop_size.width) * min_dim) + return super().center_crop( + image, + SizeDict(height=cropped_height, width=cropped_width), + **kwargs, + ) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """Custom preprocessing for Perceiver: center_crop -> resize -> rescale and normalize.""" + processed_images = [] + for image in images: + if do_center_crop: + image = self.center_crop(image, size=size, crop_size=crop_size) + if do_resize: + image = self.resize(image=image, size=size, resample=resample) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["PerceiverImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/modeling_perceiver.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/modeling_perceiver.py new file mode 100644 index 0000000000000000000000000000000000000000..757a45eb8726502ef06e8b27da16be0b6bb8bc82 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/modeling_perceiver.py @@ -0,0 +1,3302 @@ +# Copyright 2021 Deepmind and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Perceiver model.""" + +import abc +import math +from collections.abc import Callable, Mapping +from dataclasses import dataclass +from functools import reduce +from operator import __add__ +from typing import Any, Optional + +import numpy as np +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutputWithCrossAttentions +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import apply_chunking_to_forward +from ...utils import ModelOutput, auto_docstring, logging, torch_int +from .configuration_perceiver import PerceiverConfig + + +ModalitySizeType = Mapping[str, int] +PreprocessorOutputType = tuple[torch.Tensor, torch.Tensor | None, torch.Tensor] +PreprocessorType = Callable[..., PreprocessorOutputType] +PostprocessorType = Callable[..., Any] + +logger = logging.get_logger(__name__) + + +@auto_docstring( + custom_intro=""" + Base class for Perceiver base model's outputs, with potential hidden states, attentions and cross-attentions. + """ +) +@dataclass +class PerceiverModelOutput(ModelOutput): + r""" + logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`): + Classification (or regression if config.num_labels==1) scores (before SoftMax). + """ + + logits: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for Perceiver decoder outputs, with potential cross-attentions. + """ +) +@dataclass +class PerceiverDecoderOutput(ModelOutput): + r""" + logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`): + Output of the basic decoder. + """ + + logits: torch.FloatTensor | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for Perceiver's masked language model outputs. + """ +) +@dataclass +class PerceiverMaskedLMOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Masked language modeling (MLM) loss. + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for Perceiver's outputs of sequence/image classification models, optical flow and multimodal + autoencoding. + """ +) +@dataclass +class PerceiverClassifierOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Classification (or regression if config.num_labels==1) loss. + logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): + Classification (or regression if config.num_labels==1) scores (before SoftMax). + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +class PerceiverEmbeddings(nn.Module): + """Construct the latent embeddings.""" + + def __init__(self, config): + super().__init__() + self.latents = nn.Parameter(torch.randn(config.num_latents, config.d_latents)) + + def forward(self, batch_size: int): + return self.latents.expand(batch_size, -1, -1) # Thanks, Phil Wang + + +class PerceiverSelfAttention(nn.Module): + """Multi-headed {cross, self}-attention. Can be used both in the encoder as well as in the decoder.""" + + def __init__( + self, + config, + is_cross_attention=False, + qk_channels=None, + v_channels=None, + num_heads=1, + q_dim=None, + kv_dim=None, + ): + super().__init__() + self.num_heads = num_heads + # Q and K must have the same number of channels. + # Default to preserving Q's input's shape. + if qk_channels is None: + qk_channels = q_dim + # V's num_channels determines the shape of the output of QKV-attention. + # Default to the same number of channels used in the key-query operation. + if v_channels is None: + v_channels = qk_channels + if qk_channels % num_heads != 0: + raise ValueError(f"qk_channels ({qk_channels}) must be divisible by num_heads ({num_heads}).") + if v_channels % num_heads != 0: + raise ValueError(f"v_channels ({v_channels}) must be divisible by num_heads ({num_heads}).") + + self.qk_channels = qk_channels + self.v_channels = v_channels + self.qk_channels_per_head = self.qk_channels // num_heads + self.v_channels_per_head = self.v_channels // num_heads + + # Layer normalization + self.layernorm1 = nn.LayerNorm(q_dim) + self.layernorm2 = nn.LayerNorm(kv_dim) if is_cross_attention else nn.Identity() + + # Projection matrices + self.query = nn.Linear(q_dim, qk_channels) + self.key = nn.Linear(kv_dim, qk_channels) + self.value = nn.Linear(kv_dim, v_channels) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + def transpose_for_scores(self, x, channels_per_head): + new_x_shape = x.size()[:-1] + (self.num_heads, channels_per_head) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + inputs: torch.FloatTensor | None = None, + inputs_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + ) -> tuple[torch.Tensor]: + hidden_states = self.layernorm1(hidden_states) + inputs = self.layernorm2(inputs) + + # Project queries, keys and values to a common feature dimension. If this is instantiated as a cross-attention module, + # the keys and values come from the inputs; the attention mask needs to be such that the inputs's non-relevant tokens are not attended to. + is_cross_attention = inputs is not None + queries = self.query(hidden_states) + + if is_cross_attention: + keys = self.key(inputs) + values = self.value(inputs) + attention_mask = inputs_mask + else: + keys = self.key(hidden_states) + values = self.value(hidden_states) + + # Reshape channels for multi-head attention. + # We reshape from (batch_size, time, channels) to (batch_size, num_heads, time, channels per head) + queries = self.transpose_for_scores(queries, self.qk_channels_per_head) + keys = self.transpose_for_scores(keys, self.qk_channels_per_head) + values = self.transpose_for_scores(values, self.v_channels_per_head) + + # Take the dot product between the queries and keys to get the raw attention scores. + attention_scores = torch.matmul(queries, keys.transpose(-1, -2)) + + batch_size, num_heads, seq_len, q_head_dim = queries.shape + _, _, _, v_head_dim = values.shape + hiddens = self.num_heads * v_head_dim + + attention_scores = attention_scores / math.sqrt(q_head_dim) + + if attention_mask is not None: + # Apply the attention mask (precomputed for all layers in PerceiverModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, values) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (hiddens,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class PerceiverSelfOutput(nn.Module): + def __init__(self, config, input_channels, output_channels): + super().__init__() + self.dense = nn.Linear(input_channels, output_channels) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + return hidden_states + + +class PerceiverAttention(nn.Module): + """Attention module, including a dense block.""" + + def __init__( + self, + config, + is_cross_attention=False, + qk_channels=None, + v_channels=None, + num_heads=1, + q_dim=None, + kv_dim=None, + use_query_residual=True, + ): + super().__init__() + # MultiHead attention + if is_cross_attention and qk_channels is None: + if config.cross_attention_shape_for_attention == "q": + qk_channels = q_dim + elif config.cross_attention_shape_for_attention == "kv": + qk_channels = kv_dim + else: + raise ValueError( + f"Unknown value {config.cross_attention_shape_for_attention} for " + "cross_attention_shape_for_attention." + ) + else: + if qk_channels is None: + qk_channels = q_dim + if v_channels is None: + v_channels = qk_channels + self.self = PerceiverSelfAttention( + config, + is_cross_attention=is_cross_attention, + qk_channels=qk_channels, + v_channels=v_channels, + num_heads=num_heads, + q_dim=q_dim, + kv_dim=kv_dim, + ) + # dense block + output_channels = None + if is_cross_attention: + output_channels = q_dim + else: + if output_channels is None: + output_channels = v_channels + self.output = PerceiverSelfOutput(config, input_channels=self.self.v_channels, output_channels=output_channels) + self.use_query_residual = use_query_residual + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + inputs: torch.FloatTensor | None = None, + inputs_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + ) -> tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask, + inputs, + inputs_mask, + output_attentions, + ) + + # Output projection + attention_output = self.output(self_outputs[0]) + + # Optionally include a residual to the original queries. + # Consider omitting the residual if the semantics of query and output + # are different, e.g. if queries are positions and outputs are pixels. + if self.use_query_residual: + attention_output = attention_output + hidden_states + + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class PerceiverMLP(nn.Module): + """A Transformer-style dense module to follow attention.""" + + def __init__(self, config, input_size, widening_factor): + super().__init__() + self.dense1 = nn.Linear(input_size, widening_factor * input_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + self.dense2 = nn.Linear(widening_factor * input_size, input_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense1(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.dense2(hidden_states) + return hidden_states + + +class PerceiverLayer(nn.Module): + def __init__( + self, + config, + is_cross_attention=False, + qk_channels=None, + v_channels=None, + num_heads=1, + q_dim=None, + kv_dim=None, + widening_factor=4, + use_query_residual=True, + ): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = PerceiverAttention( + config, + is_cross_attention=is_cross_attention, + qk_channels=qk_channels, + v_channels=v_channels, + num_heads=num_heads, + q_dim=q_dim, + kv_dim=kv_dim, + use_query_residual=use_query_residual, + ) + self.layernorm = nn.LayerNorm(q_dim) + self.mlp = PerceiverMLP(config, input_size=q_dim, widening_factor=widening_factor) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + inputs: torch.FloatTensor | None = None, + inputs_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + ) -> tuple[torch.Tensor]: + attention_outputs = self.attention( + hidden_states, + attention_mask, + inputs, + inputs_mask, + output_attentions, + ) + attention_output = attention_outputs[0] + + outputs = attention_outputs[1:] # add attentions if we output attention weights + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + + layer_output = layer_output + attention_output # residual connection + + outputs = (layer_output,) + outputs + + return outputs + + def feed_forward_chunk(self, attention_output): + layer_output = self.layernorm(attention_output) + layer_output = self.mlp(layer_output) + return layer_output + + +class PerceiverEncoder(nn.Module): + """The Perceiver Encoder: a scalable, fully attentional encoder.""" + + def __init__(self, config, kv_dim=None): + super().__init__() + self.config = config + + # Check that we can use multihead-attention with these shapes. + if config.d_latents % config.num_self_attention_heads != 0: + raise ValueError( + f"num_z_channels ({config.d_latents}) must be divisible by" + f" num_self_attend_heads ({config.num_self_attention_heads})." + ) + if config.d_latents % config.num_cross_attention_heads != 0: + raise ValueError( + f"num_z_channels ({config.d_latents}) must be divisible by" + f" num_cross_attend_heads ({config.num_cross_attention_heads})." + ) + + # Construct the cross attention layer. + self.cross_attention = PerceiverLayer( + config, + is_cross_attention=True, + qk_channels=config.qk_channels, + v_channels=config.v_channels, + num_heads=config.num_cross_attention_heads, + q_dim=config.d_latents, + kv_dim=kv_dim, + widening_factor=config.cross_attention_widening_factor, + use_query_residual=config.use_query_residual, + ) + + # Construct a single block of self-attention layers. + # We get deeper architectures by applying this block more than once. + self_attention_layers = [] + for _ in range(config.num_self_attends_per_block): + layer = PerceiverLayer( + config, + is_cross_attention=False, + qk_channels=config.qk_channels, + v_channels=config.v_channels, + num_heads=config.num_self_attention_heads, + q_dim=config.d_latents, + kv_dim=config.d_latents, + widening_factor=config.self_attention_widening_factor, + ) + self_attention_layers.append(layer) + + self.self_attends = nn.ModuleList(self_attention_layers) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + inputs: torch.FloatTensor | None = None, + inputs_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + output_hidden_states: bool | None = False, + return_dict: bool | None = True, + ) -> tuple | BaseModelOutputWithCrossAttentions: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions else None + + # Apply the cross-attention between the latents (hidden_states) and inputs: + layer_outputs = self.cross_attention( + hidden_states, + attention_mask=attention_mask, + inputs=inputs, + inputs_mask=inputs_mask, + output_attentions=output_attentions, + ) + hidden_states = layer_outputs[0] + + if output_attentions: + all_cross_attentions = all_cross_attentions + (layer_outputs[1],) + + # Apply the block of self-attention layers more than once: + for _ in range(self.config.num_blocks): + for i, layer_module in enumerate(self.self_attends): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, all_hidden_states, all_self_attentions, all_cross_attentions] + if v is not None + ) + return BaseModelOutputWithCrossAttentions( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +@auto_docstring +class PerceiverPreTrainedModel(PreTrainedModel): + config: PerceiverConfig + base_model_prefix = "perceiver" + main_input_name = "inputs" + input_modalities = ("image",) # techinically can be anything but HF impl has only image processor + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, (nn.Linear, nn.Conv2d)): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + elif hasattr(module, "latents"): + init.normal_(module.latents, mean=0.0, std=self.config.initializer_range) + elif hasattr(module, "position_embeddings") and isinstance(module, PerceiverTrainablePositionEncoding): + init.normal_(module.position_embeddings, mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.ParameterDict): + for modality in module: + init.normal_(module[modality], mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag + if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): + init.zeros_(module.weight[module.padding_idx]) + elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d)): + init.zeros_(module.bias) + init.ones_(module.weight) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + +@auto_docstring( + custom_intro=""" + The Perceiver: a scalable, fully attentional architecture. + + + + Note that it's possible to fine-tune Perceiver on higher resolution images than the ones it has been trained on, by + setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained + position embeddings to the higher resolution. + + + """ +) +class PerceiverModel(PerceiverPreTrainedModel): + def __init__( + self, + config, + decoder: Optional["PerceiverAbstractDecoder"] = None, + input_preprocessor: PreprocessorType = None, + output_postprocessor: PostprocessorType = None, + ): + r""" + decoder (`PerceiverDecoder`, *optional*): + Decoder module that transforms latent representations into task predictions. + input_preprocessor (`PreprocessorType`, *optional*): + Preprocessor that encodes raw inputs into tensors for the model. + output_postprocessor (`PostprocessorType`, *optional*): + Postprocessor that transforms model outputs into final predictions. + """ + super().__init__(config) + self.config = config + + self.input_preprocessor = input_preprocessor + self.output_postprocessor = output_postprocessor + self.embeddings = PerceiverEmbeddings(config) + self.encoder = PerceiverEncoder( + config, kv_dim=input_preprocessor.num_channels if input_preprocessor is not None else config.d_model + ) + self.decoder = decoder + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.latents + + def set_input_embeddings(self, value): + self.embeddings.latents = value + + @auto_docstring + def forward( + self, + inputs: torch.FloatTensor, + attention_mask: torch.FloatTensor | None = None, + subsampled_output_points: dict[str, torch.Tensor] | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + interpolate_pos_encoding: bool = False, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PerceiverModelOutput: + r""" + inputs (`torch.FloatTensor`): + Inputs to the perceiver. Can be anything: images, text, audio, video, etc. + subsampled_output_points (`dict[str, torch.Tensor]`, *optional*): + Dictionary of tensors used as queries for the decoder. The decoder maps these queries to the latent + representation of the model. Used for subsampled decoding, e.g. when only decoding certain image patches. + + Examples: + + ```python + >>> from transformers import PerceiverConfig, PerceiverTokenizer, PerceiverImageProcessor, PerceiverModel + >>> from transformers.models.perceiver.modeling_perceiver import ( + ... PerceiverTextPreprocessor, + ... PerceiverImagePreprocessor, + ... PerceiverClassificationDecoder, + ... ) + >>> import torch + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + + >>> # EXAMPLE 1: using the Perceiver to classify texts + >>> # - we define a TextPreprocessor, which can be used to embed tokens + >>> # - we define a ClassificationDecoder, which can be used to decode the + >>> # final hidden states of the latents to classification logits + >>> # using trainable position embeddings + >>> config = PerceiverConfig() + >>> preprocessor = PerceiverTextPreprocessor(config) + >>> decoder = PerceiverClassificationDecoder( + ... config, + ... num_channels=config.d_latents, + ... trainable_position_encoding_kwargs=dict(num_channels=config.d_latents, index_dims=1), + ... use_query_residual=True, + ... ) + >>> model = PerceiverModel(config, input_preprocessor=preprocessor, decoder=decoder) + + >>> # you can then do a forward pass as follows: + >>> tokenizer = PerceiverTokenizer() + >>> text = "hello world" + >>> inputs = tokenizer(text, return_tensors="pt").input_ids + + >>> with torch.no_grad(): + ... outputs = model(inputs=inputs) + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 2] + + >>> # to train, one can train the model using standard cross-entropy: + >>> criterion = torch.nn.CrossEntropyLoss() + + >>> labels = torch.tensor([1]) + >>> loss = criterion(logits, labels) + + >>> # EXAMPLE 2: using the Perceiver to classify images + >>> # - we define an ImagePreprocessor, which can be used to embed images + >>> config = PerceiverConfig(image_size=224) + >>> preprocessor = PerceiverImagePreprocessor( + ... config, + ... prep_type="conv1x1", + ... spatial_downsample=1, + ... out_channels=256, + ... position_encoding_type="trainable", + ... concat_or_add_pos="concat", + ... project_pos_dim=256, + ... trainable_position_encoding_kwargs=dict( + ... num_channels=256, + ... index_dims=config.image_size**2, + ... ), + ... ) + + >>> model = PerceiverModel( + ... config, + ... input_preprocessor=preprocessor, + ... decoder=PerceiverClassificationDecoder( + ... config, + ... num_channels=config.d_latents, + ... trainable_position_encoding_kwargs=dict(num_channels=config.d_latents, index_dims=1), + ... use_query_residual=True, + ... ), + ... ) + + >>> # you can then do a forward pass as follows: + >>> image_processor = PerceiverImageProcessor() + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> inputs = image_processor(image, return_tensors="pt").pixel_values + + >>> with torch.no_grad(): + ... outputs = model(inputs=inputs) + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 2] + + >>> # to train, one can train the model using standard cross-entropy: + >>> criterion = torch.nn.CrossEntropyLoss() + + >>> labels = torch.tensor([1]) + >>> loss = criterion(logits, labels) + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if self.input_preprocessor is not None: + inputs, modality_sizes, inputs_without_pos = self.input_preprocessor( + inputs, interpolate_pos_encoding=interpolate_pos_encoding + ) + else: + modality_sizes = None + inputs_without_pos = None + if inputs.size()[-1] != self.config.d_model: + raise ValueError( + f"Last dimension of the inputs: {inputs.size()[-1]} doesn't correspond to config.d_model:" + f" {self.config.d_model}. Make sure to set config.d_model appropriately." + ) + + batch_size, seq_length, _ = inputs.size() + device = inputs.device + + # If no attention mask is provided, make them all ones + if attention_mask is None: + attention_mask = torch.ones((batch_size, seq_length), device=device) + # Make the attention mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + extended_attention_mask = self.invert_attention_mask(attention_mask) + + embedding_output = self.embeddings(batch_size=batch_size) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=None, + inputs=inputs, + inputs_mask=extended_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + logits = None + if self.decoder: + if subsampled_output_points is not None: + output_modality_sizes = { + "audio": subsampled_output_points["audio"].shape[0], + "image": subsampled_output_points["image"].shape[0], + "label": 1, + } + else: + output_modality_sizes = modality_sizes + decoder_query = self.decoder.decoder_query( + inputs, modality_sizes, inputs_without_pos, subsampled_points=subsampled_output_points + ) + decoder_outputs = self.decoder( + decoder_query, + z=sequence_output, + query_mask=extended_attention_mask, + output_attentions=output_attentions, + ) + logits = decoder_outputs.logits + + # add cross-attentions of decoder + if output_attentions and decoder_outputs.cross_attentions is not None: + if return_dict: + encoder_outputs.cross_attentions = ( + encoder_outputs.cross_attentions + decoder_outputs.cross_attentions + ) + else: + encoder_outputs = encoder_outputs + decoder_outputs.cross_attentions + + if self.output_postprocessor: + logits = self.output_postprocessor(logits, modality_sizes=output_modality_sizes) + + if not return_dict: + if logits is not None: + return (logits, sequence_output) + encoder_outputs[1:] + else: + return (sequence_output,) + encoder_outputs[1:] + + return PerceiverModelOutput( + logits=logits, + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Example use of Perceiver for masked language modeling. + """ +) +class PerceiverForMaskedLM(PerceiverPreTrainedModel): + def __init__(self, config: PerceiverConfig): + super().__init__(config) + + text_preprocessor = PerceiverTextPreprocessor(config) + + trainable_position_encoding_kwargs_decoder = { + "num_channels": text_preprocessor.num_channels, + "index_dims": config.max_position_embeddings, + } + + self.perceiver = PerceiverModel( + config, + input_preprocessor=text_preprocessor, + decoder=PerceiverBasicDecoder( + config, + output_num_channels=config.d_latents, + output_index_dims=config.max_position_embeddings, # we need to define the seq_len of the inputs beforehand + num_channels=text_preprocessor.num_channels, + qk_channels=8 * 32, + v_channels=text_preprocessor.num_channels, + num_heads=8, + use_query_residual=False, + final_project=False, + trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder, + ), + ) + self.embedding_decoder = PerceiverEmbeddingDecoder(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + inputs: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + labels: torch.Tensor | None = None, + return_dict: bool | None = None, + input_ids: torch.Tensor | None = None, + **kwargs, + ) -> tuple | PerceiverMaskedLMOutput: + r""" + inputs (`torch.FloatTensor`): + Inputs to the perceiver. Can be anything: images, text, audio, video, etc. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + + Examples: + + ```python + >>> from transformers import AutoTokenizer, PerceiverForMaskedLM + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("deepmind/language-perceiver") + >>> model = PerceiverForMaskedLM.from_pretrained("deepmind/language-perceiver") + + >>> # training + >>> text = "This is an incomplete sentence where some words are missing." + >>> inputs = tokenizer(text, padding="max_length", return_tensors="pt") + >>> # mask " missing." + >>> inputs["input_ids"][0, 52:61] = tokenizer.mask_token_id + >>> labels = tokenizer(text, padding="max_length", return_tensors="pt").input_ids + + >>> outputs = model(**inputs, labels=labels) + >>> loss = outputs.loss + >>> round(loss.item(), 2) + 19.87 + + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 2048, 262] + + >>> # inference + >>> text = "This is an incomplete sentence where some words are missing." + >>> encoding = tokenizer(text, padding="max_length", return_tensors="pt") + + >>> # mask bytes corresponding to " missing.". Note that the model performs much better if the masked span starts with a space. + >>> encoding["input_ids"][0, 52:61] = tokenizer.mask_token_id + + >>> # forward pass + >>> with torch.no_grad(): + ... outputs = model(**encoding) + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 2048, 262] + + >>> masked_tokens_predictions = logits[0, 52:61].argmax(dim=-1).tolist() + >>> tokenizer.decode(masked_tokens_predictions) + ' missing.' + ```""" + if inputs is not None and input_ids is not None: + raise ValueError("You cannot use both `inputs` and `input_ids`") + elif inputs is None and input_ids is not None: + inputs = input_ids + + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.perceiver( + inputs=inputs, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = self.embedding_decoder( + outputs.logits if return_dict else outputs[0], embedding_layer=self.perceiver.input_preprocessor.embeddings + ) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return PerceiverMaskedLMOutput( + loss=masked_lm_loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Example use of Perceiver for text classification. + """ +) +class PerceiverForSequenceClassification(PerceiverPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1} + + self.num_labels = config.num_labels + self.perceiver = PerceiverModel( + config, + input_preprocessor=PerceiverTextPreprocessor(config), + decoder=PerceiverClassificationDecoder( + config, + num_channels=config.d_latents, + trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder, + use_query_residual=True, + ), + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + inputs: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + labels: torch.Tensor | None = None, + return_dict: bool | None = None, + input_ids: torch.Tensor | None = None, + **kwargs, + ) -> tuple | PerceiverClassifierOutput: + r""" + inputs (`torch.FloatTensor`): + Inputs to the perceiver. Can be anything: images, text, audio, video, etc. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the classification/regression loss. Indices should be in `[0, ..., config.num_labels - + 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > + 1` a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import AutoTokenizer, PerceiverForSequenceClassification + + >>> tokenizer = AutoTokenizer.from_pretrained("deepmind/language-perceiver") + >>> model = PerceiverForSequenceClassification.from_pretrained("deepmind/language-perceiver") + + >>> text = "hello world" + >>> inputs = tokenizer(text, return_tensors="pt").input_ids + >>> outputs = model(inputs=inputs) + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 2] + ```""" + if inputs is not None and input_ids is not None: + raise ValueError("You cannot use both `inputs` and `input_ids`") + elif inputs is None and input_ids is not None: + inputs = input_ids + + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.perceiver( + inputs=inputs, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + logits = outputs.logits if return_dict else outputs[0] + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return PerceiverClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Example use of Perceiver for image classification, for tasks such as ImageNet. + + This model uses learned position embeddings. In other words, this model is not given any privileged information about + the structure of images. As shown in the paper, this model can achieve a top-1 accuracy of 72.7 on ImageNet. + + [`PerceiverForImageClassificationLearned`] uses [`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`] + (with `prep_type="conv1x1"`) to preprocess the input images, and + [`~models.perceiver.modeling_perceiver.PerceiverClassificationDecoder`] to decode the latent representation of + [`PerceiverModel`] into classification logits. + """ +) +class PerceiverForImageClassificationLearned(PerceiverPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + trainable_position_encoding_kwargs_preprocessor = {"num_channels": 256, "index_dims": config.image_size**2} + trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1} + + self.num_labels = config.num_labels + self.perceiver = PerceiverModel( + config, + input_preprocessor=PerceiverImagePreprocessor( + config, + prep_type="conv1x1", + spatial_downsample=1, + out_channels=256, + position_encoding_type="trainable", + concat_or_add_pos="concat", + project_pos_dim=256, + trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_preprocessor, + ), + decoder=PerceiverClassificationDecoder( + config, + num_channels=config.d_latents, + trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder, + use_query_residual=True, + ), + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + inputs: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + labels: torch.Tensor | None = None, + interpolate_pos_encoding: bool = False, + return_dict: bool | None = None, + pixel_values: torch.Tensor | None = None, + **kwargs, + ) -> tuple | PerceiverClassifierOutput: + r""" + inputs (`torch.FloatTensor`): + Inputs to the perceiver. Can be anything: images, text, audio, video, etc. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, PerceiverForImageClassificationLearned + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> image_processor = AutoImageProcessor.from_pretrained("deepmind/vision-perceiver-learned") + >>> model = PerceiverForImageClassificationLearned.from_pretrained("deepmind/vision-perceiver-learned") + + >>> inputs = image_processor(images=image, return_tensors="pt").pixel_values + >>> outputs = model(inputs=inputs) + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 1000] + + >>> # model predicts one of the 1000 ImageNet classes + >>> predicted_class_idx = logits.argmax(-1).item() + >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) + Predicted class: tabby, tabby cat + ```""" + if inputs is not None and pixel_values is not None: + raise ValueError("You cannot use both `inputs` and `pixel_values`") + elif inputs is None and pixel_values is not None: + inputs = pixel_values + + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.perceiver( + inputs=inputs, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + interpolate_pos_encoding=interpolate_pos_encoding, + return_dict=return_dict, + ) + logits = outputs.logits if return_dict else outputs[0] + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return PerceiverClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Example use of Perceiver for image classification, for tasks such as ImageNet. + + This model uses fixed 2D Fourier position embeddings. As shown in the paper, this model can achieve a top-1 accuracy of + 79.0 on ImageNet, and 84.5 when pre-trained on a large-scale dataset (i.e. JFT). + + [`PerceiverForImageClassificationLearned`] uses [`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`] + (with `prep_type="pixels"`) to preprocess the input images, and + [`~models.perceiver.modeling_perceiver.PerceiverClassificationDecoder`] to decode the latent representation of + [`PerceiverModel`] into classification logits. + """ +) +class PerceiverForImageClassificationFourier(PerceiverPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + fourier_position_encoding_kwargs_preprocessor = { + "concat_pos": True, + "max_resolution": (224, 224), + "num_bands": 64, + "sine_only": False, + } + trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1} + + self.num_labels = config.num_labels + self.perceiver = PerceiverModel( + config, + input_preprocessor=PerceiverImagePreprocessor( + config, + prep_type="pixels", + spatial_downsample=1, + fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_preprocessor, + ), + decoder=PerceiverClassificationDecoder( + config, + num_channels=config.d_latents, + trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder, + use_query_residual=True, + ), + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + inputs: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + labels: torch.Tensor | None = None, + return_dict: bool | None = None, + pixel_values: torch.Tensor | None = None, + **kwargs, + ) -> tuple | PerceiverClassifierOutput: + r""" + inputs (`torch.FloatTensor`): + Inputs to the perceiver. Can be anything: images, text, audio, video, etc. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, PerceiverForImageClassificationFourier + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> image_processor = AutoImageProcessor.from_pretrained("deepmind/vision-perceiver-fourier") + >>> model = PerceiverForImageClassificationFourier.from_pretrained("deepmind/vision-perceiver-fourier") + + >>> inputs = image_processor(images=image, return_tensors="pt").pixel_values + >>> outputs = model(inputs=inputs) + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 1000] + + >>> # model predicts one of the 1000 ImageNet classes + >>> predicted_class_idx = logits.argmax(-1).item() + >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) + Predicted class: tabby, tabby cat + ```""" + if inputs is not None and pixel_values is not None: + raise ValueError("You cannot use both `inputs` and `pixel_values`") + elif inputs is None and pixel_values is not None: + inputs = pixel_values + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.perceiver( + inputs=inputs, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + logits = outputs.logits if return_dict else outputs[0] + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return PerceiverClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Example use of Perceiver for image classification, for tasks such as ImageNet. + + This model uses a 2D conv+maxpool preprocessing network. As shown in the paper, this model can achieve a top-1 accuracy + of 82.1 on ImageNet. + + [`PerceiverForImageClassificationLearned`] uses [`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`] + (with `prep_type="conv"`) to preprocess the input images, and + [`~models.perceiver.modeling_perceiver.PerceiverClassificationDecoder`] to decode the latent representation of + [`PerceiverModel`] into classification logits. + """ +) +class PerceiverForImageClassificationConvProcessing(PerceiverPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + fourier_position_encoding_kwargs_preprocessor = { + "concat_pos": True, + "max_resolution": (56, 56), + "num_bands": 64, + "sine_only": False, + } + trainable_position_encoding_kwargs_decoder = {"num_channels": config.d_latents, "index_dims": 1} + + self.num_labels = config.num_labels + self.perceiver = PerceiverModel( + config, + input_preprocessor=PerceiverImagePreprocessor( + config, + prep_type="conv", + spatial_downsample=1, + position_encoding_type="fourier", + fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_preprocessor, + ), + decoder=PerceiverClassificationDecoder( + config, + num_channels=config.d_latents, + trainable_position_encoding_kwargs=trainable_position_encoding_kwargs_decoder, + use_query_residual=True, + ), + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + inputs: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + labels: torch.Tensor | None = None, + return_dict: bool | None = None, + pixel_values: torch.Tensor | None = None, + **kwargs, + ) -> tuple | PerceiverClassifierOutput: + r""" + inputs (`torch.FloatTensor`): + Inputs to the perceiver. Can be anything: images, text, audio, video, etc. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, PerceiverForImageClassificationConvProcessing + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> image_processor = AutoImageProcessor.from_pretrained("deepmind/vision-perceiver-conv") + >>> model = PerceiverForImageClassificationConvProcessing.from_pretrained("deepmind/vision-perceiver-conv") + + >>> inputs = image_processor(images=image, return_tensors="pt").pixel_values + >>> outputs = model(inputs=inputs) + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 1000] + + >>> # model predicts one of the 1000 ImageNet classes + >>> predicted_class_idx = logits.argmax(-1).item() + >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) + Predicted class: tabby, tabby cat + ```""" + if inputs is not None and pixel_values is not None: + raise ValueError("You cannot use both `inputs` and `pixel_values`") + elif inputs is None and pixel_values is not None: + inputs = pixel_values + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.perceiver( + inputs=inputs, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + logits = outputs.logits if return_dict else outputs[0] + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return PerceiverClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Example use of Perceiver for optical flow, for tasks such as Sintel and KITTI. [`PerceiverForOpticalFlow`] uses + [`~models.perceiver.modeling_perceiver.PerceiverImagePreprocessor`] (with *prep_type="patches"*) to preprocess the + input images, and [`~models.perceiver.modeling_perceiver.PerceiverOpticalFlowDecoder`] to decode the latent + representation of [`PerceiverModel`]. + + As input, one concatenates 2 subsequent frames along the channel dimension and extract a 3 x 3 patch around each pixel + (leading to 3 x 3 x 3 x 2 = 54 values for each pixel). Fixed Fourier position encodings are used to encode the position + of each pixel in the patch. Next, one applies the Perceiver encoder. To decode, one queries the latent representation + using the same encoding used for the input. + """ +) +class PerceiverForOpticalFlow(PerceiverPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + fourier_position_encoding_kwargs_preprocessor = { + "num_bands": 64, + "max_resolution": config.train_size, + "sine_only": False, + "concat_pos": True, + } + fourier_position_encoding_kwargs_decoder = { + "concat_pos": True, + "max_resolution": config.train_size, + "num_bands": 64, + "sine_only": False, + } + + image_preprocessor = PerceiverImagePreprocessor( + config, + prep_type="patches", + spatial_downsample=1, + conv_after_patching=True, + conv_after_patching_in_channels=54, + temporal_downsample=2, + position_encoding_type="fourier", + # position_encoding_kwargs + fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_preprocessor, + ) + + self.perceiver = PerceiverModel( + config, + input_preprocessor=image_preprocessor, + decoder=PerceiverOpticalFlowDecoder( + config, + num_channels=image_preprocessor.num_channels, + output_image_shape=config.train_size, + rescale_factor=100.0, + # decoder kwargs + use_query_residual=False, + output_num_channels=2, + # We query the decoder using the first frame features + # rather than a standard decoder position encoding. + position_encoding_type="fourier", + fourier_position_encoding_kwargs=fourier_position_encoding_kwargs_decoder, + ), + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + inputs: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + labels: torch.Tensor | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PerceiverClassifierOutput: + r""" + inputs (`torch.FloatTensor`): + Inputs to the perceiver. Can be anything: images, text, audio, video, etc. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the optical flow loss. Indices should be in `[0, ..., config.num_labels - 1]`. + + Examples: + + ```python + >>> from transformers import PerceiverForOpticalFlow + >>> import torch + + >>> model = PerceiverForOpticalFlow.from_pretrained("deepmind/optical-flow-perceiver") + + >>> # in the Perceiver IO paper, the authors extract a 3 x 3 patch around each pixel, + >>> # leading to 3 x 3 x 3 = 27 values for each pixel (as each pixel also has 3 color channels) + >>> # patches have shape (batch_size, num_frames, num_channels, height, width) + >>> # the authors train on resolutions of 368 x 496 + >>> patches = torch.randn(1, 2, 27, 368, 496) + >>> outputs = model(inputs=patches) + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 368, 496, 2] + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + loss = None + if labels is not None: + raise NotImplementedError("Optical flow training is not yet supported") + + outputs = self.perceiver( + inputs=inputs, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + logits = outputs.logits if return_dict else outputs[0] + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return PerceiverClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Example use of Perceiver for multimodal (video) autoencoding, for tasks such as Kinetics-700. + + [`PerceiverForMultimodalAutoencoding`] uses [`~models.perceiver.modeling_perceiver.PerceiverMultimodalPreprocessor`] to + preprocess the 3 modalities: images, audio and class labels. This preprocessor uses modality-specific preprocessors to + preprocess every modality separately, after which they are concatenated. Trainable position embeddings are used to pad + each modality to the same number of channels to make concatenation along the time dimension possible. Next, one applies + the Perceiver encoder. + + [`~models.perceiver.modeling_perceiver.PerceiverMultimodalDecoder`] is used to decode the latent representation of + [`PerceiverModel`]. This decoder uses each modality-specific decoder to construct queries. The decoder queries are + created based on the inputs after preprocessing. However, autoencoding an entire video in a single forward pass is + computationally infeasible, hence one only uses parts of the decoder queries to do cross-attention with the latent + representation. This is determined by the subsampled indices for each modality, which can be provided as additional + input to the forward pass of [`PerceiverForMultimodalAutoencoding`]. + + [`~models.perceiver.modeling_perceiver.PerceiverMultimodalDecoder`] also pads the decoder queries of the different + modalities to the same number of channels, in order to concatenate them along the time dimension. Next, cross-attention + is performed with the latent representation of [`PerceiverModel`]. + + Finally, [`~models.perceiver.modeling_perceiver.PerceiverMultiModalPostprocessor`] is used to turn this tensor into an + actual video. It first splits up the output into the different modalities, and then applies the respective + postprocessor for each modality. + + Note that, by masking the classification label during evaluation (i.e. simply providing a tensor of zeros for the + "label" modality), this auto-encoding model becomes a Kinetics 700 video classifier. + """ +) +class PerceiverForMultimodalAutoencoding(PerceiverPreTrainedModel): + def __init__(self, config: PerceiverConfig): + super().__init__(config) + + n_audio_samples = config.num_frames * config.audio_samples_per_frame + + input_preprocessor = PerceiverMultimodalPreprocessor( + min_padding_size=4, + modalities={ + "audio": PerceiverAudioPreprocessor( + config, + position_encoding_type="fourier", + fourier_position_encoding_kwargs={ + "num_bands": 192, + "max_resolution": (n_audio_samples,), + "sine_only": False, + "concat_pos": True, + }, + prep_type="patches", + samples_per_patch=config.samples_per_patch, + ), + "image": PerceiverImagePreprocessor( + config, + position_encoding_type="fourier", + fourier_position_encoding_kwargs={ + "num_bands": 32, + "max_resolution": (config.num_frames, config.image_size, config.image_size), + "sine_only": False, + "concat_pos": True, + }, + prep_type="patches", + spatial_downsample=4, + temporal_downsample=1, + ), + "label": PerceiverOneHotPreprocessor(config), + }, + mask_probs={"image": 0.0, "audio": 0.0, "label": 1.0}, + ) + + image_decoder = PerceiverBasicVideoAutoencodingDecoder( + config, + # Autoencoding, don't pass inputs to the queries. + concat_preprocessed_input=False, + output_shape=config.output_shape, + output_num_channels=config.output_num_channels, + use_query_residual=False, + position_encoding_only=True, + position_encoding_type="fourier", + fourier_position_encoding_kwargs={ + "num_bands": 32, + "max_resolution": (config.num_frames, config.image_size, config.image_size), + "sine_only": False, + "concat_pos": True, + }, + ) + + decoder = PerceiverMultimodalDecoder( + config, + # Autoencoding, don't pass inputs to the queries. + concat_preprocessed_input=False, + # Modality specific decoders are used ONLY to generate queries. + # All modalties are decoded together using a unified decoder. + modalities={ + "audio": PerceiverBasicDecoder( + config, + # Autoencoding, don't pass inputs to the queries. + concat_preprocessed_input=False, + output_index_dims=(n_audio_samples // config.samples_per_patch,), + output_num_channels=config.output_num_channels, + use_query_residual=False, + position_encoding_only=True, + position_encoding_type="fourier", + fourier_position_encoding_kwargs={ + "num_bands": 192, + "max_resolution": (n_audio_samples,), + "sine_only": False, + "concat_pos": True, + }, + ), + "image": image_decoder, + "label": PerceiverClassificationDecoder( + config, + # Autoencoding, don't pass inputs to the queries. + concat_preprocessed_input=False, + use_query_residual=False, + position_encoding_only=True, + position_encoding_type="trainable", + trainable_position_encoding_kwargs={ + "num_channels": config._label_trainable_num_channels, + "index_dims": 1, + }, + ), + }, + num_outputs=None, + output_num_channels=config.output_num_channels, + use_query_residual=False, + ) + + output_postprocessor = PerceiverMultimodalPostprocessor( + modalities={ + "audio": PerceiverAudioPostprocessor(config, in_channels=config.output_num_channels), + "image": PerceiverProjectionPostprocessor(in_channels=config.output_num_channels, out_channels=3), + "label": PerceiverClassificationPostprocessor(config, in_channels=config.output_num_channels), + } + ) + + self.perceiver = PerceiverModel( + config, + input_preprocessor=input_preprocessor, + decoder=decoder, + output_postprocessor=output_postprocessor, + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + inputs: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + subsampled_output_points: dict[str, torch.Tensor] | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + labels: torch.Tensor | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | PerceiverClassifierOutput: + r""" + inputs (`torch.FloatTensor`): + Inputs to the perceiver. Can be anything: images, text, audio, video, etc. + subsampled_output_points (`dict[str, torch.Tensor]`, *optional*): + Dictionary of tensors used as queries for the decoder. The decoder maps these queries to the latent + representation of the model. Used for subsampled decoding, e.g. when only decoding certain image patches. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import PerceiverForMultimodalAutoencoding + >>> import torch + >>> import numpy as np + + >>> # create multimodal inputs + >>> images = torch.randn((1, 16, 3, 224, 224)) + >>> audio = torch.randn((1, 30720, 1)) + >>> inputs = dict(image=images, audio=audio, label=torch.zeros((images.shape[0], 700))) + + >>> model = PerceiverForMultimodalAutoencoding.from_pretrained("deepmind/multimodal-perceiver") + + >>> # in the Perceiver IO paper, videos are auto-encoded in chunks + >>> # each chunk subsamples different index dimensions of the image and audio modality decoder queries + >>> nchunks = 128 + >>> image_chunk_size = np.prod((16, 224, 224)) // nchunks + >>> audio_chunk_size = audio.shape[1] // model.config.samples_per_patch // nchunks + >>> # process the first chunk + >>> chunk_idx = 0 + >>> subsampling = { + ... "image": torch.arange(image_chunk_size * chunk_idx, image_chunk_size * (chunk_idx + 1)), + ... "audio": torch.arange(audio_chunk_size * chunk_idx, audio_chunk_size * (chunk_idx + 1)), + ... "label": None, + ... } + + >>> outputs = model(inputs=inputs, subsampled_output_points=subsampling) + >>> logits = outputs.logits + >>> list(logits["audio"].shape) + [1, 240] + + >>> list(logits["image"].shape) + [1, 6272, 3] + + >>> list(logits["label"].shape) + [1, 700] + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + loss = None + if labels is not None: + raise NotImplementedError("Multimodal autoencoding training is not yet supported") + + outputs = self.perceiver( + inputs=inputs, + attention_mask=attention_mask, + subsampled_output_points=subsampled_output_points, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + logits = outputs.logits if return_dict else outputs[0] + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return PerceiverClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +# Below: position encodings + + +def build_position_encoding( + position_encoding_type, + out_channels=None, + project_pos_dim=-1, + trainable_position_encoding_kwargs=None, + fourier_position_encoding_kwargs=None, +): + """ + Builds the position encoding. + + Args: + - out_channels: refers to the number of channels of the position encodings. + - project_pos_dim: if specified, will project the position encodings to this dimension. + + """ + + if position_encoding_type == "trainable": + if not trainable_position_encoding_kwargs: + raise ValueError("Make sure to pass trainable_position_encoding_kwargs") + output_pos_enc = PerceiverTrainablePositionEncoding(**trainable_position_encoding_kwargs) + elif position_encoding_type == "fourier": + # We don't use the index_dims argument, as this is only known during the forward pass + if not fourier_position_encoding_kwargs: + raise ValueError("Make sure to pass fourier_position_encoding_kwargs") + output_pos_enc = PerceiverFourierPositionEncoding(**fourier_position_encoding_kwargs) + else: + raise ValueError(f"Unknown position encoding type: {position_encoding_type}.") + + # Optionally, project the position encoding to a target dimension: + positions_projection = nn.Linear(out_channels, project_pos_dim) if project_pos_dim > 0 else nn.Identity() + + return output_pos_enc, positions_projection + + +# Below: Perceiver decoders + + +class PerceiverAbstractDecoder(nn.Module, metaclass=abc.ABCMeta): + """Perceiver abstract decoder.""" + + @abc.abstractmethod + def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None): + raise NotImplementedError + + @property + @abc.abstractmethod + def num_query_channels(self): + raise NotImplementedError + + @abc.abstractmethod + def forward(self, query, z, query_mask=None): + raise NotImplementedError + + +class PerceiverProjectionDecoder(PerceiverAbstractDecoder): + """ + Baseline projection decoder (no cross-attention). + + Args: + config ([`PerceiverConfig`]): + Model configuration. + """ + + def __init__(self, config): + super().__init__() + self.classifier = nn.Linear(config.d_latents, config.num_labels) + + def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None): + return None + + def forward( + self, query: torch.Tensor, z: torch.FloatTensor, query_mask: torch.FloatTensor | None = None + ) -> torch.FloatTensor: + # (batch_size, num_latents, d_latents) -> (batch_size, d_latents) + z = torch.mean(z, dim=1) + # (batch_size, d_latents) -> (batch_size, config.num_labels) + logits = self.classifier(z) + return logits + + +class PerceiverBasicDecoder(PerceiverAbstractDecoder): + """ + Cross-attention-based decoder. This class can be used to decode the final hidden states of the latents using a + cross-attention operation, in which the latents produce keys and values. + + The shape of the output of this class depends on how one defines the output queries (also called decoder queries). + + Args: + config ([*PerceiverConfig*]): + Model configuration. + output_num_channels (`int`, *optional*): + The number of channels in the output. Will only be used in case *final_project* is set to `True`. + position_encoding_type (`str`, *optional*, defaults to "trainable"): + The type of position encoding to use. Can be either "trainable", "fourier", or "none". + output_index_dims (`int`, *optional*): + The number of dimensions of the output queries. Ignored if 'position_encoding_type' == 'none'. + num_channels (`int`, *optional*, defaults to 128): + The number of channels of the decoder queries. Ignored if 'position_encoding_type' == 'none'. + qk_channels (`int`, *optional*): + The number of channels of the queries and keys in the cross-attention layer. + v_channels (`int`, *optional*): + The number of channels of the values in the cross-attention layer. + num_heads (`int`, *optional*, defaults to 1): + The number of attention heads in the cross-attention layer. + widening_factor (`int`, *optional*, defaults to 1): + The widening factor of the cross-attention layer. + use_query_residual (`bool`, *optional*, defaults to `False`): + Whether to use a residual connection between the query and the output of the cross-attention layer. + concat_preprocessed_input (`bool`, *optional*, defaults to `False`): + Whether to concatenate the preprocessed input to the query. + final_project (`bool`, *optional*, defaults to `True`): + Whether to project the output of the cross-attention layer to a target dimension. + position_encoding_only (`bool`, *optional*, defaults to `False`): + Whether to only use this class to define output queries. + """ + + def __init__( + self, + config: PerceiverConfig, + output_num_channels: int, + position_encoding_type: str | None = "trainable", + # The following 2 arguments are ignored if position_encoding_type == 'none': + output_index_dims: int | None = None, + num_channels: int | None = 128, + subsampled_index_dims: int | None = None, + qk_channels: int | None = None, + v_channels: int | None = None, + num_heads: int | None = 1, + widening_factor: int | None = 1, + use_query_residual: bool | None = False, + concat_preprocessed_input: bool | None = False, + final_project: bool | None = True, + position_encoding_only: bool | None = False, + **position_encoding_kwargs, + ) -> None: + super().__init__() + + self.output_num_channels = output_num_channels + # If `none`, the decoder will not construct any position encodings. + # You should construct your own when querying the decoder. + self.output_position_encodings = None + self.position_encoding_type = position_encoding_type + self.position_encoding_kwargs = position_encoding_kwargs + if position_encoding_type != "none": + self.output_position_encodings, self.positions_projection = build_position_encoding( + position_encoding_type=position_encoding_type, **position_encoding_kwargs + ) + + self.output_index_dims = output_index_dims + self.num_channels = num_channels + if subsampled_index_dims is None: + subsampled_index_dims = output_index_dims + self.subsampled_index_dims = subsampled_index_dims + self.concat_preprocessed_input = concat_preprocessed_input + self.final_project = final_project + self.position_encoding_only = position_encoding_only + + # for multimodal autoencoding, we don't need the decoder cross-attention and final layer + # so then we will set position_encoding_only to True + if not self.position_encoding_only: + self.decoding_cross_attention = PerceiverLayer( + config, + is_cross_attention=True, + qk_channels=qk_channels, + v_channels=v_channels, + num_heads=num_heads, + q_dim=num_channels, + kv_dim=config.d_latents, + widening_factor=widening_factor, + use_query_residual=use_query_residual, + ) + self.final_layer = nn.Linear(num_channels, output_num_channels) if final_project else nn.Identity() + + @property + def num_query_channels(self) -> int: + if self.position_encoding_type == "none": # Queries come from elsewhere + raise ValueError( + "You cannot calculate number of decoder query channels when position_encoding_type is set to none" + ) + if self.position_encoding_only: + if "project_pos_dim" in self.position_encoding_kwargs: + return self.position_encoding_kwargs["project_pos_dim"] + return self.output_position_encodings.output_size() + if self.final_project: + return self.output_num_channels + return self.num_channels + + def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None): + if self.position_encoding_type == "none": # Queries come from elsewhere + raise ValueError("You cannot construct decoder queries when position_encoding_type is set to none") + if subsampled_points is not None: + # subsampled_points are the indices if the inputs would be flattened + # however, the inputs aren't flattened, that's why we use unravel_index + # to get the indices for the unflattened array + # unravel_index returns a tuple (x_idx, y_idx, ...) + # stack to get the [n, d] tensor of coordinates + indices = torch.unravel_index(subsampled_points, self.output_index_dims) + pos = torch.stack(indices, dim=1) + batch_size = inputs.shape[0] + # Map these coordinates to [-1, 1] + pos = -1 + 2 * pos / torch.tensor(self.output_index_dims)[None, :] + pos = torch.broadcast_to(pos[None], [batch_size, pos.shape[0], pos.shape[1]]) + # Construct the position encoding. + if self.position_encoding_type == "trainable": + pos_emb = self.output_position_encodings(batch_size) + elif self.position_encoding_type == "fourier": + pos_emb = self.output_position_encodings( + self.output_index_dims, batch_size=batch_size, device=inputs.device, dtype=inputs.dtype, pos=pos + ) + + # Optionally project them to a target dimension. + pos_emb = self.positions_projection(pos_emb) + pos_emb = torch.reshape(pos_emb, [pos_emb.shape[0], -1, pos_emb.shape[-1]]) + else: + batch_size = inputs.shape[0] + index_dims = inputs.shape[2:] + + # Construct the position encoding. + if self.position_encoding_type == "trainable": + pos_emb = self.output_position_encodings(batch_size) + elif self.position_encoding_type == "fourier": + pos_emb = self.output_position_encodings( + index_dims, batch_size, device=inputs.device, dtype=inputs.dtype + ) + + # Optionally project them to a target dimension. + pos_emb = self.positions_projection(pos_emb) + + if self.concat_preprocessed_input: + if inputs_without_pos is None: + raise ValueError("Value is required for inputs_without_pos if concat_preprocessed_input is True") + pos_emb = torch.cat([inputs_without_pos, pos_emb], dim=-1) + + return pos_emb + + def forward( + self, + query: torch.Tensor, + z: torch.FloatTensor, + query_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + ) -> PerceiverDecoderOutput: + # Cross-attention decoding. + # key, value: B x N x K; query: B x M x K + # Attention maps -> B x N x M + # Output -> B x M x K + cross_attentions = () if output_attentions else None + + layer_outputs = self.decoding_cross_attention( + query, + attention_mask=query_mask, + inputs=z, + inputs_mask=None, + output_attentions=output_attentions, + ) + output = layer_outputs[0] + + if output_attentions: + cross_attentions = cross_attentions + (layer_outputs[1],) + + logits = self.final_layer(output) + + return PerceiverDecoderOutput(logits=logits, cross_attentions=cross_attentions) + + +class PerceiverClassificationDecoder(PerceiverAbstractDecoder): + """ + Cross-attention based classification decoder. Light-weight wrapper of [`PerceiverBasicDecoder`] for logit output. + Will turn the output of the Perceiver encoder which is of shape (batch_size, num_latents, d_latents) to a tensor of + shape (batch_size, num_labels). The queries are of shape (batch_size, 1, num_labels). + + Args: + config ([`PerceiverConfig`]): + Model configuration. + """ + + def __init__(self, config, **decoder_kwargs): + super().__init__() + + self.num_labels = config.num_labels + self.decoder = PerceiverBasicDecoder( + config, + output_num_channels=self.num_labels, + output_index_dims=1, # Predict a single logit array. + **decoder_kwargs, + ) + + @property + def num_query_channels(self) -> int: + return self.decoder.num_query_channels + + def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None): + return self.decoder.decoder_query( + inputs, modality_sizes, inputs_without_pos, subsampled_points=subsampled_points + ) + + def forward( + self, + query: torch.Tensor, + z: torch.FloatTensor, + query_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + ) -> PerceiverDecoderOutput: + decoder_outputs = self.decoder(query, z, output_attentions=output_attentions) + + # B x 1 x num_classes -> B x num_classes + logits = decoder_outputs.logits[:, 0, :] + + return PerceiverDecoderOutput(logits=logits, cross_attentions=decoder_outputs.cross_attentions) + + +class PerceiverOpticalFlowDecoder(PerceiverAbstractDecoder): + """Cross-attention based optical flow decoder.""" + + def __init__(self, config, output_image_shape, output_num_channels=2, rescale_factor=100.0, **decoder_kwargs): + super().__init__() + + self.output_image_shape = output_image_shape + self.output_num_channels = output_num_channels + self.rescale_factor = rescale_factor + self.decoder = PerceiverBasicDecoder(config, output_num_channels=output_num_channels, **decoder_kwargs) + + @property + def num_query_channels(self) -> int: + return self.decoder.num_query_channels + + def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None): + if subsampled_points is not None: + raise ValueError("FlowDecoder doesn't support subsampling yet.") + return inputs + + def forward( + self, + query: torch.Tensor, + z: torch.FloatTensor, + query_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + ) -> PerceiverDecoderOutput: + decoder_outputs = self.decoder(query, z, output_attentions=output_attentions) + preds = decoder_outputs.logits + # Output flow and rescale. + preds /= self.rescale_factor + preds = preds.reshape([preds.shape[0]] + list(self.output_image_shape) + [preds.shape[-1]]) + return PerceiverDecoderOutput(logits=preds, cross_attentions=decoder_outputs.cross_attentions) + + +class PerceiverBasicVideoAutoencodingDecoder(PerceiverAbstractDecoder): + """ + Cross-attention based video-autoencoding decoder. Light-weight wrapper of [*PerceiverBasicDecoder*] with video + reshaping logic. + + Args: + config ([*PerceiverConfig*]): + Model configuration. + output_shape (`list[int]`): + Shape of the output as (batch_size, num_frames, height, width), excluding the channel dimension. + position_encoding_type (`str`): + The type of position encoding to use. Can be either "trainable", "fourier", or "none". + """ + + def __init__( + self, config: PerceiverConfig, output_shape: list[int], position_encoding_type: str, **decoder_kwargs + ) -> None: + super().__init__() + if len(output_shape) != 4: # B, T, H, W + raise ValueError(f"Expected rank 4 output_shape, got {output_shape}.") + # Build the decoder components: + self.output_shape = output_shape + self.output_num_channels = decoder_kwargs["output_num_channels"] + + self.decoder = PerceiverBasicDecoder( + config, + output_index_dims=self.output_shape[1:4], # T*H*W + position_encoding_type=position_encoding_type, + **decoder_kwargs, + ) + + @property + def num_query_channels(self) -> int: + return self.decoder.num_query_channels + + def decoder_query(self, inputs, modality_sizes=None, inputs_without_pos=None, subsampled_points=None): + return self.decoder.decoder_query( + inputs, + modality_sizes=modality_sizes, + inputs_without_pos=inputs_without_pos, + subsampled_points=subsampled_points, + ) + + def forward( + self, query: torch.Tensor, z: torch.FloatTensor, query_mask: torch.FloatTensor | None = None + ) -> PerceiverDecoderOutput: + decoder_outputs = self.decoder(query, z) + logits = decoder_outputs.logits + + logits = torch.reshape(logits, self.output_shape + [logits.shape[-1]]) + return PerceiverDecoderOutput(logits=logits, cross_attentions=decoder_outputs.cross_attentions) + + +def restructure(modality_sizes: ModalitySizeType, inputs: torch.Tensor) -> Mapping[str, torch.Tensor]: + """ + Partitions a [B, N, C] tensor into tensors for each modality. + + Args: + modality_sizes + dict specifying the size of the modality + inputs: + input tensor + + Returns: + dict mapping name of modality to its associated tensor. + """ + outputs = {} + index = 0 + # Apply a predictable ordering to the modalities + for modality in sorted(modality_sizes.keys()): + size = modality_sizes[modality] + inp = inputs[:, index : index + size] + index += size + outputs[modality] = inp + return outputs + + +class PerceiverMultimodalDecoder(PerceiverAbstractDecoder): + """ + Multimodal decoding by composing uni-modal decoders. The *modalities* argument of the constructor is a dictionary + mapping modality name to the decoder of that modality. That decoder will be used to construct queries for that + modality. Modality-specific queries are padded with trainable modality-specific parameters, after which they are + concatenated along the time dimension. + + Next, there is a shared cross attention operation across all modalities. + + Args: + config ([*PerceiverConfig*]): + Model configuration. + modalities (`dict[str, PerceiverAbstractDecoder]`): + Dictionary mapping modality name to the decoder of that modality. + num_outputs (`int`): + The number of outputs of the decoder. + output_num_channels (`int`): + The number of channels in the output. + min_padding_size (`int`, *optional*, defaults to 2): + The minimum padding size for all modalities. The final output will have num_channels equal to the maximum + channels across all modalities plus min_padding_size. + subsampled_index_dims (`dict[str, PerceiverAbstractDecoder]`, *optional*): + Dictionary mapping modality name to the subsampled index dimensions to use for the decoder query of that + modality. + """ + + def __init__( + self, + config: PerceiverConfig, + modalities: dict[str, PerceiverAbstractDecoder], + num_outputs: int, + output_num_channels: int, + min_padding_size: int | None = 2, + subsampled_index_dims: dict[str, PerceiverAbstractDecoder] | None = None, + **decoder_kwargs, + ) -> None: + super().__init__() + self.modalities = nn.ModuleDict(modalities) + self.subsampled_index_dims = subsampled_index_dims + self.min_padding_size = min_padding_size + self.output_num_channels = output_num_channels + self.num_outputs = num_outputs + self.decoder = PerceiverBasicDecoder( + config, + output_index_dims=(num_outputs,), + output_num_channels=output_num_channels, + position_encoding_type="none", + num_channels=self.num_query_channels, + **decoder_kwargs, + ) + self.padding = nn.ParameterDict( + { + modality: nn.Parameter(torch.randn(1, self.num_query_channels - decoder.num_query_channels)) + for modality, decoder in modalities.items() + } + ) + + @property + def num_query_channels(self) -> int: + max_channel_size = max(decoder.num_query_channels for _, decoder in self.modalities.items()) + common_channel_size = max_channel_size + self.min_padding_size + return common_channel_size + + def decoder_query(self, inputs, modality_sizes, inputs_without_pos=None, subsampled_points=None): + # Partition the flat inputs among the different modalities + inputs = restructure(modality_sizes, inputs) + + # Obtain modality-specific decoders' queries + subsampled_points = subsampled_points or {} + + decoder_queries = {} + for modality, decoder in self.modalities.items(): + # Get input_without_pos for this modality if it exists. + input_without_pos = None + if inputs_without_pos is not None: + input_without_pos = inputs_without_pos.get(modality, None) + query = decoder.decoder_query( + inputs=inputs[modality], + modality_sizes=None, + inputs_without_pos=input_without_pos, + subsampled_points=subsampled_points.get(modality, None), + ) + decoder_queries[modality] = query + + # Pad all queries with trainable position encodings to make them have the same channels + + def embed(modality, x): + x = torch.reshape(x, [x.shape[0], np.prod(x.shape[1:-1]), x.shape[-1]]) + pos = self.padding[modality] + pos = torch.broadcast_to(pos, [x.shape[0], x.shape[1], self.num_query_channels - x.shape[2]]) + return torch.cat([x, pos], dim=2) + + # Apply a predictable ordering to the modalities + return torch.cat( + [embed(modality, decoder_queries[modality]) for modality in sorted(self.modalities.keys())], dim=1 + ) + + def forward( + self, + query: torch.Tensor, + z: torch.FloatTensor, + query_mask: torch.FloatTensor | None = None, + output_attentions: bool | None = False, + ) -> torch.Tensor: + # B x 1 x num_classes -> B x num_classes + decoder_outputs = self.decoder(query, z, output_attentions=output_attentions) + + return decoder_outputs + + +# Below: IO pre- and post-processor classes for Perceiver. +def space_to_depth(frames: torch.Tensor, temporal_block_size: int = 1, spatial_block_size: int = 1) -> torch.Tensor: + """ + Space to depth transform. Rearranges blocks of spatial data, into depth. + + This function assumes the channels to be first, but will place the channels last after transformation. + """ + if len(frames.shape) == 4: + batch_size, num_channels, height, width = frames.shape + # split up dimensions (height by spatial_block_size, width by spatial_block_size) + frames = frames.view( + batch_size, + num_channels, + height // spatial_block_size, + spatial_block_size, + width // spatial_block_size, + spatial_block_size, + ) + # move blocks to last dimension: (batch_size, H//bs, W//bs, bs, bs, C) + frames = frames.permute(0, 2, 4, 3, 5, 1).contiguous() + # concatenate blocks along channel dimension: (batch_size, H//bs, W//bs, bs*bs*C) + frames = frames.view( + batch_size, + height // spatial_block_size, + width // spatial_block_size, + (spatial_block_size**2) * num_channels, + ) + return frames + elif len(frames.shape) == 5: + batch_size, time, num_channels, height, width = frames.shape + # split up dimensions (time by temporal_block_size, height by spatial_block_size, width by spatial_block_size) + frames = frames.view( + batch_size, + time // temporal_block_size, + temporal_block_size, + num_channels, + height // spatial_block_size, + spatial_block_size, + width // spatial_block_size, + spatial_block_size, + ) + # move blocks to last dimension: (batch_size, T//ts, H//bs, W//bs, ts, bs, bs, C) + frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous() + # concatenate blocks along channel dimension: (batch_size, T//ts, H//bs, W//bs, ts*bs*bs*C) + frames = frames.view( + batch_size, + time // temporal_block_size, + height // spatial_block_size, + width // spatial_block_size, + temporal_block_size * (spatial_block_size**2) * num_channels, + ) + return frames + else: + raise ValueError( + "Frames should be of rank 4 (batch, channels, height, width)" + " or rank 5 (batch, time, channels, height, width)" + ) + + +class Conv2dSamePadding(nn.Conv2d): + """ + Conv2d layer with padding="same" support. Source: + https://gist.github.com/sumanmichael/4de9dee93f972d47c80c4ade8e149ea6 + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.zero_pad_2d = nn.ZeroPad2d( + reduce(__add__, [(k // 2 + (k - 2 * (k // 2)) - 1, k // 2) for k in self.kernel_size[::-1]]) + ) + + def forward(self, input): + return self._conv_forward(self.zero_pad_2d(input), self.weight, self.bias) + + +class Conv2DDownsample(nn.Module): + """Downsamples 4x by applying a 2D convolution and doing max pooling.""" + + def __init__( + self, + num_layers: int = 1, + in_channels: int = 3, + out_channels: int = 64, + use_batchnorm: bool = True, + ): + """ + Constructs a Conv2DDownsample model. + + Args: + in_channels (`int`, *optional*, defaults to 3): + The number of input channels. + out_channels (`int`, *optional*, defaults to 64): + The number of conv output channels. + use_batchnorm (`bool`, *optional*, defaults to `True`): + Whether to use batchnorm. + """ + super().__init__() + + self.conv = Conv2dSamePadding( + in_channels=in_channels, out_channels=out_channels, kernel_size=7, stride=2, bias=False + ) + self.batchnorm = nn.BatchNorm2d(num_features=out_channels) if use_batchnorm else nn.Identity() + self.relu = nn.ReLU() + self.max_pool = nn.MaxPool2d(kernel_size=3, stride=2) + + def forward(self, inputs: torch.Tensor) -> torch.Tensor: + out = self.conv(inputs) + out = self.batchnorm(out) + out = self.relu(out) + out = self.max_pool(out) + return out + + +def generate_fourier_features(pos, num_bands, max_resolution=(224, 224), concat_pos=True, sine_only=False): + """ + Generate a Fourier frequency position encoding with linear spacing. + + Args: + pos (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`): + The Tensor containing the position of n points in d dimensional space. + num_bands (`int`): + The number of frequency bands (K) to use. + max_resolution (`tuple[int]`, *optional*, defaults to (224, 224)): + The maximum resolution (i.e. the number of pixels per dim). A tuple representing resolution for each dimension. + concat_pos (`bool`, *optional*, defaults to `True`): + Whether to concatenate the input position encoding to the Fourier features. + sine_only (`bool`, *optional*, defaults to `False`): + Whether to use a single phase (sin) or two (sin/cos) for each frequency band. + + Returns: + `torch.FloatTensor` of shape `(batch_size, sequence_length, n_channels)`: The Fourier position embeddings. If + `concat_pos` is `True` and `sine_only` is `False`, output dimensions are ordered as: [dim_1, dim_2, ..., dim_d, + sin(pi*f_1*dim_1), ..., sin(pi*f_K*dim_1), ..., sin(pi*f_1*dim_d), ..., sin(pi*f_K*dim_d), cos(pi*f_1*dim_1), + ..., cos(pi*f_K*dim_1), ..., cos(pi*f_1*dim_d), ..., cos(pi*f_K*dim_d)], where dim_i is pos[:, i] and f_k is the + kth frequency band. + """ + + batch_size = pos.shape[0] + + min_freq = 1.0 + # Nyquist frequency at the target resolution: + freq_bands = torch.stack( + [torch.linspace(start=min_freq, end=res / 2, steps=num_bands) for res in max_resolution], dim=0 + ) + + # Get frequency bands for each spatial dimension. + # Output is size [n, d * num_bands] + per_pos_features = pos[0, :, :][:, :, None] * freq_bands[None, :, :] + per_pos_features = torch.reshape(per_pos_features, [-1, np.prod(per_pos_features.shape[1:])]) + + if sine_only: + # Output is size [n, d * num_bands] + per_pos_features = torch.sin(np.pi * (per_pos_features)) + else: + # Output is size [n, 2 * d * num_bands] + per_pos_features = torch.cat( + [torch.sin(np.pi * per_pos_features), torch.cos(np.pi * per_pos_features)], dim=-1 + ) + # Concatenate the raw input positions. + if concat_pos: + # Adds d bands to the encoding. + per_pos_features = torch.cat([pos, per_pos_features.expand(batch_size, -1, -1)], dim=-1) + return per_pos_features + + +def build_linear_positions(index_dims, output_range=(-1.0, 1.0)): + """ + Generate an array of position indices for an N-D input array. + + Args: + index_dims (`list[int]`): + The shape of the index dimensions of the input array. + output_range (`tuple[float]`, *optional*, defaults to `(-1.0, 1.0)`): + The min and max values taken by each input index dimension. + + Returns: + `torch.FloatTensor` of shape `(index_dims[0], index_dims[1], .., index_dims[-1], N)`. + """ + + def _linspace(n_xels_per_dim): + return torch.linspace(start=output_range[0], end=output_range[1], steps=n_xels_per_dim, dtype=torch.float32) + + dim_ranges = [_linspace(n_xels_per_dim) for n_xels_per_dim in index_dims] + array_index_grid = torch.meshgrid(*dim_ranges, indexing="ij") + + return torch.stack(array_index_grid, dim=-1) + + +class PerceiverAbstractPositionEncoding(nn.Module, metaclass=abc.ABCMeta): + """Perceiver abstract position encoding.""" + + @property + @abc.abstractmethod + def num_dimensions(self) -> int: + raise NotImplementedError + + @abc.abstractmethod + def output_size(self, *args, **kwargs) -> int: + raise NotImplementedError + + @abc.abstractmethod + def forward(self, batch_size, pos): + raise NotImplementedError + + +class PerceiverTrainablePositionEncoding(PerceiverAbstractPositionEncoding): + """Trainable position encoding.""" + + def __init__(self, index_dims, num_channels=128): + super().__init__() + self._num_channels = num_channels + self._index_dims = index_dims + index_dim = np.prod(index_dims) + self.position_embeddings = nn.Parameter(torch.randn(index_dim, num_channels)) + + @property + def num_dimensions(self) -> int: + if isinstance(self._index_dims, int): + return 1 + return len(self._index_dims) + + def output_size(self, *args, **kwargs) -> int: + return self._num_channels + + def interpolate_pos_encoding(self, position_embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + num_positions = position_embeddings.shape[0] + new_height = new_width = torch_int(num_positions**0.5) + + # always interpolate when tracing to ensure the exported model works for dynamic input shapes + if not torch.jit.is_tracing() and height == new_height and width == new_width: + return position_embeddings + + position_embeddings = position_embeddings.reshape(1, new_height, new_width, self._num_channels).permute( + 0, 3, 1, 2 + ) + + position_embeddings = nn.functional.interpolate( + position_embeddings, + size=(height, width), + mode="bicubic", + align_corners=False, + ) + position_embeddings = position_embeddings.reshape(1, self._num_channels, -1).permute(0, 2, 1).squeeze(0) + return position_embeddings + + def forward( + self, batch_size: int, interpolate_pos_encoding: bool = False, input_size: torch.Size | None = None + ) -> torch.Tensor: + position_embeddings = self.position_embeddings + + if interpolate_pos_encoding: + height, width = input_size + position_embeddings = self.interpolate_pos_encoding(position_embeddings, height, width) + + if batch_size is not None: + position_embeddings = position_embeddings.expand(batch_size, -1, -1) + return position_embeddings + + +def _check_or_build_spatial_positions(pos, index_dims, batch_size): + """ + Checks or builds spatial position features (x, y, ...). + + Args: + pos (`torch.FloatTensor`): + None, or an array of position features. If None, position features are built. Otherwise, their size is checked. + index_dims (`list[int]`): + An iterable giving the spatial/index size of the data to be featurized. + batch_size (`int`): + The batch size of the data to be featurized. + + Returns: + `torch.FloatTensor` of shape `(batch_size, prod(index_dims))` an array of position features. + """ + if pos is None: + pos = build_linear_positions(index_dims) + # equivalent to `torch.broadcast_to(pos[None], (batch_size,) + pos.shape)` + # but `torch.broadcast_to` cannot be converted to ONNX + pos = pos[None].expand((batch_size,) + pos.shape) + pos = torch.reshape(pos, [batch_size, np.prod(index_dims), -1]) + else: + # Just a warning label: you probably don't want your spatial features to + # have a different spatial layout than your pos coordinate system. + # But feel free to override if you think it'll work! + if pos.shape[-1] != len(index_dims): + raise ValueError("Spatial features have the wrong number of dimensions.") + return pos + + +class PerceiverFourierPositionEncoding(PerceiverAbstractPositionEncoding): + """Fourier (Sinusoidal) position encoding.""" + + def __init__(self, num_bands, max_resolution, concat_pos=True, sine_only=False): + super().__init__() + self.num_bands = num_bands + self.max_resolution = max_resolution + self.concat_pos = concat_pos + self.sine_only = sine_only + + @property + def num_dimensions(self) -> int: + return len(self.max_resolution) + + def output_size(self): + """Returns size of positional encodings last dimension.""" + num_dims = len(self.max_resolution) + encoding_size = self.num_bands * num_dims + if not self.sine_only: + encoding_size *= 2 + if self.concat_pos: + encoding_size += self.num_dimensions + + return encoding_size + + def forward( + self, + index_dims: list[int], + batch_size: int, + device: torch.device, + dtype: torch.dtype, + pos: torch.FloatTensor | None = None, + ) -> torch.FloatTensor: + pos = _check_or_build_spatial_positions(pos, index_dims, batch_size) + fourier_pos_enc = generate_fourier_features( + pos, + num_bands=self.num_bands, + max_resolution=self.max_resolution, + concat_pos=self.concat_pos, + sine_only=self.sine_only, + ).to(device=device, dtype=dtype) + return fourier_pos_enc + + +class AbstractPreprocessor(nn.Module): + @property + def num_channels(self) -> int: + """Returns size of preprocessor output.""" + raise NotImplementedError() + + +class PerceiverTextPreprocessor(AbstractPreprocessor): + """ + Text preprocessing for Perceiver Encoder. Can be used to embed `inputs` and add positional encodings. + + The dimensionality of the embeddings is determined by the `d_model` attribute of the configuration. + + Args: + config ([`PerceiverConfig`]): + Model configuration. + """ + + def __init__(self, config: PerceiverConfig) -> None: + super().__init__() + self.config = config + self.embeddings = nn.Embedding(num_embeddings=config.vocab_size, embedding_dim=config.d_model) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.d_model) + + @property + def num_channels(self) -> int: + return self.config.d_model + + def forward( + self, + inputs: torch.LongTensor, + pos: torch.Tensor | None = None, + network_input_is_1d: bool = True, + interpolate_pos_encoding: bool = False, + ): + embeddings_without_pos = self.embeddings(inputs) + + seq_length = inputs.shape[1] + position_ids = torch.arange(0, seq_length, device=inputs.device) + embeddings = embeddings_without_pos + self.position_embeddings(position_ids) + + return embeddings, None, embeddings_without_pos + + +class PerceiverEmbeddingDecoder(nn.Module): + """ + Module to decode embeddings (for masked language modeling). + + Args: + config ([`PerceiverConfig`]): + Model configuration. + """ + + def __init__(self, config: PerceiverConfig) -> None: + super().__init__() + self.config = config + self.vocab_size = config.vocab_size + self.bias = nn.Parameter(torch.zeros(self.vocab_size)) + + def forward(self, hidden_states: torch.Tensor, embedding_layer: torch.Tensor) -> torch.Tensor: + batch_size, seq_len, d_model = hidden_states.shape + # Flatten batch dim + output = torch.matmul(hidden_states.reshape([-1, d_model]), embedding_layer.weight.transpose(0, 1)) + output = output + self.bias + + return output.reshape([batch_size, seq_len, self.vocab_size]) + + +class PerceiverMultimodalPostprocessor(nn.Module): + """ + Multimodal postprocessing for Perceiver. Can be used to combine modality-specific postprocessors into a single + postprocessor. + + Args: + modalities (`Mapping[str, PostprocessorType]`): + Dictionary mapping modality name to postprocessor class for that modality. + input_is_dict (`bool`, *optional*, defaults to `False`): + If True, input is assumed to be dictionary structured, and outputs keep the same dictionary shape. If + False, input is a tensor which is sliced up during postprocessing by *modality_sizes*. + """ + + def __init__(self, modalities: Mapping[str, PostprocessorType], input_is_dict: bool = False): + super().__init__() + self.modalities = nn.ModuleDict(modalities) + self.input_is_dict = input_is_dict + + def forward( + self, inputs: torch.Tensor, pos: torch.Tensor | None = None, modality_sizes=None + ) -> Mapping[str, torch.Tensor]: + if not self.input_is_dict: + # Slice up modalities by their sizes. + if modality_sizes is None: + raise ValueError("Modality sizes should be specified if input is not a dictionary.") + inputs = restructure(modality_sizes=modality_sizes, inputs=inputs) + + outputs = { + modality: postprocessor(inputs[modality], pos=pos, modality_sizes=None) + for modality, postprocessor in self.modalities.items() + } + return outputs + + +class PerceiverClassificationPostprocessor(nn.Module): + """ + Classification postprocessing for Perceiver. Can be used to convert the decoder output to classification logits. + + Args: + config ([*PerceiverConfig*]): + Model configuration. + in_channels (`int`): + Number of channels in the input. + """ + + def __init__(self, config: PerceiverConfig, in_channels: int) -> None: + super().__init__() + self.classifier = nn.Linear(in_channels, config.num_labels) + + def forward(self, inputs, pos: torch.Tensor | None = None, modality_sizes=None) -> torch.Tensor: + logits = self.classifier(inputs) + return logits[:, 0, :] + + +class PerceiverAudioPostprocessor(nn.Module): + """ + Audio postprocessing for Perceiver. Can be used to convert the decoder output to audio features. + + Args: + config ([*PerceiverConfig*]): + Model configuration. + in_channels (`int`): + Number of channels in the input. + postproc_type (`str`, *optional*, defaults to `"patches"`): + Postprocessor type to use. Currently, only "patches" is supported. + """ + + def __init__(self, config: PerceiverConfig, in_channels: int, postproc_type: str = "patches") -> None: + super().__init__() + + if postproc_type != "patches": # to be supported: 'conv', 'patches', 'pixels' + raise ValueError("Invalid postproc_type!") + + # Architecture parameters: + self.classifier = nn.Linear(in_channels, config.samples_per_patch) + + def forward(self, inputs: torch.Tensor, pos: torch.Tensor | None = None, modality_sizes=None) -> torch.Tensor: + logits = self.classifier(inputs) + return torch.reshape(logits, [inputs.shape[0], -1]) + + +class PerceiverProjectionPostprocessor(nn.Module): + """ + Projection postprocessing for Perceiver. Can be used to project the channels of the decoder output to a lower + dimension. + + Args: + in_channels (`int`): + Number of channels in the input. + out_channels (`int`): + Number of channels in the output. + """ + + def __init__(self, in_channels: int, out_channels: int) -> None: + super().__init__() + self.classifier = nn.Linear(in_channels, out_channels) + + def forward(self, inputs: torch.Tensor, pos: torch.Tensor | None = None, modality_sizes=None) -> torch.Tensor: + logits = self.classifier(inputs) + return logits + + +class PerceiverImagePreprocessor(AbstractPreprocessor): + """ + Image preprocessing for Perceiver Encoder. + + Note: the *out_channels* argument refers to the output channels of a convolutional layer, if *prep_type* is set to + "conv1x1" or "conv". If one adds absolute position embeddings, one must make sure the *num_channels* of the + position encoding kwargs are set equal to the *out_channels*. + + Args: + config ([*PerceiverConfig*]): + Model configuration. + prep_type (`str`, *optional*, defaults to `"conv"`): + Preprocessing type. Can be "conv1x1", "conv", "patches", "pixels". + spatial_downsample (`int`, *optional*, defaults to 4): + Spatial downsampling factor. + temporal_downsample (`int`, *optional*, defaults to 1): + Temporal downsampling factor (only relevant in case a time dimension is present). + position_encoding_type (`str`, *optional*, defaults to `"fourier"`): + Position encoding type. Can be "fourier" or "trainable". + in_channels (`int`, *optional*, defaults to 3): + Number of channels in the input. + out_channels (`int`, *optional*, defaults to 64): + Number of channels in the output. + conv_after_patching (`bool`, *optional*, defaults to `False`): + Whether to apply a convolutional layer after patching. + conv_after_patching_in_channels (`int`, *optional*, defaults to 54): + Number of channels in the input of the convolutional layer after patching. + conv2d_use_batchnorm (`bool`, *optional*, defaults to `True`): + Whether to use batch normalization in the convolutional layer. + concat_or_add_pos (`str`, *optional*, defaults to `"concat"`): + How to concatenate the position encoding to the input. Can be "concat" or "add". + project_pos_dim (`int`, *optional*, defaults to -1): + Dimension of the position encoding to project to. If -1, no projection is applied. + **position_encoding_kwargs (`Dict`, *optional*): + Keyword arguments for the position encoding. + """ + + def __init__( + self, + config, + prep_type="conv", + spatial_downsample: int = 4, + temporal_downsample: int = 1, + position_encoding_type: str = "fourier", + in_channels: int = 3, + out_channels: int = 64, + conv_after_patching: bool = False, + conv_after_patching_in_channels: int = 54, # only relevant when conv_after_patching = True + conv2d_use_batchnorm: bool = True, + concat_or_add_pos: str = "concat", + project_pos_dim: int = -1, + **position_encoding_kwargs, + ): + super().__init__() + self.config = config + + if prep_type not in ("conv", "patches", "pixels", "conv1x1"): + raise ValueError(f"Prep_type {prep_type} is invalid") + + if concat_or_add_pos not in ["concat", "add"]: + raise ValueError(f"Invalid value {concat_or_add_pos} for concat_or_add_pos.") + + self.in_channels = in_channels + self.prep_type = prep_type + self.spatial_downsample = spatial_downsample + self.temporal_downsample = temporal_downsample + self.position_encoding_type = position_encoding_type + self.concat_or_add_pos = concat_or_add_pos + self.conv_after_patching = conv_after_patching + self.out_channels = out_channels + + if self.prep_type == "conv": + # Downsampling with conv is currently restricted + convnet_num_layers = math.log(spatial_downsample, 4) + convnet_num_layers_is_int = convnet_num_layers == np.round(convnet_num_layers) + if not convnet_num_layers_is_int or temporal_downsample != 1: + raise ValueError( + "Only powers of 4 expected for spatial and 1 expected for temporal downsampling with conv." + ) + self.convnet = Conv2DDownsample( + in_channels=in_channels, + num_layers=int(convnet_num_layers), + out_channels=out_channels, + use_batchnorm=conv2d_use_batchnorm, + ) + + elif self.prep_type == "conv1x1": + if temporal_downsample != 1: + raise ValueError("Conv1x1 does not downsample in time.") + self.convnet_1x1 = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=(1, 1), + # spatial_downsample is unconstrained for 1x1 convolutions. + stride=(spatial_downsample, spatial_downsample), + ) + + # Position embeddings + self.project_pos_dim = project_pos_dim + self.position_embeddings, self.positions_projection = build_position_encoding( + position_encoding_type=position_encoding_type, + out_channels=out_channels, + project_pos_dim=project_pos_dim, + **position_encoding_kwargs, + ) + + # Optional convolutional layer after patches. + self.conv_after_patches = ( + nn.Linear(conv_after_patching_in_channels, self.out_channels) if conv_after_patching else nn.Identity() + ) + + @property + def num_channels(self) -> int: + # Let's assume that the number of resolutions (in the context of image preprocessing) + # of the input data is 2 or 3 depending on whether we are processing image or video respectively. + # In this case, for convenience, we will declare is_temporal variable, + # which will show whether the data has a temporal dimension or not. + is_temporal = self.position_embeddings.num_dimensions > 2 + + # position embedding + if self.project_pos_dim > 0: + pos_dim = self.project_pos_dim + else: + pos_dim = self.position_embeddings.output_size() + if self.concat_or_add_pos == "add": + return pos_dim + + # inputs + if self.conv_after_patching or self.prep_type in ("conv1x1", "conv"): + inp_dim = self.out_channels + elif self.prep_type == "pixels": + inp_dim = self.in_channels + if not is_temporal: + inp_dim = math.ceil(inp_dim / self.spatial_downsample) + elif self.prep_type == "patches": + if self.conv_after_patching: + inp_dim = self.out_channels + else: + inp_dim = self.in_channels * self.spatial_downsample**2 + if is_temporal: + inp_dim *= self.temporal_downsample + + return inp_dim + pos_dim + + def _build_network_inputs( + self, inputs: torch.Tensor, network_input_is_1d: bool = True, interpolate_pos_encoding: bool = False + ): + """ + Construct the final input, including position encoding. + + This method expects the inputs to always have channels as last dimension. + + """ + batch_size = inputs.shape[0] + input_size = inputs.shape[1:3] + index_dims = inputs.shape[1:-1] + indices = np.prod(index_dims) + + # Flatten input features to a 1D index dimension if necessary. + if len(inputs.shape) > 3 and network_input_is_1d: + inputs = torch.reshape(inputs, [batch_size, indices, -1]) + + # Construct the position encoding. + if self.position_encoding_type == "trainable": + pos_enc = self.position_embeddings(batch_size, interpolate_pos_encoding, input_size) + elif self.position_encoding_type == "fourier": + pos_enc = self.position_embeddings(index_dims, batch_size, device=inputs.device, dtype=inputs.dtype) + + # Optionally project them to a target dimension. + pos_enc = self.positions_projection(pos_enc) + + if not network_input_is_1d: + # Reshape pos to match the input feature shape + # if the network takes non-1D inputs + sh = inputs.shape + pos_enc = torch.reshape(pos_enc, list(sh)[:-1] + [-1]) + if self.concat_or_add_pos == "concat": + inputs_with_pos = torch.cat([inputs, pos_enc], dim=-1) + elif self.concat_or_add_pos == "add": + inputs_with_pos = inputs + pos_enc + return inputs_with_pos, inputs + + def forward( + self, + inputs: torch.Tensor, + pos: torch.Tensor | None = None, + network_input_is_1d: bool = True, + interpolate_pos_encoding: bool = False, + ): + if self.prep_type == "conv": + # Convnet image featurization. + # Downsamples spatially by a factor of 4 + inputs = self.convnet(inputs) + + elif self.prep_type == "conv1x1": + # map inputs to self.out_channels + inputs = self.convnet_1x1(inputs) + + elif self.prep_type == "pixels": + # if requested, downsamples in the crudest way + if inputs.ndim == 4: + inputs = inputs[:: self.spatial_downsample, :: self.spatial_downsample] + elif inputs.ndim == 5: + inputs = inputs[ + :, :: self.temporal_downsample, :, :: self.spatial_downsample, :: self.spatial_downsample + ] + else: + raise ValueError("Unsupported data format for pixels.") + + elif self.prep_type == "patches": + # Space2depth featurization. + # Video: B x T x C x H x W + inputs = space_to_depth( + inputs, temporal_block_size=self.temporal_downsample, spatial_block_size=self.spatial_downsample + ) + + if inputs.ndim == 5 and inputs.shape[1] == 1: + # for flow + inputs = inputs.squeeze(dim=1) + + # Optionally apply conv layer. + inputs = self.conv_after_patches(inputs) + + if self.prep_type != "patches": + # move channels to last dimension, as the _build_network_inputs method below expects this + if inputs.ndim == 4: + inputs = inputs.permute(0, 2, 3, 1) + elif inputs.ndim == 5: + inputs = inputs.permute(0, 1, 3, 4, 2) + else: + raise ValueError("Unsupported data format for conv1x1.") + + inputs, inputs_without_pos = self._build_network_inputs(inputs, network_input_is_1d, interpolate_pos_encoding) + modality_sizes = None # Size for each modality, only needed for multimodal + + return inputs, modality_sizes, inputs_without_pos + + +class PerceiverOneHotPreprocessor(AbstractPreprocessor): + """ + One-hot preprocessor for Perceiver Encoder. Can be used to add a dummy index dimension to the input. + + Args: + config ([`PerceiverConfig`]): + Model configuration. + """ + + def __init__(self, config: PerceiverConfig) -> None: + super().__init__() + self.config: PerceiverConfig = config + + @property + def num_channels(self) -> int: + return self.config.num_labels + + def forward(self, inputs: torch.Tensor, pos: torch.Tensor | None = None, network_input_is_1d: bool = True): + # Add a dummy index dimension. + inputs = inputs[:, None, :] + + # No position encodings, so the 1st (input) and 3rd (inputs_without_pos) + # outputs are identical. + return inputs, None, inputs + + +class PerceiverAudioPreprocessor(AbstractPreprocessor): + """ + Audio preprocessing for Perceiver Encoder. + + Args: + config ([*PerceiverConfig*]): + Model configuration. + prep_type (`str`, *optional*, defaults to `"patches"`): + Preprocessor type to use. Only "patches" is supported. + samples_per_patch (`int`, *optional*, defaults to 96): + Number of samples per patch. + position_encoding_type (`str`, *optional*, defaults to `"fourier"`): + Type of position encoding to use. Can be "trainable" or "fourier". + concat_or_add_pos (`str`, *optional*, defaults to `"concat"`): + How to concatenate the position encoding to the input. Can be "concat" or "add". + out_channels (`int`, *optional*, defaults to 64): + Number of channels in the output. + project_pos_dim (`int`, *optional*, defaults to -1): + Dimension of the position encoding to project to. If -1, no projection is applied. + **position_encoding_kwargs (`Dict`, *optional*): + Keyword arguments for the position encoding. + """ + + def __init__( + self, + config, + prep_type: str = "patches", + samples_per_patch: int = 96, + position_encoding_type: str = "fourier", + concat_or_add_pos: str = "concat", + out_channels=64, + project_pos_dim=-1, + **position_encoding_kwargs, + ): + super().__init__() + self.config = config + + if prep_type != "patches": + raise ValueError(f"Prep_type {prep_type} is invalid, can only be 'patches'.") + + if concat_or_add_pos not in ["concat", "add"]: + raise ValueError(f"Concat_or_pos {concat_or_add_pos} is invalid, can only be 'concat' or 'add'.") + + self.samples_per_patch = samples_per_patch + self.position_encoding_type = position_encoding_type + self.concat_or_add_pos = concat_or_add_pos + self.project_pos_dim = project_pos_dim + + # Position embeddings + self.position_embeddings, self.positions_projection = build_position_encoding( + position_encoding_type=position_encoding_type, + out_channels=out_channels, + project_pos_dim=project_pos_dim, + **position_encoding_kwargs, + ) + + @property + def num_channels(self) -> int: + # position embedding + if self.project_pos_dim > 0: + pos_dim = self.project_pos_dim + else: + pos_dim = self.position_embeddings.output_size() + if self.concat_or_add_pos == "add": + return pos_dim + return self.samples_per_patch + pos_dim + + def _build_network_inputs(self, inputs): + """Construct the final input, including position encoding.""" + batch_size = inputs.shape[0] + index_dims = inputs.shape[1:-1] + + # Construct the position encoding. + if self.position_encoding_type == "trainable": + pos_enc = self.position_embeddings(batch_size) + elif self.position_encoding_type == "fourier": + pos_enc = self.position_embeddings(index_dims, batch_size, device=inputs.device, dtype=inputs.dtype) + + # Optionally project them to a target dimension. + pos_enc = self.positions_projection(pos_enc) + + if self.concat_or_add_pos == "concat": + inputs_with_pos = torch.cat([inputs, pos_enc], dim=-1) + elif self.concat_or_add_pos == "add": + inputs_with_pos = inputs + pos_enc + + return inputs_with_pos, inputs + + def forward( + self, + inputs: torch.Tensor, + pos: torch.Tensor | None = None, + network_input_is_1d: bool = True, + interpolate_pos_encoding: bool = False, + ): + inputs = torch.reshape(inputs, [inputs.shape[0], -1, self.samples_per_patch]) + + inputs, inputs_without_pos = self._build_network_inputs(inputs) + modality_sizes = None # Size for each modality, only needed for multimodal + + return inputs, modality_sizes, inputs_without_pos + + +class PerceiverMultimodalPreprocessor(AbstractPreprocessor): + """ + Multimodal preprocessing for Perceiver Encoder. + + Inputs for each modality are preprocessed, then padded with trainable position embeddings to have the same number + of channels. + + Args: + modalities (`Mapping[str, PreprocessorType]`): + Dict mapping modality name to preprocessor. + mask_probs (`dict[str, float]`): + Dict mapping modality name to masking probability of that modality. + min_padding_size (`int`, *optional*, defaults to 2): + The minimum padding size for all modalities. The final output will have num_channels equal to the maximum + channels across all modalities plus min_padding_size. + """ + + def __init__( + self, + modalities: Mapping[str, PreprocessorType], + mask_probs: Mapping[str, float] | None = None, + min_padding_size: int = 2, + ): + super().__init__() + self.modalities = nn.ModuleDict(modalities) + self.min_padding_size = min_padding_size + self.mask_probs = mask_probs if mask_probs is not None else {} + self.padding = nn.ParameterDict( + { + modality: nn.Parameter(torch.randn(1, self.num_channels - preprocessor.num_channels)) + for modality, preprocessor in modalities.items() + } + ) + self.mask = nn.ParameterDict( + {modality: nn.Parameter(torch.randn(1, self.num_channels)) for modality, _ in self.mask_probs.items()} + ) + + @property + def num_channels(self) -> int: + max_channel_size = max(processor.num_channels for _, processor in self.modalities.items()) + common_channel_size = max_channel_size + self.min_padding_size + return common_channel_size + + def forward( + self, + inputs: Mapping[str, torch.Tensor], + pos: torch.Tensor | None = None, + network_input_is_1d: bool = True, + interpolate_pos_encoding: bool = False, + ) -> PreprocessorOutputType: + padded = {} + modality_sizes = {} + inputs_without_pos = {} + for modality, preprocessor in self.modalities.items(): + # preprocess each modality using the respective preprocessor. + output, _, inputs_without_pos[modality] = preprocessor( + inputs[modality], pos=pos, network_input_is_1d=network_input_is_1d + ) + + # pad to the same common_channel_size. + batch_size, num_samples, num_channels = output.shape + pos_enc = self.padding[modality].expand(batch_size, -1, -1) + + padding = torch.broadcast_to( + pos_enc, + [batch_size, num_samples, self.num_channels - num_channels], + ) + output_padded = torch.cat([output, padding], dim=2) + + # mask if required + if modality in self.mask_probs: + mask_token = self.mask[modality].expand(batch_size, -1, -1) + mask_prob = self.mask_probs[modality] + mask = torch.bernoulli(torch.full([batch_size, num_samples], mask_prob)) + mask = torch.unsqueeze(mask, dim=2).to(mask_token.device) + output_padded = (1 - mask) * output_padded + mask * mask_token + + padded[modality] = output_padded + modality_sizes[modality] = output_padded.shape[1] + + # Apply a predictable ordering to the modalities + padded_ls = [padded[k] for k in sorted(padded.keys())] + + # Finally, concatenate along the time dimension + final_inputs = torch.cat(padded_ls, dim=1) + + return final_inputs, modality_sizes, inputs_without_pos + + +__all__ = [ + "PerceiverForImageClassificationConvProcessing", + "PerceiverForImageClassificationFourier", + "PerceiverForImageClassificationLearned", + "PerceiverForMaskedLM", + "PerceiverForMultimodalAutoencoding", + "PerceiverForOpticalFlow", + "PerceiverForSequenceClassification", + "PerceiverLayer", + "PerceiverModel", + "PerceiverPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/tokenization_perceiver.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/tokenization_perceiver.py new file mode 100644 index 0000000000000000000000000000000000000000..6abaf31aed11e601ef288a1d12d2bbedab36abb1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perceiver/tokenization_perceiver.py @@ -0,0 +1,197 @@ +# Copyright 2021 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for Perceiver.""" + +from ...tokenization_python import AddedToken, PreTrainedTokenizer +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +class PerceiverTokenizer(PreTrainedTokenizer): + """ + Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + pad_token (`str`, *optional*, defaults to `"[PAD]"`): + The token used for padding, for example when batching sequences of different lengths. + bos_token (`str`, *optional*, defaults to `"[BOS]"`): + The BOS token (reserved in the vocab, but not actually used). + eos_token (`str`, *optional*, defaults to `"[EOS]"`): + The end of sequence token (reserved in the vocab, but not actually used). + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + mask_token (`str`, *optional*, defaults to `"[MASK]"`): + The MASK token, useful for masked language modeling. + cls_token (`str`, *optional*, defaults to `"[CLS]"`): + The CLS token (reserved in the vocab, but not actually used). + sep_token (`str`, *optional*, defaults to `"[SEP]"`): + The separator token, which is used when building a sequence from two sequences. + + """ + + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + pad_token="[PAD]", + bos_token="[BOS]", + eos_token="[EOS]", + mask_token="[MASK]", + cls_token="[CLS]", + sep_token="[SEP]", + model_max_length=2048, + **kwargs, + ) -> None: + pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token + bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token + eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token + mask_token = AddedToken(mask_token, lstrip=False, rstrip=False) if isinstance(mask_token, str) else mask_token + cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token + sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token + + self._utf_vocab_size = 2**8 # utf is 8 bits + + # Since these tokens are not part of the vocabulary, we manually add them + self._added_tokens_decoder: dict[str, int] = { + 0: pad_token, + 1: bos_token, + 2: eos_token, + 3: mask_token, + 4: cls_token, + 5: sep_token, + } + self._num_special_tokens = len(self._added_tokens_decoder) + super().__init__( + pad_token=pad_token, + bos_token=bos_token, + eos_token=eos_token, + mask_token=mask_token, + cls_token=cls_token, + sep_token=sep_token, + model_max_length=model_max_length, + **kwargs, + ) + + def get_vocab(self) -> dict[str, int]: + vocab = {} + for i in range(self._utf_vocab_size): + token = chr(i) + vocab[token] = i + self._num_special_tokens + vocab.update(self.added_tokens_encoder) + return vocab + + @property + def vocab_size(self): + return self._utf_vocab_size + + def get_special_tokens_mask( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None, already_has_special_tokens: bool = False + ) -> list[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + # normal case: some special tokens + if token_ids_1 is None: + return [1] + [0] * len(token_ids_0) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + + def build_inputs_with_special_tokens( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks. A sequence has the + following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`list[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + if token_ids_1 is None: + return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + else: + return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.sep_token_id] + + def _tokenize(self, text: str) -> list[str]: + """Take as input a string and return a list of strings (tokens) for words/sub-words""" + tokens = [chr(i) for i in text.encode("utf-8")] + return tokens + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + if len(token) != 1: + token_id = self.unk_token_id + else: + token_id = ord(token) + self._num_special_tokens + return token_id + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = chr(index - self._num_special_tokens) + return token + + # TODO @ArthurZ refactor this as well.... + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + bstring = b"" + for token in tokens: + if token in self.added_tokens_encoder: + tok_string = str(token).encode("utf-8") + else: + tok_string = bytes([ord(token)]) + bstring += tok_string + string = bstring.decode("utf-8", errors="replace") + return string + + # PerceiverTokenizer has no vocab file + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + return () + + +__all__ = ["PerceiverTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4e1484d4080e2d3bf25c79cbfa46a0837b5dd59d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_perception_lm import * + from .image_processing_perception_lm import * + from .modeling_perception_lm import * + from .processing_perception_lm import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/configuration_perception_lm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/configuration_perception_lm.py new file mode 100644 index 0000000000000000000000000000000000000000..15d98015be78706f01f2bacc82220d413f25f99d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/configuration_perception_lm.py @@ -0,0 +1,64 @@ +# Copyright 2025 Meta Platforms, Inc. and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PerceptionLM model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig +from ..timm_wrapper.configuration_timm_wrapper import TimmWrapperConfig + + +@auto_docstring(checkpoint="facebook/Perception-LM-1B") +@strict +class PerceptionLMConfig(PreTrainedConfig): + r""" + vision_use_cls_token (`bool`, *optional*, defaults to `True`): + Whether CLS token is used in the vision backbone. If used, we remove CLS token embedding from vision output. + projector_pooling_ratio (`int`, *optional*, defaults to 1): + The pooling ratio used in the multimodal projector. + """ + + model_type = "perception_lm" + sub_configs = {"text_config": AutoConfig, "vision_config": TimmWrapperConfig} + + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + vision_use_cls_token: bool = True + projector_pooling_ratio: int = 1 + image_token_id: int = 128002 + video_token_id: int = 128003 + tie_word_embeddings: bool | None = None + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = TimmWrapperConfig(**self.vision_config) + elif isinstance(self.vision_config, TimmWrapperConfig): + pass + elif self.vision_config is None: + self.vision_config = TimmWrapperConfig() + + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "llama") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["llama"]() + + if self.tie_word_embeddings is None: + self.tie_word_embeddings = getattr(self.text_config, "tie_word_embeddings", False) + + super().__post_init__(**kwargs) + + +__all__ = ["PerceptionLMConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/image_processing_perception_lm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/image_processing_perception_lm.py new file mode 100644 index 0000000000000000000000000000000000000000..8de148bba50b16c5bf871fa4035c785e9db897ab --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/image_processing_perception_lm.py @@ -0,0 +1,308 @@ +# Copyright 2025 Meta Platforms, Inc. and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for PerceptionLM.""" + +import math +from functools import reduce + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +class PerceptionLMImageProcessorKwargs(ImagesKwargs, total=False): + r""" + vision_input_type (`str`, *optional*, defaults to `"thumb+tile"`): + Vision processing strategy. `"thumb+tile"` uses both thumbnails and multiple tiles for + multi-scale processing, otherwise uses single tile for lower memory usage. + tile_size (`int`, *optional*, defaults to `448`): + Height and width dimension (in pixels) of each tile used for image processing. + max_num_tiles (`int`, *optional*, defaults to `36`): + Maximum number of tiles an image can be split into based on its aspect ratio. + """ + + vision_input_type: str | None + tile_size: int + max_num_tiles: int + + +@auto_docstring +class PerceptionLMImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + do_resize = True + do_center_crop = False + do_rescale = True + do_normalize = True + do_convert_rgb = True + vision_input_type = "thumb+tile" + tile_size = 448 + max_num_tiles = 36 + size = {"width": 448, "height": 448} # for backward compatibility in tests + valid_kwargs = PerceptionLMImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[PerceptionLMImageProcessorKwargs]) -> None: + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[PerceptionLMImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + @staticmethod + def _factors(n: int): + """Return all factors of a number.""" + return set( + reduce( + list.__add__, + ([i, n // i] for i in range(1, int(n**0.5) + 1) if n % i == 0), + ) + ) + + def _find_supported_aspect_ratios(self): + """ + This function computes all the allowed aspect ratios for a fixed + number of input chunks. The order of returned items matters for the result of `_fit_image_to_canvas` function. + If tie exists in `_fit_image_to_canvas`, the latter in `_find_supported_aspect_ratios` wins. + + For example, with `num_tiles=5`, it will return: + { + 0.2: [(1, 5)], + 5.0: [(5, 1)], + 0.25: [(1, 4)], + 1.0: [(2, 2), (1, 1)], + 4.0: [(4, 1)], + 0.3333333333333333: [(1, 3)], + 3.0: [(3, 1)], + 0.5: [(1, 2)], + 2.0: [(2, 1)] + } + """ + asp_dict = {} + for chunk_size in range(self.max_num_tiles, 0, -1): + _factors = sorted(self._factors(chunk_size)) + _asp_ratios = [(x, chunk_size // x) for x in _factors] + for ratio in _asp_ratios: + k = ratio[0] / ratio[1] + if k not in asp_dict: + asp_dict[k] = [ratio] + else: + asp_dict[k].append(ratio) + return asp_dict + + def _get_image_height_width( + self, image_width: int, image_height: int, target_width: int, target_height: int + ) -> tuple[int, int]: + """ + Given image width, height and target width, height for the canvas, return the dimensions of how the image would be resized + with aspect ratio preservation. + """ + scale = image_width / image_height + + if scale > 1.0: + # Width is larger than height + + # Rescaling factor is the minimum of the two scaling factors. Else one side would be outside of the canvas. + rescaling_factor = min(target_width / image_width, target_height / image_height) + + # Set new width to target width and height to the rescaled height. + new_w = rescaling_factor * image_width + new_h = math.floor(new_w / scale) + + else: + # Height is larger than width + + # Rescaling factor is the minimum of the two scaling factors. Else one side would be outside of the canvas. + rescaling_factor = min(target_width / image_width, target_height / image_height) + + # Set new height to target height and width to the rescaled width. + new_h = rescaling_factor * image_height + new_w = math.floor(new_h * scale) + + return new_w, new_h + + def _fit_image_to_canvas(self, img_width: int, img_height: int, tile_size: int): + """ + Given an image width, height and target number of chunks this function will see if the image + can be fit into any of the canvases that can be build from arranging the tiles in a grid. + If the image can be fit onto several canvases, it will return the canvas where the shorter edge + of the image will be largest. + """ + # Initialize the optimal canvas to None. If no canvas is found where image fits, function returns None. + optimal_canvas = None + optimal_image_width_height = None + + scale = img_width / img_height + + # Gather all potential supported image resolutions and iterate through them to find best match + potential_arrangements = [ + item for sublist in self._find_supported_aspect_ratios().values() for item in sublist + ] + for n_w, n_h in potential_arrangements: + # Compute the canvas size + canvas_width, canvas_height = n_w * tile_size, n_h * tile_size + + # Check if image can fit into the canvas without downsampling + if canvas_width >= img_width and canvas_height >= img_height: + # If we did not find a good canvas yet, we will use the current one + if optimal_canvas is None: + # Set optimal canvas and determine the actual image height and width in the canvas with aspect ratio preserving resampling + optimal_canvas = (n_w, n_h) + optimal_image_width_height = self._get_image_height_width( + image_width=img_width, + image_height=img_height, + target_width=n_w * tile_size, + target_height=n_h * tile_size, + ) + else: + # If we already found an optimal canvas before, we will check if the shorter edge of the image will be larger than the current optimal canvas. + # This means we can potentially upsample the image resolution which is beneficial to performance. + image_width_height = self._get_image_height_width( + image_width=img_width, + image_height=img_height, + target_width=n_w * tile_size, + target_height=n_h * tile_size, + ) + # Llama3V dynamic tiling. Prioritize biggest canvas. + if (scale < 1.0 and (image_width_height[0] >= optimal_image_width_height[0])) or ( + scale >= 1.0 and (image_width_height[1] >= optimal_image_width_height[1]) + ): + optimal_canvas = (n_w, n_h) + optimal_image_width_height = image_width_height + return optimal_canvas + + def _find_closest_aspect_ratio(self, img_width: int, img_height: int, tile_size: int) -> tuple: + """ + Given an image width, height and target number of chunks + this function will find the closest supported aspect ratio. + """ + target_aspect_ratio = img_width / img_height + asp_dict = self._find_supported_aspect_ratios() + closest_aspect_ratio = None + if target_aspect_ratio >= 1: + closest_aspect_ratio = min( + [k for k in asp_dict if k <= target_aspect_ratio], + key=lambda x: abs(x - target_aspect_ratio), + ) + tiles_given_aspect_ratio = asp_dict[closest_aspect_ratio] + # select largest width + return max(tiles_given_aspect_ratio, key=lambda x: x[0]) + else: + closest_aspect_ratio = min( + [k for k in asp_dict if k > target_aspect_ratio], + key=lambda x: abs(1 / x - 1 / target_aspect_ratio), + ) + tiles_given_aspect_ratio = asp_dict[closest_aspect_ratio] + # select largest height + return max(tiles_given_aspect_ratio, key=lambda x: x[1]) + + def _split(self, image: torch.Tensor, ncw: int, nch: int) -> torch.Tensor: + # Split image into number of required tiles (width x height) + batch_size, num_channels, height, width = image.size() + image = image.view(batch_size, num_channels, nch, height // nch, ncw, width // ncw) + # Permute dimensions to reorder the axes + image = image.permute(0, 2, 4, 1, 3, 5).contiguous() + # Reshape into the desired output shape (batch_size * 4, num_channels, width/2, height/2) + image = image.view(batch_size, ncw * nch, num_channels, height // nch, width // ncw) + return image + + def resize( + self, + image: "torch.Tensor", + tile_size: int, + max_num_tiles: int, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + **kwargs, + ) -> tuple["torch.Tensor", tuple[int, int]]: + """ + Custom resize method for PerceptionLM that handles tiling logic. + """ + height, width = image.shape[-2:] + if max_num_tiles > 1: + aspect_ratio = self._fit_image_to_canvas(img_width=width, img_height=height, tile_size=tile_size) + if aspect_ratio is None: + # If we did not find a canvas, we have to find the closest aspect ratio and downsample the image + aspect_ratio = self._find_closest_aspect_ratio(img_width=width, img_height=height, tile_size=tile_size) + else: + aspect_ratio = (1, 1) + new_width, new_height = aspect_ratio[0] * tile_size, aspect_ratio[1] * tile_size + + image = super().resize(image, SizeDict(height=new_height, width=new_width), resample=resample) + return image, aspect_ratio + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + vision_input_type: str, + tile_size: int, + max_num_tiles: int, + return_tensors: str | TensorType | None, + disable_grouping: bool | None, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched transformation + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + if vision_input_type == "thumb+tile": + thumbnails, _ = self.resize(stacked_images, tile_size, max_num_tiles=1, resample=resample) + images_for_tiling, (tiles_w, tiles_h) = self.resize( + stacked_images, tile_size, max_num_tiles=max_num_tiles, resample=resample + ) + image_tiles = self._split(images_for_tiling, tiles_w, tiles_h) + stacked_images = torch.cat([thumbnails.unsqueeze(1), image_tiles], dim=1) + else: # vanilla single tile for low memory devices + stacked_images, _ = self.resize(stacked_images, tile_size, max_num_tiles=1, resample=resample) + + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, + do_rescale, + rescale_factor, + do_normalize, + image_mean, + image_std, + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + processed_images = [p[None] if p.ndim == 3 else p for p in processed_images] # add tiles dimension if needed + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["PerceptionLMImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/modeling_perception_lm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/modeling_perception_lm.py new file mode 100644 index 0000000000000000000000000000000000000000..d7a92decc14f31abb825411dd79e09dfc3d86ee6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/modeling_perception_lm.py @@ -0,0 +1,456 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/perception_lm/modular_perception_lm.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_perception_lm.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Meta Platforms, Inc. and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass + +import torch +import torch.nn.functional as F +from torch import nn + +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check +from ..auto import AutoModel +from .configuration_perception_lm import PerceptionLMConfig + + +class PerceptionLMAdaptiveAvgPooling(nn.Module): + def __init__(self, pooling_ratio=2): + super().__init__() + self.pooling_ratio = pooling_ratio + + def forward(self, hidden_states): + b, num_tokens, c = hidden_states.shape + h = int(math.sqrt(num_tokens)) + if h * h != num_tokens: + raise ValueError(f"num_tokens {num_tokens} is expected to be a square number") + + shape = (h // self.pooling_ratio, h // self.pooling_ratio) + hidden_states = hidden_states.permute(0, 2, 1).reshape(b, -1, h, h) + hidden_states = F.adaptive_avg_pool2d(hidden_states, shape) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + + return hidden_states + + +class PerceptionLMMultiModalProjector(nn.Module): + def __init__(self, config: PerceptionLMConfig): + super().__init__() + input_size = config.vision_config.model_args["embed_dim"] + output_size = config.text_config.hidden_size + self.linear_1 = nn.Linear( + in_features=input_size, + out_features=output_size, + bias=True, + ) + self.gelu = nn.GELU() + self.linear_2 = nn.Linear( + in_features=output_size, + out_features=output_size, + bias=True, + ) + self.pooling = ( + PerceptionLMAdaptiveAvgPooling(config.projector_pooling_ratio) + if config.projector_pooling_ratio > 1 + else nn.Identity() + ) + + def forward(self, features): + features = features.permute(1, 0, 2) # NLD -> LND + features = self.linear_1(features) + features = self.gelu(features) + features = self.linear_2(features) + features = features.permute(1, 0, 2) # LND -> NLD + features = self.pooling(features) + return features + + +@auto_docstring +class PerceptionLMPreTrainedModel(PreTrainedModel): + config: PerceptionLMConfig + base_model_prefix = "model" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + _skip_keys_device_placement = "past_key_values" + + _supports_flash_attn = True + _supports_sdpa = True + + _can_compile_fullgraph = True + _supports_flex_attn = True + _supports_attention_backend = True + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for PerceptionLM outputs, with hidden states and attentions. + """ +) +class PerceptionLMModelOutputWithPast(BaseModelOutputWithPast): + r""" + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + Image hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + video_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_videos, sequence_length, hidden_size)`. + Video hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + image_hidden_states: torch.FloatTensor | None = None + + video_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for PerceptionLM causal language model (or autoregressive) outputs. + """ +) +@dataclass +class PerceptionLMCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + Image hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + video_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_videos, sequence_length, hidden_size)`. + Video hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + image_hidden_states: torch.FloatTensor | None = None + + video_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring +class PerceptionLMModel(PerceptionLMPreTrainedModel): + def __init__(self, config: PerceptionLMConfig): + super().__init__(config) + self.vision_tower = AutoModel.from_config(config.vision_config) + self.multi_modal_projector = PerceptionLMMultiModalProjector(config) + self.language_model = AutoModel.from_config(config.text_config) + self.post_init() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + @can_return_tuple + @auto_docstring( + custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection." + ) + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + image_outputs = self.vision_tower(pixel_values.flatten(0, 1), return_dict=True, **kwargs) + last_hidden_state = image_outputs.last_hidden_state + if self.config.vision_use_cls_token: + last_hidden_state = last_hidden_state[:, 1:, :] + image_features = self.multi_modal_projector(last_hidden_state) + image_outputs.pooler_output = image_features + + return image_outputs + + def get_placeholder_mask( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.FloatTensor, + image_features: torch.FloatTensor | None = None, + video_features: torch.FloatTensor | None = None, + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + special_video_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_video_mask = special_video_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + special_video_mask = input_ids == self.config.video_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if image_features is not None: + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.size()[:-1].numel()}", + ) + + n_video_tokens = special_video_mask.sum() + special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if video_features is not None: + torch_compilable_check( + inputs_embeds[special_video_mask].numel() == video_features.numel(), + f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.size()[:-1].numel()}", + ) + return special_image_mask, special_video_mask + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **lm_kwargs, + ) -> tuple | PerceptionLMModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None: + raise ValueError( + "You cannot specify both (pixel_values or pixel_values_videos) and inputs_embeds at the same time, and must specify either one" + ) + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + image_features = None + if pixel_values is not None: + image_features = self.get_image_features(pixel_values=pixel_values, return_dict=True).pooler_output + image_features = image_features.to(inputs_embeds.device, dtype=inputs_embeds.dtype) + special_image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + video_features = None + if pixel_values_videos is not None: + video_features = self.get_image_features(pixel_values=pixel_values_videos, return_dict=True).pooler_output + video_features = video_features.to(inputs_embeds.device, dtype=inputs_embeds.dtype) + _, special_video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features) + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + return_dict=True, + logits_to_keep=logits_to_keep, + **lm_kwargs, + ) + return PerceptionLMModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + hidden_states=outputs.hidden_states, + past_key_values=outputs.past_key_values, + attentions=outputs.attentions, + image_hidden_states=image_features if pixel_values is not None else None, + video_hidden_states=(video_features if pixel_values_videos is not None else None), + ) + + +@auto_docstring +class PerceptionLMForConditionalGeneration(PerceptionLMPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"} + + def __init__(self, config: PerceptionLMConfig): + super().__init__(config) + self.model = PerceptionLMModel(config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + def get_output_embeddings(self) -> nn.Module: + return self.lm_head + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **lm_kwargs, + ) -> tuple | PerceptionLMCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + from transformers import AutoProcessor, AutoModelForImageTextToText + from huggingface_hub import hf_hub_download + + MODEL_PATH = "facebook/Perception-LM-1B" + processor = AutoProcessor.from_pretrained(MODEL_PATH, use_fast=True) + model = AutoModelForImageTextToText.from_pretrained(MODEL_PATH).to("cuda") + test_image_file = hf_hub_download( + repo_id="shumingh/perception_lm_test_images", + filename="14496_0.PNG", + repo_type="dataset", + ) + conversation = [ + { + "role": "user", + "content": [ + { + "type": "image", + "url": test_image_file, + }, + {"type": "text", "text": "Describe the bar plot in the image."}, + ], + } + ] + + inputs = processor.apply_chat_template( + [conversation], + add_generation_prompt=True, + tokenize=True, + return_dict=True, + return_tensors="pt", + ) + inputs = inputs.to(model.device) + generate_ids = model.generate(**inputs, max_new_tokens=256) + input_length = inputs["input_ids"].shape[1] + generate_ids_without_inputs = generate_ids[:, input_length:] + + for output in processor.batch_decode(generate_ids_without_inputs, skip_special_tokens=True): + print(output) + ```""" + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **lm_kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.text_config.vocab_size, + **lm_kwargs, + ) + + return PerceptionLMCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + video_hidden_states=outputs.video_hidden_states, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + inputs_embeds=None, + pixel_values=None, + pixel_values_videos=None, + attention_mask=None, + logits_to_keep=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + logits_to_keep=logits_to_keep, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + if is_first_iteration or not kwargs.get("use_cache", True): + # Pixel values are used only in the first iteration if available + # In subsequent iterations, they are already merged with text and cached + # NOTE: first iteration doesn't have to be prefill, it can be the first + # iteration with a question and cached system prompt (continue generate from cache) + model_inputs["pixel_values"] = pixel_values + model_inputs["pixel_values_videos"] = pixel_values_videos + return model_inputs + + +__all__ = ["PerceptionLMForConditionalGeneration", "PerceptionLMPreTrainedModel", "PerceptionLMModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/modular_perception_lm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/modular_perception_lm.py new file mode 100644 index 0000000000000000000000000000000000000000..4c09a6d22a7868f195adb619994e4001ca5a8bef --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/modular_perception_lm.py @@ -0,0 +1,414 @@ +# Copyright 2025 Meta Platforms, Inc. and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PerceptionLM model.""" + +import math + +import torch +import torch.nn.functional as F +from torch import nn + +from ...cache_utils import Cache +from ...modeling_outputs import BaseModelOutputWithPooling +from ...processing_utils import Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + logging, + torch_compilable_check, +) +from ..auto import AutoModel +from ..llava.modeling_llava import ( + LlavaCausalLMOutputWithPast, + LlavaForConditionalGeneration, + LlavaModel, + LlavaModelOutputWithPast, + LlavaPreTrainedModel, +) +from .configuration_perception_lm import PerceptionLMConfig + + +logger = logging.get_logger(__name__) + + +class PerceptionLMAdaptiveAvgPooling(nn.Module): + def __init__(self, pooling_ratio=2): + super().__init__() + self.pooling_ratio = pooling_ratio + + def forward(self, hidden_states): + b, num_tokens, c = hidden_states.shape + h = int(math.sqrt(num_tokens)) + if h * h != num_tokens: + raise ValueError(f"num_tokens {num_tokens} is expected to be a square number") + + shape = (h // self.pooling_ratio, h // self.pooling_ratio) + hidden_states = hidden_states.permute(0, 2, 1).reshape(b, -1, h, h) + hidden_states = F.adaptive_avg_pool2d(hidden_states, shape) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + + return hidden_states + + +class PerceptionLMMultiModalProjector(nn.Module): + def __init__(self, config: PerceptionLMConfig): + super().__init__() + input_size = config.vision_config.model_args["embed_dim"] + output_size = config.text_config.hidden_size + self.linear_1 = nn.Linear( + in_features=input_size, + out_features=output_size, + bias=True, + ) + self.gelu = nn.GELU() + self.linear_2 = nn.Linear( + in_features=output_size, + out_features=output_size, + bias=True, + ) + self.pooling = ( + PerceptionLMAdaptiveAvgPooling(config.projector_pooling_ratio) + if config.projector_pooling_ratio > 1 + else nn.Identity() + ) + + def forward(self, features): + features = features.permute(1, 0, 2) # NLD -> LND + features = self.linear_1(features) + features = self.gelu(features) + features = self.linear_2(features) + features = features.permute(1, 0, 2) # LND -> NLD + features = self.pooling(features) + return features + + +class PerceptionLMPreTrainedModel(LlavaPreTrainedModel): + base_model_prefix = "model" + + +class PerceptionLMModelOutputWithPast(LlavaModelOutputWithPast): + r""" + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + Image hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + video_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_videos, sequence_length, hidden_size)`. + Video hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + video_hidden_states: torch.FloatTensor | None = None + + +class PerceptionLMCausalLMOutputWithPast(LlavaCausalLMOutputWithPast): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + Image hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + video_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_videos, sequence_length, hidden_size)`. + Video hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + video_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring +class PerceptionLMModel(LlavaModel): + def __init__(self, config: PerceptionLMConfig): + super().__init__(config) + self.vision_tower = AutoModel.from_config(config.vision_config) + self.multi_modal_projector = PerceptionLMMultiModalProjector(config) + self.language_model = AutoModel.from_config(config.text_config) + + @can_return_tuple + @auto_docstring( + custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection." + ) + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + image_outputs = self.vision_tower(pixel_values.flatten(0, 1), return_dict=True, **kwargs) + last_hidden_state = image_outputs.last_hidden_state + if self.config.vision_use_cls_token: + last_hidden_state = last_hidden_state[:, 1:, :] + image_features = self.multi_modal_projector(last_hidden_state) + image_outputs.pooler_output = image_features + + return image_outputs + + def get_placeholder_mask( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.FloatTensor, + image_features: torch.FloatTensor | None = None, + video_features: torch.FloatTensor | None = None, + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + special_video_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_video_mask = special_video_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + special_video_mask = input_ids == self.config.video_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if image_features is not None: + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.size()[:-1].numel()}", + ) + + n_video_tokens = special_video_mask.sum() + special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if video_features is not None: + torch_compilable_check( + inputs_embeds[special_video_mask].numel() == video_features.numel(), + f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.size()[:-1].numel()}", + ) + return special_image_mask, special_video_mask + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **lm_kwargs, + ) -> tuple | PerceptionLMModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None: + raise ValueError( + "You cannot specify both (pixel_values or pixel_values_videos) and inputs_embeds at the same time, and must specify either one" + ) + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + image_features = None + if pixel_values is not None: + image_features = self.get_image_features(pixel_values=pixel_values, return_dict=True).pooler_output + image_features = image_features.to(inputs_embeds.device, dtype=inputs_embeds.dtype) + special_image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + video_features = None + if pixel_values_videos is not None: + video_features = self.get_image_features(pixel_values=pixel_values_videos, return_dict=True).pooler_output + video_features = video_features.to(inputs_embeds.device, dtype=inputs_embeds.dtype) + _, special_video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_video_mask, video_features) + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + return_dict=True, + logits_to_keep=logits_to_keep, + **lm_kwargs, + ) + return PerceptionLMModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + hidden_states=outputs.hidden_states, + past_key_values=outputs.past_key_values, + attentions=outputs.attentions, + image_hidden_states=image_features if pixel_values is not None else None, + video_hidden_states=(video_features if pixel_values_videos is not None else None), + ) + + +@auto_docstring +class PerceptionLMForConditionalGeneration(LlavaForConditionalGeneration): + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + inputs_embeds=None, + pixel_values=None, + pixel_values_videos=None, + attention_mask=None, + logits_to_keep=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + logits_to_keep=logits_to_keep, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + if is_first_iteration or not kwargs.get("use_cache", True): + # Pixel values are used only in the first iteration if available + # In subsequent iterations, they are already merged with text and cached + # NOTE: first iteration doesn't have to be prefill, it can be the first + # iteration with a question and cached system prompt (continue generate from cache) + model_inputs["pixel_values"] = pixel_values + model_inputs["pixel_values_videos"] = pixel_values_videos + return model_inputs + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **lm_kwargs, + ) -> tuple | PerceptionLMCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + from transformers import AutoProcessor, AutoModelForImageTextToText + from huggingface_hub import hf_hub_download + + MODEL_PATH = "facebook/Perception-LM-1B" + processor = AutoProcessor.from_pretrained(MODEL_PATH, use_fast=True) + model = AutoModelForImageTextToText.from_pretrained(MODEL_PATH).to("cuda") + test_image_file = hf_hub_download( + repo_id="shumingh/perception_lm_test_images", + filename="14496_0.PNG", + repo_type="dataset", + ) + conversation = [ + { + "role": "user", + "content": [ + { + "type": "image", + "url": test_image_file, + }, + {"type": "text", "text": "Describe the bar plot in the image."}, + ], + } + ] + + inputs = processor.apply_chat_template( + [conversation], + add_generation_prompt=True, + tokenize=True, + return_dict=True, + return_tensors="pt", + ) + inputs = inputs.to(model.device) + generate_ids = model.generate(**inputs, max_new_tokens=256) + input_length = inputs["input_ids"].shape[1] + generate_ids_without_inputs = generate_ids[:, input_length:] + + for output in processor.batch_decode(generate_ids_without_inputs, skip_special_tokens=True): + print(output) + ```""" + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **lm_kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, + labels=labels, + vocab_size=self.config.text_config.vocab_size, + **lm_kwargs, + ) + + return PerceptionLMCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + video_hidden_states=outputs.video_hidden_states, + ) + + def get_image_features(self, **kwargs): + raise AttributeError("Not needed for PerceptionLM") + + +__all__ = [ + "PerceptionLMForConditionalGeneration", + "PerceptionLMPreTrainedModel", + "PerceptionLMModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/processing_perception_lm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/processing_perception_lm.py new file mode 100644 index 0000000000000000000000000000000000000000..56633fcc3856d02f9c7af9353460c8d0073b7998 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/processing_perception_lm.py @@ -0,0 +1,196 @@ +# Copyright 2025 Meta Platforms, Inc. and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for PerceptionLM. +""" + +from collections.abc import Iterable + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput, get_image_size, to_numpy_array +from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring, logging +from ...video_utils import VideoInput + + +logger = logging.get_logger(__name__) + + +class PerceptionLMProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": False, + "return_mm_token_type_ids": False, + }, + } + + +@auto_docstring +class PerceptionLMProcessor(ProcessorMixin): + def __init__( + self, + video_processor=None, + image_processor=None, + tokenizer=None, + patch_size=None, + chat_template=None, + pooling_ratio=2, + **kwargs, + ): + r""" + patch_size (`int`, *optional*): + Patch size from the vision tower. + pooling_ratio (`int`, *optional*, defaults to 2): + Pooling ratio for vision tokens. If not 1, 2D adaptive pooling is applied over projected vision tokens. + """ + self.patch_size = patch_size + self.pooling_ratio = pooling_ratio + self.image_token = tokenizer.image_token + self.video_token = tokenizer.video_token + self.image_token_id = tokenizer.image_token_id + self.video_token_id = tokenizer.video_token_id + super().__init__(video_processor, image_processor, tokenizer, chat_template=chat_template) + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + videos: VideoInput | None = None, + **kwargs: Unpack[PerceptionLMProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is provided. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is provided). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is provided. + - **pixel_values_videos** -- Video pixel values to be fed to a model. Returned when `videos` is provided. + """ + if text is None: + raise ValueError( + "You have to specify at least `text` input. Optionally, you can also specify `images` or `videos`." + ) + + output_kwargs = self._merge_kwargs( + PerceptionLMProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + if images is not None: + image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + else: + image_inputs = {} + + if videos is not None: + videos_inputs = self.video_processor(videos, **output_kwargs["videos_kwargs"]) + else: + videos_inputs = {} + + if isinstance(text, str): + text = [text] + elif not isinstance(text, list) and not isinstance(text[0], str): + raise TypeError("Invalid input text. Please provide a string, or a list of strings") + + # try to expand inputs in processing if we have the necessary parts + prompt_strings = [] + + pixel_values = iter(image_inputs.get("pixel_values", [])) + pixel_values_videos = iter(videos_inputs.get("pixel_values_videos", [])) + for sample in text: + # Replace the media token with the expanded media token sequence + sample = self._expand_media_tokens(sample, self.tokenizer.image_token, pixel_values) + sample = self._expand_media_tokens(sample, self.tokenizer.video_token, pixel_values_videos) + prompt_strings.append(sample) + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False) + text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None) + self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image", "video"]) + + if return_mm_token_type_ids: + text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"]) + return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors) + + def _expand_media_tokens(self, sample, media_token: str, media_iter: Iterable): + media_count = sample.count(media_token) + if media_count > 0: + media_list = [next(media_iter) for _ in range(media_count)] + sample_splits = sample.split(media_token) + media_token_list = [] + for media in media_list: + height, width = get_image_size(to_numpy_array(media)) + num_tiles = media.shape[0] + num_media_tokens = ( + (height // self.patch_size // self.pooling_ratio) + * (width // self.patch_size // self.pooling_ratio) + * num_tiles + ) + media_token_list.append(num_media_tokens) + sample = "" + for i, num_media_tokens in enumerate(media_token_list): + sample += sample_splits[i] + sample += media_token * num_media_tokens + sample += sample_splits[-1] + return sample + + def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs): + """ + Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. + + Args: + image_sizes (`list[list[int]]`, *optional*): + The input sizes formatted as (height, width) per each image. + + Returns: + `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided + input modalities, along with other useful data. + """ + + vision_data = {} + if image_sizes is not None: + images_kwargs = PerceptionLMProcessorKwargs._defaults.get("images_kwargs", {}) + images_kwargs.update(kwargs) + tile_size = images_kwargs.get("tile_size", None) or self.image_processor.tile_size + vision_input_type = images_kwargs.get("vision_input_type", None) or self.image_processor.vision_input_type + + num_image_tokens = [] + num_image_patches = [] + for height, width in image_sizes: + if vision_input_type == "thumb+tile": + aspect_ratio = self.image_processor._fit_image_to_canvas( + img_width=width, img_height=height, tile_size=tile_size + ) + if aspect_ratio is None: + aspect_ratio = self.image_processor._find_closest_aspect_ratio( + img_width=width, img_height=height, tile_size=tile_size + ) + num_tiles = aspect_ratio[0] * aspect_ratio[1] + 1 # base image and tiles + else: + num_tiles = 1 + + num_image_tokens.append( + (tile_size // self.patch_size // self.pooling_ratio) + * (tile_size // self.patch_size // self.pooling_ratio) + * num_tiles + ) + num_image_patches.append(num_tiles) + + vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) + return MultiModalData(**vision_data) + + +__all__ = ["PerceptionLMProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/video_processing_perception_lm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/video_processing_perception_lm.py new file mode 100644 index 0000000000000000000000000000000000000000..aa9d2c16fcb1c3e66cf6d7032aa17f43088f9ef4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/perception_lm/video_processing_perception_lm.py @@ -0,0 +1,31 @@ +# Copyright 2025 Meta Platforms, Inc. and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Video processor class for PerceptionLM.""" + +from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, PILImageResampling +from ...video_processing_utils import BaseVideoProcessor + + +class PerceptionLMVideoProcessor(BaseVideoProcessor): + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 448, "width": 448} + do_resize = True + do_center_crop = False + do_rescale = True + do_normalize = True + do_convert_rgb = True + + +__all__ = ["PerceptionLMVideoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cb71eae2547c59a4f9ba7ecbafda56fb9c86b494 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_persimmon import * + from .modeling_persimmon import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/configuration_persimmon.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/configuration_persimmon.py new file mode 100644 index 0000000000000000000000000000000000000000..3a7bceff101a3b7c1b9351db71033f6fb8988a17 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/configuration_persimmon.py @@ -0,0 +1,66 @@ +# Copyright 2023 Adept AI and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Persimmon model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="adept/persimmon-8b-base") +@strict +class PersimmonConfig(PreTrainedConfig): + r""" + qk_layernorm (`bool`, *optional*, default to `True`): + Whether or not to normalize the Queries and Keys after projecting the hidden states + + Example: + + ```python + >>> from transformers import PersimmonModel, PersimmonConfig + + >>> # Initializing a Persimmon persimmon-7b style configuration + >>> configuration = PersimmonConfig() + ```""" + + model_type = "persimmon" + keys_to_ignore_at_inference = ["past_key_values"] + + vocab_size: int = 262144 + hidden_size: int = 4096 + intermediate_size: int = 16384 + num_hidden_layers: int = 36 + num_attention_heads: int = 64 + hidden_act: str = "relu2" + max_position_embeddings: int = 16384 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-5 + use_cache: bool = True + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + qk_layernorm: bool = True + hidden_dropout: float | int = 0.0 + attention_dropout: float | int = 0.0 + pad_token_id: int | None = None + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 2 + + def __post_init__(self, **kwargs): + kwargs.setdefault("partial_rotary_factor", 0.5) # assign default for BC + super().__post_init__(**kwargs) + + +__all__ = ["PersimmonConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/modeling_persimmon.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/modeling_persimmon.py new file mode 100644 index 0000000000000000000000000000000000000000..e0516ed7da9ad3ec0685ae92eeaefd9e4121dc06 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/persimmon/modeling_persimmon.py @@ -0,0 +1,553 @@ +# Copyright 2023 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Persimmon model.""" + +from collections.abc import Callable +from typing import Optional + +import torch +from torch import nn + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...masking_utils import create_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import ( + GenericForSequenceClassification, + GenericForTokenClassification, + GradientCheckpointingLayer, +) +from ...modeling_outputs import ( + BaseModelOutputWithPast, + CausalLMOutputWithPast, +) +from ...modeling_rope_utils import ( + ROPE_INIT_FUNCTIONS, + dynamic_rope_update, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_persimmon import PersimmonConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Persimmon +class PersimmonRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: PersimmonConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + # Ignore copy + def compute_default_rope_parameters( + config: PersimmonConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0) + head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + dim = int(head_dim * partial_rotary_factor) + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +# Copied from transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXMLP with GPTNeoX->Persimmon +class PersimmonMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size) + self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size) + self.act = ACT2FN[config.hidden_act] + + def forward(self, hidden_states): + hidden_states = self.dense_h_to_4h(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.dense_4h_to_h(hidden_states) + return hidden_states + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PersimmonAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: PersimmonConfig, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + + self.rotary_ndims = int(self.head_dim * config.rope_parameters["partial_rotary_factor"]) + self.is_causal = True + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=True) + self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True) + self.qk_layernorm = config.qk_layernorm + self.scaling = self.head_dim**-0.5 + + if self.qk_layernorm: + self.q_layernorm = nn.LayerNorm( + config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True + ) + self.k_layernorm = nn.LayerNorm( + config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True + ) + self.attention_dropout = nn.Dropout(config.attention_dropout) + + def _split_heads(self, fused_qkv: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory + storage as `fused_qkv` + + Args: + fused_qkv (`torch.tensor`): [batch_size, seq_length, num_heads * 3 * head_dim] + + Returns: + query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim] + value: [batch_size, seq_length, num_heads, head_dim] + """ + batch_size, seq_length, three_times_hidden_size = fused_qkv.shape + fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim) + return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :] + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + bsz, q_len, _ = hidden_states.size() + + # [batch_size, seq_length, 3 x hidden_size] + fused_qkv = self.query_key_value(hidden_states) + + # 3 x [batch_size, seq_length, num_heads, head_dim] + (query_states, key_states, value_states) = self._split_heads(fused_qkv) + + if self.qk_layernorm: + query_states = self.q_layernorm(query_states) + key_states = self.k_layernorm(key_states) + + # [batch_size, num_heads, seq_length, head_dim] -> [batch_size, seq_length, num_heads, head_dim] + query_states = query_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + + cos, sin = position_embeddings + query_rot, query_pass = ( + query_states[..., : self.rotary_ndims], + query_states[..., self.rotary_ndims :], + ) + key_rot, key_pass = ( + key_states[..., : self.rotary_ndims], + key_states[..., self.rotary_ndims :], + ) + # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] + query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) + + # [batch_size, seq_length, num_heads, head_dim] + query_states = torch.cat((query_rot, query_pass), dim=-1) + key_states = torch.cat((key_rot, key_pass), dim=-1) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.config.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, q_len, -1) + attn_output = self.dense(attn_output) + + return attn_output, attn_weights + + +class PersimmonDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PersimmonConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = PersimmonAttention(config=config, layer_idx=layer_idx) + self.mlp = PersimmonMLP(config) + self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> torch.Tensor: + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = hidden_states + residual + + return hidden_states + + +@auto_docstring +class PersimmonPreTrainedModel(PreTrainedModel): + config: PersimmonConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PersimmonDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _can_compile_fullgraph = True + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PersimmonDecoderLayer, + "attentions": PersimmonAttention, + } + + +@auto_docstring +class PersimmonModel(PersimmonPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PersimmonDecoderLayer`] + + Args: + config: PersimmonConfig + """ + + def __init__(self, config: PersimmonConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [PersimmonDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.rotary_emb = PersimmonRotaryEmbedding(config=self.config) + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.final_layernorm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class PersimmonForCausalLM(PersimmonPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + + # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with LLAMA->PERSIMMON,Llama->Persimmon + def __init__(self, config): + super().__init__(config) + self.model = PersimmonModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, PersimmonForCausalLM + + >>> model = PersimmonForCausalLM.from_pretrained("adept/persimmon-8b-base") + >>> tokenizer = AutoTokenizer.from_pretrained("adept/persimmon-8b-base") + + >>> prompt = "human: Hey, what should I eat for dinner?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + 'human: Hey, what should I eat for dinner?\n\ncat: 🐱\n\nhuman: 😐\n\n' + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class PersimmonForSequenceClassification(GenericForSequenceClassification, PersimmonPreTrainedModel): ... + + +class PersimmonForTokenClassification(GenericForTokenClassification, PersimmonPreTrainedModel): ... + + +__all__ = [ + "PersimmonForCausalLM", + "PersimmonModel", + "PersimmonPreTrainedModel", + "PersimmonForSequenceClassification", + "PersimmonForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cffe33da73ee42eb20a2e7f33ea9351bb7da75c2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2023 Microsoft and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_phi import * + from .modeling_phi import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/configuration_phi.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/configuration_phi.py new file mode 100644 index 0000000000000000000000000000000000000000..2a65f0f16aecb1d6008a0eac053c87101ee76cae --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/configuration_phi.py @@ -0,0 +1,92 @@ +# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Phi model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="microsoft/phi-1") +@strict +class PhiConfig(PreTrainedConfig): + r""" + qk_layernorm (`bool`, *optional*, defaults to `False`): + Whether or not to normalize the Queries and Keys after projecting the hidden states. + + Example: + + ```python + >>> from transformers import PhiModel, PhiConfig + + >>> # Initializing a Phi-1 style configuration + >>> configuration = PhiConfig.from_pretrained("microsoft/phi-1") + + >>> # Initializing a model from the configuration + >>> model = PhiModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "phi" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.dense": "rowwise", + "layers.*.mlp.fc1": "colwise", + "layers.*.mlp.fc2": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "embed_dropout": (["inputs_embeds"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "final_layernorm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 51200 + hidden_size: int = 2048 + intermediate_size: int = 8192 + num_hidden_layers: int = 24 + num_attention_heads: int = 32 + num_key_value_heads: int | None = None + resid_pdrop: float | int = 0.0 + embd_pdrop: float | int = 0.0 + attention_dropout: float | int | None = 0.0 + hidden_act: str = "gelu_new" + max_position_embeddings: int = 2048 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-5 + use_cache: bool = True + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + qk_layernorm: bool = False + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 2 + pad_token_id: int | None = None + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + kwargs.setdefault("partial_rotary_factor", 0.5) # assign default for BC + super().__post_init__(**kwargs) + + +__all__ = ["PhiConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/modeling_phi.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/modeling_phi.py new file mode 100644 index 0000000000000000000000000000000000000000..e3f97a01ee4c01d07b6b4d8101877fea51a7ba79 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/modeling_phi.py @@ -0,0 +1,494 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/phi/modular_phi.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_phi.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn as nn + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_func_from_hub, use_kernelized_func +from ...masking_utils import create_causal_mask +from ...modeling_layers import ( + GenericForSequenceClassification, + GenericForTokenClassification, + GradientCheckpointingLayer, +) +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import can_return_tuple, maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_phi import PhiConfig + + +class PhiRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: PhiConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PhiConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0) + head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + dim = int(head_dim * partial_rotary_factor) + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class PhiAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: PhiConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) + self.dense = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True) + self.rotary_ndims = int(self.head_dim * config.rope_parameters["partial_rotary_factor"]) + self.qk_layernorm = config.qk_layernorm + if self.qk_layernorm: + self.q_layernorm = nn.LayerNorm( + config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True + ) + self.k_layernorm = nn.LayerNorm( + config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + if self.qk_layernorm: + query_states = self.q_layernorm(query_states) + key_states = self.k_layernorm(key_states) + + cos, sin = position_embeddings + # Partial rotary embedding + query_rot, query_pass = ( + query_states[..., : self.rotary_ndims], + query_states[..., self.rotary_ndims :], + ) + key_rot, key_pass = ( + key_states[..., : self.rotary_ndims], + key_states[..., self.rotary_ndims :], + ) + # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] + query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) + + # [batch_size, seq_length, num_heads, head_dim] + query_states = torch.cat((query_rot, query_pass), dim=-1) + key_states = torch.cat((key_rot, key_pass), dim=-1) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.dense(attn_output) + return attn_output, attn_weights + + +class PhiMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class PhiDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PhiConfig, layer_idx: int): + super().__init__() + self.self_attn = PhiAttention(config, layer_idx=layer_idx) + self.mlp = PhiMLP(config) + self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.resid_dropout = nn.Dropout(config.resid_pdrop) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + attn_outputs, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + attn_outputs = self.resid_dropout(attn_outputs) + + feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states)) + hidden_states = attn_outputs + feed_forward_hidden_states + residual + + return hidden_states + + +@auto_docstring +class PhiPreTrainedModel(PreTrainedModel): + config: PhiConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PhiDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PhiDecoderLayer, + "attentions": PhiAttention, + } + + +@auto_docstring +class PhiModel(PhiPreTrainedModel): + def __init__(self, config: PhiConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.rotary_emb = PhiRotaryEmbedding(config=config) + self.gradient_checkpointing = False + self.embed_dropout = nn.Dropout(config.embd_pdrop) + self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + inputs_embeds = self.embed_dropout(inputs_embeds) + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.final_layernorm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class PhiForCausalLM(PhiPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = PhiModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, PhiForCausalLM + + >>> model = PhiForCausalLM.from_pretrained("meta-phi/Phi-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-phi/Phi-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class PhiForSequenceClassification(GenericForSequenceClassification, PhiPreTrainedModel): + pass + + +class PhiForTokenClassification(GenericForTokenClassification, PhiPreTrainedModel): + pass + + +__all__ = [ + "PhiPreTrainedModel", + "PhiModel", + "PhiForCausalLM", + "PhiForSequenceClassification", + "PhiForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/modular_phi.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/modular_phi.py new file mode 100644 index 0000000000000000000000000000000000000000..b0afe5712e2afc8caa4da5ec5a24d18449bfeb49 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi/modular_phi.py @@ -0,0 +1,288 @@ +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn as nn + +from ...cache_utils import Cache, DynamicCache +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPast, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..clip.modeling_clip import CLIPMLP +from ..llama.modeling_llama import ( + LlamaAttention, + LlamaForCausalLM, + LlamaForSequenceClassification, + LlamaForTokenClassification, + LlamaModel, + LlamaPreTrainedModel, + LlamaRotaryEmbedding, + apply_rotary_pos_emb, + eager_attention_forward, +) +from .configuration_phi import PhiConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "microsoft/phi-1" +_CONFIG_FOR_DOC = "PhiConfig" + + +class PhiRotaryEmbedding(LlamaRotaryEmbedding): + @staticmethod + def compute_default_rope_parameters( + config: PhiConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0) + head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + dim = int(head_dim * partial_rotary_factor) + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + +class PhiAttention(LlamaAttention): + def __init__(self, config: PhiConfig, layer_idx: int): + super().__init__(config, layer_idx) + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) + self.dense = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True) + del self.o_proj + self.rotary_ndims = int(self.head_dim * config.rope_parameters["partial_rotary_factor"]) + self.qk_layernorm = config.qk_layernorm + if self.qk_layernorm: + self.q_layernorm = nn.LayerNorm( + config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True + ) + self.k_layernorm = nn.LayerNorm( + config.hidden_size // config.num_attention_heads, eps=config.layer_norm_eps, elementwise_affine=True + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + if self.qk_layernorm: + query_states = self.q_layernorm(query_states) + key_states = self.k_layernorm(key_states) + + cos, sin = position_embeddings + # Partial rotary embedding + query_rot, query_pass = ( + query_states[..., : self.rotary_ndims], + query_states[..., self.rotary_ndims :], + ) + key_rot, key_pass = ( + key_states[..., : self.rotary_ndims], + key_states[..., self.rotary_ndims :], + ) + # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor] + query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) + + # [batch_size, seq_length, num_heads, head_dim] + query_states = torch.cat((query_rot, query_pass), dim=-1) + key_states = torch.cat((key_rot, key_pass), dim=-1) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.dense(attn_output) + return attn_output, attn_weights + + +class PhiMLP(CLIPMLP): + pass + + +class PhiDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PhiConfig, layer_idx: int): + super().__init__() + self.self_attn = PhiAttention(config, layer_idx=layer_idx) + self.mlp = PhiMLP(config) + self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.resid_dropout = nn.Dropout(config.resid_pdrop) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + attn_outputs, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + attn_outputs = self.resid_dropout(attn_outputs) + + feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states)) + hidden_states = attn_outputs + feed_forward_hidden_states + residual + + return hidden_states + + +class PhiPreTrainedModel(LlamaPreTrainedModel): + _can_record_outputs = { + "hidden_states": PhiDecoderLayer, + "attentions": PhiAttention, + } + + +class PhiModel(LlamaModel): + def __init__(self, config: PhiConfig): + super().__init__(config) + self.layers = nn.ModuleList( + [PhiDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.embed_dropout = nn.Dropout(config.embd_pdrop) + self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + del self.norm + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + inputs_embeds = self.embed_dropout(inputs_embeds) + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.final_layernorm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class PhiForCausalLM(LlamaForCausalLM): + def __init__(self, config): + super().__init__(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True) + + +class PhiForSequenceClassification(LlamaForSequenceClassification): + pass + + +class PhiForTokenClassification(LlamaForTokenClassification): + pass + + +__all__ = [ + "PhiPreTrainedModel", + "PhiModel", + "PhiForCausalLM", + "PhiForSequenceClassification", + "PhiForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0cb1e7a9cd04fb32cb8eb29516d95c0fc8e9d108 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_phi3 import * + from .modeling_phi3 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/configuration_phi3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/configuration_phi3.py new file mode 100644 index 0000000000000000000000000000000000000000..f85502f8205d73af51426f75dccb7a400d3e1118 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/configuration_phi3.py @@ -0,0 +1,152 @@ +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Phi-3 model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="microsoft/Phi-3-mini-4k-instruct") +@strict +class Phi3Config(PreTrainedConfig): + r""" + original_max_position_embeddings (`int`, *optional*, defaults to 4096): + The maximum sequence length that this model was trained with. This is used to determine the size of the + original RoPE embeddings when using long scaling. + + Example: + + ```python + >>> from transformers import Phi3Model, Phi3Config + + >>> # Initializing a Phi-3 style configuration + >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct") + + >>> # Initializing a model from the configuration + >>> model = Phi3Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "phi3" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.qkv_proj": "colwise_gather_output", # we need to replicate here due to the slicing of qkv + "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the slicing of qkv + "layers.*.mlp.gate_up_proj": "colwise_gather_output", # we need to replicate here due to the `chunk` operation + "layers.*.mlp.down_proj": "rowwise_split_input", # input is replicated due to the `chunk` operation + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 32064 + hidden_size: int = 3072 + intermediate_size: int = 8192 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_key_value_heads: int | None = None + resid_pdrop: float | int = 0.0 + embd_pdrop: float | int = 0.0 + attention_dropout: float | int = 0.0 + hidden_act: str = "silu" + max_position_embeddings: int = 4096 + original_max_position_embeddings: int = 4096 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-5 + use_cache: bool = True + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 32000 + pad_token_id: int | None = 32000 + sliding_window: int | None = None + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + super().__post_init__(**kwargs) + + def convert_rope_params_to_dict( + self, default_theta: int | float = 10_000.0, ignore_keys: set | None = None, **kwargs + ): + rope_scaling = kwargs.pop("rope_scaling", None) + self.rope_parameters = rope_scaling or self.rope_parameters + self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {} + + # Standardize and validate the correctness of rotary position embeddings parameters + self.rope_parameters.setdefault("rope_theta", kwargs.pop("rope_theta", default_theta)) + self.rope_parameters.setdefault("partial_rotary_factor", kwargs.get("partial_rotary_factor", 1.0)) + self.standardize_rope_params() + + # For backward compatibility if previous version used "su" or "yarn" + rope_parameters_type = self.rope_parameters.get("rope_type", None) + if rope_parameters_type is not None and rope_parameters_type in ["su", "yarn"]: + self.rope_parameters["rope_type"] = "longrope" + return kwargs + + def validate_rope(self): + """ + Validate the `rope_parameters` configuration. + """ + super().validate_rope() + + # Run Phi3 specific validation + if not isinstance(self.rope_parameters, dict): + raise ValueError(f"`rope_parameters` must be a dictionary but got {self.rope_parameters}") + rope_parameters_type = self.rope_parameters.get("rope_type", None) + rope_parameters_short_factor = self.rope_parameters.get("short_factor", None) + rope_parameters_long_factor = self.rope_parameters.get("long_factor", None) + rotary_ndims = int( + self.hidden_size // self.num_attention_heads * self.rope_parameters["partial_rotary_factor"] + ) + if rope_parameters_type not in ["default", "longrope"]: + raise ValueError(f"`rope_parameters`'s type field must be one of ['longrope'], got {rope_parameters_type}") + + if rope_parameters_short_factor is not None: + if not ( + isinstance(rope_parameters_short_factor, list) + and all(isinstance(x, (int, float)) for x in rope_parameters_short_factor) + ): + raise ValueError( + f"`rope_parameters`'s short_factor field must be a list of numbers, got {rope_parameters_short_factor}" + ) + if not len(rope_parameters_short_factor) == rotary_ndims // 2: + raise ValueError( + f"`rope_parameters`'s short_factor field must have length {rotary_ndims // 2}, got {len(rope_parameters_short_factor)}" + ) + + if rope_parameters_long_factor is not None: + if not ( + isinstance(rope_parameters_long_factor, list) + and all(isinstance(x, (int, float)) for x in rope_parameters_long_factor) + ): + raise ValueError( + f"`rope_parameters`'s long_factor field must be a list of numbers, got {rope_parameters_long_factor}" + ) + if not len(rope_parameters_long_factor) == rotary_ndims // 2: + raise ValueError( + f"`rope_parameters`'s long_factor field must have length {rotary_ndims // 2}, got {len(rope_parameters_long_factor)}" + ) + + +__all__ = ["Phi3Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/modeling_phi3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/modeling_phi3.py new file mode 100644 index 0000000000000000000000000000000000000000..b07735f8a2e6f819801354f984488d11e8d36315 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/modeling_phi3.py @@ -0,0 +1,557 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/phi3/modular_phi3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_phi3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections.abc import Callable +from typing import Optional + +import torch +from torch import nn + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import ( + GenericForSequenceClassification, + GenericForTokenClassification, + GradientCheckpointingLayer, +) +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_phi3 import Phi3Config + + +class Phi3MLP(nn.Module): + def __init__(self, config): + super().__init__() + + self.config = config + self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + self.activation_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + up_states = self.gate_up_proj(hidden_states) + + gate, up_states = up_states.chunk(2, dim=-1) + up_states = up_states * self.activation_fn(gate) + + return self.down_proj(up_states) + + +class Phi3RotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: Phi3Config, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: Phi3Config | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0) + head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + dim = int(head_dim * partial_rotary_factor) + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + rotary_dim = cos.shape[-1] + q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] + k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] + + q_embed = torch.cat([(q_rot * cos) + (rotate_half(q_rot) * sin), q_pass], dim=-1) + k_embed = torch.cat([(k_rot * cos) + (rotate_half(k_rot) * sin), k_pass], dim=-1) + return q_embed, k_embed + + +class Phi3Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: Phi3Config, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.num_key_value_heads = config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) + self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + qkv = self.qkv_proj(hidden_states) + query_pos = self.config.num_attention_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=getattr(self.config, "sliding_window", None), + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +@use_kernel_forward_from_hub("RMSNorm") +class Phi3RMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + Phi3RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class Phi3DecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Phi3Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = Phi3Attention(config=config, layer_idx=layer_idx) + self.mlp = Phi3MLP(config) + self.input_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.config = config + self.resid_attn_dropout = nn.Dropout(config.resid_pdrop) + self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + self.resid_attn_dropout(hidden_states) # main diff with Llama + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.resid_mlp_dropout(hidden_states) # main diff with Llama + return hidden_states + + +@auto_docstring +class Phi3PreTrainedModel(PreTrainedModel): + config: Phi3Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Phi3DecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": Phi3DecoderLayer, + "attentions": Phi3Attention, + } + _version = "0.0.5" + + +@auto_docstring +class Phi3Model(Phi3PreTrainedModel): + def __init__(self, config: Phi3Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Phi3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = Phi3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = Phi3RotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask + causal_mask = mask_function( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +@auto_docstring +class Phi3ForCausalLM(Phi3PreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = Phi3Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, Phi3ForCausalLM + + >>> model = Phi3ForCausalLM.from_pretrained("meta-phi3/Phi3-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-phi3/Phi3-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + logits_to_keep=None, + **kwargs, + ): + # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the + # process + + # When the first time input length reached long and short factor switching point, enforce re-compute cache + # It will cause downside of slower at this single token position, however, better than current failure. + if ( + past_key_values + and hasattr(self.config, "original_max_position_embeddings") + and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1 + ): + past_length = past_key_values.get_seq_length() + if past_length <= self.config.original_max_position_embeddings: + past_key_values = None + + model_inputs = super().prepare_inputs_for_generation( + input_ids=input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **kwargs, + ) + return model_inputs + + +class Phi3ForSequenceClassification(GenericForSequenceClassification, Phi3PreTrainedModel): + pass + + +class Phi3ForTokenClassification(GenericForTokenClassification, Phi3PreTrainedModel): + pass + + +__all__ = [ + "Phi3PreTrainedModel", + "Phi3Model", + "Phi3ForCausalLM", + "Phi3ForSequenceClassification", + "Phi3ForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/modular_phi3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/modular_phi3.py new file mode 100644 index 0000000000000000000000000000000000000000..4229981cc0a8bbe22840197680741cfd15a5018b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi3/modular_phi3.py @@ -0,0 +1,264 @@ +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""PyTorch Phi-3 model.""" + +from collections.abc import Callable + +import torch +from torch import nn + +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...processing_utils import Unpack +from ...utils import logging +from ..mistral.modeling_mistral import ( + MistralDecoderLayer, + MistralForCausalLM, + MistralForSequenceClassification, + MistralForTokenClassification, + MistralPreTrainedModel, + eager_attention_forward, + rotate_half, +) +from ..phi.modeling_phi import PhiRotaryEmbedding +from .configuration_phi3 import Phi3Config + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "microsoft/Phi-3-mini-4k-instruct" +_CONFIG_FOR_DOC = "Phi3Config" + + +class Phi3MLP(nn.Module): + def __init__(self, config): + super().__init__() + + self.config = config + self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + self.activation_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + up_states = self.gate_up_proj(hidden_states) + + gate, up_states = up_states.chunk(2, dim=-1) + up_states = up_states * self.activation_fn(gate) + + return self.down_proj(up_states) + + +class Phi3RotaryEmbedding(PhiRotaryEmbedding): + pass + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + rotary_dim = cos.shape[-1] + q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] + k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] + + q_embed = torch.cat([(q_rot * cos) + (rotate_half(q_rot) * sin), q_pass], dim=-1) + k_embed = torch.cat([(k_rot * cos) + (rotate_half(k_rot) * sin), k_pass], dim=-1) + return q_embed, k_embed + + +class Phi3Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: Phi3Config, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.num_key_value_heads = config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) + self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + qkv = self.qkv_proj(hidden_states) + query_pos = self.config.num_attention_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=getattr(self.config, "sliding_window", None), + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Phi3DecoderLayer(MistralDecoderLayer): + def __init__(self, config: Phi3Config, layer_idx: int): + super().__init__(config, layer_idx) + self.config = config + self.self_attn = Phi3Attention(config=config, layer_idx=layer_idx) + self.mlp = Phi3MLP(config) + self.resid_attn_dropout = nn.Dropout(config.resid_pdrop) + self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + self.resid_attn_dropout(hidden_states) # main diff with Llama + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.resid_mlp_dropout(hidden_states) # main diff with Llama + return hidden_states + + +class Phi3PreTrainedModel(MistralPreTrainedModel): + _version = "0.0.5" + + +class Phi3ForCausalLM(MistralForCausalLM): + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + logits_to_keep=None, + **kwargs, + ): + # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the + # process + + # When the first time input length reached long and short factor switching point, enforce re-compute cache + # It will cause downside of slower at this single token position, however, better than current failure. + if ( + past_key_values + and hasattr(self.config, "original_max_position_embeddings") + and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1 + ): + past_length = past_key_values.get_seq_length() + if past_length <= self.config.original_max_position_embeddings: + past_key_values = None + + model_inputs = GenerationMixin.prepare_inputs_for_generation( + self, + input_ids=input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **kwargs, + ) + return model_inputs + + +class Phi3ForSequenceClassification(MistralForSequenceClassification): + pass + + +class Phi3ForTokenClassification(MistralForTokenClassification): + pass + + +__all__ = [ + "Phi3PreTrainedModel", + "Phi3Model", # noqa: F822 + "Phi3ForCausalLM", + "Phi3ForSequenceClassification", + "Phi3ForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d71ce6aba14246a3b80bd8a93da080f90aaa5ea1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/__init__.py @@ -0,0 +1,32 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_phi4_multimodal import * + from .feature_extraction_phi4_multimodal import * + from .image_processing_phi4_multimodal import * + from .modeling_phi4_multimodal import * + from .processing_phi4_multimodal import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/configuration_phi4_multimodal.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/configuration_phi4_multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..b73741305566c9776c0316d2cfeb7db553c04739 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/configuration_phi4_multimodal.py @@ -0,0 +1,300 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/phi4_multimodal/modular_phi4_multimodal.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_phi4_multimodal.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="microsoft/Phi-4-multimodal-instruct") +@strict +class Phi4MultimodalVisionConfig(PreTrainedConfig): + r""" + crop_size (`int`, *optional*, defaults to 448): + Crop size for the input images. + feature_layer (`int`, *optional*, defaults to -2): + The index of the layer of the encoder from which to extract image features. + + Example: + + ```python + >>> from transformers import Phi4MultimodalVisionConfig + + >>> # Initializing a Phi4MultimodalVisionConfig with microsoft/Phi-4-multimodal-instruct style configuration + >>> configuration = Phi4MultimodalVisionConfig() + ```""" + + model_type = "phi4_multimodal_vision" + base_config_key = "vision_config" + + hidden_size: int = 1152 + intermediate_size: int = 4304 + num_hidden_layers: int = 27 + num_attention_heads: int = 16 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 448 + patch_size: int | list[int] | tuple[int, int] = 14 + hidden_act: str = "gelu_pytorch_tanh" + layer_norm_eps: float = 1e-6 + attention_dropout: float | int = 0.0 + crop_size: int = 448 + image_token_id: int = 200010 + feature_layer: int = -2 + + +@auto_docstring(checkpoint="microsoft/Phi-4-multimodal-instruct") +@strict +class Phi4MultimodalAudioConfig(PreTrainedConfig): + r""" + num_blocks (`int`, *optional*, defaults to 24): + Number of hidden layers in the Transformer encoder. + activation (`str`, *optional*, defaults to `"swish"`): + The non-linear activation function in the MLPs. + chunk_size (`int`, *optional*, defaults to -1): + The chunk size to create the masks. + left_chunk (`int`, *optional*, defaults to 18): + The left chunk to create the masks. + dropout_rate (`float`, *optional*, defaults to 0.0): + The dropout ratio. + ext_pw_out_channel (`int`, *optional*, defaults to 1024): + Number of out channels in the point-wise conv modules. + depthwise_separable_out_channel (`int`, *optional*, defaults to 1024): + Number of out channels in the depth-wise separable conv modules. + depthwise_multiplier (`int`, *optional*, defaults to 1): + Input size multiplier for the depth-wise separable conv modules. + kernel_size (`int`, *optional*, defaults to 3): + Kernel size for the depth-wise separable conv modules. + conv_activation (`str`, *optional*, defaults to `"swish"`): + The non-linear activation function in the conv modules. + input_size (`int`, *optional*, defaults to 80): + Input size for the audio model. + conv_glu_type (`str`, *optional*, defaults to `"swish"`): + The non-linear activation function in the point-wise conv modules. + time_reduction (`int`, *optional*, defaults to 8): + Time reduction (subsampling factor). + bias_max_distance (`int`, *optional*, defaults to 1000): + Max distance for the relative attention bias module. + bias_symmetric (`bool`, *optional*, defaults to `False`): + Whether the relative attention bias should be symmetric or not. + nemo_activation (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function in the nemo conv modules. + nemo_conv_channels (`int`, *optional*, defaults to 1024): + Number of channels in the nemo conv modules. + downsample_rate (`int`, *optional*, defaults to 1): + Downsample rate for the audio feature extractor. + feature_layer (`int`, *optional*, defaults to -2): + The index of the layer of the encoder from which to extract audio features. + + Example: + + ```python + >>> from transformers import Phi4MultimodalAudioConfig + + >>> # Initializing a Phi4MultimodalAudioConfig with microsoft/Phi-4-multimodal-instruct style configuration + >>> configuration = Phi4MultimodalAudioConfig() + ```""" + + model_type = "phi4_multimodal_audio" + + hidden_size: int = 1024 + intermediate_size: int = 1536 + num_blocks: int = 24 + num_attention_heads: int = 16 + activation: str = "swish" + chunk_size: int = -1 + left_chunk: int = 18 + dropout_rate: float | int = 0.0 + ext_pw_out_channel: int = 1024 + depthwise_separable_out_channel: int = 1024 + depthwise_multiplier: int = 1 + kernel_size: int = 3 + conv_activation: str = "swish" + input_size: int = 80 + conv_glu_type: str = "swish" + time_reduction: int = 8 + bias_max_distance: int = 1000 + bias_symmetric: bool = False + nemo_activation: str = "relu" + nemo_conv_channels: int = 1024 + downsample_rate: int = 1 + initializer_range: float = 0.02 + audio_token_id: int = 200011 + feature_layer: int = -2 + + def __post_init__(self, **kwargs): + nemo_final_size = self.input_size + for _ in range(int(math.log2(self.time_reduction))): + nemo_final_size = math.floor((nemo_final_size - 1) / 2 + 1) + self.nemo_final_size = nemo_final_size + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.time_reduction % 2 != 0: + raise ValueError("`time_reduction` should be a multiple of 2!") + + +@auto_docstring(checkpoint="microsoft/Phi-4-multimodal-instruct") +@strict +class Phi4MultimodalConfig(PreTrainedConfig): + r""" + original_max_position_embeddings (`int`, *optional*, defaults to 4096): + The maximum sequence length that this model was trained with. This is used to determine the size of the + original RoPE embeddings when using long scaling. + + Example: + + ```python + >>> from transformers import Phi4MultimodalModel, Phi4MultimodalConfig + + >>> # Initializing a Phi4Multimodal style configuration + >>> configuration = Phi4MultimodalConfig.from_pretrained("microsoft/Phi-4-multimodal-instruct") + + >>> # Initializing a model from the configuration + >>> model = Phi4MultimodalModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "phi4_multimodal" + keys_to_ignore_at_inference = ["past_key_values"] + base_model_tp_plan = { + "layers.*.self_attn.qkv_proj": "colwise_gather_output", # we need to replicate here due to the slicing of qkv + "layers.*.self_attn.o_proj": "rowwise_split_input", # input is replicated due to the slicing of qkv + "layers.*.mlp.gate_up_proj": "colwise_gather_output", # we need to replicate here due to the `chunk` operation + "layers.*.mlp.down_proj": "rowwise_split_input", # input is replicated due to the `chunk` operation + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 200064 + hidden_size: int = 3072 + intermediate_size: int = 8192 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_key_value_heads: int | None = 8 + resid_pdrop: float | int = 0.0 + embd_pdrop: float | int = 0.0 + attention_dropout: float | int = 0.0 + hidden_act: str = "silu" + max_position_embeddings: int = 131072 + original_max_position_embeddings: int | None = 4096 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-5 + use_cache: bool = True + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + bos_token_id: int | None = 199999 + eos_token_id: int | list[int] | None = None + pad_token_id: int | None = 199999 + sliding_window: int | None = None + + sub_configs = {"audio_config": Phi4MultimodalAudioConfig, "vision_config": Phi4MultimodalVisionConfig} + vision_config: dict | PreTrainedConfig | None = None + audio_config: dict | PreTrainedConfig | None = None + + def __post_init__(self, **kwargs): + self.eos_token_id = self.eos_token_id or [199999, 200020] + if isinstance(self.vision_config, dict): + self.vision_config = Phi4MultimodalVisionConfig(**self.vision_config) + elif self.vision_config is None: + self.vision_config = Phi4MultimodalVisionConfig() + + if isinstance(self.audio_config, dict): + self.audio_config = Phi4MultimodalAudioConfig(**self.audio_config) + elif self.audio_config is None: + self.audio_config = Phi4MultimodalAudioConfig() + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + super().__post_init__(**kwargs) + + def convert_rope_params_to_dict( + self, default_theta: int | float = 10_000.0, ignore_keys: set | None = None, **kwargs + ): + rope_scaling = kwargs.pop("rope_scaling", None) + self.rope_parameters = rope_scaling or self.rope_parameters + self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {} + + # Standardize and validate the correctness of rotary position embeddings parameters + self.rope_parameters.setdefault("rope_theta", kwargs.pop("rope_theta", default_theta)) + self.rope_parameters.setdefault("partial_rotary_factor", kwargs.get("partial_rotary_factor", 1.0)) + self.standardize_rope_params() + + # For backward compatibility if previous version used "su" or "yarn" + rope_parameters_type = self.rope_parameters.get("rope_type", None) + if rope_parameters_type is not None and rope_parameters_type in ["su", "yarn"]: + self.rope_parameters["rope_type"] = "longrope" + return kwargs + + def validate_rope(self): + """ + Validate the `rope_parameters` configuration. + """ + super().validate_rope() + + # Run Phi4Multimodal specific validation + if not isinstance(self.rope_parameters, dict): + raise ValueError(f"`rope_parameters` must be a dictionary but got {self.rope_parameters}") + rope_parameters_type = self.rope_parameters.get("rope_type", None) + rope_parameters_short_factor = self.rope_parameters.get("short_factor", None) + rope_parameters_long_factor = self.rope_parameters.get("long_factor", None) + rotary_ndims = int( + self.hidden_size // self.num_attention_heads * self.rope_parameters["partial_rotary_factor"] + ) + if rope_parameters_type not in ["default", "longrope"]: + raise ValueError(f"`rope_parameters`'s type field must be one of ['longrope'], got {rope_parameters_type}") + + if rope_parameters_short_factor is not None: + if not ( + isinstance(rope_parameters_short_factor, list) + and all(isinstance(x, (int, float)) for x in rope_parameters_short_factor) + ): + raise ValueError( + f"`rope_parameters`'s short_factor field must be a list of numbers, got {rope_parameters_short_factor}" + ) + if not len(rope_parameters_short_factor) == rotary_ndims // 2: + raise ValueError( + f"`rope_parameters`'s short_factor field must have length {rotary_ndims // 2}, got {len(rope_parameters_short_factor)}" + ) + + if rope_parameters_long_factor is not None: + if not ( + isinstance(rope_parameters_long_factor, list) + and all(isinstance(x, (int, float)) for x in rope_parameters_long_factor) + ): + raise ValueError( + f"`rope_parameters`'s long_factor field must be a list of numbers, got {rope_parameters_long_factor}" + ) + if not len(rope_parameters_long_factor) == rotary_ndims // 2: + raise ValueError( + f"`rope_parameters`'s long_factor field must have length {rotary_ndims // 2}, got {len(rope_parameters_long_factor)}" + ) + + +__all__ = ["Phi4MultimodalVisionConfig", "Phi4MultimodalAudioConfig", "Phi4MultimodalConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..9ce98251e50e028d48a3e8fc21929ae9ac3d32d2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py @@ -0,0 +1,281 @@ +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Processor class for Phi4Multimodal +""" + +import numpy as np + +from ...audio_utils import AudioInput, mel_filter_bank +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...image_processing_utils import BatchFeature +from ...utils import TensorType, is_torch_available, logging + + +if is_torch_available(): + import torch + + +logger = logging.get_logger(__name__) + + +class Phi4MultimodalFeatureExtractor(SequenceFeatureExtractor): + model_input_names = ["audio_input_features", "audio_embed_sizes", "audio_attention_mask"] + + def __init__( + self, + feature_size: int = 80, + sampling_rate: int = 16000, + hop_length: int = 160, + n_fft: int = 512, + win_length: int = 400, + preemphasis: float = 0.97, + padding_value: float = 0.0, + audio_compression_rate: int = 8, + audio_downsample_rate: int = 1, + audio_feat_stride: int = 1, + mel_min_frequency: float = 0, + mel_max_frequency: float = 7690, + **kwargs, + ): + super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) + + self.hop_length = hop_length + self.n_fft = n_fft + self.win_length = win_length + self.preemphasis = preemphasis + self.padding_value = padding_value + self.audio_compression_rate = audio_compression_rate + self.audio_downsample_rate = audio_downsample_rate + self.audio_feat_stride = audio_feat_stride + + self.mel_filters = mel_filter_bank( + num_frequency_bins=self.n_fft // 2 + 1, + num_mel_filters=self.feature_size, + min_frequency=mel_min_frequency, + max_frequency=mel_max_frequency, + sampling_rate=self.sampling_rate, + triangularize_in_mel_space=True, + mel_scale="kaldi", + ) + + def __call__( + self, + raw_speech: AudioInput, + sampling_rate: int | None = None, + pad_to_multiple_of: int | None = None, + padding: str | None = "longest", + max_length: int | None = None, + truncation: bool = False, + return_tensors: str | TensorType | None = None, + return_attention_mask: bool | None = True, + device: str | None = "cpu", + **kwargs, + ) -> BatchFeature: + """ + Main method to featurize and prepare for the model one or several audio sequence(s). Implementation uses PyTorch for + the STFT computation if available, otherwise a slower NumPy based one. + + Args: + raw_speech (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`): + The sequence or batch of sequences to be processed. Each sequence can be a numpy array or PyTorch tensor. + For batched inputs, sequences can be a list of numpy arrays or PyTorch tensors, or a single numpy array or + PyTorch tensor with first dimension being the batch size. + sampling_rate (`int`, *optional*): + The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass + `sampling_rate` at the forward call to prevent silent errors. + pad_to_multiple_of (`int`, *optional*, defaults to None): + If set will pad the sequence to a multiple of the provided value. + padding (`str`, *optional*, defaults to "longest"): + Padding strategy. Can be "longest" to pad to the longest sequence in the batch, or a specific length. + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length. + truncation (`bool`, *optional*, defaults to False): + Activates truncation to cut input sequences longer than *max_length* to *max_length*. + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of numpy arrays. Acceptable values are: + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + return_attention_mask (`bool`, *optional*, defaults to `True`): + Whether to return the extracted audio input features' attention mask. + device (`str`, *optional*, defaults to "cpu"): + Specifies the device for computation of the audio features. (e.g., "cpu", "cuda") + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + - **audio_input_features** -- Audio features extracted from the raw audio input, shape (batch_size, max_feature_length, feature_size). + - **audio_lengths** -- Length of each audio sample in the batch, shape (batch_size,). + - **audio_attention_mask** -- Attention mask for the audio input, shape (batch_size, max_feature_length). + If `return_tensors` is not specified, the fields will be PyTorch tensors if PyTorch is available, otherwise NumPy arrays. + """ + if sampling_rate is not None: + if sampling_rate != self.sampling_rate: + raise ValueError( + f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a" + f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input" + f" was sampled with {self.sampling_rate} and not {sampling_rate}." + ) + else: + logger.warning( + f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. " + "Failing to do so can result in silent errors that might be hard to debug." + ) + + # Convert to torch tensor + if isinstance(raw_speech, np.ndarray): + raw_speech = torch.tensor(raw_speech) + elif isinstance(raw_speech, (list, tuple)) and isinstance(raw_speech[0], np.ndarray): + raw_speech = [torch.tensor(speech) for speech in raw_speech] + + is_batched_torch = isinstance(raw_speech, torch.Tensor) and len(raw_speech.shape) > 1 + if is_batched_torch and len(raw_speech.shape) > 2: + logger.warning( + f"Only mono-channel audio is supported for input to {self.__class__.__name__}. " + "We will take the mean of the channels to convert to mono." + ) + raw_speech = raw_speech.mean(-1) + + is_batched_sequence = isinstance(raw_speech, (list, tuple)) + if is_batched_sequence: + for speech in raw_speech: + if len(speech.shape) > 1: + logger.warning( + f"Only mono-channel audio is supported for input to {self.__class__.__name__}. " + "We will take the mean of the channels to convert to mono." + ) + speech = speech.mean(-1) + + if is_batched_torch or is_batched_sequence: + raw_speech = [speech[:, None].to(torch.float32) for speech in raw_speech] + else: + raw_speech = [raw_speech[:, None].to(torch.float32)] + + audio_lengths = [len(speech) for speech in raw_speech] + + # convert into correct format for padding + batched_speech = BatchFeature(data={"audio_input_features": raw_speech, "audio_lengths": audio_lengths}) + padded_inputs = self.pad( + batched_speech, + padding=padding, + max_length=max_length, + truncation=truncation, + pad_to_multiple_of=pad_to_multiple_of, + return_tensors="pt", + ) + input_features = padded_inputs.audio_input_features.squeeze(-1) + audio_lengths = padded_inputs.audio_lengths + + input_features = self._torch_extract_fbank_features(input_features, audio_lengths, device) + + feature_lengths = (audio_lengths - self.win_length) // self.hop_length + 1 + feature_lengths = feature_lengths * self.audio_feat_stride + audio_embed_sizes = self._compute_audio_embed_size(feature_lengths) + + feature_attention_mask = ( + torch.arange(0, feature_lengths.max()) if is_torch_available() else np.arange(0, feature_lengths.max()) + ) + feature_attention_mask = ( + feature_attention_mask[None, :] < feature_lengths[:, None] if len(feature_lengths) > 1 else None + ) + + data = { + "audio_input_features": input_features, + "audio_embed_sizes": audio_embed_sizes, + } + if feature_attention_mask is not None and return_attention_mask: + data["audio_attention_mask"] = feature_attention_mask + + return BatchFeature(data=data, tensor_type=return_tensors) + + # TODO; @eustlb, move this to audio_utils in a general spectogram_batch function that handles torch and numpy + def _torch_extract_fbank_features( + self, waveform: "torch.FloatTensor", audio_lengths: "torch.Tensor", device: str = "cpu" + ) -> "torch.FloatTensor": + """ + Compute the log mel-scaled spectrogram of batched waveforms using PyTorch's FFT implementation. + + Args: + waveform (torch.FloatTensor` of shape `(batch_size, max_audio_length)`): + The batched waveforms. + audio_lengths (`torch.Tensor` of shape `(batch_size,)`): + The lengths of the waveforms along the max_audio_length dimension. + device (`str`, *optional*, defaults to "cpu"): + The device to run the computation on. (e.g., "cpu", "cuda") + + Returns: + `torch.FloatTensor` of shape `(batch_size, max_feature_length, feature_size)`: + The log mel-scaled spectrogram of the batched waveforms. + """ + fft_window = torch.hamming_window(self.win_length, periodic=False, device=device, dtype=torch.float64) + + # batched implementation + batch_size = waveform.shape[0] + frames = waveform.unfold(-1, self.win_length, self.hop_length) + + # --- + # the unbatched (and unpaded) original implementation skips last few audio values that can't be included in a frame + # we need to ensure that the corresponding frames for the padded input also mask these values + if batch_size > 1: + frames = frames.clone() + # concerned batch indices + to_mask_batch_idxs = torch.arange(batch_size)[audio_lengths != audio_lengths.max()] + if to_mask_batch_idxs.numel() > 0: + batch_idxs_down = (audio_lengths[to_mask_batch_idxs] - self.win_length) // self.hop_length + 1 + batch_idxs_up = (audio_lengths[to_mask_batch_idxs] // self.hop_length) - 1 + offset_idx = batch_idxs_down.min() + max_idx = batch_idxs_up.max() + + mask = torch.arange(max_idx - offset_idx, device=device).expand(to_mask_batch_idxs.shape[0], -1) + mask = ((batch_idxs_down - offset_idx).unsqueeze(1) <= mask) & ( + mask < (batch_idxs_up - offset_idx).unsqueeze(1) + ) + mask = mask.unsqueeze(-1).expand(-1, -1, self.win_length) + masked_frames = frames[to_mask_batch_idxs, offset_idx:max_idx].masked_fill_(mask, 0) + frames[to_mask_batch_idxs, offset_idx:max_idx] = masked_frames + # --- + + # apply pre-emphasis first order filter on fft windows + frames_prev = torch.roll(frames, 1, dims=-1) + frames_prev[:, :, 0] = frames_prev[:, :, 1] + frames = (frames - self.preemphasis * frames_prev) * 32768 + + # apply fft + S = torch.fft.rfft(fft_window * frames.view(-1, self.win_length), n=self.n_fft, dim=1) + S = S.view(frames.shape[0], -1, S.shape[-1]) + S = S.to(torch.complex64) + + spec = torch.abs(S) + spec_power = spec**2 + + # apply triangular mel filter bank + mel_filters = torch.from_numpy(self.mel_filters).to(device, torch.float32) + log_spec = torch.clamp(spec_power @ mel_filters, min=1.0) + log_spec = torch.log(log_spec) + + return log_spec + + def _compute_audio_embed_size(self, audio_frames): + integer = audio_frames // self.audio_compression_rate + remainder = audio_frames % self.audio_compression_rate + result = integer + (remainder > 0).to(integer.dtype) + + integer = result // self.audio_downsample_rate + remainder = result % self.audio_downsample_rate + result = integer + (remainder > 0).to(integer.dtype) # qformer compression + + return result + + +__all__ = ["Phi4MultimodalFeatureExtractor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/image_processing_phi4_multimodal.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/image_processing_phi4_multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..8cf56ad043f332364c83b27d2f6ab26bdd154d25 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/image_processing_phi4_multimodal.py @@ -0,0 +1,250 @@ +# Copyright 2025 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Phi4Multimodal.""" + +import math + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput, PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +class Phi4MultimodalImageProcessorKwargs(ImagesKwargs, total=False): + r""" + patch_size (`int`, *optional*): + The size of the patch. + dynamic_hd (`int`, *optional*): + The maximum number of crops per image. + """ + + patch_size: int + dynamic_hd: int + + +@auto_docstring +class Phi4MultimodalImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + size = {"height": 448, "width": 448} + patch_size = 14 + dynamic_hd = 36 + image_mean = [0.5, 0.5, 0.5] + image_std = [0.5, 0.5, 0.5] + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + valid_kwargs = Phi4MultimodalImageProcessorKwargs + model_input_names = ["image_pixel_values", "image_sizes", "image_attention_mask"] + + def __init__(self, **kwargs: Unpack[Phi4MultimodalImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[Phi4MultimodalImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def find_closest_aspect_ratio(self, aspect_ratio, target_ratios, width, height, image_size): + best_ratio_diff = float("inf") + best_ratio = (1, 1) + area = width * height + for ratio in target_ratios: + target_aspect_ratio = ratio[0] / ratio[1] + ratio_diff = abs(aspect_ratio - target_aspect_ratio) + if ratio_diff < best_ratio_diff: + best_ratio_diff = ratio_diff + best_ratio = ratio + elif ratio_diff == best_ratio_diff: + if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: + best_ratio = ratio + return best_ratio + + def dynamic_preprocess(self, image, image_size, patch_size, mask_size, max_num=36, min_num=1): + orig_height, orig_width = image.shape[-2:] + + w_crop_num = math.ceil(orig_width / float(image_size)) + h_crop_num = math.ceil(orig_height / float(image_size)) + if w_crop_num * h_crop_num > max_num: + aspect_ratio = orig_width / orig_height + + # calculate the existing image aspect ratio + target_ratios = { + (i, j) + for n in range(min_num, max_num + 1) + for i in range(1, n + 1) + for j in range(1, n + 1) + if i * j <= max_num and i * j >= min_num + } + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + # find the closest aspect ratio to the target + target_aspect_ratio = self.find_closest_aspect_ratio( + aspect_ratio, target_ratios, orig_width, orig_height, image_size + ) + + # calculate the target width and height + target_width = image_size * target_aspect_ratio[0] + target_height = image_size * target_aspect_ratio[1] + else: + target_width = image_size * w_crop_num + target_height = image_size * h_crop_num + target_aspect_ratio = (w_crop_num, h_crop_num) + + # Calculate the ratio + ratio_width = target_width / orig_width + ratio_height = target_height / orig_height + if ratio_width < ratio_height: + new_size = (target_width, int(orig_height * ratio_width)) + padding_width = 0 + padding_height = target_height - int(orig_height * ratio_width) + else: + new_size = (int(orig_width * ratio_height), target_height) + padding_width = target_width - int(orig_width * ratio_height) + padding_height = 0 + + attention_mask = torch.ones( + (int(mask_size * target_aspect_ratio[1]), int(mask_size * target_aspect_ratio[0])), + device=image.device, + ) + if padding_width >= patch_size: + attention_mask[:, -math.floor(padding_width / patch_size) :] = 0 + if padding_height >= patch_size: + attention_mask[-math.floor(padding_height / patch_size) :, :] = 0 + + if min(new_size[1], target_height) < 10 or min(new_size[0], target_width) < 10: + raise ValueError(f"the aspect ratio is very extreme {new_size}") + + image = tvF.resize(image, [new_size[1], new_size[0]]) + resized_img = tvF.pad(image, [0, 0, padding_width, padding_height], fill=[255, 255, 255]) + + return resized_img, attention_mask + + def pad_to_max_num_crops(self, images, max_crops=5): + """ + images: B x 3 x H x W, B<=max_crops + """ + B, _, H, W = images.shape + if max_crops > B: + pad = torch.zeros(max_crops - B, 3, H, W, dtype=images.dtype, device=images.device) + images = torch.cat([images, pad], dim=0) + return images + + def pad_mask_to_max_num_crops(self, masks, max_crops=5): + B, H, W = masks.shape + if max_crops > B: + pad = torch.ones(max_crops - B, H, W, dtype=masks.dtype, device=masks.device) + masks = torch.cat([masks, pad], dim=0) + return masks + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + patch_size: int, + dynamic_hd: int, + return_tensors: str | TensorType | None, + **kwargs, + ): + if size.height != size.width: + raise ValueError("Phi4MultimodalImageProcessor only supports square sizes.") + mask_size = size.height // patch_size + images_transformed = [] + masks_transformed = [] + images_tokens = [] + image_sizes = [] + for image in images: + resized_image, attention_mask = self.dynamic_preprocess( + image, size.height, patch_size, mask_size, max_num=dynamic_hd + ) + processed_image = self.rescale_and_normalize( + resized_image, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + global_image = self.resize(processed_image, size, resample=resample, antialias=False) + height, width = processed_image.shape[-2:] + mask_height, mask_width = attention_mask.shape[-2:] + global_attention_mask = torch.ones((1, mask_size, mask_size), device=processed_image.device) + + hd_image_reshape = processed_image.reshape( + 1, 3, height // size.height, size.height, width // size.width, size.width + ) + hd_image_reshape = hd_image_reshape.permute(0, 2, 4, 1, 3, 5) + hd_image_reshape = hd_image_reshape.reshape(-1, 3, size.height, size.width).contiguous() + + attention_mask_reshape = attention_mask.reshape( + mask_height // mask_size, mask_size, mask_width // mask_size, mask_size + ) + attention_mask_reshape = attention_mask_reshape.transpose(1, 2) + attention_mask_reshape = attention_mask_reshape.reshape(-1, mask_size, mask_size).contiguous() + + downsample_attention_mask = attention_mask_reshape[:, 0::2, 0::2] + downsample_attention_mask = downsample_attention_mask.reshape( + mask_height // mask_size, + mask_width // mask_size, + mask_size // 2 + mask_size % 2, + mask_size // 2 + mask_size % 2, + ) + downsample_attention_mask = downsample_attention_mask.transpose(1, 2) + downsample_attention_mask = downsample_attention_mask.reshape( + downsample_attention_mask.size(0) * downsample_attention_mask.size(1), + downsample_attention_mask.size(2) * downsample_attention_mask.size(3), + ) + + num_img_tokens = ( + 256 + + 1 + + int(downsample_attention_mask.sum().item()) + + int(downsample_attention_mask[:, 0].sum().item()) + + 16 + ) + + hd_image_reshape = torch.cat([global_image.unsqueeze(0), hd_image_reshape], dim=0) + hd_attention_mask_reshape = torch.cat([global_attention_mask, attention_mask_reshape], dim=0) + + images_transformed.append(hd_image_reshape) + masks_transformed.append(hd_attention_mask_reshape) + images_tokens.append(num_img_tokens) + image_sizes.append([height, width]) + max_crops = hd_image_reshape.size(0) + max_crops = max(img.size(0) for img in images_transformed) + images_transformed = [self.pad_to_max_num_crops(im, max_crops) for im in images_transformed] + images_transformed = torch.stack(images_transformed, dim=0) + masks_transformed = [self.pad_mask_to_max_num_crops(mask, max_crops) for mask in masks_transformed] + masks_transformed = torch.stack(masks_transformed, dim=0) + image_sizes = torch.tensor(image_sizes, dtype=torch.long) + + data = { + "image_pixel_values": images_transformed, + "image_sizes": image_sizes, + "image_attention_mask": masks_transformed, + "num_img_tokens": images_tokens, + } + + return BatchFeature(data=data, tensor_type=return_tensors) + + +__all__ = ["Phi4MultimodalImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/modeling_phi4_multimodal.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/modeling_phi4_multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..9e6c0339098ddfda547172d06f4cd3a2293dfc9b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/modeling_phi4_multimodal.py @@ -0,0 +1,1756 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/phi4_multimodal/modular_phi4_multimodal.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_phi4_multimodal.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable +from typing import Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub +from ...masking_utils import create_bidirectional_mask, create_causal_mask, create_sliding_window_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPast, + BaseModelOutputWithPooling, + CausalLMOutputWithPast, +) +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import auto_docstring, torch_int +from ...utils.generic import TransformersKwargs, can_return_tuple, maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_phi4_multimodal import Phi4MultimodalAudioConfig, Phi4MultimodalConfig, Phi4MultimodalVisionConfig + + +class Phi4MultimodalVisionMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +def simple_eager_attention_forward( + module: nn.Module, + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float | int = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Phi4MultimodalVisionAttention(nn.Module): + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = True + self.attention_dropout = config.attention_dropout + + self.k_proj = nn.Linear(config.hidden_size, config.hidden_size) + self.v_proj = nn.Linear(config.hidden_size, config.hidden_size) + self.q_proj = nn.Linear(config.hidden_size, config.hidden_size) + self.out_proj = nn.Linear(config.hidden_size, config.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, simple_eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1) + attn_output = self.out_proj(attn_output) + return attn_output, attn_weights + + +class Phi4MultimodalVisionEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.self_attn = Phi4MultimodalVisionAttention(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = Phi4MultimodalVisionMLP(config) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class Phi4MultimodalVisionEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`Phi4MultimodalVisionEncoderLayer`]. + + Args: + config: Phi4MultimodalVisionConfig + """ + + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList( + [Phi4MultimodalVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)] + ) + self.gradient_checkpointing = False + + # Ignore copy + @auto_docstring + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + **kwargs, + ) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +@auto_docstring +class Phi4MultimodalVisionPreTrainedModel(PreTrainedModel): + config: Phi4MultimodalVisionConfig + base_model_prefix = "phi4_vision" + input_modalities = ("image",) + supports_gradient_checkpointing = True + + _no_split_modules = ["Phi4MultimodalVisionEncoderLayer"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + + _can_record_outputs = { + "hidden_states": Phi4MultimodalVisionEncoderLayer, + "attentions": Phi4MultimodalVisionAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, Phi4MultimodalVisionEmbeddings): + width = ( + self.config.hidden_size + if isinstance(self.config, Phi4MultimodalVisionConfig) + else self.config.hidden_size + ) + init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) + elif isinstance(module, nn.Embedding): + init.default_flax_embed_init_(module.weight) + elif isinstance(module, Phi4MultimodalVisionAttention): + init.normal_(module.q_proj.weight) + init.normal_(module.k_proj.weight) + init.normal_(module.v_proj.weight) + init.normal_(module.out_proj.weight) + init.zeros_(module.q_proj.bias) + init.zeros_(module.k_proj.bias) + init.zeros_(module.v_proj.bias) + init.zeros_(module.out_proj.bias) + elif isinstance(module, Phi4MultimodalVisionMLP): + init.normal_(module.fc1.weight) + init.normal_(module.fc2.weight) + init.normal_(module.fc1.bias, std=1e-6) + init.normal_(module.fc2.bias, std=1e-6) + elif isinstance(module, Phi4MultimodalVisionMultiheadAttentionPoolingHead): + init.normal_(module.probe) + init.normal_(module.attention.in_proj_weight) + init.zeros_(module.attention.in_proj_bias) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + init.lecun_normal_(module.weight) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + + +class Phi4MultimodalVisionEmbeddings(nn.Module): + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__() + self.config = config + self.patch_size = config.patch_size + self.num_patches_per_side = config.image_size // self.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=config.hidden_size, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + self.position_embedding = nn.Embedding(self.num_patches_per_side**2, config.hidden_size) + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support torch.jit tracing and no class embeddings. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + + num_patches = embeddings.shape[1] + num_positions = self.position_embedding.weight.shape[0] + + # always interpolate when tracing to ensure the exported model works for dynamic input shapes + if not torch.jit.is_tracing() and num_patches == num_positions and height == width: + return self.position_embedding(self.position_ids) + + patch_pos_embed = self.position_embedding.weight.unsqueeze(0) + + dim = embeddings.shape[-1] + + new_height = height // self.patch_size + new_width = width // self.patch_size + + sqrt_num_positions = torch_int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(new_height, new_width), + mode="bicubic", + align_corners=False, + ) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return patch_pos_embed + + def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor: + batch_size, _, max_im_h, max_im_w = pixel_values.shape + + patch_embeds = self.patch_embedding(pixel_values) + embeddings = patch_embeds.flatten(2).transpose(1, 2) + + max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size + boundaries = torch.arange( + 1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side, device=pixel_values.device + ) + position_ids = torch.full( + size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0, device=pixel_values.device + ) + + nb_patches_h = patch_attention_mask[:, :, 0].sum(dim=1) # (batch_size,) + nb_patches_w = patch_attention_mask[:, 0, :].sum(dim=1) # (batch_size,) + + step_h = 1.0 / nb_patches_h # (batch_size,) + step_w = 1.0 / nb_patches_w # (batch_size,) + + max_patches_h = patch_attention_mask.size(1) + max_patches_w = patch_attention_mask.size(2) + h_indices = torch.arange(max_patches_h, device=position_ids.device, dtype=torch.float32) + w_indices = torch.arange(max_patches_w, device=position_ids.device, dtype=torch.float32) + + fractional_coords_h = h_indices[None, :] * step_h[:, None] + fractional_coords_w = w_indices[None, :] * step_w[:, None] + + fractional_coords_h = torch.clamp(fractional_coords_h, max=(1.0 - 1e-6)) + fractional_coords_w = torch.clamp(fractional_coords_w, max=(1.0 - 1e-6)) + + fractional_coords_h = fractional_coords_h.to(pixel_values.dtype) + fractional_coords_w = fractional_coords_w.to(pixel_values.dtype) + + bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True) + bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True) + + pos_ids = bucket_coords_h[:, :, None] * self.num_patches_per_side + bucket_coords_w[:, None, :] + pos_ids = pos_ids.reshape(batch_size, -1) + + position_ids[patch_attention_mask.view(batch_size, -1)] = pos_ids[patch_attention_mask.view(batch_size, -1)] + + embeddings = embeddings + self.position_embedding(position_ids) + return embeddings + + +class Phi4MultimodalVisionMultiheadAttentionPoolingHead(nn.Module): + """Multihead Attention Pooling.""" + + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__() + + self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True) + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = Phi4MultimodalVisionMLP(config) + + def forward(self, hidden_state, attention_mask): + batch_size = hidden_state.shape[0] + probe = self.probe.repeat(batch_size, 1, 1) + + hidden_state = self.attention( + query=probe, key=hidden_state, value=hidden_state, key_padding_mask=~attention_mask + )[0] + + residual = hidden_state + hidden_state = self.layernorm(hidden_state) + hidden_state = residual + self.mlp(hidden_state) + + return hidden_state[:, 0] + + +class Phi4MultimodalVisionModel(Phi4MultimodalVisionPreTrainedModel): + config: Phi4MultimodalVisionConfig + main_input_name = "pixel_values" + + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__(config) + self.config = config + + self.embeddings = Phi4MultimodalVisionEmbeddings(config) + self.encoder = Phi4MultimodalVisionEncoder(config) + self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.head = Phi4MultimodalVisionMultiheadAttentionPoolingHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.embeddings.patch_embedding + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + pixel_values, + patch_attention_mask: torch.BoolTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + batch_size = pixel_values.size(0) + if patch_attention_mask is None: + patch_attention_mask = torch.ones( + size=( + batch_size, + pixel_values.size(2) // self.config.patch_size, + pixel_values.size(3) // self.config.patch_size, + ), + dtype=torch.bool, + device=pixel_values.device, + ) + + hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask) + + patch_attention_mask = patch_attention_mask.view(batch_size, -1) + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=patch_attention_mask, + ) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + pooled_output = self.head( + hidden_state=last_hidden_state, + attention_mask=patch_attention_mask, + ) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + ) + + +class Phi4MultimodalImageEmbedding(nn.Module): + """Image embedding.""" + + def __init__(self, config: Phi4MultimodalConfig): + super().__init__() + self.config = config + self.layer_idx = config.vision_config.feature_layer + self.crop_size = config.vision_config.crop_size + self.image_dim_out = config.vision_config.hidden_size + + n_patches = config.vision_config.image_size // config.vision_config.patch_size + if n_patches % 2 != 0: + self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1)) + n_patches += 1 + self.num_img_tokens = (n_patches // 2) ** 2 + + self.drop = nn.Dropout(config.embd_pdrop) + self.img_processor = Phi4MultimodalVisionModel._from_config(config.vision_config) + self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2) + self.img_projection_up = nn.Linear(self.image_dim_out, config.hidden_size) + self.img_projection_down = nn.Linear(config.hidden_size, config.hidden_size) + self.global_img_feature_extensor = nn.Parameter(torch.zeros([1, 1, self.image_dim_out])) + self.sub_img_feature_extensor = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out])) + + def get_img_features(self, img_embeds: torch.FloatTensor, attention_mask=None) -> torch.FloatTensor: + img_processor_output = self.img_processor( + img_embeds, patch_attention_mask=attention_mask, output_hidden_states=True + ) + img_feature = img_processor_output.hidden_states[self.layer_idx] + + patch_feature = img_feature + # reshape to 2D tensor + width = int(math.sqrt(patch_feature.size(1))) + patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1)) + # convert to NCHW + patch_feature = patch_feature.permute(0, 3, 1, 2) + if getattr(self, "img_processor_padding", None) is not None: + patch_feature = self.img_processor_padding(patch_feature) + patch_feature = self.image_token_compression(patch_feature) + # convert to NHWC + patch_feature = patch_feature.permute(0, 2, 3, 1) + patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1)) + return patch_feature + + def forward( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.Tensor, + image_pixel_values: torch.FloatTensor, + image_sizes: torch.Tensor | None = None, + image_attention_mask: torch.Tensor | None = None, + ) -> torch.FloatTensor: + image_pixel_values = image_pixel_values.to(self.img_processor.embeddings.patch_embedding.weight.dtype) + + target_device = self.img_projection_up.bias.device + target_dtype = self.img_projection_up.bias.dtype + + batch_size = image_pixel_values.shape[0] + + img_features = self.get_img_features( + image_pixel_values.flatten(0, 1), + attention_mask=image_attention_mask.flatten(0, 1).to(dtype=bool, device=target_device), + ) + base_feat_size = int(np.sqrt(img_features.shape[1])) + img_features = img_features.view(batch_size, -1, base_feat_size**2, self.image_dim_out) + image_sizes = image_sizes.view(-1, 2) + + output_imgs = [] + for idx in range(batch_size): + height, width = image_sizes[idx] + height_ratio = height // self.crop_size + width_ratio = width // self.crop_size + area_ratio = height_ratio * width_ratio + + global_img = img_features[idx, :1] + global_img = global_img.reshape(1, base_feat_size, base_feat_size, self.image_dim_out).contiguous() + temporary_extensor = self.sub_img_feature_extensor.repeat(1, base_feat_size, 1, 1) + global_img = torch.cat([global_img, temporary_extensor], dim=2).reshape(1, -1, self.image_dim_out) + + sub_img = img_features[idx, 1:] + sub_img = sub_img[:area_ratio] + sub_img = ( + sub_img.reshape(height_ratio, width_ratio, base_feat_size, base_feat_size, self.image_dim_out) + .transpose(1, 2) + .reshape(1, height_ratio * base_feat_size, width_ratio * base_feat_size, self.image_dim_out) + .contiguous() + ) + + if image_attention_mask is not None: + reshaped_image_attention_mask = ( + image_attention_mask[idx, 1 : area_ratio + 1, 0::2, 0::2] + .reshape(height_ratio, width_ratio, base_feat_size, base_feat_size) + .transpose(1, 2) + .reshape(1, height_ratio * base_feat_size, width_ratio * base_feat_size) + ) + useful_height = int(reshaped_image_attention_mask[0, :, 0].sum().item()) + useful_width = int(reshaped_image_attention_mask[0, 0, :].sum().item()) + sub_img = sub_img[:, :useful_height, :useful_width] + temporary_extensor = self.sub_img_feature_extensor.repeat(1, useful_height, 1, 1) + else: + temporary_extensor = self.sub_img_feature_extensor.repeat(1, height_ratio * base_feat_size, 1, 1) + + sub_img = torch.cat([sub_img, temporary_extensor], dim=2).reshape(1, -1, self.image_dim_out) + + # Merge global and sub + output_imgs.append(torch.cat([sub_img, self.global_img_feature_extensor, global_img], dim=1)) + + img_set_tensor = [] + for output_img in output_imgs: + output_img = output_img.to(device=target_device, dtype=target_dtype) + img_feature_proj = self.img_projection_up(output_img) + img_feature_proj = nn.functional.gelu(img_feature_proj) + img_feature_proj = self.img_projection_down(img_feature_proj) + img_set_tensor.append(img_feature_proj) + + merged_img_set_tensor = torch.cat(img_set_tensor, dim=1).squeeze(0) + merged_img_set_tensor = merged_img_set_tensor.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device) + + with torch.no_grad(): + positions_tuple = torch.nonzero(input_ids == self.config.vision_config.image_token_id, as_tuple=True) + + # Temporarily disable autocast to avoid issue on bf16 tensors + # Ref: https://github.com/pytorch/pytorch/issues/132715 + with maybe_autocast(device_type=inputs_embeds.device.type, enabled=False): + image_embeds = inputs_embeds.index_put( + indices=positions_tuple, values=merged_img_set_tensor, accumulate=False + ) + + image_embeds = self.drop(image_embeds) + + return image_embeds + + +########################################################## AUDIO ############################################# + + +class Phi4MultimodalAudioMLP(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.act_fn = ACT2FN[config.activation] + self.gate_up_proj = nn.Linear(config.hidden_size, config.intermediate_size * 2) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states): + hidden_states = self.layer_norm(hidden_states) + up_states = self.gate_up_proj(hidden_states) + up_states, gate = up_states.chunk(2, dim=-1) + up_states = up_states * self.act_fn(gate) + up_states = self.dropout(up_states) + hidden_states = self.down_proj(up_states) + out = self.dropout(hidden_states) + + return out + + +class Phi4MultimodalAudioAttention(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.config = config + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.dropout_rate + self.is_causal = True + + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs, + ): + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, simple_eager_attention_forward + ) + + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output + + +class Phi4MultimodalAudioDepthWiseSeparableConv1d(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig, padding: int = 0): + super().__init__() + self.dw_conv = nn.Conv1d( + config.hidden_size, + config.hidden_size * config.depthwise_multiplier, + config.kernel_size, + 1, + padding=padding, + groups=config.hidden_size, + ) + self.pw_conv = nn.Conv1d( + config.hidden_size * config.depthwise_multiplier, config.depthwise_separable_out_channel, 1, 1, 0 + ) + + def forward(self, hidden_states): + return self.pw_conv(self.dw_conv(hidden_states)) + + +class Phi4MultimodalAudioGluPointWiseConv(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.config = config + self.output_dim = config.ext_pw_out_channel + + self.ext_pw_conv_1d = nn.Conv1d(config.hidden_size, config.ext_pw_out_channel * 2, kernel_size=1, stride=1) + self.glu_act = ACT2FN[config.conv_glu_type] + self.b1 = nn.Parameter(torch.zeros(1, config.ext_pw_out_channel, 1)) + self.b2 = nn.Parameter(torch.zeros(1, config.ext_pw_out_channel, 1)) + + def forward(self, hidden_states): + # we assume the input always has the #channel (#dim) in the last dimension of the + # tensor, so need to switch the dimension first for 1D-Conv case + hidden_states = hidden_states.permute([0, 2, 1]) + hidden_states = self.ext_pw_conv_1d(hidden_states) + out = hidden_states[:, 0 : self.output_dim, :] + self.b1 + out = out * self.glu_act(hidden_states[:, self.output_dim : self.output_dim * 2, :] + self.b2) + return out.permute([0, 2, 1]) + + +class Phi4MultimodalAudioConvModule(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.config = config + self.kernel_size = config.kernel_size + + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.glu = Phi4MultimodalAudioGluPointWiseConv(config) + self.dw_sep_conv_1d = Phi4MultimodalAudioDepthWiseSeparableConv1d(config, padding=config.kernel_size - 1) + self.act = ACT2FN[config.conv_activation] + self.ext_pw_conv_1d = nn.Conv1d(config.hidden_size, config.ext_pw_out_channel, kernel_size=1, stride=1) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states: torch.Tensor): + hidden_states = self.glu(self.layer_norm(hidden_states)) + hidden_states = self.dw_sep_conv_1d(hidden_states.permute([0, 2, 1])) + + if self.kernel_size > 1: + hidden_states = hidden_states[:, :, : -(self.kernel_size - 1)] + + hidden_states = self.act(hidden_states) + hidden_states = self.ext_pw_conv_1d(hidden_states) + out = self.dropout(hidden_states.permute([0, 2, 1])) + return out + + +class Phi4MultimodalAudioConformerEncoderLayer(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + + self.feed_forward_in = Phi4MultimodalAudioMLP(config) + self.self_attn = Phi4MultimodalAudioAttention(config) + self.conv = Phi4MultimodalAudioConvModule(config) + self.feed_forward_out = Phi4MultimodalAudioMLP(config) + self.layer_norm_att = nn.LayerNorm(config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + ): + residual = hidden_states + 0.5 * self.feed_forward_in(hidden_states) + hidden_states = self.layer_norm_att(residual) + + hidden_states = residual + self.self_attn(hidden_states, attention_mask) + hidden_states = hidden_states + self.conv(hidden_states) + hidden_states = hidden_states + 0.5 * self.feed_forward_out(hidden_states) + + out = self.layer_norm(hidden_states) + + return out + + +class Phi4MultimodalAudioNemoConvSubsampling(torch.nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.subsampling_factor = config.time_reduction + self.sampling_num = int(math.log2(self.subsampling_factor)) + self.act_fn = ACT2FN[config.nemo_activation] + conv_channels = config.nemo_conv_channels + + layers = [ + nn.Conv2d(1, conv_channels, kernel_size=3, stride=2, padding=1), + self.act_fn, + ] + for _ in range(self.sampling_num - 1): + layers.extend( + [ + nn.Conv2d(conv_channels, conv_channels, kernel_size=3, stride=2, padding=1, groups=conv_channels), + nn.Conv2d(conv_channels, conv_channels, kernel_size=1, stride=1, padding=0, groups=1), + self.act_fn, + ] + ) + + # Aggregate the layers + self.conv = torch.nn.Sequential(*layers) + self.out = torch.nn.Linear(conv_channels * config.nemo_final_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor, mask: torch.Tensor | None): + # Unsqueeze Channel Axis + hidden_states = hidden_states.unsqueeze(1) + hidden_states = self.conv(hidden_states) + + # Flatten Channel and Frequency Axes + b, _, t, _ = hidden_states.size() + hidden_states = self.out(hidden_states.transpose(1, 2).reshape(b, t, -1)) + + if mask is None: + return hidden_states, None + + max_audio_length = hidden_states.shape[1] + feature_lens = mask.sum(1) + padding_length = torch.ceil(feature_lens / self.subsampling_factor) + arange_ = torch.arange(0, max_audio_length, device=hidden_states.device) + pad_mask = arange_.expand(padding_length.size(0), -1) < padding_length.unsqueeze(1) + return hidden_states, pad_mask.unsqueeze(1) + + +class Phi4MultimodalAudioRelativeAttentionBias(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + + self.max_distance = config.bias_max_distance + self.symmetric = config.bias_symmetric + self.num_buckets = self.max_distance + if not config.bias_symmetric: + self.num_buckets *= 2 + self.bias_values = nn.Embedding(self.num_buckets, config.num_attention_heads) + + def forward(self, x): + # instantiate bias compatible with shape of x + max_pos = x.size(1) + context_position = torch.arange(max_pos, device=x.device, dtype=torch.long)[:, None] + memory_position = torch.arange(max_pos, device=x.device, dtype=torch.long)[None, :] + relative_position = memory_position - context_position + # clipping to a maximum distance using ops that play well with ONNX export + relative_position = relative_position.masked_fill(relative_position < -self.max_distance, -self.max_distance) + relative_position = relative_position.masked_fill( + relative_position > self.max_distance - 1, self.max_distance - 1 + ) + + # mapping from relative position to index in the bias parameter + bias_idx = relative_position + bias_idx = bias_idx.abs() if self.symmetric else bias_idx + self.num_buckets // 2 + + att_bias = self.bias_values(bias_idx) + att_bias = att_bias.permute(2, 0, 1).unsqueeze(0) + + return att_bias + + +class Phi4MultimodalAudioMeanVarianceNormLayer(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.register_buffer("global_mean", torch.zeros(config.input_size)) + self.register_buffer("global_invstd", torch.ones(config.input_size)) + + def forward(self, x): + return (x - self.global_mean) * self.global_invstd + + +@auto_docstring +class Phi4MultimodalAudioPreTrainedModel(PreTrainedModel): + config: Phi4MultimodalAudioConfig + input_modalities = "audio" + supports_gradient_checkpointing = True + _no_split_modules = ["Phi4MultimodalAudioConformerEncoderLayer"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Phi4MultimodalAudioGluPointWiseConv): + init.zeros_(module.b1) + init.zeros_(module.b2) + elif isinstance(module, Phi4MultimodalAudioMeanVarianceNormLayer): + init.zeros_(module.global_mean) + init.ones_(module.global_invstd) + + +def unfold_tensor(tensor, max_seq_len): + """ + For a given tensor with shape of (N, T, D), if sequence length T is longer than max_seq_len, + this function unfold it to a (NT', max_seq_len, D) where T' is T // max_seq_len. + Args: + tensor: N, T, D + """ + _, _, D = tensor.shape + tensor = tensor.transpose(-1, -2) + # N x D x 1 x T => N x (D x max_seq_len) x T' + tensor = F.unfold(tensor[..., None, :], kernel_size=(1, max_seq_len), stride=(1, max_seq_len)) + + new_bsz, _, slen = tensor.shape + tensor = tensor.view(new_bsz, -1, max_seq_len, slen) + tensor = tensor.permute(0, 3, 2, 1) + tensor = tensor.view(-1, max_seq_len, D).contiguous() + return tensor + + +def adaptive_enc_mask(x_len, chunk_start_idx, left_window=0, right_window=0): + """ + The function is very important for Transformer Transducer Streaming mode + Args: + xs_len (int): sequence length + chunk_start_idx (list): first idx of each chunk, such as [0,18,36,48]. It also supports adaptive chunk size [0,10,15,45] + left_window (int): how many left chunks can be seen + right_window (int): how many right chunks can be seen. It is used for chunk overlap model. + Returns: + mask (torch.Tensor): a mask tensor for streaming model + """ + chunk_start_idx = torch.Tensor(chunk_start_idx).long() + start_pad = torch.nn.functional.pad( + chunk_start_idx, (1, 0) + ) # append 0 to the beginning, so it becomes [0, 0, 18, 36, 48] + end_pad = torch.nn.functional.pad( + chunk_start_idx, (0, 1), value=x_len + ) # append x_len to the end, so it becomes [0,18,36,48, x_len] + seq_range = torch.arange(0, x_len).unsqueeze(-1) + idx = ((seq_range < end_pad) & (seq_range >= start_pad)).nonzero()[:, 1] + seq_range_expand = torch.arange(0, x_len).unsqueeze(0).expand(x_len, -1) + idx_left = idx - left_window + idx_left[idx_left < 0] = 0 + boundary_left = start_pad[idx_left] + mask_left = seq_range_expand >= boundary_left.unsqueeze(-1) + idx_right = idx + right_window + idx_right[idx_right > len(chunk_start_idx)] = len(chunk_start_idx) + boundary_right = end_pad[idx_right] + mask_right = seq_range_expand < boundary_right.unsqueeze(-1) + return mask_left & mask_right + + +class Phi4MultimodalAudioModel(Phi4MultimodalAudioPreTrainedModel): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__(config) + self.config = config + + self.encoder_embedding = Phi4MultimodalAudioMeanVarianceNormLayer(config) + self.embed = Phi4MultimodalAudioNemoConvSubsampling(config) + self.relative_attention_bias_layer = Phi4MultimodalAudioRelativeAttentionBias(config) + self.encoders = nn.ModuleList( + [Phi4MultimodalAudioConformerEncoderLayer(config) for _ in range(config.num_blocks)] + ) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + def _streaming_mask(self, seq_len, batch_size, chunk_size, left_chunk): + # Create mask matrix for streaming + # S stores start index. if chunksize is 18, s is [0,18,36,....] + chunk_start_idx = np.arange(0, seq_len, chunk_size) + # avoid randomness when run evaluation or decoding + if self.training and np.random.rand() > 0.5: + # Either first or last chunk is not complete. + # If only the last one is not complete, EOS is not effective + chunk_start_idx = seq_len - chunk_start_idx + chunk_start_idx = chunk_start_idx[::-1] + chunk_start_idx = chunk_start_idx[:-1] + chunk_start_idx = np.insert(chunk_start_idx, 0, 0) + + enc_streaming_mask = ( + adaptive_enc_mask(seq_len, chunk_start_idx, left_window=left_chunk) + .unsqueeze(0) + .expand([batch_size, -1, -1]) + ) + return enc_streaming_mask + + def forward_embeddings(self, hidden_states, masks): + """Forwarding the inputs through the top embedding layers""" + seq_len = math.ceil(hidden_states.shape[1] / self.config.time_reduction) + if seq_len <= 0: + raise ValueError( + f"The sequence length after time reduction is invalid: {seq_len}. Your input feature is too short." + ) + + batch_size = hidden_states.shape[0] + + enc_streaming_mask = self._streaming_mask(seq_len, batch_size, self.config.chunk_size, self.config.left_chunk) + enc_streaming_mask = enc_streaming_mask.to(hidden_states.device) + + hidden_states, masks = self.embed(hidden_states, masks) + + streaming_mask = enc_streaming_mask + if streaming_mask is not None and masks is not None: + hs_mask = masks & streaming_mask + elif masks is not None: + hs_mask = masks + else: + hs_mask = streaming_mask + + return hidden_states, hs_mask, masks + + def calculate_hs_mask(self, hidden_states, device, mask): + max_audio_length = hidden_states.shape[1] + batch_size = hidden_states.shape[0] + enc_streaming_mask = self._streaming_mask( + max_audio_length, batch_size, self.config.chunk_size, self.config.left_chunk + ) + enc_streaming_mask = enc_streaming_mask.to(device) + if mask is None: + return enc_streaming_mask + + feature_lens = mask.sum(1) + padding_length = feature_lens + pad_mask = torch.arange(0, max_audio_length, device=device).expand( + padding_length.size(0), -1 + ) < padding_length.unsqueeze(1) + pad_mask = pad_mask.unsqueeze(1) + pad_mask = pad_mask & enc_streaming_mask + return pad_mask + + def forward(self, hidden_states: torch.Tensor, mask: torch.Tensor | None, **kwargs): + hidden_states = self.encoder_embedding(hidden_states) + hidden_states, hs_mask, mask = self.forward_embeddings(hidden_states, mask) + + unfolded = False + bs, seq_len, _ = hidden_states.shape + max_seq_len = 500 # maximum position for absolute positional encoding + if seq_len > max_seq_len: + # audio sequence is longer than max_seq_len, unfold it into chunks of max_seq_len + unfolded = True + # the unfold op will drop residual frames, pad it to the multiple of max_seq_len + if seq_len % max_seq_len > 0: + chunk_pad_size = max_seq_len - (seq_len % max_seq_len) + else: + chunk_pad_size = 0 + if chunk_pad_size > 0: + hidden_states_pad = F.pad(hidden_states, (0, 0, 0, chunk_pad_size), "constant", 0) + hidden_states = hidden_states_pad.to(hidden_states.device) + + hidden_states = unfold_tensor(hidden_states, max_seq_len) + masks_unfold = None + if mask is not None: + # revise hs_mask here because the previous calculated hs_mask did not consider extra pad + subsampled_pad_mask = mask.squeeze(1) # [bz, subsampled_unmask_seq_len] + extra_padded_subsamlped_pad_mask = F.pad( + subsampled_pad_mask, (0, chunk_pad_size), "constant", False + ) # extra padding to the pad mask + extra_padded_subsamlped_pad_mask = extra_padded_subsamlped_pad_mask.unsqueeze(-1).float() + masks_unfold = unfold_tensor( + extra_padded_subsamlped_pad_mask, max_seq_len + ) # unfold the pad mask like we did to the input tensor + masks_unfold = masks_unfold.squeeze(-1).bool() # unfold op does not support bool tensor + hs_mask = self.calculate_hs_mask( + hidden_states, hidden_states.device, masks_unfold + ) # calculate hs_mask based on the unfolded pad mask + + relative_attention_bias = self.relative_attention_bias_layer(hidden_states) + attention_mask = hs_mask.unsqueeze(1) + relative_attention_bias + + for layer in self.encoders: + hidden_states = layer(hidden_states, attention_mask) + + if unfolded: + embed_dim = hidden_states.shape[-1] + hidden_states = hidden_states.reshape(bs, -1, embed_dim) + # if we ever padded before unfolding, we need to remove the padding + if chunk_pad_size > 0: + hidden_states = hidden_states[:, :-chunk_pad_size, :] + + return hidden_states + + +class Phi4MultimodalAudioEmbedding(nn.Module): + def __init__(self, config: Phi4MultimodalConfig): + super().__init__() + self.config = config + self.layer_idx = config.audio_config.feature_layer + + self.drop = nn.Dropout(config.embd_pdrop) + self.encoder = Phi4MultimodalAudioModel._from_config(config.audio_config) + self.up_proj_for_speech = nn.Linear( + config.audio_config.hidden_size * config.audio_config.downsample_rate, config.hidden_size + ) + self.down_proj_for_speech = nn.Linear(config.hidden_size, config.hidden_size) + self.up_proj_for_vision_speech = nn.Linear( + config.audio_config.hidden_size * config.audio_config.downsample_rate, config.hidden_size + ) + self.down_proj_for_vision_speech = nn.Linear(config.hidden_size, config.hidden_size) + + def forward( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.Tensor, + audio_input_features: torch.FloatTensor, + audio_embed_sizes=None, + audio_attention_mask=None, + audio_projection_mode="speech", + ) -> torch.FloatTensor: + with torch.no_grad(): + positions_tuple = torch.nonzero(input_ids == self.config.audio_config.audio_token_id, as_tuple=True) + + up_proj = self.up_proj_for_speech if audio_projection_mode == "speech" else self.up_proj_for_vision_speech + down_proj = ( + self.down_proj_for_speech if audio_projection_mode == "speech" else self.down_proj_for_vision_speech + ) + + target_device = up_proj.bias.device + target_dtype = up_proj.bias.dtype + + audio_input_features = audio_input_features.to(device=target_device, dtype=target_dtype) + + audio_encoder_hidden_states = self.encoder(audio_input_features, audio_attention_mask) + audio_encoder_hidden_states = up_proj(audio_encoder_hidden_states) + audio_encoder_hidden_states = nn.functional.gelu(audio_encoder_hidden_states) + audio_embeds = down_proj(audio_encoder_hidden_states) + + merged_audio_embeds = torch.cat( + [audio_embeds[i, : audio_embed_sizes[i], :] for i in range(len(audio_embed_sizes))], dim=0 + ) + merged_audio_embeds = merged_audio_embeds.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device) + # Temporarily disable autocast to avoid issue on bf16 tensors + # Ref: https://github.com/pytorch/pytorch/issues/132715 + with maybe_autocast(device_type=inputs_embeds.device.type, enabled=False): + audio_embeds = inputs_embeds.index_put( + indices=positions_tuple, values=merged_audio_embeds, accumulate=False + ) + + audio_embeds = self.drop(audio_embeds) + + return audio_embeds + + +@use_kernel_forward_from_hub("RMSNorm") +class Phi4MultimodalRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + Phi4MultimodalRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class Phi4MultimodalMLP(nn.Module): + def __init__(self, config): + super().__init__() + + self.config = config + self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) + self.activation_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + up_states = self.gate_up_proj(hidden_states) + + gate, up_states = up_states.chunk(2, dim=-1) + up_states = up_states * self.activation_fn(gate) + + return self.down_proj(up_states) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + + rotary_dim = cos.shape[-1] + q_rot, q_pass = q[..., :rotary_dim], q[..., rotary_dim:] + k_rot, k_pass = k[..., :rotary_dim], k[..., rotary_dim:] + + q_embed = torch.cat([(q_rot * cos) + (rotate_half(q_rot) * sin), q_pass], dim=-1) + k_embed = torch.cat([(k_rot * cos) + (rotate_half(k_rot) * sin), k_pass], dim=-1) + return q_embed, k_embed + + +class Phi4MultimodalAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: Phi4MultimodalConfig, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.num_key_value_heads = config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) + self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + qkv = self.qkv_proj(hidden_states) + query_pos = self.config.num_attention_heads * self.head_dim + query_states = qkv[..., :query_pos] + key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim] + value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :] + + query_states = query_states.view(hidden_shape).transpose(1, 2) + key_states = key_states.view(hidden_shape).transpose(1, 2) + value_states = value_states.view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=getattr(self.config, "sliding_window", None), + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Phi4MultimodalDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Phi4MultimodalConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + self.self_attn = Phi4MultimodalAttention(config=config, layer_idx=layer_idx) + self.mlp = Phi4MultimodalMLP(config) + self.input_layernorm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.config = config + self.resid_attn_dropout = nn.Dropout(config.resid_pdrop) + self.resid_mlp_dropout = nn.Dropout(config.resid_pdrop) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + self.resid_attn_dropout(hidden_states) # main diff with Llama + + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.resid_mlp_dropout(hidden_states) # main diff with Llama + return hidden_states + + +class Phi4MultimodalFeatureEmbedding(nn.Module): + """Image-audio embedding.""" + + def __init__(self, config: Phi4MultimodalConfig) -> None: + super().__init__() + self.config = config + self.image_token_id = config.vision_config.image_token_id + self.audio_token_id = config.audio_config.audio_token_id + self.image_embed = Phi4MultimodalImageEmbedding(config) + self.audio_embed = Phi4MultimodalAudioEmbedding(config) + + def forward( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.Tensor, + image_pixel_values: torch.FloatTensor | None = None, + audio_input_features: torch.FloatTensor | None = None, + image_sizes=None, + image_attention_mask=None, + audio_embed_sizes=None, + audio_attention_mask=None, + ) -> torch.FloatTensor: + with torch.no_grad(): + image_position_mask = (input_ids == self.config.vision_config.image_token_id).unsqueeze(-1) + non_image_position_mask = ~image_position_mask + + image_embeds = None + audio_embeds = None + if image_pixel_values is not None and (input_ids == self.image_token_id).any(): + image_embeds = self.image_embed( + input_ids, + inputs_embeds, + image_pixel_values=image_pixel_values, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + ) + if audio_input_features is not None and (input_ids == self.audio_token_id).any(): + audio_projection_mode = "vision" if image_pixel_values is not None else "speech" + audio_embeds = self.audio_embed( + input_ids, + inputs_embeds, + audio_input_features=audio_input_features, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + audio_projection_mode=audio_projection_mode, + ) + + # merge image and audio + if image_embeds is not None and audio_embeds is not None: + inputs_embeds = image_embeds * image_position_mask + audio_embeds * non_image_position_mask + elif image_embeds is not None: + inputs_embeds = image_embeds + elif audio_embeds is not None: + inputs_embeds = audio_embeds + + return inputs_embeds + + +@auto_docstring +class Phi4MultimodalPreTrainedModel(PreTrainedModel): + config: Phi4MultimodalConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Phi4MultimodalDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": Phi4MultimodalDecoderLayer, + "attentions": Phi4MultimodalAttention, + } + _version = "0.0.5" + input_modalities = ("image", "audio", "text") + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Phi4MultimodalImageEmbedding): + init.zeros_(module.global_img_feature_extensor) + init.zeros_(module.sub_img_feature_extensor) + + +class Phi4MultimodalRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: Phi4MultimodalConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: Phi4MultimodalConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0) + head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + dim = int(head_dim * partial_rotary_factor) + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +@auto_docstring +class Phi4MultimodalModel(Phi4MultimodalPreTrainedModel): + def __init__(self, config: Phi4MultimodalConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + + self.layers = nn.ModuleList( + [Phi4MultimodalDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = Phi4MultimodalRotaryEmbedding(config=config) + + self.gradient_checkpointing = False + self.embed_dropout = nn.Dropout(config.embd_pdrop) + + self.embed_tokens_extend = Phi4MultimodalFeatureEmbedding(config) + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + image_pixel_values: torch.FloatTensor | None = None, + image_sizes: torch.LongTensor | None = None, + image_attention_mask=None, + audio_input_features: torch.FloatTensor | None = None, + audio_embed_sizes=None, + audio_attention_mask=None, + use_cache: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPast: + r""" + image_pixel_values (`torch.FloatTensor`, *optional*): + If the input contains images, these correspond to the pixel values after transformations (as returned by + the Processor) + image_sizes (`torch.LongTensor`, *optional*): + If the input contains images, these correspond to size of each image. + image_attention_mask (`torch.LongTensor`, *optional*): + Attention mask for the images. + audio_input_features (`torch.FloatTensor`, *optional*): + If the input contains audio samples, these correspond to the values after transformation (as returned by + the Processor). + audio_embed_sizes (`torch.Tensor`, *optional*): + Size of the audio inputs. + audio_attention_mask (`torch.Tensor, *optional*): + Attention mask for the audio inputs. + """ + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + inputs_embeds = self.embed_tokens_extend( + input_ids, + inputs_embeds, + image_pixel_values=image_pixel_values, + audio_input_features=audio_input_features, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + ) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask + causal_mask = mask_function( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +@auto_docstring +class Phi4MultimodalForCausalLM(Phi4MultimodalPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = Phi4MultimodalModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + image_pixel_values: torch.FloatTensor | None = None, + image_sizes: torch.LongTensor | None = None, + image_attention_mask=None, + audio_input_features: torch.FloatTensor | None = None, + audio_embed_sizes=None, + audio_attention_mask=None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> CausalLMOutputWithPast: + r""" + image_pixel_values (`torch.FloatTensor`, *optional*): + If the input contains images, these correspond to the pixel values after transformations (as returned by + the Processor) + image_sizes (`torch.LongTensor`, *optional*): + If the input contains images, these correspond to size of each image. + image_attention_mask (`torch.LongTensor`, *optional*): + Attention mask for the images. + audio_input_features (`torch.FloatTensor`, *optional*): + If the input contains audio samples, these correspond to the values after transformation (as returned by + the Processor). + audio_embed_sizes (`torch.Tensor`, *optional*): + Size of the audio inputs. + audio_attention_mask (`torch.Tensor, *optional*): + Attention mask for the audio inputs. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + ```python + >>> from transformers import AutoTokenizer, Phi4MultimodalForCausalLM + >>> model = Phi4MultimodalForCausalLM.from_pretrained("TBA") + >>> tokenizer = AutoTokenizer.from_pretrained("TBA") + >>> prompt = "This is an example script ." + >>> inputs = tokenizer(prompt, return_tensors="pt") + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + 'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum' + ```""" + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + image_pixel_values=image_pixel_values, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + audio_input_features=audio_input_features, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + image_pixel_values=None, + image_sizes=None, + image_attention_mask=None, + audio_input_features=None, + audio_embed_sizes=None, + audio_attention_mask=None, + position_ids=None, + use_cache=True, + logits_to_keep=0, + **kwargs, + ): + # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the + # process + + # When the first time input length reached long and short factor switching point, enforce re-compute cache + # It will cause downside of slower at this single token position, however, better than current failure. + if ( + past_key_values + and self.config.rope_parameters + and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1 + ): + past_length = past_key_values.get_seq_length() + if past_length <= self.config.original_max_position_embeddings: + past_key_values = None + + model_inputs = super().prepare_inputs_for_generation( + input_ids=input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + image_pixel_values=image_pixel_values, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + audio_input_features=audio_input_features, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + position_ids=position_ids, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **kwargs, + ) + return model_inputs + + +__all__ = [ + "Phi4MultimodalAudioPreTrainedModel", + "Phi4MultimodalAudioModel", + "Phi4MultimodalVisionPreTrainedModel", + "Phi4MultimodalVisionModel", + "Phi4MultimodalPreTrainedModel", + "Phi4MultimodalModel", + "Phi4MultimodalForCausalLM", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/modular_phi4_multimodal.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/modular_phi4_multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..4e02bbc4506e04ff73ea2ee1db56c8334cb6ab6c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/modular_phi4_multimodal.py @@ -0,0 +1,1532 @@ +# Copyright 2025 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable + +import numpy as np +import torch +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...configuration_utils import PreTrainedConfig +from ...masking_utils import create_bidirectional_mask, create_causal_mask, create_sliding_window_causal_mask +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPast, + BaseModelOutputWithPooling, + CausalLMOutputWithPast, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import auto_docstring, logging +from ...utils.generic import ( + TransformersKwargs, + can_return_tuple, + maybe_autocast, + merge_with_config_defaults, +) +from ...utils.output_capturing import capture_outputs +from ..phi3.configuration_phi3 import Phi3Config +from ..phi3.modeling_phi3 import ( + Phi3DecoderLayer, + Phi3ForCausalLM, + Phi3Model, + Phi3PreTrainedModel, + Phi3RMSNorm, +) +from ..siglip.configuration_siglip import SiglipVisionConfig +from ..siglip.modeling_siglip import ( + SiglipEncoder, + SiglipEncoderLayer, + SiglipMLP, + SiglipMultiheadAttentionPoolingHead, + SiglipPreTrainedModel, + SiglipVisionEmbeddings, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="microsoft/Phi-4-multimodal-instruct") +@strict +class Phi4MultimodalVisionConfig(SiglipVisionConfig): + r""" + crop_size (`int`, *optional*, defaults to 448): + Crop size for the input images. + feature_layer (`int`, *optional*, defaults to -2): + The index of the layer of the encoder from which to extract image features. + + Example: + + ```python + >>> from transformers import Phi4MultimodalVisionConfig + + >>> # Initializing a Phi4MultimodalVisionConfig with microsoft/Phi-4-multimodal-instruct style configuration + >>> configuration = Phi4MultimodalVisionConfig() + ```""" + + model_type = "phi4_multimodal_vision" + + hidden_size: int = 1152 + intermediate_size: int = 4304 + num_hidden_layers: int = 27 + num_attention_heads: int = 16 + image_size: int | list[int] | tuple[int, int] = 448 + patch_size: int | list[int] | tuple[int, int] = 14 + crop_size: int = 448 + image_token_id: int = 200010 + feature_layer: int = -2 + + +@auto_docstring(checkpoint="microsoft/Phi-4-multimodal-instruct") +@strict +class Phi4MultimodalAudioConfig(PreTrainedConfig): + r""" + num_blocks (`int`, *optional*, defaults to 24): + Number of hidden layers in the Transformer encoder. + activation (`str`, *optional*, defaults to `"swish"`): + The non-linear activation function in the MLPs. + chunk_size (`int`, *optional*, defaults to -1): + The chunk size to create the masks. + left_chunk (`int`, *optional*, defaults to 18): + The left chunk to create the masks. + dropout_rate (`float`, *optional*, defaults to 0.0): + The dropout ratio. + ext_pw_out_channel (`int`, *optional*, defaults to 1024): + Number of out channels in the point-wise conv modules. + depthwise_separable_out_channel (`int`, *optional*, defaults to 1024): + Number of out channels in the depth-wise separable conv modules. + depthwise_multiplier (`int`, *optional*, defaults to 1): + Input size multiplier for the depth-wise separable conv modules. + kernel_size (`int`, *optional*, defaults to 3): + Kernel size for the depth-wise separable conv modules. + conv_activation (`str`, *optional*, defaults to `"swish"`): + The non-linear activation function in the conv modules. + input_size (`int`, *optional*, defaults to 80): + Input size for the audio model. + conv_glu_type (`str`, *optional*, defaults to `"swish"`): + The non-linear activation function in the point-wise conv modules. + time_reduction (`int`, *optional*, defaults to 8): + Time reduction (subsampling factor). + bias_max_distance (`int`, *optional*, defaults to 1000): + Max distance for the relative attention bias module. + bias_symmetric (`bool`, *optional*, defaults to `False`): + Whether the relative attention bias should be symmetric or not. + nemo_activation (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function in the nemo conv modules. + nemo_conv_channels (`int`, *optional*, defaults to 1024): + Number of channels in the nemo conv modules. + downsample_rate (`int`, *optional*, defaults to 1): + Downsample rate for the audio feature extractor. + feature_layer (`int`, *optional*, defaults to -2): + The index of the layer of the encoder from which to extract audio features. + + Example: + + ```python + >>> from transformers import Phi4MultimodalAudioConfig + + >>> # Initializing a Phi4MultimodalAudioConfig with microsoft/Phi-4-multimodal-instruct style configuration + >>> configuration = Phi4MultimodalAudioConfig() + ```""" + + model_type = "phi4_multimodal_audio" + + hidden_size: int = 1024 + intermediate_size: int = 1536 + num_blocks: int = 24 + num_attention_heads: int = 16 + activation: str = "swish" + chunk_size: int = -1 + left_chunk: int = 18 + dropout_rate: float | int = 0.0 + ext_pw_out_channel: int = 1024 + depthwise_separable_out_channel: int = 1024 + depthwise_multiplier: int = 1 + kernel_size: int = 3 + conv_activation: str = "swish" + input_size: int = 80 + conv_glu_type: str = "swish" + time_reduction: int = 8 + bias_max_distance: int = 1000 + bias_symmetric: bool = False + nemo_activation: str = "relu" + nemo_conv_channels: int = 1024 + downsample_rate: int = 1 + initializer_range: float = 0.02 + audio_token_id: int = 200011 + feature_layer: int = -2 + + def __post_init__(self, **kwargs): + nemo_final_size = self.input_size + for _ in range(int(math.log2(self.time_reduction))): + nemo_final_size = math.floor((nemo_final_size - 1) / 2 + 1) + self.nemo_final_size = nemo_final_size + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.time_reduction % 2 != 0: + raise ValueError("`time_reduction` should be a multiple of 2!") + + +@auto_docstring(checkpoint="microsoft/Phi-4-multimodal-instruct") +@strict +class Phi4MultimodalConfig(Phi3Config): + r""" + original_max_position_embeddings (`int`, *optional*, defaults to 4096): + The maximum sequence length that this model was trained with. This is used to determine the size of the + original RoPE embeddings when using long scaling. + + Example: + + ```python + >>> from transformers import Phi4MultimodalModel, Phi4MultimodalConfig + + >>> # Initializing a Phi4Multimodal style configuration + >>> configuration = Phi4MultimodalConfig.from_pretrained("microsoft/Phi-4-multimodal-instruct") + + >>> # Initializing a model from the configuration + >>> model = Phi4MultimodalModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + sub_configs = {"audio_config": Phi4MultimodalAudioConfig, "vision_config": Phi4MultimodalVisionConfig} + + vocab_size: int = 200064 + num_key_value_heads: int | None = 8 + max_position_embeddings: int = 131072 + bos_token_id: int | None = 199999 + eos_token_id: int | list[int] | None = None + pad_token_id: int | None = 199999 + original_max_position_embeddings: int | None = 4096 + vision_config: dict | PreTrainedConfig | None = None + audio_config: dict | PreTrainedConfig | None = None + + def __post_init__(self, **kwargs): + self.eos_token_id = self.eos_token_id or [199999, 200020] + if isinstance(self.vision_config, dict): + self.vision_config = Phi4MultimodalVisionConfig(**self.vision_config) + elif self.vision_config is None: + self.vision_config = Phi4MultimodalVisionConfig() + + if isinstance(self.audio_config, dict): + self.audio_config = Phi4MultimodalAudioConfig(**self.audio_config) + elif self.audio_config is None: + self.audio_config = Phi4MultimodalAudioConfig() + super().__post_init__(**kwargs) + + +class Phi4MultimodalVisionMLP(SiglipMLP): + pass + + +def simple_eager_attention_forward( + module: nn.Module, + query_states: torch.Tensor, + key_states: torch.Tensor, + value_states: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float | int = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Phi4MultimodalVisionAttention(nn.Module): + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = True + self.attention_dropout = config.attention_dropout + + self.k_proj = nn.Linear(config.hidden_size, config.hidden_size) + self.v_proj = nn.Linear(config.hidden_size, config.hidden_size) + self.q_proj = nn.Linear(config.hidden_size, config.hidden_size) + self.out_proj = nn.Linear(config.hidden_size, config.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, simple_eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1) + attn_output = self.out_proj(attn_output) + return attn_output, attn_weights + + +class Phi4MultimodalVisionEncoderLayer(SiglipEncoderLayer): + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__(config) + self.self_attn = Phi4MultimodalVisionAttention(config) + self.mlp = Phi4MultimodalVisionMLP(config) + + +class Phi4MultimodalVisionEncoder(SiglipEncoder): + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__(config) + self.layers = nn.ModuleList( + [Phi4MultimodalVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)] + ) + + +class Phi4MultimodalVisionPreTrainedModel(SiglipPreTrainedModel): + config: Phi4MultimodalVisionConfig + base_model_prefix = "phi4_vision" + input_modalities = ("image",) + supports_gradient_checkpointing = True + + _no_split_modules = ["Phi4MultimodalVisionEncoderLayer"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_record_outputs = { + "hidden_states": Phi4MultimodalVisionEncoderLayer, + "attentions": Phi4MultimodalVisionAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, Phi4MultimodalVisionEmbeddings): + width = ( + self.config.hidden_size + if isinstance(self.config, Phi4MultimodalVisionConfig) + else self.config.hidden_size + ) + init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) + elif isinstance(module, nn.Embedding): + init.default_flax_embed_init_(module.weight) + elif isinstance(module, Phi4MultimodalVisionAttention): + init.normal_(module.q_proj.weight) + init.normal_(module.k_proj.weight) + init.normal_(module.v_proj.weight) + init.normal_(module.out_proj.weight) + init.zeros_(module.q_proj.bias) + init.zeros_(module.k_proj.bias) + init.zeros_(module.v_proj.bias) + init.zeros_(module.out_proj.bias) + elif isinstance(module, Phi4MultimodalVisionMLP): + init.normal_(module.fc1.weight) + init.normal_(module.fc2.weight) + init.normal_(module.fc1.bias, std=1e-6) + init.normal_(module.fc2.bias, std=1e-6) + elif isinstance(module, Phi4MultimodalVisionMultiheadAttentionPoolingHead): + init.normal_(module.probe) + init.normal_(module.attention.in_proj_weight) + init.zeros_(module.attention.in_proj_bias) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + init.lecun_normal_(module.weight) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + + +class Phi4MultimodalVisionEmbeddings(SiglipVisionEmbeddings): + def __init__(self, config: Phi4MultimodalVisionConfig): + nn.Module.__init__(self) + self.config = config + self.patch_size = config.patch_size + self.num_patches_per_side = config.image_size // self.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=config.hidden_size, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + self.position_embedding = nn.Embedding(self.num_patches_per_side**2, config.hidden_size) + + def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor: + batch_size, _, max_im_h, max_im_w = pixel_values.shape + + patch_embeds = self.patch_embedding(pixel_values) + embeddings = patch_embeds.flatten(2).transpose(1, 2) + + max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size + boundaries = torch.arange( + 1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side, device=pixel_values.device + ) + position_ids = torch.full( + size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0, device=pixel_values.device + ) + + nb_patches_h = patch_attention_mask[:, :, 0].sum(dim=1) # (batch_size,) + nb_patches_w = patch_attention_mask[:, 0, :].sum(dim=1) # (batch_size,) + + step_h = 1.0 / nb_patches_h # (batch_size,) + step_w = 1.0 / nb_patches_w # (batch_size,) + + max_patches_h = patch_attention_mask.size(1) + max_patches_w = patch_attention_mask.size(2) + h_indices = torch.arange(max_patches_h, device=position_ids.device, dtype=torch.float32) + w_indices = torch.arange(max_patches_w, device=position_ids.device, dtype=torch.float32) + + fractional_coords_h = h_indices[None, :] * step_h[:, None] + fractional_coords_w = w_indices[None, :] * step_w[:, None] + + fractional_coords_h = torch.clamp(fractional_coords_h, max=(1.0 - 1e-6)) + fractional_coords_w = torch.clamp(fractional_coords_w, max=(1.0 - 1e-6)) + + fractional_coords_h = fractional_coords_h.to(pixel_values.dtype) + fractional_coords_w = fractional_coords_w.to(pixel_values.dtype) + + bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True) + bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True) + + pos_ids = bucket_coords_h[:, :, None] * self.num_patches_per_side + bucket_coords_w[:, None, :] + pos_ids = pos_ids.reshape(batch_size, -1) + + position_ids[patch_attention_mask.view(batch_size, -1)] = pos_ids[patch_attention_mask.view(batch_size, -1)] + + embeddings = embeddings + self.position_embedding(position_ids) + return embeddings + + +class Phi4MultimodalVisionMultiheadAttentionPoolingHead(SiglipMultiheadAttentionPoolingHead): + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__(config) + self.mlp = Phi4MultimodalVisionMLP(config) + + def forward(self, hidden_state, attention_mask): + batch_size = hidden_state.shape[0] + probe = self.probe.repeat(batch_size, 1, 1) + + hidden_state = self.attention( + query=probe, key=hidden_state, value=hidden_state, key_padding_mask=~attention_mask + )[0] + + residual = hidden_state + hidden_state = self.layernorm(hidden_state) + hidden_state = residual + self.mlp(hidden_state) + + return hidden_state[:, 0] + + +class Phi4MultimodalVisionModel(Phi4MultimodalVisionPreTrainedModel): + config: Phi4MultimodalVisionConfig + main_input_name = "pixel_values" + + def __init__(self, config: Phi4MultimodalVisionConfig): + super().__init__(config) + self.config = config + + self.embeddings = Phi4MultimodalVisionEmbeddings(config) + self.encoder = Phi4MultimodalVisionEncoder(config) + self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.head = Phi4MultimodalVisionMultiheadAttentionPoolingHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.embeddings.patch_embedding + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + pixel_values, + patch_attention_mask: torch.BoolTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + batch_size = pixel_values.size(0) + if patch_attention_mask is None: + patch_attention_mask = torch.ones( + size=( + batch_size, + pixel_values.size(2) // self.config.patch_size, + pixel_values.size(3) // self.config.patch_size, + ), + dtype=torch.bool, + device=pixel_values.device, + ) + + hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask) + + patch_attention_mask = patch_attention_mask.view(batch_size, -1) + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=patch_attention_mask, + ) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + pooled_output = self.head( + hidden_state=last_hidden_state, + attention_mask=patch_attention_mask, + ) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + ) + + +class Phi4MultimodalImageEmbedding(nn.Module): + """Image embedding.""" + + def __init__(self, config: Phi4MultimodalConfig): + super().__init__() + self.config = config + self.layer_idx = config.vision_config.feature_layer + self.crop_size = config.vision_config.crop_size + self.image_dim_out = config.vision_config.hidden_size + + n_patches = config.vision_config.image_size // config.vision_config.patch_size + if n_patches % 2 != 0: + self.img_processor_padding = nn.ReflectionPad2d((0, 1, 0, 1)) + n_patches += 1 + self.num_img_tokens = (n_patches // 2) ** 2 + + self.drop = nn.Dropout(config.embd_pdrop) + self.img_processor = Phi4MultimodalVisionModel._from_config(config.vision_config) + self.image_token_compression = nn.AvgPool2d(kernel_size=2, stride=2) + self.img_projection_up = nn.Linear(self.image_dim_out, config.hidden_size) + self.img_projection_down = nn.Linear(config.hidden_size, config.hidden_size) + self.global_img_feature_extensor = nn.Parameter(torch.zeros([1, 1, self.image_dim_out])) + self.sub_img_feature_extensor = nn.Parameter(torch.zeros([1, 1, 1, self.image_dim_out])) + + def get_img_features(self, img_embeds: torch.FloatTensor, attention_mask=None) -> torch.FloatTensor: + img_processor_output = self.img_processor( + img_embeds, patch_attention_mask=attention_mask, output_hidden_states=True + ) + img_feature = img_processor_output.hidden_states[self.layer_idx] + + patch_feature = img_feature + # reshape to 2D tensor + width = int(math.sqrt(patch_feature.size(1))) + patch_feature = patch_feature.view(-1, width, width, patch_feature.size(-1)) + # convert to NCHW + patch_feature = patch_feature.permute(0, 3, 1, 2) + if getattr(self, "img_processor_padding", None) is not None: + patch_feature = self.img_processor_padding(patch_feature) + patch_feature = self.image_token_compression(patch_feature) + # convert to NHWC + patch_feature = patch_feature.permute(0, 2, 3, 1) + patch_feature = patch_feature.view(-1, patch_feature.size(1) * patch_feature.size(2), patch_feature.size(-1)) + return patch_feature + + def forward( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.Tensor, + image_pixel_values: torch.FloatTensor, + image_sizes: torch.Tensor | None = None, + image_attention_mask: torch.Tensor | None = None, + ) -> torch.FloatTensor: + image_pixel_values = image_pixel_values.to(self.img_processor.embeddings.patch_embedding.weight.dtype) + + target_device = self.img_projection_up.bias.device + target_dtype = self.img_projection_up.bias.dtype + + batch_size = image_pixel_values.shape[0] + + img_features = self.get_img_features( + image_pixel_values.flatten(0, 1), + attention_mask=image_attention_mask.flatten(0, 1).to(dtype=bool, device=target_device), + ) + base_feat_size = int(np.sqrt(img_features.shape[1])) + img_features = img_features.view(batch_size, -1, base_feat_size**2, self.image_dim_out) + image_sizes = image_sizes.view(-1, 2) + + output_imgs = [] + for idx in range(batch_size): + height, width = image_sizes[idx] + height_ratio = height // self.crop_size + width_ratio = width // self.crop_size + area_ratio = height_ratio * width_ratio + + global_img = img_features[idx, :1] + global_img = global_img.reshape(1, base_feat_size, base_feat_size, self.image_dim_out).contiguous() + temporary_extensor = self.sub_img_feature_extensor.repeat(1, base_feat_size, 1, 1) + global_img = torch.cat([global_img, temporary_extensor], dim=2).reshape(1, -1, self.image_dim_out) + + sub_img = img_features[idx, 1:] + sub_img = sub_img[:area_ratio] + sub_img = ( + sub_img.reshape(height_ratio, width_ratio, base_feat_size, base_feat_size, self.image_dim_out) + .transpose(1, 2) + .reshape(1, height_ratio * base_feat_size, width_ratio * base_feat_size, self.image_dim_out) + .contiguous() + ) + + if image_attention_mask is not None: + reshaped_image_attention_mask = ( + image_attention_mask[idx, 1 : area_ratio + 1, 0::2, 0::2] + .reshape(height_ratio, width_ratio, base_feat_size, base_feat_size) + .transpose(1, 2) + .reshape(1, height_ratio * base_feat_size, width_ratio * base_feat_size) + ) + useful_height = int(reshaped_image_attention_mask[0, :, 0].sum().item()) + useful_width = int(reshaped_image_attention_mask[0, 0, :].sum().item()) + sub_img = sub_img[:, :useful_height, :useful_width] + temporary_extensor = self.sub_img_feature_extensor.repeat(1, useful_height, 1, 1) + else: + temporary_extensor = self.sub_img_feature_extensor.repeat(1, height_ratio * base_feat_size, 1, 1) + + sub_img = torch.cat([sub_img, temporary_extensor], dim=2).reshape(1, -1, self.image_dim_out) + + # Merge global and sub + output_imgs.append(torch.cat([sub_img, self.global_img_feature_extensor, global_img], dim=1)) + + img_set_tensor = [] + for output_img in output_imgs: + output_img = output_img.to(device=target_device, dtype=target_dtype) + img_feature_proj = self.img_projection_up(output_img) + img_feature_proj = nn.functional.gelu(img_feature_proj) + img_feature_proj = self.img_projection_down(img_feature_proj) + img_set_tensor.append(img_feature_proj) + + merged_img_set_tensor = torch.cat(img_set_tensor, dim=1).squeeze(0) + merged_img_set_tensor = merged_img_set_tensor.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device) + + with torch.no_grad(): + positions_tuple = torch.nonzero(input_ids == self.config.vision_config.image_token_id, as_tuple=True) + + # Temporarily disable autocast to avoid issue on bf16 tensors + # Ref: https://github.com/pytorch/pytorch/issues/132715 + with maybe_autocast(device_type=inputs_embeds.device.type, enabled=False): + image_embeds = inputs_embeds.index_put( + indices=positions_tuple, values=merged_img_set_tensor, accumulate=False + ) + + image_embeds = self.drop(image_embeds) + + return image_embeds + + +########################################################## AUDIO ############################################# + + +class Phi4MultimodalAudioMLP(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.act_fn = ACT2FN[config.activation] + self.gate_up_proj = nn.Linear(config.hidden_size, config.intermediate_size * 2) + self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states): + hidden_states = self.layer_norm(hidden_states) + up_states = self.gate_up_proj(hidden_states) + up_states, gate = up_states.chunk(2, dim=-1) + up_states = up_states * self.act_fn(gate) + up_states = self.dropout(up_states) + hidden_states = self.down_proj(up_states) + out = self.dropout(hidden_states) + + return out + + +class Phi4MultimodalAudioAttention(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.config = config + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.dropout_rate + self.is_causal = True + + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=True) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs, + ): + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, simple_eager_attention_forward + ) + + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output + + +class Phi4MultimodalAudioDepthWiseSeparableConv1d(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig, padding: int = 0): + super().__init__() + self.dw_conv = nn.Conv1d( + config.hidden_size, + config.hidden_size * config.depthwise_multiplier, + config.kernel_size, + 1, + padding=padding, + groups=config.hidden_size, + ) + self.pw_conv = nn.Conv1d( + config.hidden_size * config.depthwise_multiplier, config.depthwise_separable_out_channel, 1, 1, 0 + ) + + def forward(self, hidden_states): + return self.pw_conv(self.dw_conv(hidden_states)) + + +class Phi4MultimodalAudioGluPointWiseConv(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.config = config + self.output_dim = config.ext_pw_out_channel + + self.ext_pw_conv_1d = nn.Conv1d(config.hidden_size, config.ext_pw_out_channel * 2, kernel_size=1, stride=1) + self.glu_act = ACT2FN[config.conv_glu_type] + self.b1 = nn.Parameter(torch.zeros(1, config.ext_pw_out_channel, 1)) + self.b2 = nn.Parameter(torch.zeros(1, config.ext_pw_out_channel, 1)) + + def forward(self, hidden_states): + # we assume the input always has the #channel (#dim) in the last dimension of the + # tensor, so need to switch the dimension first for 1D-Conv case + hidden_states = hidden_states.permute([0, 2, 1]) + hidden_states = self.ext_pw_conv_1d(hidden_states) + out = hidden_states[:, 0 : self.output_dim, :] + self.b1 + out = out * self.glu_act(hidden_states[:, self.output_dim : self.output_dim * 2, :] + self.b2) + return out.permute([0, 2, 1]) + + +class Phi4MultimodalAudioConvModule(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.config = config + self.kernel_size = config.kernel_size + + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.glu = Phi4MultimodalAudioGluPointWiseConv(config) + self.dw_sep_conv_1d = Phi4MultimodalAudioDepthWiseSeparableConv1d(config, padding=config.kernel_size - 1) + self.act = ACT2FN[config.conv_activation] + self.ext_pw_conv_1d = nn.Conv1d(config.hidden_size, config.ext_pw_out_channel, kernel_size=1, stride=1) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states: torch.Tensor): + hidden_states = self.glu(self.layer_norm(hidden_states)) + hidden_states = self.dw_sep_conv_1d(hidden_states.permute([0, 2, 1])) + + if self.kernel_size > 1: + hidden_states = hidden_states[:, :, : -(self.kernel_size - 1)] + + hidden_states = self.act(hidden_states) + hidden_states = self.ext_pw_conv_1d(hidden_states) + out = self.dropout(hidden_states.permute([0, 2, 1])) + return out + + +class Phi4MultimodalAudioConformerEncoderLayer(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + + self.feed_forward_in = Phi4MultimodalAudioMLP(config) + self.self_attn = Phi4MultimodalAudioAttention(config) + self.conv = Phi4MultimodalAudioConvModule(config) + self.feed_forward_out = Phi4MultimodalAudioMLP(config) + self.layer_norm_att = nn.LayerNorm(config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + ): + residual = hidden_states + 0.5 * self.feed_forward_in(hidden_states) + hidden_states = self.layer_norm_att(residual) + + hidden_states = residual + self.self_attn(hidden_states, attention_mask) + hidden_states = hidden_states + self.conv(hidden_states) + hidden_states = hidden_states + 0.5 * self.feed_forward_out(hidden_states) + + out = self.layer_norm(hidden_states) + + return out + + +class Phi4MultimodalAudioNemoConvSubsampling(torch.nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.subsampling_factor = config.time_reduction + self.sampling_num = int(math.log2(self.subsampling_factor)) + self.act_fn = ACT2FN[config.nemo_activation] + conv_channels = config.nemo_conv_channels + + layers = [ + nn.Conv2d(1, conv_channels, kernel_size=3, stride=2, padding=1), + self.act_fn, + ] + for _ in range(self.sampling_num - 1): + layers.extend( + [ + nn.Conv2d(conv_channels, conv_channels, kernel_size=3, stride=2, padding=1, groups=conv_channels), + nn.Conv2d(conv_channels, conv_channels, kernel_size=1, stride=1, padding=0, groups=1), + self.act_fn, + ] + ) + + # Aggregate the layers + self.conv = torch.nn.Sequential(*layers) + self.out = torch.nn.Linear(conv_channels * config.nemo_final_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor, mask: torch.Tensor | None): + # Unsqueeze Channel Axis + hidden_states = hidden_states.unsqueeze(1) + hidden_states = self.conv(hidden_states) + + # Flatten Channel and Frequency Axes + b, _, t, _ = hidden_states.size() + hidden_states = self.out(hidden_states.transpose(1, 2).reshape(b, t, -1)) + + if mask is None: + return hidden_states, None + + max_audio_length = hidden_states.shape[1] + feature_lens = mask.sum(1) + padding_length = torch.ceil(feature_lens / self.subsampling_factor) + arange_ = torch.arange(0, max_audio_length, device=hidden_states.device) + pad_mask = arange_.expand(padding_length.size(0), -1) < padding_length.unsqueeze(1) + return hidden_states, pad_mask.unsqueeze(1) + + +class Phi4MultimodalAudioRelativeAttentionBias(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + + self.max_distance = config.bias_max_distance + self.symmetric = config.bias_symmetric + self.num_buckets = self.max_distance + if not config.bias_symmetric: + self.num_buckets *= 2 + self.bias_values = nn.Embedding(self.num_buckets, config.num_attention_heads) + + def forward(self, x): + # instantiate bias compatible with shape of x + max_pos = x.size(1) + context_position = torch.arange(max_pos, device=x.device, dtype=torch.long)[:, None] + memory_position = torch.arange(max_pos, device=x.device, dtype=torch.long)[None, :] + relative_position = memory_position - context_position + # clipping to a maximum distance using ops that play well with ONNX export + relative_position = relative_position.masked_fill(relative_position < -self.max_distance, -self.max_distance) + relative_position = relative_position.masked_fill( + relative_position > self.max_distance - 1, self.max_distance - 1 + ) + + # mapping from relative position to index in the bias parameter + bias_idx = relative_position + bias_idx = bias_idx.abs() if self.symmetric else bias_idx + self.num_buckets // 2 + + att_bias = self.bias_values(bias_idx) + att_bias = att_bias.permute(2, 0, 1).unsqueeze(0) + + return att_bias + + +class Phi4MultimodalAudioMeanVarianceNormLayer(nn.Module): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__() + self.register_buffer("global_mean", torch.zeros(config.input_size)) + self.register_buffer("global_invstd", torch.ones(config.input_size)) + + def forward(self, x): + return (x - self.global_mean) * self.global_invstd + + +@auto_docstring +class Phi4MultimodalAudioPreTrainedModel(PreTrainedModel): + config: Phi4MultimodalAudioConfig + input_modalities = "audio" + supports_gradient_checkpointing = True + _no_split_modules = ["Phi4MultimodalAudioConformerEncoderLayer"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Phi4MultimodalAudioGluPointWiseConv): + init.zeros_(module.b1) + init.zeros_(module.b2) + elif isinstance(module, Phi4MultimodalAudioMeanVarianceNormLayer): + init.zeros_(module.global_mean) + init.ones_(module.global_invstd) + + +class Phi4MultimodalAudioModel(Phi4MultimodalAudioPreTrainedModel): + def __init__(self, config: Phi4MultimodalAudioConfig): + super().__init__(config) + self.config = config + + self.encoder_embedding = Phi4MultimodalAudioMeanVarianceNormLayer(config) + self.embed = Phi4MultimodalAudioNemoConvSubsampling(config) + self.relative_attention_bias_layer = Phi4MultimodalAudioRelativeAttentionBias(config) + self.encoders = nn.ModuleList( + [Phi4MultimodalAudioConformerEncoderLayer(config) for _ in range(config.num_blocks)] + ) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + def _streaming_mask(self, seq_len, batch_size, chunk_size, left_chunk): + # Create mask matrix for streaming + # S stores start index. if chunksize is 18, s is [0,18,36,....] + chunk_start_idx = np.arange(0, seq_len, chunk_size) + # avoid randomness when run evaluation or decoding + if self.training and np.random.rand() > 0.5: + # Either first or last chunk is not complete. + # If only the last one is not complete, EOS is not effective + chunk_start_idx = seq_len - chunk_start_idx + chunk_start_idx = chunk_start_idx[::-1] + chunk_start_idx = chunk_start_idx[:-1] + chunk_start_idx = np.insert(chunk_start_idx, 0, 0) + + enc_streaming_mask = ( + adaptive_enc_mask(seq_len, chunk_start_idx, left_window=left_chunk) + .unsqueeze(0) + .expand([batch_size, -1, -1]) + ) + return enc_streaming_mask + + def forward_embeddings(self, hidden_states, masks): + """Forwarding the inputs through the top embedding layers""" + seq_len = math.ceil(hidden_states.shape[1] / self.config.time_reduction) + if seq_len <= 0: + raise ValueError( + f"The sequence length after time reduction is invalid: {seq_len}. Your input feature is too short." + ) + + batch_size = hidden_states.shape[0] + + enc_streaming_mask = self._streaming_mask(seq_len, batch_size, self.config.chunk_size, self.config.left_chunk) + enc_streaming_mask = enc_streaming_mask.to(hidden_states.device) + + hidden_states, masks = self.embed(hidden_states, masks) + + streaming_mask = enc_streaming_mask + if streaming_mask is not None and masks is not None: + hs_mask = masks & streaming_mask + elif masks is not None: + hs_mask = masks + else: + hs_mask = streaming_mask + + return hidden_states, hs_mask, masks + + def calculate_hs_mask(self, hidden_states, device, mask): + max_audio_length = hidden_states.shape[1] + batch_size = hidden_states.shape[0] + enc_streaming_mask = self._streaming_mask( + max_audio_length, batch_size, self.config.chunk_size, self.config.left_chunk + ) + enc_streaming_mask = enc_streaming_mask.to(device) + if mask is None: + return enc_streaming_mask + + feature_lens = mask.sum(1) + padding_length = feature_lens + pad_mask = torch.arange(0, max_audio_length, device=device).expand( + padding_length.size(0), -1 + ) < padding_length.unsqueeze(1) + pad_mask = pad_mask.unsqueeze(1) + pad_mask = pad_mask & enc_streaming_mask + return pad_mask + + def forward(self, hidden_states: torch.Tensor, mask: torch.Tensor | None, **kwargs): + hidden_states = self.encoder_embedding(hidden_states) + hidden_states, hs_mask, mask = self.forward_embeddings(hidden_states, mask) + + unfolded = False + bs, seq_len, _ = hidden_states.shape + max_seq_len = 500 # maximum position for absolute positional encoding + if seq_len > max_seq_len: + # audio sequence is longer than max_seq_len, unfold it into chunks of max_seq_len + unfolded = True + # the unfold op will drop residual frames, pad it to the multiple of max_seq_len + if seq_len % max_seq_len > 0: + chunk_pad_size = max_seq_len - (seq_len % max_seq_len) + else: + chunk_pad_size = 0 + if chunk_pad_size > 0: + hidden_states_pad = F.pad(hidden_states, (0, 0, 0, chunk_pad_size), "constant", 0) + hidden_states = hidden_states_pad.to(hidden_states.device) + + hidden_states = unfold_tensor(hidden_states, max_seq_len) + masks_unfold = None + if mask is not None: + # revise hs_mask here because the previous calculated hs_mask did not consider extra pad + subsampled_pad_mask = mask.squeeze(1) # [bz, subsampled_unmask_seq_len] + extra_padded_subsamlped_pad_mask = F.pad( + subsampled_pad_mask, (0, chunk_pad_size), "constant", False + ) # extra padding to the pad mask + extra_padded_subsamlped_pad_mask = extra_padded_subsamlped_pad_mask.unsqueeze(-1).float() + masks_unfold = unfold_tensor( + extra_padded_subsamlped_pad_mask, max_seq_len + ) # unfold the pad mask like we did to the input tensor + masks_unfold = masks_unfold.squeeze(-1).bool() # unfold op does not support bool tensor + hs_mask = self.calculate_hs_mask( + hidden_states, hidden_states.device, masks_unfold + ) # calculate hs_mask based on the unfolded pad mask + + relative_attention_bias = self.relative_attention_bias_layer(hidden_states) + attention_mask = hs_mask.unsqueeze(1) + relative_attention_bias + + for layer in self.encoders: + hidden_states = layer(hidden_states, attention_mask) + + if unfolded: + embed_dim = hidden_states.shape[-1] + hidden_states = hidden_states.reshape(bs, -1, embed_dim) + # if we ever padded before unfolding, we need to remove the padding + if chunk_pad_size > 0: + hidden_states = hidden_states[:, :-chunk_pad_size, :] + + return hidden_states + + +def unfold_tensor(tensor, max_seq_len): + """ + For a given tensor with shape of (N, T, D), if sequence length T is longer than max_seq_len, + this function unfold it to a (NT', max_seq_len, D) where T' is T // max_seq_len. + Args: + tensor: N, T, D + """ + _, _, D = tensor.shape + tensor = tensor.transpose(-1, -2) + # N x D x 1 x T => N x (D x max_seq_len) x T' + tensor = F.unfold(tensor[..., None, :], kernel_size=(1, max_seq_len), stride=(1, max_seq_len)) + + new_bsz, _, slen = tensor.shape + tensor = tensor.view(new_bsz, -1, max_seq_len, slen) + tensor = tensor.permute(0, 3, 2, 1) + tensor = tensor.view(-1, max_seq_len, D).contiguous() + return tensor + + +def adaptive_enc_mask(x_len, chunk_start_idx, left_window=0, right_window=0): + """ + The function is very important for Transformer Transducer Streaming mode + Args: + xs_len (int): sequence length + chunk_start_idx (list): first idx of each chunk, such as [0,18,36,48]. It also supports adaptive chunk size [0,10,15,45] + left_window (int): how many left chunks can be seen + right_window (int): how many right chunks can be seen. It is used for chunk overlap model. + Returns: + mask (torch.Tensor): a mask tensor for streaming model + """ + chunk_start_idx = torch.Tensor(chunk_start_idx).long() + start_pad = torch.nn.functional.pad( + chunk_start_idx, (1, 0) + ) # append 0 to the beginning, so it becomes [0, 0, 18, 36, 48] + end_pad = torch.nn.functional.pad( + chunk_start_idx, (0, 1), value=x_len + ) # append x_len to the end, so it becomes [0,18,36,48, x_len] + seq_range = torch.arange(0, x_len).unsqueeze(-1) + idx = ((seq_range < end_pad) & (seq_range >= start_pad)).nonzero()[:, 1] + seq_range_expand = torch.arange(0, x_len).unsqueeze(0).expand(x_len, -1) + idx_left = idx - left_window + idx_left[idx_left < 0] = 0 + boundary_left = start_pad[idx_left] + mask_left = seq_range_expand >= boundary_left.unsqueeze(-1) + idx_right = idx + right_window + idx_right[idx_right > len(chunk_start_idx)] = len(chunk_start_idx) + boundary_right = end_pad[idx_right] + mask_right = seq_range_expand < boundary_right.unsqueeze(-1) + return mask_left & mask_right + + +class Phi4MultimodalAudioEmbedding(nn.Module): + def __init__(self, config: Phi4MultimodalConfig): + super().__init__() + self.config = config + self.layer_idx = config.audio_config.feature_layer + + self.drop = nn.Dropout(config.embd_pdrop) + self.encoder = Phi4MultimodalAudioModel._from_config(config.audio_config) + self.up_proj_for_speech = nn.Linear( + config.audio_config.hidden_size * config.audio_config.downsample_rate, config.hidden_size + ) + self.down_proj_for_speech = nn.Linear(config.hidden_size, config.hidden_size) + self.up_proj_for_vision_speech = nn.Linear( + config.audio_config.hidden_size * config.audio_config.downsample_rate, config.hidden_size + ) + self.down_proj_for_vision_speech = nn.Linear(config.hidden_size, config.hidden_size) + + def forward( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.Tensor, + audio_input_features: torch.FloatTensor, + audio_embed_sizes=None, + audio_attention_mask=None, + audio_projection_mode="speech", + ) -> torch.FloatTensor: + with torch.no_grad(): + positions_tuple = torch.nonzero(input_ids == self.config.audio_config.audio_token_id, as_tuple=True) + + up_proj = self.up_proj_for_speech if audio_projection_mode == "speech" else self.up_proj_for_vision_speech + down_proj = ( + self.down_proj_for_speech if audio_projection_mode == "speech" else self.down_proj_for_vision_speech + ) + + target_device = up_proj.bias.device + target_dtype = up_proj.bias.dtype + + audio_input_features = audio_input_features.to(device=target_device, dtype=target_dtype) + + audio_encoder_hidden_states = self.encoder(audio_input_features, audio_attention_mask) + audio_encoder_hidden_states = up_proj(audio_encoder_hidden_states) + audio_encoder_hidden_states = nn.functional.gelu(audio_encoder_hidden_states) + audio_embeds = down_proj(audio_encoder_hidden_states) + + merged_audio_embeds = torch.cat( + [audio_embeds[i, : audio_embed_sizes[i], :] for i in range(len(audio_embed_sizes))], dim=0 + ) + merged_audio_embeds = merged_audio_embeds.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device) + # Temporarily disable autocast to avoid issue on bf16 tensors + # Ref: https://github.com/pytorch/pytorch/issues/132715 + with maybe_autocast(device_type=inputs_embeds.device.type, enabled=False): + audio_embeds = inputs_embeds.index_put( + indices=positions_tuple, values=merged_audio_embeds, accumulate=False + ) + + audio_embeds = self.drop(audio_embeds) + + return audio_embeds + + +#################################################### TEXT #################################################### + + +class Phi4MultimodalRMSNorm(Phi3RMSNorm): + pass + + +class Phi4MultimodalDecoderLayer(Phi3DecoderLayer): + pass + + +class Phi4MultimodalFeatureEmbedding(nn.Module): + """Image-audio embedding.""" + + def __init__(self, config: Phi4MultimodalConfig) -> None: + super().__init__() + self.config = config + self.image_token_id = config.vision_config.image_token_id + self.audio_token_id = config.audio_config.audio_token_id + self.image_embed = Phi4MultimodalImageEmbedding(config) + self.audio_embed = Phi4MultimodalAudioEmbedding(config) + + def forward( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.Tensor, + image_pixel_values: torch.FloatTensor | None = None, + audio_input_features: torch.FloatTensor | None = None, + image_sizes=None, + image_attention_mask=None, + audio_embed_sizes=None, + audio_attention_mask=None, + ) -> torch.FloatTensor: + with torch.no_grad(): + image_position_mask = (input_ids == self.config.vision_config.image_token_id).unsqueeze(-1) + non_image_position_mask = ~image_position_mask + + image_embeds = None + audio_embeds = None + if image_pixel_values is not None and (input_ids == self.image_token_id).any(): + image_embeds = self.image_embed( + input_ids, + inputs_embeds, + image_pixel_values=image_pixel_values, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + ) + if audio_input_features is not None and (input_ids == self.audio_token_id).any(): + audio_projection_mode = "vision" if image_pixel_values is not None else "speech" + audio_embeds = self.audio_embed( + input_ids, + inputs_embeds, + audio_input_features=audio_input_features, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + audio_projection_mode=audio_projection_mode, + ) + + # merge image and audio + if image_embeds is not None and audio_embeds is not None: + inputs_embeds = image_embeds * image_position_mask + audio_embeds * non_image_position_mask + elif image_embeds is not None: + inputs_embeds = image_embeds + elif audio_embeds is not None: + inputs_embeds = audio_embeds + + return inputs_embeds + + +class Phi4MultimodalPreTrainedModel(Phi3PreTrainedModel): + input_modalities = ("image", "audio", "text") + + @torch.no_grad() + def _init_weights(self, module): + PreTrainedModel._init_weights(self, module) + if isinstance(module, Phi4MultimodalImageEmbedding): + init.zeros_(module.global_img_feature_extensor) + init.zeros_(module.sub_img_feature_extensor) + + +class Phi4MultimodalModel(Phi3Model): + def __init__(self, config: Phi4MultimodalConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.embed_dropout = nn.Dropout(config.embd_pdrop) + + self.embed_tokens_extend = Phi4MultimodalFeatureEmbedding(config) + + self.layers = nn.ModuleList( + [Phi4MultimodalDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = Phi4MultimodalRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + image_pixel_values: torch.FloatTensor | None = None, + image_sizes: torch.LongTensor | None = None, + image_attention_mask=None, + audio_input_features: torch.FloatTensor | None = None, + audio_embed_sizes=None, + audio_attention_mask=None, + use_cache: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPast: + r""" + image_pixel_values (`torch.FloatTensor`, *optional*): + If the input contains images, these correspond to the pixel values after transformations (as returned by + the Processor) + image_sizes (`torch.LongTensor`, *optional*): + If the input contains images, these correspond to size of each image. + image_attention_mask (`torch.LongTensor`, *optional*): + Attention mask for the images. + audio_input_features (`torch.FloatTensor`, *optional*): + If the input contains audio samples, these correspond to the values after transformation (as returned by + the Processor). + audio_embed_sizes (`torch.Tensor`, *optional*): + Size of the audio inputs. + audio_attention_mask (`torch.Tensor, *optional*): + Attention mask for the audio inputs. + """ + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + inputs_embeds = self.embed_tokens_extend( + input_ids, + inputs_embeds, + image_pixel_values=image_pixel_values, + audio_input_features=audio_input_features, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + ) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask + causal_mask = mask_function( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +class Phi4MultimodalForCausalLM(Phi3ForCausalLM): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + + def __init__(self, config): + super().__init__(config) + self.model = Phi4MultimodalModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + image_pixel_values: torch.FloatTensor | None = None, + image_sizes: torch.LongTensor | None = None, + image_attention_mask=None, + audio_input_features: torch.FloatTensor | None = None, + audio_embed_sizes=None, + audio_attention_mask=None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> CausalLMOutputWithPast: + r""" + image_pixel_values (`torch.FloatTensor`, *optional*): + If the input contains images, these correspond to the pixel values after transformations (as returned by + the Processor) + image_sizes (`torch.LongTensor`, *optional*): + If the input contains images, these correspond to size of each image. + image_attention_mask (`torch.LongTensor`, *optional*): + Attention mask for the images. + audio_input_features (`torch.FloatTensor`, *optional*): + If the input contains audio samples, these correspond to the values after transformation (as returned by + the Processor). + audio_embed_sizes (`torch.Tensor`, *optional*): + Size of the audio inputs. + audio_attention_mask (`torch.Tensor, *optional*): + Attention mask for the audio inputs. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + ```python + >>> from transformers import AutoTokenizer, Phi4MultimodalForCausalLM + >>> model = Phi4MultimodalForCausalLM.from_pretrained("TBA") + >>> tokenizer = AutoTokenizer.from_pretrained("TBA") + >>> prompt = "This is an example script ." + >>> inputs = tokenizer(prompt, return_tensors="pt") + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + 'This is an example script .\n Certainly! Below is a sample script that demonstrates a simple task, such as calculating the sum' + ```""" + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + image_pixel_values=image_pixel_values, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + audio_input_features=audio_input_features, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + image_pixel_values=None, + image_sizes=None, + image_attention_mask=None, + audio_input_features=None, + audio_embed_sizes=None, + audio_attention_mask=None, + position_ids=None, + use_cache=True, + logits_to_keep=0, + **kwargs, + ): + # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the + # process + + # When the first time input length reached long and short factor switching point, enforce re-compute cache + # It will cause downside of slower at this single token position, however, better than current failure. + if ( + past_key_values + and self.config.rope_parameters + and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1 + ): + past_length = past_key_values.get_seq_length() + if past_length <= self.config.original_max_position_embeddings: + past_key_values = None + + model_inputs = super().prepare_inputs_for_generation( + input_ids=input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + image_pixel_values=image_pixel_values, + image_sizes=image_sizes, + image_attention_mask=image_attention_mask, + audio_input_features=audio_input_features, + audio_embed_sizes=audio_embed_sizes, + audio_attention_mask=audio_attention_mask, + position_ids=position_ids, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **kwargs, + ) + return model_inputs + + +__all__ = [ + "Phi4MultimodalAudioPreTrainedModel", + "Phi4MultimodalAudioModel", + "Phi4MultimodalVisionPreTrainedModel", + "Phi4MultimodalVisionModel", + "Phi4MultimodalPreTrainedModel", + "Phi4MultimodalModel", + "Phi4MultimodalForCausalLM", + "Phi4MultimodalVisionConfig", + "Phi4MultimodalAudioConfig", + "Phi4MultimodalConfig", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/processing_phi4_multimodal.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/processing_phi4_multimodal.py new file mode 100644 index 0000000000000000000000000000000000000000..325b27ed361c9234cebbebabb02068cce4e32e67 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phi4_multimodal/processing_phi4_multimodal.py @@ -0,0 +1,133 @@ +# Copyright 2025 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Processor class for Phi4Multimodal +""" + +import re + +from ...audio_utils import AudioInput +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import TextInput +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +class Phi4MultimodalProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "audio_kwargs": { + "device": "cpu", + }, + } + + +@auto_docstring +class Phi4MultimodalProcessor(ProcessorMixin): + def __init__( + self, + image_processor, + audio_processor, + tokenizer, + **kwargs, + ): + self.image_token = tokenizer.image_token + self.image_token_id = tokenizer.image_token_id + self.audio_token = tokenizer.audio_token + self.audio_token_id = tokenizer.audio_token_id + super().__init__(image_processor, audio_processor, tokenizer, **kwargs) + + @auto_docstring + def __call__( + self, + text: TextInput | list[TextInput], + images: ImageInput | None = None, + audio: AudioInput | None = None, + **kwargs: Unpack[Phi4MultimodalProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model. + - **input_image_embeds** -- Pixel values to be fed to a model. + - **image_sizes** -- List of tuples specifying the size of each image in `input_image_embeds`. + - **image_attention_mask** -- List of attention masks for each image in `input_image_embeds`. + - **input_audio_embeds** -- Audio embeddings to be fed to a model. + - **audio_embed_sizes** -- List of integers specifying the size of each audio in `input_audio_embeds`. + """ + + output_kwargs = self._merge_kwargs(Phi4MultimodalProcessorKwargs, self.tokenizer.init_kwargs, **kwargs) + image_kwargs = output_kwargs["images_kwargs"] + audio_kwargs = output_kwargs["audio_kwargs"] + + image_inputs = self.image_processor(images, **image_kwargs) if images is not None else {} + audio_inputs = self.audio_processor(audio, **audio_kwargs) if audio is not None else {} + + # We pop here for images as we don't need it later + num_img_tokens = image_inputs.pop("num_img_tokens", []) + audio_embed_sizes = audio_inputs.get("audio_embed_sizes", []) + + # Replace certain special tokens for compatibility + if isinstance(text, str): + text = [text] + elif not isinstance(text, list) and not isinstance(text[0], str): + raise TypeError("Invalid input text. Please provide a string, or a list of strings") + + image_token = self.tokenizer.image_token + audio_token = self.tokenizer.audio_token + + # Check that the number of special tokens is sound + concatenated_prompt = "".join(text) + if concatenated_prompt.count(image_token) != len(num_img_tokens): + raise ValueError( + "You should add as much image tokens `<|image|>` in your prompt as you pass `images` to the processor. ", + f"Input contains {concatenated_prompt.count(image_token)} tokens != {len(num_img_tokens)} images", + ) + if concatenated_prompt.count(audio_token) != len(audio_embed_sizes): + raise ValueError( + "You should add as much audio tokens `<|audio|>` in your prompt as you pass `audios` to the processor. " + f"Input contains {concatenated_prompt.count(audio_token)} tokens != {len(audio_embed_sizes)} audios" + ) + + # Add appropriate number of image/audio tokens (note that the count of replacement is dynamic) + image_count_iter = iter(num_img_tokens) + audio_count_iter = iter(audio_embed_sizes) + processed_text = [ + re.sub(re.escape(image_token), lambda _: image_token * next(image_count_iter), t) for t in text + ] + processed_text = [ + re.sub(re.escape(audio_token), lambda _: audio_token * next(audio_count_iter), t) for t in processed_text + ] + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + text_inputs = self.tokenizer(processed_text, **output_kwargs["text_kwargs"]) + self._check_special_mm_tokens(processed_text, text_inputs, modalities=["image"]) + + # prepare batch feature + data = { + **text_inputs, + **image_inputs, + **audio_inputs, + } + + return BatchFeature(data=data, tensor_type=return_tensors) + + +__all__ = ["Phi4MultimodalProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e0849f5c5006e59ab19fee37230056d0add2744c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 Microsoft and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_phimoe import * + from .modeling_phimoe import * + +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/configuration_phimoe.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/configuration_phimoe.py new file mode 100644 index 0000000000000000000000000000000000000000..e20f94085be0ef72977931cf0a50e6bdf21c5036 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/configuration_phimoe.py @@ -0,0 +1,105 @@ +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""PyTorch Phi-MoE model.""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="microsoft/Phi-3.5-MoE-instruct") +@strict +class PhimoeConfig(PreTrainedConfig): + r""" + num_local_experts (`int`, *optional*, defaults to 16): + Number of experts per Sparse MLP layer. + input_jitter_noise (`float`, *optional*, defaults to 0.0): + Input jitter noise + lm_head_bias (`bool`, *optional*, defaults to `False`): + LM head bias + + Example: + + ```python + >>> from transformers import PhimoeModel, PhimoeConfig + >>> # Initializing a Phi-3 style configuration + >>> configuration = PhimoeConfig.from_pretrained("microsoft/Phi-3.5-MoE-instruct") + >>> # Initializing a model from the configuration + >>> model = PhimoeModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "phimoe" + keys_to_ignore_at_inference = ["past_key_values"] + default_theta = 1000000.0 + + vocab_size: int = 32064 + hidden_size: int = 4096 + intermediate_size: int = 6400 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_key_value_heads: int = 8 + hidden_act: str = "silu" + max_position_embeddings: int = 4096 * 32 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-5 + use_cache: bool = True + pad_token_id: int | None = None + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 2 + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + sliding_window: int | None = None + attention_dropout: float | int = 0.0 + num_experts_per_tok: int = 2 + num_local_experts: int = 16 + output_router_logits: bool = False + router_aux_loss_coef: float = 0.001 + router_jitter_noise: float = 0.01 + input_jitter_noise: float = 0.0 + attention_bias: bool = False + lm_head_bias: bool = False + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + super().__post_init__(**kwargs) + + def validate_rope(self): + """ + Validate the `rope_parameters` configuration. + """ + super().validate_rope() + + # Run model-specific rope validation + if self.rope_parameters["rope_type"] != "default": + if "original_max_position_embeddings" in self.rope_parameters: + self.original_max_position_embeddings = self.rope_parameters["original_max_position_embeddings"] + rope_parameters_short_mscale = self.rope_parameters.get("short_mscale", None) + rope_parameters_long_mscale = self.rope_parameters.get("long_mscale", None) + if not isinstance(rope_parameters_short_mscale, (int, float)): + raise TypeError( + f"`rope_parameters`'s short_mscale field must be a number, got {rope_parameters_short_mscale}" + ) + if not isinstance(rope_parameters_long_mscale, (int, float)): + raise TypeError( + f"`rope_parameters`'s long_mscale field must be a number, got {rope_parameters_long_mscale}" + ) + + +__all__ = ["PhimoeConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/modeling_phimoe.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/modeling_phimoe.py new file mode 100644 index 0000000000000000000000000000000000000000..23bc944c522af075df13ed8d4aa0d9d906c95afc --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/modeling_phimoe.py @@ -0,0 +1,916 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/phimoe/modular_phimoe.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_phimoe.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections.abc import Callable +from typing import Optional + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_experts_implementation, use_kernel_func_from_hub, use_kernelized_func +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer +from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_phimoe import PhimoeConfig + + +class PhimoeRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: PhimoeConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + self.rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PhimoeConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids=None, layer_type=None): + if layer_type is not None: + raise ValueError( + f"{self.__class__.__name__} does not support layer types, but got `layer_type={layer_type}`" + ) + + mscale = None + seq_len = torch.max(position_ids) + 1 + if self.config.rope_parameters["rope_type"] != "default" and seq_len: + mscale = ( + self.config.rope_parameters["long_mscale"] + if seq_len > self.config.rope_parameters["original_max_position_embeddings"] + else self.config.rope_parameters["short_mscale"] + ) + inv_freq, attention_scaling = self.rope_init_fn(self.config, x.device, seq_len) + mscale = attention_scaling if mscale is None else mscale + inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * mscale + sin = emb.sin() * mscale + return cos.to(x.dtype), sin.to(x.dtype) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class PhimoeAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: PhimoeConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class PhimoeMultiplier(torch.autograd.Function): + @staticmethod + def forward( + ctx, + scores: torch.Tensor, + multiplier: torch.Tensor, + selected_experts: torch.Tensor, + masked_gates: torch.Tensor, + mask_for_one: torch.Tensor, + ): + """ + Forward pass for the custom autograd function. + + Args: + ctx: Context object to save information for backward computation. + scores (torch.Tensor): Input scores tensor. + multiplier (torch.Tensor): Multiplier tensor. + selected_experts (torch.Tensor): Tensor of selected experts. + masked_gates (torch.Tensor): Masked gates tensor. + mask_for_one (torch.Tensor): Mask for one tensor. + + Returns: + torch.Tensor: Result of the forward pass. + """ + ctx.save_for_backward(multiplier, selected_experts, masked_gates) + return multiplier * mask_for_one + + @staticmethod + def backward( + ctx, + grad_at_output: torch.Tensor, + ): + """ + Backward pass for the custom autograd function. + + Args: + ctx: Context object with saved tensors from the forward pass. + grad_at_output (torch.Tensor): Gradient at the output. + + Returns: + tuple[torch.Tensor, None, None, None, None]: Gradients for the inputs. + """ + multiplier, selected_experts, masked_gates = ctx.saved_tensors + + grad_at_output = grad_at_output * multiplier + + grad_at_scores_expanded = masked_gates * grad_at_output.mul(-1) + grad_at_scores_expanded.scatter_add_( + dim=-1, + index=selected_experts, + src=grad_at_output, + ) + + return ( + grad_at_scores_expanded, + None, + None, + None, + None, + ) + + +@use_experts_implementation +class PhimoeExperts(nn.Module): + """Collection of expert weights stored as 3D tensors.""" + + def __init__(self, config: PhimoeConfig): + super().__init__() + self.num_experts = config.num_local_experts + self.hidden_dim = config.hidden_size + self.intermediate_dim = config.intermediate_size + self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim)) + self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim)) + self.act_fn = ACT2FN[config.hidden_act] + + def forward( + self, + hidden_states: torch.Tensor, + top_k_index: torch.Tensor, + top_k_weights: torch.Tensor, + ) -> torch.Tensor: + final_hidden_states = torch.zeros_like(hidden_states) + with torch.no_grad(): + expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts) + expert_mask = expert_mask.permute(2, 1, 0) + expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero() + + for expert_idx in expert_hit: + expert_idx = expert_idx[0] + if expert_idx == self.num_experts: + continue + top_k_pos, token_idx = torch.where(expert_mask[expert_idx]) + current_state = hidden_states[token_idx] + gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1) + current_hidden_states = self.act_fn(gate) * up + current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx]) + current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None] + final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype)) + + return final_hidden_states + + +def sparsemixer(scores, jitter_eps, training, top_k=2): + """ + Sparse mixer function to select top-k experts and compute multipliers. + Based on the paper: https://huggingface.co/papers/2409.12136 + We first replace the TopK(·) function as random sampling of discrete variables + in model training. Then, following Liu et al. (2023a) and Liu et al. (2023b), we apply Heun's + third order method to approximate the expert routing gradient and construct a modified + back-propagation to give a mathematically sound gradient estimation for expert routing. + + Args: + scores (torch.Tensor): Input scores tensor. + jitter_eps (float): Jitter epsilon for numerical stability. + training (bool): Flag indicating if the model is in training mode. + top_k (int): Number of top experts to select. + + Returns: + tuple[torch.Tensor, torch.Tensor]: Multiplier and selected experts tensors. + """ + with torch.no_grad(): + # Compute mask for sparsity + mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True) + factor = scores.abs().clamp(min=mask_logits_threshold) + mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps) + + # Apply mask + masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf")) + if training: + selected_experts = ( + ( + masked_gates + - torch.empty_like(masked_gates, memory_format=torch.legacy_contiguous_format).exponential_().log() + ) + .max(dim=-1)[1] + .unsqueeze(-1) + ) # Gumbel sampling, more robust than the multinomial method + else: + selected_experts = max_ind + + # Compute scores for gradients + masked_gates = torch.softmax(masked_gates, dim=-1) + multiplier_o = masked_gates.gather(dim=-1, index=selected_experts) + + if training: + # Compute midpoint mask + max_scores, max_ind = masked_gates.max(dim=-1, keepdim=True) + mask_for_one = torch.logical_or( + selected_experts == max_ind, + torch.rand_like(max_scores) > 0.75, # Heun's third-order method + ) + # 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5 + mask_for_one = torch.add(0.3333, mask_for_one, alpha=0.6667).type_as(masked_gates) + + multiplier = PhimoeMultiplier.apply( + scores, + multiplier_o, + selected_experts, + masked_gates, + mask_for_one, + ) + else: + multiplier = multiplier_o + + # Masked out first expert + masked_scores = torch.scatter( + scores, + -1, + selected_experts, + float("-inf"), + ) + with torch.no_grad(): + # Compute mask for sparsity + mask_logits_threshold, max_ind = masked_scores.max(dim=-1, keepdim=True) + factor = scores.abs().clamp(min=mask_logits_threshold) + mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps) + + # Apply mask + masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold, float("-inf")) + if training: + selected_experts_top2 = ( + ( + masked_gates_top2 + - torch.empty_like(masked_gates_top2, memory_format=torch.legacy_contiguous_format) + .exponential_() + .log() + ) + .max(dim=-1)[1] + .unsqueeze(-1) + ) # Gumbel sampling, more robust than the multinomial method + else: + selected_experts_top2 = max_ind + # Compute scores for gradients + masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1) + multiplier_top2_o = masked_gates_top2.gather(dim=-1, index=selected_experts_top2) + + if training: + # Compute midpoint mask + max_scores, max_ind = masked_gates_top2.max(dim=-1, keepdim=True) + mask_for_one_top2 = torch.logical_or( + selected_experts_top2 == max_ind, + torch.rand_like(max_scores).uniform_() > 0.75, # Heun's third-order method + ) + # 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5 + mask_for_one_top2 = torch.add(0.3333, mask_for_one_top2, alpha=0.6667).type_as(masked_gates_top2) + + multiplier_top2 = PhimoeMultiplier.apply( + scores, + multiplier_top2_o, + selected_experts_top2, + masked_gates_top2, + mask_for_one_top2, + ) + else: + multiplier_top2 = multiplier_top2_o + + multiplier = torch.concat((multiplier, multiplier_top2), dim=-1) + selected_experts = torch.concat((selected_experts, selected_experts_top2), dim=-1) + + return ( + multiplier, + selected_experts, + ) + + +class PhimoeTopKRouter(nn.Linear): + def __init__(self, config: PhimoeConfig): + super().__init__(config.hidden_size, config.num_local_experts, bias=False) + self.router_jitter_noise = config.router_jitter_noise + self.input_jitter_noise = config.input_jitter_noise + self.top_k = config.num_experts_per_tok + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + if self.training and self.input_jitter_noise > 0: + hidden_states *= torch.empty_like(hidden_states).uniform_( + 1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise + ) + router_logits = super().forward(hidden_states) + routing_weights, selected_experts = sparsemixer( + router_logits, jitter_eps=self.router_jitter_noise, training=self.training, top_k=self.top_k + ) + return router_logits, routing_weights, selected_experts + + +class PhimoeSparseMoeBlock(nn.Module): + """ + This implementation is + strictly equivalent to standard MoE with full capacity (no + dropped tokens). It's faster since it formulates MoE operations + in terms of block-sparse operations to accommodate imbalanced + assignments of tokens to experts, whereas standard MoE either + (1) drop tokens at the cost of reduced performance or (2) set + capacity factor to number of experts and thus waste computation + and memory on padding. + """ + + def __init__(self, config): + super().__init__() + self.hidden_dim = config.hidden_size + self.ffn_dim = config.intermediate_size + self.num_experts = config.num_local_experts + self.top_k = config.num_experts_per_tok + self.router = PhimoeTopKRouter(config) + self.experts = PhimoeExperts(config) + self.input_jitter_noise = config.input_jitter_noise + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + batch_size, sequence_length, hidden_dim = hidden_states.shape + if self.training and self.input_jitter_noise > 0: + hidden_states *= torch.empty_like(hidden_states).uniform_( + 1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise + ) + + batch_size, sequence_length, hidden_dim = hidden_states.shape + hidden_states = hidden_states.reshape(-1, hidden_dim) + _, routing_weights, selected_experts = self.router(hidden_states) + final_hidden_states = self.experts(hidden_states, selected_experts, routing_weights) + return final_hidden_states.reshape(batch_size, sequence_length, hidden_dim) + + +class PhimoeDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PhimoeConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = PhimoeAttention(config, layer_idx) + + self.mlp = PhimoeSparseMoeBlock(config) + + # Phimoe uses nn.LayerNorm + self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True) + self.post_attention_layernorm = nn.LayerNorm( + config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + position_embeddings=position_embeddings, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + **kwargs, + ) + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class PhimoePreTrainedModel(PreTrainedModel): + config: PhimoeConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PhimoeDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "router_logits": OutputRecorder(PhimoeTopKRouter, index=0), + "hidden_states": PhimoeDecoderLayer, + "attentions": PhimoeAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + std = self.config.initializer_range + if isinstance(module, PhimoeExperts): + init.normal_(module.gate_up_proj, mean=0.0, std=std) + init.normal_(module.down_proj, mean=0.0, std=std) + elif isinstance(module, PhimoeTopKRouter): + init.normal_(module.weight, mean=0.0, std=std) + + +@auto_docstring +class PhimoeModel(PhimoePreTrainedModel): + def __init__(self, config: PhimoeConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [PhimoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True) + self.rotary_emb = PhimoeRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> MoeModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + mask_function = create_causal_mask if self.config.sliding_window is None else create_sliding_window_causal_mask + causal_mask = mask_function( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return MoeModelOutputWithPast( # only diff with Mistral is the output type, we need MoE + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +def load_balancing_loss_func( + gate_logits: torch.Tensor | tuple[torch.Tensor] | None, + num_experts: int | None = None, + top_k=2, + attention_mask: torch.Tensor | None = None, +) -> torch.Tensor | int: + r""" + Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch. + + See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss + function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between + experts is too unbalanced. + + Args: + gate_logits: + Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of + shape [batch_size X sequence_length, num_experts]. + num_experts: + Number of experts + top_k: + The number of experts to route per-token, can be also interpreted as the `top-k` routing + parameter. + attention_mask (`torch.Tensor`, *optional*): + The attention_mask used in forward function + shape [batch_size X sequence_length] if not None. + + Returns: + The auxiliary loss. + """ + if gate_logits is None or not isinstance(gate_logits, tuple): + return 0 + + if isinstance(gate_logits, tuple): + compute_device = gate_logits[0].device + concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0) + + routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1) + + _, selected_experts = torch.topk(routing_weights, top_k, dim=-1) + + expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts) + + if attention_mask is None: + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.mean(expert_mask.float(), dim=0) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.mean(routing_weights, dim=0) + else: + batch_size, sequence_length = attention_mask.shape + num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length) + + # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask + expert_attention_mask = ( + attention_mask[None, :, :, None, None] + .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts)) + .reshape(-1, top_k, num_experts) + .to(compute_device) + ) + + # Compute the percentage of tokens routed to each experts + tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum( + expert_attention_mask, dim=0 + ) + + # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert + router_per_expert_attention_mask = ( + attention_mask[None, :, :, None] + .expand((num_hidden_layers, batch_size, sequence_length, num_experts)) + .reshape(-1, num_experts) + .to(compute_device) + ) + + # Compute the average probability of routing to these experts + router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum( + router_per_expert_attention_mask, dim=0 + ) + + overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0)) + return overall_loss * num_experts + + +@auto_docstring +class PhimoeForCausalLM(PhimoePreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = PhimoeModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=self.config.lm_head_bias) + self.router_aux_loss_coef = config.router_aux_loss_coef + self.num_experts = config.num_local_experts + self.num_experts_per_tok = config.num_experts_per_tok + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_router_logits: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> MoeCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, PhimoeForCausalLM + + >>> model = PhimoeForCausalLM.from_pretrained("mistralai/Phimoe-8x7B-v0.1") + >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Phimoe-8x7B-v0.1") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + + output_router_logits = ( + output_router_logits if output_router_logits is not None else self.config.output_router_logits + ) + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs: MoeModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_router_logits=output_router_logits, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **kwargs) + + aux_loss = None + if output_router_logits: + aux_loss = load_balancing_loss_func( + outputs.router_logits, + self.num_experts, + self.num_experts_per_tok, + attention_mask, + ) + if labels is not None: + loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device + + return MoeCausalLMOutputWithPast( + loss=loss, + aux_loss=aux_loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + router_logits=outputs.router_logits, + ) + + # Copied from transformers.models.phi3.modeling_phi3.Phi3ForCausalLM.prepare_inputs_for_generation + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + logits_to_keep=None, + **kwargs, + ): + # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the + # process + + # When the first time input length reached long and short factor switching point, enforce re-compute cache + # It will cause downside of slower at this single token position, however, better than current failure. + if ( + past_key_values + and hasattr(self.config, "original_max_position_embeddings") + and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1 + ): + past_length = past_key_values.get_seq_length() + if past_length <= self.config.original_max_position_embeddings: + past_key_values = None + + model_inputs = super().prepare_inputs_for_generation( + input_ids=input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **kwargs, + ) + return model_inputs + + +class PhimoeForSequenceClassification(GenericForSequenceClassification, PhimoePreTrainedModel): ... + + +__all__ = ["PhimoePreTrainedModel", "PhimoeModel", "PhimoeForCausalLM", "PhimoeForSequenceClassification"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/modular_phimoe.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/modular_phimoe.py new file mode 100644 index 0000000000000000000000000000000000000000..4ab1b447a03a8a1d6be6bb627f072ec7469be534 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phimoe/modular_phimoe.py @@ -0,0 +1,410 @@ +# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""PyTorch Phimoe model.""" + +from collections.abc import Callable + +import torch +from torch import nn + +from ...modeling_layers import ( + GenericForSequenceClassification, +) +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS +from ...utils.generic import maybe_autocast +from ...utils.output_capturing import OutputRecorder +from ..llama.modeling_llama import LlamaAttention +from ..mixtral.modeling_mixtral import ( + MixtralDecoderLayer, + MixtralExperts, + MixtralForCausalLM, + MixtralModel, + MixtralPreTrainedModel, + MixtralRotaryEmbedding, +) +from .configuration_phimoe import PhimoeConfig + + +class PhimoeRotaryEmbedding(MixtralRotaryEmbedding): + def __init__(self, config: PhimoeConfig, device=None): + nn.Module.__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + self.rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + def forward(self, x, position_ids=None, layer_type=None): + if layer_type is not None: + raise ValueError( + f"{self.__class__.__name__} does not support layer types, but got `layer_type={layer_type}`" + ) + + mscale = None + seq_len = torch.max(position_ids) + 1 + if self.config.rope_parameters["rope_type"] != "default" and seq_len: + mscale = ( + self.config.rope_parameters["long_mscale"] + if seq_len > self.config.rope_parameters["original_max_position_embeddings"] + else self.config.rope_parameters["short_mscale"] + ) + inv_freq, attention_scaling = self.rope_init_fn(self.config, x.device, seq_len) + mscale = attention_scaling if mscale is None else mscale + inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * mscale + sin = emb.sin() * mscale + return cos.to(x.dtype), sin.to(x.dtype) + + +class PhimoeAttention(LlamaAttention): + pass + + +class PhimoeMultiplier(torch.autograd.Function): + @staticmethod + def forward( + ctx, + scores: torch.Tensor, + multiplier: torch.Tensor, + selected_experts: torch.Tensor, + masked_gates: torch.Tensor, + mask_for_one: torch.Tensor, + ): + """ + Forward pass for the custom autograd function. + + Args: + ctx: Context object to save information for backward computation. + scores (torch.Tensor): Input scores tensor. + multiplier (torch.Tensor): Multiplier tensor. + selected_experts (torch.Tensor): Tensor of selected experts. + masked_gates (torch.Tensor): Masked gates tensor. + mask_for_one (torch.Tensor): Mask for one tensor. + + Returns: + torch.Tensor: Result of the forward pass. + """ + ctx.save_for_backward(multiplier, selected_experts, masked_gates) + return multiplier * mask_for_one + + @staticmethod + def backward( + ctx, + grad_at_output: torch.Tensor, + ): + """ + Backward pass for the custom autograd function. + + Args: + ctx: Context object with saved tensors from the forward pass. + grad_at_output (torch.Tensor): Gradient at the output. + + Returns: + tuple[torch.Tensor, None, None, None, None]: Gradients for the inputs. + """ + multiplier, selected_experts, masked_gates = ctx.saved_tensors + + grad_at_output = grad_at_output * multiplier + + grad_at_scores_expanded = masked_gates * grad_at_output.mul(-1) + grad_at_scores_expanded.scatter_add_( + dim=-1, + index=selected_experts, + src=grad_at_output, + ) + + return ( + grad_at_scores_expanded, + None, + None, + None, + None, + ) + + +def sparsemixer(scores, jitter_eps, training, top_k=2): + """ + Sparse mixer function to select top-k experts and compute multipliers. + Based on the paper: https://huggingface.co/papers/2409.12136 + We first replace the TopK(·) function as random sampling of discrete variables + in model training. Then, following Liu et al. (2023a) and Liu et al. (2023b), we apply Heun's + third order method to approximate the expert routing gradient and construct a modified + back-propagation to give a mathematically sound gradient estimation for expert routing. + + Args: + scores (torch.Tensor): Input scores tensor. + jitter_eps (float): Jitter epsilon for numerical stability. + training (bool): Flag indicating if the model is in training mode. + top_k (int): Number of top experts to select. + + Returns: + tuple[torch.Tensor, torch.Tensor]: Multiplier and selected experts tensors. + """ + with torch.no_grad(): + # Compute mask for sparsity + mask_logits_threshold, max_ind = scores.max(dim=-1, keepdim=True) + factor = scores.abs().clamp(min=mask_logits_threshold) + mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps) + + # Apply mask + masked_gates = scores.masked_fill(mask_logits_threshold, float("-inf")) + if training: + selected_experts = ( + ( + masked_gates + - torch.empty_like(masked_gates, memory_format=torch.legacy_contiguous_format).exponential_().log() + ) + .max(dim=-1)[1] + .unsqueeze(-1) + ) # Gumbel sampling, more robust than the multinomial method + else: + selected_experts = max_ind + + # Compute scores for gradients + masked_gates = torch.softmax(masked_gates, dim=-1) + multiplier_o = masked_gates.gather(dim=-1, index=selected_experts) + + if training: + # Compute midpoint mask + max_scores, max_ind = masked_gates.max(dim=-1, keepdim=True) + mask_for_one = torch.logical_or( + selected_experts == max_ind, + torch.rand_like(max_scores) > 0.75, # Heun's third-order method + ) + # 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5 + mask_for_one = torch.add(0.3333, mask_for_one, alpha=0.6667).type_as(masked_gates) + + multiplier = PhimoeMultiplier.apply( + scores, + multiplier_o, + selected_experts, + masked_gates, + mask_for_one, + ) + else: + multiplier = multiplier_o + + # Masked out first expert + masked_scores = torch.scatter( + scores, + -1, + selected_experts, + float("-inf"), + ) + with torch.no_grad(): + # Compute mask for sparsity + mask_logits_threshold, max_ind = masked_scores.max(dim=-1, keepdim=True) + factor = scores.abs().clamp(min=mask_logits_threshold) + mask_logits_threshold = ((mask_logits_threshold - scores) / factor) > (2 * jitter_eps) + + # Apply mask + masked_gates_top2 = masked_scores.masked_fill(mask_logits_threshold, float("-inf")) + if training: + selected_experts_top2 = ( + ( + masked_gates_top2 + - torch.empty_like(masked_gates_top2, memory_format=torch.legacy_contiguous_format) + .exponential_() + .log() + ) + .max(dim=-1)[1] + .unsqueeze(-1) + ) # Gumbel sampling, more robust than the multinomial method + else: + selected_experts_top2 = max_ind + # Compute scores for gradients + masked_gates_top2 = torch.softmax(masked_gates_top2, dim=-1) + multiplier_top2_o = masked_gates_top2.gather(dim=-1, index=selected_experts_top2) + + if training: + # Compute midpoint mask + max_scores, max_ind = masked_gates_top2.max(dim=-1, keepdim=True) + mask_for_one_top2 = torch.logical_or( + selected_experts_top2 == max_ind, + torch.rand_like(max_scores).uniform_() > 0.75, # Heun's third-order method + ) + # 1 -> 1.0 & 0 -> 1./3: lambda x: (x + 0.5) / 1.5 + mask_for_one_top2 = torch.add(0.3333, mask_for_one_top2, alpha=0.6667).type_as(masked_gates_top2) + + multiplier_top2 = PhimoeMultiplier.apply( + scores, + multiplier_top2_o, + selected_experts_top2, + masked_gates_top2, + mask_for_one_top2, + ) + else: + multiplier_top2 = multiplier_top2_o + + multiplier = torch.concat((multiplier, multiplier_top2), dim=-1) + selected_experts = torch.concat((selected_experts, selected_experts_top2), dim=-1) + + return ( + multiplier, + selected_experts, + ) + + +class PhimoeExperts(MixtralExperts): + pass + + +class PhimoeTopKRouter(nn.Linear): + def __init__(self, config: PhimoeConfig): + super().__init__(config.hidden_size, config.num_local_experts, bias=False) + self.router_jitter_noise = config.router_jitter_noise + self.input_jitter_noise = config.input_jitter_noise + self.top_k = config.num_experts_per_tok + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + if self.training and self.input_jitter_noise > 0: + hidden_states *= torch.empty_like(hidden_states).uniform_( + 1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise + ) + router_logits = super().forward(hidden_states) + routing_weights, selected_experts = sparsemixer( + router_logits, jitter_eps=self.router_jitter_noise, training=self.training, top_k=self.top_k + ) + return router_logits, routing_weights, selected_experts + + +class PhimoeSparseMoeBlock(nn.Module): + """ + This implementation is + strictly equivalent to standard MoE with full capacity (no + dropped tokens). It's faster since it formulates MoE operations + in terms of block-sparse operations to accommodate imbalanced + assignments of tokens to experts, whereas standard MoE either + (1) drop tokens at the cost of reduced performance or (2) set + capacity factor to number of experts and thus waste computation + and memory on padding. + """ + + def __init__(self, config): + super().__init__() + self.hidden_dim = config.hidden_size + self.ffn_dim = config.intermediate_size + self.num_experts = config.num_local_experts + self.top_k = config.num_experts_per_tok + self.router = PhimoeTopKRouter(config) + self.experts = PhimoeExperts(config) + self.input_jitter_noise = config.input_jitter_noise + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + batch_size, sequence_length, hidden_dim = hidden_states.shape + if self.training and self.input_jitter_noise > 0: + hidden_states *= torch.empty_like(hidden_states).uniform_( + 1.0 - self.input_jitter_noise, 1.0 + self.input_jitter_noise + ) + + batch_size, sequence_length, hidden_dim = hidden_states.shape + hidden_states = hidden_states.reshape(-1, hidden_dim) + _, routing_weights, selected_experts = self.router(hidden_states) + final_hidden_states = self.experts(hidden_states, selected_experts, routing_weights) + return final_hidden_states.reshape(batch_size, sequence_length, hidden_dim) + + +class PhimoeDecoderLayer(MixtralDecoderLayer): + def __init__(self, config: PhimoeConfig, layer_idx: int): + super().__init__(config, layer_idx) + + # Phimoe uses nn.LayerNorm + self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True) + self.post_attention_layernorm = nn.LayerNorm( + config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True + ) + + +class PhimoePreTrainedModel(MixtralPreTrainedModel): + _can_record_outputs = { + "router_logits": OutputRecorder(PhimoeTopKRouter, index=0), + "hidden_states": PhimoeDecoderLayer, + "attentions": PhimoeAttention, + } + + +class PhimoeModel(MixtralModel): + def __init__(self, config: PhimoeConfig): + super().__init__(config) + self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps, elementwise_affine=True) + + +class PhimoeForCausalLM(MixtralForCausalLM): + def __init__(self, config): + super().__init__(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=self.config.lm_head_bias) + + # Copied from transformers.models.phi3.modeling_phi3.Phi3ForCausalLM.prepare_inputs_for_generation + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + logits_to_keep=None, + **kwargs, + ): + # Overwritten -- this model may need to switch between short and long rope, invalidating the cache in the + # process + + # When the first time input length reached long and short factor switching point, enforce re-compute cache + # It will cause downside of slower at this single token position, however, better than current failure. + if ( + past_key_values + and hasattr(self.config, "original_max_position_embeddings") + and input_ids.shape[1] >= self.config.original_max_position_embeddings + 1 + ): + past_length = past_key_values.get_seq_length() + if past_length <= self.config.original_max_position_embeddings: + past_key_values = None + + model_inputs = super().prepare_inputs_for_generation( + input_ids=input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + use_cache=use_cache, + logits_to_keep=logits_to_keep, + **kwargs, + ) + return model_inputs + + +class PhimoeForSequenceClassification(GenericForSequenceClassification, PhimoePreTrainedModel): ... + + +__all__ = [ + "PhimoePreTrainedModel", + "PhimoeModel", + "PhimoeForCausalLM", + "PhimoeForSequenceClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phobert/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phobert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5c14d34aff61362159e0b45e5895ceb08850faf5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phobert/__init__.py @@ -0,0 +1,26 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .tokenization_phobert import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phobert/tokenization_phobert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phobert/tokenization_phobert.py new file mode 100644 index 0000000000000000000000000000000000000000..6dd66b7da72d507405e49d1ec1534e160b6215a7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/phobert/tokenization_phobert.py @@ -0,0 +1,349 @@ +# Copyright (c) 2020, VinAI Research and the HuggingFace Inc. team. +# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for PhoBERT""" + +import os +import re +from shutil import copyfile + +from ...tokenization_python import PreTrainedTokenizer +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = { + "vocab_file": "vocab.txt", + "merges_file": "bpe.codes", +} + + +def get_pairs(word): + """ + Return set of symbol pairs in a word. + + Word is represented as tuple of symbols (symbols being variable-length strings). + """ + pairs = set() + prev_char = word[0] + for char in word[1:]: + pairs.add((prev_char, char)) + prev_char = char + + pairs = set(pairs) + return pairs + + +class PhobertTokenizer(PreTrainedTokenizer): + """ + Construct a PhoBERT tokenizer. Based on Byte-Pair-Encoding. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + merges_file (`str`): + Path to the merges file. + bos_token (`st`, *optional*, defaults to `""`): + The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. + + + + When building a sequence using special tokens, this is not the token that is used for the beginning of + sequence. The token used is the `cls_token`. + + + + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + sep_token (`str`, *optional*, defaults to `""`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + cls_token (`str`, *optional*, defaults to `""`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + mask_token (`str`, *optional*, defaults to `""`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + """ + + vocab_files_names = VOCAB_FILES_NAMES + + def __init__( + self, + vocab_file, + merges_file, + bos_token="", + eos_token="", + sep_token="", + cls_token="", + unk_token="", + pad_token="", + mask_token="", + **kwargs, + ): + self.vocab_file = vocab_file + self.merges_file = merges_file + + self.encoder = {} + self.encoder[str(bos_token)] = 0 + self.encoder[str(pad_token)] = 1 + self.encoder[str(eos_token)] = 2 + self.encoder[str(unk_token)] = 3 + + self.add_from_file(vocab_file) + + self.decoder = {v: k for k, v in self.encoder.items()} + + with open(merges_file, encoding="utf-8") as merges_handle: + merges = merges_handle.read().split("\n")[:-1] + merges = [tuple(merge.split()[:-1]) for merge in merges] + + self.bpe_ranks = dict(zip(merges, range(len(merges)))) + self.cache = {} + + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + sep_token=sep_token, + cls_token=cls_token, + pad_token=pad_token, + mask_token=mask_token, + **kwargs, + ) + + def build_inputs_with_special_tokens( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A PhoBERT sequence has the following format: + + - single sequence: ` X ` + - pair of sequences: ` A B ` + + Args: + token_ids_0 (`list[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + + if token_ids_1 is None: + return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + cls = [self.cls_token_id] + sep = [self.sep_token_id] + return cls + token_ids_0 + sep + sep + token_ids_1 + sep + + def get_special_tokens_mask( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None, already_has_special_tokens: bool = False + ) -> list[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + if token_ids_1 is None: + return [1] + ([0] * len(token_ids_0)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] + + def create_token_type_ids_from_sequences( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. PhoBERT does not + make use of token type ids, therefore a list of zeros is returned. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of zeros. + """ + + sep = [self.sep_token_id] + cls = [self.cls_token_id] + + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] + + @property + def vocab_size(self): + return len(self.encoder) + + def get_vocab(self): + return dict(self.encoder, **self.added_tokens_encoder) + + def bpe(self, token): + if token in self.cache: + return self.cache[token] + word = tuple(token) + word = tuple(list(word[:-1]) + [word[-1] + ""]) + pairs = get_pairs(word) + + if not pairs: + return token + + while True: + bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) + if bigram not in self.bpe_ranks: + break + first, second = bigram + new_word = [] + i = 0 + while i < len(word): + try: + j = word.index(first, i) + except ValueError: + new_word.extend(word[i:]) + break + else: + new_word.extend(word[i:j]) + i = j + + if word[i] == first and i < len(word) - 1 and word[i + 1] == second: + new_word.append(first + second) + i += 2 + else: + new_word.append(word[i]) + i += 1 + new_word = tuple(new_word) + word = new_word + if len(word) == 1: + break + else: + pairs = get_pairs(word) + word = "@@ ".join(word) + word = word[:-4] + self.cache[token] = word + return word + + def _tokenize(self, text): + """Tokenize a string.""" + split_tokens = [] + + words = re.findall(r"\S+\n?", text) + + for token in words: + split_tokens.extend(list(self.bpe(token).split(" "))) + return split_tokens + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.encoder.get(token, self.encoder.get(self.unk_token)) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.decoder.get(index, self.unk_token) + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + out_string = " ".join(tokens).replace("@@ ", "").strip() + return out_string + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + out_merge_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + if os.path.abspath(self.merges_file) != os.path.abspath(out_merge_file): + copyfile(self.merges_file, out_merge_file) + + return out_vocab_file, out_merge_file + + # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True): + # filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)) + # tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens) + # tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far) + # return ''.join(tokens_generated_so_far) + + def add_from_file(self, f): + """ + Loads a pre-existing dictionary from a text file and adds its symbols to this instance. + """ + if isinstance(f, str): + try: + with open(f, "r", encoding="utf-8") as fd: + self.add_from_file(fd) + except FileNotFoundError as fnfe: + raise fnfe + except UnicodeError: + raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset") + return + + lines = f.readlines() + for lineTmp in lines: + line = lineTmp.strip() + idx = line.rfind(" ") + if idx == -1: + raise ValueError("Incorrect dictionary format, expected ' '") + word = line[:idx] + self.encoder[word] = len(self.encoder) + + +__all__ = ["PhobertTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a33948c10592d23294b21b84fb41260b6e1ef66e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pi0 import * + from .image_processing_pi0 import * + from .modeling_pi0 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/configuration_pi0.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/configuration_pi0.py new file mode 100644 index 0000000000000000000000000000000000000000..ac4aa4dddb8c3cba4eff5da31228da8e580a821d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/configuration_pi0.py @@ -0,0 +1,140 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pi0/modular_pi0.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pi0.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="lerobot/pi0_base") +@strict +class PI0Config(PreTrainedConfig): + r""" + vlm_config (`dict`, *optional*): + Configuration for the vlm backbone (PaliGemmaModel). + dit_config (`dict`, *optional*): + Configuration for the DiT backbone. Defaults to a Gemma 300M variant. + chunk_size (`int`, *optional*, defaults to 50): + Number of action steps to predict per chunk. + max_state_dim (`int`, *optional*, defaults to 32): + Maximum state vector dimension (shorter vectors are zero-padded). + max_action_dim (`int`, *optional*, defaults to 32): + Maximum action vector dimension (shorter vectors are zero-padded). + num_inference_steps (`int`, *optional*, defaults to 10): + Number of denoising steps during inference. + time_sampling_beta_alpha (`float`, *optional*, defaults to 1.5): + Alpha parameter for Beta distribution used to sample diffusion time during training. + time_sampling_beta_beta (`float`, *optional*, defaults to 1.0): + Beta parameter for Beta distribution used to sample diffusion time during training. + time_sampling_scale (`float`, *optional*, defaults to 0.999): + Scale factor for sampled time values. + time_sampling_offset (`float`, *optional*, defaults to 0.001): + Offset added to sampled time values. + min_period (`float`, *optional*, defaults to 0.004): + Minimum period for sinusoidal time embedding. + max_period (`float`, *optional*, defaults to 4.0): + Maximum period for sinusoidal time embedding. + loss_reduction (`str`, *optional*, defaults to `"mean"`): + The reduction to use on MSE loss. + + Example: + ```python + >>> from transformers import PI0ForConditionalGeneration, PI0Config + + >>> config = PI0Config() + >>> model = PI0ForConditionalGeneration(config) + ``` + """ + + model_type = "pi0" + sub_configs = {"vlm_config": AutoConfig, "dit_config": AutoConfig} + + vlm_config: dict | PreTrainedConfig | None = None + dit_config: dict | PreTrainedConfig | None = None + chunk_size: int = 50 + max_state_dim: int = 32 + max_action_dim: int = 32 + num_inference_steps: int = 10 + time_sampling_beta_alpha: float = 1.5 + time_sampling_beta_beta: float = 1.0 + time_sampling_scale: float = 0.999 + time_sampling_offset: float = 0.001 + min_period: float = 4e-3 + max_period: float = 4.0 + loss_reduction: str = "mean" + + def __post_init__(self, **kwargs): + if isinstance(self.vlm_config, dict): + vlm_model_type = self.vlm_config.get("model_type", "paligemma") + self.vlm_config = CONFIG_MAPPING[vlm_model_type](**self.vlm_config) + elif self.vlm_config is None: + self.vlm_config = CONFIG_MAPPING["paligemma"]( + text_config={ + "model_type": "gemma", + "hidden_size": 2048, + "num_hidden_layers": 18, + "intermediate_size": 16384, + "num_attention_heads": 8, + "num_key_value_heads": 1, + "vocab_size": 257152, + }, + vision_config={ + "model_type": "siglip_vision_model", + "intermediate_size": 4304, + "hidden_size": 1152, + "patch_size": 14, + "image_size": 224, + "num_hidden_layers": 27, + "num_attention_heads": 16, + "vocab_size": 257152, + "vision_use_head": False, + }, + projection_dim=2048, + image_token_id=257152, + ) + + if isinstance(self.dit_config, dict): + dit_model_type = self.dit_config.get("model_type", "gemma") + self.dit_config = CONFIG_MAPPING[dit_model_type](**self.dit_config) + elif self.dit_config is None: + self.dit_config = CONFIG_MAPPING["gemma"]( + hidden_size=1024, + num_hidden_layers=18, + intermediate_size=4096, + num_attention_heads=8, + num_key_value_heads=1, + head_dim=256, + vocab_size=self.vlm_config.text_config.vocab_size, + ) + + # Force bidirectional attention + self.dit_config.is_causal = False + self.dit_config.use_bidirectional_attention = True + self.vlm_config.text_config.use_bidirectional_attention = True + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.dit_config.hidden_size % 2 != 0: + raise ValueError(f"DiT hidden dim=({self.config.dit_config.hidden_size}) must be divisible by 2") + + +__all__ = ["PI0Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/image_processing_pi0.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/image_processing_pi0.py new file mode 100644 index 0000000000000000000000000000000000000000..c0cc0b6ea69a7f2dbf3dc8a8d68cb4bce5672e86 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/image_processing_pi0.py @@ -0,0 +1,40 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pi0/modular_pi0.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pi0.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from ...image_processing_backends import TorchvisionBackend +from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, PILImageResampling +from ...utils import auto_docstring + + +@auto_docstring +class PI0ImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"max_height": 224, "max_width": 224} + default_to_square = False + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + pad_size = {"height": 224, "width": 224} + do_pad = True + + +__all__ = ["PI0ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/modeling_pi0.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/modeling_pi0.py new file mode 100644 index 0000000000000000000000000000000000000000..8fd8abe48d7b43fef341db519b773c977eead7ee --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/modeling_pi0.py @@ -0,0 +1,392 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pi0/modular_pi0.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pi0.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable + +import torch +import torch.nn.functional as F +from torch import nn + +from ... import initialization as init +from ...cache_utils import Cache +from ...masking_utils import create_bidirectional_mask +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast +from ..auto import AutoModel +from .configuration_pi0 import PI0Config + + +class PI0TimestepEmbeddings(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + sinusoid_freq = self.compute_freqs(config) + self.register_buffer("sinusoid_freq", sinusoid_freq, persistent=False) + + @staticmethod + def compute_freqs(config): + fraction = torch.linspace(0.0, 1.0, config.dit_config.hidden_size // 2, dtype=torch.float32) + period = config.min_period * (config.max_period / config.min_period) ** fraction + sinusoid_freq = 1.0 / period * 2 * math.pi + return sinusoid_freq + + def forward(self, time): + device_type = time.device.type if isinstance(time.device.type, str) and time.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + sinusoid_freq = self.sinusoid_freq[None, :] + emb = sinusoid_freq * time[:, None] + time_embeds = torch.cat([emb.sin(), emb.cos()], dim=1) + return time_embeds + + +class PI0ActionTimeEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.sinusoid_embeds = PI0TimestepEmbeddings(config) + self.action_in_proj = nn.Linear(config.max_action_dim, config.dit_config.hidden_size) + self.state_proj = nn.Linear(config.max_state_dim, config.dit_config.hidden_size) + self.action_time_mlp_in = nn.Linear(2 * config.dit_config.hidden_size, config.dit_config.hidden_size) + self.action_time_mlp_out = nn.Linear(config.dit_config.hidden_size, config.dit_config.hidden_size) + + def forward(self, state, noise, timestep): + state_embeds = self.state_proj(state) + action_embeds = self.action_in_proj(noise) + + time_embeds = self.sinusoid_embeds(timestep) + time_embeds = time_embeds[:, None, :].expand_as(action_embeds).to(dtype=action_embeds.dtype) + + action_time_embeds = torch.cat([action_embeds, time_embeds], dim=2) + action_time_embeds = self.action_time_mlp_out(F.silu(self.action_time_mlp_in(action_time_embeds))) + action_embeds_merged = torch.cat([state_embeds[:, None, :], action_time_embeds], dim=1) + return action_embeds_merged + + +@auto_docstring +class PI0PreTrainedModel(PreTrainedModel): + config: PI0Config + base_model_prefix = "model" + main_input_name = "state" + supports_gradient_checkpointing = True + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _can_compile_fullgraph = True + _supports_attention_backend = True + input_modalities = ("image", "text") + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, PI0TimestepEmbeddings): + init.copy_(module.sinusoid_freq, module.compute_freqs(module.config)) + + +def blockwise_bidirectional_mask(block_boundaries: torch.Tensor) -> Callable: + def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool: + q_block = torch.bucketize(q_idx, block_boundaries) + kv_block = torch.bucketize(kv_idx, block_boundaries) + return kv_block <= q_block + + return inner_mask + + +@auto_docstring +class PI0Model(PI0PreTrainedModel): + def __init__(self, config: PI0Config): + super().__init__(config) + self.dit = AutoModel.from_config(config.dit_config) + self.vlm = AutoModel.from_config(config.vlm_config) + self.post_init() + + def get_input_embeddings(self): + return self.vlm.get_input_embeddings() + + def set_input_embeddings(self, value): + self.vlm.set_input_embeddings(value) + + def embed_prefix(self, input_ids, pixel_values, pixel_attention_mask, attention_mask=None): + max_num_cameras = pixel_attention_mask.shape[1] + pixel_values = pixel_values.flatten(0, 1) + image_features = self.vlm.get_image_features(pixel_values).pooler_output + image_features = image_features.reshape(-1, max_num_cameras, image_features.shape[1], image_features.shape[2]) + + total_image_features = [] + for batch_idx, mask in enumerate(pixel_attention_mask): + unpadded_image_features = image_features[batch_idx][mask] + total_image_features.append(unpadded_image_features) + total_image_features = torch.cat(total_image_features, dim=0) + + llm_input_ids = input_ids.clone() + llm_input_ids[input_ids == self.config.vlm_config.image_token_id] = 0 + inputs_embeds = self.vlm.get_input_embeddings()(llm_input_ids) + special_image_mask = ( + (input_ids == self.config.vlm_config.image_token_id) + .unsqueeze(-1) + .expand_as(inputs_embeds) + .to(inputs_embeds.device) + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, total_image_features) + + return inputs_embeds + + @can_return_tuple + @auto_docstring + def forward( + self, + action_embeds: torch.Tensor, # aka `suffix_emb` (noise + state + timestep) + input_ids: torch.Tensor | None = None, + pixel_values: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + pixel_attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.Tensor | None = None, # aka `prefix_emb` or merged image+text emb + past_key_values: Cache | None = None, # must-have for prefix tuning + **kwargs, + ) -> BaseModelOutputWithPast: + r""" + action_embeds (`torch.Tensor`, *optional*): + The embeddings of input actions and robot states. + pixel_attention_mask (`torch.Tensor`, *optional*): + The mask indicating padded positions in the input image. + """ + if pixel_values is not None and past_key_values is None: + if attention_mask is not None and position_ids is None: + position_ids = attention_mask.cumsum(-1) - 1 + + if inputs_embeds is None: + inputs_embeds = self.embed_prefix(input_ids, pixel_values, pixel_attention_mask) + + token_type_ids = torch.zeros_like(inputs_embeds)[:, :, 0] + past_key_values = self.vlm( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + token_type_ids=token_type_ids, + use_cache=True, + ).past_key_values + + if attention_mask is not None and attention_mask.ndim != 2: + raise ValueError("Only two-dimensional attention masks are accepted for now!") + + # Merge masks if needed, same for position ids + dit_position_ids = dit_attention_mask = None + if attention_mask is not None: + noise_mask = torch.ones( + action_embeds.shape[0], + action_embeds.shape[1], + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + dit_attention_mask = torch.cat([attention_mask, noise_mask], dim=1) + dit_position_ids = (torch.cumsum(dit_attention_mask, dim=1) - 1)[:, -action_embeds.shape[1] :] + + # We have three blocks: vlm-inputss, state and actions from which only 1 token is `state` + # The mask should be bidirectional within each block and to prev blocks, but not to next blocks + vlm_input_length = past_key_values.get_seq_length() + block_sizes = torch.tensor([vlm_input_length + 1, action_embeds.shape[1] - 1], device=action_embeds.device) + block_boundaries = torch.cumsum(block_sizes, dim=0) - 1 + bidirectional_mask = create_bidirectional_mask( + config=self.config.dit_config, + inputs_embeds=action_embeds, + attention_mask=dit_attention_mask, + past_key_values=past_key_values, + and_mask_function=blockwise_bidirectional_mask(block_boundaries), + ) + + dit_output = self.dit( + inputs_embeds=action_embeds, + attention_mask=bidirectional_mask, + position_ids=dit_position_ids, + past_key_values=past_key_values, + **kwargs, + ) + return dit_output + + +class PI0ForConditionalGeneration(PI0PreTrainedModel): + """PI0 model with action projection heads and flow matching.""" + + _tp_plan = {"action_out_proj": "colwise_gather_output"} + + def __init__(self, config: PI0Config): + super().__init__(config) + self.model = PI0Model(config) + self.expert_hidden_size = config.dit_config.hidden_size + self.embed_action_time = PI0ActionTimeEmbedding(config) + self.action_out_proj = nn.Linear(self.expert_hidden_size, config.max_action_dim) + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + state: torch.FloatTensor, + noise: torch.FloatTensor | None = None, + timestep: torch.FloatTensor | None = None, + input_ids: torch.Tensor | None = None, + pixel_values: torch.Tensor | None = None, + pixel_attention_mask: torch.BoolTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | None = None, + actions: torch.FloatTensor = None, # aka labels + **kwargs, + ) -> CausalLMOutputWithPast: + r""" + state (`torch.Tensor`, *optional*): + Current robot state. + noise (`torch.Tensor`, *optional*): + Random noise at current timestep that needs to be denoised + timestep (`torch.Tensor`, *optional*): + Current denoising timestep. + pixel_attention_mask (`torch.Tensor`, *optional*): + The mask indicating padded positions in the input image. + actions (`torch.Tensor`, *optional*): + Input actions that need to be predicted. Used only when training to compiute loss. + """ + batch_size = state.shape[0] + + # 1.Sample the timestep + if timestep is None: + alpha_t = torch.tensor(self.config.time_sampling_beta_alpha, dtype=torch.float32) + beta_t = torch.tensor(self.config.time_sampling_beta_beta, dtype=torch.float32) + dist = torch.distributions.Beta(alpha_t, beta_t) + time_beta = dist.sample((batch_size,)).to(state.device) + timestep = (time_beta * self.config.time_sampling_scale + self.config.time_sampling_offset).float() + + # 2. Create random noise if not provided + if noise is None: + noise = torch.randn( + batch_size, + self.config.chunk_size, + self.config.max_action_dim, + device=state.device, + dtype=state.dtype, + ) + + # 3. If training: merge noise with the ground truth actions (aka labels) + # Target velocity is the label we want to predict and will compute loss upon + if actions is not None: + time_expanded = timestep[:, None, None] + noisy_actions = (time_expanded * noise + (1 - time_expanded) * actions).to(actions.dtype) + target_velocity = noise - actions + else: + noisy_actions = noise + + # 4. Embed 'state + noise + actions' for DiT blocks + action_time_embeds = self.embed_action_time(state, noisy_actions, timestep) + + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + pixel_attention_mask=pixel_attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + action_embeds=action_time_embeds, + past_key_values=past_key_values, + **kwargs, + ) + last_hidden_states = outputs.last_hidden_state[:, -self.config.chunk_size :] + predicted_velocity = self.action_out_proj(last_hidden_states) + + loss = None + if actions is not None: + # Let the users reduce loss themselves and return fine-grained per sample loss + loss = F.mse_loss(target_velocity, predicted_velocity, reduction=self.config.loss_reduction) + + return CausalLMOutputWithPast( + loss=loss, + logits=predicted_velocity, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + @torch.no_grad() + def sample_actions( + self, + state: torch.FloatTensor, + input_ids: torch.LongTensor, + pixel_values: torch.FloatTensor, + noise: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + pixel_attention_mask: torch.BoolTensor | None = None, + num_steps: int | None = None, + **kwargs, + ) -> torch.FloatTensor: + """Run flow matching inference to generate actions.""" + + num_steps = num_steps or self.config.num_inference_steps + batch_size = input_ids.shape[0] + device = input_ids.device + + # 1. Sample random noise + if noise is None: + noise = torch.normal( + mean=0.0, + std=1.0, + size=( + batch_size, + self.config.chunk_size, + self.config.max_action_dim, + ), + dtype=pixel_values.dtype, + device=device, + ) + + # 2. Run VLM once and obtain prefix cache. Must infer positions here! + if attention_mask is not None: + position_ids = attention_mask.cumsum(-1) - 1 + inputs_embeds = self.model.embed_prefix(input_ids, pixel_values, pixel_attention_mask) + past_key_values = self.model.vlm( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + use_cache=True, + return_dict=True, + ).past_key_values + prefix_length = past_key_values.get_seq_length() + + # 3. Denoise `num_steps` times + dt = -1.0 / num_steps + for step in range(num_steps): + time = 1.0 + step * dt + time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(batch_size) + output = self( + state=state, + noise=noise, + timestep=time_tensor, + pixel_attention_mask=pixel_attention_mask, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + + # We need to keep only the "vlm-prefix", no attention to past denoising steps! + past_key_values.crop(prefix_length) + noise = noise + dt * output.logits + return noise + + +__all__ = ["PI0PreTrainedModel", "PI0Model", "PI0ForConditionalGeneration"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/modular_pi0.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/modular_pi0.py new file mode 100644 index 0000000000000000000000000000000000000000..f79ac3c2775a9cce01d608c867384605ada38de7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/modular_pi0.py @@ -0,0 +1,649 @@ +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PI0 model: PaliGemma + Action Expert with flow matching for robot action prediction.""" + +import math +from collections.abc import Callable + +import numpy as np +import torch +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict +from torch import nn + +from ... import initialization as init +from ...cache_utils import Cache +from ...configuration_utils import PreTrainedConfig +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput, make_nested_list_of_images +from ...masking_utils import create_bidirectional_mask +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_utils import PreTrainedModel +from ...processing_utils import ProcessingKwargs, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring, can_return_tuple, logging +from ...utils.generic import maybe_autocast +from ...utils.import_utils import requires +from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel +from ..paligemma.processing_paligemma import PaligemmaProcessor +from ..siglip.image_processing_siglip import SiglipImageProcessor + + +logger = logging.get_logger(__name__) + + +@auto_docstring +class PI0ImageProcessor(SiglipImageProcessor): + size = {"max_height": 224, "max_width": 224} + pad_size = {"height": 224, "width": 224} + do_pad = True + + +class PI0ProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": "max_length", + "max_length": 48, + "padding_side": "right", + }, + "common_kwargs": {"return_tensors": "pt"}, + } + + +@auto_docstring +@requires(backends=("vision", "torch")) +class PI0Processor(PaligemmaProcessor): + def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): + self.height, self.width = image_processor.size["height"], image_processor.size["width"] + state_mean = kwargs.get("state_mean", [-0.0419, 0.0354, 0.8257, 2.9083, -0.5562, -0.1665, 0.0283, -0.0286]) + state_std = kwargs.get("state_std", [0.1074, 0.1442, 0.2572, 0.3441, 1.2344, 0.3580, 0.0133, 0.0132]) + actions_mean = kwargs.get("actions_mean", [0.0182, 0.0586, -0.0559, 0.0046, 0.0029, -0.0077, -0.0916]) + actions_std = kwargs.get("actions_std", [0.2825, 0.3590, 0.3674, 0.0377, 0.0543, 0.0872, 0.9958]) + + self.state_mean = torch.tensor(state_mean) + self.state_std = torch.tensor(state_std) + self.actions_mean = torch.tensor(actions_mean) + self.actions_std = torch.tensor(actions_std) + self.max_state_dim = kwargs.get("max_state_dim", 32) + self.chunk_size = kwargs.get("chunk_size", 50) + super().__init__(image_processor, tokenizer) + + def __call__( + self, + images: ImageInput | list[ImageInput] | list[list[ImageInput]] | None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + actions: list | np.ndarray | torch.Tensor | None = None, + state: list | np.ndarray | torch.Tensor | None = None, + **kwargs: Unpack[PI0ProcessorKwargs], + ) -> BatchFeature: + r""" + actions (`list | np.ndarray | torch.Tensor`, *optional*): + Actions to be predicted by the model. If provided, padding, mean and std normalization will be applied. + state (`list | np.ndarray | torch.Tensor`, *optional*): + Robotic states to be predicted by the model. If provided, padding, mean and std normalization will be applied. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix` + is provided, the `input_ids` will also contain the suffix input ids. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **pixel_attention_mask** -- Pixel values padding mask to be fed to a model. Returned when `images` is not `None`. + - **state** -- Robot state compatible with model if `state` is not None + - **actions** -- Label-actions compatible with training if `actions` is not None + """ + output_kwargs = self._merge_kwargs( + PI0ProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs + ) + + if text is None: + logger.warning_once("You are using PI0 without a text prefix. The processor will use an empty prompt.") + text = "" + + if isinstance(text, str): + text = [text] + + batched_images = make_nested_list_of_images(images) + if len(batched_images) != len(text): + raise ValueError( + f"Received {len(batched_images)} image samples for {len(text)} prompts. " + "Each prompt should be associated with one sample (with one or more camera images)." + ) + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + output_kwargs["images_kwargs"].pop("return_tensors", None) + + prompt_strings = [] + for sample, image_list in zip(text, batched_images): + sample = ( + f"{self.image_token * self.image_seq_length * len(image_list)}{self.tokenizer.bos_token}{sample}\n" + ) + prompt_strings.append(sample) + + text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"]) + + # Here is the diff from PaliGemma. Ideally we'd create a new ImageProcessor if it were a VLM + max_num_cameras = max(len(sample_images) for sample_images in batched_images) + pixel_attention_mask = torch.zeros((len(batched_images), max_num_cameras), dtype=torch.bool) + padded_pixel_values = torch.zeros(len(batched_images), max_num_cameras, 3, self.height, self.width) + + for batch, sample_images in enumerate(batched_images): + processed = self.image_processor(sample_images, return_tensors="pt", **output_kwargs["images_kwargs"]) + + num_cameras = len(sample_images) + pixel_attention_mask[batch, :num_cameras] = True + padded_pixel_values[batch, :num_cameras] = processed["pixel_values"] + + return_data = { + **text_inputs, + "pixel_values": padded_pixel_values, + "pixel_attention_mask": pixel_attention_mask, + } + + if actions is not None: + actions = (torch.tensor(actions) - self.actions_mean) / (self.actions_std + 1e-08) + if actions.shape[-1] < self.max_state_dim: + actions = F.pad(actions, (0, self.max_state_dim - actions.shape[-1])) + return_data["actions"] = actions.view(-1, self.chunk_size, self.max_state_dim) + + if state is not None: + state = (torch.tensor(state) - self.state_mean) / (self.state_std + 1e-08) + if state.shape[-1] < self.max_state_dim: + state = F.pad(state, (0, self.max_state_dim - state.shape[-1])) + return_data["state"] = state.view(-1, self.max_state_dim) + + return BatchFeature(data=return_data, tensor_type=return_tensors) + + @property + def model_input_names(self): + return super().model_input_names + ["pixel_attention_mask"] + + +@auto_docstring(checkpoint="lerobot/pi0_base") +@strict +class PI0Config(PreTrainedConfig): + r""" + vlm_config (`dict`, *optional*): + Configuration for the vlm backbone (PaliGemmaModel). + dit_config (`dict`, *optional*): + Configuration for the DiT backbone. Defaults to a Gemma 300M variant. + chunk_size (`int`, *optional*, defaults to 50): + Number of action steps to predict per chunk. + max_state_dim (`int`, *optional*, defaults to 32): + Maximum state vector dimension (shorter vectors are zero-padded). + max_action_dim (`int`, *optional*, defaults to 32): + Maximum action vector dimension (shorter vectors are zero-padded). + num_inference_steps (`int`, *optional*, defaults to 10): + Number of denoising steps during inference. + time_sampling_beta_alpha (`float`, *optional*, defaults to 1.5): + Alpha parameter for Beta distribution used to sample diffusion time during training. + time_sampling_beta_beta (`float`, *optional*, defaults to 1.0): + Beta parameter for Beta distribution used to sample diffusion time during training. + time_sampling_scale (`float`, *optional*, defaults to 0.999): + Scale factor for sampled time values. + time_sampling_offset (`float`, *optional*, defaults to 0.001): + Offset added to sampled time values. + min_period (`float`, *optional*, defaults to 0.004): + Minimum period for sinusoidal time embedding. + max_period (`float`, *optional*, defaults to 4.0): + Maximum period for sinusoidal time embedding. + loss_reduction (`str`, *optional*, defaults to `"mean"`): + The reduction to use on MSE loss. + + Example: + ```python + >>> from transformers import PI0ForConditionalGeneration, PI0Config + + >>> config = PI0Config() + >>> model = PI0ForConditionalGeneration(config) + ``` + """ + + model_type = "pi0" + sub_configs = {"vlm_config": AutoConfig, "dit_config": AutoConfig} + + vlm_config: dict | PreTrainedConfig | None = None + dit_config: dict | PreTrainedConfig | None = None + chunk_size: int = 50 + max_state_dim: int = 32 + max_action_dim: int = 32 + num_inference_steps: int = 10 + time_sampling_beta_alpha: float = 1.5 + time_sampling_beta_beta: float = 1.0 + time_sampling_scale: float = 0.999 + time_sampling_offset: float = 0.001 + min_period: float = 4e-3 + max_period: float = 4.0 + loss_reduction: str = "mean" + + def __post_init__(self, **kwargs): + if isinstance(self.vlm_config, dict): + vlm_model_type = self.vlm_config.get("model_type", "paligemma") + self.vlm_config = CONFIG_MAPPING[vlm_model_type](**self.vlm_config) + elif self.vlm_config is None: + self.vlm_config = CONFIG_MAPPING["paligemma"]( + text_config={ + "model_type": "gemma", + "hidden_size": 2048, + "num_hidden_layers": 18, + "intermediate_size": 16384, + "num_attention_heads": 8, + "num_key_value_heads": 1, + "vocab_size": 257152, + }, + vision_config={ + "model_type": "siglip_vision_model", + "intermediate_size": 4304, + "hidden_size": 1152, + "patch_size": 14, + "image_size": 224, + "num_hidden_layers": 27, + "num_attention_heads": 16, + "vocab_size": 257152, + "vision_use_head": False, + }, + projection_dim=2048, + image_token_id=257152, + ) + + if isinstance(self.dit_config, dict): + dit_model_type = self.dit_config.get("model_type", "gemma") + self.dit_config = CONFIG_MAPPING[dit_model_type](**self.dit_config) + elif self.dit_config is None: + self.dit_config = CONFIG_MAPPING["gemma"]( + hidden_size=1024, + num_hidden_layers=18, + intermediate_size=4096, + num_attention_heads=8, + num_key_value_heads=1, + head_dim=256, + vocab_size=self.vlm_config.text_config.vocab_size, + ) + + # Force bidirectional attention + self.dit_config.is_causal = False + self.dit_config.use_bidirectional_attention = True + self.vlm_config.text_config.use_bidirectional_attention = True + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.dit_config.hidden_size % 2 != 0: + raise ValueError(f"DiT hidden dim=({self.config.dit_config.hidden_size}) must be divisible by 2") + + +def blockwise_bidirectional_mask(block_boundaries: torch.Tensor) -> Callable: + def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool: + q_block = torch.bucketize(q_idx, block_boundaries) + kv_block = torch.bucketize(kv_idx, block_boundaries) + return kv_block <= q_block + + return inner_mask + + +class PI0TimestepEmbeddings(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + sinusoid_freq = self.compute_freqs(config) + self.register_buffer("sinusoid_freq", sinusoid_freq, persistent=False) + + @staticmethod + def compute_freqs(config): + fraction = torch.linspace(0.0, 1.0, config.dit_config.hidden_size // 2, dtype=torch.float32) + period = config.min_period * (config.max_period / config.min_period) ** fraction + sinusoid_freq = 1.0 / period * 2 * math.pi + return sinusoid_freq + + def forward(self, time): + device_type = time.device.type if isinstance(time.device.type, str) and time.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + sinusoid_freq = self.sinusoid_freq[None, :] + emb = sinusoid_freq * time[:, None] + time_embeds = torch.cat([emb.sin(), emb.cos()], dim=1) + return time_embeds + + +class PI0ActionTimeEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.sinusoid_embeds = PI0TimestepEmbeddings(config) + self.action_in_proj = nn.Linear(config.max_action_dim, config.dit_config.hidden_size) + self.state_proj = nn.Linear(config.max_state_dim, config.dit_config.hidden_size) + self.action_time_mlp_in = nn.Linear(2 * config.dit_config.hidden_size, config.dit_config.hidden_size) + self.action_time_mlp_out = nn.Linear(config.dit_config.hidden_size, config.dit_config.hidden_size) + + def forward(self, state, noise, timestep): + state_embeds = self.state_proj(state) + action_embeds = self.action_in_proj(noise) + + time_embeds = self.sinusoid_embeds(timestep) + time_embeds = time_embeds[:, None, :].expand_as(action_embeds).to(dtype=action_embeds.dtype) + + action_time_embeds = torch.cat([action_embeds, time_embeds], dim=2) + action_time_embeds = self.action_time_mlp_out(F.silu(self.action_time_mlp_in(action_time_embeds))) + action_embeds_merged = torch.cat([state_embeds[:, None, :], action_time_embeds], dim=1) + return action_embeds_merged + + +@auto_docstring +class PI0PreTrainedModel(PreTrainedModel): + config: PI0Config + base_model_prefix = "model" + main_input_name = "state" + supports_gradient_checkpointing = True + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _can_compile_fullgraph = True + _supports_attention_backend = True + input_modalities = ("image", "text") + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, PI0TimestepEmbeddings): + init.copy_(module.sinusoid_freq, module.compute_freqs(module.config)) + + +@auto_docstring +class PI0Model(PI0PreTrainedModel): + def __init__(self, config: PI0Config): + super().__init__(config) + self.dit = AutoModel.from_config(config.dit_config) + self.vlm = AutoModel.from_config(config.vlm_config) + self.post_init() + + def get_input_embeddings(self): + return self.vlm.get_input_embeddings() + + def set_input_embeddings(self, value): + self.vlm.set_input_embeddings(value) + + def embed_prefix(self, input_ids, pixel_values, pixel_attention_mask, attention_mask=None): + max_num_cameras = pixel_attention_mask.shape[1] + pixel_values = pixel_values.flatten(0, 1) + image_features = self.vlm.get_image_features(pixel_values).pooler_output + image_features = image_features.reshape(-1, max_num_cameras, image_features.shape[1], image_features.shape[2]) + + total_image_features = [] + for batch_idx, mask in enumerate(pixel_attention_mask): + unpadded_image_features = image_features[batch_idx][mask] + total_image_features.append(unpadded_image_features) + total_image_features = torch.cat(total_image_features, dim=0) + + llm_input_ids = input_ids.clone() + llm_input_ids[input_ids == self.config.vlm_config.image_token_id] = 0 + inputs_embeds = self.vlm.get_input_embeddings()(llm_input_ids) + special_image_mask = ( + (input_ids == self.config.vlm_config.image_token_id) + .unsqueeze(-1) + .expand_as(inputs_embeds) + .to(inputs_embeds.device) + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, total_image_features) + + return inputs_embeds + + @can_return_tuple + @auto_docstring + def forward( + self, + action_embeds: torch.Tensor, # aka `suffix_emb` (noise + state + timestep) + input_ids: torch.Tensor | None = None, + pixel_values: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + pixel_attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.Tensor | None = None, # aka `prefix_emb` or merged image+text emb + past_key_values: Cache | None = None, # must-have for prefix tuning + **kwargs, + ) -> BaseModelOutputWithPast: + r""" + action_embeds (`torch.Tensor`, *optional*): + The embeddings of input actions and robot states. + pixel_attention_mask (`torch.Tensor`, *optional*): + The mask indicating padded positions in the input image. + """ + if pixel_values is not None and past_key_values is None: + if attention_mask is not None and position_ids is None: + position_ids = attention_mask.cumsum(-1) - 1 + + if inputs_embeds is None: + inputs_embeds = self.embed_prefix(input_ids, pixel_values, pixel_attention_mask) + + token_type_ids = torch.zeros_like(inputs_embeds)[:, :, 0] + past_key_values = self.vlm( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + token_type_ids=token_type_ids, + use_cache=True, + ).past_key_values + + if attention_mask is not None and attention_mask.ndim != 2: + raise ValueError("Only two-dimensional attention masks are accepted for now!") + + # Merge masks if needed, same for position ids + dit_position_ids = dit_attention_mask = None + if attention_mask is not None: + noise_mask = torch.ones( + action_embeds.shape[0], + action_embeds.shape[1], + dtype=attention_mask.dtype, + device=attention_mask.device, + ) + dit_attention_mask = torch.cat([attention_mask, noise_mask], dim=1) + dit_position_ids = (torch.cumsum(dit_attention_mask, dim=1) - 1)[:, -action_embeds.shape[1] :] + + # We have three blocks: vlm-inputss, state and actions from which only 1 token is `state` + # The mask should be bidirectional within each block and to prev blocks, but not to next blocks + vlm_input_length = past_key_values.get_seq_length() + block_sizes = torch.tensor([vlm_input_length + 1, action_embeds.shape[1] - 1], device=action_embeds.device) + block_boundaries = torch.cumsum(block_sizes, dim=0) - 1 + bidirectional_mask = create_bidirectional_mask( + config=self.config.dit_config, + inputs_embeds=action_embeds, + attention_mask=dit_attention_mask, + past_key_values=past_key_values, + and_mask_function=blockwise_bidirectional_mask(block_boundaries), + ) + + dit_output = self.dit( + inputs_embeds=action_embeds, + attention_mask=bidirectional_mask, + position_ids=dit_position_ids, + past_key_values=past_key_values, + **kwargs, + ) + return dit_output + + +class PI0ForConditionalGeneration(PI0PreTrainedModel): + """PI0 model with action projection heads and flow matching.""" + + _tp_plan = {"action_out_proj": "colwise_gather_output"} + + def __init__(self, config: PI0Config): + super().__init__(config) + self.model = PI0Model(config) + self.expert_hidden_size = config.dit_config.hidden_size + self.embed_action_time = PI0ActionTimeEmbedding(config) + self.action_out_proj = nn.Linear(self.expert_hidden_size, config.max_action_dim) + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + state: torch.FloatTensor, + noise: torch.FloatTensor | None = None, + timestep: torch.FloatTensor | None = None, + input_ids: torch.Tensor | None = None, + pixel_values: torch.Tensor | None = None, + pixel_attention_mask: torch.BoolTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values: Cache | None = None, + actions: torch.FloatTensor = None, # aka labels + **kwargs, + ) -> CausalLMOutputWithPast: + r""" + state (`torch.Tensor`, *optional*): + Current robot state. + noise (`torch.Tensor`, *optional*): + Random noise at current timestep that needs to be denoised + timestep (`torch.Tensor`, *optional*): + Current denoising timestep. + pixel_attention_mask (`torch.Tensor`, *optional*): + The mask indicating padded positions in the input image. + actions (`torch.Tensor`, *optional*): + Input actions that need to be predicted. Used only when training to compiute loss. + """ + batch_size = state.shape[0] + + # 1.Sample the timestep + if timestep is None: + alpha_t = torch.tensor(self.config.time_sampling_beta_alpha, dtype=torch.float32) + beta_t = torch.tensor(self.config.time_sampling_beta_beta, dtype=torch.float32) + dist = torch.distributions.Beta(alpha_t, beta_t) + time_beta = dist.sample((batch_size,)).to(state.device) + timestep = (time_beta * self.config.time_sampling_scale + self.config.time_sampling_offset).float() + + # 2. Create random noise if not provided + if noise is None: + noise = torch.randn( + batch_size, + self.config.chunk_size, + self.config.max_action_dim, + device=state.device, + dtype=state.dtype, + ) + + # 3. If training: merge noise with the ground truth actions (aka labels) + # Target velocity is the label we want to predict and will compute loss upon + if actions is not None: + time_expanded = timestep[:, None, None] + noisy_actions = (time_expanded * noise + (1 - time_expanded) * actions).to(actions.dtype) + target_velocity = noise - actions + else: + noisy_actions = noise + + # 4. Embed 'state + noise + actions' for DiT blocks + action_time_embeds = self.embed_action_time(state, noisy_actions, timestep) + + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + pixel_attention_mask=pixel_attention_mask, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + action_embeds=action_time_embeds, + past_key_values=past_key_values, + **kwargs, + ) + last_hidden_states = outputs.last_hidden_state[:, -self.config.chunk_size :] + predicted_velocity = self.action_out_proj(last_hidden_states) + + loss = None + if actions is not None: + # Let the users reduce loss themselves and return fine-grained per sample loss + loss = F.mse_loss(target_velocity, predicted_velocity, reduction=self.config.loss_reduction) + + return CausalLMOutputWithPast( + loss=loss, + logits=predicted_velocity, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + @torch.no_grad() + def sample_actions( + self, + state: torch.FloatTensor, + input_ids: torch.LongTensor, + pixel_values: torch.FloatTensor, + noise: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + pixel_attention_mask: torch.BoolTensor | None = None, + num_steps: int | None = None, + **kwargs, + ) -> torch.FloatTensor: + """Run flow matching inference to generate actions.""" + + num_steps = num_steps or self.config.num_inference_steps + batch_size = input_ids.shape[0] + device = input_ids.device + + # 1. Sample random noise + if noise is None: + noise = torch.normal( + mean=0.0, + std=1.0, + size=( + batch_size, + self.config.chunk_size, + self.config.max_action_dim, + ), + dtype=pixel_values.dtype, + device=device, + ) + + # 2. Run VLM once and obtain prefix cache. Must infer positions here! + if attention_mask is not None: + position_ids = attention_mask.cumsum(-1) - 1 + inputs_embeds = self.model.embed_prefix(input_ids, pixel_values, pixel_attention_mask) + past_key_values = self.model.vlm( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + use_cache=True, + return_dict=True, + ).past_key_values + prefix_length = past_key_values.get_seq_length() + + # 3. Denoise `num_steps` times + dt = -1.0 / num_steps + for step in range(num_steps): + time = 1.0 + step * dt + time_tensor = torch.tensor(time, dtype=torch.float32, device=device).expand(batch_size) + output = self( + state=state, + noise=noise, + timestep=time_tensor, + pixel_attention_mask=pixel_attention_mask, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + + # We need to keep only the "vlm-prefix", no attention to past denoising steps! + past_key_values.crop(prefix_length) + noise = noise + dt * output.logits + return noise + + +__all__ = [ + "PI0Config", + "PI0PreTrainedModel", + "PI0Model", + "PI0ForConditionalGeneration", + "PI0Processor", + "PI0ImageProcessor", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/processing_pi0.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/processing_pi0.py new file mode 100644 index 0000000000000000000000000000000000000000..6fe2d94ff08cff254b7bf5ae4ddf276600a30045 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pi0/processing_pi0.py @@ -0,0 +1,201 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pi0/modular_pi0.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pi0.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Physical Intelligence and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import torch +import torch.nn.functional as F + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput, make_nested_list_of_images +from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import AddedToken, PreTokenizedInput, TextInput +from ...utils import auto_docstring, logging +from ...utils.import_utils import requires + + +logger = logging.get_logger(__name__) + + +class PI0ProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": "max_length", + "max_length": 48, + "padding_side": "right", + }, + "common_kwargs": {"return_tensors": "pt"}, + } + + +IMAGE_TOKEN = "" +EXTRA_TOKENS = [f"4}>" for i in range(1024)] + [f"3}>" for i in range(128)] + + +@auto_docstring +@requires(backends=("vision", "torch")) +class PI0Processor(ProcessorMixin): + def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): + self.height, self.width = image_processor.size["height"], image_processor.size["width"] + state_mean = kwargs.get("state_mean", [-0.0419, 0.0354, 0.8257, 2.9083, -0.5562, -0.1665, 0.0283, -0.0286]) + state_std = kwargs.get("state_std", [0.1074, 0.1442, 0.2572, 0.3441, 1.2344, 0.3580, 0.0133, 0.0132]) + actions_mean = kwargs.get("actions_mean", [0.0182, 0.0586, -0.0559, 0.0046, 0.0029, -0.0077, -0.0916]) + actions_std = kwargs.get("actions_std", [0.2825, 0.3590, 0.3674, 0.0377, 0.0543, 0.0872, 0.9958]) + + self.state_mean = torch.tensor(state_mean) + self.state_std = torch.tensor(state_std) + self.actions_mean = torch.tensor(actions_mean) + self.actions_std = torch.tensor(actions_std) + self.max_state_dim = kwargs.get("max_state_dim", 32) + self.chunk_size = kwargs.get("chunk_size", 50) + if not hasattr(image_processor, "image_seq_length"): + raise ValueError("Image processor is missing an `image_seq_length` attribute.") + + self.image_seq_length = image_processor.image_seq_length + + if not hasattr(tokenizer, "image_token"): + image_token = AddedToken(IMAGE_TOKEN, normalized=False, special=True) + tokens_to_add = {"additional_special_tokens": [image_token]} + tokenizer.add_special_tokens(tokens_to_add) + self.image_token_id = tokenizer.convert_tokens_to_ids(IMAGE_TOKEN) + self.image_token = IMAGE_TOKEN + else: + self.image_token_id = tokenizer.image_token_id + self.image_token = tokenizer.image_token + + tokenizer.add_tokens(EXTRA_TOKENS) + tokenizer.add_bos_token = False + tokenizer.add_eos_token = False + + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + @auto_docstring + def __call__( + self, + images: ImageInput | list[ImageInput] | list[list[ImageInput]] | None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + actions: list | np.ndarray | torch.Tensor | None = None, + state: list | np.ndarray | torch.Tensor | None = None, + **kwargs: Unpack[PI0ProcessorKwargs], + ) -> BatchFeature: + r""" + actions (`list | np.ndarray | torch.Tensor`, *optional*): + Actions to be predicted by the model. If provided, padding, mean and std normalization will be applied. + state (`list | np.ndarray | torch.Tensor`, *optional*): + Robotic states to be predicted by the model. If provided, padding, mean and std normalization will be applied. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. If `suffix` + is provided, the `input_ids` will also contain the suffix input ids. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **pixel_attention_mask** -- Pixel values padding mask to be fed to a model. Returned when `images` is not `None`. + - **state** -- Robot state compatible with model if `state` is not None + - **actions** -- Label-actions compatible with training if `actions` is not None + """ + output_kwargs = self._merge_kwargs( + PI0ProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs + ) + + if text is None: + logger.warning_once("You are using PI0 without a text prefix. The processor will use an empty prompt.") + text = "" + + if isinstance(text, str): + text = [text] + + batched_images = make_nested_list_of_images(images) + if len(batched_images) != len(text): + raise ValueError( + f"Received {len(batched_images)} image samples for {len(text)} prompts. " + "Each prompt should be associated with one sample (with one or more camera images)." + ) + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + output_kwargs["images_kwargs"].pop("return_tensors", None) + + prompt_strings = [] + for sample, image_list in zip(text, batched_images): + sample = ( + f"{self.image_token * self.image_seq_length * len(image_list)}{self.tokenizer.bos_token}{sample}\n" + ) + prompt_strings.append(sample) + + text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"]) + + # Here is the diff from PaliGemma. Ideally we'd create a new ImageProcessor if it were a VLM + max_num_cameras = max(len(sample_images) for sample_images in batched_images) + pixel_attention_mask = torch.zeros((len(batched_images), max_num_cameras), dtype=torch.bool) + padded_pixel_values = torch.zeros(len(batched_images), max_num_cameras, 3, self.height, self.width) + + for batch, sample_images in enumerate(batched_images): + processed = self.image_processor(sample_images, return_tensors="pt", **output_kwargs["images_kwargs"]) + + num_cameras = len(sample_images) + pixel_attention_mask[batch, :num_cameras] = True + padded_pixel_values[batch, :num_cameras] = processed["pixel_values"] + + return_data = { + **text_inputs, + "pixel_values": padded_pixel_values, + "pixel_attention_mask": pixel_attention_mask, + } + + if actions is not None: + actions = (torch.tensor(actions) - self.actions_mean) / (self.actions_std + 1e-08) + if actions.shape[-1] < self.max_state_dim: + actions = F.pad(actions, (0, self.max_state_dim - actions.shape[-1])) + return_data["actions"] = actions.view(-1, self.chunk_size, self.max_state_dim) + + if state is not None: + state = (torch.tensor(state) - self.state_mean) / (self.state_std + 1e-08) + if state.shape[-1] < self.max_state_dim: + state = F.pad(state, (0, self.max_state_dim - state.shape[-1])) + return_data["state"] = state.view(-1, self.max_state_dim) + + return BatchFeature(data=return_data, tensor_type=return_tensors) + + def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs): + """ + Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. + + Args: + image_sizes (list[list[str]], *optional*): + The input sizes formatted as (height, width) per each image. + Returns: + `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided + input modalities, along with other useful data. + """ + vision_data = {} + if image_sizes is not None: + num_image_tokens = [self.image_seq_length] * len(image_sizes) + num_image_patches = [1] * len(image_sizes) + vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) + return MultiModalData(**vision_data) + + @property + def model_input_names(self): + return super().model_input_names + ["pixel_attention_mask"] + + +__all__ = ["PI0Processor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..40adb189ff1fd18746d184a82874cc2a53b4a327 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pix2struct import * + from .image_processing_pil_pix2struct import * + from .image_processing_pix2struct import * + from .modeling_pix2struct import * + from .processing_pix2struct import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/configuration_pix2struct.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/configuration_pix2struct.py new file mode 100644 index 0000000000000000000000000000000000000000..13ce5576b01abec4d36e305782e3c653a8ab81ab --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/configuration_pix2struct.py @@ -0,0 +1,209 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Pix2Struct model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="google/pix2struct-base") +@strict +class Pix2StructTextConfig(PreTrainedConfig): + r""" + relative_attention_num_buckets (`int`, *optional*, defaults to 32): + The number of buckets to use for each attention layer. + relative_attention_max_distance (`int`, *optional*, defaults to 128): + The maximum distance of the longer sequences for the bucket separation. + dense_act_fn (`Union[Callable, str]`, *optional*, defaults to `"gelu_new"`): + The non-linear activation function (function or string). + + Example: + + ```python + >>> from transformers import Pix2StructTextConfig, Pix2StructTextModel + + >>> # Initializing a Pix2StructTextConfig with google/pix2struct-base style configuration + >>> configuration = Pix2StructTextConfig() + + >>> # Initializing a Pix2StructTextModel (with random weights) from the google/pix2struct-base style configuration + >>> model = Pix2StructTextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pix2struct_text_model" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "hidden_size": "hidden_size", + "num_attention_heads": "num_heads", + "num_hidden_layers": "num_layers", + "decoder_attention_heads": "num_heads", + "encoder_attention_heads": "num_heads", + "encoder_layers": "num_layers", + "decoder_layers": "num_layers", + } + + vocab_size: int = 50244 + hidden_size: int = 768 + d_kv: int = 64 + d_ff: int = 2048 + num_layers: int = 12 + num_heads: int = 12 + relative_attention_num_buckets: int = 32 + relative_attention_max_distance: int = 128 + dropout_rate: float | int = 0.1 + layer_norm_epsilon: float = 1e-6 + initializer_factor: float = 1.0 + dense_act_fn: str = "gelu_new" + decoder_start_token_id: int = 0 + use_cache: bool = False + pad_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 1 + bos_token_id: int | None = None + tie_word_embeddings: bool = False + is_decoder: bool = True + add_cross_attention: bool = False + + +@auto_docstring(checkpoint="google/pix2struct-base") +@strict +class Pix2StructVisionConfig(PreTrainedConfig): + r""" + patch_embed_hidden_size (`int`, *optional*, defaults to 768): + Dimensionality of the input patch_embedding layer in the Transformer encoder. + d_ff (`int`, *optional*, defaults to 2048): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + d_kv (`int`, *optional*, defaults to 64): + Dimensionality of the key, query, value projections per attention head. + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported. + dense_act_fn (`Union[Callable, str]`, *optional*, defaults to `"gelu_new"`): + The non-linear activation function (function or string). + seq_len (`int`, *optional*, defaults to 4096): + Maximum sequence length (here number of patches) supported by the model. + relative_attention_num_buckets (`int`, *optional*, defaults to 32): + The number of buckets to use for each attention layer. + relative_attention_max_distance (`int`, *optional*, defaults to 128): + The maximum distance (in tokens) to use for each attention layer. + + Example: + + ```python + >>> from transformers import Pix2StructVisionConfig, Pix2StructVisionModel + + >>> # Initializing a Pix2StructVisionConfig with google/pix2struct-base style configuration + >>> configuration = Pix2StructVisionConfig() + + >>> # Initializing a Pix2StructVisionModel (with random weights) from the google/pix2struct-base style configuration + >>> model = Pix2StructVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pix2struct_vision_model" + + hidden_size: int = 768 + patch_embed_hidden_size: int = 768 + d_ff: int = 2048 + d_kv: int = 64 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + dense_act_fn: str = "gelu_new" + layer_norm_eps: float = 1e-6 + dropout_rate: float | int = 0.0 + attention_dropout: float | int = 0.0 + initializer_range: float = 1e-10 + initializer_factor: float = 1.0 + seq_len: int = 4096 + relative_attention_num_buckets: int = 32 + relative_attention_max_distance: int = 128 + + +@auto_docstring(checkpoint="google/pix2struct-base") +@strict +class Pix2StructConfig(PreTrainedConfig): + r""" + is_vqa (`bool`, *optional*, defaults to `False`): + Whether the model has been fine-tuned for VQA or not. + + Example: + + ```python + >>> from transformers import Pix2StructConfig, Pix2StructForConditionalGeneration + + >>> # Initializing a Pix2StructConfig with google/pix2struct-base style configuration + >>> configuration = Pix2StructConfig() + + >>> # Initializing a Pix2StructForConditionalGeneration (with random weights) from the google/pix2struct-base style configuration + >>> model = Pix2StructForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Pix2StructConfig from a Pix2StructTextConfig and a Pix2StructVisionConfig + + >>> # Initializing a Pix2Struct text and Pix2Struct vision configuration + >>> config_text = Pix2StructTextConfig() + >>> config_vision = Pix2StructVisionConfig() + + >>> config = Pix2StructConfig(text_config=config_text, vision_config=config_vision) + ```""" + + model_type = "pix2struct" + sub_configs = {"text_config": Pix2StructTextConfig, "vision_config": Pix2StructVisionConfig} + + text_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + initializer_factor: float = 1.0 + initializer_range: float = 0.02 + is_vqa: bool = False + tie_word_embeddings: bool = False + is_encoder_decoder: bool = True + + def __post_init__(self, **kwargs): + if self.text_config is None: + self.text_config = Pix2StructTextConfig( + is_encoder_decoder=self.is_encoder_decoder, + tie_word_embeddings=self.tie_word_embeddings, + ) + logger.info("`text_config` is `None`. initializing the `Pix2StructTextConfig` with default values.") + elif isinstance(self.text_config, dict): + self.text_config["is_encoder_decoder"] = self.is_encoder_decoder + self.text_config["tie_word_embeddings"] = self.tie_word_embeddings + self.text_config = Pix2StructTextConfig(**self.text_config) + + if self.vision_config is None: + self.vision_config = Pix2StructVisionConfig() + logger.info("`vision_config` is `None`. initializing the `Pix2StructVisionConfig` with default values.") + elif isinstance(self.vision_config, dict): + self.vision_config = Pix2StructVisionConfig(**self.vision_config) + + self.decoder_start_token_id = self.text_config.decoder_start_token_id + self.pad_token_id = self.text_config.pad_token_id + self.eos_token_id = self.text_config.eos_token_id + + self.text_config.initializer_range = self.initializer_range + self.vision_config.initializer_range = self.initializer_range + + super().__post_init__(**kwargs) + + +__all__ = ["Pix2StructConfig", "Pix2StructTextConfig", "Pix2StructVisionConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/image_processing_pil_pix2struct.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/image_processing_pil_pix2struct.py new file mode 100644 index 0000000000000000000000000000000000000000..82c127abb29a6d96c2d8d9e16b06f850b947c922 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/image_processing_pil_pix2struct.py @@ -0,0 +1,408 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Pix2Struct.""" + +import io +import math +import textwrap + +import numpy as np +from huggingface_hub import hf_hub_download +from PIL import Image, ImageDraw, ImageFont + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_transforms import to_channel_dimension_format, to_pil_image +from ...image_utils import ChannelDimension, ImageInput, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_torch_available, requires_backends +from ...utils.import_utils import requires + + +if is_torch_available(): + import torch + + +DEFAULT_FONT_PATH = "ybelkada/fonts" + + +# Adapted from transformers.models.pix2struct.image_processing_pix2struct.Pix2StructImageProcessorKwargs +class Pix2StructImageProcessorKwargs(ImagesKwargs, total=False): + """ + max_patches (`int`, *optional*): + Maximum number of patches to extract. + patch_size (`dict[str, int]`, *optional*, defaults to `{"height": 16, "width": 16}`): + The patch size to use for the image. According to Pix2Struct paper and code, the patch size is 16x16. + is_vqa (`bool`, *optional*, defaults to `False`): + Whether or not the image processor is for the VQA task. If `True` and `header_text` is passed in, text is + rendered onto the input images. + header_text (`Union[list[str], str]`, *optional*): + Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`. + """ + + max_patches: int + patch_size: dict[str, int] + is_vqa: bool + header_text: list[str] | str | None + + +# Adapted from transformers.models.pix2struct.image_processing_pix2struct.render_text +# Adapted from https://github.com/google-research/pix2struct/blob/0e1779af0f4db4b652c1d92b3bbd2550a7399123/pix2struct/preprocessing/preprocessing_utils.py#L106 +def render_text( + text: str, + text_size: int = 36, + text_color: str = "black", + background_color: str = "white", + left_padding: int = 5, + right_padding: int = 5, + top_padding: int = 5, + bottom_padding: int = 5, + font_bytes: bytes | None = None, + font_path: str | None = None, +) -> Image.Image: + """ + Render text. This script is entirely adapted from the original script that can be found here: + https://github.com/google-research/pix2struct/blob/main/pix2struct/preprocessing/preprocessing_utils.py + + Args: + text (`str`, *optional*, defaults to ): + Text to render. + text_size (`int`, *optional*, defaults to 36): + Size of the text. + text_color (`str`, *optional*, defaults to `"black"`): + Color of the text. + background_color (`str`, *optional*, defaults to `"white"`): + Color of the background. + left_padding (`int`, *optional*, defaults to 5): + Padding on the left. + right_padding (`int`, *optional*, defaults to 5): + Padding on the right. + top_padding (`int`, *optional*, defaults to 5): + Padding on the top. + bottom_padding (`int`, *optional*, defaults to 5): + Padding on the bottom. + font_bytes (`bytes`, *optional*): + Bytes of the font to use. If `None`, the default font will be used. + font_path (`str`, *optional*): + Path to the font to use. If `None`, the default font will be used. + """ + requires_backends(render_text, "vision") + # Add new lines so that each line is no more than 80 characters. + + wrapper = textwrap.TextWrapper(width=80) + lines = wrapper.wrap(text=text) + wrapped_text = "\n".join(lines) + + if font_bytes is not None and font_path is None: + font = io.BytesIO(font_bytes) + elif font_path is not None: + font = font_path + else: + font = hf_hub_download(DEFAULT_FONT_PATH, "Arial.TTF") + font = ImageFont.truetype(font, encoding="UTF-8", size=text_size) + + # Use a temporary canvas to determine the width and height in pixels when + # rendering the text. + temp_img = Image.new("RGB", (1, 1)) + temp_draw = ImageDraw.Draw(temp_img) + _, _, w, h = temp_draw.textbbox((0, 0), wrapped_text, font=font) + + text_width = w + left_padding + right_padding + text_height = h + top_padding + bottom_padding + + # Create the actual image with the text. + img = Image.new("RGB", (text_width, text_height), background_color) + draw = ImageDraw.Draw(img) + draw.text((left_padding, top_padding), wrapped_text, fill=text_color, font=font) + + return img + + +# Adapted from transformers.models.pix2struct.image_processing_pix2struct.torch_extract_patches +if is_torch_available(): + import torch + + # Disable as it causes issues with torch.compile + @torch.compiler.disable + def torch_extract_patches(image_tensor, patch_height, patch_width): + """ + Extract patches from image tensor. Returns tensor of shape (batch, rows, columns, patch_height*patch_width*channels). + + Args: + image_tensor (`torch.Tensor`): + Image tensor of shape (batch, channels, height, width). + patch_height (`int`): + Height of patches to extract. + patch_width (`int`): + Width of patches to extract. + """ + batch_size, channels, height, width = image_tensor.shape + patches = torch.nn.functional.unfold( + image_tensor, (patch_height, patch_width), stride=(patch_height, patch_width) + ) + patches = patches.reshape(batch_size, channels, patch_height, patch_width, -1) + patches = patches.permute(0, 4, 2, 3, 1).reshape( + batch_size, height // patch_height, width // patch_width, channels * patch_height * patch_width + ) + return patches + + +@auto_docstring +@requires(backends=("torch",)) +class Pix2StructImageProcessorPil(PilBackend): + rescale_factor = None + do_normalize = True + do_convert_rgb = True + patch_size = {"height": 16, "width": 16} + max_patches = 2048 + is_vqa = False + valid_kwargs = Pix2StructImageProcessorKwargs + model_input_names = ["flattened_patches", "attention_mask"] + + def _standardize_kwargs(self, patch_size: dict[str, int] | SizeDict | None = None, **kwargs) -> dict: + """ + Process custom Pix2Struct kwargs, specifically converting patch_size to SizeDict. + """ + kwargs = super()._standardize_kwargs(**kwargs) + if patch_size is not None and not isinstance(patch_size, SizeDict): + kwargs["patch_size"] = SizeDict(**get_size_dict(size=patch_size, param_name="patch_size")) + else: + kwargs["patch_size"] = patch_size + + return kwargs + + def _validate_preprocess_kwargs(self, **kwargs): + """ + Skip standard validation as Pix2Struct uses custom preprocessing. + """ + # Pix2Struct doesn't use standard resize/rescale/normalize parameters + # so we skip the default validation + pass + + def render_header( + self, image: np.ndarray, header: str, font_bytes: bytes | None = None, font_path: str | None = None + ) -> np.ndarray: + """ + Render header text on image using numpy arrays. + + Args: + image (`np.ndarray`): + Image array in channel-first format (C, H, W). + header (`str`): + Header text to render. + font_bytes (`bytes`, *optional*): + Font bytes to use for rendering. + font_path (`str`, *optional*): + Path to font file to use for rendering. + + Returns: + `np.ndarray`: Image with header in channel-first format (C, H, W). + """ + # Convert numpy array to PIL + + image_pil = to_pil_image(image, input_data_format=ChannelDimension.FIRST) + + # Render header text as PIL image + header_image = render_text(header, font_bytes=font_bytes, font_path=font_path) + + # Calculate new dimensions + new_width = max(header_image.width, image_pil.width) + new_height = int(image_pil.height * (new_width / image_pil.width)) + new_header_height = int(header_image.height * (new_width / header_image.width)) + + # Create new image and paste header and original image + new_image = Image.new("RGB", (new_width, new_height + new_header_height), "white") + new_image.paste(header_image.resize((new_width, new_header_height)), (0, 0)) + new_image.paste(image_pil.resize((new_width, new_height)), (0, new_header_height)) + + # Convert back to numpy array (channel-first) + + result = np.array(new_image).astype(np.uint8) + result = to_channel_dimension_format(result, ChannelDimension.FIRST, input_channel_dim=ChannelDimension.LAST) + + return result + + def normalize(self, image: np.ndarray) -> np.ndarray: + """ + Normalize image using per-image mean and standard deviation. + + Args: + image (`np.ndarray`): + Image array of shape (C, H, W). + + Returns: + `np.ndarray`: Normalized image of shape (C, H, W). + """ + if image.dtype == np.uint8: + image = image.astype(np.float32) + + # Compute mean and std + mean = np.mean(image) + std = np.std(image) + adjusted_stddev = max(std, 1.0 / math.sqrt(np.prod(image.shape))) + + return (image - mean) / adjusted_stddev + + def extract_flattened_patches(self, image: np.ndarray, max_patches: int, patch_size: SizeDict) -> np.ndarray: + """ + Extract flattened patches from an image. Uses torch for patch extraction. + + Args: + image (`np.ndarray`): + Image array of shape (channels, height, width). + max_patches (`int`): + Maximum number of patches to extract. + patch_size (`SizeDict`): + Dictionary containing patch height and width. + + Returns: + `np.ndarray`: Flattened patches with row/column IDs of shape (max_patches, patch_dim). + """ + requires_backends(self, "torch") + # Convert to torch for patch extraction (pix2struct requires torch for unfold) + image_torch = torch.from_numpy(image) + patch_height, patch_width = patch_size.height, patch_size.width + channels, image_height, image_width = image_torch.shape + + # Calculate scale to maximize patches while respecting max_patches + scale = (max_patches * (patch_height / image_height) * (patch_width / image_width)) ** 0.5 + num_feasible_rows = max(min(int(scale * image_height / patch_height), max_patches), 1) + num_feasible_cols = max(min(int(scale * image_width / patch_width), max_patches), 1) + resized_height = max(num_feasible_rows * patch_height, 1) + resized_width = max(num_feasible_cols * patch_width, 1) + + # Resize image + image_torch = image_torch.unsqueeze(0) # Add batch dimension + image_torch = torch.nn.functional.interpolate( + image_torch.float(), + size=(resized_height, resized_width), + mode="bilinear", + align_corners=False, + antialias=True, + ) + image_torch = image_torch.squeeze(0) + + # Extract patches: [1, rows, columns, patch_height * patch_width * channels] + patches = torch_extract_patches(image_torch.unsqueeze(0), patch_height, patch_width) + + rows, columns, depth = patches.shape[1], patches.shape[2], patches.shape[3] + + # Reshape to [rows * columns, depth] + patches = patches.squeeze(0).reshape(rows * columns, depth) + + # Create row and column IDs + row_ids = torch.arange(rows).reshape(rows, 1).repeat(1, columns).reshape(rows * columns, 1) + col_ids = torch.arange(columns).reshape(1, columns).repeat(rows, 1).reshape(rows * columns, 1) + + # Offset by 1 so IDs don't contain zeros (which represent padding) + row_ids = (row_ids + 1).float() + col_ids = (col_ids + 1).float() + + # Concatenate row_ids, col_ids, and patches: [rows * columns, 2 + depth] + result = torch.cat([row_ids, col_ids, patches], dim=-1) + + # Pad to max_patches: [max_patches, 2 + depth] + result = torch.nn.functional.pad(result, [0, 0, 0, max_patches - (rows * columns)]).float() + + # Convert back to numpy + return result.numpy() + + @auto_docstring + def preprocess( + self, + images: ImageInput, + header_text: str | list[str] | None = None, + **kwargs: Unpack[Pix2StructImageProcessorKwargs], + ) -> BatchFeature: + r""" + header_text (`Union[str, list[str]]`, *optional*): + Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`. + """ + return super().preprocess(images, header_text=header_text, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + header_text: str | list[str] | None = None, + do_convert_rgb: bool = True, + input_data_format: ChannelDimension = ChannelDimension.FIRST, + **kwargs: Unpack[Pix2StructImageProcessorKwargs], + ) -> BatchFeature: + """ + Preprocess images for Pix2Struct. + """ + # Prepare images (converts to numpy arrays) + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format + ) + + # Handle VQA mode with header rendering + is_vqa = kwargs.get("is_vqa", self.is_vqa) + if is_vqa: + if header_text is None: + raise ValueError("A header text must be provided for VQA models.") + + font_bytes = kwargs.pop("font_bytes", None) + font_path = kwargs.pop("font_path", None) + + if isinstance(header_text, str): + header_text = [header_text] * len(images) + + # Render headers + images = [ + self.render_header(image, header_text[i], font_bytes=font_bytes, font_path=font_path) + for i, image in enumerate(images) + ] + + return self._preprocess(images, **kwargs) + + def _preprocess( + self, + images: list[np.ndarray], + do_normalize: bool, + max_patches: int, + patch_size: SizeDict, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess images to extract flattened patches. + """ + flattened_patches = [] + attention_masks = [] + + for image in images: + # Normalize image with per-image mean and std + if do_normalize: + image = self.normalize(image) + + patches = self.extract_flattened_patches(image=image, max_patches=max_patches, patch_size=patch_size) + mask = (patches.sum(axis=-1) != 0).astype(np.float32) + + flattened_patches.append(patches) + attention_masks.append(mask) + + # Stack if return_tensors is set + if return_tensors: + requires_backends(self, "torch") + flattened_patches = torch.stack([torch.from_numpy(p) for p in flattened_patches], dim=0) + attention_masks = torch.stack([torch.from_numpy(m) for m in attention_masks], dim=0) + + return BatchFeature( + data={"flattened_patches": flattened_patches, "attention_mask": attention_masks}, + tensor_type=return_tensors, + ) + + +__all__ = ["Pix2StructImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/image_processing_pix2struct.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/image_processing_pix2struct.py new file mode 100644 index 0000000000000000000000000000000000000000..8072b8b62e94c6791b26ff716fd4f35e16c28e4a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/image_processing_pix2struct.py @@ -0,0 +1,434 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Pix2Struct.""" + +import io +import textwrap +from typing import Union + +import torch +import torchvision.transforms.v2.functional as tvF +from huggingface_hub import hf_hub_download +from PIL import Image, ImageDraw, ImageFont + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ChannelDimension, ImageInput, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires_backends + + +DEFAULT_FONT_PATH = "ybelkada/fonts" + + +class Pix2StructImageProcessorKwargs(ImagesKwargs, total=False): + """ + max_patches (`int`, *optional*): + Maximum number of patches to extract. + patch_size (`dict[str, int]`, *optional*, defaults to `{"height": 16, "width": 16}`): + The patch size to use for the image. According to Pix2Struct paper and code, the patch size is 16x16. + is_vqa (`bool`, *optional*, defaults to `False`): + Whether or not the image processor is for the VQA task. If `True` and `header_text` is passed in, text is + rendered onto the input images. + header_text (`Union[list[str], str]`, *optional*): + Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`. + """ + + max_patches: int + patch_size: dict[str, int] + is_vqa: bool + header_text: list[str] | str | None + + +# Adapted from https://github.com/google-research/pix2struct/blob/0e1779af0f4db4b652c1d92b3bbd2550a7399123/pix2struct/preprocessing/preprocessing_utils.py#L106 +def render_text( + text: str, + text_size: int = 36, + text_color: str = "black", + background_color: str = "white", + left_padding: int = 5, + right_padding: int = 5, + top_padding: int = 5, + bottom_padding: int = 5, + font_bytes: bytes | None = None, + font_path: str | None = None, +) -> Image.Image: + """ + Render text. This script is entirely adapted from the original script that can be found here: + https://github.com/google-research/pix2struct/blob/main/pix2struct/preprocessing/preprocessing_utils.py + + Args: + text (`str`, *optional*, defaults to ): + Text to render. + text_size (`int`, *optional*, defaults to 36): + Size of the text. + text_color (`str`, *optional*, defaults to `"black"`): + Color of the text. + background_color (`str`, *optional*, defaults to `"white"`): + Color of the background. + left_padding (`int`, *optional*, defaults to 5): + Padding on the left. + right_padding (`int`, *optional*, defaults to 5): + Padding on the right. + top_padding (`int`, *optional*, defaults to 5): + Padding on the top. + bottom_padding (`int`, *optional*, defaults to 5): + Padding on the bottom. + font_bytes (`bytes`, *optional*): + Bytes of the font to use. If `None`, the default font will be used. + font_path (`str`, *optional*): + Path to the font to use. If `None`, the default font will be used. + """ + requires_backends(render_text, "vision") + # Add new lines so that each line is no more than 80 characters. + + wrapper = textwrap.TextWrapper(width=80) + lines = wrapper.wrap(text=text) + wrapped_text = "\n".join(lines) + + if font_bytes is not None and font_path is None: + font = io.BytesIO(font_bytes) + elif font_path is not None: + font = font_path + else: + font = hf_hub_download(DEFAULT_FONT_PATH, "Arial.TTF") + font = ImageFont.truetype(font, encoding="UTF-8", size=text_size) + + # Use a temporary canvas to determine the width and height in pixels when + # rendering the text. + temp_img = Image.new("RGB", (1, 1)) + temp_draw = ImageDraw.Draw(temp_img) + _, _, w, h = temp_draw.textbbox((0, 0), wrapped_text, font=font) + + text_width = w + left_padding + right_padding + text_height = h + top_padding + bottom_padding + + # Create the actual image with the text. + img = Image.new("RGB", (text_width, text_height), background_color) + draw = ImageDraw.Draw(img) + draw.text((left_padding, top_padding), wrapped_text, fill=text_color, font=font) + + return img + + +# Disable as it causes issues with torch.compile +@torch.compiler.disable +def torch_extract_patches(image_tensor, patch_height, patch_width): + """ + Extract patches from image tensor. Returns tensor of shape (batch, rows, columns, patch_height*patch_width*channels). + + Args: + image_tensor (`torch.Tensor`): + Image tensor of shape (batch, channels, height, width). + patch_height (`int`): + Height of patches to extract. + patch_width (`int`): + Width of patches to extract. + """ + batch_size, channels, height, width = image_tensor.shape + patches = torch.nn.functional.unfold(image_tensor, (patch_height, patch_width), stride=(patch_height, patch_width)) + patches = patches.reshape(batch_size, channels, patch_height, patch_width, -1) + patches = patches.permute(0, 4, 2, 3, 1).reshape( + batch_size, height // patch_height, width // patch_width, channels * patch_height * patch_width + ) + return patches + + +@auto_docstring +class Pix2StructImageProcessor(TorchvisionBackend): + rescale_factor = None + do_normalize = True + do_convert_rgb = True + patch_size = {"height": 16, "width": 16} + max_patches = 2048 + is_vqa = False + valid_kwargs = Pix2StructImageProcessorKwargs + model_input_names = ["flattened_patches", "attention_mask"] + + def _standardize_kwargs( + self, + patch_size: dict[str, int] | SizeDict | None = None, + **kwargs, + ) -> dict: + """ + Process custom Pix2Struct kwargs, specifically converting patch_size to SizeDict. + """ + kwargs = super()._standardize_kwargs(**kwargs) + if patch_size is not None and not isinstance(patch_size, SizeDict): + kwargs["patch_size"] = SizeDict(**get_size_dict(size=patch_size, param_name="patch_size")) + else: + kwargs["patch_size"] = patch_size + + return kwargs + + def _validate_preprocess_kwargs(self, **kwargs): + """ + Skip standard validation as Pix2Struct uses custom preprocessing. + """ + # Pix2Struct doesn't use standard resize/rescale/normalize parameters + # so we skip the default validation + pass + + def render_header( + self, + image: "torch.Tensor", + header: str, + font_bytes: bytes | None = None, + font_path: str | None = None, + ) -> "torch.Tensor": + """ + Render header text on image using torch tensors. + + Args: + image (`torch.Tensor`): + Image tensor in channel-first format (C, H, W). + header (`str`): + Header text to render. + font_bytes (`bytes`, *optional*): + Font bytes to use for rendering. + font_path (`str`, *optional*): + Path to font file to use for rendering. + + Returns: + `torch.Tensor`: Image with header in channel-first format (C, H, W). + """ + device = image.device + dtype = image.dtype + + # Convert tensor to PIL (channel-first to channel-last for PIL) + if image.dtype == torch.uint8: + image_pil = tvF.to_pil_image(image) + else: + # If float, convert to uint8 first + image_uint8 = (image * 255).clamp(0, 255).to(torch.uint8) + image_pil = tvF.to_pil_image(image_uint8) + + # Render header text as PIL image + header_image = render_text(header, font_bytes=font_bytes, font_path=font_path) + + # Calculate new dimensions + new_width = max(header_image.width, image_pil.width) + new_height = int(image_pil.height * (new_width / image_pil.width)) + new_header_height = int(header_image.height * (new_width / header_image.width)) + + # Create new image and paste header and original image + new_image = Image.new("RGB", (new_width, new_height + new_header_height), "white") + new_image.paste(header_image.resize((new_width, new_header_height)), (0, 0)) + new_image.paste(image_pil.resize((new_width, new_height)), (0, new_header_height)) + + # Convert back to tensor (channel-first) + result = tvF.pil_to_tensor(new_image).to(device) + + # Convert back to original dtype if needed + if dtype != torch.uint8: + result = result.float() / 255.0 + + return result + + def normalize(self, images: "torch.Tensor") -> "torch.Tensor": + """ + Normalize batched images using per-image mean and standard deviation. + + Args: + images (`torch.Tensor`): + Batched float image tensor of shape (B, C, H, W). + + Returns: + `torch.Tensor`: Normalized images of shape (B, C, H, W). + """ + # Compute mean and std per image along spatial and channel dimensions + mean = images.mean(dim=(1, 2, 3), keepdim=True) # Shape: (B, 1, 1, 1) + std = images.std(dim=(1, 2, 3), keepdim=True) # Shape: (B, 1, 1, 1) + + num_elements_per_image = images.shape[1] * images.shape[2] * images.shape[3] + min_std = 1.0 / num_elements_per_image**0.5 + adjusted_stddev = torch.maximum(std, torch.tensor(min_std, device=std.device)) + + return (images - mean) / adjusted_stddev + + def extract_flattened_patches( + self, + images: "torch.Tensor", + max_patches: int, + patch_size: SizeDict, + ) -> "torch.Tensor": + """ + Extract flattened patches from a batch of images. + + Args: + images (`torch.Tensor`): + Batched images tensor of shape (batch, channels, height, width). + max_patches (`int`): + Maximum number of patches to extract. + patch_size (`SizeDict`): + Dictionary containing patch height and width. + + Returns: + `torch.Tensor`: Batched flattened patches with row/column IDs of shape (batch, max_patches, patch_dim). + """ + patch_height, patch_width = patch_size.height, patch_size.width + batch_size, channels, image_height, image_width = images.shape + + # Calculate scale to maximize patches while respecting max_patches + scale = (max_patches * (patch_height / image_height) * (patch_width / image_width)) ** 0.5 + num_feasible_rows = max(min(int(scale * image_height / patch_height), max_patches), 1) + num_feasible_cols = max(min(int(scale * image_width / patch_width), max_patches), 1) + resized_height = max(num_feasible_rows * patch_height, 1) + resized_width = max(num_feasible_cols * patch_width, 1) + + # Resize images (batched) using parent class method + resize_size = SizeDict(height=resized_height, width=resized_width) + images = self.resize(image=images, size=resize_size, resample=tvF.InterpolationMode.BILINEAR, antialias=True) + + # Extract patches: [batch, rows, columns, patch_height * patch_width * channels] + patches = torch_extract_patches(images, patch_height, patch_width) + + batch_size, rows, columns, depth = patches.shape + + # Reshape to [batch, rows * columns, depth] + patches = patches.reshape(batch_size, rows * columns, depth) + + # Create row and column IDs + row_ids = ( + torch.arange(rows, device=images.device).reshape(rows, 1).repeat(1, columns).reshape(1, rows * columns, 1) + ) + col_ids = ( + torch.arange(columns, device=images.device) + .reshape(1, columns) + .repeat(rows, 1) + .reshape(1, rows * columns, 1) + ) + + # Expand to batch size + row_ids = row_ids.expand(batch_size, -1, -1) + col_ids = col_ids.expand(batch_size, -1, -1) + + # Offset by 1 so IDs don't contain zeros (which represent padding) + row_ids = (row_ids + 1).float() + col_ids = (col_ids + 1).float() + + # Concatenate row_ids, col_ids, and patches: [batch, rows * columns, 2 + depth] + result = torch.cat([row_ids, col_ids, patches], dim=-1) + + # Pad to max_patches: [batch, max_patches, 2 + depth] + result = torch.nn.functional.pad(result, [0, 0, 0, max_patches - (rows * columns)]).float() + + return result + + @auto_docstring + def preprocess( + self, + images: ImageInput, + header_text: str | list[str] | None = None, + **kwargs: Unpack[Pix2StructImageProcessorKwargs], + ) -> BatchFeature: + r""" + header_text (`Union[str, list[str]]`, *optional*): + Text to render as a header. Only has an effect if `image_processor.is_vqa` is `True`. + """ + return super().preprocess(images, header_text=header_text, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + header_text: str | list[str] | None = None, + do_convert_rgb: bool = True, + input_data_format: ChannelDimension = ChannelDimension.FIRST, + device: Union[str, "torch.device"] | None = None, + **kwargs: Unpack[Pix2StructImageProcessorKwargs], + ) -> BatchFeature: + """ + Preprocess images for Pix2Struct. + """ + # Prepare images (converts to torch tensors) + images = self._prepare_image_like_inputs( + images=images, + do_convert_rgb=do_convert_rgb, + input_data_format=input_data_format, + device=device, + ) + + # Handle VQA mode with header rendering + is_vqa = kwargs.get("is_vqa", self.is_vqa) + if is_vqa: + if header_text is None: + raise ValueError("A header text must be provided for VQA models.") + + font_bytes = kwargs.pop("font_bytes", None) + font_path = kwargs.pop("font_path", None) + + if isinstance(header_text, str): + header_text = [header_text] * len(images) + + # Render headers using torch-native method + images = [ + self.render_header(image, header_text[i], font_bytes=font_bytes, font_path=font_path) + for i, image in enumerate(images) + ] + + return self._preprocess(images, **kwargs) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_normalize: bool, + max_patches: int, + patch_size: SizeDict, + return_tensors: str | TensorType | None, + disable_grouping: bool, + **kwargs, + ) -> BatchFeature: + """ + Preprocess images to extract flattened patches. + """ + # Group images by shape first for efficient batch processing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + + flattened_patches_grouped = {} + attention_masks_grouped = {} + + for shape, stacked_images in grouped_images.items(): + # Convert to float if needed (for resize and other operations) + if stacked_images.dtype == torch.uint8: + stacked_images = stacked_images.float() + + # Normalize batched images with per-image mean and std + if do_normalize: + stacked_images = self.normalize(stacked_images) + + patches = self.extract_flattened_patches( + images=stacked_images, max_patches=max_patches, patch_size=patch_size + ) + masks = (patches.sum(dim=-1) != 0).float() + + flattened_patches_grouped[shape] = patches + attention_masks_grouped[shape] = masks + + flattened_patches = reorder_images(flattened_patches_grouped, grouped_images_index) + attention_masks = reorder_images(attention_masks_grouped, grouped_images_index) + + # Stack if return_tensors is set + if return_tensors: + flattened_patches = torch.stack(flattened_patches, dim=0) + attention_masks = torch.stack(attention_masks, dim=0) + + return BatchFeature( + data={"flattened_patches": flattened_patches, "attention_mask": attention_masks}, + tensor_type=return_tensors, + ) + + +__all__ = ["Pix2StructImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/modeling_pix2struct.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/modeling_pix2struct.py new file mode 100644 index 0000000000000000000000000000000000000000..942accf66764f112cbb669dda8054b5ebe0ec436 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/modeling_pix2struct.py @@ -0,0 +1,1337 @@ +# Copyright 2023 The HuggingFace Inc. & Google team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Pix2Struct modeling file""" + +import math + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, + CausalLMOutputWithCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) +from ...modeling_utils import PreTrainedModel +from ...utils import ( + DUMMY_INPUTS, + DUMMY_MASK, + auto_docstring, + is_torchdynamo_compiling, + logging, +) +from .configuration_pix2struct import Pix2StructConfig, Pix2StructTextConfig, Pix2StructVisionConfig + + +logger = logging.get_logger(__name__) + +# General docstring + + +# Adapted from transformers.models.t5.modeling_t5.T5LayerNorm with T5->Pix2Struct +class Pix2StructLayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Construct a layernorm module in the T5 style. No bias and no subtraction of mean. + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + # T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean + # Square Layer Normalization https://huggingface.co/papers/1910.07467 thus variance is calculated + # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for + # half-precision inputs is done in fp32 + + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + + return self.weight * hidden_states + + +class Pix2StructVisionEmbeddings(nn.Module): + r""" + Construct the embeddings from patch. In `Pix2Struct` the input is different from classic Vision-transformer models. + Here the input is a sequence of `seq_len` flattened patches that also combines padding patches (tokens). Each patch + is represented by a vector of `hidden_size` values. + """ + + def __init__(self, config: Pix2StructConfig) -> None: + super().__init__() + self.patch_projection = nn.Linear(config.patch_embed_hidden_size, config.hidden_size) + + self.row_embedder = nn.Embedding(config.seq_len, config.hidden_size) + self.column_embedder = nn.Embedding(config.seq_len, config.hidden_size) + + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, flattened_patches: torch.Tensor) -> torch.Tensor: + # the row and column indices are stored in the first and second position of the flattened_patches + # flattened_patches: `batch_size`, `seq_len`, `hidden_size` + 2 + row_indices = flattened_patches[:, :, 0].long() + col_indices = flattened_patches[:, :, 1].long() + + flattened_patches = flattened_patches[:, :, 2:] + + embeddings = self.patch_projection(flattened_patches) + row_embeddings = self.row_embedder(row_indices) + col_embeddings = self.column_embedder(col_indices) + + # sum all embeddings together + embeddings = embeddings + row_embeddings + col_embeddings + + embeddings = self.dropout(embeddings) + + return embeddings + + +class Pix2StructVisionAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.hidden_size = config.hidden_size + self.key_value_proj_dim = config.d_kv + self.n_heads = config.num_attention_heads + self.dropout = config.attention_dropout + self.inner_dim = self.n_heads * self.key_value_proj_dim + + self.query = nn.Linear(self.hidden_size, self.inner_dim, bias=False) + self.key = nn.Linear(self.hidden_size, self.inner_dim, bias=False) + self.value = nn.Linear(self.hidden_size, self.inner_dim, bias=False) + self.output = nn.Linear(self.inner_dim, self.hidden_size, bias=False) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + position_bias=None, + output_attentions=False, + ): + """ + Self-attention block + """ + # Input is (batch_size, seq_length, dim) + # Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length) + batch_size, seq_length = hidden_states.shape[:2] + + def to_projection_shape(states): + """projection""" + return states.contiguous().view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) + + # get query states + # (batch_size, n_heads, seq_length, dim_per_head) + query_states = to_projection_shape(self.query(hidden_states)) + + # get key/value states + key_states = to_projection_shape(self.key(hidden_states)) + value_states = to_projection_shape(self.value(hidden_states)) + + # compute scores + # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 + scores = torch.matmul(query_states, key_states.transpose(3, 2)) + + if position_bias is None: + position_bias = torch.zeros( + (1, self.n_heads, seq_length, seq_length), device=scores.device, dtype=scores.dtype + ) + if self.gradient_checkpointing and self.training: + position_bias.requires_grad = True + + if attention_mask.dim() == 2: + position_bias = position_bias + attention_mask[:, None, None, :].to(position_bias.device) + elif attention_mask is not None: + # (batch_size, n_heads, seq_length, key_length) + position_bias = position_bias + attention_mask.to(position_bias.device) + elif not is_torchdynamo_compiling(): + attention_mask = torch.ones( + (batch_size, seq_length), device=position_bias.device, dtype=position_bias.dtype + ) + position_bias = position_bias + attention_mask.to(position_bias.device) + + position_bias = 1 - position_bias + + position_bias_masked = position_bias.masked_fill(position_bias == 1, torch.finfo(scores.dtype).min) + scores += position_bias_masked + scores = torch.max(scores, torch.tensor(torch.finfo(scores.dtype).min)) + + # (batch_size, n_heads, seq_length, key_length) + attn_weights = nn.functional.softmax(scores, dim=-1, dtype=torch.float32).type_as(scores) + + # (batch_size, n_heads, seq_length, key_length) + attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.matmul(attn_weights, value_states) + + # (batch_size, seq_length, dim) + attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim) + + attn_output = self.output(attn_output) + + outputs = (attn_output,) + (position_bias,) + + if output_attentions: + outputs = outputs + (attn_weights,) + return outputs + + +# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5DenseGatedActDense->Pix2StructVisionMlp,T5Config->Pix2StructVisionConfig,config.d_model->config.hidden_size,dropout_rate->dropout_rate +class Pix2StructVisionMlp(nn.Module): + def __init__(self, config: Pix2StructVisionConfig): + super().__init__() + self.wi_0 = nn.Linear(config.hidden_size, config.d_ff, bias=False) + self.wi_1 = nn.Linear(config.hidden_size, config.d_ff, bias=False) + self.wo = nn.Linear(config.d_ff, config.hidden_size, bias=False) + self.dropout = nn.Dropout(config.dropout_rate) + self.act = ACT2FN[config.dense_act_fn] + + def forward(self, hidden_states): + hidden_gelu = self.act(self.wi_0(hidden_states)) + hidden_linear = self.wi_1(hidden_states) + hidden_states = hidden_gelu * hidden_linear + hidden_states = self.dropout(hidden_states) + + # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32. + # See https://github.com/huggingface/transformers/issues/20287 + # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None`` + if ( + isinstance(self.wo.weight, torch.Tensor) + and hidden_states.dtype != self.wo.weight.dtype + and self.wo.weight.dtype != torch.int8 + ): + hidden_states = hidden_states.to(self.wo.weight.dtype) + + hidden_states = self.wo(hidden_states) + return hidden_states + + +class Pix2StructVisionLayer(GradientCheckpointingLayer): + def __init__(self, config: Pix2StructConfig) -> None: + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = Pix2StructVisionAttention(config) + self.mlp = Pix2StructVisionMlp(config) + self.pre_mlp_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.pre_attention_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + ) -> tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor]: + residual = hidden_states + + # in Pix2StructVision, layernorm is applied before self-attention + hidden_states = self.pre_attention_layer_norm(hidden_states) + + self_attention_outputs = self.attention( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection + hidden_states = attention_output + residual + + # in Pix2StructVision, layernorm is also applied after self-attention + layer_output = self.pre_mlp_layer_norm(hidden_states) + layer_output = self.mlp(layer_output) + hidden_states # second residual connection + + outputs = (layer_output,) + outputs + + return outputs + + +class Pix2StructVisionEncoder(nn.Module): + def __init__(self, config: Pix2StructVisionConfig) -> None: + super().__init__() + self.config = config + self.layer = nn.ModuleList([Pix2StructVisionLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> tuple | BaseModelOutput: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module(hidden_states, attention_mask, output_attentions) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +@auto_docstring +class Pix2StructPreTrainedModel(PreTrainedModel): + config: Pix2StructConfig + input_modalities = ("image", "text") + + _can_compile_fullgraph = False + + @property + def dummy_inputs(self): + input_ids = torch.tensor(DUMMY_INPUTS) + input_mask = torch.tensor(DUMMY_MASK) + dummy_inputs = { + "decoder_input_ids": input_ids, + "input_ids": input_ids, + "decoder_attention_mask": input_mask, + } + return dummy_inputs + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + factor = self.config.initializer_factor # Used for testing weights initialization + if isinstance(module, Pix2StructLayerNorm): + init.constant_(module.weight, factor * 1.0) + elif isinstance(module, Pix2StructTextDenseGatedActDense): + hidden_size = ( + self.config.text_config.hidden_size + if isinstance(self.config, Pix2StructConfig) + else self.config.hidden_size + ) + d_ff = self.config.text_config.d_ff if isinstance(self.config, Pix2StructConfig) else self.config.d_ff + + init.normal_(module.wi_0.weight, mean=0.0, std=factor * ((hidden_size) ** -0.5)) + if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: + init.zeros_(module.wi_0.bias) + init.normal_(module.wi_1.weight, mean=0.0, std=factor * ((hidden_size) ** -0.5)) + if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: + init.zeros_(module.wi_1.bias) + init.normal_(module.wo.weight, mean=0.0, std=factor * ((d_ff) ** -0.5)) + if hasattr(module.wo, "bias") and module.wo.bias is not None: + init.zeros_(module.wo.bias) + elif isinstance(module, Pix2StructTextAttention): + hidden_size = ( + self.config.text_config.hidden_size + if isinstance(self.config, Pix2StructConfig) + else self.config.hidden_size + ) + key_value_proj_dim = ( + self.config.text_config.d_kv if isinstance(self.config, Pix2StructConfig) else self.config.hidden_size + ) + n_heads = ( + self.config.text_config.num_heads + if isinstance(self.config, Pix2StructConfig) + else self.config.num_heads + ) + + init.normal_(module.query.weight, mean=0.0, std=factor * ((hidden_size * key_value_proj_dim) ** -0.5)) + init.normal_(module.key.weight, mean=0.0, std=factor * (hidden_size**-0.5)) + init.normal_(module.value.weight, mean=0.0, std=factor * (hidden_size**-0.5)) + init.normal_(module.output.weight, mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)) + if module.has_relative_attention_bias: + init.normal_(module.relative_attention_bias.weight, mean=0.0, std=factor * ((hidden_size) ** -0.5)) + elif isinstance(module, nn.Embedding): + hidden_size = ( + self.config.text_config.hidden_size + if isinstance(self.config, Pix2StructConfig) + else self.config.hidden_size + ) + + init.normal_(module.weight, mean=0.0, std=factor * ((hidden_size) ** -0.5)) + # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag + if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): + init.zeros_(module.weight[module.padding_idx]) + elif isinstance(module, Pix2StructTextModel): + hidden_size = ( + self.config.text_config.hidden_size + if isinstance(self.config, Pix2StructConfig) + else self.config.hidden_size + ) + + init.normal_(module.lm_head.weight, mean=0.0, std=factor * ((hidden_size) ** -0.5)) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + init.trunc_normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, Pix2StructLayerNorm): + if module.weight is not None: + init.ones_(module.weight) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag + if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): + init.zeros_(module.weight[module.padding_idx]) + + # Copied from transformers.models.t5.modeling_t5.T5PreTrainedModel._shift_right with T5->Pix2Struct + def _shift_right(self, input_ids): + decoder_start_token_id = self.config.decoder_start_token_id + pad_token_id = self.config.pad_token_id + + if decoder_start_token_id is None: + raise ValueError( + "self.model.config.decoder_start_token_id has to be defined. In Pix2Struct it is usually set to the pad_token_id. " + "See Pix2Struct docs for more information." + ) + + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() + shifted_input_ids[..., 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +@auto_docstring +class Pix2StructVisionModel(Pix2StructPreTrainedModel): + config: Pix2StructVisionConfig + main_input_name = "flattened_patches" + input_modalities = ("image",) + supports_gradient_checkpointing = True + _no_split_modules = ["Pix2StructVisionLayer"] + + def __init__(self, config: Pix2StructVisionConfig): + super().__init__(config) + self.config = config + + self.embeddings = Pix2StructVisionEmbeddings(config) + self.encoder = Pix2StructVisionEncoder(config) + + self.layernorm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.patch_projection + + @auto_docstring + def forward( + self, + flattened_patches: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPooling: + r""" + flattened_patches (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_channels x patch_height x patch_width)`): + Flattened and padded pixel values. These values can be obtained using [`AutoImageProcessor`]. See + [`Pix2StructVisionImageProcessor.__call__`] for details. Check the [original + paper](https://huggingface.co/papers/2210.03347) (figure 5) for more details. + + Example: + + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + >>> from transformers import AutoProcessor, Pix2StructVisionModel + + >>> image_processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base") + >>> model = Pix2StructVisionModel.from_pretrained("google/pix2struct-textcaps-base") + + >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 2048, 768] + ``` + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if flattened_patches is None: + raise ValueError("You have to specify flattened_patches") + + if attention_mask is None: + # check where `flattened_patches` is not 0 + attention_mask = (flattened_patches.sum(dim=-1) != 0).float() + + embedding_output = self.embeddings(flattened_patches) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.layernorm(sequence_output) + + if not return_dict: + head_outputs = (sequence_output,) + return head_outputs + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->Pix2StructText,d_model->hidden_size +class Pix2StructTextDenseGatedActDense(nn.Module): + def __init__(self, config: Pix2StructTextConfig): + super().__init__() + self.wi_0 = nn.Linear(config.hidden_size, config.d_ff, bias=False) + self.wi_1 = nn.Linear(config.hidden_size, config.d_ff, bias=False) + self.wo = nn.Linear(config.d_ff, config.hidden_size, bias=False) + self.dropout = nn.Dropout(config.dropout_rate) + self.act = ACT2FN[config.dense_act_fn] + + def forward(self, hidden_states): + hidden_gelu = self.act(self.wi_0(hidden_states)) + hidden_linear = self.wi_1(hidden_states) + hidden_states = hidden_gelu * hidden_linear + hidden_states = self.dropout(hidden_states) + + # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32. + # See https://github.com/huggingface/transformers/issues/20287 + # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None`` + if ( + isinstance(self.wo.weight, torch.Tensor) + and hidden_states.dtype != self.wo.weight.dtype + and self.wo.weight.dtype != torch.int8 + ): + hidden_states = hidden_states.to(self.wo.weight.dtype) + + hidden_states = self.wo(hidden_states) + return hidden_states + + +class Pix2StructTextLayerFF(nn.Module): + def __init__(self, config: Pix2StructTextConfig): + super().__init__() + self.DenseReluDense = Pix2StructTextDenseGatedActDense(config) + + self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + # Copied from transformers.models.t5.modeling_t5.T5LayerFF.forward + def forward(self, hidden_states): + forwarded_states = self.layer_norm(hidden_states) + forwarded_states = self.DenseReluDense(forwarded_states) + hidden_states = hidden_states + self.dropout(forwarded_states) + return hidden_states + + +class Pix2StructTextAttention(nn.Module): + def __init__(self, config: Pix2StructTextConfig, has_relative_attention_bias=False, layer_idx: int | None = None): + super().__init__() + self.has_relative_attention_bias = has_relative_attention_bias + self.relative_attention_num_buckets = config.relative_attention_num_buckets + self.relative_attention_max_distance = config.relative_attention_max_distance + self.hidden_size = config.hidden_size + self.key_value_proj_dim = config.d_kv + self.n_heads = config.num_heads + self.dropout = config.dropout_rate + self.inner_dim = self.n_heads * self.key_value_proj_dim + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.query = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + self.key = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + self.value = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + self.output = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + + if self.has_relative_attention_bias: + self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads) + + self.gradient_checkpointing = False + + @staticmethod + # Copied from transformers.models.t5.modeling_t5.T5Attention._relative_position_bucket + def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): + """ + Adapted from Mesh Tensorflow: + https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 + + Translate relative position to a bucket number for relative attention. The relative position is defined as + memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to + position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for + small absolute relative_position and larger buckets for larger absolute relative_positions. All relative + positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. + This should allow for more graceful generalization to longer sequences than the model has been trained on + + Args: + relative_position: an int32 Tensor + bidirectional: a boolean - whether the attention is bidirectional + num_buckets: an integer + max_distance: an integer + + Returns: + a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) + """ + relative_buckets = 0 + if bidirectional: + num_buckets //= 2 + relative_buckets += (relative_position > 0).to(torch.long) * num_buckets + relative_position = torch.abs(relative_position) + else: + relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) + # now relative_position is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = relative_position < max_exact + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + relative_position_if_large = max_exact + ( + torch.log(relative_position.float() / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_position_if_large = torch.min( + relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) + ) + + relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) + return relative_buckets + + # Adapted from transformers.models.t5.modeling_t5.T5Attention.compute_bias + def compute_bias(self, query_length, key_length, device=None, past_seen_tokens=0): + """Compute binned relative position bias""" + if device is None: + device = self.relative_attention_bias.weight.device + context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] + past_seen_tokens + memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] + relative_position = memory_position - context_position # shape (query_length, key_length) + relative_position_bucket = self._relative_position_bucket( + relative_position, # shape (query_length, key_length) + bidirectional=False, + num_buckets=self.relative_attention_num_buckets, + max_distance=self.relative_attention_max_distance, + ) + values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads) + values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) + return values + + # Adapted from transformers.models.t5.modeling_t5.T5Attention.forward + def forward( + self, + hidden_states, + mask=None, + key_value_states=None, + position_bias=None, + past_key_values=None, + output_attentions=False, + **kwargs, + ): + """ + Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). + """ + # Input is (batch_size, seq_length, dim) + # Mask is (batch_size, 1, 1, key_length) (non-causal) or (batch_size, 1, seq_length, key_length) (causal decoder) + batch_size, seq_length = hidden_states.shape[:2] + past_seen_tokens = past_key_values.get_seq_length(self.layer_idx) if past_key_values is not None else 0 + # We clone here for StaticCache, as we get the value before updating it, but use it after and it's the same ref + past_seen_tokens = past_seen_tokens.clone() if isinstance(past_seen_tokens, torch.Tensor) else past_seen_tokens + + # if key_value_states are provided this layer is used as a cross-attention layer for the decoder + is_cross_attention = key_value_states is not None + + query_states = self.query(hidden_states) + query_states = query_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) + + # Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache` + if past_key_values is not None and isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.key(current_states) + value_states = self.value(current_states) + key_states = key_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) + value_states = value_states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2) + + if past_key_values is not None: + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention: + past_key_values.is_updated[self.layer_idx] = True + + # compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 + scores = torch.matmul(query_states, key_states.transpose(3, 2)) + + if position_bias is None: + key_length = key_states.shape[-2] + if not self.has_relative_attention_bias: + position_bias = torch.zeros( + (1, self.n_heads, seq_length, key_length), device=scores.device, dtype=scores.dtype + ) + if self.gradient_checkpointing and self.training: + position_bias.requires_grad = True + else: + position_bias = self.compute_bias( + seq_length, key_length, device=scores.device, past_seen_tokens=past_seen_tokens + ) + + if mask is not None: + causal_mask = mask[:, :, :, : key_states.shape[-2]] + position_bias = position_bias + causal_mask + + position_bias_masked = position_bias + scores += position_bias_masked + + # (batch_size, n_heads, seq_length, key_length) + attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores) + attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.matmul(attn_weights, value_states) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(batch_size, -1, self.inner_dim) + attn_output = self.output(attn_output) + + outputs = (attn_output, position_bias) + + if output_attentions: + outputs = outputs + (attn_weights,) + return outputs + + +# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5LayerNorm->Pix2StructLayerNorm,T5Attention->Pix2StructTextAttention,T5LayerSelfAttention->Pix2StructTextLayerSelfAttention,self.SelfAttention->self.attention,config.d_model->config.hidden_size +class Pix2StructTextLayerSelfAttention(nn.Module): + def __init__(self, config, has_relative_attention_bias=False, layer_idx: int | None = None): + super().__init__() + self.attention = Pix2StructTextAttention( + config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx + ) + self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward( + self, + hidden_states, + attention_mask=None, + position_bias=None, + past_key_values=None, + use_cache=False, + output_attentions=False, + **kwargs, + ): + normed_hidden_states = self.layer_norm(hidden_states) + attention_output = self.attention( + normed_hidden_states, + mask=attention_mask, + position_bias=position_bias, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + ) + hidden_states = hidden_states + self.dropout(attention_output[0]) + outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5LayerNorm->Pix2StructLayerNorm,T5Attention->Pix2StructTextAttention,T5LayerCrossAttention->Pix2StructTextLayerCrossAttention,self.EncDecAttention->self.attention,config.d_model->config.hidden_size +class Pix2StructTextLayerCrossAttention(nn.Module): + def __init__(self, config, layer_idx: int | None = None): + super().__init__() + self.attention = Pix2StructTextAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx) + self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward( + self, + hidden_states, + key_value_states, + attention_mask=None, + position_bias=None, + past_key_values=None, + output_attentions=False, + **kwargs, + ): + normed_hidden_states = self.layer_norm(hidden_states) + attention_output = self.attention( + normed_hidden_states, + mask=attention_mask, + key_value_states=key_value_states, + position_bias=position_bias, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + layer_output = hidden_states + self.dropout(attention_output[0]) + outputs = (layer_output,) + attention_output[1:] # add attentions if we output them + return outputs + + +class Pix2StructTextBlock(GradientCheckpointingLayer): + def __init__(self, config, has_relative_attention_bias=False, layer_idx: int | None = None): + super().__init__() + + self.self_attention = Pix2StructTextLayerSelfAttention( + config, + has_relative_attention_bias=has_relative_attention_bias, + layer_idx=layer_idx, + ) + + self.encoder_decoder_attention = Pix2StructTextLayerCrossAttention( + config, + layer_idx=layer_idx, + ) + + self.mlp = Pix2StructTextLayerFF(config) + + def forward( + self, + hidden_states, + attention_mask=None, + position_bias=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + encoder_decoder_position_bias=None, + past_key_values=None, + use_cache=False, + output_attentions=False, + return_dict=True, + **kwargs, + ): + self_attention_outputs = self.self_attention( + hidden_states, + attention_mask=attention_mask, + position_bias=position_bias, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + ) + hidden_states = self_attention_outputs[0] + attention_outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights + + # clamp inf values to enable fp16 training + if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + do_cross_attention = encoder_hidden_states is not None + if do_cross_attention: + cross_attention_outputs = self.encoder_decoder_attention( + hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + position_bias=encoder_decoder_position_bias, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + hidden_states = cross_attention_outputs[0] + + # clamp inf values to enable fp16 training + if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + # Keep cross-attention outputs and relative position weights + attention_outputs = attention_outputs + cross_attention_outputs[1:] + + # Apply Feed Forward layer + hidden_states = self.mlp(hidden_states) + + # clamp inf values to enable fp16 training + if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + return outputs + attention_outputs + + +@auto_docstring( + custom_intro=""" + The standalone text decoder of Pix2Struct + """ +) +class Pix2StructTextModel(Pix2StructPreTrainedModel): + config: Pix2StructTextConfig + input_modalities = ("text",) + _no_split_modules = ["Pix2StructTextBlock"] + _tied_weights_keys = {"lm_head.weight": "embed_tokens.weight"} + supports_gradient_checkpointing = True + + def __init__(self, config): + super().__init__(config) + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) + + self.layer = nn.ModuleList( + [ + Pix2StructTextBlock(config, has_relative_attention_bias=bool(i == 0), layer_idx=i) + for i in range(config.num_layers) + ] + ) + self.final_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + self.gradient_checkpointing = False + + def set_input_embeddings(self, new_embeddings): + self.embed_tokens = new_embeddings + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + inputs_embeds: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + labels: torch.LongTensor | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.FloatTensor, ...] | CausalLMOutputWithCrossAttentions: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Pix2StructText is a model with relative position + embeddings so you should be able to pad the inputs on both the right and the left. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for detail. + + [What are input IDs?](../glossary#input-ids) + + To know more on how to prepare `input_ids` for pretraining take a look a [Pix2StructText + Training](./t5#training). + + Example: + + ```python + >>> from transformers import AutoProcessor, Pix2StructTextModel + + >>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base") + >>> model = Pix2StructTextModel.from_pretrained("google/pix2struct-textcaps-base") + + >>> inputs = processor(text="Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> loss = outputs.loss + ``` + """ + use_cache = use_cache if use_cache is not None else self.config.use_cache + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if self.gradient_checkpointing and self.training and use_cache: + logger.warning( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if inputs_embeds is None: + assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings" + inputs_embeds = self.embed_tokens(input_ids) + + batch_size, seq_length = input_shape + + if use_cache and past_key_values is None: + if self.config.is_encoder_decoder: + past_key_values = EncoderDecoderCache( + DynamicCache(config=self.config), DynamicCache(config=self.config) + ) + else: + past_key_values = DynamicCache(config=self.config) + + if attention_mask is None: + # required mask seq length can be calculated via length of past + mask_seq_length = ( + past_key_values.get_seq_length() + seq_length if past_key_values is not None else seq_length + ) + attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) + + if self.config.is_decoder: + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + else: + causal_mask = attention_mask[:, None, None, :] + causal_mask = causal_mask.to(dtype=inputs_embeds.dtype) + causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + all_cross_attentions = () if (output_attentions) else None + position_bias = None + encoder_decoder_position_bias = None + + hidden_states = self.dropout(inputs_embeds) + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module( + hidden_states, + causal_mask, + position_bias, + encoder_hidden_states, + encoder_extended_attention_mask, + encoder_decoder_position_bias, # as a positional argument for gradient checkpointing + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + # We share the position biases between the layers - the first layer store them + # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), + # (cross-attention position bias), (cross-attention weights) + position_bias = layer_outputs[1] + if encoder_hidden_states is not None: + encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[2],) + if encoder_hidden_states is not None: + all_cross_attentions = all_cross_attentions + (layer_outputs[4],) + + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.dropout(hidden_states) + + logits = self.lm_head(hidden_states) + + # Add last layer + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(logits.device) + loss_fct = nn.CrossEntropyLoss(ignore_index=-100, reduction="mean") + + loss = loss_fct(logits.contiguous().view(-1, logits.size(-1)), labels.contiguous().view(-1)) + + if not return_dict: + return tuple( + v + for v in [ + loss, + logits, + past_key_values, + all_hidden_states, + all_attentions, + all_cross_attentions, + ] + if v is not None + ) + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attentions, + cross_attentions=all_cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + A conditional generation model with a language modeling head. Can be used for sequence generation tasks. + """ +) +class Pix2StructForConditionalGeneration(Pix2StructPreTrainedModel, GenerationMixin): + config: Pix2StructConfig + main_input_name = "flattened_patches" + + def __init__(self, config: Pix2StructConfig): + super().__init__(config) + + self.encoder = Pix2StructVisionModel(config.vision_config) + self.decoder = Pix2StructTextModel(config.text_config) + + self.is_vqa = config.is_vqa + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.decoder.get_input_embeddings() + + def set_input_embeddings(self, new_embeddings): + self.decoder.set_input_embeddings(new_embeddings) + + def get_output_embeddings(self) -> nn.Module: + return self.decoder.get_output_embeddings() + + def set_output_embeddings(self, new_embeddings): + self.decoder.set_output_embeddings(new_embeddings) + + @auto_docstring + def forward( + self, + flattened_patches: torch.FloatTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.BoolTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + labels: torch.LongTensor | None = None, + decoder_inputs_embeds: torch.Tensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.FloatTensor] | Seq2SeqModelOutput: + r""" + flattened_patches (`torch.FloatTensor` of shape `(batch_size, seq_length, hidden_size)`): + Flattened pixel patches. the `hidden_size` is obtained by the following formula: `hidden_size` = + `num_channels` * `patch_size` * `patch_size` + + The process of flattening the pixel patches is done by `Pix2StructProcessor`. + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Pix2StructText uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + To know more on how to prepare `decoder_input_ids` for pretraining take a look at [Pix2StructText + Training](./t5#training). + decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss for the decoder. + + Example: + + Inference: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration + + >>> processor = AutoProcessor.from_pretrained("google/pix2struct-textcaps-base") + >>> model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base") + + >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> # autoregressive generation + >>> generated_ids = model.generate(**inputs, max_new_tokens=50) + >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + >>> print(generated_text) + A stop sign is on a street corner. + + >>> # conditional generation + >>> text = "A picture of" + >>> inputs = processor(text=text, images=image, return_tensors="pt", add_special_tokens=False) + + >>> generated_ids = model.generate(**inputs, max_new_tokens=50) + >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] + >>> print(generated_text) + A picture of a stop sign with a red stop sign + ``` + + Training: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, Pix2StructForConditionalGeneration + + >>> processor = AutoProcessor.from_pretrained("google/pix2struct-base") + >>> model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-base") + + >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> text = "A stop sign is on the street corner." + + >>> inputs = processor(images=image, return_tensors="pt") + >>> labels = processor(text=text, return_tensors="pt").input_ids + + >>> # forward pass + >>> outputs = model(**inputs, labels=labels) + >>> loss = outputs.loss + >>> print(f"{loss.item():.5f}") + 5.94282 + ```""" + use_cache = use_cache if use_cache is not None else self.config.text_config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # Encode if needed (training, first prediction pass) + if encoder_outputs is None: + encoder_outputs = self.encoder( + flattened_patches=flattened_patches, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + hidden_states = encoder_outputs[0] + + if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: + # get decoder inputs from shifting lm labels to the right + decoder_input_ids = self._shift_right(labels) + decoder_attention_mask = ( + decoder_attention_mask + if decoder_attention_mask is not None + else decoder_input_ids.ne(self.config.pad_token_id).float() + ) + # Always attend to the first token + decoder_attention_mask[:, 0] = 1 + + # Decode + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + inputs_embeds=decoder_inputs_embeds, + past_key_values=past_key_values, + encoder_hidden_states=hidden_states, + encoder_attention_mask=attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + labels=labels, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return Seq2SeqLMOutput( + loss=decoder_outputs.loss, + logits=decoder_outputs.logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +__all__ = [ + "Pix2StructPreTrainedModel", + "Pix2StructForConditionalGeneration", + "Pix2StructVisionModel", + "Pix2StructTextModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/processing_pix2struct.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/processing_pix2struct.py new file mode 100644 index 0000000000000000000000000000000000000000..189c539daaf0de0fd8e7cda7a099ad689f6a1f1d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pix2struct/processing_pix2struct.py @@ -0,0 +1,109 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for Pix2Struct. +""" + +from ...feature_extraction_utils import BatchFeature +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput +from ...utils import auto_docstring, logging + + +class Pix2StructProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "add_special_tokens": True, + "padding": False, + "stride": 0, + "return_overflowing_tokens": False, + "return_special_tokens_mask": False, + "return_offsets_mapping": False, + "return_token_type_ids": False, + "return_length": False, + "verbose": True, + }, + "images_kwargs": { + "max_patches": 2048, + }, + } + + +logger = logging.get_logger(__name__) + + +@auto_docstring +class Pix2StructProcessor(ProcessorMixin): + def __init__(self, image_processor, tokenizer): + tokenizer.return_token_type_ids = False + super().__init__(image_processor, tokenizer) + + @auto_docstring + def __call__( + self, + images=None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[Pix2StructProcessorKwargs], + ) -> BatchEncoding | BatchFeature: + if images is None and text is None: + raise ValueError("You have to specify either images or text.") + + output_kwargs = self._merge_kwargs( + Pix2StructProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + add_special_tokens = output_kwargs["text_kwargs"].pop("add_special_tokens", None) + # Get only text + if images is None and not self.image_processor.is_vqa: + output_kwargs["text_kwargs"]["add_special_tokens"] = ( + add_special_tokens if add_special_tokens is not None else True + ) + text_encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"]) + return text_encoding + + if not self.image_processor.is_vqa: + # add pixel_values + encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"]) + else: + # add pixel_values and bbox + output_kwargs["images_kwargs"].setdefault("header_text", text) + encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"]) + + if text is not None and not self.image_processor.is_vqa: + output_kwargs["text_kwargs"]["add_special_tokens"] = ( + add_special_tokens if add_special_tokens is not None else False + ) + text_encoding = self.tokenizer(text=text, **output_kwargs["text_kwargs"]) + + if "attention_mask" in text_encoding: + text_encoding["decoder_attention_mask"] = text_encoding.pop("attention_mask") + if "input_ids" in text_encoding: + text_encoding["decoder_input_ids"] = text_encoding.pop("input_ids") + else: + text_encoding = None + + if text_encoding is not None: + encoding_image_processor.update(text_encoding) + + return encoding_image_processor + + @property + def model_input_names(self): + image_processor_input_names = self.image_processor.model_input_names + decoder_ids = ["decoder_attention_mask", "decoder_input_ids"] + return image_processor_input_names + decoder_ids + + +__all__ = ["Pix2StructProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..38435cc643c4606d9d118d47c9ec765ecf413d0f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Pixio model configuration""" + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pixio import * + from .modeling_pixio import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/configuration_pixio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/configuration_pixio.py new file mode 100644 index 0000000000000000000000000000000000000000..43ad9c4701cec173f25b3bd12c26502162213721 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/configuration_pixio.py @@ -0,0 +1,85 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pixio/modular_pixio.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pixio.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import BackboneConfigMixin +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="facebook/pixio-huge") +@strict +class PixioConfig(BackboneConfigMixin, PreTrainedConfig): + r""" + apply_layernorm (`bool`, *optional*, defaults to `True`): + Whether to apply layer normalization to the feature maps in case the model is used as backbone. + reshape_hidden_states (`bool`, *optional*, defaults to `True`): + Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in + case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size, + seq_len, hidden_size)`. + n_cls_tokens (`int`, *optional*, defaults to 8): + Number of class tokens in the Transformer encoder. + + Example: + + ```python + >>> from transformers import PixioConfig, PixioModel + + >>> # Initializing a Pixio pixio-huge style configuration + >>> configuration = PixioConfig() + + >>> # Initializing a model (with random weights) from the pixio-huge style configuration + >>> model = PixioModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pixio" + + hidden_size: int = 1280 + num_hidden_layers: int = 32 + num_attention_heads: int = 16 + mlp_ratio: int = 4 + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.0 + attention_probs_dropout_prob: float | int = 0.0 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-6 + image_size: int | list[int] | tuple[int, int] = 256 + patch_size: int | list[int] | tuple[int, int] = 16 + num_channels: int = 3 + qkv_bias: bool = True + drop_path_rate: float | int = 0.0 + _out_features: list[str] | None = None + _out_indices: list[int] | None = None + apply_layernorm: bool = True + reshape_hidden_states: bool = True + n_cls_tokens: int = 8 + + def __post_init__(self, **kwargs): + self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, self.num_hidden_layers + 1)] + self.set_output_features_output_indices( + out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None) + ) + super().__post_init__(**kwargs) + + +__all__ = ["PixioConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/modeling_pixio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/modeling_pixio.py new file mode 100644 index 0000000000000000000000000000000000000000..a6d08cfa5fb0dffb6f0a472cb2cb379f72cdf5b9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/modeling_pixio.py @@ -0,0 +1,507 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pixio/modular_pixio.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pixio.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections.abc +from collections.abc import Callable + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...backbone_utils import BackboneMixin, filter_output_hidden_states +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, is_tracing +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pixio import PixioConfig + + +class PixioPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config: PixioConfig): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + f" Expected {self.num_channels} but got {num_channels}." + ) + if not interpolate_pos_encoding: + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model" + f" ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2) + return embeddings + + +class PixioEmbeddings(nn.Module): + """ + Construct the CLS tokens, position and patch embeddings. + """ + + def __init__(self, config: PixioConfig) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.randn(1, config.n_cls_tokens, config.hidden_size)) + self.mask_token = None + self.patch_embeddings = PixioPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + config.n_cls_tokens, config.hidden_size)) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.n_cls_tokens = config.n_cls_tokens + self.patch_size = config.patch_size + self.config = config + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support tracing and interpolation at torch.float32 precision. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + num_patches = embeddings.shape[1] - self.n_cls_tokens + num_positions = self.position_embeddings.shape[1] - self.n_cls_tokens + + if not is_tracing() and num_patches == num_positions and height == width: + return self.position_embeddings + + class_pos_embed = self.position_embeddings[:, : self.n_cls_tokens] + patch_pos_embed = self.position_embeddings[:, self.n_cls_tokens :] + + dim = embeddings.shape[-1] + + new_height = height // self.patch_size + new_width = width // self.patch_size + + sqrt_num_positions = int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + target_dtype = patch_pos_embed.dtype + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.to(torch.float32), + size=(new_height, new_width), + mode="bicubic", + align_corners=False, + ).to(dtype=target_dtype) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + + return torch.cat((class_pos_embed, patch_pos_embed), dim=1) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + batch_size, _, height, width = pixel_values.shape + target_dtype = self.patch_embeddings.projection.weight.dtype + embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype)) + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + + embeddings = self.dropout(embeddings) + + return embeddings + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PixioSelfAttention(nn.Module): + def __init__(self, config: PixioConfig): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size {config.hidden_size} is not a multiple of the number of attention " + f"heads {config.num_attention_heads}." + ) + + self.config = config + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.dropout_prob = config.attention_probs_dropout_prob + self.scaling = self.attention_head_size**-0.5 + self.is_causal = False + + self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + batch_size = hidden_states.shape[0] + new_shape = batch_size, -1, self.num_attention_heads, self.attention_head_size + + key_layer = self.key(hidden_states).view(*new_shape).transpose(1, 2) + value_layer = self.value(hidden_states).view(*new_shape).transpose(1, 2) + query_layer = self.query(hidden_states).view(*new_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + context_layer, attention_probs = attention_interface( + self, + query_layer, + key_layer, + value_layer, + None, + is_causal=self.is_causal, + scaling=self.scaling, + dropout=0.0 if not self.training else self.dropout_prob, + **kwargs, + ) + + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.reshape(new_context_layer_shape) + + return context_layer, attention_probs + + +class PixioSelfOutput(nn.Module): + """ + The residual connection is defined in PixioLayer instead of here (as is the case with other models), due to the + layernorm applied before each block. + """ + + def __init__(self, config: PixioConfig): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class PixioAttention(nn.Module): + def __init__(self, config: PixioConfig): + super().__init__() + self.attention = PixioSelfAttention(config) + self.output = PixioSelfOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + self_attn_output, _ = self.attention(hidden_states, **kwargs) + output = self.output(self_attn_output, hidden_states) + return output + + +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +class PixioDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float | None = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return f"p={self.drop_prob}" + + +class PixioMLP(nn.Module): + def __init__(self, config) -> None: + super().__init__() + in_features = out_features = config.hidden_size + hidden_features = int(config.hidden_size * config.mlp_ratio) + self.fc1 = nn.Linear(in_features, hidden_features, bias=True) + if isinstance(config.hidden_act, str): + self.activation = ACT2FN[config.hidden_act] + else: + self.activation = config.hidden_act + self.fc2 = nn.Linear(hidden_features, out_features, bias=True) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + hidden_state = self.fc1(hidden_state) + hidden_state = self.activation(hidden_state) + hidden_state = self.fc2(hidden_state) + return hidden_state + + +class PixioLayer(GradientCheckpointingLayer): + def __init__(self, config: PixioConfig) -> None: + super().__init__() + + self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attention = PixioAttention(config) + self.drop_path = PixioDropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity() + + self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = PixioMLP(config) + + def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> torch.Tensor: + hidden_states_norm = self.norm1(hidden_states) + self_attention_output = self.attention(hidden_states_norm, **kwargs) + + hidden_states = self.drop_path(self_attention_output) + hidden_states + + layer_output = self.norm2(hidden_states) + layer_output = self.mlp(layer_output) + + layer_output = self.drop_path(layer_output) + hidden_states + + return layer_output + + +@auto_docstring +class PixioPreTrainedModel(PreTrainedModel): + config: PixioConfig + base_model_prefix = "pixio" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + _no_split_modules = ["PixioEmbeddings", "PixioLayer"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PixioLayer, + "attentions": PixioSelfAttention, + } + + @torch.no_grad() + def _init_weights(self, module: nn.Linear | nn.Conv2d | nn.LayerNorm): + """Initialize the weights""" + if isinstance(module, nn.Linear | nn.Conv2d): + init.trunc_normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, PixioEmbeddings): + init.trunc_normal_(module.position_embeddings, mean=0.0, std=self.config.initializer_range) + init.trunc_normal_(module.cls_token, mean=0.0, std=self.config.initializer_range) + if module.mask_token is not None: + init.zeros_(module.mask_token) + + +class PixioEncoder(PixioPreTrainedModel): + def __init__(self, config: PixioConfig): + super().__init__(config) + self.layer = nn.ModuleList([PixioLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BaseModelOutput: + for layer_module in self.layer: + hidden_states = layer_module(hidden_states, **kwargs) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +@auto_docstring +class PixioModel(PixioPreTrainedModel): + def __init__(self, config: PixioConfig): + super().__init__(config) + self.config = config + + self.embeddings = PixioEmbeddings(config) + self.encoder = PixioEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.post_init() + + def get_input_embeddings(self) -> PixioPatchEmbeddings: + return self.embeddings.patch_embeddings + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + embedding_output = self.embeddings(pixel_values) + + encoder_outputs: BaseModelOutput = self.encoder(embedding_output, **kwargs) + sequence_output = encoder_outputs.last_hidden_state + sequence_output = self.layernorm(sequence_output) + pooled_output = sequence_output[:, : self.embeddings.n_cls_tokens, :].mean(dim=1) + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + Pixio backbone, to be used with frameworks like DETR and MaskFormer. + """ +) +class PixioBackbone(BackboneMixin, PixioPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)] + self.embeddings = PixioEmbeddings(config) + self.encoder = PixioEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> PixioPatchEmbeddings: + return self.embeddings.patch_embeddings + + @can_return_tuple + @filter_output_hidden_states + @auto_docstring + def forward(self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BackboneOutput: + r""" + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoBackbone + >>> import torch + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> processor = AutoImageProcessor.from_pretrained("facebook/pixio-huge") + >>> model = AutoBackbone.from_pretrained( + ... "facebook/pixio-huge", out_features=["stage7", "stage15", "stage23", "stage31"] + ... ) + + >>> inputs = processor(image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + [1, 1280, 16, 16] + ```""" + kwargs["output_hidden_states"] = True # required to extract layers for the stages + + embedding_output = self.embeddings(pixel_values) + output: BaseModelOutput = self.encoder(embedding_output, **kwargs) + hidden_states = output.hidden_states + + feature_maps = [] + for stage, hidden_state in zip(self.stage_names, hidden_states): + if stage in self.out_features: + if self.config.apply_layernorm: + hidden_state = self.layernorm(hidden_state) + if self.config.reshape_hidden_states: + hidden_state = hidden_state[:, self.embeddings.n_cls_tokens :] + batch_size, _, height, width = pixel_values.shape + patch_size = self.config.patch_size + hidden_state = hidden_state.reshape(batch_size, height // patch_size, width // patch_size, -1) + hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() + feature_maps.append(hidden_state) + + return BackboneOutput( + feature_maps=tuple(feature_maps), + hidden_states=hidden_states, + attentions=output.attentions, + ) + + +__all__ = ["PixioModel", "PixioPreTrainedModel", "PixioBackbone"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/modular_pixio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/modular_pixio.py new file mode 100644 index 0000000000000000000000000000000000000000..5b1718b82ab0483d4b27aae9f201835df566f004 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixio/modular_pixio.py @@ -0,0 +1,318 @@ +# Copyright 2025 Meta AI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Pixio model.""" + +import torch +from huggingface_hub.dataclasses import strict +from torch import nn + +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, is_tracing +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..dinov2.configuration_dinov2 import Dinov2Config +from ..dinov2.modeling_dinov2 import ( + Dinov2Backbone, + Dinov2DropPath, + Dinov2MLP, +) +from ..vit.modeling_vit import ViTAttention, ViTPatchEmbeddings, ViTPreTrainedModel, ViTSelfAttention + + +@auto_docstring(checkpoint="facebook/pixio-huge") +@strict +class PixioConfig(Dinov2Config): + r""" + apply_layernorm (`bool`, *optional*, defaults to `True`): + Whether to apply layer normalization to the feature maps in case the model is used as backbone. + reshape_hidden_states (`bool`, *optional*, defaults to `True`): + Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in + case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size, + seq_len, hidden_size)`. + n_cls_tokens (`int`, *optional*, defaults to 8): + Number of class tokens in the Transformer encoder. + + Example: + + ```python + >>> from transformers import PixioConfig, PixioModel + + >>> # Initializing a Pixio pixio-huge style configuration + >>> configuration = PixioConfig() + + >>> # Initializing a model (with random weights) from the pixio-huge style configuration + >>> model = PixioModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pixio" + + hidden_size: int = 1280 + num_hidden_layers: int = 32 + num_attention_heads: int = 16 + n_cls_tokens: int = 8 + image_size: int | list[int] | tuple[int, int] = 256 + patch_size: int | list[int] | tuple[int, int] = 16 + + layerscale_value = AttributeError() + use_swiglu_ffn = AttributeError() + use_mask_token = AttributeError() + + +class PixioPatchEmbeddings(ViTPatchEmbeddings): + pass + + +class PixioEmbeddings(nn.Module): + """ + Construct the CLS tokens, position and patch embeddings. + """ + + def __init__(self, config: PixioConfig) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.randn(1, config.n_cls_tokens, config.hidden_size)) + self.mask_token = None + self.patch_embeddings = PixioPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + config.n_cls_tokens, config.hidden_size)) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.n_cls_tokens = config.n_cls_tokens + self.patch_size = config.patch_size + self.config = config + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support tracing and interpolation at torch.float32 precision. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + num_patches = embeddings.shape[1] - self.n_cls_tokens + num_positions = self.position_embeddings.shape[1] - self.n_cls_tokens + + if not is_tracing() and num_patches == num_positions and height == width: + return self.position_embeddings + + class_pos_embed = self.position_embeddings[:, : self.n_cls_tokens] + patch_pos_embed = self.position_embeddings[:, self.n_cls_tokens :] + + dim = embeddings.shape[-1] + + new_height = height // self.patch_size + new_width = width // self.patch_size + + sqrt_num_positions = int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + target_dtype = patch_pos_embed.dtype + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed.to(torch.float32), + size=(new_height, new_width), + mode="bicubic", + align_corners=False, + ).to(dtype=target_dtype) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + + return torch.cat((class_pos_embed, patch_pos_embed), dim=1) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + batch_size, _, height, width = pixel_values.shape + target_dtype = self.patch_embeddings.projection.weight.dtype + embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype)) + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + + embeddings = self.dropout(embeddings) + + return embeddings + + +class PixioSelfAttention(ViTSelfAttention): + pass + + +class PixioAttention(ViTAttention): + def __init__(self, config: PixioConfig): + super().__init__(config) + self.attention = PixioSelfAttention(config) + + +class PixioDropPath(Dinov2DropPath): + pass + + +class PixioMLP(Dinov2MLP): + pass + + +class PixioLayer(GradientCheckpointingLayer): + def __init__(self, config: PixioConfig) -> None: + super().__init__() + + self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attention = PixioAttention(config) + self.drop_path = PixioDropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity() + + self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = PixioMLP(config) + + def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> torch.Tensor: + hidden_states_norm = self.norm1(hidden_states) + self_attention_output = self.attention(hidden_states_norm, **kwargs) + + hidden_states = self.drop_path(self_attention_output) + hidden_states + + layer_output = self.norm2(hidden_states) + layer_output = self.mlp(layer_output) + + layer_output = self.drop_path(layer_output) + hidden_states + + return layer_output + + +class PixioPreTrainedModel(ViTPreTrainedModel): + _can_record_outputs = { + "hidden_states": PixioLayer, + "attentions": PixioSelfAttention, + } + + +class PixioEncoder(PixioPreTrainedModel): + def __init__(self, config: PixioConfig): + super().__init__(config) + self.layer = nn.ModuleList([PixioLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BaseModelOutput: + for layer_module in self.layer: + hidden_states = layer_module(hidden_states, **kwargs) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +@auto_docstring +class PixioModel(PixioPreTrainedModel): + def __init__(self, config: PixioConfig): + super().__init__(config) + self.config = config + + self.embeddings = PixioEmbeddings(config) + self.encoder = PixioEncoder(config) + + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.post_init() + + def get_input_embeddings(self) -> PixioPatchEmbeddings: + return self.embeddings.patch_embeddings + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + embedding_output = self.embeddings(pixel_values) + + encoder_outputs: BaseModelOutput = self.encoder(embedding_output, **kwargs) + sequence_output = encoder_outputs.last_hidden_state + sequence_output = self.layernorm(sequence_output) + pooled_output = sequence_output[:, : self.embeddings.n_cls_tokens, :].mean(dim=1) + + return BaseModelOutputWithPooling( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + Pixio backbone, to be used with frameworks like DETR and MaskFormer. + """ +) +class PixioBackbone(Dinov2Backbone): + def forward(self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BackboneOutput: + r""" + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoBackbone + >>> import torch + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> processor = AutoImageProcessor.from_pretrained("facebook/pixio-huge") + >>> model = AutoBackbone.from_pretrained( + ... "facebook/pixio-huge", out_features=["stage7", "stage15", "stage23", "stage31"] + ... ) + + >>> inputs = processor(image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + [1, 1280, 16, 16] + ```""" + kwargs["output_hidden_states"] = True # required to extract layers for the stages + + embedding_output = self.embeddings(pixel_values) + output: BaseModelOutput = self.encoder(embedding_output, **kwargs) + hidden_states = output.hidden_states + + feature_maps = [] + for stage, hidden_state in zip(self.stage_names, hidden_states): + if stage in self.out_features: + if self.config.apply_layernorm: + hidden_state = self.layernorm(hidden_state) + if self.config.reshape_hidden_states: + hidden_state = hidden_state[:, self.embeddings.n_cls_tokens :] + batch_size, _, height, width = pixel_values.shape + patch_size = self.config.patch_size + hidden_state = hidden_state.reshape(batch_size, height // patch_size, width // patch_size, -1) + hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() + feature_maps.append(hidden_state) + + return BackboneOutput( + feature_maps=tuple(feature_maps), + hidden_states=hidden_states, + attentions=output.attentions, + ) + + +__all__ = ["PixioConfig", "PixioModel", "PixioPreTrainedModel", "PixioBackbone"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3335ba43c39e7f5b80bef6a66174eb5508cdba9e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pixtral import * + from .image_processing_pil_pixtral import * + from .image_processing_pixtral import * + from .modeling_pixtral import * + from .processing_pixtral import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/configuration_pixtral.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/configuration_pixtral.py new file mode 100644 index 0000000000000000000000000000000000000000..095425e4054b9ec50959cf854de07e07b67638ae --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/configuration_pixtral.py @@ -0,0 +1,60 @@ +# Copyright 2024 HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Pixtral model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="mistral-labs/pixtral-12b") +@strict +class PixtralVisionConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PixtralVisionModel, PixtralVisionConfig + + >>> # Initializing a Pixtral-12B style configuration + >>> config = PixtralVisionConfig() + + >>> # Initializing a model (with randomly initialized weights) from the configuration + >>> model = PixtralVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pixtral" + + hidden_size: int = 1024 + intermediate_size: int = 4096 + num_hidden_layers: int = 24 + num_attention_heads: int = 16 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 1024 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "gelu" + attention_dropout: float | int = 0.0 + rope_parameters: RopeParameters | dict | None = None + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + self.head_dim = self.hidden_size // self.num_attention_heads + super().__post_init__(**kwargs) + + +__all__ = ["PixtralVisionConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/image_processing_pil_pixtral.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/image_processing_pil_pixtral.py new file mode 100644 index 0000000000000000000000000000000000000000..2c3a5cfbfef58bbb8598da4de88ea4e1a3e27013 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/image_processing_pil_pixtral.py @@ -0,0 +1,227 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Pixtral.""" + +import math + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_transforms import PaddingMode, pad +from ...image_utils import ( + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + get_image_size, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +# Adapted from transformers.models.pixtral.image_processing_pixtral.PixtralImageProcessorKwargs +class PixtralImageProcessorKwargs(ImagesKwargs, total=False): + """ + patch_size (`Union[dict[str, int], int]` *optional*, defaults to `{"height": 16, "width": 16}`): + Size of the patches in the model, used to calculate the output image size. + """ + + patch_size: dict[str, int] | int + + +# Adapted from transformers.models.pixtral.image_processing_pixtral._num_image_tokens +def _num_image_tokens(image_size: tuple[int, int], patch_size: tuple[int, int]) -> int: + """ + Calculate the number of image tokens given the image size and patch size. + + Args: + image_size (`tuple[int, int]`): + The size of the image as `(height, width)`. + patch_size (`tuple[int, int]`): + The patch size as `(height, width)`. + + Returns: + `int`: The number of image tokens. + """ + height, width = image_size + patch_height, patch_width = patch_size if isinstance(patch_size, (tuple, list)) else (patch_size, patch_size) + num_width_tokens = (width - 1) // patch_width + 1 + num_height_tokens = (height - 1) // patch_height + 1 + return num_height_tokens, num_width_tokens + + +# Adapted from transformers.models.pixtral.image_processing_pixtral.get_resize_output_image_size +def get_resize_output_image_size( + input_image: ImageInput, + size: int | tuple[int, int] | list[int] | tuple[int], + patch_size: int | tuple[int, int] | list[int] | tuple[int], + input_data_format: str | ChannelDimension | None = None, +) -> tuple: + """ + Find the target (height, width) dimension of the output image after resizing given the input image and the desired + size. + + Args: + input_image (`ImageInput`): + The image to resize. + size (`int` or `tuple[int, int]`): + Max image size an input image can be. Must be a dictionary with the key "longest_edge". + patch_size (`int` or `tuple[int, int]`): + The patch_size as `(height, width)` to use for resizing the image. If patch_size is an integer, `(patch_size, patch_size)` + will be used + input_data_format (`ChannelDimension`, *optional*): + The channel dimension format of the input image. If unset, will use the inferred format from the input. + + Returns: + `tuple`: The target (height, width) dimension of the output image after resizing. + """ + max_height, max_width = size if isinstance(size, (tuple, list)) else (size, size) + patch_height, patch_width = patch_size if isinstance(patch_size, (tuple, list)) else (patch_size, patch_size) + height, width = get_image_size(input_image, input_data_format) + + ratio = max(height / max_height, width / max_width) + + if ratio > 1: + # Original implementation uses `round` which utilises bankers rounding, which can lead to surprising results + # Here we use floor to ensure the image is always smaller than the given "longest_edge" + height = int(math.floor(height / ratio)) + width = int(math.floor(width / ratio)) + + num_height_tokens, num_width_tokens = _num_image_tokens((height, width), (patch_height, patch_width)) + return num_height_tokens * patch_height, num_width_tokens * patch_width + + +@auto_docstring +class PixtralImageProcessorPil(PilBackend): + resample = PILImageResampling.BICUBIC + image_mean = [0.48145466, 0.4578275, 0.40821073] + image_std = [0.26862954, 0.26130258, 0.27577711] + patch_size = {"height": 16, "width": 16} + size = {"longest_edge": 1024} + default_to_square = True + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + valid_kwargs = PixtralImageProcessorKwargs + + model_input_names = ["pixel_values", "image_sizes"] + + def __init__(self, **kwargs: Unpack[PixtralImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[PixtralImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def resize( + self, + image: np.ndarray, + size: SizeDict, + patch_size: SizeDict, + resample: "PILImageResampling | None" = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image. The longest edge is resized to size["longest_edge"], with aspect ratio preserved. + Output dimensions are aligned to patch_size. + """ + if size.longest_edge: + size_tuple = (size.longest_edge, size.longest_edge) + elif size.height and size.width: + size_tuple = (size.height, size.width) + else: + raise ValueError("size must contain either 'longest_edge' or 'height' and 'width'.") + + if patch_size.height and patch_size.width: + patch_size_tuple = (patch_size.height, patch_size.width) + else: + raise ValueError("patch_size must contain 'height' and 'width'.") + + output_size = get_resize_output_image_size( + image, size=size_tuple, patch_size=patch_size_tuple, input_data_format=ChannelDimension.FIRST + ) + return super().resize( + image, size=SizeDict(height=output_size[0], width=output_size[1]), resample=resample, **kwargs + ) + + def _pad_for_batching( + self, + pixel_values: list[np.ndarray], + image_sizes: list[tuple[int, int]], + ) -> np.ndarray: + """Pad images to form a batch of same shape.""" + max_shape = (max(s[0] for s in image_sizes), max(s[1] for s in image_sizes)) + padded = [] + for img, size in zip(pixel_values, image_sizes): + pad_h = max_shape[0] - size[0] + pad_w = max_shape[1] - size[1] + padded_img = pad( + img, + padding=((0, pad_h), (0, pad_w)), + mode=PaddingMode.CONSTANT, + constant_values=0, + input_data_format=ChannelDimension.FIRST, + ) + padded.append(padded_img) + return np.stack(padded) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + patch_size: dict[str, int] | SizeDict | None = None, + **kwargs, + ) -> BatchFeature: + patch_size = get_size_dict(patch_size or self.patch_size, default_to_square=True) + patch_size_sd = SizeDict(**patch_size) + + processed_images = [] + batch_image_sizes = [] + + for image in images: + if do_resize: + image = self.resize(image, size=size, patch_size=patch_size_sd, resample=resample) + if do_center_crop: + image = self.center_crop(image, crop_size) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + + processed_images.append(image) + batch_image_sizes.append(get_image_size(image, channel_dim=ChannelDimension.FIRST)) + + padded_images = self._pad_for_batching( + pixel_values=processed_images, + image_sizes=batch_image_sizes, + ) + + return BatchFeature( + data={"pixel_values": padded_images, "image_sizes": batch_image_sizes}, tensor_type=return_tensors + ) + + +__all__ = ["PixtralImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/image_processing_pixtral.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/image_processing_pixtral.py new file mode 100644 index 0000000000000000000000000000000000000000..7988e446ec59a145ae55895d5e48ae1d74d5131e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/image_processing_pixtral.py @@ -0,0 +1,238 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Pixtral.""" + +import math + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, SizeDict, get_image_size +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +def _num_image_tokens(image_size: tuple[int, int], patch_size: tuple[int, int]) -> int: + """ + Calculate the number of image tokens given the image size and patch size. + + Args: + image_size (`tuple[int, int]`): + The size of the image as `(height, width)`. + patch_size (`tuple[int, int]`): + The patch size as `(height, width)`. + + Returns: + `int`: The number of image tokens. + """ + height, width = image_size + patch_height, patch_width = patch_size if isinstance(patch_size, (tuple, list)) else (patch_size, patch_size) + num_width_tokens = (width - 1) // patch_width + 1 + num_height_tokens = (height - 1) // patch_height + 1 + return num_height_tokens, num_width_tokens + + +def get_resize_output_image_size( + input_image: ImageInput, + size: int | tuple[int, int] | list[int] | tuple[int], + patch_size: int | tuple[int, int] | list[int] | tuple[int], + input_data_format: str | ChannelDimension | None = None, +) -> tuple: + """ + Find the target (height, width) dimension of the output image after resizing given the input image and the desired + size. + + Args: + input_image (`ImageInput`): + The image to resize. + size (`int` or `tuple[int, int]`): + Max image size an input image can be. Must be a dictionary with the key "longest_edge". + patch_size (`int` or `tuple[int, int]`): + The patch_size as `(height, width)` to use for resizing the image. If patch_size is an integer, `(patch_size, patch_size)` + will be used + input_data_format (`ChannelDimension`, *optional*): + The channel dimension format of the input image. If unset, will use the inferred format from the input. + + Returns: + `tuple`: The target (height, width) dimension of the output image after resizing. + """ + max_height, max_width = size if isinstance(size, (tuple, list)) else (size, size) + patch_height, patch_width = patch_size if isinstance(patch_size, (tuple, list)) else (patch_size, patch_size) + height, width = get_image_size(input_image, input_data_format) + + ratio = max(height / max_height, width / max_width) + + if ratio > 1: + # Original implementation uses `round` which utilises bankers rounding, which can lead to surprising results + # Here we use floor to ensure the image is always smaller than the given "longest_edge" + height = int(math.floor(height / ratio)) + width = int(math.floor(width / ratio)) + + num_height_tokens, num_width_tokens = _num_image_tokens((height, width), (patch_height, patch_width)) + return num_height_tokens * patch_height, num_width_tokens * patch_width + + +class PixtralImageProcessorKwargs(ImagesKwargs, total=False): + """ + patch_size (`Union[dict[str, int], int]` *optional*, defaults to `{"height": 16, "width": 16}`): + Size of the patches in the model, used to calculate the output image size. + """ + + patch_size: dict[str, int] | int + + +@auto_docstring +class PixtralImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = [0.48145466, 0.4578275, 0.40821073] + image_std = [0.26862954, 0.26130258, 0.27577711] + patch_size = {"height": 16, "width": 16} + size = {"longest_edge": 1024} + default_to_square = True + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + valid_kwargs = PixtralImageProcessorKwargs + + model_input_names = ["pixel_values", "image_sizes"] + + def __init__(self, **kwargs: Unpack[PixtralImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[PixtralImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def resize( + self, + image: "torch.Tensor", + size: SizeDict, + patch_size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + **kwargs, + ) -> "torch.Tensor": + """ + Resize an image. The longest edge of the image is resized to size["longest_edge"], with the aspect ratio + preserved. Output dimensions are aligned to patch_size. + + Args: + image (`torch.Tensor`): + Image to resize. + size (`SizeDict`): + Dict containing the longest possible edge of the image. + patch_size (`SizeDict`): + Patch size used to calculate the size of the output image. + resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): + Resampling filter to use when resizing the image. + """ + if size.longest_edge: + size_tuple = (size.longest_edge, size.longest_edge) + elif size.height and size.width: + size_tuple = (size.height, size.width) + else: + raise ValueError("size must contain either 'longest_edge' or 'height' and 'width'.") + + if patch_size.height and patch_size.width: + patch_size_tuple = (patch_size.height, patch_size.width) + else: + raise ValueError("patch_size must contain 'height' and 'width'.") + + output_size = get_resize_output_image_size(image, size=size_tuple, patch_size=patch_size_tuple) + return super().resize( + image, size=SizeDict(height=output_size[0], width=output_size[1]), resample=resample, **kwargs + ) + + def _pad_for_batching( + self, + pixel_values: list["torch.Tensor"], + image_sizes: list[tuple[int, int]], + ) -> "torch.Tensor": + """ + Pads images to form a batch of same shape. + + Args: + pixel_values (`list[torch.Tensor]`): + A list of pixel values, each of shape (channels, height, width). + image_sizes (`list[tuple[int, int]]`): + A list of (height, width) for each image. + + Returns: + `torch.Tensor`: Stacked and padded images. + """ + max_shape = (max(s[0] for s in image_sizes), max(s[1] for s in image_sizes)) + padded = [ + torch.nn.functional.pad(img, pad=(0, max_shape[1] - size[1], 0, max_shape[0] - size[0])) + for img, size in zip(pixel_values, image_sizes) + ] + return torch.stack(padded) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + patch_size: dict[str, int] | SizeDict | None = None, + **kwargs, + ) -> BatchFeature: + patch_size = get_size_dict(patch_size or self.patch_size, default_to_square=True) + patch_size_sd = SizeDict(**patch_size) + + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize( + image=stacked_images, size=size, patch_size=patch_size_sd, resample=resample + ) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + batch_image_sizes = [grouped_images_index[i][0] for i in range(len(grouped_images_index))] + + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + padded_images = self._pad_for_batching( + pixel_values=processed_images, + image_sizes=batch_image_sizes, + ) + + return BatchFeature( + data={"pixel_values": padded_images, "image_sizes": batch_image_sizes}, tensor_type=return_tensors + ) + + +__all__ = ["PixtralImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/modeling_pixtral.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/modeling_pixtral.py new file mode 100644 index 0000000000000000000000000000000000000000..e7c92bfe4df7084c54eb3ae0f4c3fe13a40e8ef2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/modeling_pixtral.py @@ -0,0 +1,485 @@ +# Copyright 2024 Mistral and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Pixtral model.""" + +from collections.abc import Callable +from typing import Optional + +import torch +from torch import nn + +from ...activations import ACT2FN +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput +from ...modeling_rope_utils import dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pixtral import PixtralVisionConfig + + +logger = logging.get_logger(__name__) + + +def position_ids_in_meshgrid(patch_embeds_list, max_width): + positions = [] + for patch in patch_embeds_list: + height, width = patch.shape[-2:] + mesh = torch.meshgrid(torch.arange(height), torch.arange(width), indexing="ij") + h_grid, v_grid = torch.stack(mesh, dim=-1).reshape(-1, 2).chunk(2, -1) + ids = h_grid * max_width + v_grid + positions.append(ids[:, 0]) + return torch.cat(positions) + + +class PixtralRotaryEmbedding(nn.Module): + """ + The key with pixtral embedding is just that you have a frequency for each pixel positions. + If you have height x width pixels (or embedding pixels), then the frequency used for ROPE + is given by indexing the pre_computed frequency on the width and height. + + What you output is of dimension (batch, height * width, dim) with dim the embed dim. + + This simply means that for each image hidden state, you are going to add + a corresponding positional embedding, based on its index in the grid. + """ + + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: PixtralVisionConfig, device=None, layer_type=None): + super().__init__() + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + raise ValueError( + f"{self.__class__.__name__} does not support non-default RoPE, but got `rope_type={self.rope_type}`" + ) + + inv_freq, attention_scaling = rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PixtralVisionConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Here is the diff from Llama RoPE + max_patches_per_side = config.image_size // config.patch_size + h = torch.arange(max_patches_per_side) + w = torch.arange(max_patches_per_side) + + freqs = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim)) + freqs_h = torch.outer(h, freqs[::2]).float() + freqs_w = torch.outer(w, freqs[1::2]).float() + inv_freq = torch.cat( + [ + freqs_h[:, None, :].repeat(1, max_patches_per_side, 1), + freqs_w[None, :, :].repeat(max_patches_per_side, 1, 1), + ], + dim=-1, + ).reshape(-1, dim // 2) # we reshape to only index on the position indexes, not tuple of indexes + # Different from paper, but it uses a different permutation in order to obtain the same calculation + + # TODO maybe make it torch compatible later on. We can also just slice + inv_freq = torch.cat((inv_freq, inv_freq), dim=-1) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + freqs = self.inv_freq[position_ids] + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + emb = freqs + cos = emb.cos() + sin = emb.sin() + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +# Copied from transformers.models.llama.modeling_llama.rotate_half +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PixtralAttention(nn.Module): + """ + Multi-headed attention compatible with ALL_ATTENTION_FUNCTIONS. + """ + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + self.is_causal = False + + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.dropout = config.attention_dropout + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) + self.o_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + batch_size, patches, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(batch_size, patches, self.num_heads, self.head_dim).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, unsqueeze_dim=0) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(batch_size, patches, -1).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +# Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Pixtral +class PixtralMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Pixtral +class PixtralRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + PixtralRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class PixtralAttentionLayer(GradientCheckpointingLayer): + def __init__(self, config): + super().__init__() + self.attention_norm = PixtralRMSNorm(config.hidden_size, eps=1e-5) + self.feed_forward = PixtralMLP(config) + self.attention = PixtralAttention(config) + self.ffn_norm = PixtralRMSNorm(config.hidden_size, eps=1e-5) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + Input to the layer of shape `(batch, seq_len, embed_dim)`. + attention_mask (`torch.FloatTensor`): + Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values. + """ + residual = hidden_states + + hidden_states = self.attention_norm(hidden_states) + hidden_states, _ = self.attention( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.ffn_norm(hidden_states) + hidden_states = self.feed_forward(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class PixtralTransformer(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layers = torch.nn.ModuleList() + for _ in range(config.num_hidden_layers): + self.layers.append(PixtralAttentionLayer(config)) + self.gradient_checkpointing = False + + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Embeddings which serve as input to the Transformer. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + """ + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +@auto_docstring +class PixtralPreTrainedModel(PreTrainedModel): + config: PixtralVisionConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + _supports_attention_backend = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _no_split_modules = ["PixtralAttentionLayer"] + _can_record_outputs = { + "hidden_states": PixtralAttentionLayer, + "attentions": PixtralAttention, + } + + +def generate_block_attention_mask(patch_embeds_list, tensor): + dtype = tensor.dtype + device = tensor.device + seq_len = tensor.shape[1] + d_min = torch.finfo(dtype).min + causal_mask = torch.full((seq_len, seq_len), fill_value=d_min, dtype=dtype, device=device) + + block_end_idx = torch.tensor(patch_embeds_list).cumsum(-1) + block_start_idx = torch.tensor([0] + patch_embeds_list[:-1]).cumsum(-1) + for start, end in zip(block_start_idx, block_end_idx): + causal_mask[start:end, start:end] = 0 + + causal_mask = causal_mask[None, None, :, :].expand(tensor.shape[0], 1, -1, -1) + return causal_mask + + +@auto_docstring +class PixtralVisionModel(PixtralPreTrainedModel): + base_model_prefix = "vision_encoder" + + def __init__(self, config): + super().__init__(config) + self.config = config + self.patch_conv = nn.Conv2d( + in_channels=config.num_channels, + out_channels=config.hidden_size, + kernel_size=config.patch_size, + stride=config.patch_size, + bias=False, + ) + self.patch_size = config.patch_size + self.ln_pre = PixtralRMSNorm(config.hidden_size, eps=1e-5) + self.transformer = PixtralTransformer(config) + self.patch_positional_embedding = PixtralRotaryEmbedding(config) + + self.post_init() + + def get_input_embeddings(self): + return self.patch_conv + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + image_sizes: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + if image_sizes is None: + batch_size, _, height, width = pixel_values.shape + image_sizes = [(height, width)] * batch_size + + # pass images through initial convolution independently + target_dtype = self.patch_conv.weight.dtype + patch_embeds = self.patch_conv(pixel_values.to(dtype=target_dtype)) + patch_embeds_list = [ + embed[..., : (size[0] // self.patch_size), : (size[1] // self.patch_size)] + for embed, size in zip(patch_embeds, image_sizes) + ] + + # flatten to a single sequence + patch_embeds = torch.cat([p.flatten(1).T for p in patch_embeds_list], dim=0).unsqueeze(0) + patch_embeds = self.ln_pre(patch_embeds) + + # positional embeddings + position_ids = position_ids_in_meshgrid( + patch_embeds_list, max_width=self.config.image_size // self.config.patch_size + ) + kwargs["position_ids"] = position_ids.unsqueeze(0).to(patch_embeds.device, non_blocking=True) + + position_embeddings = self.patch_positional_embedding(patch_embeds, position_ids) + + if is_flash_attention_requested(self.config): + # We only rely on position_ids when using flash attention + attention_mask = None + else: + attention_mask = generate_block_attention_mask( + [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], patch_embeds + ) + + return self.transformer( + patch_embeds, + attention_mask=attention_mask, + position_embeddings=position_embeddings, + **kwargs, + ) + + +__all__ = ["PixtralVisionModel", "PixtralPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/processing_pixtral.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/processing_pixtral.py new file mode 100644 index 0000000000000000000000000000000000000000..eef1dc674e7be55ae31636d00499df6b2c01fc8b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pixtral/processing_pixtral.py @@ -0,0 +1,221 @@ +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for Pixtral. +""" + +import numpy as np + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput, is_valid_image +from ...processing_utils import ( + MultiModalData, + ProcessingKwargs, + ProcessorMixin, + Unpack, +) +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring, is_vision_available, logging +from ...utils.import_utils import requires + + +if is_vision_available(): + from .image_processing_pixtral import get_resize_output_image_size + + +logger = logging.get_logger(__name__) + + +class PixtralProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": False, + "return_mm_token_type_ids": False, + }, + "common_kwargs": { + "return_tensors": "pt", + }, + } + + +# Copied from transformers.models.idefics2.processing_idefics2.is_url +def is_url(val) -> bool: + return isinstance(val, str) and val.startswith("http") + + +# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url +def is_image_or_image_url(elem): + return is_url(elem) or is_valid_image(elem) + + +@auto_docstring +@requires(backends=("torchvision", "torch")) +class PixtralProcessor(ProcessorMixin): + def __init__( + self, + image_processor=None, + tokenizer=None, + patch_size: int = 16, + spatial_merge_size: int = 1, + chat_template=None, + image_token="[IMG]", # set the default and let users change if they have peculiar special tokens in rare cases + image_break_token="[IMG_BREAK]", + image_end_token="[IMG_END]", + **kwargs, + ): + r""" + patch_size (`int`, *optional*, defaults to 16): + Patch size from the vision tower. + spatial_merge_size (`int`, *optional*, defaults to 1): + The downsampling factor for the spatial merge operation. + image_token (`str`, *optional*, defaults to `"[IMG]"`): + Special token used to denote image location. + image_break_token (`str`, *optional*, defaults to `"[IMG_BREAK]"`): + Special token used to denote the end of a line of pixels in an image. + image_end_token (`str`, *optional*, defaults to `"[IMG_END]"`): + Special token used to denote the end of an image input. + """ + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + self.patch_size = patch_size + self.spatial_merge_size = spatial_merge_size + self.image_token = image_token + self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token) + self.image_break_token = image_break_token + self.image_end_token = image_end_token + self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token) + self.image_break_token_id = tokenizer.convert_tokens_to_ids(self.image_break_token) + self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token) + self.image_ids = [self.image_token_id, self.image_break_token_id, self.image_end_token_id] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[PixtralProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + """ + + output_kwargs = self._merge_kwargs( + PixtralProcessorKwargs, + tokenizer_init_kwargs=getattr(self.tokenizer, "init_kwargs", {}), + **kwargs, + ) + + patch_size = self.patch_size * self.spatial_merge_size + + if images is not None: + output_kwargs["images_kwargs"]["patch_size"] = patch_size + image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"]) + else: + image_inputs = {} + + if isinstance(text, str): + text = [text] + elif not isinstance(text, list) and not isinstance(text[0], str): + raise TypeError("Invalid input text. Please provide a string, or a list of strings") + + # try to expand inputs in processing if we have the necessary parts + prompt_strings = text + if image_inputs.get("pixel_values") is not None: + # Replace the image token with the expanded image token sequence + image_sizes = iter(image_inputs["image_sizes"]) + prompt_strings = [] + replace_strings = [] + + for sample in text: + while self.image_token in sample: + height, width = next(image_sizes) + num_height_tokens = height // patch_size + num_width_tokens = width // patch_size + replace_tokens = [ + [self.image_token] * num_width_tokens + [self.image_break_token] + ] * num_height_tokens + # Flatten list + replace_tokens = [item for sublist in replace_tokens for item in sublist] + replace_tokens[-1] = self.image_end_token + replace_str = "".join(replace_tokens) + replace_strings.append(replace_str) + sample = sample.replace(self.image_token, "", 1) + + while "" in sample: + replace_str = replace_strings.pop(0) + sample = sample.replace("", replace_str, 1) + prompt_strings.append(sample) + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False) + # Remove return_token_type_ids as MistralCommonBackend doesn't support it + output_kwargs["text_kwargs"].pop("return_token_type_ids", None) + text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None) + self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"]) + + if return_mm_token_type_ids: + text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"]) + return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors) + + def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs): + """ + Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. + + Args: + image_sizes (`list[list[int]]`, *optional*): + The input sizes formatted as (height, width) per each image. + + Returns: + `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided + input modalities, along with other useful data. + """ + vision_data = {} + if image_sizes is not None: + images_kwargs = PixtralProcessorKwargs._defaults.get("images_kwargs", {}) + images_kwargs.update(kwargs) + + size = images_kwargs.get("size", None) or self.image_processor.size + patch_size = self.patch_size * self.spatial_merge_size + + num_image_tokens = [] + for height, width in image_sizes: + resized_height, resized_width = get_resize_output_image_size( + np.zeros((height, width, 3)), + size=(size["longest_edge"], size["longest_edge"]), + patch_size=(patch_size, patch_size), + ) + num_height_tokens = resized_height // patch_size + num_width_tokens = resized_width // patch_size + num_image_tokens.append((num_width_tokens + 1) * num_height_tokens) + + num_image_patches = [1] * len(image_sizes) + vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) + + return MultiModalData(**vision_data) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return tokenizer_input_names + image_processor_input_names + ["image_sizes"] + + +__all__ = ["PixtralProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..33e5618ca7e1727c0ce28458d952ce0bfbbfee59 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_plbart import * + from .modeling_plbart import * + from .tokenization_plbart import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/configuration_plbart.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/configuration_plbart.py new file mode 100644 index 0000000000000000000000000000000000000000..9fd3f171f46ed34a12276e4ff2086ceb5609cf00 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/configuration_plbart.py @@ -0,0 +1,78 @@ +# Copyright 2022, UCLA NLP, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PLBART model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="uclanlp/plbart-base") +@strict +class PLBartConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PLBartConfig, PLBartModel + + >>> # Initializing a PLBART uclanlp/plbart-base style configuration + >>> configuration = PLBartConfig() + + >>> # Initializing a model (with random weights) from the uclanlp/plbart-base style configuration + >>> model = PLBartModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "plbart" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "num_attention_heads": "encoder_attention_heads", + "hidden_size": "d_model", + "initializer_range": "init_std", + "num_hidden_layers": "encoder_layers", + } + + vocab_size: int = 50005 + max_position_embeddings: int = 1024 + encoder_layers: int = 6 + encoder_ffn_dim: int = 3072 + encoder_attention_heads: int = 12 + decoder_layers: int = 6 + decoder_ffn_dim: int = 3072 + decoder_attention_heads: int = 12 + encoder_layerdrop: float | int = 0.0 + decoder_layerdrop: float | int = 0.0 + use_cache: bool = True + is_encoder_decoder: bool = True + activation_function: str = "gelu" + d_model: int = 768 + dropout: float | int = 0.1 + attention_dropout: float | int = 0.1 + activation_dropout: float | int = 0.0 + init_std: float = 0.02 + classifier_dropout: float | int = 0.0 + scale_embedding: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 2 + forced_eos_token_id: int | list[int] | None = 2 + is_decoder: bool = False + tie_word_embeddings: bool = True + + +__all__ = ["PLBartConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/modeling_plbart.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/modeling_plbart.py new file mode 100644 index 0000000000000000000000000000000000000000..418e44d7029ac4863489dcdc8880ccd1091a1609 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/modeling_plbart.py @@ -0,0 +1,1149 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/plbart/modular_plbart.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_plbart.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2022, UCLA NLP, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, + Seq2SeqSequenceClassifierOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + is_torchdynamo_compiling, + logging, + torch_compilable_check, +) +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_plbart import PLBartConfig + + +logger = logging.get_logger(__name__) + + +class PLBartScaledWordEmbedding(nn.Embedding): + """ + This module overrides nn.Embeddings' forward by multiplying with embeddings scale. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float | None = 1.0): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.embed_scale = embed_scale + + def forward(self, input_ids: torch.Tensor): + return super().forward(input_ids) * self.embed_scale + + +@auto_docstring +class PLBartPreTrainedModel(PreTrainedModel): + config: PLBartConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PLBartDecoderLayer", "PLBartEncoderLayer"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, PLBartForConditionalGeneration): + init.zeros_(module.final_logits_bias) + + +class PLBartLearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int): + # PLBart is set up so that if padding_idx is specified then offset the embedding ids by 2 + # and adjust num_embeddings appropriately. Other models don't have this hack + self.offset = 2 + super().__init__(num_embeddings + self.offset, embedding_dim) + + def forward( + self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor | None = None + ): + """`input_ids' shape is expected to be [bsz x seqlen].""" + + if position_ids is None: + bsz, seq_len = input_ids.shape[:2] + position_ids = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device + ).expand(bsz, -1) + else: + position_ids = position_ids.unsqueeze(0) + + return super().forward(position_ids + self.offset) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PLBartAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: PLBartConfig | None = None, + layer_idx: int | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + if layer_idx is None and self.is_decoder: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + # TODO: we need a refactor so that the different attention modules can get their specific kwargs + # ATM, we have mixed things encoder, decoder, and encoder-decoder attn + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + + # get query proj + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.k_proj(current_states) + value_states = self.v_proj(current_states) + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = key_states.view(kv_shape).transpose(1, 2) + value_states = value_states.view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class PLBartEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PLBartConfig, layer_idx: int | None = None): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = PLBartAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + layer_idx=layer_idx, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.FloatTensor, + attention_mask: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states, _ = self.self_attn( + hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + if hidden_states.dtype == torch.float16 and not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +class PLBartEncoder(PLBartPreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`PLBartEncoderLayer`]. + + Args: + config: PLBartConfig + embed_tokens (nn.Embedding): output embedding + """ + + _can_record_outputs = { + "hidden_states": PLBartEncoderLayer, + "attentions": PLBartAttention, + } + + def __init__(self, config: PLBartConfig): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + self.padding_idx = config.pad_token_id + self.max_source_positions = config.max_position_embeddings + embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = PLBartScaledWordEmbedding( + config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale + ) + + self.embed_positions = PLBartLearnedPositionalEmbedding( + config.max_position_embeddings, + embed_dim, + ) + self.layers = nn.ModuleList([PLBartEncoderLayer(config, layer_idx=i) for i in range(config.encoder_layers)]) + self.layernorm_embedding = nn.LayerNorm(embed_dim) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + embed_pos = self.embed_positions(inputs_embeds[:, :, -1]) # needed for the shape only + embed_pos = embed_pos.to(inputs_embeds.device) + + hidden_states = inputs_embeds + embed_pos + hidden_states = self.layernorm_embedding(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + for idx, encoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if not to_drop: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + **kwargs, + ) + + return BaseModelOutput( + last_hidden_state=hidden_states, + ) + + +class PLBartDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PLBartConfig, layer_idx: int | None = None): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = PLBartAttention( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + is_causal=True, + config=config, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = PLBartAttention( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + config=config, + layer_idx=layer_idx, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = True, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Cross-Attention Block + if encoder_hidden_states is not None: + residual = hidden_states + + hidden_states, _ = self.encoder_attn( + hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states + + +class PLBartDecoder(PLBartPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PLBartDecoderLayer`] + + Args: + config: PLBartConfig + embed_tokens (nn.Embedding): output embedding + """ + + _can_record_outputs = { + "hidden_states": PLBartDecoderLayer, + "attentions": OutputRecorder(PLBartAttention, index=1, layer_name="self_attn"), + "cross_attentions": OutputRecorder(PLBartAttention, index=1, layer_name="encoder_attn"), + } + + def __init__(self, config: PLBartConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = PLBartScaledWordEmbedding( + config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale + ) + + self.embed_positions = PLBartLearnedPositionalEmbedding( + config.max_position_embeddings, + config.d_model, + ) + self.layers = nn.ModuleList([PLBartDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)]) + + self.layernorm_embedding = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPastAndCrossAttentions: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of decoder_input_ids or decoder_inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # initialize `past_key_values` + if use_cache and past_key_values is None: + past_key_values = ( + EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + if encoder_hidden_states is not None or self.config.is_encoder_decoder + else DynamicCache(config=self.config) + ) + + batch_size, seq_length = inputs_embeds.size()[:-1] + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(seq_length, device=inputs_embeds.device) + past_key_values_length + + if attention_mask is None and not is_torchdynamo_compiling(): + # required mask seq length can be calculated via length of past cache + mask_seq_length = past_key_values_length + seq_length + attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) + + self_attn_cache = ( + past_key_values.self_attention_cache + if isinstance(past_key_values, EncoderDecoderCache) + else past_key_values + ) + + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=self_attn_cache, + ) + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + # embed positions + positions = self.embed_positions(input, past_key_values_length, position_ids=position_ids) + positions = positions.to(inputs_embeds.device) + + hidden_states = inputs_embeds + positions + hidden_states = self.layernorm_embedding(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + hidden_states = decoder_layer( + hidden_states, + attention_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int): + """ + Shift input ids one token to the right, and wrap the last non pad token (the token) Note that PLBart does not + have a single `decoder_start_token_id` in contrast to other Bart-like models. + """ + prev_output_tokens = input_ids.clone() + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) + + index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) + decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() + prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() + prev_output_tokens[:, 0] = decoder_start_tokens + + return prev_output_tokens + + +@auto_docstring +class PLBartModel(PLBartPreTrainedModel): + _tied_weights_keys = { + "encoder.embed_tokens.weight": "shared.weight", + "decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: PLBartConfig): + super().__init__(config) + + padding_idx, vocab_size = config.pad_token_id, config.vocab_size + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + self.shared = PLBartScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale) + + self.encoder = PLBartEncoder(config) + self.decoder = PLBartDecoder(config) + + self.post_init() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, value): + self.shared = value + self.encoder.embed_tokens = self.shared + self.decoder.embed_tokens = self.shared + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.Tensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | Seq2SeqModelOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint. + See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that + varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (: + obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: + generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. + """ + # different to other models, PLBart automatically creates decoder_input_ids from + # input_ids if no decoder_input_ids are provided + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id) + + if encoder_outputs is None: + encoder_outputs: BaseModelOutput = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + **kwargs, + ) + elif not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The PLBART Model with a language modeling head. Can be used for code-to-text, text-to-code and code-to-code. + """ +) +class PLBartForConditionalGeneration(PLBartPreTrainedModel, GenerationMixin): + base_model_prefix = "model" + _keys_to_ignore_on_load_missing = ["final_logits_bias"] + _tied_weights_keys = { + "lm_head.weight": "model.shared.weight", + } + + def __init__(self, config: PLBartConfig): + super().__init__(config) + self.model = PLBartModel(config) + self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) + self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) + + self.post_init() + + def resize_token_embeddings( + self, new_num_tokens: int, pad_to_multiple_of: int | None = None, mean_resizing: bool = True + ) -> nn.Embedding: + new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing) + self._resize_final_logits_bias(new_embeddings.weight.shape[0]) + return new_embeddings + + def _resize_final_logits_bias(self, new_num_tokens: int) -> None: + old_num_tokens = self.final_logits_bias.shape[-1] + if new_num_tokens <= old_num_tokens: + new_bias = self.final_logits_bias[:, :new_num_tokens] + else: + extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) + new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) + self.register_buffer("final_logits_bias", new_bias) + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.Tensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.Tensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | Seq2SeqLMOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint. + See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that + varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (: + obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: + generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example Mask-filling: + + ```python + >>> from transformers import AutoTokenizer, PLBartForConditionalGeneration + + >>> model = PLBartForConditionalGeneration.from_pretrained("uclanlp/plbart-base") + >>> tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-base") + + >>> # en_XX is the language symbol id for English + >>> TXT = " Is 0 the Fibonacci number ? en_XX" + >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="pt").input_ids + + >>> logits = model(input_ids).logits + >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item() + >>> probs = logits[0, masked_index].softmax(dim=0) + >>> values, predictions = probs.topk(5) + + >>> tokenizer.decode(predictions).split() + ['first', 'same', 'highest', 'result', 'number'] + ``` + """ + if labels is not None: + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id) + + outputs: Seq2SeqModelOutput = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + lm_logits = self.lm_head(outputs.last_hidden_state) + lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id) + + +class PLBartClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__( + self, + input_dim: int, + inner_dim: int, + num_classes: int, + pooler_dropout: float, + ): + super().__init__() + self.dense = nn.Linear(input_dim, inner_dim) + self.dropout = nn.Dropout(p=pooler_dropout) + self.out_proj = nn.Linear(inner_dim, num_classes) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dropout(hidden_states) + hidden_states = self.dense(hidden_states) + hidden_states = torch.tanh(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.out_proj(hidden_states) + return hidden_states + + +@auto_docstring( + custom_intro=""" + PLBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. + for GLUE tasks. + """ +) +class PLBartForSequenceClassification(PLBartPreTrainedModel): + def __init__(self, config: PLBartConfig, **kwargs): + super().__init__(config, **kwargs) + self.model = PLBartModel(config) + self.classification_head = PLBartClassificationHead( + config.d_model, + config.d_model, + config.num_labels, + config.classifier_dropout, + ) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Seq2SeqSequenceClassifierOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint. + See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that + varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (: + obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: + generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + if labels is not None: + use_cache = False + + if input_ids is None and inputs_embeds is not None: + raise NotImplementedError( + f"Passing input embeddings is currently not supported for {self.__class__.__name__}" + ) + + outputs: Seq2SeqModelOutput = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + hidden_states = outputs[0] # last hidden state + + eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device) + + torch_compilable_check( + torch.unique_consecutive(eos_mask.sum(1)).numel() == 1, + "All examples must have the same number of tokens.", + ) + sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[ + :, -1, : + ] + logits = self.classification_head(sentence_representation) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.config.num_labels == 1: + self.config.problem_type = "regression" + elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.config.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + return Seq2SeqSequenceClassifierOutput( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + +class PLBartDecoderWrapper(PLBartPreTrainedModel): + """ + This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is + used in combination with the [`EncoderDecoderModel`] framework. + """ + + def __init__(self, config): + super().__init__(config) + self.decoder = PLBartDecoder(config) + self.post_init() + + def forward(self, *args, **kwargs): + return self.decoder(*args, **kwargs) + + +@auto_docstring( + custom_intro=""" + PLBART decoder with a language modeling head on top (linear layer with weights tied to the input embeddings). + """ +) +class PLBartForCausalLM(PLBartPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "lm_head.weight": "model.decoder.embed_tokens.weight", + } + + def __init__(self, config): + config.is_decoder = True + config.is_encoder_decoder = False + super().__init__(config) + self.model = PLBartDecoderWrapper(config) + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | CausalLMOutputWithCrossAttentions: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, PLBartForCausalLM + + >>> tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-base") + >>> model = PLBartForCausalLM.from_pretrained("uclanlp/plbart-base") + >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size] + >>> list(logits.shape) == expected_shape + True + ```""" + + outputs: BaseModelOutputWithPastAndCrossAttentions = self.model.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + labels = labels.to(logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1)) + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +__all__ = [ + "PLBartForCausalLM", + "PLBartForConditionalGeneration", + "PLBartForSequenceClassification", + "PLBartModel", + "PLBartPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/modular_plbart.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/modular_plbart.py new file mode 100644 index 0000000000000000000000000000000000000000..97430e8e9841b9f56eefde052fcd6c810997c3b9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/modular_plbart.py @@ -0,0 +1,391 @@ +# Copyright 2022, UCLA NLP, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PLBART model.""" + +import math + +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + +from ... import initialization as init +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...modeling_outputs import ( + BaseModelOutput, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..bart.modeling_bart import ( + BartClassificationHead, + BartDecoder, + BartEncoder, + BartForCausalLM, + BartScaledWordEmbedding, +) +from ..bigbird_pegasus.modeling_bigbird_pegasus import BigBirdPegasusForSequenceClassification +from ..mbart.modeling_mbart import shift_tokens_right +from .configuration_plbart import PLBartConfig + + +class PLBartScaledWordEmbedding(BartScaledWordEmbedding): + pass + + +@auto_docstring +class PLBartPreTrainedModel(PreTrainedModel): + config: PLBartConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PLBartDecoderLayer", "PLBartEncoderLayer"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, PLBartForConditionalGeneration): + init.zeros_(module.final_logits_bias) + + +class PLBartEncoder(BartEncoder): + pass + + +class PLBartDecoder(BartDecoder): + pass + + +@auto_docstring +class PLBartModel(PLBartPreTrainedModel): + _tied_weights_keys = { + "encoder.embed_tokens.weight": "shared.weight", + "decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: PLBartConfig): + super().__init__(config) + + padding_idx, vocab_size = config.pad_token_id, config.vocab_size + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + self.shared = PLBartScaledWordEmbedding(vocab_size, config.d_model, padding_idx, embed_scale=embed_scale) + + self.encoder = PLBartEncoder(config) + self.decoder = PLBartDecoder(config) + + self.post_init() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, value): + self.shared = value + self.encoder.embed_tokens = self.shared + self.decoder.embed_tokens = self.shared + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.Tensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | Seq2SeqModelOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint. + See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that + varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (: + obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: + generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. + """ + # different to other models, PLBart automatically creates decoder_input_ids from + # input_ids if no decoder_input_ids are provided + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right(input_ids, self.config.pad_token_id) + + if encoder_outputs is None: + encoder_outputs: BaseModelOutput = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + **kwargs, + ) + elif not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The PLBART Model with a language modeling head. Can be used for code-to-text, text-to-code and code-to-code. + """ +) +class PLBartForConditionalGeneration(PLBartPreTrainedModel, GenerationMixin): + base_model_prefix = "model" + _keys_to_ignore_on_load_missing = ["final_logits_bias"] + _tied_weights_keys = { + "lm_head.weight": "model.shared.weight", + } + + def __init__(self, config: PLBartConfig): + super().__init__(config) + self.model = PLBartModel(config) + self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings))) + self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False) + + self.post_init() + + def resize_token_embeddings( + self, new_num_tokens: int, pad_to_multiple_of: int | None = None, mean_resizing: bool = True + ) -> nn.Embedding: + new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing) + self._resize_final_logits_bias(new_embeddings.weight.shape[0]) + return new_embeddings + + def _resize_final_logits_bias(self, new_num_tokens: int) -> None: + old_num_tokens = self.final_logits_bias.shape[-1] + if new_num_tokens <= old_num_tokens: + new_bias = self.final_logits_bias[:, :new_num_tokens] + else: + extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device) + new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1) + self.register_buffer("final_logits_bias", new_bias) + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.Tensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.Tensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | Seq2SeqLMOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint. + See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that + varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (: + obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: + generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example Mask-filling: + + ```python + >>> from transformers import AutoTokenizer, PLBartForConditionalGeneration + + >>> model = PLBartForConditionalGeneration.from_pretrained("uclanlp/plbart-base") + >>> tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-base") + + >>> # en_XX is the language symbol id for English + >>> TXT = " Is 0 the Fibonacci number ? en_XX" + >>> input_ids = tokenizer([TXT], add_special_tokens=False, return_tensors="pt").input_ids + + >>> logits = model(input_ids).logits + >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item() + >>> probs = logits[0, masked_index].softmax(dim=0) + >>> values, predictions = probs.topk(5) + + >>> tokenizer.decode(predictions).split() + ['first', 'same', 'highest', 'result', 'number'] + ``` + """ + if labels is not None: + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right(labels, self.config.pad_token_id) + + outputs: Seq2SeqModelOutput = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + lm_logits = self.lm_head(outputs.last_hidden_state) + lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.pad_token_id) + + +class PLBartClassificationHead(BartClassificationHead): + pass + + +class PLBartForSequenceClassification(BigBirdPegasusForSequenceClassification): + def forward(**super_kwargs): + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`] or [`PLBartMultiTokenizer`] depending on the checkpoint. + See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + PLBart uses a specific language id token as the starting token for `decoder_input_ids` generation that + varies according to source and target language, *e.g.* 50003 for *en_XX*, and 50001 for *java*. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (: + obj:*torch.LongTensor* of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: + generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + super().forward(**super_kwargs) + + +class PLBartForCausalLM(BartForCausalLM): + @can_return_tuple + @auto_docstring + def forward(**super_kwargs): + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, PLBartForCausalLM + + >>> tokenizer = AutoTokenizer.from_pretrained("uclanlp/plbart-base") + >>> model = PLBartForCausalLM.from_pretrained("uclanlp/plbart-base") + >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size] + >>> list(logits.shape) == expected_shape + True + ```""" + super().forward(**super_kwargs) + + +__all__ = [ + "PLBartForCausalLM", + "PLBartForConditionalGeneration", + "PLBartForSequenceClassification", + "PLBartModel", + "PLBartPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/tokenization_plbart.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/tokenization_plbart.py new file mode 100644 index 0000000000000000000000000000000000000000..f848e25c24c7fbfc7ffb8ec334aed8d9a3e1e820 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/plbart/tokenization_plbart.py @@ -0,0 +1,347 @@ +# Copyright 2022, UCLA NLP, The Facebook AI Research Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any + +from ...tokenization_python import BatchEncoding +from ...tokenization_utils_base import AddedToken +from ...tokenization_utils_sentencepiece import SentencePieceBackend +from ...utils import logging +from ...utils.import_utils import requires + + +logger = logging.get_logger(__name__) + +SPIECE_UNDERLINE = "▁" + +VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} + + +FAIRSEQ_LANGUAGE_CODES = { + "base": ["__java__", "__python__", "__en_XX__"], + "multi": ["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"], +} + +FAIRSEQ_LANGUAGE_CODES_MAP = { + "java": "__java__", + "python": "__python__", + "en_XX": "__en_XX__", + "javascript": "__javascript__", + "php": "__php__", + "ruby": "__ruby__", + "go": "__go__", +} + + +@requires(backends=("sentencepiece",)) +class PLBartTokenizer(SentencePieceBackend): + """ + Construct an PLBART tokenizer. + + Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on + [SentencePiece](https://github.com/google/sentencepiece). + + The tokenization method is ` ` for source language documents, and ` + ` for target language documents. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + src_lang (`str`, *optional*): + A string representing the source language. + tgt_lang (`str`, *optional*): + A string representing the target language. + bos_token (`str`, *optional*, defaults to `""`): + The start of sequence token. + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + sep_token (`str`, *optional*, defaults to `""`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + cls_token (`str`, *optional*, defaults to `""`): + The cls token, which is a special token used as the first token for all tasks. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + mask_token(`str`, *optional*, defaults to `""`): + The token used for masking values. This is the token used when training this model with masking tasks. This + is only used in the `"base"` tokenizer type. For `"multi"` tokenizer, masking is never done for the + downstream tasks. + language_codes (`str`, *optional*, defaults to `"base"`): + What language codes to use. Should be one of `"base"` or `"multi"`. + sp_model_kwargs (`dict`, *optional*): + Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for + SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, + to set: + - `enable_sampling`: Enable subword regularization. + - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. + - `nbest_size = {0,1}`: No sampling is performed. + - `nbest_size > 1`: samples from the nbest_size results. + - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) + using forward-filtering-and-backward-sampling algorithm. + - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for + BPE-dropout. + + Examples: + + ```python + >>> from transformers import PLBartTokenizer + + >>> tokenizer = PLBartTokenizer.from_pretrained("uclanlp/plbart-python-en_XX", src_lang="python", tgt_lang="en_XX") + >>> example_python_phrase = "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])" + >>> expected_translation_english = "Returns the maximum value of a b c." + >>> inputs = tokenizer(example_python_phrase, text_target=expected_translation_english, return_tensors="pt") + ```""" + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + + prefix_tokens: list[int] = [] + suffix_tokens: list[int] = [] + + def __init__( + self, + vocab_file, + bos_token="", + eos_token="", + sep_token="", + cls_token="", + unk_token="", + pad_token="", + mask_token="", + language_codes="base", + src_lang=None, + tgt_lang=None, + sp_model_kwargs: dict[str, Any] | None = None, + additional_special_tokens=None, + clean_up_tokenization_spaces=True, + **kwargs, + ): + # Mask token behave like a normal word, i.e. include the space before it + mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token + + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + src_lang = self._convert_lang_code_special_format(src_lang) + tgt_lang = self._convert_lang_code_special_format(tgt_lang) + self.language_codes = language_codes + fairseq_language_codes = FAIRSEQ_LANGUAGE_CODES[self.language_codes] + + # Original fairseq vocab and spm vocab must be "aligned": + # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 + # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- + # fairseq | '' | '' | '' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-' + # spm | '' | '' | '' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' + + # Mimic fairseq token-to-id alignment for the first 4 token + self.vocab_file = vocab_file + self.lang_code_to_id = {} + self.id_to_lang_code = {} + self.fairseq_tokens_to_ids = {"": 0, "": 1, "": 2, "": 3} + self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} + self.fairseq_offset = 1 + _additional_special_tokens = list(fairseq_language_codes) + + if additional_special_tokens is not None: + _additional_special_tokens.extend( + [t for t in additional_special_tokens if t not in _additional_special_tokens] + ) + + super().__init__( + vocab_file=vocab_file, + bos_token=bos_token, + eos_token=eos_token, + unk_token=unk_token, + sep_token=sep_token, + cls_token=cls_token, + pad_token=pad_token, + mask_token=mask_token, + src_lang=src_lang, + tgt_lang=tgt_lang, + additional_special_tokens=_additional_special_tokens, + sp_model_kwargs=self.sp_model_kwargs, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + language_codes=language_codes, + special_tokens_pattern="prefix_suffix", + token_type_ids_pattern="all_zeros", + **kwargs, + ) + + # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab + self.sp_model_size = len(self.sp_model) + self.lang_code_to_id = { + code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(fairseq_language_codes) + } + self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()} + self.fairseq_tokens_to_ids = {"": 0, "": 1, "": 2, "": 3} + + if self.language_codes == "base": + self.fairseq_tokens_to_ids[""] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + + self.fairseq_tokens_to_ids.update(self.lang_code_to_id) + self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} + reserved_tokens = {"", "", "", "", ""} + reserved_tokens.update(FAIRSEQ_LANGUAGE_CODES[self.language_codes]) + + removed = False + for token in reserved_tokens: + idx = self._added_tokens_encoder.pop(token, None) + if idx is not None: + self._added_tokens_decoder.pop(idx, None) + removed = True + if removed: + self._update_trie() + self._update_total_vocab_size() + + synced = False + for token, idx in self._added_tokens_encoder.items(): + if idx in self._added_tokens_decoder: + continue + self._added_tokens_decoder[idx] = AddedToken( + token, special=True, normalized=False, lstrip=False, rstrip=False + ) + synced = True + if synced: + self._update_trie() + self._update_total_vocab_size() + + if self.language_codes == "base": + self._src_lang = src_lang + self.cur_lang_code_id = ( + self.lang_code_to_id[self._src_lang] if self._src_lang is not None else self._src_lang + ) + else: + self._src_lang = src_lang if src_lang is not None else "__en_XX__" + self.cur_lang_code_id = self.lang_code_to_id[self._src_lang] + + self.tgt_lang = tgt_lang + self.set_src_lang_special_tokens(self._src_lang) + + @property + def vocab_size(self): + lang_code_count = len(getattr(self, "lang_code_to_id", {})) + fairseq_offset = getattr(self, "fairseq_offset", 1) + base_vocab = len(self.sp_model) if hasattr(self, "sp_model") else 0 + if getattr(self, "language_codes", "base") == "base": + return base_vocab + lang_code_count + fairseq_offset + 1 # +1 for mask token + return base_vocab + lang_code_count + fairseq_offset + + def get_vocab(self): + """Override to use fairseq vocabulary structure""" + vocab = self.fairseq_tokens_to_ids.copy() + for i in range(self.sp_model.get_piece_size()): + sp_token = self.sp_model.IdToPiece(i) + # Map SP token to fairseq ID: SP ID 0 maps to unk_token_id, others map to SP_ID + fairseq_offset + vocab_id = self.unk_token_id if i == 0 else (i + self.fairseq_offset) + if sp_token not in vocab: + vocab[sp_token] = vocab_id + # Add any additional tokens + vocab.update({token: idx for token, idx in self._added_tokens_encoder.items() if token not in vocab}) + return vocab + + @property + def src_lang(self) -> str: + return self._src_lang + + @src_lang.setter + def src_lang(self, new_src_lang: str) -> None: + new_src_lang = self._convert_lang_code_special_format(new_src_lang) + self._src_lang = new_src_lang + self.set_src_lang_special_tokens(self._src_lang) + + def _build_translation_inputs( + self, raw_inputs, return_tensors: str, src_lang: str | None, tgt_lang: str | None, **extra_kwargs + ): + """Used by translation pipeline, to prepare inputs for the generate function""" + if src_lang is None or tgt_lang is None: + raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") + self.src_lang = self._convert_lang_code_special_format(src_lang) + self.tgt_lang = self._convert_lang_code_special_format(tgt_lang) + inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs) + tgt_lang_id = self.convert_tokens_to_ids(self.tgt_lang) + inputs["forced_bos_token_id"] = tgt_lang_id + return inputs + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + if token in self.fairseq_tokens_to_ids: + return self.fairseq_tokens_to_ids[token] + spm_id = self.sp_model.PieceToId(token) + + # Need to return unknown token if the SP model returned 0 + return spm_id + self.fairseq_offset if spm_id else self.unk_token_id + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + if index in self.fairseq_ids_to_tokens: + return self.fairseq_ids_to_tokens[index] + return self.sp_model.IdToPiece(index - self.fairseq_offset) + + def prepare_seq2seq_batch( + self, + src_texts: list[str], + src_lang: str = "en_XX", + tgt_texts: list[str] | None = None, + tgt_lang: str = "python", + **kwargs, + ) -> BatchEncoding: + self.src_lang = self._convert_lang_code_special_format(src_lang) + self.tgt_lang = self._convert_lang_code_special_format(tgt_lang) + return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs) + + def _switch_to_input_mode(self): + return self.set_src_lang_special_tokens(self.src_lang) + + def _switch_to_target_mode(self): + return self.set_tgt_lang_special_tokens(self.tgt_lang) + + def set_src_lang_special_tokens(self, src_lang) -> None: + """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].""" + src_lang = self._convert_lang_code_special_format(src_lang) + self.cur_lang_code = self.lang_code_to_id[src_lang] if src_lang is not None else None + self.prefix_tokens = [] + if self.cur_lang_code is not None: + self.suffix_tokens = [self.eos_token_id, self.cur_lang_code] + else: + self.suffix_tokens = [self.eos_token_id] + + def set_tgt_lang_special_tokens(self, lang: str) -> None: + """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code].""" + lang = self._convert_lang_code_special_format(lang) + + self.cur_lang_code = self.lang_code_to_id[lang] if lang is not None else None + self.prefix_tokens = [] + if self.cur_lang_code is not None: + self.suffix_tokens = [self.eos_token_id, self.cur_lang_code] + else: + self.suffix_tokens = [self.eos_token_id] + + def _convert_lang_code_special_format(self, lang: str) -> str: + """Convert Language Codes to format tokenizer uses if required""" + lang = FAIRSEQ_LANGUAGE_CODES_MAP.get(lang, lang) + return lang + + def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=None, **kwargs): + """Override to use self.clean_up_tokenization_spaces as default for batched input.""" + return super().decode( + token_ids=token_ids, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=self.clean_up_tokenization_spaces, + **kwargs, + ) + + +__all__ = ["PLBartTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7cf9a0042e6f7213eed4b09ba83963c6fb116504 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_poolformer import * + from .feature_extraction_poolformer import * + from .image_processing_pil_poolformer import * + from .image_processing_poolformer import * + from .modeling_poolformer import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/configuration_poolformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/configuration_poolformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ad75633644626c07717fd34df81fc4a35840fe6c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/configuration_poolformer.py @@ -0,0 +1,77 @@ +# Copyright 2022 Sea AI Labs and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PoolFormer model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="sail/poolformer_s12") +@strict +class PoolFormerConfig(PreTrainedConfig): + r""" + stride (`int`, *optional*, defaults to 16): + The stride of the input patch. + pool_size (`int`, *optional*, defaults to 3): + The size of the pooling window. + patch_sizes (`list`, *optional*, defaults to `[7, 3, 3, 3]`): + The size of the input patch for each encoder block. + strides (`list`, *optional*, defaults to `[4, 2, 2, 2]`): + The stride of the input patch for each encoder block. + padding (`list`, *optional*, defaults to `[2, 1, 1, 1]`): + The padding of the input patch for each encoder block. + num_encoder_blocks (`int`, *optional*, defaults to 4): + The number of encoder blocks. + use_layer_scale (`bool`, *optional*, defaults to `True`): + Whether to use layer scale. + + Example: + + ```python + >>> from transformers import PoolFormerConfig, PoolFormerModel + + >>> # Initializing a PoolFormer sail/poolformer_s12 style configuration + >>> configuration = PoolFormerConfig() + + >>> # Initializing a model (with random weights) from the sail/poolformer_s12 style configuration + >>> model = PoolFormerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "poolformer" + + num_channels: int = 3 + patch_size: int | list[int] | tuple[int, int] = 16 + stride: int = 16 + pool_size: int = 3 + mlp_ratio: float = 4.0 + depths: list[int] | tuple[int, ...] = (2, 2, 6, 2) + hidden_sizes: list[int] | tuple[int, ...] = (64, 128, 320, 512) + patch_sizes: list[int] | tuple[int, ...] = (7, 3, 3, 3) + strides: list[int] | tuple[int, ...] = (4, 2, 2, 2) + padding: list[int] | tuple[int, ...] = (2, 1, 1, 1) + num_encoder_blocks: int = 4 + drop_path_rate: float | int = 0.0 + hidden_act: str = "gelu" + use_layer_scale: bool = True + layer_scale_init_value: float = 1e-5 + initializer_range: float = 0.02 + + +__all__ = ["PoolFormerConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/image_processing_pil_poolformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/image_processing_pil_poolformer.py new file mode 100644 index 0000000000000000000000000000000000000000..b2e5fef4e4647223bd013cfb92ee39c1ba00227f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/image_processing_pil_poolformer.py @@ -0,0 +1,133 @@ +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for PoolFormer.""" + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import get_resize_output_image_size +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +# Adapted from transformers.models.poolformer.image_processing_poolformer.PoolFormerImageProcessorKwargs +class PoolFormerImageProcessorKwargs(ImagesKwargs, total=False): + r""" + crop_pct (`float`, *optional*, defaults to `self.crop_pct`): + Percentage of the image to crop. Only has an effect if `do_resize` is set to `True`. + """ + + crop_pct: float + + +@auto_docstring +class PoolFormerImageProcessorPil(PilBackend): + """PIL backend for PoolFormer with custom resize (crop_pct).""" + + valid_kwargs = PoolFormerImageProcessorKwargs + + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"shortest_edge": 224} + default_to_square = False + crop_size = {"height": 224, "width": 224} + crop_pct = 0.9 + do_resize = True + do_center_crop = True + do_rescale = True + do_normalize = True + do_convert_rgb = None + + def __init__(self, **kwargs: Unpack[PoolFormerImageProcessorKwargs]): + super().__init__(**kwargs) + + def resize( + self, + image: np.ndarray, + size: SizeDict, + resample: "PILImageResampling | None" = None, + crop_pct: float | None = None, + **kwargs, + ) -> np.ndarray: + """Resize with crop_pct: scale size by 1/crop_pct when crop_pct is set.""" + + if crop_pct is not None: + if size.shortest_edge: + scale_size = int(size.shortest_edge / crop_pct) + elif size.height and size.width: + if size.height == size.width: + scale_size = int(size.height / crop_pct) + else: + scale_size = (int(size.height / crop_pct), int(size.width / crop_pct)) + else: + raise ValueError(f"Invalid size for resize: {size}") + output_size = get_resize_output_image_size( + image, size=scale_size, default_to_square=False, input_data_format=ChannelDimension.FIRST + ) + size = SizeDict(height=output_size[0], width=output_size[1]) + return super().resize( + image, + size=size, + resample=resample, + **kwargs, + ) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + return_tensors: str | TensorType | None, + crop_pct: float = 0.9, + **kwargs, + ) -> BatchFeature: + """Custom preprocessing for PoolFormer (pass crop_pct to resize).""" + processed_images = [] + for image in images: + if do_resize: + image = self.resize(image, size, resample, crop_pct=crop_pct) + if do_center_crop: + image = self.center_crop(image, crop_size) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["PoolFormerImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/image_processing_poolformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/image_processing_poolformer.py new file mode 100644 index 0000000000000000000000000000000000000000..a316f2efb0594a62fbaa0cef365d53026c096038 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/image_processing_poolformer.py @@ -0,0 +1,135 @@ +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for PoolFormer.""" + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import get_resize_output_image_size, group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +class PoolFormerImageProcessorKwargs(ImagesKwargs, total=False): + r""" + crop_pct (`float`, *optional*, defaults to `self.crop_pct`): + Percentage of the image to crop. Only has an effect if `do_resize` is set to `True`. + """ + + crop_pct: float + + +@auto_docstring +class PoolFormerImageProcessor(TorchvisionBackend): + """Torchvision backend for PoolFormer with custom resize (crop_pct).""" + + valid_kwargs = PoolFormerImageProcessorKwargs + + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"shortest_edge": 224} + default_to_square = False + crop_size = {"height": 224, "width": 224} + crop_pct = 0.9 + do_resize = True + do_center_crop = True + do_rescale = True + do_normalize = True + do_convert_rgb = None + + def __init__(self, **kwargs: Unpack[PoolFormerImageProcessorKwargs]): + super().__init__(**kwargs) + + def resize( + self, + image: "torch.Tensor", + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + crop_pct: float | None = None, + **kwargs, + ) -> "torch.Tensor": + """Resize with crop_pct: scale size by 1/crop_pct when crop_pct is set.""" + if crop_pct is not None: + if size.shortest_edge: + scale_size = int(size.shortest_edge / crop_pct) + elif size.height and size.width: + if size.height == size.width: + scale_size = int(size.height / crop_pct) + else: + scale_size = (int(size.height / crop_pct), int(size.width / crop_pct)) + else: + raise ValueError(f"Invalid size for resize: {size}") + new_size = get_resize_output_image_size( + image, size=scale_size, default_to_square=False, input_data_format=ChannelDimension.FIRST + ) + size = SizeDict(height=new_size[0], width=new_size[1]) + return super().resize(image, size=size, resample=resample, **kwargs) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + crop_pct: float = 0.9, + **kwargs, + ) -> BatchFeature: + """Custom preprocessing for PoolFormer (pass crop_pct to resize).""" + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(stacked_images, size, resample, crop_pct=crop_pct) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["PoolFormerImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/modeling_poolformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/modeling_poolformer.py new file mode 100644 index 0000000000000000000000000000000000000000..4dd2b52fffa2c47271941581d1cbdc9a0bd4643d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/poolformer/modeling_poolformer.py @@ -0,0 +1,382 @@ +# Copyright 2022 Sea AI Lab and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PoolFormer model.""" + +import collections.abc + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, logging +from .configuration_poolformer import PoolFormerConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->PoolFormer +class PoolFormerDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float | None = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return f"p={self.drop_prob}" + + +class PoolFormerEmbeddings(nn.Module): + """ + Construct Patch Embeddings. + """ + + def __init__(self, hidden_size, num_channels, patch_size, stride, padding, norm_layer=None): + super().__init__() + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + stride = stride if isinstance(stride, collections.abc.Iterable) else (stride, stride) + padding = padding if isinstance(padding, collections.abc.Iterable) else (padding, padding) + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=stride, padding=padding) + self.norm = norm_layer(hidden_size) if norm_layer else nn.Identity() + + def forward(self, pixel_values): + embeddings = self.projection(pixel_values) + embeddings = self.norm(embeddings) + return embeddings + + +class PoolFormerGroupNorm(nn.GroupNorm): + """ + Group Normalization with 1 group. Input: tensor in shape [B, C, H, W] + """ + + def __init__(self, num_channels, **kwargs): + super().__init__(1, num_channels, **kwargs) + + +class PoolFormerPooling(nn.Module): + def __init__(self, pool_size): + super().__init__() + self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False) + + def forward(self, hidden_states): + return self.pool(hidden_states) - hidden_states + + +class PoolFormerOutput(nn.Module): + def __init__(self, config, dropout_prob, hidden_size, intermediate_size): + super().__init__() + self.conv1 = nn.Conv2d(hidden_size, intermediate_size, 1) + self.conv2 = nn.Conv2d(intermediate_size, hidden_size, 1) + self.drop = PoolFormerDropPath(dropout_prob) + if isinstance(config.hidden_act, str): + self.act_fn = ACT2FN[config.hidden_act] + else: + self.act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.conv1(hidden_states) + hidden_states = self.act_fn(hidden_states) + hidden_states = self.drop(hidden_states) + hidden_states = self.conv2(hidden_states) + hidden_states = self.drop(hidden_states) + + return hidden_states + + +class PoolFormerLayer(nn.Module): + """This corresponds to the 'PoolFormerBlock' class in the original implementation.""" + + def __init__(self, config, num_channels, pool_size, hidden_size, intermediate_size, drop_path): + super().__init__() + self.pooling = PoolFormerPooling(pool_size) + self.output = PoolFormerOutput(config, drop_path, hidden_size, intermediate_size) + self.before_norm = PoolFormerGroupNorm(num_channels) + self.after_norm = PoolFormerGroupNorm(num_channels) + + # Useful for training neural nets + self.drop_path = PoolFormerDropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.use_layer_scale = config.use_layer_scale + if config.use_layer_scale: + self.layer_scale_1 = nn.Parameter( + config.layer_scale_init_value * torch.ones(num_channels), requires_grad=True + ) + self.layer_scale_2 = nn.Parameter( + config.layer_scale_init_value * torch.ones(num_channels), requires_grad=True + ) + + def forward(self, hidden_states): + if self.use_layer_scale: + pooling_output = self.pooling(self.before_norm(hidden_states)) + scaled_op = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * pooling_output + # First residual connection + hidden_states = hidden_states + self.drop_path(scaled_op) + outputs = () + + layer_output = self.output(self.after_norm(hidden_states)) + scaled_op = self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * layer_output + # Second residual connection + output = hidden_states + self.drop_path(scaled_op) + + outputs = (output,) + outputs + return outputs + + else: + pooling_output = self.drop_path(self.pooling(self.before_norm(hidden_states))) + # First residual connection + hidden_states = pooling_output + hidden_states + outputs = () + + # Second residual connection inside the PoolFormerOutput block + layer_output = self.drop_path(self.output(self.after_norm(hidden_states))) + output = hidden_states + layer_output + + outputs = (output,) + outputs + return outputs + + +class PoolFormerEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + # stochastic depth decay rule + dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths), device="cpu")] + + # patch embeddings + embeddings = [] + for i in range(config.num_encoder_blocks): + embeddings.append( + PoolFormerEmbeddings( + patch_size=config.patch_sizes[i], + stride=config.strides[i], + padding=config.padding[i], + num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1], + hidden_size=config.hidden_sizes[i], + ) + ) + self.patch_embeddings = nn.ModuleList(embeddings) + + # Transformer blocks + blocks = [] + cur = 0 + for i in range(config.num_encoder_blocks): + # each block consists of layers + layers = [] + if i != 0: + cur += config.depths[i - 1] + for j in range(config.depths[i]): + layers.append( + PoolFormerLayer( + config, + num_channels=config.hidden_sizes[i], + pool_size=config.pool_size, + hidden_size=config.hidden_sizes[i], + intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio), + drop_path=dpr[cur + j], + ) + ) + blocks.append(nn.ModuleList(layers)) + + self.block = nn.ModuleList(blocks) + + def forward(self, pixel_values, output_hidden_states=False, return_dict=True): + all_hidden_states = () if output_hidden_states else None + + hidden_states = pixel_values + for idx, layers in enumerate(zip(self.patch_embeddings, self.block)): + embedding_layer, block_layer = layers + # Get patch embeddings from hidden_states + hidden_states = embedding_layer(hidden_states) + # Send the embeddings through the blocks + for _, blk in enumerate(block_layer): + layer_outputs = blk(hidden_states) + hidden_states = layer_outputs[0] + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states] if v is not None) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=all_hidden_states) + + +@auto_docstring +class PoolFormerPreTrainedModel(PreTrainedModel): + config: PoolFormerConfig + base_model_prefix = "poolformer" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = ["PoolFormerLayer"] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, PoolFormerLayer): + if hasattr(module, "layer_scale_1"): + init.constant_(module.layer_scale_1, self.config.layer_scale_init_value) + init.constant_(module.layer_scale_2, self.config.layer_scale_init_value) + + +@auto_docstring +class PoolFormerModel(PoolFormerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + self.encoder = PoolFormerEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + # Input embeddings correspond to the very first patch-embedding stage. + return self.encoder.patch_embeddings[0] + + def set_input_embeddings(self, value): + self.encoder.patch_embeddings[0] = value + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithNoAttention: + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + encoder_outputs = self.encoder( + pixel_values, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + if not return_dict: + return (sequence_output, None) + encoder_outputs[1:] + + return BaseModelOutputWithNoAttention( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + ) + + +class PoolFormerFinalPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + + def forward(self, hidden_states): + output = self.dense(hidden_states) + return output + + +@auto_docstring( + custom_intro=""" + PoolFormer Model transformer with an image classification head on top + """ +) +class PoolFormerForImageClassification(PoolFormerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.poolformer = PoolFormerModel(config) + + # Final norm + self.norm = PoolFormerGroupNorm(config.hidden_sizes[-1]) + # Classifier head + self.classifier = ( + nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity() + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.poolformer.get_input_embeddings() + + def set_input_embeddings(self, value): + self.poolformer.set_input_embeddings(value) + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | ImageClassifierOutputWithNoAttention: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.poolformer( + pixel_values, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.classifier(self.norm(sequence_output).mean([-2, -1])) + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states) + + +__all__ = ["PoolFormerForImageClassification", "PoolFormerModel", "PoolFormerPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cbbfcbf157b58aa582e2a8b2a99c7d561372904b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pop2piano import * + from .modeling_pop2piano import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/configuration_pop2piano.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/configuration_pop2piano.py new file mode 100644 index 0000000000000000000000000000000000000000..2086119b9193d98b48d91b125bc0b35983432cd9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/configuration_pop2piano.py @@ -0,0 +1,70 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Pop2Piano model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="sweetcocoa/pop2piano") +@strict +class Pop2PianoConfig(PreTrainedConfig): + r""" + composer_vocab_size (`int`, *optional*, defaults to 21): + Denotes the number of composers. + relative_attention_num_buckets (`int`, *optional*, defaults to 32): + The number of buckets to use for each attention layer. + relative_attention_max_distance (`int`, *optional*, defaults to 128): + The maximum distance of the longer sequences for the bucket separation. + feed_forward_proj (`string`, *optional*, defaults to `"gated-gelu"`): + Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. + dense_act_fn (`string`, *optional*, defaults to `"relu"`): + Type of Activation Function to be used in `Pop2PianoDenseActDense` and in `Pop2PianoDenseGatedActDense`. + """ + + model_type = "pop2piano" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = {"num_hidden_layers": "num_layers", "hidden_size": "d_model", "num_attention_heads": "num_heads"} + + vocab_size: int = 2400 + composer_vocab_size: int = 21 + d_model: int = 512 + d_kv: int = 64 + d_ff: int = 2048 + num_layers: int = 6 + num_decoder_layers: int | None = None + num_heads: int = 8 + relative_attention_num_buckets: int = 32 + relative_attention_max_distance: int = 128 + dropout_rate: float | int = 0.1 + layer_norm_epsilon: float = 1e-6 + initializer_factor: float = 1.0 + feed_forward_proj: str = "gated-gelu" + is_encoder_decoder: bool = True + use_cache: bool = True + pad_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 1 + dense_act_fn: str = "relu" + is_decoder: bool = False + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + self.num_decoder_layers = self.num_decoder_layers if self.num_decoder_layers is not None else self.num_layers + self.is_gated_act = self.feed_forward_proj.split("-")[0] == "gated" + super().__post_init__(**kwargs) + + +__all__ = ["Pop2PianoConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/feature_extraction_pop2piano.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/feature_extraction_pop2piano.py new file mode 100644 index 0000000000000000000000000000000000000000..4e770fcb1b71f6471620cf6a4ae77861c6f51ff9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/feature_extraction_pop2piano.py @@ -0,0 +1,452 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Feature extractor class for Pop2Piano""" + +import warnings + +import numpy +import numpy as np + +from ...audio_utils import mel_filter_bank, spectrogram +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...feature_extraction_utils import BatchFeature +from ...utils import ( + TensorType, + is_essentia_available, + is_librosa_available, + is_scipy_available, + logging, + requires_backends, +) +from ...utils.import_utils import requires + + +if is_essentia_available(): + import essentia.standard + +if is_librosa_available(): + import librosa + +if is_scipy_available(): + import scipy + + +logger = logging.get_logger(__name__) + + +@requires(backends=("essentia", "librosa", "scipy", "torch")) +class Pop2PianoFeatureExtractor(SequenceFeatureExtractor): + r""" + Constructs a Pop2Piano feature extractor. + + This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains + most of the main methods. Users should refer to this superclass for more information regarding those methods. + + This class extracts rhythm and preprocesses the audio before it is passed to the model. First the audio is passed + to `RhythmExtractor2013` algorithm which extracts the beat_times, beat positions and estimates their confidence as + well as tempo in bpm, then beat_times is interpolated and to get beatsteps. Later we calculate + extrapolated_beatsteps from it to be used in tokenizer. On the other hand audio is resampled to self.sampling_rate + and preprocessed and then log mel spectogram is computed from that to be used in our transformer model. + + Args: + sampling_rate (`int`, *optional*, defaults to 22050): + Target Sampling rate of audio signal. It's the sampling rate that we forward to the model. + padding_value (`int`, *optional*, defaults to 0): + Padding value used to pad the audio. Should correspond to silences. + window_size (`int`, *optional*, defaults to 4096): + Length of the window in samples to which the Fourier transform is applied. + hop_length (`int`, *optional*, defaults to 1024): + Step size between each window of the waveform, in samples. + min_frequency (`float`, *optional*, defaults to 10.0): + Lowest frequency that will be used in the log-mel spectrogram. + feature_size (`int`, *optional*, defaults to 512): + The feature dimension of the extracted features. + num_bars (`int`, *optional*, defaults to 2): + Determines interval between each sequence. + """ + + model_input_names = ["input_features", "beatsteps", "extrapolated_beatstep"] + + def __init__( + self, + sampling_rate: int = 22050, + padding_value: int = 0, + window_size: int = 4096, + hop_length: int = 1024, + min_frequency: float = 10.0, + feature_size: int = 512, + num_bars: int = 2, + **kwargs, + ): + super().__init__( + feature_size=feature_size, + sampling_rate=sampling_rate, + padding_value=padding_value, + **kwargs, + ) + self.sampling_rate = sampling_rate + self.padding_value = padding_value + self.window_size = window_size + self.hop_length = hop_length + self.min_frequency = min_frequency + self.feature_size = feature_size + self.num_bars = num_bars + self.mel_filters = mel_filter_bank( + num_frequency_bins=(self.window_size // 2) + 1, + num_mel_filters=self.feature_size, + min_frequency=self.min_frequency, + max_frequency=float(self.sampling_rate // 2), + sampling_rate=self.sampling_rate, + norm=None, + mel_scale="htk", + ) + + def mel_spectrogram(self, sequence: np.ndarray): + """ + Generates MelSpectrogram. + + Args: + sequence (`numpy.ndarray`): + The sequence of which the mel-spectrogram will be computed. + """ + mel_specs = [] + for seq in sequence: + window = np.hanning(self.window_size + 1)[:-1] + mel_specs.append( + spectrogram( + waveform=seq, + window=window, + frame_length=self.window_size, + hop_length=self.hop_length, + power=2.0, + mel_filters=self.mel_filters, + ) + ) + mel_specs = np.array(mel_specs) + + return mel_specs + + def extract_rhythm(self, audio: np.ndarray): + """ + This algorithm(`RhythmExtractor2013`) extracts the beat positions and estimates their confidence as well as + tempo in bpm for an audio signal. For more information please visit + https://essentia.upf.edu/reference/std_RhythmExtractor2013.html . + + Args: + audio(`numpy.ndarray`): + raw audio waveform which is passed to the Rhythm Extractor. + """ + requires_backends(self, ["essentia"]) + essentia_tracker = essentia.standard.RhythmExtractor2013(method="multifeature") + bpm, beat_times, confidence, estimates, essentia_beat_intervals = essentia_tracker(audio) + + return bpm, beat_times, confidence, estimates, essentia_beat_intervals + + def interpolate_beat_times( + self, beat_times: numpy.ndarray, steps_per_beat: numpy.ndarray, n_extend: numpy.ndarray + ): + """ + This method takes beat_times and then interpolates that using `scipy.interpolate.interp1d` and the output is + then used to convert raw audio to log-mel-spectrogram. + + Args: + beat_times (`numpy.ndarray`): + beat_times is passed into `scipy.interpolate.interp1d` for processing. + steps_per_beat (`int`): + used as an parameter to control the interpolation. + n_extend (`int`): + used as an parameter to control the interpolation. + """ + + requires_backends(self, ["scipy"]) + beat_times_function = scipy.interpolate.interp1d( + np.arange(beat_times.size), + beat_times, + bounds_error=False, + fill_value="extrapolate", + ) + + ext_beats = beat_times_function( + np.linspace(0, beat_times.size + n_extend - 1, beat_times.size * steps_per_beat + n_extend) + ) + + return ext_beats + + def preprocess_mel(self, audio: np.ndarray, beatstep: np.ndarray): + """ + Preprocessing for log-mel-spectrogram + + Args: + audio (`numpy.ndarray` of shape `(audio_length, )` ): + Raw audio waveform to be processed. + beatstep (`numpy.ndarray`): + Interpolated values of the raw audio. If beatstep[0] is greater than 0.0, then it will be shifted by + the value at beatstep[0]. + """ + + if audio is not None and len(audio.shape) != 1: + raise ValueError( + f"Expected `audio` to be a single channel audio input of shape `(n, )` but found shape {audio.shape}." + ) + if beatstep[0] > 0.0: + beatstep = beatstep - beatstep[0] + + num_steps = self.num_bars * 4 + num_target_steps = len(beatstep) + extrapolated_beatstep = self.interpolate_beat_times( + beat_times=beatstep, steps_per_beat=1, n_extend=(self.num_bars + 1) * 4 + 1 + ) + + sample_indices = [] + max_feature_length = 0 + for i in range(0, num_target_steps, num_steps): + start_idx = i + end_idx = min(i + num_steps, num_target_steps) + start_sample = int(extrapolated_beatstep[start_idx] * self.sampling_rate) + end_sample = int(extrapolated_beatstep[end_idx] * self.sampling_rate) + sample_indices.append((start_sample, end_sample)) + max_feature_length = max(max_feature_length, end_sample - start_sample) + padded_batch = [] + for start_sample, end_sample in sample_indices: + feature = audio[start_sample:end_sample] + padded_feature = np.pad( + feature, + ((0, max_feature_length - feature.shape[0]),), + "constant", + constant_values=0, + ) + padded_batch.append(padded_feature) + + padded_batch = np.asarray(padded_batch) + return padded_batch, extrapolated_beatstep + + def _pad(self, features: np.ndarray, add_zero_line=True): + features_shapes = [each_feature.shape for each_feature in features] + attention_masks, padded_features = [], [] + for i, each_feature in enumerate(features): + # To pad "input_features". + if len(each_feature.shape) == 3: + features_pad_value = max([*zip(*features_shapes)][1]) - features_shapes[i][1] + attention_mask = np.ones(features_shapes[i][:2], dtype=np.int64) + feature_padding = ((0, 0), (0, features_pad_value), (0, 0)) + attention_mask_padding = (feature_padding[0], feature_padding[1]) + + # To pad "beatsteps" and "extrapolated_beatstep". + else: + each_feature = each_feature.reshape(1, -1) + features_pad_value = max([*zip(*features_shapes)][0]) - features_shapes[i][0] + attention_mask = np.ones(features_shapes[i], dtype=np.int64).reshape(1, -1) + feature_padding = attention_mask_padding = ((0, 0), (0, features_pad_value)) + + each_padded_feature = np.pad(each_feature, feature_padding, "constant", constant_values=self.padding_value) + attention_mask = np.pad( + attention_mask, attention_mask_padding, "constant", constant_values=self.padding_value + ) + + if add_zero_line: + # if it is batched then we separate each examples using zero array + zero_array_len = max([*zip(*features_shapes)][1]) + + # we concatenate the zero array line here + each_padded_feature = np.concatenate( + [each_padded_feature, np.zeros([1, zero_array_len, self.feature_size])], axis=0 + ) + attention_mask = np.concatenate( + [attention_mask, np.zeros([1, zero_array_len], dtype=attention_mask.dtype)], axis=0 + ) + + padded_features.append(each_padded_feature) + attention_masks.append(attention_mask) + + padded_features = np.concatenate(padded_features, axis=0).astype(np.float32) + attention_masks = np.concatenate(attention_masks, axis=0).astype(np.int64) + + return padded_features, attention_masks + + def pad( + self, + inputs: BatchFeature, + is_batched: bool, + return_attention_mask: bool, + return_tensors: str | TensorType | None = None, + ): + """ + Pads the inputs to same length and returns attention_mask. + + Args: + inputs (`BatchFeature`): + Processed audio features. + is_batched (`bool`): + Whether inputs are batched or not. + return_attention_mask (`bool`): + Whether to return attention mask or not. + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + If nothing is specified, it will return list of `np.ndarray` arrays. + Return: + `BatchFeature` with attention_mask, attention_mask_beatsteps and attention_mask_extrapolated_beatstep added + to it: + - **attention_mask** numpy.ndarray of shape `(batch_size, max_input_features_seq_length)` -- + Example : + 1, 1, 1, 0, 0 (audio 1, also here it is padded to max length of 5 that's why there are 2 zeros at + the end indicating they are padded) + + 0, 0, 0, 0, 0 (zero pad to separate audio 1 and 2) + + 1, 1, 1, 1, 1 (audio 2) + + 0, 0, 0, 0, 0 (zero pad to separate audio 2 and 3) + + 1, 1, 1, 1, 1 (audio 3) + - **attention_mask_beatsteps** numpy.ndarray of shape `(batch_size, max_beatsteps_seq_length)` + - **attention_mask_extrapolated_beatstep** numpy.ndarray of shape `(batch_size, + max_extrapolated_beatstep_seq_length)` + """ + + processed_features_dict = {} + for feature_name, feature_value in inputs.items(): + if feature_name == "input_features": + padded_feature_values, attention_mask = self._pad(feature_value, add_zero_line=True) + processed_features_dict[feature_name] = padded_feature_values + if return_attention_mask: + processed_features_dict["attention_mask"] = attention_mask + else: + padded_feature_values, attention_mask = self._pad(feature_value, add_zero_line=False) + processed_features_dict[feature_name] = padded_feature_values + if return_attention_mask: + processed_features_dict[f"attention_mask_{feature_name}"] = attention_mask + + # If we are processing only one example, we should remove the zero array line since we don't need it to + # separate examples from each other. + if not is_batched and not return_attention_mask: + processed_features_dict["input_features"] = processed_features_dict["input_features"][:-1, ...] + + outputs = BatchFeature(processed_features_dict, tensor_type=return_tensors) + + return outputs + + def __call__( + self, + audio: np.ndarray | list[float] | list[np.ndarray] | list[list[float]], + sampling_rate: int | list[int], + steps_per_beat: int = 2, + resample: bool | None = True, + return_attention_mask: bool | None = False, + return_tensors: str | TensorType | None = None, + **kwargs, + ) -> BatchFeature: + """ + Main method to featurize and prepare for the model. + + Args: + audio (`np.ndarray`, `List`): + The audio or batch of audio to be processed. Each audio can be a numpy array, a list of float values, a + list of numpy arrays or a list of list of float values. + sampling_rate (`int`): + The sampling rate at which the `audio` input was sampled. It is strongly recommended to pass + `sampling_rate` at the forward call to prevent silent errors. + steps_per_beat (`int`, *optional*, defaults to 2): + This is used in interpolating `beat_times`. + resample (`bool`, *optional*, defaults to `True`): + Determines whether to resample the audio to `sampling_rate` or not before processing. Must be True + during inference. + return_attention_mask (`bool` *optional*, defaults to `False`): + Denotes if attention_mask for input_features, beatsteps and extrapolated_beatstep will be given as + output or not. Automatically set to True for batched inputs. + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + If nothing is specified, it will return list of `np.ndarray` arrays. + """ + + requires_backends(self, ["librosa"]) + is_batched = isinstance(audio, (list, tuple)) and isinstance(audio[0], (np.ndarray, tuple, list)) + if is_batched: + # This enables the user to process files of different sampling_rate at same time + if not isinstance(sampling_rate, list): + raise ValueError( + "Please give sampling_rate of each audio separately when you are passing multiple raw_audios at the same time. " + f"Received {sampling_rate}, expected [audio_1_sr, ..., audio_n_sr]." + ) + return_attention_mask = True if return_attention_mask is None else return_attention_mask + else: + audio = [audio] + sampling_rate = [sampling_rate] + return_attention_mask = False if return_attention_mask is None else return_attention_mask + + batch_input_features, batch_beatsteps, batch_ext_beatstep = [], [], [] + for single_raw_audio, single_sampling_rate in zip(audio, sampling_rate): + bpm, beat_times, confidence, estimates, essentia_beat_intervals = self.extract_rhythm( + audio=single_raw_audio + ) + beatsteps = self.interpolate_beat_times(beat_times=beat_times, steps_per_beat=steps_per_beat, n_extend=1) + + if self.sampling_rate != single_sampling_rate and self.sampling_rate is not None: + if resample: + # Change sampling_rate to self.sampling_rate + single_raw_audio = librosa.core.resample( + single_raw_audio, + orig_sr=single_sampling_rate, + target_sr=self.sampling_rate, + res_type="kaiser_best", + ) + else: + warnings.warn( + f"The sampling_rate of the provided audio is different from the target sampling_rate " + f"of the Feature Extractor, {self.sampling_rate} vs {single_sampling_rate}. " + f"In these cases it is recommended to use `resample=True` in the `__call__` method to " + f"get the optimal behaviour." + ) + + single_sampling_rate = self.sampling_rate + start_sample = int(beatsteps[0] * single_sampling_rate) + end_sample = int(beatsteps[-1] * single_sampling_rate) + + input_features, extrapolated_beatstep = self.preprocess_mel( + single_raw_audio[start_sample:end_sample], beatsteps - beatsteps[0] + ) + + mel_specs = self.mel_spectrogram(input_features.astype(np.float32)) + + # apply np.log to get log mel-spectrograms + log_mel_specs = np.log(np.clip(mel_specs, a_min=1e-6, a_max=None)) + + input_features = np.transpose(log_mel_specs, (0, -1, -2)) + + batch_input_features.append(input_features) + batch_beatsteps.append(beatsteps) + batch_ext_beatstep.append(extrapolated_beatstep) + + output = BatchFeature( + { + "input_features": batch_input_features, + "beatsteps": batch_beatsteps, + "extrapolated_beatstep": batch_ext_beatstep, + } + ) + + output = self.pad( + output, + is_batched=is_batched, + return_attention_mask=return_attention_mask, + return_tensors=return_tensors, + ) + + return output + + +__all__ = ["Pop2PianoFeatureExtractor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/modeling_pop2piano.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/modeling_pop2piano.py new file mode 100644 index 0000000000000000000000000000000000000000..8b5a49a365e093433e380c7c3f9fdd83660372cf --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/modeling_pop2piano.py @@ -0,0 +1,1065 @@ +# Copyright 2023 The Pop2Piano Authors and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Pop2Piano model.""" + +import copy +import math + +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + +from transformers.generation import GenerationConfig + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPastAndCrossAttentions, Seq2SeqLMOutput +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, is_torchdynamo_compiling, logging +from .configuration_pop2piano import Pop2PianoConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.t5.modeling_t5.T5LayerNorm with T5->Pop2Piano +class Pop2PianoLayerNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + Construct a layernorm module in the Pop2Piano style. No bias and no subtraction of mean. + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + # Pop2Piano uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean + # Square Layer Normalization https://huggingface.co/papers/1910.07467 thus variance is calculated + # w/o mean and there is no bias. Additionally we want to make sure that the accumulation for + # half-precision inputs is done in fp32 + + variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + + # convert into half-precision if necessary + if self.weight.dtype in [torch.float16, torch.bfloat16]: + hidden_states = hidden_states.to(self.weight.dtype) + + return self.weight * hidden_states + + +# Copied from transformers.models.t5.modeling_t5.T5DenseActDense with T5->Pop2Piano,t5->pop2piano +class Pop2PianoDenseActDense(nn.Module): + def __init__(self, config: Pop2PianoConfig): + super().__init__() + self.wi = nn.Linear(config.d_model, config.d_ff, bias=False) + self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) + self.dropout = nn.Dropout(config.dropout_rate) + self.act = ACT2FN[config.dense_act_fn] + + def forward(self, hidden_states): + hidden_states = self.wi(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.dropout(hidden_states) + if ( + isinstance(self.wo.weight, torch.Tensor) + and hidden_states.dtype != self.wo.weight.dtype + and self.wo.weight.dtype != torch.int8 + ): + hidden_states = hidden_states.to(self.wo.weight.dtype) + hidden_states = self.wo(hidden_states) + return hidden_states + + +# Copied from transformers.models.t5.modeling_t5.T5DenseGatedActDense with T5->Pop2Piano +class Pop2PianoDenseGatedActDense(nn.Module): + def __init__(self, config: Pop2PianoConfig): + super().__init__() + self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False) + self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False) + self.wo = nn.Linear(config.d_ff, config.d_model, bias=False) + self.dropout = nn.Dropout(config.dropout_rate) + self.act = ACT2FN[config.dense_act_fn] + + def forward(self, hidden_states): + hidden_gelu = self.act(self.wi_0(hidden_states)) + hidden_linear = self.wi_1(hidden_states) + hidden_states = hidden_gelu * hidden_linear + hidden_states = self.dropout(hidden_states) + + # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32. + # See https://github.com/huggingface/transformers/issues/20287 + # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None`` + if ( + isinstance(self.wo.weight, torch.Tensor) + and hidden_states.dtype != self.wo.weight.dtype + and self.wo.weight.dtype != torch.int8 + ): + hidden_states = hidden_states.to(self.wo.weight.dtype) + + hidden_states = self.wo(hidden_states) + return hidden_states + + +# Copied from transformers.models.t5.modeling_t5.T5LayerFF with T5->Pop2Piano +class Pop2PianoLayerFF(nn.Module): + def __init__(self, config: Pop2PianoConfig): + super().__init__() + if config.is_gated_act: + self.DenseReluDense = Pop2PianoDenseGatedActDense(config) + else: + self.DenseReluDense = Pop2PianoDenseActDense(config) + + self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward(self, hidden_states): + forwarded_states = self.layer_norm(hidden_states) + forwarded_states = self.DenseReluDense(forwarded_states) + hidden_states = hidden_states + self.dropout(forwarded_states) + return hidden_states + + +# Copied from transformers.models.t5.modeling_t5.T5Attention with T5->Pop2Piano,t5->pop2piano +class Pop2PianoAttention(nn.Module): + def __init__( + self, + config: Pop2PianoConfig, + has_relative_attention_bias=False, + layer_idx: int | None = None, + ): + super().__init__() + self.is_decoder = config.is_decoder + self.has_relative_attention_bias = has_relative_attention_bias + self.relative_attention_num_buckets = config.relative_attention_num_buckets + self.relative_attention_max_distance = config.relative_attention_max_distance + self.d_model = config.d_model + self.key_value_proj_dim = config.d_kv + self.n_heads = config.num_heads + self.dropout = config.dropout_rate + self.inner_dim = self.n_heads * self.key_value_proj_dim + self.layer_idx = layer_idx + if layer_idx is None and self.is_decoder: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.q = nn.Linear(self.d_model, self.inner_dim, bias=False) + self.k = nn.Linear(self.d_model, self.inner_dim, bias=False) + self.v = nn.Linear(self.d_model, self.inner_dim, bias=False) + self.o = nn.Linear(self.inner_dim, self.d_model, bias=False) + + if self.has_relative_attention_bias: + self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads) + + self.gradient_checkpointing = False + + @staticmethod + def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128): + """ + Adapted from Mesh Tensorflow: + https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593 + + Translate relative position to a bucket number for relative attention. The relative position is defined as + memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to + position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for + small absolute relative_position and larger buckets for larger absolute relative_positions. All relative + positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket. + This should allow for more graceful generalization to longer sequences than the model has been trained on + + Args: + relative_position: an int32 Tensor + bidirectional: a boolean - whether the attention is bidirectional + num_buckets: an integer + max_distance: an integer + + Returns: + a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets) + """ + relative_buckets = 0 + if bidirectional: + num_buckets //= 2 + relative_buckets += (relative_position > 0).to(torch.long) * num_buckets + relative_position = torch.abs(relative_position) + else: + relative_position = -torch.min(relative_position, torch.zeros_like(relative_position)) + # now relative_position is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = relative_position < max_exact + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + relative_position_if_large = max_exact + ( + torch.log(relative_position.float() / max_exact) + / math.log(max_distance / max_exact) + * (num_buckets - max_exact) + ).to(torch.long) + relative_position_if_large = torch.min( + relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1) + ) + + relative_buckets += torch.where(is_small, relative_position, relative_position_if_large) + return relative_buckets + + def compute_bias(self, query_length, key_length, device=None, past_seen_tokens=0): + """Compute binned relative position bias""" + if device is None: + device = self.relative_attention_bias.weight.device + context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None] + past_seen_tokens + memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :] + relative_position = memory_position - context_position # shape (query_length, key_length) + relative_position_bucket = self._relative_position_bucket( + relative_position, # shape (query_length, key_length) + bidirectional=(not self.is_decoder), + num_buckets=self.relative_attention_num_buckets, + max_distance=self.relative_attention_max_distance, + ) + values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads) + values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length) + return values + + def forward( + self, + hidden_states, + mask=None, + key_value_states=None, + position_bias=None, + past_key_values=None, + output_attentions=False, + **kwargs, + ): + """ + Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states). + """ + # Input is (batch_size, seq_length, dim) + # Mask is (batch_size, 1, 1, key_length) (non-causal encoder) or (batch_size, 1, seq_length, key_length) (causal decoder) + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.key_value_proj_dim) + past_seen_tokens = past_key_values.get_seq_length(self.layer_idx) if past_key_values is not None else 0 + # We clone here for StaticCache, as we get the value before updating it, but use it after and it's the same ref + past_seen_tokens = past_seen_tokens.clone() if isinstance(past_seen_tokens, torch.Tensor) else past_seen_tokens + + # if key_value_states are provided this layer is used as a cross-attention layer for the decoder + is_cross_attention = key_value_states is not None + + query_states = self.q(hidden_states).view(hidden_shape).transpose(1, 2) + + # Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache` + is_updated = False + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + kv_shape = (*current_states.shape[:-1], -1, self.key_value_proj_dim) + key_states = self.k(current_states).view(kv_shape).transpose(1, 2) + value_states = self.v(current_states).view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + # compute scores, equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9 + scores = torch.matmul(query_states, key_states.transpose(3, 2)) + + if position_bias is None: + key_length = key_states.shape[-2] + if not self.has_relative_attention_bias: + position_bias = torch.zeros( + (1, query_states.shape[1], input_shape[1], key_length), device=scores.device, dtype=scores.dtype + ) + if self.gradient_checkpointing and self.training: + position_bias.requires_grad = True + else: + position_bias = self.compute_bias( + input_shape[1], key_length, device=scores.device, past_seen_tokens=past_seen_tokens + ) + + if mask is not None: + causal_mask = mask[:, :, :, : key_states.shape[-2]] + position_bias = position_bias + causal_mask + + position_bias_masked = position_bias + scores += position_bias_masked + + # (batch_size, n_heads, seq_length, key_length) + attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores) + attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.matmul(attn_weights, value_states) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(*input_shape, -1) + attn_output = self.o(attn_output) + + outputs = (attn_output, position_bias) + + if output_attentions: + outputs = outputs + (attn_weights,) + return outputs + + +# Copied from transformers.models.t5.modeling_t5.T5LayerSelfAttention with T5->Pop2Piano,t5->pop2piano +class Pop2PianoLayerSelfAttention(nn.Module): + def __init__(self, config, has_relative_attention_bias=False, layer_idx: int | None = None): + super().__init__() + self.SelfAttention = Pop2PianoAttention( + config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx + ) + self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward( + self, + hidden_states, + attention_mask=None, + position_bias=None, + past_key_values=None, + use_cache=False, + output_attentions=False, + **kwargs, + ): + normed_hidden_states = self.layer_norm(hidden_states) + attention_output = self.SelfAttention( + normed_hidden_states, + mask=attention_mask, + position_bias=position_bias, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + ) + hidden_states = hidden_states + self.dropout(attention_output[0]) + outputs = (hidden_states,) + attention_output[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.t5.modeling_t5.T5LayerCrossAttention with T5->Pop2Piano,t5->pop2piano +class Pop2PianoLayerCrossAttention(nn.Module): + def __init__(self, config, layer_idx: int | None = None): + super().__init__() + self.EncDecAttention = Pop2PianoAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx) + self.layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + def forward( + self, + hidden_states, + key_value_states, + attention_mask=None, + position_bias=None, + past_key_values=None, + output_attentions=False, + **kwargs, + ): + normed_hidden_states = self.layer_norm(hidden_states) + attention_output = self.EncDecAttention( + normed_hidden_states, + mask=attention_mask, + key_value_states=key_value_states, + position_bias=position_bias, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + layer_output = hidden_states + self.dropout(attention_output[0]) + outputs = (layer_output,) + attention_output[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.t5.modeling_t5.T5Block with T5->Pop2Piano,t5->pop2piano +class Pop2PianoBlock(GradientCheckpointingLayer): + def __init__(self, config, has_relative_attention_bias=False, layer_idx: int | None = None): + super().__init__() + self.is_decoder = config.is_decoder + self.layer = nn.ModuleList() + self.layer.append( + Pop2PianoLayerSelfAttention( + config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx + ) + ) + if self.is_decoder: + self.layer.append(Pop2PianoLayerCrossAttention(config, layer_idx=layer_idx)) + + self.layer.append(Pop2PianoLayerFF(config)) + + def forward( + self, + hidden_states, + attention_mask=None, + position_bias=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + encoder_decoder_position_bias=None, + past_key_values=None, + use_cache=False, + output_attentions=False, + return_dict=True, + **kwargs, + ): + self_attention_outputs = self.layer[0]( + hidden_states, + attention_mask=attention_mask, + position_bias=position_bias, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + ) + hidden_states = self_attention_outputs[0] + attention_outputs = self_attention_outputs[1:] # Keep self-attention outputs and relative position weights + + # clamp inf values to enable fp16 training + if hidden_states.dtype == torch.float16: + clamp_value = torch.where( + torch.isinf(hidden_states).any(), + torch.finfo(hidden_states.dtype).max - 1000, + torch.finfo(hidden_states.dtype).max, + ) + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + do_cross_attention = self.is_decoder and encoder_hidden_states is not None + if do_cross_attention: + cross_attention_outputs = self.layer[1]( + hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + position_bias=encoder_decoder_position_bias, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + hidden_states = cross_attention_outputs[0] + + # clamp inf values to enable fp16 training + if hidden_states.dtype == torch.float16: + clamp_value = torch.where( + torch.isinf(hidden_states).any(), + torch.finfo(hidden_states.dtype).max - 1000, + torch.finfo(hidden_states.dtype).max, + ) + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + # Keep cross-attention outputs and relative position weights + attention_outputs = attention_outputs + cross_attention_outputs[1:] + + # Apply Feed Forward layer + hidden_states = self.layer[-1](hidden_states) + + # clamp inf values to enable fp16 training + if hidden_states.dtype == torch.float16: + clamp_value = torch.where( + torch.isinf(hidden_states).any(), + torch.finfo(hidden_states.dtype).max - 1000, + torch.finfo(hidden_states.dtype).max, + ) + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + return ( + outputs + attention_outputs + ) # hidden-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights) + + +@auto_docstring +class Pop2PianoPreTrainedModel(PreTrainedModel): + config: Pop2PianoConfig + base_model_prefix = "transformer" + output_modalities = ("audio",) + supports_gradient_checkpointing = True + + _can_compile_fullgraph = False + _no_split_modules = ["Pop2PianoBlock"] + _keep_in_fp32_modules = ["wo"] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + factor = self.config.initializer_factor # Used for testing weights initialization + if isinstance(module, Pop2PianoLayerNorm): + init.constant_(module.weight, factor * 1.0) + elif isinstance(module, Pop2PianoConcatEmbeddingToMel): + init.normal_(module.embedding.weight, mean=0.0, std=factor * 1.0) + elif isinstance(module, Pop2PianoForConditionalGeneration): + init.normal_(module.shared.weight, mean=0.0, std=factor * 1.0) + if hasattr(module, "lm_head"): + init.normal_(module.lm_head.weight, mean=0.0, std=factor * 1.0) + elif isinstance(module, Pop2PianoDenseActDense): + init.normal_(module.wi.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) + if hasattr(module.wi, "bias") and module.wi.bias is not None: + init.zeros_(module.wi.bias) + init.normal_(module.wo.weight, mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) + if hasattr(module.wo, "bias") and module.wo.bias is not None: + init.zeros_(module.wo.bias) + elif isinstance(module, Pop2PianoDenseGatedActDense): + init.normal_(module.wi_0.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) + if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: + init.zeros_(module.wi_0.bias) + init.normal_(module.wi_1.weight, mean=0.0, std=factor * ((self.config.d_model) ** -0.5)) + if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: + init.zeros_(module.wi_1.bias) + init.normal_(module.wo.weight, mean=0.0, std=factor * ((self.config.d_ff) ** -0.5)) + if hasattr(module.wo, "bias") and module.wo.bias is not None: + init.zeros_(module.wo.bias) + elif isinstance(module, Pop2PianoAttention): + d_model = self.config.d_model + key_value_proj_dim = self.config.d_kv + n_heads = self.config.num_heads + init.normal_(module.q.weight, mean=0.0, std=factor * ((d_model * key_value_proj_dim) ** -0.5)) + init.normal_(module.k.weight, mean=0.0, std=factor * (d_model**-0.5)) + init.normal_(module.v.weight, mean=0.0, std=factor * (d_model**-0.5)) + init.normal_(module.o.weight, mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5)) + if module.has_relative_attention_bias: + init.normal_(module.relative_attention_bias.weight, mean=0.0, std=factor * ((d_model) ** -0.5)) + + def _shift_right(self, input_ids): + decoder_start_token_id = self.config.decoder_start_token_id + pad_token_id = self.config.pad_token_id + + if decoder_start_token_id is None: + raise ValueError( + "self.model.config.decoder_start_token_id has to be defined. In Pop2Piano it is usually set to the pad_token_id." + ) + + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() + shifted_input_ids[..., 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +class Pop2PianoStack(Pop2PianoPreTrainedModel): + # Copied from transformers.models.t5.modeling_t5.T5Stack.__init__ with T5->Pop2Piano,t5->pop2piano + def __init__(self, config): + super().__init__(config) + + self.embed_tokens = nn.Embedding(config.vocab_size, config.d_model) + self.is_decoder = config.is_decoder + + self.block = nn.ModuleList( + [ + Pop2PianoBlock(config, has_relative_attention_bias=bool(i == 0), layer_idx=i) + for i in range(config.num_layers) + ] + ) + self.final_layer_norm = Pop2PianoLayerNorm(config.d_model, eps=config.layer_norm_epsilon) + self.dropout = nn.Dropout(config.dropout_rate) + + # Initialize weights and apply final processing + self.post_init() + self.gradient_checkpointing = False + + # Copied from transformers.models.t5.modeling_t5.T5Stack.set_input_embeddings + def set_input_embeddings(self, new_embeddings): + self.embed_tokens = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + inputs_embeds=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + use_cache = use_cache if use_cache is not None else self.config.use_cache + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_ids is not None and inputs_embeds is not None: + err_msg_prefix = "decoder_" if self.is_decoder else "" + raise ValueError( + f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time" + ) + elif input_ids is not None: + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + err_msg_prefix = "decoder_" if self.is_decoder else "" + raise ValueError(f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds") + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if inputs_embeds is None: + if self.embed_tokens is None: + raise ValueError("You have to initialize the model with valid token embeddings") + inputs_embeds = self.embed_tokens(input_ids) + + batch_size, seq_length = input_shape + + if use_cache is True: + if not self.is_decoder: + raise ValueError(f"`use_cache` can only be set to `True` if {self} is used as a decoder") + + if self.is_decoder: + if use_cache and past_key_values is None: + if self.config.is_encoder_decoder: + past_key_values = EncoderDecoderCache( + DynamicCache(config=self.config), DynamicCache(config=self.config) + ) + else: + past_key_values = DynamicCache(config=self.config) + elif not self.is_decoder: + # do not pass cache object down the line for encoder stack + # it messes indexing later in decoder-stack because cache object is modified in-place + past_key_values = None + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + if attention_mask is None and not is_torchdynamo_compiling(): + # required mask seq length can be calculated via length of past cache + mask_seq_length = past_key_values_length + seq_length + attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) + + if self.config.is_decoder: + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + else: + causal_mask = attention_mask[:, None, None, :] + causal_mask = causal_mask.to(dtype=inputs_embeds.dtype) + causal_mask = (1.0 - causal_mask) * torch.finfo(inputs_embeds.dtype).min + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + all_cross_attentions = () if (output_attentions and self.is_decoder) else None + position_bias = None + encoder_decoder_position_bias = None + + hidden_states = self.dropout(inputs_embeds) + + for i, layer_module in enumerate(self.block): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module( + hidden_states, + causal_mask, + position_bias, + encoder_hidden_states, + encoder_extended_attention_mask, + encoder_decoder_position_bias, # as a positional argument for gradient checkpointing + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + # We share the position biases between the layers - the first layer store them + # layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights), + # (cross-attention position bias), (cross-attention weights) + position_bias = layer_outputs[1] + if self.is_decoder and encoder_hidden_states is not None: + encoder_decoder_position_bias = layer_outputs[3 if output_attentions else 2] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[2],) + if self.is_decoder: + all_cross_attentions = all_cross_attentions + (layer_outputs[4],) + + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.dropout(hidden_states) + + # Add last layer + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + past_key_values, + all_hidden_states, + all_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_attentions, + cross_attentions=all_cross_attentions, + ) + + +class Pop2PianoConcatEmbeddingToMel(nn.Module): + """Embedding Matrix for `composer` tokens.""" + + def __init__(self, config): + super().__init__() + self.embedding = nn.Embedding(num_embeddings=config.composer_vocab_size, embedding_dim=config.d_model) + + def forward(self, feature, index_value, embedding_offset): + index_shifted = index_value - embedding_offset + composer_embedding = self.embedding(index_shifted).unsqueeze(1) + inputs_embeds = torch.cat([composer_embedding, feature], dim=1) + return inputs_embeds + + +@auto_docstring( + custom_intro=""" + Pop2Piano Model with a `language modeling` head on top. + """ +) +class Pop2PianoForConditionalGeneration(Pop2PianoPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "encoder.embed_tokens.weight": "shared.weight", + "decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: Pop2PianoConfig): + super().__init__(config) + self.config = config + self.model_dim = config.d_model + + self.shared = nn.Embedding(config.vocab_size, config.d_model) + + self.mel_conditioner = Pop2PianoConcatEmbeddingToMel(config) + + encoder_config = copy.deepcopy(config) + encoder_config.is_decoder = False + encoder_config.use_cache = False + + self.encoder = Pop2PianoStack(encoder_config) + + decoder_config = copy.deepcopy(config) + decoder_config.is_decoder = True + decoder_config.num_layers = config.num_decoder_layers + self.decoder = Pop2PianoStack(decoder_config) + + self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.shared + + def set_input_embeddings(self, new_embeddings): + self.shared = new_embeddings + self.encoder.set_input_embeddings(new_embeddings) + self.decoder.set_input_embeddings(new_embeddings) + + def get_mel_conditioner_outputs( + self, + input_features: torch.FloatTensor, + composer: str, + generation_config: GenerationConfig, + attention_mask: torch.FloatTensor | None = None, + ): + """ + This method is used to concatenate mel conditioner tokens at the front of the input_features in order to + control the type of MIDI token generated by the model. + + Args: + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + input features extracted from the feature extractor. + composer (`str`): + composer token which determines the type of MIDI tokens to be generated. + generation_config (`~generation.GenerationConfig`): + The generation is used to get the composer-feature_token pair. + attention_mask (``, *optional*): + For batched generation `input_features` are padded to have the same shape across all examples. + `attention_mask` helps to determine which areas were padded and which were not. + - 1 for tokens that are **not padded**, + - 0 for tokens that are **padded**. + """ + composer_to_feature_token = generation_config.composer_to_feature_token + if composer not in composer_to_feature_token: + raise ValueError( + f"Please choose a composer from {list(composer_to_feature_token.keys())}. Composer received - {composer}" + ) + composer_value = composer_to_feature_token[composer] + composer_value = torch.tensor(composer_value, device=self.device) + composer_value = composer_value.repeat(input_features.shape[0]) + + embedding_offset = min(composer_to_feature_token.values()) + + input_features = self.mel_conditioner( + feature=input_features, + index_value=composer_value, + embedding_offset=embedding_offset, + ) + if attention_mask is not None: + input_features[~attention_mask[:, 0].bool()] = 0.0 + + # since self.mel_conditioner adds a new array at the front of inputs_embeds we need to do the same for attention_mask to keep the shapes same + attention_mask = torch.concatenate([attention_mask[:, 0].view(-1, 1), attention_mask], axis=1) + return input_features, attention_mask + + return input_features, None + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.BoolTensor | None = None, + encoder_outputs: tuple[tuple[torch.Tensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + input_features: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.FloatTensor] | Seq2SeqLMOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Pop2Piano is a model with relative position embeddings + so you should be able to pad the inputs on both the right and the left. Indices can be obtained using + [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for detail. + [What are input IDs?](../glossary#input-ids) To know more on how to prepare `input_ids` for pretraining + take a look a [Pop2Piano Training](./Pop2Piano#training). + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using + [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + [What are decoder input IDs?](../glossary#decoder-input-ids) Pop2Piano uses the `pad_token_id` as the + starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last + `decoder_input_ids` have to be input (see `past_key_values`). To know more on how to prepare + decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., + config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for + labels in `[0, ..., config.vocab_size]` + """ + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if inputs_embeds is not None and input_features is not None: + raise ValueError("Both `inputs_embeds` and `input_features` received! Please provide only one of them") + elif input_features is not None and inputs_embeds is None: + inputs_embeds = input_features + + # Encode if needed (training, first prediction pass) + if encoder_outputs is None: + # Convert encoder inputs in embeddings if needed + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + hidden_states = encoder_outputs[0] + + if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: + # get decoder inputs from shifting lm labels to the right + decoder_input_ids = self._shift_right(labels) + + # Decode + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + inputs_embeds=decoder_inputs_embeds, + past_key_values=past_key_values, + encoder_hidden_states=hidden_states, + encoder_attention_mask=attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = decoder_outputs[0] + + if self.config.tie_word_embeddings: + sequence_output = sequence_output * (self.model_dim**-0.5) + + lm_logits = self.lm_head(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss(ignore_index=-100) + loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) + + if not return_dict: + output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs + return ((loss,) + output) if loss is not None else output + + return Seq2SeqLMOutput( + loss=loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_features, + attention_mask=None, + composer="composer1", + generation_config=None, + **kwargs, + ): + """ + Generates token ids for midi outputs. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. For an overview of generation + strategies and code examples, check out the [following guide](./generation_strategies). + + + + Parameters: + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + This is the featurized version of audio generated by `Pop2PianoFeatureExtractor`. + attention_mask: + For batched generation `input_features` are padded to have the same shape across all examples. + `attention_mask` helps to determine which areas were padded and which were not. + - 1 for tokens that are **not padded**, + - 0 for tokens that are **padded**. + composer (`str`, *optional*, defaults to `"composer1"`): + This value is passed to `Pop2PianoConcatEmbeddingToMel` to generate different embeddings for each + `"composer"`. Please make sure that the composer value is present in `composer_to_feature_token` in + `generation_config`. For an example please see + https://huggingface.co/sweetcocoa/pop2piano/blob/main/generation_config.json . + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + kwargs: + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder + specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*. + Return: + [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` + or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. + Since Pop2Piano is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible + [`~utils.ModelOutput`] types are: + - [`~generation.GenerateEncoderDecoderOutput`], + - [`~generation.GenerateBeamEncoderDecoderOutput`] + """ + + if generation_config is None: + generation_config = self.generation_config + generation_config.update(**kwargs) + + # check for composer_to_feature_token + if not hasattr(generation_config, "composer_to_feature_token"): + raise ValueError( + "`composer_to_feature_token` was not found! Please refer to " + "https://huggingface.co/sweetcocoa/pop2piano/blob/main/generation_config.json" + "and parse a dict like that." + ) + + if len(generation_config.composer_to_feature_token) != self.config.composer_vocab_size: + raise ValueError( + "config.composer_vocab_size must be same as the number of keys in " + f"generation_config.composer_to_feature_token! " + f"Found {self.config.composer_vocab_size} vs {len(generation_config.composer_to_feature_token)}." + ) + + # to control the variation of generated MIDI tokens we concatenate mel-conditioner tokens(which depends on composer_token) + # at the front of input_features. + input_features, attention_mask = self.get_mel_conditioner_outputs( + input_features=input_features, + attention_mask=attention_mask, + composer=composer, + generation_config=generation_config, + ) + + return super().generate( + inputs=None, + inputs_embeds=input_features, + attention_mask=attention_mask, + generation_config=generation_config, + **kwargs, + ) + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return self._shift_right(labels) + + +__all__ = ["Pop2PianoForConditionalGeneration", "Pop2PianoPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/processing_pop2piano.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/processing_pop2piano.py new file mode 100644 index 0000000000000000000000000000000000000000..e7588ad148163782b7fbb70b075b75424cfbf5e4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/processing_pop2piano.py @@ -0,0 +1,126 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Processor class for Pop2Piano.""" + +import os + +import numpy as np + +from ...feature_extraction_utils import BatchFeature +from ...processing_utils import ProcessorMixin +from ...tokenization_python import BatchEncoding, PaddingStrategy, TruncationStrategy +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires + + +@requires(backends=("essentia", "librosa", "pretty_midi", "scipy", "torch")) +@auto_docstring +class Pop2PianoProcessor(ProcessorMixin): + def __init__(self, feature_extractor, tokenizer): + super().__init__(feature_extractor, tokenizer) + + @auto_docstring + def __call__( + self, + audio: np.ndarray | list[float] | list[np.ndarray] = None, + sampling_rate: int | list[int] | None = None, + steps_per_beat: int = 2, + resample: bool | None = True, + notes: list | TensorType = None, + padding: bool | str | PaddingStrategy = False, + truncation: bool | str | TruncationStrategy = None, + max_length: int | None = None, + pad_to_multiple_of: int | None = None, + verbose: bool = True, + **kwargs, + ) -> BatchFeature | BatchEncoding: + # Since Feature Extractor needs both audio and sampling_rate and tokenizer needs both token_ids and + # feature_extractor_output, we must check for both. + r""" + sampling_rate (`int` or `list[int]`, *optional*): + The sampling rate of the input audio in Hz. This should match the sampling rate used by the feature + extractor. If not provided, the default sampling rate from the processor configuration will be used. + steps_per_beat (`int`, *optional*, defaults to `2`): + The number of time steps per musical beat. This parameter controls the temporal resolution of the + musical representation. A higher value provides finer temporal granularity but increases the sequence + length. Used when processing audio to extract musical features. + notes (`list` or `TensorType`, *optional*): + Pre-extracted musical notes in MIDI format. When provided, the processor skips audio feature extraction + and directly processes the notes through the tokenizer. Each note should be represented as a list or + tensor containing pitch, velocity, and timing information. + """ + if (audio is None and sampling_rate is None) and (notes is None): + raise ValueError( + "You have to specify at least audios and sampling_rate in order to use feature extractor or " + "notes to use the tokenizer part." + ) + + if audio is not None and sampling_rate is not None: + inputs = self.feature_extractor( + audio=audio, + sampling_rate=sampling_rate, + steps_per_beat=steps_per_beat, + resample=resample, + **kwargs, + ) + if notes is not None: + encoded_token_ids = self.tokenizer( + notes=notes, + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + if notes is None: + return inputs + + elif audio is None or sampling_rate is None: + return encoded_token_ids + + else: + inputs["token_ids"] = encoded_token_ids["token_ids"] + return inputs + + def batch_decode( + self, + token_ids, + feature_extractor_output: BatchFeature, + return_midi: bool = True, + ) -> BatchEncoding: + """ + This method uses [`Pop2PianoTokenizer.batch_decode`] method to convert model generated token_ids to midi_notes. + + Please refer to the docstring of the above two methods for more information. + """ + + return self.tokenizer.batch_decode( + token_ids=token_ids, feature_extractor_output=feature_extractor_output, return_midi=return_midi + ) + + def save_pretrained(self, save_directory, **kwargs): + if os.path.isfile(save_directory): + raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file") + os.makedirs(save_directory, exist_ok=True) + return super().save_pretrained(save_directory, **kwargs) + + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): + args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs) + return cls(*args) + + +__all__ = ["Pop2PianoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/tokenization_pop2piano.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/tokenization_pop2piano.py new file mode 100644 index 0000000000000000000000000000000000000000..115484ac25404e1cdde61adb9524738a5d549cff --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pop2piano/tokenization_pop2piano.py @@ -0,0 +1,714 @@ +# Copyright 2023 The Pop2Piano Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for Pop2Piano.""" + +import json +import os + +import numpy as np + +from ...feature_extraction_utils import BatchFeature +from ...tokenization_python import AddedToken, BatchEncoding, PaddingStrategy, PreTrainedTokenizer, TruncationStrategy +from ...utils import TensorType, is_pretty_midi_available, logging, requires_backends, to_numpy +from ...utils.import_utils import requires + + +if is_pretty_midi_available(): + import pretty_midi + +logger = logging.get_logger(__name__) + + +VOCAB_FILES_NAMES = { + "vocab": "vocab.json", +} + + +def token_time_to_note(number, cutoff_time_idx, current_idx): + current_idx += number + if cutoff_time_idx is not None: + current_idx = min(current_idx, cutoff_time_idx) + + return current_idx + + +def token_note_to_note(number, current_velocity, default_velocity, note_onsets_ready, current_idx, notes): + if note_onsets_ready[number] is not None: + # offset with onset + onset_idx = note_onsets_ready[number] + if onset_idx < current_idx: + # Time shift after previous note_on + offset_idx = current_idx + notes.append([onset_idx, offset_idx, number, default_velocity]) + onsets_ready = None if current_velocity == 0 else current_idx + note_onsets_ready[number] = onsets_ready + else: + note_onsets_ready[number] = current_idx + return notes + + +@requires(backends=("pretty_midi", "torch")) +class Pop2PianoTokenizer(PreTrainedTokenizer): + """ + Constructs a Pop2Piano tokenizer. This tokenizer does not require training. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab (`str`): + Path to the vocab file which contains the vocabulary. + default_velocity (`int`, *optional*, defaults to 77): + Determines the default velocity to be used while creating midi Notes. + num_bars (`int`, *optional*, defaults to 2): + Determines cutoff_time_idx in for each token. + unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"-1"`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to 1): + The end of sequence token. + pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to 0): + A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by + attention mechanisms or loss computation. + bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to 2): + The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. + """ + + model_input_names = ["token_ids", "attention_mask"] + vocab_files_names = VOCAB_FILES_NAMES + + def __init__( + self, + vocab, + default_velocity=77, + num_bars=2, + unk_token="-1", + eos_token="1", + pad_token="0", + bos_token="2", + **kwargs, + ): + unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token + eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token + pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token + bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token + + self.default_velocity = default_velocity + self.num_bars = num_bars + + # Load the vocab + with open(vocab, "rb") as file: + self.encoder = json.load(file) + + # create mappings for encoder + self.decoder = {v: k for k, v in self.encoder.items()} + + super().__init__( + unk_token=unk_token, + eos_token=eos_token, + pad_token=pad_token, + bos_token=bos_token, + **kwargs, + ) + + @property + def vocab_size(self): + """Returns the vocabulary size of the tokenizer.""" + return len(self.encoder) + + def get_vocab(self): + """Returns the vocabulary of the tokenizer.""" + return dict(self.encoder, **self.added_tokens_encoder) + + def _convert_id_to_token(self, token_id: int) -> list: + """ + Decodes the token ids generated by the transformer into notes. + + Args: + token_id (`int`): + This denotes the ids generated by the transformers to be converted to Midi tokens. + + Returns: + `List`: A list consists of token_type (`str`) and value (`int`). + """ + + token_type_value = self.decoder.get(token_id, f"{self.unk_token}_TOKEN_TIME") + token_type_value = token_type_value.split("_") + token_type, value = "_".join(token_type_value[1:]), int(token_type_value[0]) + + return [token_type, value] + + def _convert_token_to_id(self, token, token_type="TOKEN_TIME") -> int: + """ + Encodes the Midi tokens to transformer generated token ids. + + Args: + token (`int`): + This denotes the token value. + token_type (`str`): + This denotes the type of the token. There are four types of midi tokens such as "TOKEN_TIME", + "TOKEN_VELOCITY", "TOKEN_NOTE" and "TOKEN_SPECIAL". + + Returns: + `int`: returns the id of the token. + """ + return self.encoder.get(f"{token}_{token_type}", int(self.unk_token)) + + def relative_batch_tokens_ids_to_notes( + self, + tokens: np.ndarray, + beat_offset_idx: int, + bars_per_batch: int, + cutoff_time_idx: int, + ): + """ + Converts relative tokens to notes which are then used to generate pretty midi object. + + Args: + tokens (`numpy.ndarray`): + Tokens to be converted to notes. + beat_offset_idx (`int`): + Denotes beat offset index for each note in generated Midi. + bars_per_batch (`int`): + A parameter to control the Midi output generation. + cutoff_time_idx (`int`): + Denotes the cutoff time index for each note in generated Midi. + """ + + notes = None + + for index in range(len(tokens)): + _tokens = tokens[index] + _start_idx = beat_offset_idx + index * bars_per_batch * 4 + _cutoff_time_idx = cutoff_time_idx + _start_idx + _notes = self.relative_tokens_ids_to_notes( + _tokens, + start_idx=_start_idx, + cutoff_time_idx=_cutoff_time_idx, + ) + + if len(_notes) == 0: + pass + elif notes is None: + notes = _notes + else: + notes = np.concatenate((notes, _notes), axis=0) + + if notes is None: + return [] + return notes + + def relative_batch_tokens_ids_to_midi( + self, + tokens: np.ndarray, + beatstep: np.ndarray, + beat_offset_idx: int = 0, + bars_per_batch: int = 2, + cutoff_time_idx: int = 12, + ): + """ + Converts tokens to Midi. This method calls `relative_batch_tokens_ids_to_notes` method to convert batch tokens + to notes then uses `notes_to_midi` method to convert them to Midi. + + Args: + tokens (`numpy.ndarray`): + Denotes tokens which alongside beatstep will be converted to Midi. + beatstep (`np.ndarray`): + We get beatstep from feature extractor which is also used to get Midi. + beat_offset_idx (`int`, *optional*, defaults to 0): + Denotes beat offset index for each note in generated Midi. + bars_per_batch (`int`, *optional*, defaults to 2): + A parameter to control the Midi output generation. + cutoff_time_idx (`int`, *optional*, defaults to 12): + Denotes the cutoff time index for each note in generated Midi. + """ + beat_offset_idx = 0 if beat_offset_idx is None else beat_offset_idx + notes = self.relative_batch_tokens_ids_to_notes( + tokens=tokens, + beat_offset_idx=beat_offset_idx, + bars_per_batch=bars_per_batch, + cutoff_time_idx=cutoff_time_idx, + ) + midi = self.notes_to_midi(notes, beatstep, offset_sec=beatstep[beat_offset_idx]) + return midi + + # Taken from the original code + # Please see https://github.com/sweetcocoa/pop2piano/blob/fac11e8dcfc73487513f4588e8d0c22a22f2fdc5/midi_tokenizer.py#L257 + def relative_tokens_ids_to_notes(self, tokens: np.ndarray, start_idx: float, cutoff_time_idx: float | None = None): + """ + Converts relative tokens to notes which will then be used to create Pretty Midi objects. + + Args: + tokens (`numpy.ndarray`): + Relative Tokens which will be converted to notes. + start_idx (`float`): + A parameter which denotes the starting index. + cutoff_time_idx (`float`, *optional*): + A parameter used while converting tokens to notes. + """ + words = [self._convert_id_to_token(token) for token in tokens] + + current_idx = start_idx + current_velocity = 0 + note_onsets_ready = [None for i in range(sum(k.endswith("NOTE") for k in self.encoder) + 1)] + notes = [] + for token_type, number in words: + if token_type == "TOKEN_SPECIAL": + if number == 1: + break + elif token_type == "TOKEN_TIME": + current_idx = token_time_to_note( + number=number, cutoff_time_idx=cutoff_time_idx, current_idx=current_idx + ) + elif token_type == "TOKEN_VELOCITY": + current_velocity = number + + elif token_type == "TOKEN_NOTE": + notes = token_note_to_note( + number=number, + current_velocity=current_velocity, + default_velocity=self.default_velocity, + note_onsets_ready=note_onsets_ready, + current_idx=current_idx, + notes=notes, + ) + else: + raise ValueError("Token type not understood!") + + for pitch, note_onset in enumerate(note_onsets_ready): + # force offset if no offset for each pitch + if note_onset is not None: + if cutoff_time_idx is None: + cutoff = note_onset + 1 + else: + cutoff = max(cutoff_time_idx, note_onset + 1) + + offset_idx = max(current_idx, cutoff) + notes.append([note_onset, offset_idx, pitch, self.default_velocity]) + + if len(notes) == 0: + return [] + else: + notes = np.array(notes) + note_order = notes[:, 0] * 128 + notes[:, 1] + notes = notes[note_order.argsort()] + return notes + + def notes_to_midi(self, notes: np.ndarray, beatstep: np.ndarray, offset_sec: int = 0.0): + """ + Converts notes to Midi. + + Args: + notes (`numpy.ndarray`): + This is used to create Pretty Midi objects. + beatstep (`numpy.ndarray`): + This is the extrapolated beatstep that we get from feature extractor. + offset_sec (`int`, *optional*, defaults to 0.0): + This represents the offset seconds which is used while creating each Pretty Midi Note. + """ + + requires_backends(self, ["pretty_midi"]) + + new_pm = pretty_midi.PrettyMIDI(resolution=384, initial_tempo=120.0) + new_inst = pretty_midi.Instrument(program=0) + new_notes = [] + + for onset_idx, offset_idx, pitch, velocity in notes: + new_note = pretty_midi.Note( + velocity=velocity, + pitch=pitch, + start=beatstep[onset_idx] - offset_sec, + end=beatstep[offset_idx] - offset_sec, + ) + new_notes.append(new_note) + new_inst.notes = new_notes + new_pm.instruments.append(new_inst) + new_pm.remove_invalid_notes() + return new_pm + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + """ + Saves the tokenizer's vocabulary dictionary to the provided save_directory. + + Args: + save_directory (`str`): + A path to the directory where to saved. It will be created if it doesn't exist. + filename_prefix (`Optional[str]`, *optional*): + A prefix to add to the names of the files saved by the tokenizer. + """ + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + + # Save the encoder. + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"] + ) + with open(out_vocab_file, "w") as file: + file.write(json.dumps(self.encoder)) + + return (out_vocab_file,) + + def encode_plus( + self, + notes: np.ndarray | list[pretty_midi.Note], + truncation_strategy: TruncationStrategy | None = None, + max_length: int | None = None, + **kwargs, + ) -> BatchEncoding: + r""" + This is the `encode_plus` method for `Pop2PianoTokenizer`. It converts the midi notes to the transformer + generated token ids. It only works on a single batch, to process multiple batches please use + `batch_encode_plus` or `__call__` method. + + Args: + notes (`numpy.ndarray` of shape `[sequence_length, 4]` or `list` of `pretty_midi.Note` objects): + This represents the midi notes. If `notes` is a `numpy.ndarray`: + - Each sequence must have 4 values, they are `onset idx`, `offset idx`, `pitch` and `velocity`. + If `notes` is a `list` containing `pretty_midi.Note` objects: + - Each sequence must have 4 attributes, they are `start`, `end`, `pitch` and `velocity`. + truncation_strategy ([`~tokenization_utils_base.TruncationStrategy`], *optional*): + Indicates the truncation strategy that is going to be used during truncation. + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + + Returns: + `BatchEncoding` containing the tokens ids. + """ + + requires_backends(self, ["pretty_midi"]) + + # check if notes is a pretty_midi object or not, if yes then extract the attributes and put them into a numpy + # array. + if isinstance(notes[0], pretty_midi.Note): + notes = np.array( + [[each_note.start, each_note.end, each_note.pitch, each_note.velocity] for each_note in notes] + ).reshape(-1, 4) + + # to round up all the values to the closest int values. + notes = np.round(notes).astype(np.int32) + max_time_idx = notes[:, :2].max() + + times = [[] for i in range(max_time_idx + 1)] + for onset, offset, pitch, velocity in notes: + times[onset].append([pitch, velocity]) + times[offset].append([pitch, 0]) + + tokens = [] + current_velocity = 0 + for i, time in enumerate(times): + if len(time) == 0: + continue + tokens.append(self._convert_token_to_id(i, "TOKEN_TIME")) + for pitch, velocity in time: + velocity = int(velocity > 0) + if current_velocity != velocity: + current_velocity = velocity + tokens.append(self._convert_token_to_id(velocity, "TOKEN_VELOCITY")) + tokens.append(self._convert_token_to_id(pitch, "TOKEN_NOTE")) + + total_len = len(tokens) + + # truncation + if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: + tokens, _, _ = self.truncate_sequences( + ids=tokens, + num_tokens_to_remove=total_len - max_length, + truncation_strategy=truncation_strategy, + **kwargs, + ) + + return BatchEncoding({"token_ids": tokens}) + + def batch_encode_plus( + self, + notes: np.ndarray | list[pretty_midi.Note], + truncation_strategy: TruncationStrategy | None = None, + max_length: int | None = None, + **kwargs, + ) -> BatchEncoding: + r""" + This is the `batch_encode_plus` method for `Pop2PianoTokenizer`. It converts the midi notes to the transformer + generated token ids. It works on multiple batches by calling `encode_plus` multiple times in a loop. + + Args: + notes (`numpy.ndarray` of shape `[batch_size, sequence_length, 4]` or `list` of `pretty_midi.Note` objects): + This represents the midi notes. If `notes` is a `numpy.ndarray`: + - Each sequence must have 4 values, they are `onset idx`, `offset idx`, `pitch` and `velocity`. + If `notes` is a `list` containing `pretty_midi.Note` objects: + - Each sequence must have 4 attributes, they are `start`, `end`, `pitch` and `velocity`. + truncation_strategy ([`~tokenization_utils_base.TruncationStrategy`], *optional*): + Indicates the truncation strategy that is going to be used during truncation. + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + + Returns: + `BatchEncoding` containing the tokens ids. + """ + + encoded_batch_token_ids = [] + for i in range(len(notes)): + encoded_batch_token_ids.append( + self.encode_plus( + notes[i], + truncation_strategy=truncation_strategy, + max_length=max_length, + **kwargs, + )["token_ids"] + ) + + return BatchEncoding({"token_ids": encoded_batch_token_ids}) + + def __call__( + self, + notes: np.ndarray | list[pretty_midi.Note] | list[list[pretty_midi.Note]], + padding: bool | str | PaddingStrategy = False, + truncation: bool | str | TruncationStrategy = None, + max_length: int | None = None, + pad_to_multiple_of: int | None = None, + return_attention_mask: bool | None = None, + return_tensors: str | TensorType | None = None, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + r""" + This is the `__call__` method for `Pop2PianoTokenizer`. It converts the midi notes to the transformer generated + token ids. + + Args: + notes (`numpy.ndarray` of shape `[batch_size, max_sequence_length, 4]` or `list` of `pretty_midi.Note` objects): + This represents the midi notes. + + If `notes` is a `numpy.ndarray`: + - Each sequence must have 4 values, they are `onset idx`, `offset idx`, `pitch` and `velocity`. + If `notes` is a `list` containing `pretty_midi.Note` objects: + - Each sequence must have 4 attributes, they are `start`, `end`, `pitch` and `velocity`. + padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`): + Activates and controls padding. Accepts the following values: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): + Activates and controls truncation. Accepts the following values: + + - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or + to the maximum acceptable input length for the model if that argument is not provided. This will + truncate token by token, removing a token from the longest sequence in the pair if a pair of + sequences (or a batch of pairs) is provided. + - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will only + truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. + - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the + maximum acceptable input length for the model if that argument is not provided. This will only + truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. + - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths + greater than the model maximum admissible input size). + max_length (`int`, *optional*): + Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to + `None`, this will use the predefined model maximum length if a maximum length is required by one of the + truncation/padding parameters. If the model has no specific maximum input length (like XLNet) + truncation/padding to a maximum length will be deactivated. + pad_to_multiple_of (`int`, *optional*): + If set will pad the sequence to a multiple of the provided value. This is especially useful to enable + the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta). + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific tokenizer's default, defined by the `return_outputs` attribute. + + [What are attention masks?](../glossary#attention-mask) + return_tensors (`str` or [`~file_utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + verbose (`bool`, *optional*, defaults to `True`): + Whether or not to print more information and warnings. + + Returns: + `BatchEncoding` containing the token_ids. + """ + + # check if it is batched or not + # it is batched if its a list containing a list of `pretty_midi.Notes` where the outer list contains all the + # batches and the inner list contains all Notes for a single batch. Otherwise if np.ndarray is passed it will be + # considered batched if it has shape of `[batch_size, sequence_length, 4]` or ndim=3. + is_batched = notes.ndim == 3 if isinstance(notes, np.ndarray) else isinstance(notes[0], list) + + # get the truncation and padding strategy + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + if is_batched: + # If the user has not explicitly mentioned `return_attention_mask` as False, we change it to True + return_attention_mask = True if return_attention_mask is None else return_attention_mask + token_ids = self.batch_encode_plus( + notes=notes, + truncation_strategy=truncation_strategy, + max_length=max_length, + **kwargs, + ) + else: + token_ids = self.encode_plus( + notes=notes, + truncation_strategy=truncation_strategy, + max_length=max_length, + **kwargs, + ) + + # since we already have truncated sequnences we are just left to do padding + token_ids = self.pad( + token_ids, + padding=padding_strategy, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=return_attention_mask, + return_tensors=return_tensors, + verbose=verbose, + ) + + return token_ids + + def batch_decode( + self, + token_ids, + feature_extractor_output: BatchFeature, + return_midi: bool = True, + ): + r""" + This is the `batch_decode` method for `Pop2PianoTokenizer`. It converts the token_ids generated by the + transformer to midi_notes and returns them. + + Args: + token_ids (`Union[np.ndarray, torch.Tensor]`): + Output token_ids of `Pop2PianoConditionalGeneration` model. + feature_extractor_output (`BatchFeature`): + Denotes the output of `Pop2PianoFeatureExtractor.__call__`. It must contain `"beatstep"` and + `"extrapolated_beatstep"`. Also `"attention_mask_beatsteps"` and + `"attention_mask_extrapolated_beatstep"` + should be present if they were returned by the feature extractor. + return_midi (`bool`, *optional*, defaults to `True`): + Whether to return midi object or not. + Returns: + If `return_midi` is True: + - `BatchEncoding` containing both `notes` and `pretty_midi.pretty_midi.PrettyMIDI` objects. + If `return_midi` is False: + - `BatchEncoding` containing `notes`. + """ + + # check if they have attention_masks(attention_mask, attention_mask_beatsteps, attention_mask_extrapolated_beatstep) or not + attention_masks_present = bool( + hasattr(feature_extractor_output, "attention_mask") + and hasattr(feature_extractor_output, "attention_mask_beatsteps") + and hasattr(feature_extractor_output, "attention_mask_extrapolated_beatstep") + ) + + # if we are processing batched inputs then we must need attention_masks + if not attention_masks_present and feature_extractor_output["beatsteps"].shape[0] > 1: + raise ValueError( + "attention_mask, attention_mask_beatsteps and attention_mask_extrapolated_beatstep must be present " + "for batched inputs! But one of them were not present." + ) + + # check for length mismatch between inputs_embeds, beatsteps and extrapolated_beatstep + if attention_masks_present: + # since we know about the number of examples in token_ids from attention_mask + if ( + sum(feature_extractor_output["attention_mask"][:, 0] == 0) + != feature_extractor_output["beatsteps"].shape[0] + or feature_extractor_output["beatsteps"].shape[0] + != feature_extractor_output["extrapolated_beatstep"].shape[0] + ): + raise ValueError( + "Length mistamtch between token_ids, beatsteps and extrapolated_beatstep! Found " + f"token_ids length - {token_ids.shape[0]}, beatsteps shape - {feature_extractor_output['beatsteps'].shape[0]} " + f"and extrapolated_beatsteps shape - {feature_extractor_output['extrapolated_beatstep'].shape[0]}" + ) + if feature_extractor_output["attention_mask"].shape[0] != token_ids.shape[0]: + raise ValueError( + f"Found attention_mask of length - {feature_extractor_output['attention_mask'].shape[0]} but token_ids of length - {token_ids.shape[0]}" + ) + else: + # if there is no attention mask present then it's surely a single example + if ( + feature_extractor_output["beatsteps"].shape[0] != 1 + or feature_extractor_output["extrapolated_beatstep"].shape[0] != 1 + ): + raise ValueError( + "Length mistamtch of beatsteps and extrapolated_beatstep! Since attention_mask is not present the number of examples must be 1, " + f"But found beatsteps length - {feature_extractor_output['beatsteps'].shape[0]}, extrapolated_beatsteps length - {feature_extractor_output['extrapolated_beatstep'].shape[0]}." + ) + + if attention_masks_present: + # check for zeros(since token_ids are separated by zero arrays) + batch_idx = np.where(feature_extractor_output["attention_mask"][:, 0] == 0)[0] + else: + batch_idx = [token_ids.shape[0]] + + notes_list = [] + pretty_midi_objects_list = [] + start_idx = 0 + for index, end_idx in enumerate(batch_idx): + each_tokens_ids = token_ids[start_idx:end_idx] + # check where the whole example ended by searching for eos_token_id and getting the upper bound + each_tokens_ids = each_tokens_ids[:, : np.max(np.where(each_tokens_ids == int(self.eos_token))[1]) + 1] + beatsteps = feature_extractor_output["beatsteps"][index] + extrapolated_beatstep = feature_extractor_output["extrapolated_beatstep"][index] + + # if attention mask is present then mask out real array/tensor + if attention_masks_present: + attention_mask_beatsteps = feature_extractor_output["attention_mask_beatsteps"][index] + attention_mask_extrapolated_beatstep = feature_extractor_output[ + "attention_mask_extrapolated_beatstep" + ][index] + beatsteps = beatsteps[: np.max(np.where(attention_mask_beatsteps == 1)[0]) + 1] + extrapolated_beatstep = extrapolated_beatstep[ + : np.max(np.where(attention_mask_extrapolated_beatstep == 1)[0]) + 1 + ] + + each_tokens_ids = to_numpy(each_tokens_ids) + beatsteps = to_numpy(beatsteps) + extrapolated_beatstep = to_numpy(extrapolated_beatstep) + + pretty_midi_object = self.relative_batch_tokens_ids_to_midi( + tokens=each_tokens_ids, + beatstep=extrapolated_beatstep, + bars_per_batch=self.num_bars, + cutoff_time_idx=(self.num_bars + 1) * 4, + ) + + for note in pretty_midi_object.instruments[0].notes: + note.start += beatsteps[0] + note.end += beatsteps[0] + notes_list.append(note) + + pretty_midi_objects_list.append(pretty_midi_object) + start_idx += end_idx + 1 # 1 represents the zero array + + if return_midi: + return BatchEncoding({"notes": notes_list, "pretty_midi_objects": pretty_midi_objects_list}) + + return BatchEncoding({"notes": notes_list}) + + +__all__ = ["Pop2PianoTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..961039282748414733d7d5a107b19ad1735ed7ed --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_chart2table import * + from .image_processing_pil_pp_chart2table import * + from .image_processing_pp_chart2table import * + from .processing_pp_chart2table import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/configuration_pp_chart2table.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/configuration_pp_chart2table.py new file mode 100644 index 0000000000000000000000000000000000000000..61794f02b5a302f09ac18483f6c49f79a3df9a6e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/configuration_pp_chart2table.py @@ -0,0 +1,131 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_chart2table/modular_pp_chart2table.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_chart2table.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="facebook/sam-vit-huge") +@strict +class PPChart2TableVisionConfig(PreTrainedConfig): + r""" + output_channels (`int`, *optional*, defaults to 256): + Dimensionality of the output channels in the Patch Encoder. + use_abs_pos (`bool`, *optional*, defaults to `True`): + Whether to use absolute position embedding. + use_rel_pos (`bool`, *optional*, defaults to `True`): + Whether to use relative position embedding. + window_size (`int`, *optional*, defaults to 14): + Window size for relative position. + global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`): + The indexes of the global attention layers. + mlp_dim (`int`, *optional*, defaults to 3072): + The dimensionality of the MLP layer in the Transformer encoder. + """ + + base_config_key = "vision_config" + hidden_size: int = 768 + output_channels: int = 256 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 1024 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-06 + attention_dropout: float | int = 0.0 + initializer_range: float = 1e-10 + qkv_bias: bool = True + use_abs_pos: bool = True + use_rel_pos: bool = True + window_size: int = 14 + global_attn_indexes: list[int] | tuple[int, ...] = (2, 5, 8, 11) + mlp_dim: int = 3072 + + +@auto_docstring(checkpoint="PaddlePaddle/PP-Chart2Table_safetensors") +@strict +class PPChart2TableConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import GotOcr2ForConditionalGeneration, PPChart2TableConfig + + >>> # Initializing a PPChart2Table style configuration + >>> configuration = PPChart2TableConfig() + + >>> # Initializing a model from the PaddlePaddle/PP-Chart2Table_safetensors style configuration + >>> model = GotOcr2ForConditionalGeneration(configuration) # underlying architecture is Got Ocr 2 + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pp_chart2table" + attribute_map = { + "image_token_id": "image_token_index", + } + sub_configs = {"text_config": AutoConfig, "vision_config": PPChart2TableVisionConfig} + + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + image_token_index: int = 151859 + image_seq_length: int = 576 + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if self.vision_config is None: + self.vision_config = PPChart2TableVisionConfig() + elif isinstance(self.vision_config, dict): + self.vision_config = PPChart2TableVisionConfig(**self.vision_config) + + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "qwen2") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["qwen2"]( + vocab_size=151860, + hidden_size=1024, + intermediate_size=2816, + num_hidden_layers=24, + num_attention_heads=16, + num_key_value_heads=16, + hidden_act="silu", + max_position_embeddings=32768, + initializer_range=0.02, + rms_norm_eps=1e-6, + use_cache=True, + tie_word_embeddings=self.tie_word_embeddings, + rope_theta=1000000.0, + rope_parameters=None, + use_sliding_window=False, + sliding_window=4096, + max_window_layers=21, + attention_dropout=0.0, + ) + + super().__post_init__(**kwargs) + + +__all__ = ["PPChart2TableConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/image_processing_pil_pp_chart2table.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/image_processing_pil_pp_chart2table.py new file mode 100644 index 0000000000000000000000000000000000000000..40cce468b5dcf07b662c45e802edd4fd125f3bcf --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/image_processing_pil_pp_chart2table.py @@ -0,0 +1,36 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_chart2table/modular_pp_chart2table.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_chart2table.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ...image_processing_backends import PilBackend +from ...utils import auto_docstring + + +@auto_docstring +class PPChart2TableImageProcessorPil(PilBackend): + resample = 3 + image_mean = [0.48145466, 0.4578275, 0.40821073] + image_std = [0.26862954, 0.26130258, 0.27577711] + size = {"height": 1024, "width": 1024} + do_resize = True + do_rescale = True + do_normalize = True + + +__all__ = ["PPChart2TableImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/image_processing_pp_chart2table.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/image_processing_pp_chart2table.py new file mode 100644 index 0000000000000000000000000000000000000000..b38027aecef96f5e0562f083bf878239f1555796 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/image_processing_pp_chart2table.py @@ -0,0 +1,36 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_chart2table/modular_pp_chart2table.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_chart2table.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from ...image_processing_backends import TorchvisionBackend +from ...utils import auto_docstring + + +@auto_docstring +class PPChart2TableImageProcessor(TorchvisionBackend): + resample = 3 + image_mean = [0.48145466, 0.4578275, 0.40821073] + image_std = [0.26862954, 0.26130258, 0.27577711] + size = {"height": 1024, "width": 1024} + do_resize = True + do_rescale = True + do_normalize = True + + +__all__ = ["PPChart2TableImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/modular_pp_chart2table.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/modular_pp_chart2table.py new file mode 100644 index 0000000000000000000000000000000000000000..d4953b067fe751102e42412723f8ea2842b72314 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/modular_pp_chart2table.py @@ -0,0 +1,94 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...feature_extraction_utils import BatchFeature +from ...image_processing_backends import PilBackend, TorchvisionBackend +from ...image_utils import ImageInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring, logging +from ..got_ocr2.configuration_got_ocr2 import GotOcr2Config + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PP-Chart2Table_safetensors") +@strict +class PPChart2TableConfig(GotOcr2Config): + model_type = "pp_chart2table" + + r""" + Example: + + ```python + >>> from transformers import GotOcr2ForConditionalGeneration, PPChart2TableConfig + + >>> # Initializing a PPChart2Table style configuration + >>> configuration = PPChart2TableConfig() + + >>> # Initializing a model from the PaddlePaddle/PP-Chart2Table_safetensors style configuration + >>> model = GotOcr2ForConditionalGeneration(configuration) # underlying architecture is Got Ocr 2 + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + +@auto_docstring +class PPChart2TableImageProcessor(TorchvisionBackend): + resample = 3 + image_mean = [0.48145466, 0.4578275, 0.40821073] + image_std = [0.26862954, 0.26130258, 0.27577711] + size = {"height": 1024, "width": 1024} + do_resize = True + do_rescale = True + do_normalize = True + + +@auto_docstring +class PPChart2TableImageProcessorPil(PilBackend): + resample = 3 + image_mean = [0.48145466, 0.4578275, 0.40821073] + image_std = [0.26862954, 0.26130258, 0.27577711] + size = {"height": 1024, "width": 1024} + do_resize = True + do_rescale = True + do_normalize = True + + +@auto_docstring +class PPChart2TableProcessor(ProcessorMixin): + def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + def __call__( + self, + images: ImageInput = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[ProcessingKwargs], + ) -> BatchFeature: + if text is None or images is None: + raise ValueError("Both `images` and `text` must be provided") + return super().__call__(images=images, text=text, **kwargs) + + +__all__ = [ + "PPChart2TableConfig", + "PPChart2TableImageProcessor", + "PPChart2TableImageProcessorPil", + "PPChart2TableProcessor", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/processing_pp_chart2table.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/processing_pp_chart2table.py new file mode 100644 index 0000000000000000000000000000000000000000..6f5e4554b731d7564eaa07751a440d02e8529b44 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_chart2table/processing_pp_chart2table.py @@ -0,0 +1,45 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_chart2table/modular_pp_chart2table.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_chart2table.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring + + +@auto_docstring +class PPChart2TableProcessor(ProcessorMixin): + def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): + super().__init__(image_processor, tokenizer, chat_template=chat_template) + + def __call__( + self, + images: ImageInput = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + **kwargs: Unpack[ProcessingKwargs], + ) -> BatchFeature: + if text is None or images is None: + raise ValueError("Both `images` and `text` must be provided") + return super().__call__(images=images, text=text, **kwargs) + + +__all__ = ["PPChart2TableProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5c971aba9111bf143a16c7bbdaa27dd6cec2bdb8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_doclayout_v2 import * + from .image_processing_pp_doclayout_v2 import * + from .modeling_pp_doclayout_v2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/configuration_pp_doclayout_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/configuration_pp_doclayout_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..08c28c0e3de78bb21980b93b28043d93c6c15cf5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/configuration_pp_doclayout_v2.py @@ -0,0 +1,266 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_doclayout_v2/modular_pp_doclayout_v2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_doclayout_v2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PaddlePaddle/PP-DocLayoutV2_safetensors") +@strict +class PPDocLayoutV2ReadingOrderConfig(PreTrainedConfig): + r""" + has_relative_attention_bias (`bool`, *optional*, defaults to `True`): + Whether or not to use a relative attention bias in the self-attention mechanism. + has_spatial_attention_bias (`bool`, *optional*, defaults to `True`): + Whether or not to use a spatial attention bias in the self-attention mechanism. + rel_pos_bins (`int`, *optional*, defaults to 32): + The number of relative position bins to be used in the self-attention mechanism. + max_rel_pos (`int`, *optional*, defaults to 128): + The maximum number of relative positions to be used in the self-attention mechanism. + rel_2d_pos_bins (`int`, *optional*, defaults to 64): + The number of 2D relative position bins in the self-attention mechanism. + max_rel_2d_pos (`int`, *optional*, defaults to 256): + The maximum number of relative 2D positions in the self-attention mechanism. + max_2d_position_embeddings (`int`, *optional*, defaults to 1024): + The maximum value that the 2D position embedding might ever be used with. Typically set this to something + large just in case (e.g., 1024). + start_token_id (`int`, *optional*, defaults to 0): + Token id representing the start of a sequence. + end_token_id (`int`, *optional*, defaults to 2): + Token id representing the end of a sequence. + pred_token_id (`int`, *optional*, defaults to 3): + Token id representing valid prediction positions (placeholders) in the sequence. + coordinate_size (`int`, *optional*, defaults to 171): + Dimension of the coordinate embeddings. + shape_size (`int`, *optional*, defaults to 170): + Dimension of the width and height embeddings. + num_classes (`int`, *optional*, defaults to 20): + Number of labels or classes for the layout elements. + relation_bias_embed_dim (`int`, *optional*, defaults to 16): + Embedding dimension for the relation bias. + relation_bias_theta (`float`, *optional*, defaults to 10000): + Temperature parameter used for relation bias scaling. + relation_bias_scale (`float`, *optional*, defaults to 100): + Scale parameter for the relation bias. + global_pointer_head_size (`int`, *optional*, defaults to 64): + The size of the global pointer head. + gp_dropout_value (`float`, *optional*, defaults to 0.0): + The dropout probability in the global pointer head. + """ + + hidden_size: int = 512 + num_attention_heads: int = 8 + attention_probs_dropout_prob: float | int = 0.1 + has_relative_attention_bias: bool = False + has_spatial_attention_bias: bool = True + layer_norm_eps: float = 1e-5 + hidden_dropout_prob: float | int = 0.1 + intermediate_size: int = 2048 + hidden_act: str = "gelu" + num_hidden_layers: int = 6 + rel_pos_bins: int = 32 + max_rel_pos: int = 128 + rel_2d_pos_bins: int = 64 + max_rel_2d_pos: int = 256 + max_position_embeddings: int = 514 + max_2d_position_embeddings: int = 1024 + type_vocab_size: int = 1 + vocab_size: int = 4 + initializer_range: float = 0.01 + start_token_id: int = 0 + pad_token_id: int | None = 1 + end_token_id: int = 2 + pred_token_id: int = 3 + coordinate_size: int = 171 + shape_size: int = 170 + num_classes: int = 20 + relation_bias_embed_dim: int = 16 + relation_bias_theta: int = 10000 + relation_bias_scale: int = 100 + global_pointer_head_size: int = 64 + gp_dropout_value: float | int = 0.0 + + +@auto_docstring(checkpoint="PaddlePaddle/PP-DocLayoutV2_safetensors") +@strict +class PPDocLayoutV2Config(PreTrainedConfig): + r""" + initializer_bias_prior_prob (`float`, *optional*): + The prior probability used by the bias initializer to initialize biases for `enc_score_head` and `class_embed`. + If `None`, `prior_prob` computed as `prior_prob = 1 / (num_labels + 1)` while initializing model weights. + freeze_backbone_batch_norms (`bool`, *optional*, defaults to `True`): + Whether to freeze the batch normalization layers in the backbone. + encoder_hidden_dim (`int`, *optional*, defaults to 256): + Dimension of the layers in hybrid encoder. + encoder_in_channels (`list`, *optional*, defaults to `[512, 1024, 2048]`): + Multi level features input for encoder. + feat_strides (`list[int]`, *optional*, defaults to `[8, 16, 32]`): + Strides used in each feature map. + encoder_layers (`int`, *optional*, defaults to 1): + Total of layers to be used by the encoder. + encode_proj_layers (`list[int]`, *optional*, defaults to `[2]`): + Indexes of the projected layers to be used in the encoder. + positional_encoding_temperature (`int`, *optional*, defaults to 10000): + The temperature parameter used to create the positional encodings. + encoder_activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + eval_size (`tuple[int, int]`, *optional*): + Height and width used to computes the effective height and width of the position embeddings after taking + into account the stride. + normalize_before (`bool`, *optional*, defaults to `False`): + Determine whether to apply layer normalization in the transformer encoder layer before self-attention and + feed-forward modules. + hidden_expansion (`float`, *optional*, defaults to 1.0): + Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. + num_queries (`int`, *optional*, defaults to 300): + Number of object queries. + decoder_in_channels (`list`, *optional*, defaults to `[256, 256, 256]`): + Multi level features dimension for decoder + decoder_ffn_dim (`int`, *optional*, defaults to 1024): + Dimension of the "intermediate" (often named feed-forward) layer in decoder. + num_feature_levels (`int`, *optional*, defaults to 3): + The number of input feature levels. + decoder_n_points (`int`, *optional*, defaults to 4): + The number of sampled keys in each feature level for each attention head in the decoder. + decoder_activation_function (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + num_denoising (`int`, *optional*, defaults to 100): + The total number of denoising tasks or queries to be used for contrastive denoising. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + The fraction of denoising labels to which random noise should be added. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale or magnitude of noise to be added to the bounding boxes. + learn_initial_query (`bool`, *optional*, defaults to `False`): + Indicates whether the initial query embeddings for the decoder should be learned during training + anchor_image_size (`tuple[int, int]`, *optional*): + Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. + disable_custom_kernels (`bool`, *optional*, defaults to `True`): + Whether to disable custom kernels. + class_thresholds (`list[float]`, *optional*): + The thresholds for each label. + class_order (`list[int]`, *optional*): + The priority for each label. + reading_order_config (`dict`, *optional*): + The configuration of a `PPDocLayoutV2ReadingOrder`. + + Examples: + + ```python + >>> from transformers import PPDocLayoutV2Config, PPDocLayoutV2ForObjectDetection + + >>> # Initializing a PP-DocLayoutV2 configuration + >>> configuration = PPDocLayoutV2Config() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = PPDocLayoutV2ForObjectDetection(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pp_doclayout_v2" + sub_configs = {"backbone_config": AutoConfig, "reading_order_config": PPDocLayoutV2ReadingOrderConfig} + + layer_types = ("basic", "bottleneck") + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + initializer_range: float = 0.01 + initializer_bias_prior_prob: float | None = None + layer_norm_eps: float = 1e-5 + batch_norm_eps: float = 1e-5 + backbone_config: PreTrainedConfig | dict | None = None + freeze_backbone_batch_norms: bool = True + encoder_hidden_dim: int = 256 + encoder_in_channels: list[int] | tuple[int, ...] | None = (512, 1024, 2048) + feat_strides: list[int] | tuple[int, ...] | None = (8, 16, 32) + encoder_layers: int = 1 + encoder_ffn_dim: int = 1024 + encoder_attention_heads: int = 8 + dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + encode_proj_layers: list[int] | tuple[int, ...] | None = (2,) + positional_encoding_temperature: int = 10000 + encoder_activation_function: str = "gelu" + activation_function: str = "silu" + eval_size: list[int] | None = None + normalize_before: bool = False + hidden_expansion: float = 1.0 + d_model: int = 256 + num_queries: int = 300 + decoder_in_channels: list[int] | tuple[int, ...] | None = (256, 256, 256) + decoder_ffn_dim: int = 1024 + num_feature_levels: int = 3 + decoder_n_points: int = 4 + decoder_layers: int = 6 + decoder_attention_heads: int = 8 + decoder_activation_function: str = "relu" + attention_dropout: float | int = 0.0 + num_denoising: int = 100 + label_noise_ratio: float = 0.5 + box_noise_scale: float = 1.0 + learn_initial_query: bool = False + anchor_image_size: list[int] | None = None + disable_custom_kernels: bool = True + is_encoder_decoder: bool = True + class_thresholds: list[float] | None = None + class_order: list[int] | None = None + reading_order_config: PreTrainedConfig | dict | None = None + + def __post_init__(self, **kwargs): + if isinstance(self.reading_order_config, dict): + self.reading_order_config = self.sub_configs["reading_order_config"](**self.reading_order_config) + elif self.reading_order_config is None: + self.reading_order_config = self.sub_configs["reading_order_config"]() + + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [0, 0.05, 0.05, 0.05, 0.05], + "out_features": ["stage2", "stage3", "stage4"], + }, + **kwargs, + ) + + self.encoder_in_channels = list(self.encoder_in_channels) + self.feat_strides = list(self.feat_strides) + self.encode_proj_layers = list(self.encode_proj_layers) + self.eval_size = list(self.eval_size) if self.eval_size is not None else None + self.decoder_in_channels = list(self.decoder_in_channels) + self.anchor_image_size = list(self.anchor_image_size) if self.anchor_image_size is not None else None + + super().__post_init__(**kwargs) + + +__all__ = ["PPDocLayoutV2Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/image_processing_pp_doclayout_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/image_processing_pp_doclayout_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..ec8dce9c8b2538c094d19e8ee1382cf997cc80c3 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/image_processing_pp_doclayout_v2.py @@ -0,0 +1,193 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_doclayout_v2/modular_pp_doclayout_v2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_doclayout_v2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import PILImageResampling, SizeDict +from ...utils import auto_docstring +from ...utils.generic import TensorType + + +@auto_docstring +class PPDocLayoutV2ImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = [0, 0, 0] + image_std = [1, 1, 1] + size = {"height": 800, "width": 800} + do_resize = True + do_rescale = True + do_normalize = True + + # We require `self.resize(..., antialias=False)` to approximate the output of `cv2.resize` + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample, antialias=False) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + def _get_order_seqs(self, order_logits): + """ + Computes the order sequences for a batch of inputs based on logits. + + This function takes in the order logits, calculates order scores using a sigmoid activation, + and determines the order sequences by ranking the votes derived from the scores. + + Args: + order_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_queries)`): + Stacked order logits. + + Returns: + torch.Tensor: A tensor of shape `(batch_size, num_queries)`: + Containing the computed order sequences for each input in the batch. Each row represents the ranked order of elements for the corresponding input in the batch. + """ + order_scores = torch.sigmoid(order_logits) + batch_size, sequence_length, _ = order_scores.shape + + order_votes = order_scores.triu(diagonal=1).sum(dim=1) + (1.0 - order_scores.transpose(1, 2)).tril( + diagonal=-1 + ).sum(dim=1) + + order_pointers = torch.argsort(order_votes, dim=1) + order_seq = torch.empty_like(order_pointers) + ranks = torch.arange(sequence_length, device=order_pointers.device, dtype=order_pointers.dtype).expand( + batch_size, -1 + ) + order_seq.scatter_(1, order_pointers, ranks) + + return order_seq + + def extract_custom_vertices(self): + raise AttributeError("Not needed for PPDocLayoutV2") + + def post_process_object_detection( + self, + outputs, + threshold: float = 0.5, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + PPDocLayoutV2 is identical to PPDocLayoutV3, except that it does not return `polygon_points`. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. + """ + boxes = outputs.pred_boxes + logits = outputs.logits + order_logits = outputs.order_logits + + order_seqs = self._get_order_seqs(order_logits) + + box_centers, box_dims = torch.split(boxes, 2, dim=-1) + top_left_coords = box_centers - 0.5 * box_dims + bottom_right_coords = box_centers + 0.5 * box_dims + boxes = torch.cat([top_left_coords, bottom_right_coords], dim=-1) + + if target_sizes is not None: + if len(logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + if isinstance(target_sizes, list): + img_height, img_width = torch.as_tensor(target_sizes).unbind(1) + else: + img_height, img_width = target_sizes.unbind(1) + scale_factor = torch.stack([img_width, img_height, img_width, img_height], dim=1).to(boxes.device) + boxes = boxes * scale_factor[:, None, :] + + num_top_queries = logits.shape[1] + num_classes = logits.shape[2] + + scores = torch.nn.functional.sigmoid(logits) + scores, index = torch.topk(scores.flatten(1), num_top_queries, dim=-1) + labels = index % num_classes + index = index // num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + order_seqs = order_seqs.gather(dim=1, index=index) + + results = [] + for score, label, box, order_seq in zip(scores, labels, boxes, order_seqs): + order_seq = order_seq[score >= threshold] + order_seq, indices = torch.sort(order_seq) + results.append( + { + "scores": score[score >= threshold][indices], + "labels": label[score >= threshold][indices], + "boxes": box[score >= threshold][indices], + "order_seq": order_seq, + } + ) + + return results + + +__all__ = ["PPDocLayoutV2ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/modeling_pp_doclayout_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/modeling_pp_doclayout_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..9b536f4346d1e7965a555f76c7d4e4fd7f48b43a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/modeling_pp_doclayout_v2.py @@ -0,0 +1,2480 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_doclayout_v2/modular_pp_doclayout_v2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_doclayout_v2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import warnings +from collections.abc import Callable +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2CLS, ACT2FN +from ...backbone_utils import load_backbone +from ...image_transforms import center_to_corners_format, corners_to_center_format +from ...integrations import use_kernel_forward_from_hub +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import apply_chunking_to_forward, compile_compatible_method_lru_cache +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + can_return_tuple, + torch_compilable_check, + torch_int, +) +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pp_doclayout_v2 import PPDocLayoutV2Config + + +class PPDocLayoutV2GlobalPointer(nn.Module): + def __init__(self, config): + super().__init__() + self.head_size = config.global_pointer_head_size + self.dense = nn.Linear(config.hidden_size, self.head_size * 2) + self.dropout = nn.Dropout(config.gp_dropout_value) + + def forward(self, inputs): + batch_size, sequence_length, _ = inputs.shape + query_key_projection = self.dense(inputs).reshape(batch_size, sequence_length, 2, self.head_size) + query_key_projection = self.dropout(query_key_projection) + queries, keys = torch.unbind(query_key_projection, dim=2) + + logits = (queries @ keys.transpose(-2, -1)) / (self.head_size**0.5) + mask = torch.tril(torch.ones(sequence_length, sequence_length, device=logits.device)).bool() + logits = logits.masked_fill(mask.unsqueeze(0), -1e4) + + return logits + + +class PPDocLayoutV2PositionRelationEmbedding(nn.Module): + inv_freq: torch.Tensor + + def __init__(self, config, device=None): + super().__init__() + self.config = config + self.embed_dim = config.relation_bias_embed_dim + self.scale = config.relation_bias_scale + self.pos_proj = nn.Conv2d( + in_channels=self.embed_dim * 4, out_channels=config.num_attention_heads, kernel_size=1 + ) + inv_freq, self.attention_scaling = self.compute_default_rope_parameters(config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PPDocLayoutV2Config | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.relation_bias_theta + dim = config.relation_bias_embed_dim + half_dim = dim // 2 + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / half_dim) + ) + return inv_freq, attention_factor + + def box_relative_encoding( + self, source_boxes: torch.Tensor, target_boxes: torch.Tensor = None, epsilon: float = 1e-5 + ): + source_boxes, target_boxes = source_boxes.unsqueeze(-2), target_boxes.unsqueeze(-3) + source_coordinates, source_dim = source_boxes[..., :2], source_boxes[..., 2:] + target_coordinates, target_dim = target_boxes[..., :2], target_boxes[..., 2:] + + coordinate_difference = torch.abs(source_coordinates - target_coordinates) + relative_coordinates = torch.log(coordinate_difference / (source_dim + epsilon) + 1.0) + relative_dim = torch.log((source_dim + epsilon) / (target_dim + epsilon)) + + relative_encoding = torch.cat([relative_coordinates, relative_dim], dim=-1) + + return relative_encoding + + def get_position_embedding(self, x: torch.Tensor, scale: float = 100.0): + embedding = (x * scale).unsqueeze(-1) * self.inv_freq + embedding = torch.cat((embedding.sin(), embedding.cos()), dim=-1).flatten(start_dim=-2).to(x.dtype) + + return embedding + + def forward(self, source_boxes: torch.Tensor, target_boxes: torch.Tensor = None): + if target_boxes is None: + target_boxes = source_boxes + with torch.no_grad(): + relative_encoding = self.box_relative_encoding(source_boxes, target_boxes) + position_embedding = self.get_position_embedding(relative_encoding, self.scale) + position_embedding = position_embedding.permute(0, 3, 1, 2) + out = self.pos_proj(position_embedding) + return out + + +class PPDocLayoutV2ReadingOrderSelfAttention(nn.Module): + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.has_relative_attention_bias = config.has_relative_attention_bias + self.has_spatial_attention_bias = config.has_spatial_attention_bias + + def cogview_attention(self, attention_scores, alpha=32): + """ + https://huggingface.co/papers/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation + (PB-Relax). A replacement of the original nn.Softmax(dim=-1)(attention_scores). Seems the new attention_probs + will result in a slower speed and a little bias. Can use torch.allclose(standard_attention_probs, + cogview_attention_probs, atol=1e-08) for comparison. The smaller atol (e.g., 1e-08), the better. + """ + scaled_attention_scores = attention_scores / alpha + max_value = scaled_attention_scores.amax(dim=(-1)).unsqueeze(-1) + new_attention_scores = (scaled_attention_scores - max_value) * alpha + return nn.Softmax(dim=-1)(new_attention_scores) + + def forward( + self, hidden_states, attention_mask=None, rel_pos=None, rel_2d_pos=None, **kwargs: Unpack[TransformersKwargs] + ): + batch_size, seq_length, _ = hidden_states.shape + query_layer = ( + self.query(hidden_states) + .view(batch_size, -1, self.num_attention_heads, self.attention_head_size) + .transpose(1, 2) + ) + key_layer = ( + self.key(hidden_states) + .view(batch_size, -1, self.num_attention_heads, self.attention_head_size) + .transpose(1, 2) + ) + value_layer = ( + self.value(hidden_states) + .view(batch_size, -1, self.num_attention_heads, self.attention_head_size) + .transpose(1, 2) + ) + + # Take the dot product between "query" and "key" to get the raw attention scores. + # The attention scores QT K/√d could be significantly larger than input elements, and result in overflow. + # Changing the computational order into QT(K/√d) alleviates the problem. (https://huggingface.co/papers/2105.13290) + attention_scores = torch.matmul(query_layer / math.sqrt(self.attention_head_size), key_layer.transpose(-1, -2)) + + # NOTE: + # PPDocLayoutV2ReadingOrder applies unscaled rel_2d_pos + # unlike LayoutLMv3 which uses (rel_pos + rel_2d_pos) / math.sqrt(self.attention_head_size) + if rel_2d_pos is not None: + attention_scores += rel_2d_pos + elif self.has_relative_attention_bias: + attention_scores += rel_pos / math.sqrt(self.attention_head_size) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + # Use the trick of the CogView paper to stabilize training + attention_probs = self.cogview_attention(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer, attention_probs + + +class PPDocLayoutV2ReadingOrderSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.norm(hidden_states + input_tensor) + return hidden_states + + +class PPDocLayoutV2ReadingOrderIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class PPDocLayoutV2ReadingOrderOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.norm(hidden_states + input_tensor) + return hidden_states + + +class PPDocLayoutV2ReadingOrderAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.self = PPDocLayoutV2ReadingOrderSelfAttention(config) + self.output = PPDocLayoutV2ReadingOrderSelfOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + rel_pos=None, + rel_2d_pos=None, + **kwargs: Unpack[TransformersKwargs], + ): + residual = hidden_states + attention_output, _ = self.self( + hidden_states, + attention_mask, + rel_pos=rel_pos, + rel_2d_pos=rel_2d_pos, + **kwargs, + ) + attention_output = self.output(attention_output, residual) + return attention_output + + +class PPDocLayoutV2ReadingOrderLayer(GradientCheckpointingLayer): + def __init__(self, config): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = PPDocLayoutV2ReadingOrderAttention(config) + self.intermediate = PPDocLayoutV2ReadingOrderIntermediate(config) + self.output = PPDocLayoutV2ReadingOrderOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + output_attentions=False, + rel_pos=None, + rel_2d_pos=None, + **kwargs: Unpack[TransformersKwargs], + ): + attention_output = self.attention( + hidden_states, + attention_mask, + rel_pos=rel_pos, + rel_2d_pos=rel_2d_pos, + ) + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + + return layer_output + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class PPDocLayoutV2ReadingOrderEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([PPDocLayoutV2ReadingOrderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + self.has_relative_attention_bias = config.has_relative_attention_bias + self.has_spatial_attention_bias = config.has_spatial_attention_bias + + if self.has_relative_attention_bias: + self.rel_pos_bins = config.rel_pos_bins + self.max_rel_pos = config.max_rel_pos + self.rel_pos_bias = nn.Linear(self.rel_pos_bins, config.num_attention_heads, bias=False) + + if self.has_spatial_attention_bias: + self.max_rel_2d_pos = config.max_rel_2d_pos + self.rel_2d_pos_bins = config.rel_2d_pos_bins + self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False) + self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_bins, config.num_attention_heads, bias=False) + self.rel_bias_module = PPDocLayoutV2PositionRelationEmbedding(config) + + def relative_position_bucket(self, relative_position, bidirectional=True, num_buckets=32, max_distance=128): + ret = 0 + if bidirectional: + num_buckets //= 2 + ret += (relative_position > 0).long() * num_buckets + n = torch.abs(relative_position) + else: + n = torch.max(-relative_position, torch.zeros_like(relative_position)) + # now n is in the range [0, inf) + + # half of the buckets are for exact increments in positions + max_exact = num_buckets // 2 + is_small = n < max_exact + + # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance + val_if_large = max_exact + ( + torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) + ).to(torch.long) + val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) + + ret += torch.where(is_small, n, val_if_large) + return ret + + def _cal_1d_pos_emb(self, position_ids): + rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1) + + rel_pos = self.relative_position_bucket( + rel_pos_mat, + num_buckets=self.rel_pos_bins, + max_distance=self.max_rel_pos, + ) + # Since this is a simple indexing operation that is independent of the input, + # no need to track gradients for this operation + # + # Without this no_grad context, training speed slows down significantly + with torch.no_grad(): + rel_pos = self.rel_pos_bias.weight.t()[rel_pos].permute(0, 3, 1, 2) + rel_pos = rel_pos.contiguous() + return rel_pos + + def _cal_2d_pos_emb(self, bbox): + x_min, y_min, x_max, y_max = ( + bbox[..., 0], + bbox[..., 1], + bbox[..., 2], + bbox[..., 3], + ) + + width = (x_max - x_min).clamp(min=1e-3) + height = (y_max - y_min).clamp(min=1e-3) + + center_x = (x_min + x_max) * 0.5 + center_y = (y_min + y_max) * 0.5 + + center_width_height_bbox = torch.stack([center_x, center_y, width, height], dim=-1) + + result = self.rel_bias_module(center_width_height_bbox) + + return result + + def forward( + self, + hidden_states, + bbox=None, + attention_mask=None, + position_ids=None, + patch_height=None, + patch_width=None, + **kwargs: Unpack[TransformersKwargs], + ): + rel_pos = self._cal_1d_pos_emb(position_ids) if self.has_relative_attention_bias else None + rel_2d_pos = self._cal_2d_pos_emb(bbox) if self.has_spatial_attention_bias else None + + for layer_module in self.layer: + hidden_states = layer_module( + hidden_states, + attention_mask, + rel_pos=rel_pos, + rel_2d_pos=rel_2d_pos, + **kwargs, + ) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +class PPDocLayoutV2TextEmbeddings(nn.Module): + """ + PPDocLayoutV2 text embeddings. Same as `RobertaEmbeddings` but with added spatial (layout) embeddings. + """ + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + + self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size) + self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size) + self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size) + self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size) + self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + spatial_embed_dim = 4 * config.coordinate_size + 2 * config.shape_size + self.spatial_proj = nn.Linear(spatial_embed_dim, config.hidden_size) + + def calculate_spatial_position_embeddings(self, bbox): + try: + left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0]) + upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1]) + right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2]) + lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3]) + except IndexError as e: + raise IndexError("The `bbox` coordinate values should be within 0-1000 range.") from e + + h_position_embeddings = self.h_position_embeddings(torch.clip(bbox[:, :, 3] - bbox[:, :, 1], 0, 1023)) + w_position_embeddings = self.w_position_embeddings(torch.clip(bbox[:, :, 2] - bbox[:, :, 0], 0, 1023)) + + # below is the difference between LayoutLMEmbeddingsV2 (torch.cat) and LayoutLMEmbeddingsV1 (add) + spatial_position_embeddings = torch.cat( + [ + left_position_embeddings, + upper_position_embeddings, + right_position_embeddings, + lower_position_embeddings, + h_position_embeddings, + w_position_embeddings, + ], + dim=-1, + ) + return spatial_position_embeddings + + def create_position_ids_from_input_ids(self, input_ids, padding_idx): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding + symbols are ignored. This is modified from fairseq's `utils.make_positions`. + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask)) * mask + return incremental_indices.long() + padding_idx + + def create_position_ids_from_inputs_embeds(self, inputs_embeds): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape) + + def forward( + self, + input_ids=None, + bbox=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to( + input_ids.device + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = inputs_embeds + token_type_embeddings + + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + + # custom new spatial embeddings + spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox) + spatial_position_embeddings = self.spatial_proj(spatial_position_embeddings) + embeddings += spatial_position_embeddings + return embeddings + + +@use_kernel_forward_from_hub("MultiScaleDeformableAttention") +class MultiScaleDeformableAttention(nn.Module): + def forward( + self, + value: Tensor, + value_spatial_shapes: Tensor, + value_spatial_shapes_list: list[tuple], + level_start_index: Tensor, + sampling_locations: Tensor, + attention_weights: Tensor, + im2col_step: int, + ): + batch_size, _, num_heads, hidden_dim = value.shape + _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape + value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level_id, (height, width) in enumerate(value_spatial_shapes_list): + # batch_size, height*width, num_heads, hidden_dim + # -> batch_size, height*width, num_heads*hidden_dim + # -> batch_size, num_heads*hidden_dim, height*width + # -> batch_size*num_heads, hidden_dim, height, width + value_l_ = ( + value_list[level_id] + .flatten(2) + .transpose(1, 2) + .reshape(batch_size * num_heads, hidden_dim, height, width) + ) + # batch_size, num_queries, num_heads, num_points, 2 + # -> batch_size, num_heads, num_queries, num_points, 2 + # -> batch_size*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1) + # batch_size*num_heads, hidden_dim, num_queries, num_points + sampling_value_l_ = nn.functional.grid_sample( + value_l_, + sampling_grid_l_, + mode="bilinear", + padding_mode="zeros", + align_corners=False, + ) + sampling_value_list.append(sampling_value_l_) + # (batch_size, num_queries, num_heads, num_levels, num_points) + # -> (batch_size, num_heads, num_queries, num_levels, num_points) + # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.transpose(1, 2).reshape( + batch_size * num_heads, 1, num_queries, num_levels * num_points + ) + output = ( + (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) + .sum(-1) + .view(batch_size, num_heads * hidden_dim, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +class PPDocLayoutV2MultiscaleDeformableAttention(nn.Module): + """ + Multiscale deformable attention as proposed in Deformable DETR. + """ + + def __init__(self, config: PPDocLayoutV2Config, num_heads: int, n_points: int): + super().__init__() + + self.attn = MultiScaleDeformableAttention() + + if config.d_model % num_heads != 0: + raise ValueError( + f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" + ) + dim_per_head = config.d_model // num_heads + # check if dim_per_head is power of 2 + if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): + warnings.warn( + "You'd better set embed_dim (d_model) in PPDocLayoutV2MultiscaleDeformableAttention to make the" + " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" + " implementation." + ) + + self.im2col_step = 64 + + self.d_model = config.d_model + self.n_levels = config.num_feature_levels + self.n_heads = num_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) + self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) + self.value_proj = nn.Linear(config.d_model, config.d_model) + self.output_proj = nn.Linear(config.d_model, config.d_model) + + self.disable_custom_kernels = config.disable_custom_kernels + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states=None, + encoder_attention_mask=None, + position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # add position embeddings to the hidden states before projecting to queries and keys + if position_embeddings is not None: + hidden_states = hidden_states + position_embeddings + + batch_size, num_queries, _ = hidden_states.shape + batch_size, sequence_length, _ = encoder_hidden_states.shape + total_elements = sum(height * width for height, width in spatial_shapes_list) + torch_compilable_check( + total_elements == sequence_length, + "Make sure to align the spatial shapes with the sequence length of the encoder hidden states", + ) + + value = self.value_proj(encoder_hidden_states) + if attention_mask is not None: + # we invert the attention_mask + value = value.masked_fill(~attention_mask[..., None], float(0)) + value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2 + ) + attention_weights = self.attention_weights(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points + ) + attention_weights = F.softmax(attention_weights, -1).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points + ) + # batch_size, num_queries, n_heads, n_levels, n_points, 2 + num_coordinates = reference_points.shape[-1] + if num_coordinates == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif num_coordinates == 4: + sampling_locations = ( + reference_points[:, :, None, :, None, :2] + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + ) + else: + raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") + + output = self.attn( + value, + spatial_shapes, + spatial_shapes_list, + level_start_index, + sampling_locations, + attention_weights, + self.im2col_step, + ) + + output = self.output_proj(output) + + return output, attention_weights + + +@auto_docstring +class PPDocLayoutV2PreTrainedModel(PreTrainedModel): + config: PPDocLayoutV2Config + base_model_prefix = "pp_doclayout_v2" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = [r"PPDocLayoutV2HybridEncoder", r"PPDocLayoutV2DecoderLayer"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, PPDocLayoutV2ForObjectDetection): + if module.model.decoder.class_embed is not None: + for layer in module.model.decoder.class_embed: + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(layer.weight) + init.constant_(layer.bias, bias) + + if module.model.decoder.bbox_embed is not None: + for layer in module.model.decoder.bbox_embed: + init.constant_(layer.layers[-1].weight, 0) + init.constant_(layer.layers[-1].bias, 0) + + elif isinstance(module, PPDocLayoutV2MultiscaleDeformableAttention): + init.constant_(module.sampling_offsets.weight, 0.0) + default_dtype = torch.get_default_dtype() + thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * ( + 2.0 * math.pi / module.n_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(module.n_heads, 1, 1, 2) + .repeat(1, module.n_levels, module.n_points, 1) + ) + for i in range(module.n_points): + grid_init[:, :, i, :] *= i + 1 + + init.copy_(module.sampling_offsets.bias, grid_init.view(-1)) + init.constant_(module.attention_weights.weight, 0.0) + init.constant_(module.attention_weights.bias, 0.0) + init.xavier_uniform_(module.value_proj.weight) + init.constant_(module.value_proj.bias, 0.0) + init.xavier_uniform_(module.output_proj.weight) + init.constant_(module.output_proj.bias, 0.0) + + elif isinstance(module, PPDocLayoutV2Model): + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(module.enc_score_head.weight) + init.constant_(module.enc_score_head.bias, bias) + + elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + elif isinstance(module, nn.LayerNorm): + init.ones_(module.weight) + init.zeros_(module.bias) + + if hasattr(module, "weight_embedding") and self.config.learn_initial_query: + init.xavier_uniform_(module.weight_embedding.weight) + if hasattr(module, "denoising_class_embed") and self.config.num_denoising > 0: + init.xavier_uniform_(module.denoising_class_embed.weight) + if isinstance(module, PPDocLayoutV2TextEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + if isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + init.zeros_(module.weight.data[module.padding_idx]) + if isinstance(module, PPDocLayoutV2PositionRelationEmbedding): + inv_freq, _ = module.compute_default_rope_parameters(module.config, module.inv_freq.device) + module.register_buffer("inv_freq", inv_freq, persistent=False) + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV2 ReadingOrder Model. This model consists of an encoder and a GlobalPointer head. + It takes layout features as input and outputs logits representing the relative ordering relationships + between elements, which are used to determine the final reading sequence. + """ +) +class PPDocLayoutV2ReadingOrder(PPDocLayoutV2PreTrainedModel): + # Attention is based on LayoutLMv3 (no interface) + _supports_sdpa = False + _supports_flash_attn = False + _supports_attention_backend = False + _supports_flex_attn = False + + def __init__(self, config): + super().__init__(config) + self.embeddings = PPDocLayoutV2TextEmbeddings(config) + self.label_embeddings = nn.Embedding(config.num_classes, config.hidden_size) + self.label_features_projection = nn.Linear(config.hidden_size, config.hidden_size) + self.encoder = PPDocLayoutV2ReadingOrderEncoder(config) + self.relative_head = PPDocLayoutV2GlobalPointer(config) + self.config = config + + self.post_init() + + @auto_docstring + def forward(self, boxes, labels=None, mask=None, **kwargs: Unpack[TransformersKwargs]): + r""" + boxes (`torch.Tensor` of shape `(batch_size, sequence_length, 4)`): + Bounding box coordinates of the detected layout elements **in [0, 1000] scale**. + Format is `[x_min, y_min, x_max, y_max]`. + The tensor usually contains sorted valid boxes followed by zero-padding. + labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + The **remapped** class indices for each layout element. + These are not necessarily the raw detection class IDs, but indices mapped via + `config.class_order` (e.g., mapping text/title/figure to specific reading-order category IDs). + mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Boolean or Binary mask indicating valid detected elements after threshold filtering. + - True: Valid layout element. + - False: Padding/Empty element. + Used to determine the sequence length (`num_pred`) for the pointer mechanism. + """ + device = mask.device + batch_size, seq_len = mask.shape + num_pred = mask.sum(dim=1) + + input_ids = torch.full( + (batch_size, seq_len + 2), self.config.pad_token_id, dtype=torch.long, device=boxes.device + ) + input_ids[:, 0] = self.config.start_token_id + + pred_col_idx = torch.arange(seq_len + 2, device=device).unsqueeze(0) + pred_mask = (pred_col_idx >= 1) & (pred_col_idx <= num_pred.unsqueeze(1)) + input_ids[pred_mask] = self.config.pred_token_id + end_col_indices = num_pred + 1 + input_ids[torch.arange(batch_size, device=device), end_col_indices] = self.config.end_token_id + + pad_box = torch.zeros(size=[boxes.shape[0], 1, boxes.shape[-1]], dtype=boxes.dtype, device=boxes.device) + pad_boxes = torch.cat([pad_box, boxes, pad_box], dim=1) + bbox_embedding = self.embeddings(input_ids=input_ids, bbox=pad_boxes.long()) + + if labels is not None: + label_embs = self.label_embeddings(labels) + label_proj = self.label_features_projection(label_embs) + pad = torch.zeros( + size=[label_proj.shape[0], 1, label_proj.shape[-1]], dtype=label_proj.dtype, device=labels.device + ) + label_proj = torch.cat([pad, label_proj, pad], dim=1) + else: + label_proj = torch.zeros_like(bbox_embedding) + + final_embeddings = bbox_embedding + label_proj + final_embeddings = self.embeddings.norm(final_embeddings) + final_embeddings = self.embeddings.dropout(final_embeddings) + + attention_mask = pred_col_idx < (num_pred + 2).unsqueeze(1) + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=final_embeddings, + attention_mask=attention_mask, + ) + encoder_output = self.encoder(hidden_states=final_embeddings, bbox=pad_boxes, attention_mask=attention_mask) + encoder_output = encoder_output.last_hidden_state + token = encoder_output[:, 1 : 1 + seq_len, :] + read_order_logits = self.relative_head(token) + return read_order_logits + + +@dataclass +@auto_docstring +class PPDocLayoutV2ForObjectDetectionOutput(ModelOutput): + r""" + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~RTDetrImageProcessor.post_process_object_detection`] to retrieve the + unnormalized (absolute) bounding boxes. + order_logits (`tuple` of `torch.FloatTensor` of shape `(batch_size, num_queries, num_queries)`): + Order logits for all queries. The first dimension of each tensor is the batch size. The second dimension is the number of queries. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values + """ + + logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + order_logits: tuple[torch.FloatTensor] | None = None + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: tuple[torch.FloatTensor] | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the PP-DocLayoutV2 encoder-decoder model. + """ +) +@dataclass +class PPDocLayoutV2ModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points used for the first decoder layer. + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`): + Logits of predicted bounding boxes coordinates in the encoder stage. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: torch.FloatTensor | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +class PPDocLayoutV2MLPPredictionHead(nn.Module): + """ + Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, + height and width of a bounding box w.r.t. an image. + + """ + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the PPDocLayoutV2Decoder. This class adds two attributes to + BaseModelOutputWithCrossAttentions, namely: + - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) + - a stacked tensor of intermediate reference points. + """ +) +@dataclass +class PPDocLayoutV2DecoderOutput(ModelOutput): + r""" + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +class PPDocLayoutV2MLP(nn.Module): + def __init__( + self, config: PPDocLayoutV2Config, hidden_size: int, intermediate_size: int, activation_function: str + ): + super().__init__() + self.fc1 = nn.Linear(hidden_size, intermediate_size) + self.fc2 = nn.Linear(intermediate_size, hidden_size) + self.activation_fn = ACT2FN[activation_function] + self.activation_dropout = config.activation_dropout + self.dropout = config.dropout + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + return hidden_states + + +class PPDocLayoutV2FrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than + torchvision.models.resnet[18,34,50,101] produce nans. + """ + + def __init__(self, n): + super().__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it user-friendly + weight = self.weight.reshape(1, -1, 1, 1) + bias = self.bias.reshape(1, -1, 1, 1) + running_var = self.running_var.reshape(1, -1, 1, 1) + running_mean = self.running_mean.reshape(1, -1, 1, 1) + epsilon = 1e-5 + scale = weight * (running_var + epsilon).rsqrt() + bias = bias - running_mean * scale + return x * scale + bias + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PPDocLayoutV2SelfAttention(nn.Module): + """ + Multi-headed self-attention from 'Attention Is All You Need' paper. + + In PP_DOCLAYOUT_V2, position embeddings are added to both queries and keys (but not values) in self-attention. + """ + + def __init__( + self, + config: PPDocLayoutV2Config, + hidden_size: int, + num_attention_heads: int, + dropout: float = 0.0, + bias: bool = True, + ): + super().__init__() + self.config = config + self.head_dim = hidden_size // num_attention_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = dropout + self.is_causal = False + + self.k_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.v_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.q_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.o_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Position embeddings are added to both queries and keys (but not values). + """ + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_key_input = hidden_states + position_embeddings if position_embeddings is not None else hidden_states + + query_states = self.q_proj(query_key_input).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(query_key_input).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +def replace_batch_norm(model): + r""" + Recursively replace all `torch.nn.BatchNorm2d` with `PPDocLayoutV2FrozenBatchNorm2d`. + + Args: + model (torch.nn.Module): + input model + """ + for name, module in model.named_children(): + if isinstance(module, nn.BatchNorm2d): + new_module = PPDocLayoutV2FrozenBatchNorm2d(module.num_features) + + if module.weight.device != torch.device("meta"): + new_module.weight.copy_(module.weight) + new_module.bias.copy_(module.bias) + new_module.running_mean.copy_(module.running_mean) + new_module.running_var.copy_(module.running_var) + + model._modules[name] = new_module + + if len(list(module.children())) > 0: + replace_batch_norm(module) + + +class PPDocLayoutV2ConvEncoder(nn.Module): + """ + Convolutional backbone using the modeling_pp_doclayout_v2_resnet.py. + + nn.BatchNorm2d layers are replaced by PPDocLayoutV2FrozenBatchNorm2d as defined above. + https://github.com/lyuwenyu/RT-DETR/blob/main/PPDocLayoutV2_pytorch/src/nn/backbone/presnet.py#L142 + """ + + def __init__(self, config): + super().__init__() + + backbone = load_backbone(config) + + if config.freeze_backbone_batch_norms: + # replace batch norm by frozen batch norm + with torch.no_grad(): + replace_batch_norm(backbone) + self.model = backbone + self.intermediate_channel_sizes = self.model.channels + + def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): + # send pixel_values through the model to get list of feature maps + features = self.model(pixel_values).feature_maps + + out = [] + for feature_map in features: + # downsample pixel_mask to match shape of corresponding feature_map + mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] + out.append((feature_map, mask)) + return out + + +class PPDocLayoutV2ConvNormLayer(nn.Module): + def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride, + padding=(kernel_size - 1) // 2 if padding is None else padding, + bias=False, + ) + self.norm = nn.BatchNorm2d(out_channels, config.batch_norm_eps) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_state): + hidden_state = self.conv(hidden_state) + hidden_state = self.norm(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class PPDocLayoutV2EncoderLayer(nn.Module): + def __init__(self, config: PPDocLayoutV2Config): + super().__init__() + self.normalize_before = config.normalize_before + self.hidden_size = config.encoder_hidden_dim + + # self-attention + self.self_attn = PPDocLayoutV2SelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.num_attention_heads, + dropout=config.dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + self.dropout = config.dropout + self.mlp = PPDocLayoutV2MLP( + config, self.hidden_size, config.encoder_ffn_dim, config.encoder_activation_function + ) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + spatial_position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, hidden_size)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + spatial_position_embeddings (`torch.FloatTensor`, *optional*): + Spatial position embeddings (2D positional encodings of image locations), to be added to both + the queries and keys in self-attention (but not to values). + """ + residual = hidden_states + if self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_embeddings=spatial_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + if self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + residual = hidden_states + + hidden_states = self.mlp(hidden_states) + + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + + if self.training: + if not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +class PPDocLayoutV2RepVggBlock(nn.Module): + """ + RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again". + """ + + def __init__(self, config: PPDocLayoutV2Config): + super().__init__() + + activation = config.activation_function + hidden_channels = int(config.encoder_hidden_dim * config.hidden_expansion) + self.conv1 = PPDocLayoutV2ConvNormLayer(config, hidden_channels, hidden_channels, 3, 1, padding=1) + self.conv2 = PPDocLayoutV2ConvNormLayer(config, hidden_channels, hidden_channels, 1, 1, padding=0) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, x): + y = self.conv1(x) + self.conv2(x) + return self.activation(y) + + +class PPDocLayoutV2CSPRepLayer(nn.Module): + """ + Cross Stage Partial (CSP) network layer with RepVGG blocks. + """ + + def __init__(self, config: PPDocLayoutV2Config): + super().__init__() + + in_channels = config.encoder_hidden_dim * 2 + out_channels = config.encoder_hidden_dim + num_blocks = 3 + activation = config.activation_function + + hidden_channels = int(out_channels * config.hidden_expansion) + self.conv1 = PPDocLayoutV2ConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.conv2 = PPDocLayoutV2ConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.bottlenecks = nn.Sequential(*[PPDocLayoutV2RepVggBlock(config) for _ in range(num_blocks)]) + if hidden_channels != out_channels: + self.conv3 = PPDocLayoutV2ConvNormLayer(config, hidden_channels, out_channels, 1, 1, activation=activation) + else: + self.conv3 = nn.Identity() + + def forward(self, hidden_state): + hidden_state_1 = self.conv1(hidden_state) + hidden_state_1 = self.bottlenecks(hidden_state_1) + hidden_state_2 = self.conv2(hidden_state) + return self.conv3(hidden_state_1 + hidden_state_2) + + +class PPDocLayoutV2DecoderLayer(nn.Module): + def __init__(self, config: PPDocLayoutV2Config): + super().__init__() + self.hidden_size = config.d_model + + # self-attention + self.self_attn = PPDocLayoutV2SelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + ) + self.dropout = config.dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # cross-attention + self.encoder_attn = PPDocLayoutV2MultiscaleDeformableAttention( + config, + num_heads=config.decoder_attention_heads, + n_points=config.decoder_n_points, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # feedforward neural networks + self.mlp = PPDocLayoutV2MLP( + config, self.hidden_size, config.decoder_ffn_dim, config.decoder_activation_function + ) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + object_queries_position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + Input to the layer of shape `(batch, seq_len, hidden_size)`. + object_queries_position_embeddings (`torch.FloatTensor`, *optional*): + Position embeddings for the object query slots. These are added to both queries and keys + in the self-attention layer (not values). + reference_points (`torch.FloatTensor`, *optional*): + Reference points. + spatial_shapes (`torch.LongTensor`, *optional*): + Spatial shapes. + level_start_index (`torch.LongTensor`, *optional*): + Level start index. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, hidden_size)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + """ + residual = hidden_states + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=encoder_attention_mask, + position_embeddings=object_queries_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + + # Cross-Attention + hidden_states, _ = self.encoder_attn( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + position_embeddings=object_queries_position_embeddings, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states + + +class PPDocLayoutV2SinePositionEmbedding(nn.Module): + """ + 2D sinusoidal position embedding used in RT-DETR hybrid encoder. + """ + + def __init__(self, embed_dim: int = 256, temperature: int = 10000): + super().__init__() + self.embed_dim = embed_dim + self.temperature = temperature + + @compile_compatible_method_lru_cache(maxsize=32) + def forward( + self, + width: int, + height: int, + device: torch.device | str, + dtype: torch.dtype, + ) -> torch.Tensor: + """ + Generate 2D sinusoidal position embeddings. + + Returns: + Position embeddings of shape (1, height*width, embed_dim) + """ + grid_w = torch.arange(torch_int(width), device=device).to(dtype) + grid_h = torch.arange(torch_int(height), device=device).to(dtype) + grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="xy") + if self.embed_dim % 4 != 0: + raise ValueError("Embed dimension must be divisible by 4 for 2D sin-cos position embedding") + pos_dim = self.embed_dim // 4 + omega = torch.arange(pos_dim, device=device).to(dtype) / pos_dim + omega = 1.0 / (self.temperature**omega) + + out_w = grid_w.flatten()[..., None] @ omega[None] + out_h = grid_h.flatten()[..., None] @ omega[None] + + return torch.concat([out_h.sin(), out_h.cos(), out_w.sin(), out_w.cos()], dim=1)[None, :, :] + + +class PPDocLayoutV2AIFILayer(nn.Module): + """ + AIFI (Attention-based Intra-scale Feature Interaction) layer used in RT-DETR hybrid encoder. + """ + + def __init__(self, config: PPDocLayoutV2Config): + super().__init__() + self.config = config + self.encoder_hidden_dim = config.encoder_hidden_dim + self.eval_size = config.eval_size + + self.position_embedding = PPDocLayoutV2SinePositionEmbedding( + embed_dim=self.encoder_hidden_dim, + temperature=config.positional_encoding_temperature, + ) + self.layers = nn.ModuleList([PPDocLayoutV2EncoderLayer(config) for _ in range(config.encoder_layers)]) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`): + Feature map to process. + """ + batch_size = hidden_states.shape[0] + height, width = hidden_states.shape[2:] + + hidden_states = hidden_states.flatten(2).permute(0, 2, 1) + + if self.training or self.eval_size is None: + pos_embed = self.position_embedding( + width=width, + height=height, + device=hidden_states.device, + dtype=hidden_states.dtype, + ) + else: + pos_embed = None + + for layer in self.layers: + hidden_states = layer( + hidden_states, + attention_mask=None, + spatial_position_embeddings=pos_embed, + **kwargs, + ) + + hidden_states = ( + hidden_states.permute(0, 2, 1).reshape(batch_size, self.encoder_hidden_dim, height, width).contiguous() + ) + + return hidden_states + + +class PPDocLayoutV2HybridEncoder(PPDocLayoutV2PreTrainedModel): + """ + Hybrid encoder consisting of AIFI (Attention-based Intra-scale Feature Interaction) layers, + a top-down Feature Pyramid Network (FPN) and a bottom-up Path Aggregation Network (PAN). + More details on the paper: https://huggingface.co/papers/2304.08069 + + Args: + config: PPDocLayoutV2Config + """ + + _can_record_outputs = { + "hidden_states": PPDocLayoutV2AIFILayer, + "attentions": PPDocLayoutV2SelfAttention, + } + + def __init__(self, config: PPDocLayoutV2Config): + super().__init__(config) + self.config = config + self.in_channels = config.encoder_in_channels + self.feat_strides = config.feat_strides + self.encoder_hidden_dim = config.encoder_hidden_dim + self.encode_proj_layers = config.encode_proj_layers + self.positional_encoding_temperature = config.positional_encoding_temperature + self.eval_size = config.eval_size + self.out_channels = [self.encoder_hidden_dim for _ in self.in_channels] + self.out_strides = self.feat_strides + self.num_fpn_stages = len(self.in_channels) - 1 + self.num_pan_stages = len(self.in_channels) - 1 + + # AIFI (Attention-based Intra-scale Feature Interaction) layers + self.aifi = nn.ModuleList([PPDocLayoutV2AIFILayer(config) for _ in range(len(self.encode_proj_layers))]) + + # top-down FPN + self.lateral_convs = nn.ModuleList() + self.fpn_blocks = nn.ModuleList() + for _ in range(self.num_fpn_stages): + lateral_conv = PPDocLayoutV2ConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=1, + stride=1, + activation=config.activation_function, + ) + fpn_block = PPDocLayoutV2CSPRepLayer(config) + self.lateral_convs.append(lateral_conv) + self.fpn_blocks.append(fpn_block) + + # bottom-up PAN + self.downsample_convs = nn.ModuleList() + self.pan_blocks = nn.ModuleList() + for _ in range(self.num_pan_stages): + downsample_conv = PPDocLayoutV2ConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=3, + stride=2, + activation=config.activation_function, + ) + pan_block = PPDocLayoutV2CSPRepLayer(config) + self.downsample_convs.append(downsample_conv) + self.pan_blocks.append(pan_block) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + inputs_embeds=None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + """ + feature_maps = inputs_embeds + + # AIFI: Apply transformer encoder to specified feature levels + if self.config.encoder_layers > 0: + for i, enc_ind in enumerate(self.encode_proj_layers): + feature_maps[enc_ind] = self.aifi[i](feature_maps[enc_ind], **kwargs) + + # top-down FPN + fpn_feature_maps = [feature_maps[-1]] + for idx, (lateral_conv, fpn_block) in enumerate(zip(self.lateral_convs, self.fpn_blocks)): + backbone_feature_map = feature_maps[self.num_fpn_stages - idx - 1] + top_fpn_feature_map = fpn_feature_maps[-1] + # apply lateral block + top_fpn_feature_map = lateral_conv(top_fpn_feature_map) + fpn_feature_maps[-1] = top_fpn_feature_map + # apply fpn block + top_fpn_feature_map = F.interpolate(top_fpn_feature_map, scale_factor=2.0, mode="nearest") + fused_feature_map = torch.concat([top_fpn_feature_map, backbone_feature_map], dim=1) + new_fpn_feature_map = fpn_block(fused_feature_map) + fpn_feature_maps.append(new_fpn_feature_map) + + fpn_feature_maps.reverse() + + # bottom-up PAN + pan_feature_maps = [fpn_feature_maps[0]] + for idx, (downsample_conv, pan_block) in enumerate(zip(self.downsample_convs, self.pan_blocks)): + top_pan_feature_map = pan_feature_maps[-1] + fpn_feature_map = fpn_feature_maps[idx + 1] + downsampled_feature_map = downsample_conv(top_pan_feature_map) + fused_feature_map = torch.concat([downsampled_feature_map, fpn_feature_map], dim=1) + new_pan_feature_map = pan_block(fused_feature_map) + pan_feature_maps.append(new_pan_feature_map) + + return BaseModelOutput(last_hidden_state=pan_feature_maps) + + +def inverse_sigmoid(x, eps=1e-5): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +class PPDocLayoutV2Decoder(PPDocLayoutV2PreTrainedModel): + _can_record_outputs = { + "hidden_states": PPDocLayoutV2DecoderLayer, + "attentions": PPDocLayoutV2SelfAttention, + "cross_attentions": PPDocLayoutV2MultiscaleDeformableAttention, + } + + def __init__(self, config: PPDocLayoutV2Config): + super().__init__(config) + + self.dropout = config.dropout + self.layers = nn.ModuleList([PPDocLayoutV2DecoderLayer(config) for _ in range(config.decoder_layers)]) + self.query_pos_head = PPDocLayoutV2MLPPredictionHead(4, 2 * config.d_model, config.d_model, num_layers=2) + + # hack implementation for iterative bounding box refinement and two-stage Deformable DETR + self.bbox_embed = None + self.class_embed = None + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + The query embeddings that are passed into the decoder. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected + in `[0, 1]`: + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): + Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. + spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): + Spatial shapes of the feature maps. + level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): + Indexes for the start of each feature level. In range `[0, sequence_length]`. + """ + if inputs_embeds is not None: + hidden_states = inputs_embeds + + # decoder layers + intermediate = () + intermediate_reference_points = () + intermediate_logits = () + + reference_points = F.sigmoid(reference_points) + + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/PPDocLayoutV2_pytorch/src/zoo/PPDocLayoutV2/PPDocLayoutV2_decoder.py#L252 + for idx, decoder_layer in enumerate(self.layers): + reference_points_input = reference_points.unsqueeze(2) + object_queries_position_embeddings = self.query_pos_head(reference_points) + + hidden_states = decoder_layer( + hidden_states, + object_queries_position_embeddings=object_queries_position_embeddings, + encoder_hidden_states=encoder_hidden_states, + reference_points=reference_points_input, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + encoder_attention_mask=encoder_attention_mask, + **kwargs, + ) + + # hack implementation for iterative bounding box refinement + if self.bbox_embed is not None: + predicted_corners = self.bbox_embed[idx](hidden_states) + new_reference_points = F.sigmoid(predicted_corners + inverse_sigmoid(reference_points)) + reference_points = new_reference_points.detach() + + intermediate += (hidden_states,) + intermediate_reference_points += ( + (new_reference_points,) if self.bbox_embed is not None else (reference_points,) + ) + + if self.class_embed is not None: + logits = self.class_embed[idx](hidden_states) + intermediate_logits += (logits,) + + # Keep batch_size as first dimension + intermediate = torch.stack(intermediate, dim=1) + intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) + if self.class_embed is not None: + intermediate_logits = torch.stack(intermediate_logits, dim=1) + + return PPDocLayoutV2DecoderOutput( + last_hidden_state=hidden_states, + intermediate_hidden_states=intermediate, + intermediate_logits=intermediate_logits, + intermediate_reference_points=intermediate_reference_points, + ) + + +def get_contrastive_denoising_training_group( + targets, + num_classes, + num_queries, + class_embed, + num_denoising_queries=100, + label_noise_ratio=0.5, + box_noise_scale=1.0, +): + """ + Creates a contrastive denoising training group using ground-truth samples. It adds noise to labels and boxes. + + Args: + targets (`list[dict]`): + The target objects, each containing 'class_labels' and 'boxes' for objects in an image. + num_classes (`int`): + Total number of classes in the dataset. + num_queries (`int`): + Number of query slots in the transformer. + class_embed (`callable`): + A function or a model layer to embed class labels. + num_denoising_queries (`int`, *optional*, defaults to 100): + Number of denoising queries. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + Ratio of noise applied to labels. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale of noise applied to bounding boxes. + Returns: + `tuple` comprising various elements: + - **input_query_class** (`torch.FloatTensor`) -- + Class queries with applied label noise. + - **input_query_bbox** (`torch.FloatTensor`) -- + Bounding box queries with applied box noise. + - **attn_mask** (`torch.FloatTensor`) -- + Attention mask for separating denoising and reconstruction queries. + - **denoising_meta_values** (`dict`) -- + Metadata including denoising positive indices, number of groups, and split sizes. + """ + + if num_denoising_queries <= 0: + return None, None, None, None + + num_ground_truths = [len(t["class_labels"]) for t in targets] + device = targets[0]["class_labels"].device + + max_gt_num = max(num_ground_truths) + if max_gt_num == 0: + return None, None, None, None + + num_groups_denoising_queries = num_denoising_queries // max_gt_num + num_groups_denoising_queries = 1 if num_groups_denoising_queries == 0 else num_groups_denoising_queries + # pad gt to max_num of a batch + batch_size = len(num_ground_truths) + + input_query_class = torch.full([batch_size, max_gt_num], num_classes, dtype=torch.int32, device=device) + input_query_bbox = torch.zeros([batch_size, max_gt_num, 4], device=device) + pad_gt_mask = torch.zeros([batch_size, max_gt_num], dtype=torch.bool, device=device) + + for i in range(batch_size): + num_gt = num_ground_truths[i] + if num_gt > 0: + input_query_class[i, :num_gt] = targets[i]["class_labels"] + input_query_bbox[i, :num_gt] = targets[i]["boxes"] + pad_gt_mask[i, :num_gt] = 1 + # each group has positive and negative queries. + input_query_class = input_query_class.tile([1, 2 * num_groups_denoising_queries]) + input_query_bbox = input_query_bbox.tile([1, 2 * num_groups_denoising_queries, 1]) + pad_gt_mask = pad_gt_mask.tile([1, 2 * num_groups_denoising_queries]) + # positive and negative mask + negative_gt_mask = torch.zeros([batch_size, max_gt_num * 2, 1], device=device) + negative_gt_mask[:, max_gt_num:] = 1 + negative_gt_mask = negative_gt_mask.tile([1, num_groups_denoising_queries, 1]) + positive_gt_mask = 1 - negative_gt_mask + # contrastive denoising training positive index + positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask + denoise_positive_idx = torch.nonzero(positive_gt_mask)[:, 1] + denoise_positive_idx = torch.split( + denoise_positive_idx, [n * num_groups_denoising_queries for n in num_ground_truths] + ) + # total denoising queries + num_denoising_queries = torch_int(max_gt_num * 2 * num_groups_denoising_queries) + + if label_noise_ratio > 0: + mask = torch.rand_like(input_query_class, dtype=torch.float) < (label_noise_ratio * 0.5) + # randomly put a new one here + new_label = torch.randint_like(mask, 0, num_classes, dtype=input_query_class.dtype) + input_query_class = torch.where(mask & pad_gt_mask, new_label, input_query_class) + + if box_noise_scale > 0: + known_bbox = center_to_corners_format(input_query_bbox) + diff = torch.tile(input_query_bbox[..., 2:] * 0.5, [1, 1, 2]) * box_noise_scale + rand_sign = torch.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0 + rand_part = torch.rand_like(input_query_bbox) + rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (1 - negative_gt_mask) + rand_part *= rand_sign + known_bbox += rand_part * diff + known_bbox.clip_(min=0.0, max=1.0) + input_query_bbox = corners_to_center_format(known_bbox) + input_query_bbox = inverse_sigmoid(input_query_bbox) + + input_query_class = class_embed(input_query_class) + + target_size = num_denoising_queries + num_queries + attn_mask = torch.full([target_size, target_size], 0, dtype=torch.float, device=device) + # match query cannot see the reconstruction + attn_mask[num_denoising_queries:, :num_denoising_queries] = -torch.inf + + # reconstructions cannot see each other + for i in range(num_groups_denoising_queries): + idx_block_start = max_gt_num * 2 * i + idx_block_end = max_gt_num * 2 * (i + 1) + attn_mask[idx_block_start:idx_block_end, :idx_block_start] = -torch.inf + attn_mask[idx_block_start:idx_block_end, idx_block_end:num_denoising_queries] = -torch.inf + + denoising_meta_values = { + "dn_positive_idx": denoise_positive_idx, + "dn_num_group": num_groups_denoising_queries, + "dn_num_split": [num_denoising_queries, num_queries], + } + + return input_query_class, input_query_bbox, attn_mask, denoising_meta_values + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV2 Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top. + """ +) +class PPDocLayoutV2Model(PPDocLayoutV2PreTrainedModel): + def __init__(self, config: PPDocLayoutV2Config): + super().__init__(config) + + # Create backbone + self.backbone = PPDocLayoutV2ConvEncoder(config) + intermediate_channel_sizes = self.backbone.intermediate_channel_sizes + + # Create encoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/PPDocLayoutV2_pytorch/src/zoo/PPDocLayoutV2/hybrid_encoder.py#L212 + num_backbone_outs = len(intermediate_channel_sizes) + encoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = intermediate_channel_sizes[i] + encoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.encoder_hidden_dim, kernel_size=1, bias=False), + nn.BatchNorm2d(config.encoder_hidden_dim), + ) + ) + self.encoder_input_proj = nn.ModuleList(encoder_input_proj_list) + + # Create encoder + self.encoder = PPDocLayoutV2HybridEncoder(config) + + # denoising part + if config.num_denoising > 0: + self.denoising_class_embed = nn.Embedding( + config.num_labels + 1, config.d_model, padding_idx=config.num_labels + ) + + # decoder embedding + if config.learn_initial_query: + self.weight_embedding = nn.Embedding(config.num_queries, config.d_model) + + # encoder head + self.enc_output = nn.Sequential( + nn.Linear(config.d_model, config.d_model), + nn.LayerNorm(config.d_model, eps=config.layer_norm_eps), + ) + self.enc_score_head = nn.Linear(config.d_model, config.num_labels) + self.enc_bbox_head = PPDocLayoutV2MLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + + # init encoder output anchors and valid_mask + if config.anchor_image_size: + self.anchors, self.valid_mask = self.generate_anchors(dtype=self.dtype) + + # Create decoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/PPDocLayoutV2_pytorch/src/zoo/PPDocLayoutV2/PPDocLayoutV2_decoder.py#L412 + num_backbone_outs = len(config.decoder_in_channels) + decoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = config.decoder_in_channels[i] + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + for _ in range(config.num_feature_levels - num_backbone_outs): + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + in_channels = config.d_model + self.decoder_input_proj = nn.ModuleList(decoder_input_proj_list) + + # decoder + self.decoder = PPDocLayoutV2Decoder(config) + self.denoising_class_embed = nn.Embedding(config.num_labels, config.d_model) + + self.post_init() + + def freeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(False) + + def unfreeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(True) + + @compile_compatible_method_lru_cache(maxsize=32) + def generate_anchors(self, spatial_shapes=None, grid_size=0.05, device="cpu", dtype=torch.float32): + if spatial_shapes is None: + spatial_shapes = [ + [int(self.config.anchor_image_size[0] / s), int(self.config.anchor_image_size[1] / s)] + for s in self.config.feat_strides + ] + anchors = [] + for level, (height, width) in enumerate(spatial_shapes): + grid_y, grid_x = torch.meshgrid( + torch.arange(end=height, device=device).to(dtype), + torch.arange(end=width, device=device).to(dtype), + indexing="ij", + ) + grid_xy = torch.stack([grid_x, grid_y], -1) + grid_xy = grid_xy.unsqueeze(0) + 0.5 + grid_xy[..., 0] /= width + grid_xy[..., 1] /= height + wh = torch.ones_like(grid_xy) * grid_size * (2.0**level) + anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, height * width, 4)) + # define the valid range for anchor coordinates + eps = 1e-2 + anchors = torch.concat(anchors, 1) + valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True) + anchors = torch.log(anchors / (1 - anchors)) + anchors = torch.where(valid_mask, anchors, torch.tensor(torch.finfo(dtype).max, dtype=dtype, device=device)) + + return anchors, valid_mask + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | PPDocLayoutV2ModelOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, PPDocLayoutV2Model + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("PekingU/PPDocLayoutV2_r50vd") + >>> model = PPDocLayoutV2Model.from_pretrained("PekingU/PPDocLayoutV2_r50vd") + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 300, 256] + ```""" + if pixel_values is None and inputs_embeds is None: + raise ValueError("You have to specify either pixel_values or inputs_embeds") + + if inputs_embeds is None: + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + if pixel_mask is None: + pixel_mask = torch.ones(((batch_size, height, width)), device=device) + features = self.backbone(pixel_values, pixel_mask) + proj_feats = [self.encoder_input_proj[level](source) for level, (source, mask) in enumerate(features)] + else: + batch_size = inputs_embeds.shape[0] + device = inputs_embeds.device + proj_feats = inputs_embeds + + if encoder_outputs is None: + encoder_outputs = self.encoder( + proj_feats, + **kwargs, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput + elif not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Equivalent to def _get_encoder_input + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/PPDocLayoutV2_pytorch/src/zoo/PPDocLayoutV2/PPDocLayoutV2_decoder.py#L412 + sources = [] + for level, source in enumerate(encoder_outputs.last_hidden_state): + sources.append(self.decoder_input_proj[level](source)) + + # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage + if self.config.num_feature_levels > len(sources): + _len_sources = len(sources) + sources.append(self.decoder_input_proj[_len_sources](encoder_outputs.last_hidden_state)[-1]) + for i in range(_len_sources + 1, self.config.num_feature_levels): + sources.append(self.decoder_input_proj[i](encoder_outputs.last_hidden_state[-1])) + + # Prepare encoder inputs (by flattening) + source_flatten = [] + spatial_shapes_list = [] + spatial_shapes = torch.empty((len(sources), 2), device=device, dtype=torch.long) + for level, source in enumerate(sources): + height, width = source.shape[-2:] + spatial_shapes[level, 0] = height + spatial_shapes[level, 1] = width + spatial_shapes_list.append((height, width)) + source = source.flatten(2).transpose(1, 2) + source_flatten.append(source) + source_flatten = torch.cat(source_flatten, 1) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + + # prepare denoising training + if self.training and self.config.num_denoising > 0 and labels is not None: + ( + denoising_class, + denoising_bbox_unact, + attention_mask, + denoising_meta_values, + ) = get_contrastive_denoising_training_group( + targets=labels, + num_classes=self.config.num_labels, + num_queries=self.config.num_queries, + class_embed=self.denoising_class_embed, + num_denoising_queries=self.config.num_denoising, + label_noise_ratio=self.config.label_noise_ratio, + box_noise_scale=self.config.box_noise_scale, + ) + else: + denoising_class, denoising_bbox_unact, attention_mask, denoising_meta_values = None, None, None, None + + batch_size = len(source_flatten) + device = source_flatten.device + dtype = source_flatten.dtype + + # prepare input for decoder + if self.training or self.config.anchor_image_size is None: + # Pass spatial_shapes as tuple to make it hashable and make sure + # lru_cache is working for generate_anchors() + spatial_shapes_tuple = tuple(spatial_shapes_list) + anchors, valid_mask = self.generate_anchors(spatial_shapes_tuple, device=device, dtype=dtype) + else: + anchors, valid_mask = self.anchors, self.valid_mask + anchors, valid_mask = anchors.to(device, dtype), valid_mask.to(device, dtype) + + # use the valid_mask to selectively retain values in the feature map where the mask is `True` + memory = valid_mask.to(source_flatten.dtype) * source_flatten + + output_memory = self.enc_output(memory) + + enc_outputs_class = self.enc_score_head(output_memory) + enc_outputs_coord_logits = self.enc_bbox_head(output_memory) + anchors + + _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.config.num_queries, dim=1) + + reference_points_unact = enc_outputs_coord_logits.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_coord_logits.shape[-1]) + ) + + enc_topk_bboxes = F.sigmoid(reference_points_unact) + if denoising_bbox_unact is not None: + reference_points_unact = torch.concat([denoising_bbox_unact, reference_points_unact], 1) + + enc_topk_logits = enc_outputs_class.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_class.shape[-1]) + ) + + # extract region features + if self.config.learn_initial_query: + target = self.weight_embedding.tile([batch_size, 1, 1]) + else: + target = output_memory.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, output_memory.shape[-1])) + target = target.detach() + + if denoising_class is not None: + target = torch.concat([denoising_class, target], 1) + + init_reference_points = reference_points_unact.detach() + + # decoder + decoder_outputs = self.decoder( + inputs_embeds=target, + encoder_hidden_states=source_flatten, + encoder_attention_mask=attention_mask, + reference_points=init_reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + return PPDocLayoutV2ModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, + intermediate_logits=decoder_outputs.intermediate_logits, + intermediate_reference_points=decoder_outputs.intermediate_reference_points, + intermediate_predicted_corners=decoder_outputs.intermediate_predicted_corners, + initial_reference_points=decoder_outputs.initial_reference_points, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + init_reference_points=init_reference_points, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + enc_outputs_class=enc_outputs_class, + enc_outputs_coord_logits=enc_outputs_coord_logits, + denoising_meta_values=denoising_meta_values, + ) + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV2 Model (consisting of a backbone and encoder-decoder) outputting bounding boxes, logits and order_logits to be further + decoded into scores, classes and their reading order. + """ +) +class PPDocLayoutV2ForObjectDetection(PPDocLayoutV2PreTrainedModel): + # When using clones, all layers > 0 will be clones, but layer 0 *is* required + # We can't initialize the model on meta device as some weights are modified during the initialization + _no_split_modules = None + _keys_to_ignore_on_load_missing = ["num_batches_tracked", "rel_pos_y_bias", "rel_pos_x_bias"] + + def __init__(self, config: PPDocLayoutV2Config): + super().__init__(config) + self.model = PPDocLayoutV2Model(config) + num_pred = config.decoder_layers + self.model.decoder.class_embed = nn.ModuleList( + [torch.nn.Linear(config.d_model, config.num_labels) for _ in range(num_pred)] + ) + self.model.decoder.bbox_embed = nn.ModuleList( + [PPDocLayoutV2MLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) for _ in range(num_pred)] + ) + + self.reading_order = PPDocLayoutV2ReadingOrder(config.reading_order_config) + self.num_queries = config.num_queries + self.config = config + # if two-stage, the last class_embed and bbox_embed is for region proposal generation + self.post_init() + + def _set_aux_loss(self, outputs_class, outputs_coord): + return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)] + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | PPDocLayoutV2ForObjectDetectionOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): + Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an + embedded representation. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoModelForObjectDetection, AutoImageProcessor + >>> from PIL import Image + >>> import requests + >>> import torch + + >>> url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> model_path = "PaddlePaddle/PP-DocLayoutV2_safetensors" + >>> image_processor = AutoImageProcessor.from_pretrained(model_path) + >>> model = AutoModelForObjectDetection.from_pretrained(model_path) + + >>> # prepare image for the model + >>> inputs = image_processor(images=[image], return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]])) + + >>> # print outputs + >>> for result in results: + ... for idx, (score, label_id, box) in enumerate(zip(result["scores"], result["labels"], result["boxes"])): + ... score, label = score.item(), label_id.item() + ... box = [round(i, 2) for i in box.tolist()] + ... print(f"Order {idx + 1}: {model.config.id2label[label]}: {score:.2f} {box}") + Order 1: text: 0.99 [335.39, 184.26, 896.49, 654.48] + Order 2: paragraph_title: 0.97 [337.14, 683.49, 869.42, 798.27] + Order 3: text: 0.99 [335.71, 843.04, 891.17, 1454.15] + Order 4: text: 0.99 [920.42, 185.53, 1476.39, 464.25] + Order 5: text: 0.98 [920.62, 483.75, 1480.52, 765.34] + Order 6: text: 0.98 [920.58, 846.75, 1481.94, 1220.53] + Order 7: text: 0.97 [921.12, 1239.27, 1468.87, 1377.33] + Order 8: footnote: 0.82 [334.58, 1614.67, 1483.84, 1731.61] + Order 9: text: 0.51 [334.58, 1614.67, 1483.84, 1731.61] + Order 10: footnote: 0.83 [334.7, 1757.26, 1471.07, 1845.33] + Order 11: text: 0.87 [336.65, 1910.28, 661.33, 1939.92] + Order 12: footnote: 0.95 [336.16, 2114.52, 1450.28, 2171.74] + Order 13: number: 0.87 [106.04, 2257.37, 136.05, 2281.98] + Order 14: footer: 0.93 [338.6, 2255.94, 985.67, 2283.57] + ```""" + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + labels=labels, + **kwargs, + ) + + intermediate_reference_points = outputs.intermediate_reference_points + intermediate_logits = outputs.intermediate_logits + raw_bboxes = intermediate_reference_points[:, -1] + logits = intermediate_logits[:, -1] + + box_centers, box_sizes = raw_bboxes.split(2, dim=-1) + bboxes = torch.cat([box_centers - 0.5 * box_sizes, box_centers + 0.5 * box_sizes], dim=-1) * 1000 + bboxes = bboxes.clamp_(0.0, 1000.0) + + max_logits, class_ids = logits.max(dim=-1) + max_probs = max_logits.sigmoid() + + class_thresholds = torch.tensor(self.config.class_thresholds, dtype=torch.float32, device=logits.device) + thresholds = class_thresholds[class_ids] + mask = max_probs >= thresholds + + indices = torch.argsort(mask.to(torch.int8), dim=1, descending=True) + + sorted_class_ids = torch.take_along_dim(class_ids, indices, dim=1) + sorted_boxes = torch.take_along_dim(bboxes, indices[..., None].expand(-1, -1, 4), dim=1) + pred_boxes = torch.take_along_dim(raw_bboxes, indices[..., None].expand(-1, -1, 4), dim=1) + logits = torch.take_along_dim(logits, indices[..., None].expand(-1, -1, logits.size(-1)), dim=1) + + sorted_mask = torch.take_along_dim(mask, indices, dim=1) + + pad_boxes = torch.where(sorted_mask[..., None], sorted_boxes, torch.zeros_like(sorted_boxes)) + pad_class_ids = torch.where(sorted_mask, sorted_class_ids, torch.zeros_like(sorted_class_ids)) + + class_order = torch.tensor(self.config.class_order, dtype=torch.int32, device=logits.device) + pad_class_ids = class_order[pad_class_ids] + + order_logits = self.reading_order( + boxes=pad_boxes, + labels=pad_class_ids, + mask=mask, + ) + order_logits = order_logits[:, :, : self.num_queries] + + if labels is not None: + raise ValueError("PPDocLayoutV2ForObjectDetection does not support training") + + return PPDocLayoutV2ForObjectDetectionOutput( + logits=logits, + pred_boxes=pred_boxes, + order_logits=order_logits, + last_hidden_state=outputs.last_hidden_state, + intermediate_hidden_states=outputs.intermediate_hidden_states, + intermediate_logits=outputs.intermediate_logits, + intermediate_reference_points=outputs.intermediate_reference_points, + intermediate_predicted_corners=outputs.intermediate_predicted_corners, + initial_reference_points=outputs.initial_reference_points, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + init_reference_points=outputs.init_reference_points, + enc_topk_logits=outputs.enc_topk_logits, + enc_topk_bboxes=outputs.enc_topk_bboxes, + enc_outputs_class=outputs.enc_outputs_class, + enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, + denoising_meta_values=outputs.denoising_meta_values, + ) + + +__all__ = [ + "PPDocLayoutV2ForObjectDetection", + "PPDocLayoutV2Model", + "PPDocLayoutV2PreTrainedModel", + "PPDocLayoutV2ReadingOrder", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/modular_pp_doclayout_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/modular_pp_doclayout_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..33562a613f730f0aabf04ea9482c73833c2b9e9f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v2/modular_pp_doclayout_v2.py @@ -0,0 +1,1001 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass +from typing import Optional + +import torch +from huggingface_hub.dataclasses import strict +from torch import nn + +from ... import initialization as init +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...masking_utils import create_bidirectional_mask +from ...processing_utils import Unpack +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + can_return_tuple, + logging, +) +from ...utils.generic import TensorType +from ..auto import AutoConfig +from ..layoutlmv3.modeling_layoutlmv3 import ( + LayoutLMv3Attention, + LayoutLMv3Encoder, + LayoutLMv3Intermediate, + LayoutLMv3Layer, + LayoutLMv3Output, + LayoutLMv3SelfAttention, + LayoutLMv3SelfOutput, + LayoutLMv3TextEmbeddings, +) +from ..pp_doclayout_v3.image_processing_pp_doclayout_v3 import PPDocLayoutV3ImageProcessor +from ..pp_doclayout_v3.modeling_pp_doclayout_v3 import PPDocLayoutV3GlobalPointer +from ..rt_detr.modeling_rt_detr import ( + RTDetrForObjectDetection, + RTDetrMLPPredictionHead, + RTDetrModel, + RTDetrModelOutput, + RTDetrPreTrainedModel, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PP-DocLayoutV2_safetensors") +@strict +class PPDocLayoutV2ReadingOrderConfig(PreTrainedConfig): + r""" + has_relative_attention_bias (`bool`, *optional*, defaults to `True`): + Whether or not to use a relative attention bias in the self-attention mechanism. + has_spatial_attention_bias (`bool`, *optional*, defaults to `True`): + Whether or not to use a spatial attention bias in the self-attention mechanism. + rel_pos_bins (`int`, *optional*, defaults to 32): + The number of relative position bins to be used in the self-attention mechanism. + max_rel_pos (`int`, *optional*, defaults to 128): + The maximum number of relative positions to be used in the self-attention mechanism. + rel_2d_pos_bins (`int`, *optional*, defaults to 64): + The number of 2D relative position bins in the self-attention mechanism. + max_rel_2d_pos (`int`, *optional*, defaults to 256): + The maximum number of relative 2D positions in the self-attention mechanism. + max_2d_position_embeddings (`int`, *optional*, defaults to 1024): + The maximum value that the 2D position embedding might ever be used with. Typically set this to something + large just in case (e.g., 1024). + start_token_id (`int`, *optional*, defaults to 0): + Token id representing the start of a sequence. + end_token_id (`int`, *optional*, defaults to 2): + Token id representing the end of a sequence. + pred_token_id (`int`, *optional*, defaults to 3): + Token id representing valid prediction positions (placeholders) in the sequence. + coordinate_size (`int`, *optional*, defaults to 171): + Dimension of the coordinate embeddings. + shape_size (`int`, *optional*, defaults to 170): + Dimension of the width and height embeddings. + num_classes (`int`, *optional*, defaults to 20): + Number of labels or classes for the layout elements. + relation_bias_embed_dim (`int`, *optional*, defaults to 16): + Embedding dimension for the relation bias. + relation_bias_theta (`float`, *optional*, defaults to 10000): + Temperature parameter used for relation bias scaling. + relation_bias_scale (`float`, *optional*, defaults to 100): + Scale parameter for the relation bias. + global_pointer_head_size (`int`, *optional*, defaults to 64): + The size of the global pointer head. + gp_dropout_value (`float`, *optional*, defaults to 0.0): + The dropout probability in the global pointer head. + """ + + hidden_size: int = 512 + num_attention_heads: int = 8 + attention_probs_dropout_prob: float | int = 0.1 + has_relative_attention_bias: bool = False + has_spatial_attention_bias: bool = True + layer_norm_eps: float = 1e-5 + hidden_dropout_prob: float | int = 0.1 + intermediate_size: int = 2048 + hidden_act: str = "gelu" + num_hidden_layers: int = 6 + rel_pos_bins: int = 32 + max_rel_pos: int = 128 + rel_2d_pos_bins: int = 64 + max_rel_2d_pos: int = 256 + max_position_embeddings: int = 514 + max_2d_position_embeddings: int = 1024 + type_vocab_size: int = 1 + vocab_size: int = 4 + initializer_range: float = 0.01 + start_token_id: int = 0 + pad_token_id: int | None = 1 + end_token_id: int = 2 + pred_token_id: int = 3 + coordinate_size: int = 171 + shape_size: int = 170 + num_classes: int = 20 + relation_bias_embed_dim: int = 16 + relation_bias_theta: int = 10000 + relation_bias_scale: int = 100 + global_pointer_head_size: int = 64 + gp_dropout_value: float | int = 0.0 + + +@auto_docstring(checkpoint="PaddlePaddle/PP-DocLayoutV2_safetensors") +@strict +class PPDocLayoutV2Config(PreTrainedConfig): + r""" + initializer_bias_prior_prob (`float`, *optional*): + The prior probability used by the bias initializer to initialize biases for `enc_score_head` and `class_embed`. + If `None`, `prior_prob` computed as `prior_prob = 1 / (num_labels + 1)` while initializing model weights. + freeze_backbone_batch_norms (`bool`, *optional*, defaults to `True`): + Whether to freeze the batch normalization layers in the backbone. + encoder_hidden_dim (`int`, *optional*, defaults to 256): + Dimension of the layers in hybrid encoder. + encoder_in_channels (`list`, *optional*, defaults to `[512, 1024, 2048]`): + Multi level features input for encoder. + feat_strides (`list[int]`, *optional*, defaults to `[8, 16, 32]`): + Strides used in each feature map. + encoder_layers (`int`, *optional*, defaults to 1): + Total of layers to be used by the encoder. + encode_proj_layers (`list[int]`, *optional*, defaults to `[2]`): + Indexes of the projected layers to be used in the encoder. + positional_encoding_temperature (`int`, *optional*, defaults to 10000): + The temperature parameter used to create the positional encodings. + encoder_activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + eval_size (`tuple[int, int]`, *optional*): + Height and width used to computes the effective height and width of the position embeddings after taking + into account the stride. + normalize_before (`bool`, *optional*, defaults to `False`): + Determine whether to apply layer normalization in the transformer encoder layer before self-attention and + feed-forward modules. + hidden_expansion (`float`, *optional*, defaults to 1.0): + Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. + num_queries (`int`, *optional*, defaults to 300): + Number of object queries. + decoder_in_channels (`list`, *optional*, defaults to `[256, 256, 256]`): + Multi level features dimension for decoder + decoder_ffn_dim (`int`, *optional*, defaults to 1024): + Dimension of the "intermediate" (often named feed-forward) layer in decoder. + num_feature_levels (`int`, *optional*, defaults to 3): + The number of input feature levels. + decoder_n_points (`int`, *optional*, defaults to 4): + The number of sampled keys in each feature level for each attention head in the decoder. + decoder_activation_function (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + num_denoising (`int`, *optional*, defaults to 100): + The total number of denoising tasks or queries to be used for contrastive denoising. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + The fraction of denoising labels to which random noise should be added. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale or magnitude of noise to be added to the bounding boxes. + learn_initial_query (`bool`, *optional*, defaults to `False`): + Indicates whether the initial query embeddings for the decoder should be learned during training + anchor_image_size (`tuple[int, int]`, *optional*): + Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. + disable_custom_kernels (`bool`, *optional*, defaults to `True`): + Whether to disable custom kernels. + class_thresholds (`list[float]`, *optional*): + The thresholds for each label. + class_order (`list[int]`, *optional*): + The priority for each label. + reading_order_config (`dict`, *optional*): + The configuration of a `PPDocLayoutV2ReadingOrder`. + + Examples: + + ```python + >>> from transformers import PPDocLayoutV2Config, PPDocLayoutV2ForObjectDetection + + >>> # Initializing a PP-DocLayoutV2 configuration + >>> configuration = PPDocLayoutV2Config() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = PPDocLayoutV2ForObjectDetection(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pp_doclayout_v2" + sub_configs = {"backbone_config": AutoConfig, "reading_order_config": PPDocLayoutV2ReadingOrderConfig} + + layer_types = ("basic", "bottleneck") + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + initializer_range: float = 0.01 + initializer_bias_prior_prob: float | None = None + layer_norm_eps: float = 1e-5 + batch_norm_eps: float = 1e-5 + backbone_config: PreTrainedConfig | dict | None = None + freeze_backbone_batch_norms: bool = True + encoder_hidden_dim: int = 256 + encoder_in_channels: list[int] | tuple[int, ...] | None = (512, 1024, 2048) + feat_strides: list[int] | tuple[int, ...] | None = (8, 16, 32) + encoder_layers: int = 1 + encoder_ffn_dim: int = 1024 + encoder_attention_heads: int = 8 + dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + encode_proj_layers: list[int] | tuple[int, ...] | None = (2,) + positional_encoding_temperature: int = 10000 + encoder_activation_function: str = "gelu" + activation_function: str = "silu" + eval_size: list[int] | None = None + normalize_before: bool = False + hidden_expansion: float = 1.0 + d_model: int = 256 + num_queries: int = 300 + decoder_in_channels: list[int] | tuple[int, ...] | None = (256, 256, 256) + decoder_ffn_dim: int = 1024 + num_feature_levels: int = 3 + decoder_n_points: int = 4 + decoder_layers: int = 6 + decoder_attention_heads: int = 8 + decoder_activation_function: str = "relu" + attention_dropout: float | int = 0.0 + num_denoising: int = 100 + label_noise_ratio: float = 0.5 + box_noise_scale: float = 1.0 + learn_initial_query: bool = False + anchor_image_size: list[int] | None = None + disable_custom_kernels: bool = True + is_encoder_decoder: bool = True + class_thresholds: list[float] | None = None + class_order: list[int] | None = None + reading_order_config: PreTrainedConfig | dict | None = None + + def __post_init__(self, **kwargs): + if isinstance(self.reading_order_config, dict): + self.reading_order_config = self.sub_configs["reading_order_config"](**self.reading_order_config) + elif self.reading_order_config is None: + self.reading_order_config = self.sub_configs["reading_order_config"]() + + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [0, 0.05, 0.05, 0.05, 0.05], + "out_features": ["stage2", "stage3", "stage4"], + }, + **kwargs, + ) + + self.encoder_in_channels = list(self.encoder_in_channels) + self.feat_strides = list(self.feat_strides) + self.encode_proj_layers = list(self.encode_proj_layers) + self.eval_size = list(self.eval_size) if self.eval_size is not None else None + self.decoder_in_channels = list(self.decoder_in_channels) + self.anchor_image_size = list(self.anchor_image_size) if self.anchor_image_size is not None else None + + super().__post_init__(**kwargs) + + +class PPDocLayoutV2ImageProcessor(PPDocLayoutV3ImageProcessor): + def extract_custom_vertices(self): + raise AttributeError("Not needed for PPDocLayoutV2") + + def _mask2polygon(self): + raise AttributeError("Not needed for PPDocLayoutV2") + + def _extract_polygon_points_by_masks(self): + raise AttributeError("Not needed for PPDocLayoutV2") + + def post_process_object_detection( + self, + outputs, + threshold: float = 0.5, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + PPDocLayoutV2 is identical to PPDocLayoutV3, except that it does not return `polygon_points`. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. + """ + boxes = outputs.pred_boxes + logits = outputs.logits + order_logits = outputs.order_logits + + order_seqs = self._get_order_seqs(order_logits) + + box_centers, box_dims = torch.split(boxes, 2, dim=-1) + top_left_coords = box_centers - 0.5 * box_dims + bottom_right_coords = box_centers + 0.5 * box_dims + boxes = torch.cat([top_left_coords, bottom_right_coords], dim=-1) + + if target_sizes is not None: + if len(logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + if isinstance(target_sizes, list): + img_height, img_width = torch.as_tensor(target_sizes).unbind(1) + else: + img_height, img_width = target_sizes.unbind(1) + scale_factor = torch.stack([img_width, img_height, img_width, img_height], dim=1).to(boxes.device) + boxes = boxes * scale_factor[:, None, :] + + num_top_queries = logits.shape[1] + num_classes = logits.shape[2] + + scores = torch.nn.functional.sigmoid(logits) + scores, index = torch.topk(scores.flatten(1), num_top_queries, dim=-1) + labels = index % num_classes + index = index // num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + order_seqs = order_seqs.gather(dim=1, index=index) + + results = [] + for score, label, box, order_seq in zip(scores, labels, boxes, order_seqs): + order_seq = order_seq[score >= threshold] + order_seq, indices = torch.sort(order_seq) + results.append( + { + "scores": score[score >= threshold][indices], + "labels": label[score >= threshold][indices], + "boxes": box[score >= threshold][indices], + "order_seq": order_seq, + } + ) + + return results + + +class PPDocLayoutV2GlobalPointer(PPDocLayoutV3GlobalPointer): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, self.head_size * 2) + + +class PPDocLayoutV2PositionRelationEmbedding(nn.Module): + inv_freq: torch.Tensor + + def __init__(self, config, device=None): + super().__init__() + self.config = config + self.embed_dim = config.relation_bias_embed_dim + self.scale = config.relation_bias_scale + self.pos_proj = nn.Conv2d( + in_channels=self.embed_dim * 4, out_channels=config.num_attention_heads, kernel_size=1 + ) + inv_freq, self.attention_scaling = self.compute_default_rope_parameters(config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: PPDocLayoutV2Config | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.relation_bias_theta + dim = config.relation_bias_embed_dim + half_dim = dim // 2 + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / half_dim) + ) + return inv_freq, attention_factor + + def box_relative_encoding( + self, source_boxes: torch.Tensor, target_boxes: torch.Tensor = None, epsilon: float = 1e-5 + ): + source_boxes, target_boxes = source_boxes.unsqueeze(-2), target_boxes.unsqueeze(-3) + source_coordinates, source_dim = source_boxes[..., :2], source_boxes[..., 2:] + target_coordinates, target_dim = target_boxes[..., :2], target_boxes[..., 2:] + + coordinate_difference = torch.abs(source_coordinates - target_coordinates) + relative_coordinates = torch.log(coordinate_difference / (source_dim + epsilon) + 1.0) + relative_dim = torch.log((source_dim + epsilon) / (target_dim + epsilon)) + + relative_encoding = torch.cat([relative_coordinates, relative_dim], dim=-1) + + return relative_encoding + + def get_position_embedding(self, x: torch.Tensor, scale: float = 100.0): + embedding = (x * scale).unsqueeze(-1) * self.inv_freq + embedding = torch.cat((embedding.sin(), embedding.cos()), dim=-1).flatten(start_dim=-2).to(x.dtype) + + return embedding + + def forward(self, source_boxes: torch.Tensor, target_boxes: torch.Tensor = None): + if target_boxes is None: + target_boxes = source_boxes + with torch.no_grad(): + relative_encoding = self.box_relative_encoding(source_boxes, target_boxes) + position_embedding = self.get_position_embedding(relative_encoding, self.scale) + position_embedding = position_embedding.permute(0, 3, 1, 2) + out = self.pos_proj(position_embedding) + return out + + +class PPDocLayoutV2ReadingOrderSelfAttention(LayoutLMv3SelfAttention): + def forward( + self, hidden_states, attention_mask=None, rel_pos=None, rel_2d_pos=None, **kwargs: Unpack[TransformersKwargs] + ): + batch_size, seq_length, _ = hidden_states.shape + query_layer = ( + self.query(hidden_states) + .view(batch_size, -1, self.num_attention_heads, self.attention_head_size) + .transpose(1, 2) + ) + key_layer = ( + self.key(hidden_states) + .view(batch_size, -1, self.num_attention_heads, self.attention_head_size) + .transpose(1, 2) + ) + value_layer = ( + self.value(hidden_states) + .view(batch_size, -1, self.num_attention_heads, self.attention_head_size) + .transpose(1, 2) + ) + + # Take the dot product between "query" and "key" to get the raw attention scores. + # The attention scores QT K/√d could be significantly larger than input elements, and result in overflow. + # Changing the computational order into QT(K/√d) alleviates the problem. (https://huggingface.co/papers/2105.13290) + attention_scores = torch.matmul(query_layer / math.sqrt(self.attention_head_size), key_layer.transpose(-1, -2)) + + # NOTE: + # PPDocLayoutV2ReadingOrder applies unscaled rel_2d_pos + # unlike LayoutLMv3 which uses (rel_pos + rel_2d_pos) / math.sqrt(self.attention_head_size) + if rel_2d_pos is not None: + attention_scores += rel_2d_pos + elif self.has_relative_attention_bias: + attention_scores += rel_pos / math.sqrt(self.attention_head_size) + + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RobertaModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + # Use the trick of the CogView paper to stabilize training + attention_probs = self.cogview_attention(attention_scores) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer, attention_probs + + +class PPDocLayoutV2ReadingOrderSelfOutput(LayoutLMv3SelfOutput): + def __init__(self, config): + super().__init__() + del self.LayerNorm + self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.norm(hidden_states + input_tensor) + return hidden_states + + +class PPDocLayoutV2ReadingOrderIntermediate(LayoutLMv3Intermediate): + pass + + +class PPDocLayoutV2ReadingOrderOutput(LayoutLMv3Output): + def __init__(self, config): + super().__init__() + del self.LayerNorm + self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.norm(hidden_states + input_tensor) + return hidden_states + + +class PPDocLayoutV2ReadingOrderAttention(LayoutLMv3Attention): + def __init__(self, config): + super().__init__() + self.self = PPDocLayoutV2ReadingOrderSelfAttention(config) + self.output = PPDocLayoutV2ReadingOrderSelfOutput(config) + + +class PPDocLayoutV2ReadingOrderLayer(LayoutLMv3Layer): + def __init__(self, config): + super().__init__() + self.attention = PPDocLayoutV2ReadingOrderAttention(config) + self.intermediate = PPDocLayoutV2ReadingOrderIntermediate(config) + self.output = PPDocLayoutV2ReadingOrderOutput(config) + + +class PPDocLayoutV2ReadingOrderEncoder(LayoutLMv3Encoder): + def __init__(self, config): + super().__init__(config) + self.layer = nn.ModuleList([PPDocLayoutV2ReadingOrderLayer(config) for _ in range(config.num_hidden_layers)]) + self.rel_bias_module = PPDocLayoutV2PositionRelationEmbedding(config) + + def _cal_2d_pos_emb(self, bbox): + x_min, y_min, x_max, y_max = ( + bbox[..., 0], + bbox[..., 1], + bbox[..., 2], + bbox[..., 3], + ) + + width = (x_max - x_min).clamp(min=1e-3) + height = (y_max - y_min).clamp(min=1e-3) + + center_x = (x_min + x_max) * 0.5 + center_y = (y_min + y_max) * 0.5 + + center_width_height_bbox = torch.stack([center_x, center_y, width, height], dim=-1) + + result = self.rel_bias_module(center_width_height_bbox) + + return result + + +class PPDocLayoutV2TextEmbeddings(LayoutLMv3TextEmbeddings): + def __init__(self, config): + super().__init__(config) + + del self.LayerNorm + self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + spatial_embed_dim = 4 * config.coordinate_size + 2 * config.shape_size + self.spatial_proj = nn.Linear(spatial_embed_dim, config.hidden_size) + + def forward( + self, + input_ids=None, + bbox=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx).to( + input_ids.device + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = inputs_embeds + token_type_embeddings + + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + + # custom new spatial embeddings + spatial_position_embeddings = self.calculate_spatial_position_embeddings(bbox) + spatial_position_embeddings = self.spatial_proj(spatial_position_embeddings) + embeddings += spatial_position_embeddings + return embeddings + + +@auto_docstring +class PPDocLayoutV2PreTrainedModel(RTDetrPreTrainedModel): + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, PPDocLayoutV2TextEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + if isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + init.zeros_(module.weight.data[module.padding_idx]) + if isinstance(module, PPDocLayoutV2PositionRelationEmbedding): + inv_freq, _ = module.compute_default_rope_parameters(module.config, module.inv_freq.device) + module.register_buffer("inv_freq", inv_freq, persistent=False) + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV2 ReadingOrder Model. This model consists of an encoder and a GlobalPointer head. + It takes layout features as input and outputs logits representing the relative ordering relationships + between elements, which are used to determine the final reading sequence. + """ +) +class PPDocLayoutV2ReadingOrder(PPDocLayoutV2PreTrainedModel): + # Attention is based on LayoutLMv3 (no interface) + _supports_sdpa = False + _supports_flash_attn = False + _supports_attention_backend = False + _supports_flex_attn = False + + def __init__(self, config): + super().__init__(config) + self.embeddings = PPDocLayoutV2TextEmbeddings(config) + self.label_embeddings = nn.Embedding(config.num_classes, config.hidden_size) + self.label_features_projection = nn.Linear(config.hidden_size, config.hidden_size) + self.encoder = PPDocLayoutV2ReadingOrderEncoder(config) + self.relative_head = PPDocLayoutV2GlobalPointer(config) + self.config = config + + self.post_init() + + @auto_docstring + def forward(self, boxes, labels=None, mask=None, **kwargs: Unpack[TransformersKwargs]): + r""" + boxes (`torch.Tensor` of shape `(batch_size, sequence_length, 4)`): + Bounding box coordinates of the detected layout elements **in [0, 1000] scale**. + Format is `[x_min, y_min, x_max, y_max]`. + The tensor usually contains sorted valid boxes followed by zero-padding. + labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + The **remapped** class indices for each layout element. + These are not necessarily the raw detection class IDs, but indices mapped via + `config.class_order` (e.g., mapping text/title/figure to specific reading-order category IDs). + mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Boolean or Binary mask indicating valid detected elements after threshold filtering. + - True: Valid layout element. + - False: Padding/Empty element. + Used to determine the sequence length (`num_pred`) for the pointer mechanism. + """ + device = mask.device + batch_size, seq_len = mask.shape + num_pred = mask.sum(dim=1) + + input_ids = torch.full( + (batch_size, seq_len + 2), self.config.pad_token_id, dtype=torch.long, device=boxes.device + ) + input_ids[:, 0] = self.config.start_token_id + + pred_col_idx = torch.arange(seq_len + 2, device=device).unsqueeze(0) + pred_mask = (pred_col_idx >= 1) & (pred_col_idx <= num_pred.unsqueeze(1)) + input_ids[pred_mask] = self.config.pred_token_id + end_col_indices = num_pred + 1 + input_ids[torch.arange(batch_size, device=device), end_col_indices] = self.config.end_token_id + + pad_box = torch.zeros(size=[boxes.shape[0], 1, boxes.shape[-1]], dtype=boxes.dtype, device=boxes.device) + pad_boxes = torch.cat([pad_box, boxes, pad_box], dim=1) + bbox_embedding = self.embeddings(input_ids=input_ids, bbox=pad_boxes.long()) + + if labels is not None: + label_embs = self.label_embeddings(labels) + label_proj = self.label_features_projection(label_embs) + pad = torch.zeros( + size=[label_proj.shape[0], 1, label_proj.shape[-1]], dtype=label_proj.dtype, device=labels.device + ) + label_proj = torch.cat([pad, label_proj, pad], dim=1) + else: + label_proj = torch.zeros_like(bbox_embedding) + + final_embeddings = bbox_embedding + label_proj + final_embeddings = self.embeddings.norm(final_embeddings) + final_embeddings = self.embeddings.dropout(final_embeddings) + + attention_mask = pred_col_idx < (num_pred + 2).unsqueeze(1) + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=final_embeddings, + attention_mask=attention_mask, + ) + encoder_output = self.encoder(hidden_states=final_embeddings, bbox=pad_boxes, attention_mask=attention_mask) + encoder_output = encoder_output.last_hidden_state + token = encoder_output[:, 1 : 1 + seq_len, :] + read_order_logits = self.relative_head(token) + return read_order_logits + + +@dataclass +@auto_docstring +class PPDocLayoutV2ForObjectDetectionOutput(ModelOutput): + r""" + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~RTDetrImageProcessor.post_process_object_detection`] to retrieve the + unnormalized (absolute) bounding boxes. + order_logits (`tuple` of `torch.FloatTensor` of shape `(batch_size, num_queries, num_queries)`): + Order logits for all queries. The first dimension of each tensor is the batch size. The second dimension is the number of queries. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values + """ + + logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + order_logits: tuple[torch.FloatTensor] | None = None + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: tuple[torch.FloatTensor] | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the PP-DocLayoutV2 encoder-decoder model. + """ +) +@dataclass +class PPDocLayoutV2ModelOutput(RTDetrModelOutput): + pass + + +class PPDocLayoutV2MLPPredictionHead(RTDetrMLPPredictionHead): + pass + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV2 Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top. + """ +) +class PPDocLayoutV2Model(RTDetrModel): + def __init__(self, config: PPDocLayoutV2Config): + super().__init__() + self.denoising_class_embed = nn.Embedding(config.num_labels, config.d_model) + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV2 Model (consisting of a backbone and encoder-decoder) outputting bounding boxes, logits and order_logits to be further + decoded into scores, classes and their reading order. + """ +) +class PPDocLayoutV2ForObjectDetection(RTDetrForObjectDetection): + _keys_to_ignore_on_load_missing = ["num_batches_tracked", "rel_pos_y_bias", "rel_pos_x_bias"] + + def __init__(self, config: PPDocLayoutV2Config): + super().__init__(config) + + self.reading_order = PPDocLayoutV2ReadingOrder(config.reading_order_config) + self.num_queries = config.num_queries + self.config = config + + self.post_init() + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | PPDocLayoutV2ForObjectDetectionOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): + Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an + embedded representation. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoModelForObjectDetection, AutoImageProcessor + >>> from PIL import Image + >>> import requests + >>> import torch + + >>> url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> model_path = "PaddlePaddle/PP-DocLayoutV2_safetensors" + >>> image_processor = AutoImageProcessor.from_pretrained(model_path) + >>> model = AutoModelForObjectDetection.from_pretrained(model_path) + + >>> # prepare image for the model + >>> inputs = image_processor(images=[image], return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]])) + + >>> # print outputs + >>> for result in results: + ... for idx, (score, label_id, box) in enumerate(zip(result["scores"], result["labels"], result["boxes"])): + ... score, label = score.item(), label_id.item() + ... box = [round(i, 2) for i in box.tolist()] + ... print(f"Order {idx + 1}: {model.config.id2label[label]}: {score:.2f} {box}") + Order 1: text: 0.99 [335.39, 184.26, 896.49, 654.48] + Order 2: paragraph_title: 0.97 [337.14, 683.49, 869.42, 798.27] + Order 3: text: 0.99 [335.71, 843.04, 891.17, 1454.15] + Order 4: text: 0.99 [920.42, 185.53, 1476.39, 464.25] + Order 5: text: 0.98 [920.62, 483.75, 1480.52, 765.34] + Order 6: text: 0.98 [920.58, 846.75, 1481.94, 1220.53] + Order 7: text: 0.97 [921.12, 1239.27, 1468.87, 1377.33] + Order 8: footnote: 0.82 [334.58, 1614.67, 1483.84, 1731.61] + Order 9: text: 0.51 [334.58, 1614.67, 1483.84, 1731.61] + Order 10: footnote: 0.83 [334.7, 1757.26, 1471.07, 1845.33] + Order 11: text: 0.87 [336.65, 1910.28, 661.33, 1939.92] + Order 12: footnote: 0.95 [336.16, 2114.52, 1450.28, 2171.74] + Order 13: number: 0.87 [106.04, 2257.37, 136.05, 2281.98] + Order 14: footer: 0.93 [338.6, 2255.94, 985.67, 2283.57] + ```""" + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + labels=labels, + **kwargs, + ) + + intermediate_reference_points = outputs.intermediate_reference_points + intermediate_logits = outputs.intermediate_logits + raw_bboxes = intermediate_reference_points[:, -1] + logits = intermediate_logits[:, -1] + + box_centers, box_sizes = raw_bboxes.split(2, dim=-1) + bboxes = torch.cat([box_centers - 0.5 * box_sizes, box_centers + 0.5 * box_sizes], dim=-1) * 1000 + bboxes = bboxes.clamp_(0.0, 1000.0) + + max_logits, class_ids = logits.max(dim=-1) + max_probs = max_logits.sigmoid() + + class_thresholds = torch.tensor(self.config.class_thresholds, dtype=torch.float32, device=logits.device) + thresholds = class_thresholds[class_ids] + mask = max_probs >= thresholds + + indices = torch.argsort(mask.to(torch.int8), dim=1, descending=True) + + sorted_class_ids = torch.take_along_dim(class_ids, indices, dim=1) + sorted_boxes = torch.take_along_dim(bboxes, indices[..., None].expand(-1, -1, 4), dim=1) + pred_boxes = torch.take_along_dim(raw_bboxes, indices[..., None].expand(-1, -1, 4), dim=1) + logits = torch.take_along_dim(logits, indices[..., None].expand(-1, -1, logits.size(-1)), dim=1) + + sorted_mask = torch.take_along_dim(mask, indices, dim=1) + + pad_boxes = torch.where(sorted_mask[..., None], sorted_boxes, torch.zeros_like(sorted_boxes)) + pad_class_ids = torch.where(sorted_mask, sorted_class_ids, torch.zeros_like(sorted_class_ids)) + + class_order = torch.tensor(self.config.class_order, dtype=torch.int32, device=logits.device) + pad_class_ids = class_order[pad_class_ids] + + order_logits = self.reading_order( + boxes=pad_boxes, + labels=pad_class_ids, + mask=mask, + ) + order_logits = order_logits[:, :, : self.num_queries] + + if labels is not None: + raise ValueError("PPDocLayoutV2ForObjectDetection does not support training") + + return PPDocLayoutV2ForObjectDetectionOutput( + logits=logits, + pred_boxes=pred_boxes, + order_logits=order_logits, + last_hidden_state=outputs.last_hidden_state, + intermediate_hidden_states=outputs.intermediate_hidden_states, + intermediate_logits=outputs.intermediate_logits, + intermediate_reference_points=outputs.intermediate_reference_points, + intermediate_predicted_corners=outputs.intermediate_predicted_corners, + initial_reference_points=outputs.initial_reference_points, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + init_reference_points=outputs.init_reference_points, + enc_topk_logits=outputs.enc_topk_logits, + enc_topk_bboxes=outputs.enc_topk_bboxes, + enc_outputs_class=outputs.enc_outputs_class, + enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, + denoising_meta_values=outputs.denoising_meta_values, + ) + + +__all__ = [ + "PPDocLayoutV2ForObjectDetection", + "PPDocLayoutV2ImageProcessor", + "PPDocLayoutV2Config", + "PPDocLayoutV2Model", + "PPDocLayoutV2PreTrainedModel", + "PPDocLayoutV2ReadingOrder", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c25d7d8e98c5dfbb5881e6bfb0132b5598a5e177 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_doclayout_v3 import * + from .image_processing_pp_doclayout_v3 import * + from .modeling_pp_doclayout_v3 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/configuration_pp_doclayout_v3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/configuration_pp_doclayout_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..f490ac7bc36f54ec1c39cda2423f42fba43133cd --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/configuration_pp_doclayout_v3.py @@ -0,0 +1,192 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_doclayout_v3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PaddlePaddle/PP-DocLayoutV3_safetensors") +@strict +class PPDocLayoutV3Config(PreTrainedConfig): + r""" + initializer_bias_prior_prob (`float`, *optional*): + The prior probability used by the bias initializer to initialize biases for `enc_score_head` and `class_embed`. + If `None`, `prior_prob` computed as `prior_prob = 1 / (num_labels + 1)` while initializing model weights. + freeze_backbone_batch_norms (`bool`, *optional*, defaults to `True`): + Whether to freeze the batch normalization layers in the backbone. + encoder_in_channels (`list`, *optional*, defaults to `[512, 1024, 2048]`): + Multi level features input for encoder. + feat_strides (`list[int]`, *optional*, defaults to `[8, 16, 32]`): + Strides used in each feature map. + encode_proj_layers (`list[int]`, *optional*, defaults to `[2]`): + Indexes of the projected layers to be used in the encoder. + positional_encoding_temperature (`int`, *optional*, defaults to 10000): + The temperature parameter used to create the positional encodings. + encoder_activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + eval_size (`tuple[int, int]`, *optional*): + Height and width used to computes the effective height and width of the position embeddings after taking + into account the stride. + normalize_before (`bool`, *optional*, defaults to `False`): + Determine whether to apply layer normalization in the transformer encoder layer before self-attention and + feed-forward modules. + hidden_expansion (`float`, *optional*, defaults to 1.0): + Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. + mask_feature_channels (`list[int]`, *optional*, defaults to `[64, 64]`): + The channels of the multi-level features for mask enhancement. + x4_feat_dim (`int`, *optional*, defaults to 128): + The dimension of the x4 feature map. + d_model (`int`, *optional*, defaults to 256): + Dimension of the layers exclude hybrid encoder. + num_prototypes (`int`, *optional*, defaults to 32): + Dimension of the layers exclude mask query head. + label_noise_ratio (`float`, *optional*, defaults to 0.4): + The fraction of denoising labels to which random noise should be added. + box_noise_scale (`float`, *optional*, defaults to 0.4): + Scale or magnitude of noise to be added to the bounding boxes. + mask_enhanced (`bool`, *optional*, defaults to `True`): + Whether to use enhanced masked attention. + num_queries (`int`, *optional*, defaults to 300): + Number of object queries. + decoder_in_channels (`list`, *optional*, defaults to `[256, 256, 256]`): + Multi level features dimension for decoder + decoder_ffn_dim (`int`, *optional*, defaults to 1024): + Dimension of the "intermediate" (often named feed-forward) layer in decoder. + num_feature_levels (`int`, *optional*, defaults to 3): + The number of input feature levels. + decoder_n_points (`int`, *optional*, defaults to 4): + The number of sampled keys in each feature level for each attention head in the decoder. + decoder_activation_function (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + num_denoising (`int`, *optional*, defaults to 100): + The total number of denoising tasks or queries to be used for contrastive denoising. + learn_initial_query (`bool`, *optional*, defaults to `False`): + Indicates whether the initial query embeddings for the decoder should be learned during training + anchor_image_size (`tuple[int, int]`, *optional*): + Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. + disable_custom_kernels (`bool`, *optional*, defaults to `True`): + Whether to disable custom kernels. + global_pointer_head_size (`int`, *optional*, defaults to 64): + The size of the global pointer head. + gp_dropout_value (`float`, *optional*, defaults to 0.1): + The dropout probability in the global pointer head. + + Examples: + + ```python + >>> from transformers import PPDocLayoutV3Config, PPDocLayoutV3ForObjectDetection + + >>> # Initializing a PP-DocLayoutV3 configuration + >>> configuration = PPDocLayoutV3Config() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = PPDocLayoutV3ForObjectDetection(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pp_doclayout_v3" + sub_configs = {"backbone_config": AutoConfig} + + layer_types = ("basic", "bottleneck") + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + initializer_range: float = 0.01 + initializer_bias_prior_prob: float | None = None + layer_norm_eps: float = 1e-5 + batch_norm_eps: float = 1e-5 + tie_word_embeddings: bool = True + backbone_config: dict | PreTrainedConfig | None = None + freeze_backbone_batch_norms: bool = True + encoder_hidden_dim: int = 256 + encoder_in_channels: list[int] | tuple[int, ...] = (512, 1024, 2048) + feat_strides: list[int] | tuple[int, ...] = (8, 16, 32) + encoder_layers: int = 1 + encoder_ffn_dim: int = 1024 + encoder_attention_heads: int = 8 + dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + encode_proj_layers: list[int] | tuple[int, ...] = (2,) + positional_encoding_temperature: int = 10000 + encoder_activation_function: str = "gelu" + activation_function: str = "silu" + eval_size: int | None = None + normalize_before: bool = False + hidden_expansion: float = 1.0 + mask_feature_channels: list[int] | tuple[int, ...] = (64, 64) + x4_feat_dim: int = 128 + d_model: int = 256 + num_prototypes: int = 32 + label_noise_ratio: float = 0.4 + box_noise_scale: float = 0.4 + mask_enhanced: bool = True + num_queries: int = 300 + decoder_in_channels: list[int] | tuple[int, ...] = (256, 256, 256) + decoder_ffn_dim: int = 1024 + num_feature_levels: int = 3 + decoder_n_points: int = 4 + decoder_layers: int = 6 + decoder_attention_heads: int = 8 + decoder_activation_function: str = "relu" + attention_dropout: float | int = 0.0 + num_denoising: int = 100 + learn_initial_query: bool = False + anchor_image_size: int | None = None + disable_custom_kernels: bool = True + is_encoder_decoder: bool = True + global_pointer_head_size: int = 64 + gp_dropout_value: float | int = 0.1 + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [0, 1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [0, 0.05, 0.05, 0.05, 0.05], + "out_features": ["stage1", "stage2", "stage3", "stage4"], + }, + **kwargs, + ) + + self.encoder_in_channels = list(self.encoder_in_channels) + self.feat_strides = list(self.feat_strides) + self.encode_proj_layers = list(self.encode_proj_layers) + self.eval_size = list(self.eval_size) if self.eval_size is not None else None + self.decoder_in_channels = list(self.decoder_in_channels) + self.anchor_image_size = list(self.anchor_image_size) if self.anchor_image_size is not None else None + super().__post_init__(**kwargs) + + +__all__ = ["PPDocLayoutV3Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/image_processing_pp_doclayout_v3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/image_processing_pp_doclayout_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..ee2a51515bdb88a311628edcd5c3bd3edc0880d1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/image_processing_pp_doclayout_v3.py @@ -0,0 +1,303 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_doclayout_v3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import PILImageResampling, SizeDict +from ...utils import auto_docstring, is_cv2_available, requires_backends +from ...utils.generic import TensorType + + +if is_cv2_available(): + import cv2 + + +@auto_docstring +class PPDocLayoutV3ImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = [0, 0, 0] + image_std = [1, 1, 1] + size = {"height": 800, "width": 800} + do_resize = True + do_rescale = True + do_normalize = True + + # We require `self.resize(..., antialias=False)` to approximate the output of `cv2.resize` + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample, antialias=False) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + def _get_order_seqs(self, order_logits): + """ + Computes the order sequences for a batch of inputs based on logits. + + This function takes in the order logits, calculates order scores using a sigmoid activation, + and determines the order sequences by ranking the votes derived from the scores. + + Args: + order_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_queries)`): + Stacked order logits. + + Returns: + torch.Tensor: A tensor of shape `(batch_size, num_queries)`: + Containing the computed order sequences for each input in the batch. Each row represents the ranked order of elements for the corresponding input in the batch. + """ + order_scores = torch.sigmoid(order_logits) + batch_size, sequence_length, _ = order_scores.shape + + order_votes = order_scores.triu(diagonal=1).sum(dim=1) + (1.0 - order_scores.transpose(1, 2)).tril( + diagonal=-1 + ).sum(dim=1) + + order_pointers = torch.argsort(order_votes, dim=1) + order_seq = torch.empty_like(order_pointers) + ranks = torch.arange(sequence_length, device=order_pointers.device, dtype=order_pointers.dtype).expand( + batch_size, -1 + ) + order_seq.scatter_(1, order_pointers, ranks) + + return order_seq + + def extract_custom_vertices(self, polygon, sharp_angle_thresh=45): + poly = np.array(polygon) + n = len(poly) + res = [] + i = 0 + while i < n: + previous_point = poly[(i - 1) % n] + current_point = poly[i] + next_point = poly[(i + 1) % n] + vector_1 = previous_point - current_point + vector_2 = next_point - current_point + cross_product_value = (vector_1[1] * vector_2[0]) - (vector_1[0] * vector_2[1]) + if cross_product_value < 0: + angle_cos = np.clip( + (vector_1 @ vector_2) / (np.linalg.norm(vector_1) * np.linalg.norm(vector_2)), -1.0, 1.0 + ) + angle = np.degrees(np.arccos(angle_cos)) + if abs(angle - sharp_angle_thresh) < 1: + # Calculate the new point based on the direction of two vectors. + dir_vec = vector_1 / np.linalg.norm(vector_1) + vector_2 / np.linalg.norm(vector_2) + dir_vec = dir_vec / np.linalg.norm(dir_vec) + step_size = (np.linalg.norm(vector_1) + np.linalg.norm(vector_2)) / 2 + new_point = current_point + dir_vec * step_size + res.append(tuple(new_point)) + else: + res.append(tuple(current_point)) + i += 1 + return res + + def _mask2polygon(self, mask, epsilon_ratio=0.004): + """ + Postprocess mask by removing small noise. + Args: + mask (ndarray): The input mask of shape [H, W]. + epsilon_ratio (float): The ratio of epsilon. + Returns: + ndarray: The output mask after postprocessing. + """ + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if not contours: + return None + + contours = max(contours, key=cv2.contourArea) + epsilon = epsilon_ratio * cv2.arcLength(contours, True) + approx_contours = cv2.approxPolyDP(contours, epsilon, True) + polygon_points = approx_contours.squeeze() + polygon_points = np.atleast_2d(polygon_points) + + polygon_points = self.extract_custom_vertices(polygon_points) + + return polygon_points + + def _extract_polygon_points_by_masks(self, boxes, masks, scale_ratio): + scale_width, scale_height = scale_ratio[0] / 4, scale_ratio[1] / 4 + mask_height, mask_width = masks.shape[1:] + polygon_points = [] + + for i in range(len(boxes)): + x_min, y_min, x_max, y_max = boxes[i].astype(np.int32) + box_w, box_h = x_max - x_min, y_max - y_min + + # default rect + rect = np.array( + [[x_min, y_min], [x_max, y_min], [x_max, y_max], [x_min, y_max]], + dtype=np.float32, + ) + + if box_w <= 0 or box_h <= 0: + polygon_points.append(rect) + continue + + # crop mask + x_coordinates = [int(round((x_min * scale_width).item())), int(round((x_max * scale_width).item()))] + x_start, x_end = np.clip(x_coordinates, 0, mask_width) + y_coordinates = [int(round((y_min * scale_height).item())), int(round((y_max * scale_height).item()))] + y_start, y_end = np.clip(y_coordinates, 0, mask_height) + cropped_mask = masks[i, y_start:y_end, x_start:x_end] + if cropped_mask.size == 0 or np.sum(cropped_mask) == 0: + polygon_points.append(rect) + continue + + # resize mask to match box size + resized_mask = cv2.resize(cropped_mask.astype(np.uint8), (box_w, box_h), interpolation=cv2.INTER_NEAREST) + + polygon = self._mask2polygon(resized_mask) + if polygon is not None and len(polygon) < 4: + polygon_points.append(rect) + continue + if polygon is not None and len(polygon) > 0: + polygon = polygon + np.array([x_min, y_min]) + + polygon_points.append(polygon) + + return polygon_points + + def post_process_object_detection( + self, + outputs, + threshold: float = 0.5, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the raw output of [`PPDocLayoutV3ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, boxes and polygon_points for an image + in the batch as predicted by the model. + """ + requires_backends(self, ["torch", "cv2"]) + boxes = outputs.pred_boxes + logits = outputs.logits + order_logits = outputs.order_logits + masks = outputs.out_masks + + order_seqs = self._get_order_seqs(order_logits) + + box_centers, box_dims = torch.split(boxes, 2, dim=-1) + top_left_coords = box_centers - 0.5 * box_dims + bottom_right_coords = box_centers + 0.5 * box_dims + boxes = torch.cat([top_left_coords, bottom_right_coords], dim=-1) + + if target_sizes is not None: + if len(logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + if isinstance(target_sizes, list): + img_height, img_width = torch.as_tensor(target_sizes).unbind(1) + else: + img_height, img_width = target_sizes.unbind(1) + scale_factor = torch.stack([img_width, img_height, img_width, img_height], dim=1).to(boxes.device) + boxes = boxes * scale_factor[:, None, :] + + num_top_queries = logits.shape[1] + num_classes = logits.shape[2] + + scores = torch.nn.functional.sigmoid(logits) + scores, index = torch.topk(scores.flatten(1), num_top_queries, dim=-1) + labels = index % num_classes + index = index // num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + masks = masks.gather( + dim=1, index=index.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, masks.shape[-2], masks.shape[-1]) + ) + masks = (masks.sigmoid() > threshold).int() + order_seqs = order_seqs.gather(dim=1, index=index) + + results = [] + for score, label, box, order_seq, target_size, mask in zip( + scores, labels, boxes, order_seqs, target_sizes, masks + ): + order_seq = order_seq[score >= threshold] + order_seq, indices = torch.sort(order_seq) + polygon_points = self._extract_polygon_points_by_masks( + box[score >= threshold][indices].detach().cpu().numpy(), + mask[score >= threshold][indices].detach().cpu().numpy(), + [self.size["width"] / target_size[1], self.size["height"] / target_size[0]], + ) + results.append( + { + "scores": score[score >= threshold][indices], + "labels": label[score >= threshold][indices], + "boxes": box[score >= threshold][indices], + "polygon_points": polygon_points, + "order_seq": order_seq, + } + ) + + return results + + +__all__ = ["PPDocLayoutV3ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/modeling_pp_doclayout_v3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/modeling_pp_doclayout_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..03dc2680301ca16cc12c8a79477a3e61d9464d26 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/modeling_pp_doclayout_v3.py @@ -0,0 +1,2073 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_doclayout_v3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +import warnings +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2CLS, ACT2FN +from ...backbone_utils import load_backbone +from ...image_transforms import center_to_corners_format, corners_to_center_format +from ...integrations import use_kernel_forward_from_hub +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + torch_compilable_check, + torch_int, +) +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pp_doclayout_v3 import PPDocLayoutV3Config + + +class PPDocLayoutV3GlobalPointer(nn.Module): + def __init__(self, config): + super().__init__() + self.head_size = config.global_pointer_head_size + self.dense = nn.Linear(config.d_model, self.head_size * 2) + self.dropout = nn.Dropout(config.gp_dropout_value) + + def forward(self, inputs): + batch_size, sequence_length, _ = inputs.shape + query_key_projection = self.dense(inputs).reshape(batch_size, sequence_length, 2, self.head_size) + query_key_projection = self.dropout(query_key_projection) + queries, keys = torch.unbind(query_key_projection, dim=2) + + logits = (queries @ keys.transpose(-2, -1)) / (self.head_size**0.5) + mask = torch.tril(torch.ones(sequence_length, sequence_length, device=logits.device)).bool() + logits = logits.masked_fill(mask.unsqueeze(0), -1e4) + + return logits + + +@use_kernel_forward_from_hub("MultiScaleDeformableAttention") +class MultiScaleDeformableAttention(nn.Module): + def forward( + self, + value: Tensor, + value_spatial_shapes: Tensor, + value_spatial_shapes_list: list[tuple], + level_start_index: Tensor, + sampling_locations: Tensor, + attention_weights: Tensor, + im2col_step: int, + ): + batch_size, _, num_heads, hidden_dim = value.shape + _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape + value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level_id, (height, width) in enumerate(value_spatial_shapes_list): + # batch_size, height*width, num_heads, hidden_dim + # -> batch_size, height*width, num_heads*hidden_dim + # -> batch_size, num_heads*hidden_dim, height*width + # -> batch_size*num_heads, hidden_dim, height, width + value_l_ = ( + value_list[level_id] + .flatten(2) + .transpose(1, 2) + .reshape(batch_size * num_heads, hidden_dim, height, width) + ) + # batch_size, num_queries, num_heads, num_points, 2 + # -> batch_size, num_heads, num_queries, num_points, 2 + # -> batch_size*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1) + # batch_size*num_heads, hidden_dim, num_queries, num_points + sampling_value_l_ = nn.functional.grid_sample( + value_l_, + sampling_grid_l_, + mode="bilinear", + padding_mode="zeros", + align_corners=False, + ) + sampling_value_list.append(sampling_value_l_) + # (batch_size, num_queries, num_heads, num_levels, num_points) + # -> (batch_size, num_heads, num_queries, num_levels, num_points) + # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.transpose(1, 2).reshape( + batch_size * num_heads, 1, num_queries, num_levels * num_points + ) + output = ( + (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) + .sum(-1) + .view(batch_size, num_heads * hidden_dim, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +class PPDocLayoutV3MultiscaleDeformableAttention(nn.Module): + """ + Multiscale deformable attention as proposed in Deformable DETR. + """ + + def __init__(self, config: PPDocLayoutV3Config, num_heads: int, n_points: int): + super().__init__() + + self.attn = MultiScaleDeformableAttention() + + if config.d_model % num_heads != 0: + raise ValueError( + f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" + ) + dim_per_head = config.d_model // num_heads + # check if dim_per_head is power of 2 + if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): + warnings.warn( + "You'd better set embed_dim (d_model) in PPDocLayoutV3MultiscaleDeformableAttention to make the" + " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" + " implementation." + ) + + self.im2col_step = 64 + + self.d_model = config.d_model + self.n_levels = config.num_feature_levels + self.n_heads = num_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) + self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) + self.value_proj = nn.Linear(config.d_model, config.d_model) + self.output_proj = nn.Linear(config.d_model, config.d_model) + + self.disable_custom_kernels = config.disable_custom_kernels + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states=None, + encoder_attention_mask=None, + position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # add position embeddings to the hidden states before projecting to queries and keys + if position_embeddings is not None: + hidden_states = hidden_states + position_embeddings + + batch_size, num_queries, _ = hidden_states.shape + batch_size, sequence_length, _ = encoder_hidden_states.shape + total_elements = sum(height * width for height, width in spatial_shapes_list) + torch_compilable_check( + total_elements == sequence_length, + "Make sure to align the spatial shapes with the sequence length of the encoder hidden states", + ) + + value = self.value_proj(encoder_hidden_states) + if attention_mask is not None: + # we invert the attention_mask + value = value.masked_fill(~attention_mask[..., None], float(0)) + value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2 + ) + attention_weights = self.attention_weights(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points + ) + attention_weights = F.softmax(attention_weights, -1).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points + ) + # batch_size, num_queries, n_heads, n_levels, n_points, 2 + num_coordinates = reference_points.shape[-1] + if num_coordinates == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif num_coordinates == 4: + sampling_locations = ( + reference_points[:, :, None, :, None, :2] + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + ) + else: + raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") + + output = self.attn( + value, + spatial_shapes, + spatial_shapes_list, + level_start_index, + sampling_locations, + attention_weights, + self.im2col_step, + ) + + output = self.output_proj(output) + + return output, attention_weights + + +@auto_docstring +class PPDocLayoutV3PreTrainedModel(PreTrainedModel): + config: PPDocLayoutV3Config + base_model_prefix = "pp_doclayout_v3" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = [r"PPDocLayoutV3HybridEncoder", r"PPDocLayoutV3DecoderLayer"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, PPDocLayoutV3MultiscaleDeformableAttention): + init.constant_(module.sampling_offsets.weight, 0.0) + default_dtype = torch.get_default_dtype() + thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * ( + 2.0 * math.pi / module.n_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(module.n_heads, 1, 1, 2) + .repeat(1, module.n_levels, module.n_points, 1) + ) + for i in range(module.n_points): + grid_init[:, :, i, :] *= i + 1 + + init.copy_(module.sampling_offsets.bias, grid_init.view(-1)) + init.constant_(module.attention_weights.weight, 0.0) + init.constant_(module.attention_weights.bias, 0.0) + init.xavier_uniform_(module.value_proj.weight) + init.constant_(module.value_proj.bias, 0.0) + init.xavier_uniform_(module.output_proj.weight) + init.constant_(module.output_proj.bias, 0.0) + + elif isinstance(module, PPDocLayoutV3Model): + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(module.enc_score_head.weight) + init.constant_(module.enc_score_head.bias, bias) + init.xavier_uniform_(module.decoder.class_embed.weight) + init.constant_(module.decoder.class_embed.bias, bias) + + elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + elif isinstance(module, nn.LayerNorm): + init.ones_(module.weight) + init.zeros_(module.bias) + + if isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + init.zeros_(module.weight.data[module.padding_idx]) + + +@dataclass +class PPDocLayoutV3DecoderOutput(ModelOutput): + r""" + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + decoder_out_order_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.num_queries, config.num_queries)`): + Stacked order logits (order logits of each layer of the decoder). + decoder_out_masks (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.num_queries, 200, 200)`): + Stacked masks (masks of each layer of the decoder). + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + decoder_out_order_logits: torch.FloatTensor | None = None + decoder_out_masks: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the PP-DocLayoutV3 model. + """ +) +@dataclass +class PPDocLayoutV3ModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points used for the first decoder layer. + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`): + Logits of predicted bounding boxes coordinates in the encoder stage. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values. + out_order_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.num_queries, config.num_queries)`): + Stacked order logits (order logits of each layer of the decoder). + out_masks (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.num_queries, 200, 200)`): + Stacked masks (masks of each layer of the decoder). + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: torch.FloatTensor | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + out_order_logits: torch.FloatTensor | None = None + out_masks: torch.FloatTensor | None = None + + +class PPDocLayoutV3MLPPredictionHead(nn.Module): + """ + Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, + height and width of a bounding box w.r.t. an image. + + """ + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +class PPDocLayoutV3ConvLayer(nn.Module): + def __init__( + self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu" + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=False + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class PPDocLayoutV3ScaleHead(nn.Module): + def __init__(self, in_channels, feature_channels, fpn_stride, base_stride, align_corners=False): + super().__init__() + head_length = max(1, int(np.log2(fpn_stride) - np.log2(base_stride))) + self.layers = nn.ModuleList() + for k in range(head_length): + in_c = in_channels if k == 0 else feature_channels + self.layers.append(PPDocLayoutV3ConvLayer(in_c, feature_channels, 3, 1, "silu")) + if fpn_stride != base_stride: + self.layers.append(nn.Upsample(scale_factor=2, mode="bilinear", align_corners=align_corners)) + + def forward(self, x): + for layer in self.layers: + x = layer(x) + return x + + +class PPDocLayoutV3MaskFeatFPN(nn.Module): + def __init__( + self, + in_channels=[256, 256, 256], + fpn_strides=[32, 16, 8], + feature_channels=256, + dropout_ratio=0.0, + out_channels=256, + align_corners=False, + ): + super().__init__() + + reorder_index = np.argsort(fpn_strides, axis=0).tolist() + in_channels = [in_channels[i] for i in reorder_index] + fpn_strides = [fpn_strides[i] for i in reorder_index] + + self.reorder_index = reorder_index + self.fpn_strides = fpn_strides + self.dropout_ratio = dropout_ratio + self.align_corners = align_corners + if self.dropout_ratio > 0: + self.dropout = nn.Dropout2d(dropout_ratio) + + self.scale_heads = nn.ModuleList() + for i in range(len(fpn_strides)): + self.scale_heads.append( + PPDocLayoutV3ScaleHead( + in_channels=in_channels[i], + feature_channels=feature_channels, + fpn_stride=fpn_strides[i], + base_stride=fpn_strides[0], + align_corners=align_corners, + ) + ) + self.output_conv = PPDocLayoutV3ConvLayer(feature_channels, out_channels, 3, 1, "silu") + + def forward(self, inputs): + x = [inputs[i] for i in self.reorder_index] + + output = self.scale_heads[0](x[0]) + for i in range(1, len(self.fpn_strides)): + output = output + F.interpolate( + self.scale_heads[i](x[i]), size=output.shape[2:], mode="bilinear", align_corners=self.align_corners + ) + + if self.dropout_ratio > 0: + output = self.dropout(output) + output = self.output_conv(output) + return output + + +class PPDocLayoutV3EncoderMaskOutput(nn.Module): + def __init__(self, in_channels, num_prototypes): + super().__init__() + self.base_conv = PPDocLayoutV3ConvLayer(in_channels, in_channels, 3, 1, "silu") + self.conv = nn.Conv2d(in_channels, num_prototypes, kernel_size=1) + + def forward(self, x): + x = self.base_conv(x) + x = self.conv(x) + return x + + +class PPDocLayoutV3MLP(nn.Module): + def __init__( + self, config: PPDocLayoutV3Config, hidden_size: int, intermediate_size: int, activation_function: str + ): + super().__init__() + self.fc1 = nn.Linear(hidden_size, intermediate_size) + self.fc2 = nn.Linear(intermediate_size, hidden_size) + self.activation_fn = ACT2FN[activation_function] + self.activation_dropout = config.activation_dropout + self.dropout = config.dropout + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + return hidden_states + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PPDocLayoutV3SelfAttention(nn.Module): + """ + Multi-headed self-attention from 'Attention Is All You Need' paper. + + In PP_DOCLAYOUT_V3, position embeddings are added to both queries and keys (but not values) in self-attention. + """ + + def __init__( + self, + config: PPDocLayoutV3Config, + hidden_size: int, + num_attention_heads: int, + dropout: float = 0.0, + bias: bool = True, + ): + super().__init__() + self.config = config + self.head_dim = hidden_size // num_attention_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = dropout + self.is_causal = False + + self.k_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.v_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.q_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.o_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Position embeddings are added to both queries and keys (but not values). + """ + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_key_input = hidden_states + position_embeddings if position_embeddings is not None else hidden_states + + query_states = self.q_proj(query_key_input).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(query_key_input).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class PPDocLayoutV3ConvNormLayer(nn.Module): + def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride, + padding=(kernel_size - 1) // 2 if padding is None else padding, + bias=False, + ) + self.norm = nn.BatchNorm2d(out_channels, config.batch_norm_eps) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_state): + hidden_state = self.conv(hidden_state) + hidden_state = self.norm(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class PPDocLayoutV3EncoderLayer(nn.Module): + def __init__(self, config: PPDocLayoutV3Config): + super().__init__() + self.normalize_before = config.normalize_before + self.hidden_size = config.encoder_hidden_dim + + # self-attention + self.self_attn = PPDocLayoutV3SelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.num_attention_heads, + dropout=config.dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + self.dropout = config.dropout + self.mlp = PPDocLayoutV3MLP( + config, self.hidden_size, config.encoder_ffn_dim, config.encoder_activation_function + ) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + spatial_position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, hidden_size)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + spatial_position_embeddings (`torch.FloatTensor`, *optional*): + Spatial position embeddings (2D positional encodings of image locations), to be added to both + the queries and keys in self-attention (but not to values). + """ + residual = hidden_states + if self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_embeddings=spatial_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + if self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + residual = hidden_states + + hidden_states = self.mlp(hidden_states) + + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + + if self.training: + if not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +class PPDocLayoutV3RepVggBlock(nn.Module): + """ + RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again". + """ + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__() + + activation = config.activation_function + hidden_channels = int(config.encoder_hidden_dim * config.hidden_expansion) + self.conv1 = PPDocLayoutV3ConvNormLayer(config, hidden_channels, hidden_channels, 3, 1, padding=1) + self.conv2 = PPDocLayoutV3ConvNormLayer(config, hidden_channels, hidden_channels, 1, 1, padding=0) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, x): + y = self.conv1(x) + self.conv2(x) + return self.activation(y) + + +class PPDocLayoutV3CSPRepLayer(nn.Module): + """ + Cross Stage Partial (CSP) network layer with RepVGG blocks. + """ + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__() + + in_channels = config.encoder_hidden_dim * 2 + out_channels = config.encoder_hidden_dim + num_blocks = 3 + activation = config.activation_function + + hidden_channels = int(out_channels * config.hidden_expansion) + self.conv1 = PPDocLayoutV3ConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.conv2 = PPDocLayoutV3ConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.bottlenecks = nn.Sequential(*[PPDocLayoutV3RepVggBlock(config) for _ in range(num_blocks)]) + if hidden_channels != out_channels: + self.conv3 = PPDocLayoutV3ConvNormLayer(config, hidden_channels, out_channels, 1, 1, activation=activation) + else: + self.conv3 = nn.Identity() + + def forward(self, hidden_state): + hidden_state_1 = self.conv1(hidden_state) + hidden_state_1 = self.bottlenecks(hidden_state_1) + hidden_state_2 = self.conv2(hidden_state) + return self.conv3(hidden_state_1 + hidden_state_2) + + +class PPDocLayoutV3SinePositionEmbedding(nn.Module): + """ + 2D sinusoidal position embedding used in RT-DETR hybrid encoder. + """ + + def __init__(self, embed_dim: int = 256, temperature: int = 10000): + super().__init__() + self.embed_dim = embed_dim + self.temperature = temperature + + @compile_compatible_method_lru_cache(maxsize=32) + def forward( + self, + width: int, + height: int, + device: torch.device | str, + dtype: torch.dtype, + ) -> torch.Tensor: + """ + Generate 2D sinusoidal position embeddings. + + Returns: + Position embeddings of shape (1, height*width, embed_dim) + """ + grid_w = torch.arange(torch_int(width), device=device).to(dtype) + grid_h = torch.arange(torch_int(height), device=device).to(dtype) + grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="xy") + if self.embed_dim % 4 != 0: + raise ValueError("Embed dimension must be divisible by 4 for 2D sin-cos position embedding") + pos_dim = self.embed_dim // 4 + omega = torch.arange(pos_dim, device=device).to(dtype) / pos_dim + omega = 1.0 / (self.temperature**omega) + + out_w = grid_w.flatten()[..., None] @ omega[None] + out_h = grid_h.flatten()[..., None] @ omega[None] + + return torch.concat([out_h.sin(), out_h.cos(), out_w.sin(), out_w.cos()], dim=1)[None, :, :] + + +class PPDocLayoutV3AIFILayer(nn.Module): + """ + AIFI (Attention-based Intra-scale Feature Interaction) layer used in RT-DETR hybrid encoder. + """ + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__() + self.config = config + self.encoder_hidden_dim = config.encoder_hidden_dim + self.eval_size = config.eval_size + + self.position_embedding = PPDocLayoutV3SinePositionEmbedding( + embed_dim=self.encoder_hidden_dim, + temperature=config.positional_encoding_temperature, + ) + self.layers = nn.ModuleList([PPDocLayoutV3EncoderLayer(config) for _ in range(config.encoder_layers)]) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`): + Feature map to process. + """ + batch_size = hidden_states.shape[0] + height, width = hidden_states.shape[2:] + + hidden_states = hidden_states.flatten(2).permute(0, 2, 1) + + if self.training or self.eval_size is None: + pos_embed = self.position_embedding( + width=width, + height=height, + device=hidden_states.device, + dtype=hidden_states.dtype, + ) + else: + pos_embed = None + + for layer in self.layers: + hidden_states = layer( + hidden_states, + attention_mask=None, + spatial_position_embeddings=pos_embed, + **kwargs, + ) + + hidden_states = ( + hidden_states.permute(0, 2, 1).reshape(batch_size, self.encoder_hidden_dim, height, width).contiguous() + ) + + return hidden_states + + +class PPDocLayoutV3HybridEncoder(PPDocLayoutV3PreTrainedModel): + """ + Main difference to `RTDetrHybridEncoder`: + 1. Mask Feature Head: Added `PPDocLayoutV3MaskFeatFPN` module (`self.mask_feature_head`) for document - specific mask feature generation. + 2. Extra Conv Layers: Introduced `self.encoder_mask_lateral` and `self.encoder_mask_output` for mask feature processing and output. + """ + + _can_record_outputs = { + "hidden_states": PPDocLayoutV3AIFILayer, + "attentions": PPDocLayoutV3SelfAttention, + } + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__(config) + self.config = config + self.in_channels = config.encoder_in_channels + self.feat_strides = config.feat_strides + self.encoder_hidden_dim = config.encoder_hidden_dim + self.encode_proj_layers = config.encode_proj_layers + self.positional_encoding_temperature = config.positional_encoding_temperature + self.eval_size = config.eval_size + self.out_channels = [self.encoder_hidden_dim for _ in self.in_channels] + self.out_strides = self.feat_strides + self.num_fpn_stages = len(self.in_channels) - 1 + self.num_pan_stages = len(self.in_channels) - 1 + + # AIFI (Attention-based Intra-scale Feature Interaction) layers + self.aifi = nn.ModuleList([PPDocLayoutV3AIFILayer(config) for _ in range(len(self.encode_proj_layers))]) + + # top-down FPN + self.lateral_convs = nn.ModuleList() + self.fpn_blocks = nn.ModuleList() + for _ in range(self.num_fpn_stages): + lateral_conv = PPDocLayoutV3ConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=1, + stride=1, + activation=config.activation_function, + ) + fpn_block = PPDocLayoutV3CSPRepLayer(config) + self.lateral_convs.append(lateral_conv) + self.fpn_blocks.append(fpn_block) + + # bottom-up PAN + self.downsample_convs = nn.ModuleList() + self.pan_blocks = nn.ModuleList() + for _ in range(self.num_pan_stages): + downsample_conv = PPDocLayoutV3ConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=3, + stride=2, + activation=config.activation_function, + ) + pan_block = PPDocLayoutV3CSPRepLayer(config) + self.downsample_convs.append(downsample_conv) + self.pan_blocks.append(pan_block) + + feat_strides = config.feat_strides + mask_feature_channels = config.mask_feature_channels + self.mask_feature_head = PPDocLayoutV3MaskFeatFPN( + [self.encoder_hidden_dim] * len(feat_strides), + feat_strides, + feature_channels=mask_feature_channels[0], + out_channels=mask_feature_channels[1], + ) + self.encoder_mask_lateral = PPDocLayoutV3ConvLayer(config.x4_feat_dim, mask_feature_channels[1], 3, 1, "silu") + self.encoder_mask_output = PPDocLayoutV3EncoderMaskOutput( + in_channels=mask_feature_channels[1], num_prototypes=config.num_prototypes + ) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + inputs_embeds=None, + x4_feat=None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + """ + feature_maps = inputs_embeds + + # AIFI: Apply transformer encoder to specified feature levels + if self.config.encoder_layers > 0: + for i, enc_ind in enumerate(self.encode_proj_layers): + feature_maps[enc_ind] = self.aifi[i](feature_maps[enc_ind], **kwargs) + + # top-down FPN + fpn_feature_maps = [feature_maps[-1]] + for idx, (lateral_conv, fpn_block) in enumerate(zip(self.lateral_convs, self.fpn_blocks)): + backbone_feature_map = feature_maps[self.num_fpn_stages - idx - 1] + top_fpn_feature_map = fpn_feature_maps[-1] + # apply lateral block + top_fpn_feature_map = lateral_conv(top_fpn_feature_map) + fpn_feature_maps[-1] = top_fpn_feature_map + # apply fpn block + top_fpn_feature_map = F.interpolate(top_fpn_feature_map, scale_factor=2.0, mode="nearest") + fused_feature_map = torch.concat([top_fpn_feature_map, backbone_feature_map], dim=1) + new_fpn_feature_map = fpn_block(fused_feature_map) + fpn_feature_maps.append(new_fpn_feature_map) + + fpn_feature_maps.reverse() + + # bottom-up PAN + pan_feature_maps = [fpn_feature_maps[0]] + for idx, (downsample_conv, pan_block) in enumerate(zip(self.downsample_convs, self.pan_blocks)): + top_pan_feature_map = pan_feature_maps[-1] + fpn_feature_map = fpn_feature_maps[idx + 1] + downsampled_feature_map = downsample_conv(top_pan_feature_map) + fused_feature_map = torch.concat([downsampled_feature_map, fpn_feature_map], dim=1) + new_pan_feature_map = pan_block(fused_feature_map) + pan_feature_maps.append(new_pan_feature_map) + + mask_feat = self.mask_feature_head(pan_feature_maps) + mask_feat = F.interpolate(mask_feat, scale_factor=2, mode="bilinear", align_corners=False) + mask_feat += self.encoder_mask_lateral(x4_feat[0]) + mask_feat = self.encoder_mask_output(mask_feat) + + return PPDocLayoutV3HybridEncoderOutput( + last_hidden_state=pan_feature_maps, + mask_feat=mask_feat, + ) + + +class PPDocLayoutV3DecoderLayer(nn.Module): + def __init__(self, config: PPDocLayoutV3Config): + super().__init__() + self.hidden_size = config.d_model + + # self-attention + self.self_attn = PPDocLayoutV3SelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + ) + self.dropout = config.dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # cross-attention + self.encoder_attn = PPDocLayoutV3MultiscaleDeformableAttention( + config, + num_heads=config.decoder_attention_heads, + n_points=config.decoder_n_points, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # feedforward neural networks + self.mlp = PPDocLayoutV3MLP( + config, self.hidden_size, config.decoder_ffn_dim, config.decoder_activation_function + ) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + object_queries_position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + Input to the layer of shape `(batch, seq_len, hidden_size)`. + object_queries_position_embeddings (`torch.FloatTensor`, *optional*): + Position embeddings for the object query slots. These are added to both queries and keys + in the self-attention layer (not values). + reference_points (`torch.FloatTensor`, *optional*): + Reference points. + spatial_shapes (`torch.LongTensor`, *optional*): + Spatial shapes. + level_start_index (`torch.LongTensor`, *optional*): + Level start index. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, hidden_size)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + """ + residual = hidden_states + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=encoder_attention_mask, + position_embeddings=object_queries_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + + # Cross-Attention + hidden_states, _ = self.encoder_attn( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + position_embeddings=object_queries_position_embeddings, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states + + +def inverse_sigmoid(x, eps=1e-5): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +class PPDocLayoutV3Decoder(PPDocLayoutV3PreTrainedModel): + """ + Main difference to `RTDetrDecoder`: + A new mask generation process is introduced at each decoder layer. + """ + + _can_record_outputs = { + "hidden_states": PPDocLayoutV3DecoderLayer, + "attentions": PPDocLayoutV3SelfAttention, + "cross_attentions": PPDocLayoutV3MultiscaleDeformableAttention, + } + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__(config) + + self.dropout = config.dropout + self.layers = nn.ModuleList([PPDocLayoutV3DecoderLayer(config) for _ in range(config.decoder_layers)]) + self.query_pos_head = PPDocLayoutV3MLPPredictionHead(4, 2 * config.d_model, config.d_model, num_layers=2) + + # hack implementation for iterative bounding box refinement and two-stage Deformable DETR + self.bbox_embed = None + self.class_embed = None + + self.num_queries = config.num_queries + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + order_head=None, + global_pointer=None, + mask_query_head=None, + norm=None, + mask_feat=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + The query embeddings that are passed into the decoder. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected + in `[0, 1]`: + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): + Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. + spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): + Spatial shapes of the feature maps. + level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): + Indexes for the start of each feature level. In range `[0, sequence_length]`. + """ + if inputs_embeds is not None: + hidden_states = inputs_embeds + + # decoder layers + intermediate = () + intermediate_reference_points = () + intermediate_logits = () + decoder_out_order_logits = () + decoder_out_masks = () + + reference_points = F.sigmoid(reference_points) + + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L252 + for idx, decoder_layer in enumerate(self.layers): + reference_points_input = reference_points.unsqueeze(2) + object_queries_position_embeddings = self.query_pos_head(reference_points) + + hidden_states = decoder_layer( + hidden_states, + object_queries_position_embeddings=object_queries_position_embeddings, + encoder_hidden_states=encoder_hidden_states, + reference_points=reference_points_input, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + encoder_attention_mask=encoder_attention_mask, + **kwargs, + ) + + # hack implementation for iterative bounding box refinement + if self.bbox_embed is not None: + predicted_corners = self.bbox_embed(hidden_states) + new_reference_points = F.sigmoid(predicted_corners + inverse_sigmoid(reference_points)) + reference_points = new_reference_points.detach() + + intermediate += (hidden_states,) + intermediate_reference_points += ( + (new_reference_points,) if self.bbox_embed is not None else (reference_points,) + ) + + # get_pred_class_order_and_mask + out_query = norm(hidden_states) + mask_query_embed = mask_query_head(out_query) + batch_size, mask_dim, _ = mask_query_embed.shape + _, _, mask_h, mask_w = mask_feat.shape + out_mask = torch.bmm(mask_query_embed, mask_feat.flatten(start_dim=2)).reshape( + batch_size, mask_dim, mask_h, mask_w + ) + decoder_out_masks += (out_mask,) + + if self.class_embed is not None: + logits = self.class_embed(out_query) + intermediate_logits += (logits,) + + if order_head is not None and global_pointer is not None: + valid_query = out_query[:, -self.num_queries :] if self.num_queries is not None else out_query + order_logits = global_pointer(order_head[idx](valid_query)) + decoder_out_order_logits += (order_logits,) + + # Keep batch_size as first dimension + intermediate = torch.stack(intermediate, dim=1) + intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) + if self.class_embed is not None: + intermediate_logits = torch.stack(intermediate_logits, dim=1) + if order_head is not None and global_pointer is not None: + decoder_out_order_logits = torch.stack(decoder_out_order_logits, dim=1) + decoder_out_masks = torch.stack(decoder_out_masks, dim=1) + + return PPDocLayoutV3DecoderOutput( + last_hidden_state=hidden_states, + intermediate_hidden_states=intermediate, + intermediate_logits=intermediate_logits, + intermediate_reference_points=intermediate_reference_points, + decoder_out_order_logits=decoder_out_order_logits, + decoder_out_masks=decoder_out_masks, + ) + + +class PPDocLayoutV3FrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than + torchvision.models.resnet[18,34,50,101] produce nans. + """ + + def __init__(self, n): + super().__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it user-friendly + weight = self.weight.reshape(1, -1, 1, 1) + bias = self.bias.reshape(1, -1, 1, 1) + running_var = self.running_var.reshape(1, -1, 1, 1) + running_mean = self.running_mean.reshape(1, -1, 1, 1) + epsilon = 1e-5 + scale = weight * (running_var + epsilon).rsqrt() + bias = bias - running_mean * scale + return x * scale + bias + + +def replace_batch_norm(model): + r""" + Recursively replace all `torch.nn.BatchNorm2d` with `PPDocLayoutV3FrozenBatchNorm2d`. + + Args: + model (torch.nn.Module): + input model + """ + for name, module in model.named_children(): + if isinstance(module, nn.BatchNorm2d): + new_module = PPDocLayoutV3FrozenBatchNorm2d(module.num_features) + + if module.weight.device != torch.device("meta"): + new_module.weight.copy_(module.weight) + new_module.bias.copy_(module.bias) + new_module.running_mean.copy_(module.running_mean) + new_module.running_var.copy_(module.running_var) + + model._modules[name] = new_module + + if len(list(module.children())) > 0: + replace_batch_norm(module) + + +class PPDocLayoutV3ConvEncoder(nn.Module): + """ + Convolutional backbone using the modeling_pp_doclayout_v3_resnet.py. + + nn.BatchNorm2d layers are replaced by PPDocLayoutV3FrozenBatchNorm2d as defined above. + https://github.com/lyuwenyu/RT-DETR/blob/main/PPDocLayoutV3_pytorch/src/nn/backbone/presnet.py#L142 + """ + + def __init__(self, config): + super().__init__() + + backbone = load_backbone(config) + + if config.freeze_backbone_batch_norms: + # replace batch norm by frozen batch norm + with torch.no_grad(): + replace_batch_norm(backbone) + self.model = backbone + self.intermediate_channel_sizes = self.model.channels + + def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): + # send pixel_values through the model to get list of feature maps + features = self.model(pixel_values).feature_maps + + out = [] + for feature_map in features: + # downsample pixel_mask to match shape of corresponding feature_map + mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] + out.append((feature_map, mask)) + return out + + +def get_contrastive_denoising_training_group( + targets, + num_classes, + num_queries, + class_embed, + num_denoising_queries=100, + label_noise_ratio=0.5, + box_noise_scale=1.0, +): + """ + Creates a contrastive denoising training group using ground-truth samples. It adds noise to labels and boxes. + + Args: + targets (`list[dict]`): + The target objects, each containing 'class_labels' and 'boxes' for objects in an image. + num_classes (`int`): + Total number of classes in the dataset. + num_queries (`int`): + Number of query slots in the transformer. + class_embed (`callable`): + A function or a model layer to embed class labels. + num_denoising_queries (`int`, *optional*, defaults to 100): + Number of denoising queries. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + Ratio of noise applied to labels. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale of noise applied to bounding boxes. + Returns: + `tuple` comprising various elements: + - **input_query_class** (`torch.FloatTensor`) -- + Class queries with applied label noise. + - **input_query_bbox** (`torch.FloatTensor`) -- + Bounding box queries with applied box noise. + - **attn_mask** (`torch.FloatTensor`) -- + Attention mask for separating denoising and reconstruction queries. + - **denoising_meta_values** (`dict`) -- + Metadata including denoising positive indices, number of groups, and split sizes. + """ + + if num_denoising_queries <= 0: + return None, None, None, None + + num_ground_truths = [len(t["class_labels"]) for t in targets] + device = targets[0]["class_labels"].device + + max_gt_num = max(num_ground_truths) + if max_gt_num == 0: + return None, None, None, None + + num_groups_denoising_queries = num_denoising_queries // max_gt_num + num_groups_denoising_queries = 1 if num_groups_denoising_queries == 0 else num_groups_denoising_queries + # pad gt to max_num of a batch + batch_size = len(num_ground_truths) + + input_query_class = torch.full([batch_size, max_gt_num], num_classes, dtype=torch.int32, device=device) + input_query_bbox = torch.zeros([batch_size, max_gt_num, 4], device=device) + pad_gt_mask = torch.zeros([batch_size, max_gt_num], dtype=torch.bool, device=device) + + for i in range(batch_size): + num_gt = num_ground_truths[i] + if num_gt > 0: + input_query_class[i, :num_gt] = targets[i]["class_labels"] + input_query_bbox[i, :num_gt] = targets[i]["boxes"] + pad_gt_mask[i, :num_gt] = 1 + # each group has positive and negative queries. + input_query_class = input_query_class.tile([1, 2 * num_groups_denoising_queries]) + input_query_bbox = input_query_bbox.tile([1, 2 * num_groups_denoising_queries, 1]) + pad_gt_mask = pad_gt_mask.tile([1, 2 * num_groups_denoising_queries]) + # positive and negative mask + negative_gt_mask = torch.zeros([batch_size, max_gt_num * 2, 1], device=device) + negative_gt_mask[:, max_gt_num:] = 1 + negative_gt_mask = negative_gt_mask.tile([1, num_groups_denoising_queries, 1]) + positive_gt_mask = 1 - negative_gt_mask + # contrastive denoising training positive index + positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask + denoise_positive_idx = torch.nonzero(positive_gt_mask)[:, 1] + denoise_positive_idx = torch.split( + denoise_positive_idx, [n * num_groups_denoising_queries for n in num_ground_truths] + ) + # total denoising queries + num_denoising_queries = torch_int(max_gt_num * 2 * num_groups_denoising_queries) + + if label_noise_ratio > 0: + mask = torch.rand_like(input_query_class, dtype=torch.float) < (label_noise_ratio * 0.5) + # randomly put a new one here + new_label = torch.randint_like(mask, 0, num_classes, dtype=input_query_class.dtype) + input_query_class = torch.where(mask & pad_gt_mask, new_label, input_query_class) + + if box_noise_scale > 0: + known_bbox = center_to_corners_format(input_query_bbox) + diff = torch.tile(input_query_bbox[..., 2:] * 0.5, [1, 1, 2]) * box_noise_scale + rand_sign = torch.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0 + rand_part = torch.rand_like(input_query_bbox) + rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (1 - negative_gt_mask) + rand_part *= rand_sign + known_bbox += rand_part * diff + known_bbox.clip_(min=0.0, max=1.0) + input_query_bbox = corners_to_center_format(known_bbox) + input_query_bbox = inverse_sigmoid(input_query_bbox) + + input_query_class = class_embed(input_query_class) + + target_size = num_denoising_queries + num_queries + attn_mask = torch.full([target_size, target_size], 0, dtype=torch.float, device=device) + # match query cannot see the reconstruction + attn_mask[num_denoising_queries:, :num_denoising_queries] = -torch.inf + + # reconstructions cannot see each other + for i in range(num_groups_denoising_queries): + idx_block_start = max_gt_num * 2 * i + idx_block_end = max_gt_num * 2 * (i + 1) + attn_mask[idx_block_start:idx_block_end, :idx_block_start] = -torch.inf + attn_mask[idx_block_start:idx_block_end, idx_block_end:num_denoising_queries] = -torch.inf + + denoising_meta_values = { + "dn_positive_idx": denoise_positive_idx, + "dn_num_group": num_groups_denoising_queries, + "dn_num_split": [num_denoising_queries, num_queries], + } + + return input_query_class, input_query_bbox, attn_mask, denoising_meta_values + + +def mask_to_box_coordinate(mask, dtype): + mask = mask.bool() + + height, width = mask.shape[-2:] + + y_coords, x_coords = torch.meshgrid( + torch.arange(height, device=mask.device), torch.arange(width, device=mask.device), indexing="ij" + ) + x_coords = x_coords.to(dtype) + y_coords = y_coords.to(dtype) + + x_coords_masked = x_coords * mask + x_max = x_coords_masked.flatten(start_dim=-2).max(dim=-1).values + 1 + x_min = ( + torch.where(mask, x_coords_masked, torch.tensor(torch.finfo(dtype).max)) + .flatten(start_dim=-2) + .min(dim=-1) + .values + ) + + y_coords_masked = y_coords * mask + y_max = y_coords_masked.flatten(start_dim=-2).max(dim=-1).values + 1 + y_min = ( + torch.where(mask, y_coords_masked, torch.tensor(torch.finfo(dtype).max)) + .flatten(start_dim=-2) + .min(dim=-1) + .values + ) + + unnormalized_bbox = torch.stack([x_min, y_min, x_max, y_max], dim=-1) + + is_mask_non_empty = torch.any(mask, dim=(-2, -1)).unsqueeze(-1) + unnormalized_bbox = unnormalized_bbox * is_mask_non_empty + + norm_tensor = torch.tensor([width, height, width, height], device=mask.device, dtype=dtype) + normalized_bbox_xyxy = unnormalized_bbox / norm_tensor + + x_min_norm, y_min_norm, x_max_norm, y_max_norm = normalized_bbox_xyxy.unbind(dim=-1) + + center_x = (x_min_norm + x_max_norm) / 2 + center_y = (y_min_norm + y_max_norm) / 2 + box_width = x_max_norm - x_min_norm + box_height = y_max_norm - y_min_norm + + return torch.stack([center_x, center_y, box_width, box_height], dim=-1) + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV3 Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top. + """ +) +class PPDocLayoutV3Model(PPDocLayoutV3PreTrainedModel): + _tied_weights_keys = { + "decoder.class_embed": "enc_score_head", + "decoder.bbox_embed": "enc_bbox_head", + } + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__(config) + + # Create backbone + self.backbone = PPDocLayoutV3ConvEncoder(config) + intermediate_channel_sizes = self.backbone.intermediate_channel_sizes + + # Create encoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/PPDocLayoutV3_pytorch/src/zoo/PPDocLayoutV3/hybrid_encoder.py#L212 + num_backbone_outs = len(intermediate_channel_sizes) + + encoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = intermediate_channel_sizes[i] + encoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.encoder_hidden_dim, kernel_size=1, bias=False), + nn.BatchNorm2d(config.encoder_hidden_dim), + ) + ) + self.encoder_input_proj = nn.ModuleList(encoder_input_proj_list[1:]) + + # Create encoder + self.encoder = PPDocLayoutV3HybridEncoder(config) + + # denoising part + if config.num_denoising > 0: + self.denoising_class_embed = nn.Embedding( + config.num_labels + 1, config.d_model, padding_idx=config.num_labels + ) + + # decoder embedding + if config.learn_initial_query: + self.weight_embedding = nn.Embedding(config.num_queries, config.d_model) + + # encoder head + self.enc_output = nn.Sequential( + nn.Linear(config.d_model, config.d_model), + nn.LayerNorm(config.d_model, eps=config.layer_norm_eps), + ) + self.enc_score_head = nn.Linear(config.d_model, config.num_labels) + self.enc_bbox_head = PPDocLayoutV3MLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + + # init encoder output anchors and valid_mask + if config.anchor_image_size: + self.anchors, self.valid_mask = self.generate_anchors(dtype=self.dtype) + + # Create decoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/PPDocLayoutV3_pytorch/src/zoo/PPDocLayoutV3/PPDocLayoutV3_decoder.py#L412 + num_backbone_outs = len(config.decoder_in_channels) + decoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = config.decoder_in_channels[i] + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + for _ in range(config.num_feature_levels - num_backbone_outs): + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + in_channels = config.d_model + self.decoder_input_proj = nn.ModuleList(decoder_input_proj_list) + self.decoder = PPDocLayoutV3Decoder(config) + + self.decoder_order_head = nn.ModuleList( + [nn.Linear(config.d_model, config.d_model) for _ in range(config.decoder_layers)] + ) + self.decoder_global_pointer = PPDocLayoutV3GlobalPointer(config) + self.decoder_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps) + self.decoder.class_embed = nn.Linear(config.d_model, config.num_labels) + self.decoder.bbox_embed = PPDocLayoutV3MLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + + self.mask_enhanced = config.mask_enhanced + self.mask_query_head = PPDocLayoutV3MLPPredictionHead( + config.d_model, config.d_model, config.num_prototypes, num_layers=3 + ) + + self.post_init() + + def freeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(False) + + def unfreeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(True) + + @compile_compatible_method_lru_cache(maxsize=32) + def generate_anchors(self, spatial_shapes=None, grid_size=0.05, device="cpu", dtype=torch.float32): + if spatial_shapes is None: + spatial_shapes = [ + [int(self.config.anchor_image_size[0] / s), int(self.config.anchor_image_size[1] / s)] + for s in self.config.feat_strides + ] + anchors = [] + for level, (height, width) in enumerate(spatial_shapes): + grid_y, grid_x = torch.meshgrid( + torch.arange(end=height, device=device).to(dtype), + torch.arange(end=width, device=device).to(dtype), + indexing="ij", + ) + grid_xy = torch.stack([grid_x, grid_y], -1) + grid_xy = grid_xy.unsqueeze(0) + 0.5 + grid_xy[..., 0] /= width + grid_xy[..., 1] /= height + wh = torch.ones_like(grid_xy) * grid_size * (2.0**level) + anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, height * width, 4)) + # define the valid range for anchor coordinates + eps = 1e-2 + anchors = torch.concat(anchors, 1) + valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True) + anchors = torch.log(anchors / (1 - anchors)) + anchors = torch.where(valid_mask, anchors, torch.tensor(torch.finfo(dtype).max, dtype=dtype, device=device)) + + return anchors, valid_mask + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | PPDocLayoutV3ModelOutput: + r""" + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, PPDocLayoutV2Model + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("PekingU/PPDocLayoutV2_r50vd") + >>> model = PPDocLayoutV2Model.from_pretrained("PekingU/PPDocLayoutV2_r50vd") + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 300, 256] + ```""" + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + + if pixel_mask is None: + pixel_mask = torch.ones(((batch_size, height, width)), device=device) + + features = self.backbone(pixel_values, pixel_mask) + x4_feat = features.pop(0) + proj_feats = [self.encoder_input_proj[level](source) for level, (source, mask) in enumerate(features)] + + if encoder_outputs is None: + encoder_outputs = self.encoder( + proj_feats, + x4_feat, + **kwargs, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a PPDocLayoutV3HybridEncoderOutput when return_dict=True + elif not isinstance(encoder_outputs, PPDocLayoutV3HybridEncoderOutput): + encoder_outputs = PPDocLayoutV3HybridEncoderOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + mask_feat=encoder_outputs[-1], + ) + + # Equivalent to def _get_encoder_input + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L412 + sources = [] + for level, source in enumerate(encoder_outputs.last_hidden_state): + sources.append(self.decoder_input_proj[level](source)) + + # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage + if self.config.num_feature_levels > len(sources): + _len_sources = len(sources) + sources.append(self.decoder_input_proj[_len_sources](encoder_outputs.last_hidden_state[-1])) + for i in range(_len_sources + 1, self.config.num_feature_levels): + sources.append(self.decoder_input_proj[i](encoder_outputs.last_hidden_state[-1])) + + # Prepare encoder inputs (by flattening) + source_flatten = [] + spatial_shapes_list = [] + spatial_shapes = torch.empty((len(sources), 2), device=device, dtype=torch.long) + for level, source in enumerate(sources): + height, width = source.shape[-2:] + spatial_shapes[level, 0] = height + spatial_shapes[level, 1] = width + spatial_shapes_list.append((height, width)) + source = source.flatten(2).transpose(1, 2) + source_flatten.append(source) + source_flatten = torch.cat(source_flatten, 1) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + + # prepare denoising training + if self.training and self.config.num_denoising > 0 and labels is not None: + ( + denoising_class, + denoising_bbox_unact, + attention_mask, + denoising_meta_values, + ) = get_contrastive_denoising_training_group( + targets=labels, + num_classes=self.config.num_labels, + num_queries=self.config.num_queries, + class_embed=self.denoising_class_embed, + num_denoising_queries=self.config.num_denoising, + label_noise_ratio=self.config.label_noise_ratio, + box_noise_scale=self.config.box_noise_scale, + ) + else: + denoising_class, denoising_bbox_unact, attention_mask, denoising_meta_values = None, None, None, None + + batch_size = len(source_flatten) + device = source_flatten.device + dtype = source_flatten.dtype + + # prepare input for decoder + if self.training or self.config.anchor_image_size is None: + # Pass spatial_shapes as tuple to make it hashable and make sure + # lru_cache is working for generate_anchors() + spatial_shapes_tuple = tuple(spatial_shapes_list) + anchors, valid_mask = self.generate_anchors(spatial_shapes_tuple, device=device, dtype=dtype) + else: + anchors, valid_mask = self.anchors, self.valid_mask + anchors, valid_mask = anchors.to(device, dtype), valid_mask.to(device, dtype) + + # use the valid_mask to selectively retain values in the feature map where the mask is `True` + memory = valid_mask.to(source_flatten.dtype) * source_flatten + + output_memory = self.enc_output(memory) + + enc_outputs_class = self.enc_score_head(output_memory) + enc_outputs_coord_logits = self.enc_bbox_head(output_memory) + anchors + + _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.config.num_queries, dim=1) + + reference_points_unact = enc_outputs_coord_logits.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_coord_logits.shape[-1]) + ) + + # _get_pred_class_and_mask + batch_ind = torch.arange(memory.shape[0], device=output_memory.device).unsqueeze(1) + target = output_memory[batch_ind, topk_ind] + out_query = self.decoder_norm(target) + mask_query_embed = self.mask_query_head(out_query) + batch_size, mask_dim, _ = mask_query_embed.shape + + enc_topk_bboxes = F.sigmoid(reference_points_unact) + + enc_topk_logits = enc_outputs_class.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_class.shape[-1]) + ) + + # extract region features + if self.config.learn_initial_query: + target = self.weight_embedding.tile([batch_size, 1, 1]) + else: + target = output_memory.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, output_memory.shape[-1])) + target = target.detach() + + if denoising_class is not None: + target = torch.concat([denoising_class, target], 1) + + if self.mask_enhanced: + _, _, mask_h, mask_w = encoder_outputs.mask_feat.shape + enc_out_masks = torch.bmm(mask_query_embed, encoder_outputs.mask_feat.flatten(start_dim=2)).reshape( + batch_size, mask_dim, mask_h, mask_w + ) + reference_points = mask_to_box_coordinate(enc_out_masks > 0, dtype=reference_points_unact.dtype) + reference_points_unact = inverse_sigmoid(reference_points) + + if denoising_bbox_unact is not None: + reference_points_unact = torch.concat([denoising_bbox_unact, reference_points_unact], 1) + + init_reference_points = reference_points_unact.detach() + + # decoder + decoder_outputs = self.decoder( + inputs_embeds=target, + encoder_hidden_states=source_flatten, + encoder_attention_mask=attention_mask, + reference_points=init_reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + order_head=self.decoder_order_head, + global_pointer=self.decoder_global_pointer, + mask_query_head=self.mask_query_head, + norm=self.decoder_norm, + mask_feat=encoder_outputs.mask_feat, + **kwargs, + ) + + return PPDocLayoutV3ModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, + intermediate_logits=decoder_outputs.intermediate_logits, + intermediate_reference_points=decoder_outputs.intermediate_reference_points, + intermediate_predicted_corners=decoder_outputs.intermediate_predicted_corners, + initial_reference_points=decoder_outputs.initial_reference_points, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + out_order_logits=decoder_outputs.decoder_out_order_logits, + out_masks=decoder_outputs.decoder_out_masks, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + init_reference_points=init_reference_points, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + enc_outputs_class=enc_outputs_class, + enc_outputs_coord_logits=enc_outputs_coord_logits, + denoising_meta_values=denoising_meta_values, + ) + + +@auto_docstring +@dataclass +class PPDocLayoutV3HybridEncoderOutput(BaseModelOutput): + r""" + mask_feat (`torch.FloatTensor` of shape `(batch_size, config.num_queries, 200, 200)`): + Mask features for each query in the batch. + """ + + mask_feat: torch.FloatTensor = None + + +@dataclass +@auto_docstring +class PPDocLayoutV3ForObjectDetectionOutput(ModelOutput): + r""" + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~PPDocLayoutV3ImageProcessor.post_process_object_detection`] to retrieve the + unnormalized (absolute) bounding boxes. + order_logits (`tuple` of `torch.FloatTensor` of shape `(batch_size, num_queries, num_queries)`): + Order logits of the final layer of the decoder. + out_masks (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, height, width)`): + Masks of the final layer of the decoder. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values + """ + + logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + order_logits: torch.FloatTensor | None = None + out_masks: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: tuple[torch.FloatTensor] | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV3 Model (consisting of a backbone and encoder-decoder) outputs bounding boxes and logits sorted according to reading order, + which are further decoded into scores and classes. + """ +) +class PPDocLayoutV3ForObjectDetection(PPDocLayoutV3PreTrainedModel): + # When using clones, all layers > 0 will be clones, but layer 0 *is* required + # We can't initialize the model on meta device as some weights are modified during the initialization + _no_split_modules = None + _keys_to_ignore_on_load_missing = ["num_batches_tracked", "rel_pos_y_bias", "rel_pos_x_bias"] + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__(config) + self.model = PPDocLayoutV3Model(config) + + self.model.denoising_class_embed = nn.Embedding(config.num_labels, config.d_model) + self.num_queries = config.num_queries + # if two-stage, the last class_embed and bbox_embed is for region proposal generation + self.post_init() + + def _set_aux_loss(self, outputs_class, outputs_coord): + return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)] + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | PPDocLayoutV3ForObjectDetectionOutput: + r""" + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoModelForObjectDetection, AutoImageProcessor + >>> from PIL import Image + >>> import requests + >>> import torch + + >>> url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> model_path = "PaddlePaddle/PP-DocLayoutV3_safetensors" + >>> image_processor = AutoImageProcessor.from_pretrained(model_path) + >>> model = AutoModelForObjectDetection.from_pretrained(model_path) + + >>> # prepare image for the model + >>> inputs = image_processor(images=[image], return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]])) + + >>> # print outputs + >>> for result in results: + ... for idx, (score, label_id, box) in enumerate(zip(result["scores"], result["labels"], result["boxes"])): + ... score, label = score.item(), label_id.item() + ... box = [round(i, 2) for i in box.tolist()] + ... print(f"Order {idx + 1}: {model.config.id2label[label]}: {score:.2f} {box}") + Order 1: text: 0.99 [334.95, 184.78, 897.25, 654.83] + Order 2: paragraph_title: 0.97 [337.28, 683.92, 869.16, 798.35] + Order 3: text: 0.99 [335.75, 842.82, 892.13, 1454.32] + Order 4: text: 0.99 [920.18, 185.28, 1476.38, 464.49] + Order 5: text: 0.98 [920.47, 483.68, 1480.63, 765.72] + Order 6: text: 0.98 [920.62, 846.8, 1482.09, 1220.67] + Order 7: text: 0.97 [920.92, 1239.41, 1469.55, 1378.02] + Order 8: footnote: 0.86 [335.03, 1614.68, 1483.33, 1731.73] + Order 9: footnote: 0.83 [334.64, 1756.74, 1471.78, 1845.69] + Order 10: text: 0.81 [336.8, 1910.52, 661.64, 1939.92] + Order 11: footnote: 0.96 [336.24, 2114.42, 1450.14, 2172.12] + Order 12: number: 0.88 [106.0, 2257.5, 135.84, 2282.18] + Order 13: footer: 0.93 [338.4, 2255.52, 986.15, 2284.37] + ```""" + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + encoder_outputs=encoder_outputs, + labels=labels, + **kwargs, + ) + + intermediate_logits = outputs.intermediate_logits + intermediate_reference_points = outputs.intermediate_reference_points + order_logits = outputs.out_order_logits + out_masks = outputs.out_masks + + pred_boxes = intermediate_reference_points[:, -1] + logits = intermediate_logits[:, -1] + order_logits = order_logits[:, -1] + out_masks = out_masks[:, -1] + + if labels is not None: + raise ValueError("PPDocLayoutV3ForObjectDetection does not support training") + + return PPDocLayoutV3ForObjectDetectionOutput( + logits=logits, + pred_boxes=pred_boxes, + order_logits=order_logits, + out_masks=out_masks, + last_hidden_state=outputs.last_hidden_state, + intermediate_hidden_states=outputs.intermediate_hidden_states, + intermediate_logits=outputs.intermediate_logits, + intermediate_reference_points=outputs.intermediate_reference_points, + intermediate_predicted_corners=outputs.intermediate_predicted_corners, + initial_reference_points=outputs.initial_reference_points, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + init_reference_points=outputs.init_reference_points, + enc_topk_logits=outputs.enc_topk_logits, + enc_topk_bboxes=outputs.enc_topk_bboxes, + enc_outputs_class=outputs.enc_outputs_class, + enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, + denoising_meta_values=outputs.denoising_meta_values, + ) + + +__all__ = ["PPDocLayoutV3ForObjectDetection", "PPDocLayoutV3Model", "PPDocLayoutV3PreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..db59b179d75b867d3a5ee30981077116571402cc --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py @@ -0,0 +1,1445 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict +from torch import nn +from torchvision.transforms.v2 import functional as tvF + +from ... import initialization as init +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import ( + group_images_by_shape, + reorder_images, +) +from ...image_utils import PILImageResampling, SizeDict +from ...modeling_outputs import BaseModelOutput +from ...processing_utils import Unpack +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + is_cv2_available, + logging, + requires_backends, +) +from ...utils.generic import TensorType, can_return_tuple +from ..auto import AutoConfig +from ..resnet.modeling_resnet import ResNetConvLayer +from ..rt_detr.modeling_rt_detr import ( + RTDetrDecoder, + RTDetrDecoderOutput, + RTDetrForObjectDetection, + RTDetrHybridEncoder, + RTDetrMLPPredictionHead, + RTDetrModel, + RTDetrModelOutput, + RTDetrMultiscaleDeformableAttention, + RTDetrPreTrainedModel, + get_contrastive_denoising_training_group, + inverse_sigmoid, +) + + +if is_cv2_available(): + import cv2 + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PP-DocLayoutV3_safetensors") +@strict +class PPDocLayoutV3Config(PreTrainedConfig): + r""" + initializer_bias_prior_prob (`float`, *optional*): + The prior probability used by the bias initializer to initialize biases for `enc_score_head` and `class_embed`. + If `None`, `prior_prob` computed as `prior_prob = 1 / (num_labels + 1)` while initializing model weights. + freeze_backbone_batch_norms (`bool`, *optional*, defaults to `True`): + Whether to freeze the batch normalization layers in the backbone. + encoder_in_channels (`list`, *optional*, defaults to `[512, 1024, 2048]`): + Multi level features input for encoder. + feat_strides (`list[int]`, *optional*, defaults to `[8, 16, 32]`): + Strides used in each feature map. + encode_proj_layers (`list[int]`, *optional*, defaults to `[2]`): + Indexes of the projected layers to be used in the encoder. + positional_encoding_temperature (`int`, *optional*, defaults to 10000): + The temperature parameter used to create the positional encodings. + encoder_activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + eval_size (`tuple[int, int]`, *optional*): + Height and width used to computes the effective height and width of the position embeddings after taking + into account the stride. + normalize_before (`bool`, *optional*, defaults to `False`): + Determine whether to apply layer normalization in the transformer encoder layer before self-attention and + feed-forward modules. + hidden_expansion (`float`, *optional*, defaults to 1.0): + Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. + mask_feature_channels (`list[int]`, *optional*, defaults to `[64, 64]`): + The channels of the multi-level features for mask enhancement. + x4_feat_dim (`int`, *optional*, defaults to 128): + The dimension of the x4 feature map. + d_model (`int`, *optional*, defaults to 256): + Dimension of the layers exclude hybrid encoder. + num_prototypes (`int`, *optional*, defaults to 32): + Dimension of the layers exclude mask query head. + label_noise_ratio (`float`, *optional*, defaults to 0.4): + The fraction of denoising labels to which random noise should be added. + box_noise_scale (`float`, *optional*, defaults to 0.4): + Scale or magnitude of noise to be added to the bounding boxes. + mask_enhanced (`bool`, *optional*, defaults to `True`): + Whether to use enhanced masked attention. + num_queries (`int`, *optional*, defaults to 300): + Number of object queries. + decoder_in_channels (`list`, *optional*, defaults to `[256, 256, 256]`): + Multi level features dimension for decoder + decoder_ffn_dim (`int`, *optional*, defaults to 1024): + Dimension of the "intermediate" (often named feed-forward) layer in decoder. + num_feature_levels (`int`, *optional*, defaults to 3): + The number of input feature levels. + decoder_n_points (`int`, *optional*, defaults to 4): + The number of sampled keys in each feature level for each attention head in the decoder. + decoder_activation_function (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + num_denoising (`int`, *optional*, defaults to 100): + The total number of denoising tasks or queries to be used for contrastive denoising. + learn_initial_query (`bool`, *optional*, defaults to `False`): + Indicates whether the initial query embeddings for the decoder should be learned during training + anchor_image_size (`tuple[int, int]`, *optional*): + Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. + disable_custom_kernels (`bool`, *optional*, defaults to `True`): + Whether to disable custom kernels. + global_pointer_head_size (`int`, *optional*, defaults to 64): + The size of the global pointer head. + gp_dropout_value (`float`, *optional*, defaults to 0.1): + The dropout probability in the global pointer head. + + Examples: + + ```python + >>> from transformers import PPDocLayoutV3Config, PPDocLayoutV3ForObjectDetection + + >>> # Initializing a PP-DocLayoutV3 configuration + >>> configuration = PPDocLayoutV3Config() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = PPDocLayoutV3ForObjectDetection(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pp_doclayout_v3" + sub_configs = {"backbone_config": AutoConfig} + + layer_types = ("basic", "bottleneck") + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + initializer_range: float = 0.01 + initializer_bias_prior_prob: float | None = None + layer_norm_eps: float = 1e-5 + batch_norm_eps: float = 1e-5 + tie_word_embeddings: bool = True + backbone_config: dict | PreTrainedConfig | None = None + freeze_backbone_batch_norms: bool = True + encoder_hidden_dim: int = 256 + encoder_in_channels: list[int] | tuple[int, ...] = (512, 1024, 2048) + feat_strides: list[int] | tuple[int, ...] = (8, 16, 32) + encoder_layers: int = 1 + encoder_ffn_dim: int = 1024 + encoder_attention_heads: int = 8 + dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + encode_proj_layers: list[int] | tuple[int, ...] = (2,) + positional_encoding_temperature: int = 10000 + encoder_activation_function: str = "gelu" + activation_function: str = "silu" + eval_size: int | None = None + normalize_before: bool = False + hidden_expansion: float = 1.0 + mask_feature_channels: list[int] | tuple[int, ...] = (64, 64) + x4_feat_dim: int = 128 + d_model: int = 256 + num_prototypes: int = 32 + label_noise_ratio: float = 0.4 + box_noise_scale: float = 0.4 + mask_enhanced: bool = True + num_queries: int = 300 + decoder_in_channels: list[int] | tuple[int, ...] = (256, 256, 256) + decoder_ffn_dim: int = 1024 + num_feature_levels: int = 3 + decoder_n_points: int = 4 + decoder_layers: int = 6 + decoder_attention_heads: int = 8 + decoder_activation_function: str = "relu" + attention_dropout: float | int = 0.0 + num_denoising: int = 100 + learn_initial_query: bool = False + anchor_image_size: int | None = None + disable_custom_kernels: bool = True + is_encoder_decoder: bool = True + global_pointer_head_size: int = 64 + gp_dropout_value: float | int = 0.1 + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [0, 1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [0, 0.05, 0.05, 0.05, 0.05], + "out_features": ["stage1", "stage2", "stage3", "stage4"], + }, + **kwargs, + ) + + self.encoder_in_channels = list(self.encoder_in_channels) + self.feat_strides = list(self.feat_strides) + self.encode_proj_layers = list(self.encode_proj_layers) + self.eval_size = list(self.eval_size) if self.eval_size is not None else None + self.decoder_in_channels = list(self.decoder_in_channels) + self.anchor_image_size = list(self.anchor_image_size) if self.anchor_image_size is not None else None + super().__post_init__(**kwargs) + + +@auto_docstring +class PPDocLayoutV3ImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = [0, 0, 0] + image_std = [1, 1, 1] + size = {"height": 800, "width": 800} + do_resize = True + do_rescale = True + do_normalize = True + + # We require `self.resize(..., antialias=False)` to approximate the output of `cv2.resize` + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample, antialias=False) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + def _get_order_seqs(self, order_logits): + """ + Computes the order sequences for a batch of inputs based on logits. + + This function takes in the order logits, calculates order scores using a sigmoid activation, + and determines the order sequences by ranking the votes derived from the scores. + + Args: + order_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_queries)`): + Stacked order logits. + + Returns: + torch.Tensor: A tensor of shape `(batch_size, num_queries)`: + Containing the computed order sequences for each input in the batch. Each row represents the ranked order of elements for the corresponding input in the batch. + """ + order_scores = torch.sigmoid(order_logits) + batch_size, sequence_length, _ = order_scores.shape + + order_votes = order_scores.triu(diagonal=1).sum(dim=1) + (1.0 - order_scores.transpose(1, 2)).tril( + diagonal=-1 + ).sum(dim=1) + + order_pointers = torch.argsort(order_votes, dim=1) + order_seq = torch.empty_like(order_pointers) + ranks = torch.arange(sequence_length, device=order_pointers.device, dtype=order_pointers.dtype).expand( + batch_size, -1 + ) + order_seq.scatter_(1, order_pointers, ranks) + + return order_seq + + def extract_custom_vertices(self, polygon, sharp_angle_thresh=45): + poly = np.array(polygon) + n = len(poly) + res = [] + i = 0 + while i < n: + previous_point = poly[(i - 1) % n] + current_point = poly[i] + next_point = poly[(i + 1) % n] + vector_1 = previous_point - current_point + vector_2 = next_point - current_point + cross_product_value = (vector_1[1] * vector_2[0]) - (vector_1[0] * vector_2[1]) + if cross_product_value < 0: + angle_cos = np.clip( + (vector_1 @ vector_2) / (np.linalg.norm(vector_1) * np.linalg.norm(vector_2)), -1.0, 1.0 + ) + angle = np.degrees(np.arccos(angle_cos)) + if abs(angle - sharp_angle_thresh) < 1: + # Calculate the new point based on the direction of two vectors. + dir_vec = vector_1 / np.linalg.norm(vector_1) + vector_2 / np.linalg.norm(vector_2) + dir_vec = dir_vec / np.linalg.norm(dir_vec) + step_size = (np.linalg.norm(vector_1) + np.linalg.norm(vector_2)) / 2 + new_point = current_point + dir_vec * step_size + res.append(tuple(new_point)) + else: + res.append(tuple(current_point)) + i += 1 + return res + + def _mask2polygon(self, mask, epsilon_ratio=0.004): + """ + Postprocess mask by removing small noise. + Args: + mask (ndarray): The input mask of shape [H, W]. + epsilon_ratio (float): The ratio of epsilon. + Returns: + ndarray: The output mask after postprocessing. + """ + contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if not contours: + return None + + contours = max(contours, key=cv2.contourArea) + epsilon = epsilon_ratio * cv2.arcLength(contours, True) + approx_contours = cv2.approxPolyDP(contours, epsilon, True) + polygon_points = approx_contours.squeeze() + polygon_points = np.atleast_2d(polygon_points) + + polygon_points = self.extract_custom_vertices(polygon_points) + + return polygon_points + + def _extract_polygon_points_by_masks(self, boxes, masks, scale_ratio): + scale_width, scale_height = scale_ratio[0] / 4, scale_ratio[1] / 4 + mask_height, mask_width = masks.shape[1:] + polygon_points = [] + + for i in range(len(boxes)): + x_min, y_min, x_max, y_max = boxes[i].astype(np.int32) + box_w, box_h = x_max - x_min, y_max - y_min + + # default rect + rect = np.array( + [[x_min, y_min], [x_max, y_min], [x_max, y_max], [x_min, y_max]], + dtype=np.float32, + ) + + if box_w <= 0 or box_h <= 0: + polygon_points.append(rect) + continue + + # crop mask + x_coordinates = [int(round((x_min * scale_width).item())), int(round((x_max * scale_width).item()))] + x_start, x_end = np.clip(x_coordinates, 0, mask_width) + y_coordinates = [int(round((y_min * scale_height).item())), int(round((y_max * scale_height).item()))] + y_start, y_end = np.clip(y_coordinates, 0, mask_height) + cropped_mask = masks[i, y_start:y_end, x_start:x_end] + if cropped_mask.size == 0 or np.sum(cropped_mask) == 0: + polygon_points.append(rect) + continue + + # resize mask to match box size + resized_mask = cv2.resize(cropped_mask.astype(np.uint8), (box_w, box_h), interpolation=cv2.INTER_NEAREST) + + polygon = self._mask2polygon(resized_mask) + if polygon is not None and len(polygon) < 4: + polygon_points.append(rect) + continue + if polygon is not None and len(polygon) > 0: + polygon = polygon + np.array([x_min, y_min]) + + polygon_points.append(polygon) + + return polygon_points + + def post_process_object_detection( + self, + outputs, + threshold: float = 0.5, + target_sizes: TensorType | list[tuple] | None = None, + ): + """ + Converts the raw output of [`PPDocLayoutV3ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels, boxes and polygon_points for an image + in the batch as predicted by the model. + """ + requires_backends(self, ["torch", "cv2"]) + boxes = outputs.pred_boxes + logits = outputs.logits + order_logits = outputs.order_logits + masks = outputs.out_masks + + order_seqs = self._get_order_seqs(order_logits) + + box_centers, box_dims = torch.split(boxes, 2, dim=-1) + top_left_coords = box_centers - 0.5 * box_dims + bottom_right_coords = box_centers + 0.5 * box_dims + boxes = torch.cat([top_left_coords, bottom_right_coords], dim=-1) + + if target_sizes is not None: + if len(logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + if isinstance(target_sizes, list): + img_height, img_width = torch.as_tensor(target_sizes).unbind(1) + else: + img_height, img_width = target_sizes.unbind(1) + scale_factor = torch.stack([img_width, img_height, img_width, img_height], dim=1).to(boxes.device) + boxes = boxes * scale_factor[:, None, :] + + num_top_queries = logits.shape[1] + num_classes = logits.shape[2] + + scores = torch.nn.functional.sigmoid(logits) + scores, index = torch.topk(scores.flatten(1), num_top_queries, dim=-1) + labels = index % num_classes + index = index // num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + masks = masks.gather( + dim=1, index=index.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, masks.shape[-2], masks.shape[-1]) + ) + masks = (masks.sigmoid() > threshold).int() + order_seqs = order_seqs.gather(dim=1, index=index) + + results = [] + for score, label, box, order_seq, target_size, mask in zip( + scores, labels, boxes, order_seqs, target_sizes, masks + ): + order_seq = order_seq[score >= threshold] + order_seq, indices = torch.sort(order_seq) + polygon_points = self._extract_polygon_points_by_masks( + box[score >= threshold][indices].detach().cpu().numpy(), + mask[score >= threshold][indices].detach().cpu().numpy(), + [self.size["width"] / target_size[1], self.size["height"] / target_size[0]], + ) + results.append( + { + "scores": score[score >= threshold][indices], + "labels": label[score >= threshold][indices], + "boxes": box[score >= threshold][indices], + "polygon_points": polygon_points, + "order_seq": order_seq, + } + ) + + return results + + +class PPDocLayoutV3GlobalPointer(nn.Module): + def __init__(self, config): + super().__init__() + self.head_size = config.global_pointer_head_size + self.dense = nn.Linear(config.d_model, self.head_size * 2) + self.dropout = nn.Dropout(config.gp_dropout_value) + + def forward(self, inputs): + batch_size, sequence_length, _ = inputs.shape + query_key_projection = self.dense(inputs).reshape(batch_size, sequence_length, 2, self.head_size) + query_key_projection = self.dropout(query_key_projection) + queries, keys = torch.unbind(query_key_projection, dim=2) + + logits = (queries @ keys.transpose(-2, -1)) / (self.head_size**0.5) + mask = torch.tril(torch.ones(sequence_length, sequence_length, device=logits.device)).bool() + logits = logits.masked_fill(mask.unsqueeze(0), -1e4) + + return logits + + +class PPDocLayoutV3MultiscaleDeformableAttention(RTDetrMultiscaleDeformableAttention): + pass + + +@auto_docstring +class PPDocLayoutV3PreTrainedModel(RTDetrPreTrainedModel): + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, PPDocLayoutV3MultiscaleDeformableAttention): + init.constant_(module.sampling_offsets.weight, 0.0) + default_dtype = torch.get_default_dtype() + thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * ( + 2.0 * math.pi / module.n_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(module.n_heads, 1, 1, 2) + .repeat(1, module.n_levels, module.n_points, 1) + ) + for i in range(module.n_points): + grid_init[:, :, i, :] *= i + 1 + + init.copy_(module.sampling_offsets.bias, grid_init.view(-1)) + init.constant_(module.attention_weights.weight, 0.0) + init.constant_(module.attention_weights.bias, 0.0) + init.xavier_uniform_(module.value_proj.weight) + init.constant_(module.value_proj.bias, 0.0) + init.xavier_uniform_(module.output_proj.weight) + init.constant_(module.output_proj.bias, 0.0) + + elif isinstance(module, PPDocLayoutV3Model): + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(module.enc_score_head.weight) + init.constant_(module.enc_score_head.bias, bias) + init.xavier_uniform_(module.decoder.class_embed.weight) + init.constant_(module.decoder.class_embed.bias, bias) + + elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + elif isinstance(module, nn.LayerNorm): + init.ones_(module.weight) + init.zeros_(module.bias) + + if isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + init.zeros_(module.weight.data[module.padding_idx]) + + +def mask_to_box_coordinate(mask, dtype): + mask = mask.bool() + + height, width = mask.shape[-2:] + + y_coords, x_coords = torch.meshgrid( + torch.arange(height, device=mask.device), torch.arange(width, device=mask.device), indexing="ij" + ) + x_coords = x_coords.to(dtype) + y_coords = y_coords.to(dtype) + + x_coords_masked = x_coords * mask + x_max = x_coords_masked.flatten(start_dim=-2).max(dim=-1).values + 1 + x_min = ( + torch.where(mask, x_coords_masked, torch.tensor(torch.finfo(dtype).max)) + .flatten(start_dim=-2) + .min(dim=-1) + .values + ) + + y_coords_masked = y_coords * mask + y_max = y_coords_masked.flatten(start_dim=-2).max(dim=-1).values + 1 + y_min = ( + torch.where(mask, y_coords_masked, torch.tensor(torch.finfo(dtype).max)) + .flatten(start_dim=-2) + .min(dim=-1) + .values + ) + + unnormalized_bbox = torch.stack([x_min, y_min, x_max, y_max], dim=-1) + + is_mask_non_empty = torch.any(mask, dim=(-2, -1)).unsqueeze(-1) + unnormalized_bbox = unnormalized_bbox * is_mask_non_empty + + norm_tensor = torch.tensor([width, height, width, height], device=mask.device, dtype=dtype) + normalized_bbox_xyxy = unnormalized_bbox / norm_tensor + + x_min_norm, y_min_norm, x_max_norm, y_max_norm = normalized_bbox_xyxy.unbind(dim=-1) + + center_x = (x_min_norm + x_max_norm) / 2 + center_y = (y_min_norm + y_max_norm) / 2 + box_width = x_max_norm - x_min_norm + box_height = y_max_norm - y_min_norm + + return torch.stack([center_x, center_y, box_width, box_height], dim=-1) + + +@dataclass +class PPDocLayoutV3DecoderOutput(RTDetrDecoderOutput): + r""" + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + decoder_out_order_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.num_queries, config.num_queries)`): + Stacked order logits (order logits of each layer of the decoder). + decoder_out_masks (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.num_queries, 200, 200)`): + Stacked masks (masks of each layer of the decoder). + """ + + decoder_out_order_logits: torch.FloatTensor | None = None + decoder_out_masks: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the PP-DocLayoutV3 model. + """ +) +@dataclass +class PPDocLayoutV3ModelOutput(RTDetrModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points used for the first decoder layer. + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`): + Logits of predicted bounding boxes coordinates in the encoder stage. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values. + out_order_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.num_queries, config.num_queries)`): + Stacked order logits (order logits of each layer of the decoder). + out_masks (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, config.num_queries, 200, 200)`): + Stacked masks (masks of each layer of the decoder). + """ + + out_order_logits: torch.FloatTensor | None = None + out_masks: torch.FloatTensor | None = None + + +class PPDocLayoutV3MLPPredictionHead(RTDetrMLPPredictionHead): + pass + + +class PPDocLayoutV3ConvLayer(ResNetConvLayer): + pass + + +class PPDocLayoutV3ScaleHead(nn.Module): + def __init__(self, in_channels, feature_channels, fpn_stride, base_stride, align_corners=False): + super().__init__() + head_length = max(1, int(np.log2(fpn_stride) - np.log2(base_stride))) + self.layers = nn.ModuleList() + for k in range(head_length): + in_c = in_channels if k == 0 else feature_channels + self.layers.append(PPDocLayoutV3ConvLayer(in_c, feature_channels, 3, 1, "silu")) + if fpn_stride != base_stride: + self.layers.append(nn.Upsample(scale_factor=2, mode="bilinear", align_corners=align_corners)) + + def forward(self, x): + for layer in self.layers: + x = layer(x) + return x + + +class PPDocLayoutV3MaskFeatFPN(nn.Module): + def __init__( + self, + in_channels=[256, 256, 256], + fpn_strides=[32, 16, 8], + feature_channels=256, + dropout_ratio=0.0, + out_channels=256, + align_corners=False, + ): + super().__init__() + + reorder_index = np.argsort(fpn_strides, axis=0).tolist() + in_channels = [in_channels[i] for i in reorder_index] + fpn_strides = [fpn_strides[i] for i in reorder_index] + + self.reorder_index = reorder_index + self.fpn_strides = fpn_strides + self.dropout_ratio = dropout_ratio + self.align_corners = align_corners + if self.dropout_ratio > 0: + self.dropout = nn.Dropout2d(dropout_ratio) + + self.scale_heads = nn.ModuleList() + for i in range(len(fpn_strides)): + self.scale_heads.append( + PPDocLayoutV3ScaleHead( + in_channels=in_channels[i], + feature_channels=feature_channels, + fpn_stride=fpn_strides[i], + base_stride=fpn_strides[0], + align_corners=align_corners, + ) + ) + self.output_conv = PPDocLayoutV3ConvLayer(feature_channels, out_channels, 3, 1, "silu") + + def forward(self, inputs): + x = [inputs[i] for i in self.reorder_index] + + output = self.scale_heads[0](x[0]) + for i in range(1, len(self.fpn_strides)): + output = output + F.interpolate( + self.scale_heads[i](x[i]), size=output.shape[2:], mode="bilinear", align_corners=self.align_corners + ) + + if self.dropout_ratio > 0: + output = self.dropout(output) + output = self.output_conv(output) + return output + + +class PPDocLayoutV3EncoderMaskOutput(nn.Module): + def __init__(self, in_channels, num_prototypes): + super().__init__() + self.base_conv = PPDocLayoutV3ConvLayer(in_channels, in_channels, 3, 1, "silu") + self.conv = nn.Conv2d(in_channels, num_prototypes, kernel_size=1) + + def forward(self, x): + x = self.base_conv(x) + x = self.conv(x) + return x + + +class PPDocLayoutV3HybridEncoder(RTDetrHybridEncoder): + """ + Main difference to `RTDetrHybridEncoder`: + 1. Mask Feature Head: Added `PPDocLayoutV3MaskFeatFPN` module (`self.mask_feature_head`) for document - specific mask feature generation. + 2. Extra Conv Layers: Introduced `self.encoder_mask_lateral` and `self.encoder_mask_output` for mask feature processing and output. + """ + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__() + + feat_strides = config.feat_strides + mask_feature_channels = config.mask_feature_channels + self.mask_feature_head = PPDocLayoutV3MaskFeatFPN( + [self.encoder_hidden_dim] * len(feat_strides), + feat_strides, + feature_channels=mask_feature_channels[0], + out_channels=mask_feature_channels[1], + ) + self.encoder_mask_lateral = PPDocLayoutV3ConvLayer(config.x4_feat_dim, mask_feature_channels[1], 3, 1, "silu") + self.encoder_mask_output = PPDocLayoutV3EncoderMaskOutput( + in_channels=mask_feature_channels[1], num_prototypes=config.num_prototypes + ) + + def forward( + self, + inputs_embeds=None, + x4_feat=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + """ + feature_maps = inputs_embeds + + # AIFI: Apply transformer encoder to specified feature levels + if self.config.encoder_layers > 0: + for i, enc_ind in enumerate(self.encode_proj_layers): + feature_maps[enc_ind] = self.aifi[i](feature_maps[enc_ind], **kwargs) + + # top-down FPN + fpn_feature_maps = [feature_maps[-1]] + for idx, (lateral_conv, fpn_block) in enumerate(zip(self.lateral_convs, self.fpn_blocks)): + backbone_feature_map = feature_maps[self.num_fpn_stages - idx - 1] + top_fpn_feature_map = fpn_feature_maps[-1] + # apply lateral block + top_fpn_feature_map = lateral_conv(top_fpn_feature_map) + fpn_feature_maps[-1] = top_fpn_feature_map + # apply fpn block + top_fpn_feature_map = F.interpolate(top_fpn_feature_map, scale_factor=2.0, mode="nearest") + fused_feature_map = torch.concat([top_fpn_feature_map, backbone_feature_map], dim=1) + new_fpn_feature_map = fpn_block(fused_feature_map) + fpn_feature_maps.append(new_fpn_feature_map) + + fpn_feature_maps.reverse() + + # bottom-up PAN + pan_feature_maps = [fpn_feature_maps[0]] + for idx, (downsample_conv, pan_block) in enumerate(zip(self.downsample_convs, self.pan_blocks)): + top_pan_feature_map = pan_feature_maps[-1] + fpn_feature_map = fpn_feature_maps[idx + 1] + downsampled_feature_map = downsample_conv(top_pan_feature_map) + fused_feature_map = torch.concat([downsampled_feature_map, fpn_feature_map], dim=1) + new_pan_feature_map = pan_block(fused_feature_map) + pan_feature_maps.append(new_pan_feature_map) + + mask_feat = self.mask_feature_head(pan_feature_maps) + mask_feat = F.interpolate(mask_feat, scale_factor=2, mode="bilinear", align_corners=False) + mask_feat += self.encoder_mask_lateral(x4_feat[0]) + mask_feat = self.encoder_mask_output(mask_feat) + + return PPDocLayoutV3HybridEncoderOutput( + last_hidden_state=pan_feature_maps, + mask_feat=mask_feat, + ) + + +class PPDocLayoutV3Decoder(RTDetrDecoder): + """ + Main difference to `RTDetrDecoder`: + A new mask generation process is introduced at each decoder layer. + """ + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__() + + self.num_queries = config.num_queries + + def forward( + self, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + order_head=None, + global_pointer=None, + mask_query_head=None, + norm=None, + mask_feat=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + The query embeddings that are passed into the decoder. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected + in `[0, 1]`: + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): + Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. + spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): + Spatial shapes of the feature maps. + level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): + Indexes for the start of each feature level. In range `[0, sequence_length]`. + """ + if inputs_embeds is not None: + hidden_states = inputs_embeds + + # decoder layers + intermediate = () + intermediate_reference_points = () + intermediate_logits = () + decoder_out_order_logits = () + decoder_out_masks = () + + reference_points = F.sigmoid(reference_points) + + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L252 + for idx, decoder_layer in enumerate(self.layers): + reference_points_input = reference_points.unsqueeze(2) + object_queries_position_embeddings = self.query_pos_head(reference_points) + + hidden_states = decoder_layer( + hidden_states, + object_queries_position_embeddings=object_queries_position_embeddings, + encoder_hidden_states=encoder_hidden_states, + reference_points=reference_points_input, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + encoder_attention_mask=encoder_attention_mask, + **kwargs, + ) + + # hack implementation for iterative bounding box refinement + if self.bbox_embed is not None: + predicted_corners = self.bbox_embed(hidden_states) + new_reference_points = F.sigmoid(predicted_corners + inverse_sigmoid(reference_points)) + reference_points = new_reference_points.detach() + + intermediate += (hidden_states,) + intermediate_reference_points += ( + (new_reference_points,) if self.bbox_embed is not None else (reference_points,) + ) + + # get_pred_class_order_and_mask + out_query = norm(hidden_states) + mask_query_embed = mask_query_head(out_query) + batch_size, mask_dim, _ = mask_query_embed.shape + _, _, mask_h, mask_w = mask_feat.shape + out_mask = torch.bmm(mask_query_embed, mask_feat.flatten(start_dim=2)).reshape( + batch_size, mask_dim, mask_h, mask_w + ) + decoder_out_masks += (out_mask,) + + if self.class_embed is not None: + logits = self.class_embed(out_query) + intermediate_logits += (logits,) + + if order_head is not None and global_pointer is not None: + valid_query = out_query[:, -self.num_queries :] if self.num_queries is not None else out_query + order_logits = global_pointer(order_head[idx](valid_query)) + decoder_out_order_logits += (order_logits,) + + # Keep batch_size as first dimension + intermediate = torch.stack(intermediate, dim=1) + intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) + if self.class_embed is not None: + intermediate_logits = torch.stack(intermediate_logits, dim=1) + if order_head is not None and global_pointer is not None: + decoder_out_order_logits = torch.stack(decoder_out_order_logits, dim=1) + decoder_out_masks = torch.stack(decoder_out_masks, dim=1) + + return PPDocLayoutV3DecoderOutput( + last_hidden_state=hidden_states, + intermediate_hidden_states=intermediate, + intermediate_logits=intermediate_logits, + intermediate_reference_points=intermediate_reference_points, + decoder_out_order_logits=decoder_out_order_logits, + decoder_out_masks=decoder_out_masks, + ) + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV3 Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top. + """ +) +class PPDocLayoutV3Model(RTDetrModel): + _tied_weights_keys = { + "decoder.class_embed": "enc_score_head", + "decoder.bbox_embed": "enc_bbox_head", + } + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__(config) + + encoder_input_proj_list = [] + self.encoder_input_proj = nn.ModuleList(encoder_input_proj_list[1:]) + + self.decoder_order_head = nn.ModuleList( + [nn.Linear(config.d_model, config.d_model) for _ in range(config.decoder_layers)] + ) + self.decoder_global_pointer = PPDocLayoutV3GlobalPointer(config) + self.decoder_norm = nn.LayerNorm(config.d_model, eps=config.layer_norm_eps) + self.decoder = PPDocLayoutV3Decoder(config) + self.decoder.class_embed = nn.Linear(config.d_model, config.num_labels) + self.decoder.bbox_embed = PPDocLayoutV3MLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + + self.mask_enhanced = config.mask_enhanced + self.mask_query_head = PPDocLayoutV3MLPPredictionHead( + config.d_model, config.d_model, config.num_prototypes, num_layers=3 + ) + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | PPDocLayoutV3ModelOutput: + r""" + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, PPDocLayoutV2Model + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("PekingU/PPDocLayoutV2_r50vd") + >>> model = PPDocLayoutV2Model.from_pretrained("PekingU/PPDocLayoutV2_r50vd") + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 300, 256] + ```""" + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + + if pixel_mask is None: + pixel_mask = torch.ones(((batch_size, height, width)), device=device) + + features = self.backbone(pixel_values, pixel_mask) + x4_feat = features.pop(0) + proj_feats = [self.encoder_input_proj[level](source) for level, (source, mask) in enumerate(features)] + + if encoder_outputs is None: + encoder_outputs = self.encoder( + proj_feats, + x4_feat, + **kwargs, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a PPDocLayoutV3HybridEncoderOutput when return_dict=True + elif not isinstance(encoder_outputs, PPDocLayoutV3HybridEncoderOutput): + encoder_outputs = PPDocLayoutV3HybridEncoderOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + mask_feat=encoder_outputs[-1], + ) + + # Equivalent to def _get_encoder_input + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L412 + sources = [] + for level, source in enumerate(encoder_outputs.last_hidden_state): + sources.append(self.decoder_input_proj[level](source)) + + # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage + if self.config.num_feature_levels > len(sources): + _len_sources = len(sources) + sources.append(self.decoder_input_proj[_len_sources](encoder_outputs.last_hidden_state[-1])) + for i in range(_len_sources + 1, self.config.num_feature_levels): + sources.append(self.decoder_input_proj[i](encoder_outputs.last_hidden_state[-1])) + + # Prepare encoder inputs (by flattening) + source_flatten = [] + spatial_shapes_list = [] + spatial_shapes = torch.empty((len(sources), 2), device=device, dtype=torch.long) + for level, source in enumerate(sources): + height, width = source.shape[-2:] + spatial_shapes[level, 0] = height + spatial_shapes[level, 1] = width + spatial_shapes_list.append((height, width)) + source = source.flatten(2).transpose(1, 2) + source_flatten.append(source) + source_flatten = torch.cat(source_flatten, 1) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + + # prepare denoising training + if self.training and self.config.num_denoising > 0 and labels is not None: + ( + denoising_class, + denoising_bbox_unact, + attention_mask, + denoising_meta_values, + ) = get_contrastive_denoising_training_group( + targets=labels, + num_classes=self.config.num_labels, + num_queries=self.config.num_queries, + class_embed=self.denoising_class_embed, + num_denoising_queries=self.config.num_denoising, + label_noise_ratio=self.config.label_noise_ratio, + box_noise_scale=self.config.box_noise_scale, + ) + else: + denoising_class, denoising_bbox_unact, attention_mask, denoising_meta_values = None, None, None, None + + batch_size = len(source_flatten) + device = source_flatten.device + dtype = source_flatten.dtype + + # prepare input for decoder + if self.training or self.config.anchor_image_size is None: + # Pass spatial_shapes as tuple to make it hashable and make sure + # lru_cache is working for generate_anchors() + spatial_shapes_tuple = tuple(spatial_shapes_list) + anchors, valid_mask = self.generate_anchors(spatial_shapes_tuple, device=device, dtype=dtype) + else: + anchors, valid_mask = self.anchors, self.valid_mask + anchors, valid_mask = anchors.to(device, dtype), valid_mask.to(device, dtype) + + # use the valid_mask to selectively retain values in the feature map where the mask is `True` + memory = valid_mask.to(source_flatten.dtype) * source_flatten + + output_memory = self.enc_output(memory) + + enc_outputs_class = self.enc_score_head(output_memory) + enc_outputs_coord_logits = self.enc_bbox_head(output_memory) + anchors + + _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.config.num_queries, dim=1) + + reference_points_unact = enc_outputs_coord_logits.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_coord_logits.shape[-1]) + ) + + # _get_pred_class_and_mask + batch_ind = torch.arange(memory.shape[0], device=output_memory.device).unsqueeze(1) + target = output_memory[batch_ind, topk_ind] + out_query = self.decoder_norm(target) + mask_query_embed = self.mask_query_head(out_query) + batch_size, mask_dim, _ = mask_query_embed.shape + + enc_topk_bboxes = F.sigmoid(reference_points_unact) + + enc_topk_logits = enc_outputs_class.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_class.shape[-1]) + ) + + # extract region features + if self.config.learn_initial_query: + target = self.weight_embedding.tile([batch_size, 1, 1]) + else: + target = output_memory.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, output_memory.shape[-1])) + target = target.detach() + + if denoising_class is not None: + target = torch.concat([denoising_class, target], 1) + + if self.mask_enhanced: + _, _, mask_h, mask_w = encoder_outputs.mask_feat.shape + enc_out_masks = torch.bmm(mask_query_embed, encoder_outputs.mask_feat.flatten(start_dim=2)).reshape( + batch_size, mask_dim, mask_h, mask_w + ) + reference_points = mask_to_box_coordinate(enc_out_masks > 0, dtype=reference_points_unact.dtype) + reference_points_unact = inverse_sigmoid(reference_points) + + if denoising_bbox_unact is not None: + reference_points_unact = torch.concat([denoising_bbox_unact, reference_points_unact], 1) + + init_reference_points = reference_points_unact.detach() + + # decoder + decoder_outputs = self.decoder( + inputs_embeds=target, + encoder_hidden_states=source_flatten, + encoder_attention_mask=attention_mask, + reference_points=init_reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + order_head=self.decoder_order_head, + global_pointer=self.decoder_global_pointer, + mask_query_head=self.mask_query_head, + norm=self.decoder_norm, + mask_feat=encoder_outputs.mask_feat, + **kwargs, + ) + + return PPDocLayoutV3ModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, + intermediate_logits=decoder_outputs.intermediate_logits, + intermediate_reference_points=decoder_outputs.intermediate_reference_points, + intermediate_predicted_corners=decoder_outputs.intermediate_predicted_corners, + initial_reference_points=decoder_outputs.initial_reference_points, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + out_order_logits=decoder_outputs.decoder_out_order_logits, + out_masks=decoder_outputs.decoder_out_masks, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + init_reference_points=init_reference_points, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + enc_outputs_class=enc_outputs_class, + enc_outputs_coord_logits=enc_outputs_coord_logits, + denoising_meta_values=denoising_meta_values, + ) + + +@auto_docstring +@dataclass +class PPDocLayoutV3HybridEncoderOutput(BaseModelOutput): + r""" + mask_feat (`torch.FloatTensor` of shape `(batch_size, config.num_queries, 200, 200)`): + Mask features for each query in the batch. + """ + + mask_feat: torch.FloatTensor = None + + +@dataclass +@auto_docstring +class PPDocLayoutV3ForObjectDetectionOutput(ModelOutput): + r""" + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~PPDocLayoutV3ImageProcessor.post_process_object_detection`] to retrieve the + unnormalized (absolute) bounding boxes. + order_logits (`tuple` of `torch.FloatTensor` of shape `(batch_size, num_queries, num_queries)`): + Order logits of the final layer of the decoder. + out_masks (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, height, width)`): + Masks of the final layer of the decoder. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values + """ + + logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + order_logits: torch.FloatTensor | None = None + out_masks: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: tuple[torch.FloatTensor] | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +@auto_docstring( + custom_intro=""" + PP-DocLayoutV3 Model (consisting of a backbone and encoder-decoder) outputs bounding boxes and logits sorted according to reading order, + which are further decoded into scores and classes. + """ +) +class PPDocLayoutV3ForObjectDetection(RTDetrForObjectDetection, PPDocLayoutV3PreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked", "rel_pos_y_bias", "rel_pos_x_bias"] + + def __init__(self, config: PPDocLayoutV3Config): + super().__init__(config) + + del self.model.decoder.class_embed + del self.model.decoder.bbox_embed + del num_pred # noqa + + self.model.denoising_class_embed = nn.Embedding(config.num_labels, config.d_model) + self.num_queries = config.num_queries + + self.post_init() + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | PPDocLayoutV3ForObjectDetectionOutput: + r""" + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoModelForObjectDetection, AutoImageProcessor + >>> from PIL import Image + >>> import requests + >>> import torch + + >>> url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/layout_demo.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> model_path = "PaddlePaddle/PP-DocLayoutV3_safetensors" + >>> image_processor = AutoImageProcessor.from_pretrained(model_path) + >>> model = AutoModelForObjectDetection.from_pretrained(model_path) + + >>> # prepare image for the model + >>> inputs = image_processor(images=[image], return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]])) + + >>> # print outputs + >>> for result in results: + ... for idx, (score, label_id, box) in enumerate(zip(result["scores"], result["labels"], result["boxes"])): + ... score, label = score.item(), label_id.item() + ... box = [round(i, 2) for i in box.tolist()] + ... print(f"Order {idx + 1}: {model.config.id2label[label]}: {score:.2f} {box}") + Order 1: text: 0.99 [334.95, 184.78, 897.25, 654.83] + Order 2: paragraph_title: 0.97 [337.28, 683.92, 869.16, 798.35] + Order 3: text: 0.99 [335.75, 842.82, 892.13, 1454.32] + Order 4: text: 0.99 [920.18, 185.28, 1476.38, 464.49] + Order 5: text: 0.98 [920.47, 483.68, 1480.63, 765.72] + Order 6: text: 0.98 [920.62, 846.8, 1482.09, 1220.67] + Order 7: text: 0.97 [920.92, 1239.41, 1469.55, 1378.02] + Order 8: footnote: 0.86 [335.03, 1614.68, 1483.33, 1731.73] + Order 9: footnote: 0.83 [334.64, 1756.74, 1471.78, 1845.69] + Order 10: text: 0.81 [336.8, 1910.52, 661.64, 1939.92] + Order 11: footnote: 0.96 [336.24, 2114.42, 1450.14, 2172.12] + Order 12: number: 0.88 [106.0, 2257.5, 135.84, 2282.18] + Order 13: footer: 0.93 [338.4, 2255.52, 986.15, 2284.37] + ```""" + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + encoder_outputs=encoder_outputs, + labels=labels, + **kwargs, + ) + + intermediate_logits = outputs.intermediate_logits + intermediate_reference_points = outputs.intermediate_reference_points + order_logits = outputs.out_order_logits + out_masks = outputs.out_masks + + pred_boxes = intermediate_reference_points[:, -1] + logits = intermediate_logits[:, -1] + order_logits = order_logits[:, -1] + out_masks = out_masks[:, -1] + + if labels is not None: + raise ValueError("PPDocLayoutV3ForObjectDetection does not support training") + + return PPDocLayoutV3ForObjectDetectionOutput( + logits=logits, + pred_boxes=pred_boxes, + order_logits=order_logits, + out_masks=out_masks, + last_hidden_state=outputs.last_hidden_state, + intermediate_hidden_states=outputs.intermediate_hidden_states, + intermediate_logits=outputs.intermediate_logits, + intermediate_reference_points=outputs.intermediate_reference_points, + intermediate_predicted_corners=outputs.intermediate_predicted_corners, + initial_reference_points=outputs.initial_reference_points, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + init_reference_points=outputs.init_reference_points, + enc_topk_logits=outputs.enc_topk_logits, + enc_topk_bboxes=outputs.enc_topk_bboxes, + enc_outputs_class=outputs.enc_outputs_class, + enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, + denoising_meta_values=outputs.denoising_meta_values, + ) + + +__all__ = [ + "PPDocLayoutV3ForObjectDetection", + "PPDocLayoutV3ImageProcessor", + "PPDocLayoutV3Config", + "PPDocLayoutV3Model", + "PPDocLayoutV3PreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..066f8084a4a318df1164ad7186b6287a5b88f4fc --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_formulanet import * + from .image_processing_pp_formulanet import * + from .modeling_pp_formulanet import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/configuration_pp_formulanet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/configuration_pp_formulanet.py new file mode 100644 index 0000000000000000000000000000000000000000..e5749c68e71ef6f4d5569625a37706bff5cde887 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/configuration_pp_formulanet.py @@ -0,0 +1,155 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_formulanet/modular_pp_formulanet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_formulanet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors") +@strict +class PPFormulaNetVisionConfig(PreTrainedConfig): + r""" + output_channels (`int`, *optional*, defaults to 256): + Dimensionality of the output channels in the Patch Encoder. + window_size (`int`, *optional*, defaults to 14): + Window size for relative position. + global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`): + The indexes of the global attention layers. + mlp_dim (`int`, *optional*, defaults to 3072): + The dimensionality of the MLP layer in the Transformer encoder. + post_conv_in_channels (`int`, *optional*, defaults to 256): + Number of input channels for the post-encoder convolution layer. + post_conv_out_channels (`int`, *optional*, defaults to 1024): + Number of output channels for the post-encoder convolution layer. + post_conv_mid_channels (`int`, *optional*, defaults to 512): + Number of intermediate channels for the post-encoder convolution layer. + decoder_hidden_size (`int`, *optional*, defaults to 512): + The hidden size of the decoder that the encoder features are projected to. + """ + + base_config_key = "vision_config" + hidden_size: int = 768 + output_channels: int = 256 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + image_size: int = 512 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-06 + attention_dropout: float | int = 0.0 + initializer_range: float = 1e-10 + qkv_bias: bool = True + use_abs_pos: bool = True + use_rel_pos: bool = True + window_size: int = 14 + global_attn_indexes: list[int] | tuple[int, ...] = (2, 5, 8, 11) + mlp_dim: int = 3072 + + post_conv_in_channels: int = 256 + post_conv_out_channels: int = 1024 + post_conv_mid_channels: int = 512 + decoder_hidden_size: int = 512 + + +@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors") +@strict +class PPFormulaNetTextConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import PPFormulaNetTextConfig, PPFormulaNetTextModel + + >>> # Initializing a PP_FORMULANET facebook/pp_formulanet-large-cc25 style configuration + >>> configuration = PPFormulaNetTextConfig() + + >>> # Initializing a model (with random weights) from the facebook/pp_formulanet-large-cc25 style configuration + >>> model = PPFormulaNetTextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pp_formulanet" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "num_attention_heads": "encoder_attention_heads", + "hidden_size": "d_model", + "num_hidden_layers": "encoder_layers", + } + vocab_size: int = 50000 + max_position_embeddings: int = 2560 + encoder_layers: int = 12 + encoder_attention_heads: int = 16 + decoder_layers: int = 8 + decoder_ffn_dim: int = 2048 + decoder_attention_heads: int = 16 + decoder_layerdrop: float | int = 0.0 + use_cache: bool = True + is_encoder_decoder: bool = True + activation_function: str = "gelu" + d_model: int = 512 + dropout: float | int = 0.1 + attention_dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + init_std: float = 0.02 + scale_embedding: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 2 + decoder_start_token_id: int | None = 2 + forced_eos_token_id: int | list[int] | None = 2 + tie_word_embeddings: bool = False + base_config_key = "text_config" + + +@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors") +@strict +class PPFormulaNetConfig(PreTrainedConfig): + model_type = "pp_formulanet" + sub_configs = {"text_config": PPFormulaNetTextConfig, "vision_config": PPFormulaNetVisionConfig} + + text_config: dict | PPFormulaNetTextConfig | None = None + vision_config: dict | PPFormulaNetVisionConfig | None = None + is_encoder_decoder: bool = True + + def __post_init__(self, **kwargs): + if isinstance(self.text_config, dict): + self.text_config = PPFormulaNetTextConfig(**self.text_config) + elif self.text_config is None: + logger.info("text_config is None. Initializing the PPFormulaNetTextConfig with default values.") + self.text_config = PPFormulaNetTextConfig() + + if isinstance(self.vision_config, dict): + self.vision_config = PPFormulaNetVisionConfig(**self.vision_config) + elif self.vision_config is None: + logger.info("vision_config is None. Initializing the PPFormulaNetVisionConfig with default values.") + self.vision_config = PPFormulaNetVisionConfig() + + super().__post_init__(**kwargs) + + +__all__ = ["PPFormulaNetConfig", "PPFormulaNetTextConfig", "PPFormulaNetVisionConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/image_processing_pp_formulanet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/image_processing_pp_formulanet.py new file mode 100644 index 0000000000000000000000000000000000000000..b3a5a5f5c6f72b7631c7ec170eaf69e6350a9578 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/image_processing_pp_formulanet.py @@ -0,0 +1,298 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_formulanet/modular_pp_formulanet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_formulanet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import get_resize_output_image_size, group_images_by_shape, reorder_images +from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires + + +class PPFormulaNetImageProcessorKwargs(ImagesKwargs, total=False): + r""" + do_crop_margin (`bool`, *optional*, defaults to `self.do_crop_margin`): + Whether to crop the image margins. + do_thumbnail (`bool`, *optional*, defaults to `self.do_thumbnail`): + Whether to resize the image using thumbnail method. + do_align_long_axis (`bool`, *optional*, defaults to `self.do_align_long_axis`): + Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees. + """ + + do_crop_margin: bool + do_thumbnail: bool + do_align_long_axis: bool + + +@auto_docstring +@requires(backends=("torch",)) +class PPFormulaNetImageProcessor(TorchvisionBackend): + valid_kwargs = PPFormulaNetImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = [0.7931, 0.7931, 0.7931] + image_std = [0.1738, 0.1738, 0.1738] + size = {"height": 768, "width": 768} + do_resize = True + do_normalize = True + do_thumbnail = True + do_align_long_axis = False + do_pad = True + do_rescale = True + do_crop_margin = True + + def __init__(self, **kwargs: Unpack[PPFormulaNetImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[PPFormulaNetImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def python_find_non_zero( + self, + image: "torch.Tensor", + ): + """This is a reimplementation of a findNonZero function equivalent to cv2.""" + + non_zero_indices = torch.nonzero(image, as_tuple=False) + idxvec = non_zero_indices[:, [2, 1]] + idxvec = idxvec.reshape(-1, 1, 2) + return idxvec + + def python_bounding_rect(self, coordinates): + """This is a reimplementation of a BoundingRect function equivalent to cv2.""" + + min_values = torch.amin(coordinates, axis=(0, 1)).to(torch.int) + max_values = torch.amax(coordinates, axis=(0, 1)).to(torch.int) + + x_min, y_min = min_values[0], min_values[1] + width = max_values[0] - x_min + 1 + height = max_values[1] - y_min + 1 + return x_min, y_min, width, height + + def crop_margin( + self, + image: "torch.Tensor", + gray_threshold: int = 200, + ) -> "torch.Tensor": + """ + Crops the margin of the image. Gray pixels are considered margin (i.e., pixels with a value below the + threshold). + + Args: + image (`torch.Tensor`): + The image to be cropped. + gray_threshold (`int`, *optional*, defaults to `200`) + Value below which pixels are considered to be gray. + """ + data = tvF.rgb_to_grayscale(image, num_output_channels=1) + + max_val = torch.max(data) + min_val = torch.min(data) + + if max_val == min_val: + return image + data = (data - min_val) / (max_val - min_val) * 255 + gray = data < gray_threshold + coords = self.python_find_non_zero(gray) + x_min, y_min, width, height = self.python_bounding_rect(coords) + image = image[:, y_min : y_min + height, x_min : x_min + width] + + return image + + def align_long_axis( + self, + image: "torch.Tensor", + size: SizeDict, + ) -> "torch.Tensor": + """ + Align the long axis of the image to the longest axis of the specified size. + + Args: + image (`torch.Tensor`): + The image to be aligned. + size (`SizeDict`): + The size to align the long axis to. + Returns: + `torch.Tensor`: The aligned image. + """ + input_height, input_width = image.shape[-2:] + output_height, output_width = size.height, size.width + + if (output_width < output_height and input_width > input_height) or ( + output_width > output_height and input_width < input_height + ): + image = torch.rot90(image, 3, dims=[1, 2]) + + return image + + def thumbnail( + self, + image: "torch.Tensor", + size: SizeDict, + ) -> "torch.Tensor": + """ + Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any + corresponding dimension of the specified size. + + Args: + image (`torch.tensor`): + The image to be resized. + size (`SizeDict`): + The size to resize the image to. + """ + + input_height, input_width = image.shape[-2:] + output_height, output_width = size.height, size.width + + # We always resize to the smallest of either the input or output size. + height = min(input_height, output_height) + width = min(input_width, output_width) + + if height == input_height and width == input_width: + return image + + if input_height > input_width: + width = int(input_width * height / input_height) + elif input_width > input_height: + height = int(input_height * width / input_width) + + new_size = (height, width) + + return tvF.resize(image, new_size, interpolation=tvF.InterpolationMode.BICUBIC) + + def pad_images( + self, + image: "torch.Tensor", + size: SizeDict, + ) -> "torch.Tensor": + """ + Pads a batch of images to the specified size at the top, bottom, left and right. + + Args: + image (`torch.tensor`): + The image to be padded. + size (`SizeDict`): + The size to pad the image to. + """ + input_height, input_width = image.shape[-2:] + output_height, output_width = size.height, size.width + + delta_width = output_width - input_width + delta_height = output_height - input_height + + pad_top = delta_height // 2 + pad_left = delta_width // 2 + + pad_bottom = delta_height - pad_top + pad_right = delta_width - pad_left + + padding = (pad_left, pad_top, pad_right, pad_bottom) + return tvF.pad(image, padding) + + def resize( + self, + image: "torch.Tensor", + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + antialias: bool = True, + **kwargs, + ) -> "torch.Tensor": + """ + Resize an image to `(size.height, size.width)`. + + Args: + image (`torch.Tensor`): + Image to resize. + size (`SizeDict`): + Size of the output image. + resample (`PILImageResampling | tvF.InterpolationMode | int`, *optional*): + Resampling filter to use when resizing the image. + Returns: + `torch.Tensor`: The resized image. + """ + shortest_edge = min(size.height, size.width) + + new_size = get_resize_output_image_size( + image, size=shortest_edge, default_to_square=False, input_data_format=ChannelDimension.FIRST + ) + return super().resize( + image, SizeDict(height=new_size[0], width=new_size[1]), resample=resample, antialias=antialias, **kwargs + ) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + do_align_long_axis: bool = False, + do_thumbnail: bool = True, + do_crop_margin: bool = True, + **kwargs, + ) -> BatchFeature: + # Crop images + if do_crop_margin: + images = [self.crop_margin(image) for image in images] + + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_align_long_axis: + stacked_images = self.align_long_axis(image=stacked_images, size=size) + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample) + if do_thumbnail: + stacked_images = self.thumbnail(image=stacked_images, size=size) + if do_pad: + stacked_images = self.pad_images(image=stacked_images, size=size) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + +__all__ = ["PPFormulaNetImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/modeling_pp_formulanet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/modeling_pp_formulanet.py new file mode 100644 index 0000000000000000000000000000000000000000..652d18a9a941db25838ad56811849a3b1f43aa1f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/modeling_pp_formulanet.py @@ -0,0 +1,1165 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_formulanet/modular_pp_formulanet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_formulanet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections +import math +from collections.abc import Callable +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPooling, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + is_torchdynamo_compiling, + logging, +) +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_pp_formulanet import PPFormulaNetConfig, PPFormulaNetVisionConfig + + +logger = logging.get_logger(__name__) + + +class PPFormulaNetPreTrainedModel(PreTrainedModel): + config: PPFormulaNetConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + _keep_in_fp32_modules_strict = [] + # Note this goes for the decoder only, the encoder will inherently always use eager attention + _supports_sdpa = True + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + + # Initialize positional embeddings to zero (PPFormulaNetVisionModel holds pos_embed) + if isinstance(module, PPFormulaNetVisionModel): + if module.pos_embed is not None: + init.constant_(module.pos_embed, 0.0) + + # Initialize relative positional embeddings to zero (PPFormulaNetVisionAttention holds rel_pos_h/w) + if isinstance(module, PPFormulaNetVisionAttention): + if module.use_rel_pos: + init.constant_(module.rel_pos_h, 0.0) + init.constant_(module.rel_pos_w, 0.0) + + +# overrider for PPFormulaNetModel's encoder output +@dataclass +class PPFormulaNetVisionEncoderOutput(BaseModelOutputWithPooling): + pass + + +class PPFormulaNetVisionAttention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__(self, config, window_size): + super().__init__() + input_size = ( + (config.image_size // config.patch_size, config.image_size // config.patch_size) + if window_size == 0 + else (window_size, window_size) + ) + + self.num_attention_heads = config.num_attention_heads + head_dim = config.hidden_size // config.num_attention_heads + self.scale = head_dim**-0.5 + self.dropout = config.attention_dropout + + self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias) + self.proj = nn.Linear(config.hidden_size, config.hidden_size) + + self.use_rel_pos = config.use_rel_pos + if self.use_rel_pos: + if input_size is None: + raise ValueError("Input size must be provided if using relative positional encoding.") + + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + + Args: + q_size (int): + size of the query. + k_size (int): + size of key k. + rel_pos (`torch.Tensor`): + relative position embeddings (L, channel). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + def get_decomposed_rel_pos( + self, + query: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: tuple[int, int], + k_size: tuple[int, int], + ) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py + + Args: + query (`torch.Tensor`): + query q in the attention layer with shape (batch_size, query_height * query_width, channel). + rel_pos_h (`torch.Tensor`): + relative position embeddings (Lh, channel) for height axis. + rel_pos_w (`torch.Tensor`): + relative position embeddings (Lw, channel) for width axis. + q_size (tuple): + spatial sequence size of query q with (query_height, query_width). + k_size (tuple): + spatial sequence size of key k with (key_height, key_width). + + Returns: + decomposed_rel_pos (`torch.Tensor`): + decomposed relative position embeddings. + """ + query_height, query_width = q_size + key_height, key_width = k_size + relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h) + relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w) + + batch_size, _, dim = query.shape + reshaped_query = query.reshape(batch_size, query_height, query_width, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height) + rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width) + + decomposed_rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + + return decomposed_rel_pos + + def forward(self, hidden_states: torch.Tensor, output_attentions=None) -> tuple[torch.Tensor, torch.Tensor]: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, batch_size, nHead, height * width, channel) + qkv = ( + self.qkv(hidden_states) + .reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + .permute(2, 0, 3, 1, 4) + ) + # q, k, v with shape (batch_size * nHead, height * width, channel) + query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0) + + attn_weights = (query * self.scale) @ key.transpose(-2, -1) + + if self.use_rel_pos: + decomposed_rel_pos = self.get_decomposed_rel_pos( + query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width) + ) + decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights) + attn_weights = attn_weights + decomposed_rel_pos + + attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype) + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1) + attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + return attn_output, attn_weights + + +class PPFormulaNetMultiModalProjector(nn.Module): + def __init__(self, config): + super().__init__() + self.conv1 = nn.Conv2d( + config.post_conv_in_channels, config.post_conv_mid_channels, kernel_size=3, stride=2, padding=1, bias=False + ) + self.conv2 = nn.Conv2d( + config.post_conv_mid_channels, + config.post_conv_out_channels, + kernel_size=3, + stride=2, + padding=1, + bias=False, + ) + self.linear_1 = nn.Linear(config.post_conv_out_channels, config.post_conv_out_channels) + self.linear_2 = nn.Linear(config.post_conv_out_channels, config.decoder_hidden_size) + + def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]): + hidden_states = self.conv1(hidden_states) + hidden_states = self.conv2(hidden_states) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + hidden_states = self.linear_1(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +class PPFormulaNetMLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim) + self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size) + self.act = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.lin1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.lin2(hidden_states) + return hidden_states + + +class PPFormulaNetVisionLayer(GradientCheckpointingLayer): + def __init__(self, config, window_size): + super().__init__() + self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attn = PPFormulaNetVisionAttention(config, window_size) + self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = PPFormulaNetMLPBlock(config) + self.window_size = window_size + + def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> tuple[torch.Tensor, tuple[int, int]]: + """ + Args: + Partition into non-overlapping windows with padding if needed. + hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window + size. + + Returns: + windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel]. + (pad_height, pad_width): padded height and width before partition + """ + batch_size, height, width, channel = hidden_states.shape + + pad_h = (window_size - height % window_size) % window_size + pad_w = (window_size - width % window_size) % window_size + hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h)) + pad_height, pad_width = height + pad_h, width + pad_w + + hidden_states = hidden_states.reshape( + batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel + ) + windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel) + return windows, (pad_height, pad_width) + + def window_unpartition( + self, windows: torch.Tensor, window_size: int, padding_shape: tuple[int, int], original_shape: tuple[int, int] + ) -> torch.Tensor: + """ + Args: + Window unpartition into original sequences and removing padding. + hidden_states (tensor): + input tokens with [batch_size * num_windows, window_size, window_size, channel]. + window_size (int): + window size. + padding_shape (Tuple): + padded height and width (pad_height, pad_width). + original_shape (Tuple): original height and width (height, width) before padding. + + Returns: + hidden_states: unpartitioned sequences with [batch_size, height, width, channel]. + """ + pad_height, pad_width = padding_shape + height, width = original_shape + batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size) + hidden_states = windows.reshape( + batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1 + ) + hidden_states = ( + hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1) + ) + + hidden_states = hidden_states[:, :height, :width, :].contiguous() + return hidden_states + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.FloatTensor]: + residual = hidden_states + hidden_states = self.layer_norm1(hidden_states) + # Window partition + if self.window_size > 0: + height, width = hidden_states.shape[1], hidden_states.shape[2] + hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size) + + hidden_states, attn_weights = self.attn( + hidden_states=hidden_states, + ) + # Reverse window partition + if self.window_size > 0: + hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width)) + + hidden_states = residual + hidden_states + layernorm_output = self.layer_norm2(hidden_states) + hidden_states = hidden_states + self.mlp(layernorm_output) + return hidden_states + + +class PPFormulaNetPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values): + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).permute(0, 2, 3, 1) + return embeddings + + +class PPFormulaNetLayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +class PPFormulaNetVisionNeck(nn.Module): + def __init__(self, config: PPFormulaNetVisionConfig): + super().__init__() + self.config = config + + self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False) + self.layer_norm1 = PPFormulaNetLayerNorm(config.output_channels, data_format="channels_first") + self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False) + self.layer_norm2 = PPFormulaNetLayerNorm(config.output_channels, data_format="channels_first") + + def forward(self, hidden_states): + hidden_states = hidden_states.permute(0, 3, 1, 2) + hidden_states = self.conv1(hidden_states) + hidden_states = self.layer_norm1(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + return hidden_states + + +class PPFormulaNetVisionModel(PPFormulaNetPreTrainedModel): + _can_record_outputs = {"hidden_states": PPFormulaNetVisionLayer, "attentions": PPFormulaNetVisionAttention} + input_modalities = ("image",) + + def __init__(self, config: PPFormulaNetVisionConfig): + super().__init__(config) + self.config = config + self.image_size = config.image_size + self.patch_embed = PPFormulaNetPatchEmbeddings(config) + + self.pos_embed = None + if config.use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros( + 1, + config.image_size // config.patch_size, + config.image_size // config.patch_size, + config.hidden_size, + ) + ) + + self.layers = nn.ModuleList() + for i in range(config.num_hidden_layers): + layer = PPFormulaNetVisionLayer( + config, + window_size=config.window_size if i not in config.global_attn_indexes else 0, + ) + self.layers.append(layer) + + self.neck = PPFormulaNetVisionNeck(config) + + self.gradient_checkpointing = False + self.multi_modal_projector = PPFormulaNetMultiModalProjector(config) + self.post_init() + + def get_input_embeddings(self): + return self.patch_embed + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, pixel_values: torch.FloatTensor | None = None, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithPooling: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.patch_embed(pixel_values) + if self.pos_embed is not None: + hidden_states = hidden_states + self.pos_embed + for layer_module in self.layers: + hidden_states = layer_module(hidden_states) + hidden_states = self.neck(hidden_states) + pooler_output = self.multi_modal_projector(hidden_states) + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=pooler_output, + ) + + +class PPFormulaNetLearnedPositionalEmbedding(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int): + # PPFormulaNet is set up so that if padding_idx is specified then offset the embedding ids by 2 + # and adjust num_embeddings appropriately. Other models don't have this hack + self.offset = 2 + super().__init__(num_embeddings + self.offset, embedding_dim) + + def forward( + self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor | None = None + ): + """`input_ids' shape is expected to be [bsz x seqlen].""" + + if position_ids is None: + bsz, seq_len = input_ids.shape[:2] + position_ids = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device + ).expand(bsz, -1) + else: + position_ids = position_ids.unsqueeze(0) + + return super().forward(position_ids + self.offset) + + +class PPFormulaNetScaledWordEmbedding(nn.Embedding): + """ + This module overrides nn.Embeddings' forward by multiplying with embeddings scale. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float | None = 1.0): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.embed_scale = embed_scale + + def forward(self, input_ids: torch.Tensor): + return super().forward(input_ids) * self.embed_scale + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PPFormulaNetAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: PPFormulaNetConfig | None = None, + layer_idx: int | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + if layer_idx is None and self.is_decoder: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + # TODO: we need a refactor so that the different attention modules can get their specific kwargs + # ATM, we have mixed things encoder, decoder, and encoder-decoder attn + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + + # get query proj + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.k_proj(current_states) + value_states = self.v_proj(current_states) + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = key_states.view(kv_shape).transpose(1, 2) + value_states = value_states.view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class PPFormulaNetDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PPFormulaNetConfig, layer_idx: int | None = None): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = PPFormulaNetAttention( + embed_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + is_causal=True, + config=config, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.encoder_attn = PPFormulaNetAttention( + self.embed_dim, + config.decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + config=config, + layer_idx=layer_idx, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = True, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + past_key_values (`Cache`): cached past key and value projection states + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Cross-Attention Block + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + hidden_states, _ = self.encoder_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class PPFormulaNetTextModel(PPFormulaNetPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PPFormulaNetTextModelLayer`] + + Args: + config: PPFormulaNetConfig + embed_tokens (nn.Embedding): output embedding + """ + + _can_record_outputs = { + "hidden_states": PPFormulaNetDecoderLayer, + "attentions": OutputRecorder(PPFormulaNetAttention, index=1, layer_name="self_attn"), + "cross_attentions": OutputRecorder(PPFormulaNetAttention, index=1, layer_name="encoder_attn"), + } + + def __init__(self, config: PPFormulaNetConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.max_target_positions = config.max_position_embeddings + embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0 + + self.embed_tokens = PPFormulaNetScaledWordEmbedding( + config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale + ) + + self.embed_positions = PPFormulaNetLearnedPositionalEmbedding( + config.max_position_embeddings, + config.d_model, + ) + self.layers = nn.ModuleList( + [PPFormulaNetDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)] + ) + self.config = config + + self.layernorm_embedding = nn.LayerNorm(config.d_model) + self.layer_norm = nn.LayerNorm(config.d_model) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPastAndCrossAttentions: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values + selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks and in the + cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. + + If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `decoder_input_ids` of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + """ + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # initialize `past_key_values` + if use_cache and past_key_values is None: + past_key_values = ( + EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + if encoder_hidden_states is not None or self.config.is_encoder_decoder + else DynamicCache(config=self.config) + ) + + batch_size, seq_length = inputs_embeds.size()[:-1] + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(seq_length, device=inputs_embeds.device) + past_key_values_length + + if attention_mask is None and not is_torchdynamo_compiling(): + # required mask seq length can be calculated via length of past cache + mask_seq_length = past_key_values_length + seq_length + attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) + + self_attn_cache = ( + past_key_values.self_attention_cache + if isinstance(past_key_values, EncoderDecoderCache) + else past_key_values + ) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=self_attn_cache, + ) + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + # embed positions + position_ids = self.embed_positions(input, past_key_values_length, position_ids=position_ids) + + hidden_states = inputs_embeds + position_ids.to(inputs_embeds.device) + hidden_states = self.layernorm_embedding(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + hidden_states = decoder_layer( + hidden_states, + causal_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.layer_norm(hidden_states) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int): + """ + Shift input ids one token to the right, and wrap the last non pad token (the token) Note that PPFormulaNet does not + have a single `decoder_start_token_id` in contrast to other Bart-like models. + """ + prev_output_tokens = input_ids.clone() + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) + + index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) + decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() + prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() + prev_output_tokens[:, 0] = decoder_start_tokens + + return prev_output_tokens + + +class PPFormulaNetModel(PPFormulaNetPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.decoder = PPFormulaNetTextModel(config.text_config) + self.encoder = PPFormulaNetVisionModel(config=config.vision_config) + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor + | None = None, # Kept in the signature for compatibility to avoid duplicate-keyword errors. + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor + | None = None, # Kept in the signature for compatibility to avoid duplicate-keyword errors. + use_cache: bool | None = None, + **kwargs, + ) -> tuple | Seq2SeqModelOutput: + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right(input_ids, self.config.text_config.pad_token_id) + + if (encoder_outputs is None) ^ (pixel_values is not None): + raise ValueError("You must specify exactly one of encoder_outputs or pixel_values") + + if encoder_outputs is None: + encoder_outputs: BaseModelOutputWithPooling = self.encoder( + pixel_values=pixel_values, + **kwargs, + ) + elif not isinstance(encoder_outputs, BaseModelOutputWithPooling): + encoder_outputs = BaseModelOutputWithPooling( + last_hidden_state=encoder_outputs[0], + pooler_output=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + hidden_states=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + attentions=encoder_outputs[3] if len(encoder_outputs) > 3 else None, + ) + + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs.pooler_output, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring +class PPFormulaNetForConditionalGeneration(PPFormulaNetPreTrainedModel, GenerationMixin): + _tied_weights_keys = {} + + def __init__(self, config: PPFormulaNetConfig): + super().__init__(config) + self.model = PPFormulaNetModel(config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.post_init() + + def get_output_embeddings(self) -> nn.Module: + return self.lm_head + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Seq2SeqLMOutput: + r""" + Example: + + ```python + >>> from io import BytesIO + + >>> import httpx + >>> from PIL import Image + >>> from transformers import AutoProcessor, PPFormulaNetForConditionalGeneration + + >>> model_path = "PaddlePaddle/PP-FormulaNet_plus-L_safetensors" # or "PaddlePaddle/PP-FormulaNet-L_safetensors" + >>> model = PPFormulaNetForConditionalGeneration.from_pretrained(model_path, device_map="auto") + >>> processor = AutoProcessor.from_pretrained(model_path) + + >>> image_url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png" + >>> image = Image.open(BytesIO(httpx.get(image_url).content)).convert("RGB") + >>> inputs = processor(images=image, return_tensors="pt").to(model.device) + >>> outputs = model(**inputs) + >>> result = processor.post_process(outputs) + >>> print(result) + ['\\zeta_{0}(\\nu)=-\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}\\breve{\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,'] + ```""" + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return Seq2SeqLMOutput( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + inputs_embeds=None, + pixel_values=None, + attention_mask=None, + logits_to_keep=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + logits_to_keep=logits_to_keep, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + if is_first_iteration or not kwargs.get("use_cache", True): + # Pixel values are used only in the first iteration if available + # In subsequent iterations, they are already merged with text and cached + # NOTE: first iteration doesn't have to be prefill, it can be the first + # iteration with a question and cached system prompt (continue generate from cache) + model_inputs["pixel_values"] = pixel_values + + return model_inputs + + # override this function to compatible with `_prepare_encoder_decoder_kwargs_for_generation` + def get_encoder(self): + return self.model.get_encoder() + + +__all__ = [ + "PPFormulaNetModel", + "PPFormulaNetTextModel", + "PPFormulaNetVisionModel", + "PPFormulaNetForConditionalGeneration", + "PPFormulaNetPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/modular_pp_formulanet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/modular_pp_formulanet.py new file mode 100644 index 0000000000000000000000000000000000000000..e1124656a495adc5dc286460665742d3c8df2e87 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/modular_pp_formulanet.py @@ -0,0 +1,538 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re +from dataclasses import dataclass + +import torch +import torch.nn as nn +from huggingface_hub.dataclasses import strict + +from ... import initialization as init +from ...cache_utils import Cache +from ...configuration_utils import PreTrainedConfig +from ...image_processing_utils import BatchFeature +from ...image_utils import ( + ImageInput, +) +from ...modeling_outputs import ( + BaseModelOutputWithPooling, + Seq2SeqLMOutput, + Seq2SeqModelOutput, +) +from ...modeling_utils import PreTrainedModel +from ...processing_utils import ( + ProcessingKwargs, + Unpack, +) +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + logging, +) +from ...utils.generic import merge_with_config_defaults +from ...utils.import_utils import requires +from ...utils.output_capturing import capture_outputs +from ..florence2.modeling_florence2 import Florence2ForConditionalGeneration +from ..mbart.configuration_mbart import MBartConfig +from ..mbart.modeling_mbart import MBartDecoder, shift_tokens_right +from ..nougat.image_processing_nougat import NougatImageProcessor +from ..nougat.processing_nougat import NougatProcessor +from ..slanext.configuration_slanext import SLANeXtVisionConfig +from ..slanext.modeling_slanext import ( + SLANeXtPreTrainedModel, + SLANeXtVisionAttention, + SLANeXtVisionEncoder, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors") +@strict +class PPFormulaNetVisionConfig(SLANeXtVisionConfig): + r""" + output_channels (`int`, *optional*, defaults to 256): + Dimensionality of the output channels in the Patch Encoder. + window_size (`int`, *optional*, defaults to 14): + Window size for relative position. + global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`): + The indexes of the global attention layers. + mlp_dim (`int`, *optional*, defaults to 3072): + The dimensionality of the MLP layer in the Transformer encoder. + post_conv_in_channels (`int`, *optional*, defaults to 256): + Number of input channels for the post-encoder convolution layer. + post_conv_out_channels (`int`, *optional*, defaults to 1024): + Number of output channels for the post-encoder convolution layer. + post_conv_mid_channels (`int`, *optional*, defaults to 512): + Number of intermediate channels for the post-encoder convolution layer. + decoder_hidden_size (`int`, *optional*, defaults to 512): + The hidden size of the decoder that the encoder features are projected to. + """ + + post_conv_in_channels: int = 256 + post_conv_out_channels: int = 1024 + post_conv_mid_channels: int = 512 + decoder_hidden_size: int = 512 + + +@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors") +@strict +class PPFormulaNetTextConfig(MBartConfig): + base_config_key = "text_config" + vocab_size: int = 50000 + max_position_embeddings: int = 2560 + decoder_layers: int = 8 + decoder_ffn_dim: int = 2048 + d_model: int = 512 + scale_embedding: bool = True + decoder_start_token_id: int | None = 2 + tie_word_embeddings: bool = False + is_encoder_decoder: bool = True + classifier_dropout = AttributeError() + encoder_ffn_dim = AttributeError() + encoder_layerdrop = AttributeError() + is_decoder = AttributeError() + + +@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors") +@strict +class PPFormulaNetConfig(PreTrainedConfig): + model_type = "pp_formulanet" + sub_configs = {"text_config": PPFormulaNetTextConfig, "vision_config": PPFormulaNetVisionConfig} + + text_config: dict | PPFormulaNetTextConfig | None = None + vision_config: dict | PPFormulaNetVisionConfig | None = None + is_encoder_decoder: bool = True + + def __post_init__(self, **kwargs): + if isinstance(self.text_config, dict): + self.text_config = PPFormulaNetTextConfig(**self.text_config) + elif self.text_config is None: + logger.info("text_config is None. Initializing the PPFormulaNetTextConfig with default values.") + self.text_config = PPFormulaNetTextConfig() + + if isinstance(self.vision_config, dict): + self.vision_config = PPFormulaNetVisionConfig(**self.vision_config) + elif self.vision_config is None: + logger.info("vision_config is None. Initializing the PPFormulaNetVisionConfig with default values.") + self.vision_config = PPFormulaNetVisionConfig() + + super().__post_init__(**kwargs) + + +@auto_docstring +@requires(backends=("torch",)) +class PPFormulaNetImageProcessor(NougatImageProcessor): + image_mean = [0.7931, 0.7931, 0.7931] + image_std = [0.1738, 0.1738, 0.1738] + size = {"height": 768, "width": 768} + + +@auto_docstring +class PPFormulaNetProcessor(NougatProcessor): + r""" + [`PPFormulaNetProcessor`] offers all the functionalities of [`PPFormulaNetImageProcessor`] and [`NougatTokenizer`]. See the + [`~PPFormulaNetProcessor.__call__`] and [`~PPFormulaNetProcessor.decode`] for more information. + """ + + def __init__(self, image_processor, tokenizer): + super().__init__(image_processor, tokenizer) + + # normalize() regex + self._text_reg = re.compile(r"(\\(operatorname|mathrm|text|mathbf)\s?\*? {.*?})") + self._macro_pattern = re.compile(r"(\\[a-zA-Z]+)\s(?=\w)|\\[a-zA-Z]+\s(?=})") + self._protected_macros = {"\\operatorname", "\\mathrm", "\\text", "\\mathbf"} + + letter = r"[a-zA-Z]" + noletter = r"[\W_^\d]" + self._rule_noletter_noletter = re.compile(r"(?!\\ )(%s)\s+?(%s)" % (noletter, noletter)) + self._rule_noletter_letter = re.compile(r"(?!\\ )(%s)\s+?(%s)" % (noletter, letter)) + self._rule_letter_noletter = re.compile(r"(%s)\s+?(%s)" % (letter, noletter)) + + # remove_chinese_text_wrapping() regex + self._chinese_text_wrapping_pattern = re.compile(r"\\text\s*{([^{}]*[\u4e00-\u9fff]+[^{}]*)}") + + def __call__( + self, + images: ImageInput, + **kwargs: Unpack[ProcessingKwargs], + ) -> BatchFeature: + r""" + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. Both channels-first and channels-last formats are supported. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + """ + output_kwargs = self._merge_kwargs( + ProcessingKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + return BatchFeature({**image_inputs}) + + def normalize(self, text: str) -> str: + """Normalizes a string by removing unnecessary spaces.""" + names = [] + for x in self._text_reg.findall(text): + matches = self._macro_pattern.findall(x[0]) + for m in matches: + if m not in self._protected_macros and m.strip() != "": + text = text.replace(m, m + "XXXXXXX") + text = text.replace(" ", "") + names.append(text) + + if names: + text = self._text_reg.sub(lambda match: str(names.pop(0)), text) + + new_text = text + while True: + text = new_text + new_text = self._rule_noletter_noletter.sub(r"\1\2", text) + new_text = self._rule_noletter_letter.sub(r"\1\2", new_text) + new_text = self._rule_letter_noletter.sub(r"\1\2", new_text) + if new_text == text: + break + + return new_text.replace("XXXXXXX", " ") + + def remove_chinese_text_wrapping(self, formula: str) -> str: + def replacer(match): + return match.group(1) + + replaced_formula = self._chinese_text_wrapping_pattern.sub(replacer, formula) + return replaced_formula.replace('"', "") + + def post_process_generation(self, text: str) -> str: + """Post-processes a string by fixing text and normalizing it. + + Args: + text (str): String to post-process. + + Returns: + str: Post-processed string. + """ + text = self.remove_chinese_text_wrapping(text) + try: + from ftfy import fix_text + + text = fix_text(text) + except ImportError: + logger.warning_once( + "ftfy is not installed, skipping fix_text. " + "Output may contain unnormalized unicode, extra spaces, or escaped artifacts" + ) + text = self.normalize(text) + return text + + def post_process(self, generated_outputs, skip_special_tokens=True, **kwargs): + """ + Post-process the output of the model to decode the text. + + Args: + generated_outputs (`torch.Tensor` or `np.ndarray`): + The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` + or `(sequence_length,)`. + skip_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. + **kwargs: + Additional arguments to be passed to the tokenizer's `batch_decode method`. + + Returns: + `list[str]`: The decoded text. + """ + generated_texts = self.batch_decode(generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs) + return [self.post_process_generation(text) for text in generated_texts] + + +class PPFormulaNetPreTrainedModel(SLANeXtPreTrainedModel): + _keep_in_fp32_modules_strict = [] + base_model_prefix = "model" + # Note this goes for the decoder only, the encoder will inherently always use eager attention + _supports_sdpa = True + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + PreTrainedModel._init_weights(module) + + # Initialize positional embeddings to zero (PPFormulaNetVisionModel holds pos_embed) + if isinstance(module, PPFormulaNetVisionModel): + if module.pos_embed is not None: + init.constant_(module.pos_embed, 0.0) + + # Initialize relative positional embeddings to zero (PPFormulaNetVisionAttention holds rel_pos_h/w) + if isinstance(module, PPFormulaNetVisionAttention): + if module.use_rel_pos: + init.constant_(module.rel_pos_h, 0.0) + init.constant_(module.rel_pos_w, 0.0) + + +# overrider for PPFormulaNetModel's encoder output +@dataclass +class PPFormulaNetVisionEncoderOutput(BaseModelOutputWithPooling): + pass + + +class PPFormulaNetVisionAttention(SLANeXtVisionAttention): + pass + + +class PPFormulaNetMultiModalProjector(nn.Module): + def __init__(self, config): + super().__init__() + self.conv1 = nn.Conv2d( + config.post_conv_in_channels, config.post_conv_mid_channels, kernel_size=3, stride=2, padding=1, bias=False + ) + self.conv2 = nn.Conv2d( + config.post_conv_mid_channels, + config.post_conv_out_channels, + kernel_size=3, + stride=2, + padding=1, + bias=False, + ) + self.linear_1 = nn.Linear(config.post_conv_out_channels, config.post_conv_out_channels) + self.linear_2 = nn.Linear(config.post_conv_out_channels, config.decoder_hidden_size) + + def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]): + hidden_states = self.conv1(hidden_states) + hidden_states = self.conv2(hidden_states) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + hidden_states = self.linear_1(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +class PPFormulaNetVisionModel(SLANeXtVisionEncoder): + def __init__(self, config: PPFormulaNetVisionConfig): + super().__init__() + self.multi_modal_projector = PPFormulaNetMultiModalProjector(config) + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, pixel_values: torch.FloatTensor | None = None, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithPooling: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.patch_embed(pixel_values) + if self.pos_embed is not None: + hidden_states = hidden_states + self.pos_embed + for layer_module in self.layers: + hidden_states = layer_module(hidden_states) + hidden_states = self.neck(hidden_states) + pooler_output = self.multi_modal_projector(hidden_states) + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=pooler_output, + ) + + +@auto_docstring +class PPFormulaNetTextModel(MBartDecoder): + pass + + +class PPFormulaNetModel(PPFormulaNetPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.decoder = PPFormulaNetTextModel(config.text_config) + self.encoder = PPFormulaNetVisionModel(config=config.vision_config) + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor + | None = None, # Kept in the signature for compatibility to avoid duplicate-keyword errors. + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor + | None = None, # Kept in the signature for compatibility to avoid duplicate-keyword errors. + use_cache: bool | None = None, + **kwargs, + ) -> tuple | Seq2SeqModelOutput: + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right(input_ids, self.config.text_config.pad_token_id) + + if (encoder_outputs is None) ^ (pixel_values is not None): + raise ValueError("You must specify exactly one of encoder_outputs or pixel_values") + + if encoder_outputs is None: + encoder_outputs: BaseModelOutputWithPooling = self.encoder( + pixel_values=pixel_values, + **kwargs, + ) + elif not isinstance(encoder_outputs, BaseModelOutputWithPooling): + encoder_outputs = BaseModelOutputWithPooling( + last_hidden_state=encoder_outputs[0], + pooler_output=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + hidden_states=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + attentions=encoder_outputs[3] if len(encoder_outputs) > 3 else None, + ) + + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs.pooler_output, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring +class PPFormulaNetForConditionalGeneration(Florence2ForConditionalGeneration): + _tied_weights_keys = {} + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: list[torch.FloatTensor] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Seq2SeqLMOutput: + r""" + Example: + + ```python + >>> from io import BytesIO + + >>> import httpx + >>> from PIL import Image + >>> from transformers import AutoProcessor, PPFormulaNetForConditionalGeneration + + >>> model_path = "PaddlePaddle/PP-FormulaNet_plus-L_safetensors" # or "PaddlePaddle/PP-FormulaNet-L_safetensors" + >>> model = PPFormulaNetForConditionalGeneration.from_pretrained(model_path, device_map="auto") + >>> processor = AutoProcessor.from_pretrained(model_path) + + >>> image_url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png" + >>> image = Image.open(BytesIO(httpx.get(image_url).content)).convert("RGB") + >>> inputs = processor(images=image, return_tensors="pt").to(model.device) + >>> outputs = model(**inputs) + >>> result = processor.post_process(outputs) + >>> print(result) + ['\\zeta_{0}(\\nu)=-\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}\\breve{\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,'] + ```""" + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return Seq2SeqLMOutput( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + # override this function to compatible with `_prepare_encoder_decoder_kwargs_for_generation` + def get_encoder(self): + return self.model.get_encoder() + + def get_input_embeddings(self): + raise AttributeError("The PPFormulaNetModel does not have an `input_embedding` attribute.") + + def set_input_embeddings(self): + raise AttributeError("The PPFormulaNetModel does not have an `input_embedding` attribute.") + + def get_placeholder_mask(self): + raise AttributeError("The PPFormulaNet does not need placeholder mask.") + + def get_image_features(self): + raise AttributeError("The PPFormulaNet does not need `get_image_features`.") + + def _prepare_encoder_decoder_kwargs_for_generation(self): + raise AttributeError("The PPFormulaNet use default implementation.") + + +__all__ = [ + "PPFormulaNetProcessor", + "PPFormulaNetImageProcessor", + "PPFormulaNetConfig", + "PPFormulaNetTextConfig", + "PPFormulaNetModel", + "PPFormulaNetTextModel", + "PPFormulaNetVisionModel", + "PPFormulaNetVisionConfig", + "PPFormulaNetForConditionalGeneration", + "PPFormulaNetPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/processing_pp_formulanet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/processing_pp_formulanet.py new file mode 100644 index 0000000000000000000000000000000000000000..2c323289fb0587c48f2c970f91d53429d4c48c4a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_formulanet/processing_pp_formulanet.py @@ -0,0 +1,155 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_formulanet/modular_pp_formulanet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_formulanet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import re + +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@auto_docstring +class PPFormulaNetProcessor(ProcessorMixin): + r""" + [`PPFormulaNetProcessor`] offers all the functionalities of [`PPFormulaNetImageProcessor`] and [`NougatTokenizer`]. See the + [`~PPFormulaNetProcessor.__call__`] and [`~PPFormulaNetProcessor.decode`] for more information. + """ + + def __init__(self, image_processor, tokenizer): + super().__init__(image_processor, tokenizer) + + # normalize() regex + self._text_reg = re.compile(r"(\\(operatorname|mathrm|text|mathbf)\s?\*? {.*?})") + self._macro_pattern = re.compile(r"(\\[a-zA-Z]+)\s(?=\w)|\\[a-zA-Z]+\s(?=})") + self._protected_macros = {"\\operatorname", "\\mathrm", "\\text", "\\mathbf"} + + letter = r"[a-zA-Z]" + noletter = r"[\W_^\d]" + self._rule_noletter_noletter = re.compile(r"(?!\\ )(%s)\s+?(%s)" % (noletter, noletter)) + self._rule_noletter_letter = re.compile(r"(?!\\ )(%s)\s+?(%s)" % (noletter, letter)) + self._rule_letter_noletter = re.compile(r"(%s)\s+?(%s)" % (letter, noletter)) + + # remove_chinese_text_wrapping() regex + self._chinese_text_wrapping_pattern = re.compile(r"\\text\s*{([^{}]*[\u4e00-\u9fff]+[^{}]*)}") + + @auto_docstring + def __call__( + self, + images: ImageInput, + **kwargs: Unpack[ProcessingKwargs], + ) -> BatchFeature: + r""" + images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): + The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch + tensor. Both channels-first and channels-last formats are supported. + + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + """ + output_kwargs = self._merge_kwargs( + ProcessingKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + return BatchFeature({**image_inputs}) + + def post_process_generation(self, text: str) -> str: + """Post-processes a string by fixing text and normalizing it. + + Args: + text (str): String to post-process. + + Returns: + str: Post-processed string. + """ + text = self.remove_chinese_text_wrapping(text) + try: + from ftfy import fix_text + + text = fix_text(text) + except ImportError: + logger.warning_once( + "ftfy is not installed, skipping fix_text. " + "Output may contain unnormalized unicode, extra spaces, or escaped artifacts" + ) + text = self.normalize(text) + return text + + def normalize(self, text: str) -> str: + """Normalizes a string by removing unnecessary spaces.""" + names = [] + for x in self._text_reg.findall(text): + matches = self._macro_pattern.findall(x[0]) + for m in matches: + if m not in self._protected_macros and m.strip() != "": + text = text.replace(m, m + "XXXXXXX") + text = text.replace(" ", "") + names.append(text) + + if names: + text = self._text_reg.sub(lambda match: str(names.pop(0)), text) + + new_text = text + while True: + text = new_text + new_text = self._rule_noletter_noletter.sub(r"\1\2", text) + new_text = self._rule_noletter_letter.sub(r"\1\2", new_text) + new_text = self._rule_letter_noletter.sub(r"\1\2", new_text) + if new_text == text: + break + + return new_text.replace("XXXXXXX", " ") + + def remove_chinese_text_wrapping(self, formula: str) -> str: + def replacer(match): + return match.group(1) + + replaced_formula = self._chinese_text_wrapping_pattern.sub(replacer, formula) + return replaced_formula.replace('"', "") + + def post_process(self, generated_outputs, skip_special_tokens=True, **kwargs): + """ + Post-process the output of the model to decode the text. + + Args: + generated_outputs (`torch.Tensor` or `np.ndarray`): + The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` + or `(sequence_length,)`. + skip_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. + **kwargs: + Additional arguments to be passed to the tokenizer's `batch_decode method`. + + Returns: + `list[str]`: The decoded text. + """ + generated_texts = self.batch_decode(generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs) + return [self.post_process_generation(text) for text in generated_texts] + + +__all__ = ["PPFormulaNetProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..648b55c8f4603d43eafcfe28ec3b0d00b431cfca --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_lcnet import * + from .image_processing_pp_lcnet import * + from .modeling_pp_lcnet import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/configuration_pp_lcnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/configuration_pp_lcnet.py new file mode 100644 index 0000000000000000000000000000000000000000..78b6330d698f5f03170a5b4720913716712c1b2d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/configuration_pp_lcnet.py @@ -0,0 +1,108 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_lcnet/modular_pp_lcnet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_lcnet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import BackboneConfigMixin +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="PaddlePaddle/PP-LCNet_x1_0_doc_ori_safetensors") +@strict +class PPLCNetConfig(BackboneConfigMixin, PreTrainedConfig): + r""" + scale (`float`, *optional*, defaults to 1.0): + The scaling factor for the model's channel dimensions, used to adjust the model size and computational cost + without changing the overall architecture (e.g., 0.25, 0.5, 1.0, 1.5). + block_configs (`list[list[tuple]]`, *optional*, defaults to `None`): + Configuration for each block in each stage. Each tuple contains: + (kernel_size, in_channels, out_channels, stride, use_squeeze_excitation). + If `None`, uses the default PP-LCNet configuration. + stem_channels (`int`, *optional*, defaults to 16): + The number of output channels for the stem layer. + stem_stride (`int`, *optional*, defaults to 2): + The stride for the stem convolution layer. + reduction (`int`, *optional*, defaults to 4): + The reduction factor for feature channel dimensions in the squeeze-and-excitation (SE) blocks, used to + reduce the number of model parameters and computational complexity while maintaining feature representability. + class_expand (`int`, *optional*, defaults to 1280): + The number of hidden units in the expansion layer of the classification head, used to enhance the model's + feature representation capability before the final classification layer. + divisor (`int`, *optional*, defaults to 8): + The divisor used to ensure that various model parameters (e.g., channel dimensions, kernel sizes) are + multiples of this value, promoting efficient model implementation and resource utilization. + """ + + model_type = "pp_lcnet" + + scale: float | int = 1.0 + block_configs: list | None = None + stem_channels: int = 16 + stem_stride: int = 2 + reduction: int = 4 + class_expand: int = 1280 + divisor: int = 8 + hidden_act: str = "hardswish" + _out_features: list[str] | None = None + _out_indices: list[int] | None = None + hidden_dropout_prob: float | int = 0.2 + + def __post_init__(self, **kwargs): + # Default block configs for PP-LCNet + # Each tuple: (kernel_size, in_channels, out_channels, stride, use_squeeze_excitation) + self.block_configs = ( + [ + # Stage 1 (blocks2) + [[3, 16, 32, 1, False]], + # Stage 2 (blocks3) + [[3, 32, 64, 2, False], [3, 64, 64, 1, False]], + # Stage 3 (blocks4) + [[3, 64, 128, 2, False], [3, 128, 128, 1, False]], + # Stage 4 (blocks5) + [ + [3, 128, 256, 2, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + ], + # Stage 5 (blocks6) + [[5, 256, 512, 2, True], [5, 512, 512, 1, True]], + ] + if self.block_configs is None + else self.block_configs + ) + + self.depths = [len(blocks) for blocks in self.block_configs] + self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.block_configs) + 1)] + self.set_output_features_output_indices( + out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None) + ) + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if len(self.block_configs) != 5: + raise ValueError(f"block_configs must have 5 stages, but got {len(self.block_configs)}") + + +__all__ = ["PPLCNetConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/image_processing_pp_lcnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/image_processing_pp_lcnet.py new file mode 100644 index 0000000000000000000000000000000000000000..f4bc3eae23b362345ac41dd881e98d6a4b6ac9a9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/image_processing_pp_lcnet.py @@ -0,0 +1,133 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_lcnet/modular_pp_lcnet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_lcnet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import torch +import torchvision.transforms.v2.functional as tvF + +from ...feature_extraction_utils import BatchFeature +from ...image_processing_backends import TorchvisionBackend +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs +from ...utils import auto_docstring +from ...utils.generic import TensorType +from ...utils.import_utils import requires + + +class PPLCNetImageProcessorKwargs(ImagesKwargs, total=False): + r""" + resize_short (`int`, *optional*, defaults to 256): + target_short_edge (Union[int, None]): Desired length for the shorter edge of the image. + size_divisor (`int`, *optional*, defaults to 1): + Divisor to align image dimensions. + """ + + resize_short: int + size_divisor: int + + +@auto_docstring +@requires(backends=("torch",)) +class PPLCNetImageProcessor(TorchvisionBackend): + resample = 2 + image_mean = [0.406, 0.456, 0.485] + image_std = [0.225, 0.224, 0.229] + size = {"height": 256, "width": 256} + do_resize = True + do_rescale = True + do_normalize = True + do_center_crop = True + crop_size = 224 + resize_short = 256 + size_divisor = 1 + valid_kwargs = PPLCNetImageProcessorKwargs + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + resize_short: int, + size_divisor: int, + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + disable_grouping: bool | None = False, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + # Unlike TorchvisionBackend, which resizes to a fixed target, + # this implementation first calculates the target size dynamically to preserve + # the aspect ratio, using the shorter edge as a reference. + resize_size = size + if self.resize_short is not None: + resize_size = self.get_image_size( + stacked_images[0], target_short_edge=resize_short, size_divisor=size_divisor + ) + stacked_images = self.resize(stacked_images, size=resize_size, resample=resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + # RGB -> BGR + images = [image[[2, 1, 0], :, :] for image in processed_images] + return BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors) + + def get_image_size( + self, + image: "torch.Tensor", + target_short_edge: int | None, + size_divisor: int | None, + ) -> tuple[SizeDict, torch.Tensor]: + _, height, width = image.shape + resize_scale = target_short_edge / min(height, width) + resized_height = round(height * resize_scale) + resized_width = round(width * resize_scale) + if size_divisor is not None: + resized_height = math.ceil(resized_height / size_divisor) * size_divisor + resized_width = math.ceil(resized_width / size_divisor) * size_divisor + + return SizeDict(height=resized_height, width=resized_width) + + +__all__ = ["PPLCNetImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/modeling_pp_lcnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/modeling_pp_lcnet.py new file mode 100644 index 0000000000000000000000000000000000000000..244ca4a53a5cf350c153ff45c345454505eebeff --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/modeling_pp_lcnet.py @@ -0,0 +1,368 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_lcnet/modular_pp_lcnet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_lcnet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn +from torch import Tensor + +from ...activations import ACT2FN +from ...backbone_utils import BackboneMixin, filter_output_hidden_states +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BackboneOutput, BaseModelOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pp_lcnet import PPLCNetConfig + + +class PPLCNetConvLayer(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + stride: int = 1, + activation: str = "hardswish", + groups: int = 1, + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + bias=False, + groups=groups, + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class PPLCNetDepthwiseSeparableConvLayer(GradientCheckpointingLayer): + """ + Depthwise Separable Convolution Layer: Depthwise Conv -> SE Module (optional) -> Pointwise Conv + Core component of lightweight models (e.g., MobileNet, PP-LCNet) that significantly reduces + the number of parameters and computational cost. + """ + + def __init__( + self, + in_channels, + out_channels, + stride, + kernel_size, + use_squeeze_excitation, + config, + ): + super().__init__() + self.depthwise_convolution = PPLCNetConvLayer( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + groups=in_channels, + activation=config.hidden_act, + ) + self.squeeze_excitation_module = ( + PPLCNetSqueezeExcitationModule(in_channels, config.reduction) if use_squeeze_excitation else nn.Identity() + ) + self.pointwise_convolution = PPLCNetConvLayer( + in_channels=in_channels, + kernel_size=1, + out_channels=out_channels, + stride=1, + activation=config.hidden_act, + ) + + def forward(self, hidden_state): + hidden_state = self.depthwise_convolution(hidden_state) + hidden_state = self.squeeze_excitation_module(hidden_state) + hidden_state = self.pointwise_convolution(hidden_state) + + return hidden_state + + +class PPLCNetSqueezeExcitationModule(nn.Module): + """ + Squeeze-and-Excitation (SE) Module: Adaptive feature recalibration + Enhances the model's ability to focus on important channels by learning channel-wise attention weights. + """ + + def __init__(self, channel, reduction=4): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + + self.convolutions = nn.ModuleList() + for in_channels, out_channels, activation in [ + [channel, channel // reduction, nn.ReLU()], + [channel // reduction, channel, nn.Hardsigmoid()], + ]: + self.convolutions.append( + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=True, + ) + ) + self.convolutions.append(activation) + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.avg_pool(hidden_state) + for layer in self.convolutions: + hidden_state = layer(hidden_state) + hidden_state = residual * hidden_state + + return hidden_state + + +def make_divisible(value: int, divisor: int = 8, min_value: int | None = None) -> int: + """ + Ensure that all layers have a channel count that is divisible by `divisor`. + """ + if min_value is None: + min_value = divisor + new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_value < 0.9 * value: + new_value += divisor + return int(new_value) + + +class PPLCNetBlock(nn.Module): + def __init__(self, config, stage_index): + super().__init__() + self.config = config + + blocks = config.block_configs[stage_index] + + self.layers = nn.ModuleList() + for kernel_size, in_channels, out_channels, stride, use_squeeze_excitation in blocks: + scaled_in_channels = make_divisible(in_channels * config.scale, config.divisor) + scaled_out_channels = make_divisible(out_channels * config.scale, config.divisor) + + depthwise_block = PPLCNetDepthwiseSeparableConvLayer( + in_channels=scaled_in_channels, + out_channels=scaled_out_channels, + kernel_size=kernel_size, + stride=stride, + use_squeeze_excitation=use_squeeze_excitation, + config=config, + ) + self.layers.append(depthwise_block) + + def forward(self, hidden_states): + for layer in self.layers: + hidden_states = layer(hidden_states) + return hidden_states + + +@auto_docstring +class PPLCNetPreTrainedModel(PreTrainedModel): + """ + An abstract base class for PP-LCNet models that inherits from Hugging Face PreTrainedModel. + Provides common functionality for weight initialization and loading. + """ + + config: PPLCNetConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ("image",) + _can_compile_fullgraph = True + supports_gradient_checkpointing = True + _no_split_modules = ["PPLCNetDepthwiseSeparableConvLayer"] + _can_record_outputs = { + "hidden_states": PPLCNetBlock, + } + + +class PPLCNetEncoder(PPLCNetPreTrainedModel): + def __init__(self, config: PPLCNetConfig): + super().__init__(config) + self.config = config + + # stem + self.convolution = PPLCNetConvLayer( + in_channels=3, + kernel_size=3, + out_channels=make_divisible(config.stem_channels * config.scale, config.divisor), + stride=config.stem_stride, + activation=config.hidden_act, + ) + # stages + self.blocks = nn.ModuleList([]) + for stage_index in range(len(config.block_configs)): + block = PPLCNetBlock(config, stage_index) + self.blocks.append(block) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward(self, pixel_values: torch.Tensor, **kwargs) -> BaseModelOutputWithNoAttention: + hidden_state = self.convolution(pixel_values) + for block in self.blocks: + hidden_state = block(hidden_state) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state) + + +@auto_docstring( + custom_intro=""" + PPLCNet backbone model for feature extraction. + """ +) +class PPLCNetBackbone(BackboneMixin, PPLCNetPreTrainedModel): + has_attentions = False + + def __init__(self, config: PPLCNetConfig): + super().__init__(config) + num_features = [config.stem_channels] + for block in config.block_configs: + num_features.append(int(block[-1][2] * config.scale)) + self.num_features = num_features + self.encoder = PPLCNetEncoder(config) + + self.post_init() + + @can_return_tuple + @filter_output_hidden_states + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> BackboneOutput: + r""" + Examples: + + ```python + >>> from transformers import PPLCNetConfig, PPLCNetBackbone + >>> import torch + + >>> config = PPLCNetConfig() + >>> model = PPLCNetBackbone(config) + + >>> pixel_values = torch.randn(1, 3, 224, 224) + + >>> with torch.no_grad(): + ... outputs = model(pixel_values) + + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + ```""" + kwargs["output_hidden_states"] = True # required to extract layers for the stages + hidden_states = self.encoder(pixel_values, **kwargs).hidden_states + + feature_maps = () + for idx, stage in enumerate(self.stage_names): + if stage in self.out_features: + feature_maps += (hidden_states[idx],) + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=hidden_states, + ) + + +@auto_docstring +class PPLCNetForImageClassification(PPLCNetPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: PPLCNetConfig): + super().__init__(config) + self.encoder = PPLCNetEncoder(config) + + self.config = config + self.num_labels = config.num_labels + last_block_out_channels = config.block_configs[-1][-1][2] + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.last_convolution = nn.Conv2d( + in_channels=make_divisible(last_block_out_channels * config.scale, config.divisor), + out_channels=config.class_expand, + kernel_size=1, + stride=1, + padding=0, + bias=False, + ) + self.act_fn = ACT2FN[config.hidden_act] + self.hidden_dropout_prob = config.hidden_dropout_prob + + self.flatten = nn.Flatten(start_dim=1, end_dim=-1) + self.head = nn.Linear(config.class_expand, config.num_labels) if config.num_labels > 0 else nn.Identity() + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithNoAttention: + r""" + Examples: + + ```python + >>> import requests + >>> from PIL import Image + >>> from transformers import AutoModelForImageClassification, AutoImageProcessor + + >>> model_path = "PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors" + >>> model = AutoModelForImageClassification.from_pretrained(model_path) + >>> image_processor = AutoImageProcessor.from_pretrained(model_path) + + >>> url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/img_rot180_demo.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> predicted_label = outputs.last_hidden_state.argmax(-1).item() + >>> print(model.config.id2label[predicted_label]) + wireless_table + ```""" + outputs = self.encoder(pixel_values, **kwargs) + + last_hidden_state = self.avg_pool(outputs.last_hidden_state) + + last_hidden_state = self.last_convolution(last_hidden_state) + last_hidden_state = self.act_fn(last_hidden_state) + last_hidden_state = last_hidden_state * (1 - self.hidden_dropout_prob) + + last_hidden_state = self.flatten(last_hidden_state) + last_hidden_state = self.head(last_hidden_state) + + return BaseModelOutputWithNoAttention(last_hidden_state=last_hidden_state, hidden_states=outputs.hidden_states) + + +__all__ = ["PPLCNetBackbone", "PPLCNetForImageClassification", "PPLCNetPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/modular_pp_lcnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/modular_pp_lcnet.py new file mode 100644 index 0000000000000000000000000000000000000000..f19480fa087e31de9e1286722cae0d4cb61f1840 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet/modular_pp_lcnet.py @@ -0,0 +1,540 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import torch +import torch.nn as nn +import torchvision.transforms.v2.functional as tvF +from huggingface_hub.dataclasses import strict + +from ...activations import ACT2FN +from ...backbone_utils import BackboneConfigMixin, BackboneMixin, filter_output_hidden_states +from ...configuration_utils import PreTrainedConfig +from ...feature_extraction_utils import BatchFeature +from ...image_processing_backends import TorchvisionBackend +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import PILImageResampling, SizeDict +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BackboneOutput, + BaseModelOutputWithNoAttention, +) +from ...modeling_utils import PreTrainedModel +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, +) +from ...utils.generic import TensorType, merge_with_config_defaults +from ...utils.import_utils import requires +from ...utils.output_capturing import capture_outputs +from ..mobilenet_v2.modeling_mobilenet_v2 import make_divisible +from ..resnet.modeling_resnet import ResNetConvLayer + + +@auto_docstring(checkpoint="PaddlePaddle/PP-LCNet_x1_0_doc_ori_safetensors") +@strict +class PPLCNetConfig(BackboneConfigMixin, PreTrainedConfig): + r""" + scale (`float`, *optional*, defaults to 1.0): + The scaling factor for the model's channel dimensions, used to adjust the model size and computational cost + without changing the overall architecture (e.g., 0.25, 0.5, 1.0, 1.5). + block_configs (`list[list[tuple]]`, *optional*, defaults to `None`): + Configuration for each block in each stage. Each tuple contains: + (kernel_size, in_channels, out_channels, stride, use_squeeze_excitation). + If `None`, uses the default PP-LCNet configuration. + stem_channels (`int`, *optional*, defaults to 16): + The number of output channels for the stem layer. + stem_stride (`int`, *optional*, defaults to 2): + The stride for the stem convolution layer. + reduction (`int`, *optional*, defaults to 4): + The reduction factor for feature channel dimensions in the squeeze-and-excitation (SE) blocks, used to + reduce the number of model parameters and computational complexity while maintaining feature representability. + class_expand (`int`, *optional*, defaults to 1280): + The number of hidden units in the expansion layer of the classification head, used to enhance the model's + feature representation capability before the final classification layer. + divisor (`int`, *optional*, defaults to 8): + The divisor used to ensure that various model parameters (e.g., channel dimensions, kernel sizes) are + multiples of this value, promoting efficient model implementation and resource utilization. + """ + + model_type = "pp_lcnet" + + scale: float | int = 1.0 + block_configs: list | None = None + stem_channels: int = 16 + stem_stride: int = 2 + reduction: int = 4 + class_expand: int = 1280 + divisor: int = 8 + hidden_act: str = "hardswish" + _out_features: list[str] | None = None + _out_indices: list[int] | None = None + hidden_dropout_prob: float | int = 0.2 + + def __post_init__(self, **kwargs): + # Default block configs for PP-LCNet + # Each tuple: (kernel_size, in_channels, out_channels, stride, use_squeeze_excitation) + self.block_configs = ( + [ + # Stage 1 (blocks2) + [[3, 16, 32, 1, False]], + # Stage 2 (blocks3) + [[3, 32, 64, 2, False], [3, 64, 64, 1, False]], + # Stage 3 (blocks4) + [[3, 64, 128, 2, False], [3, 128, 128, 1, False]], + # Stage 4 (blocks5) + [ + [3, 128, 256, 2, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + ], + # Stage 5 (blocks6) + [[5, 256, 512, 2, True], [5, 512, 512, 1, True]], + ] + if self.block_configs is None + else self.block_configs + ) + + self.depths = [len(blocks) for blocks in self.block_configs] + self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.block_configs) + 1)] + self.set_output_features_output_indices( + out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None) + ) + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if len(self.block_configs) != 5: + raise ValueError(f"block_configs must have 5 stages, but got {len(self.block_configs)}") + + +class PPLCNetImageProcessorKwargs(ImagesKwargs, total=False): + r""" + resize_short (`int`, *optional*, defaults to 256): + target_short_edge (Union[int, None]): Desired length for the shorter edge of the image. + size_divisor (`int`, *optional*, defaults to 1): + Divisor to align image dimensions. + """ + + resize_short: int + size_divisor: int + + +@auto_docstring +@requires(backends=("torch",)) +class PPLCNetImageProcessor(TorchvisionBackend): + resample = 2 + image_mean = [0.406, 0.456, 0.485] + image_std = [0.225, 0.224, 0.229] + size = {"height": 256, "width": 256} + do_resize = True + do_rescale = True + do_normalize = True + do_center_crop = True + crop_size = 224 + resize_short = 256 + size_divisor = 1 + valid_kwargs = PPLCNetImageProcessorKwargs + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + resize_short: int, + size_divisor: int, + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + disable_grouping: bool | None = False, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + # Unlike TorchvisionBackend, which resizes to a fixed target, + # this implementation first calculates the target size dynamically to preserve + # the aspect ratio, using the shorter edge as a reference. + resize_size = size + if self.resize_short is not None: + resize_size = self.get_image_size( + stacked_images[0], target_short_edge=resize_short, size_divisor=size_divisor + ) + stacked_images = self.resize(stacked_images, size=resize_size, resample=resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + # RGB -> BGR + images = [image[[2, 1, 0], :, :] for image in processed_images] + return BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors) + + def get_image_size( + self, + image: "torch.Tensor", + target_short_edge: int | None, + size_divisor: int | None, + ) -> tuple[SizeDict, torch.Tensor]: + _, height, width = image.shape + resize_scale = target_short_edge / min(height, width) + resized_height = round(height * resize_scale) + resized_width = round(width * resize_scale) + if size_divisor is not None: + resized_height = math.ceil(resized_height / size_divisor) * size_divisor + resized_width = math.ceil(resized_width / size_divisor) * size_divisor + + return SizeDict(height=resized_height, width=resized_width) + + +class PPLCNetConvLayer(ResNetConvLayer): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + stride: int = 1, + activation: str = "hardswish", + groups: int = 1, + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + bias=False, + groups=groups, + ) + + +class PPLCNetDepthwiseSeparableConvLayer(GradientCheckpointingLayer): + """ + Depthwise Separable Convolution Layer: Depthwise Conv -> SE Module (optional) -> Pointwise Conv + Core component of lightweight models (e.g., MobileNet, PP-LCNet) that significantly reduces + the number of parameters and computational cost. + """ + + def __init__( + self, + in_channels, + out_channels, + stride, + kernel_size, + use_squeeze_excitation, + config, + ): + super().__init__() + self.depthwise_convolution = PPLCNetConvLayer( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + groups=in_channels, + activation=config.hidden_act, + ) + self.squeeze_excitation_module = ( + PPLCNetSqueezeExcitationModule(in_channels, config.reduction) if use_squeeze_excitation else nn.Identity() + ) + self.pointwise_convolution = PPLCNetConvLayer( + in_channels=in_channels, + kernel_size=1, + out_channels=out_channels, + stride=1, + activation=config.hidden_act, + ) + + def forward(self, hidden_state): + hidden_state = self.depthwise_convolution(hidden_state) + hidden_state = self.squeeze_excitation_module(hidden_state) + hidden_state = self.pointwise_convolution(hidden_state) + + return hidden_state + + +class PPLCNetSqueezeExcitationModule(nn.Module): + """ + Squeeze-and-Excitation (SE) Module: Adaptive feature recalibration + Enhances the model's ability to focus on important channels by learning channel-wise attention weights. + """ + + def __init__(self, channel, reduction=4): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + + self.convolutions = nn.ModuleList() + for in_channels, out_channels, activation in [ + [channel, channel // reduction, nn.ReLU()], + [channel // reduction, channel, nn.Hardsigmoid()], + ]: + self.convolutions.append( + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=True, + ) + ) + self.convolutions.append(activation) + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.avg_pool(hidden_state) + for layer in self.convolutions: + hidden_state = layer(hidden_state) + hidden_state = residual * hidden_state + + return hidden_state + + +class PPLCNetBlock(nn.Module): + def __init__(self, config, stage_index): + super().__init__() + self.config = config + + blocks = config.block_configs[stage_index] + + self.layers = nn.ModuleList() + for kernel_size, in_channels, out_channels, stride, use_squeeze_excitation in blocks: + scaled_in_channels = make_divisible(in_channels * config.scale, config.divisor) + scaled_out_channels = make_divisible(out_channels * config.scale, config.divisor) + + depthwise_block = PPLCNetDepthwiseSeparableConvLayer( + in_channels=scaled_in_channels, + out_channels=scaled_out_channels, + kernel_size=kernel_size, + stride=stride, + use_squeeze_excitation=use_squeeze_excitation, + config=config, + ) + self.layers.append(depthwise_block) + + def forward(self, hidden_states): + for layer in self.layers: + hidden_states = layer(hidden_states) + return hidden_states + + +@auto_docstring +class PPLCNetPreTrainedModel(PreTrainedModel): + """ + An abstract base class for PP-LCNet models that inherits from Hugging Face PreTrainedModel. + Provides common functionality for weight initialization and loading. + """ + + config: PPLCNetConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ("image",) + _can_compile_fullgraph = True + supports_gradient_checkpointing = True + _no_split_modules = ["PPLCNetDepthwiseSeparableConvLayer"] + _can_record_outputs = { + "hidden_states": PPLCNetBlock, + } + + +class PPLCNetEncoder(PPLCNetPreTrainedModel): + def __init__(self, config: PPLCNetConfig): + super().__init__(config) + self.config = config + + # stem + self.convolution = PPLCNetConvLayer( + in_channels=3, + kernel_size=3, + out_channels=make_divisible(config.stem_channels * config.scale, config.divisor), + stride=config.stem_stride, + activation=config.hidden_act, + ) + # stages + self.blocks = nn.ModuleList([]) + for stage_index in range(len(config.block_configs)): + block = PPLCNetBlock(config, stage_index) + self.blocks.append(block) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward(self, pixel_values: torch.Tensor, **kwargs) -> BaseModelOutputWithNoAttention: + hidden_state = self.convolution(pixel_values) + for block in self.blocks: + hidden_state = block(hidden_state) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state) + + +@auto_docstring( + custom_intro=""" + PPLCNet backbone model for feature extraction. + """ +) +class PPLCNetBackbone(BackboneMixin, PPLCNetPreTrainedModel): + has_attentions = False + + def __init__(self, config: PPLCNetConfig): + super().__init__(config) + num_features = [config.stem_channels] + for block in config.block_configs: + num_features.append(int(block[-1][2] * config.scale)) + self.num_features = num_features + self.encoder = PPLCNetEncoder(config) + + self.post_init() + + @can_return_tuple + @filter_output_hidden_states + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> BackboneOutput: + r""" + Examples: + + ```python + >>> from transformers import PPLCNetConfig, PPLCNetBackbone + >>> import torch + + >>> config = PPLCNetConfig() + >>> model = PPLCNetBackbone(config) + + >>> pixel_values = torch.randn(1, 3, 224, 224) + + >>> with torch.no_grad(): + ... outputs = model(pixel_values) + + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + ```""" + kwargs["output_hidden_states"] = True # required to extract layers for the stages + hidden_states = self.encoder(pixel_values, **kwargs).hidden_states + + feature_maps = () + for idx, stage in enumerate(self.stage_names): + if stage in self.out_features: + feature_maps += (hidden_states[idx],) + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=hidden_states, + ) + + +@auto_docstring +class PPLCNetForImageClassification(PPLCNetPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: PPLCNetConfig): + super().__init__(config) + self.encoder = PPLCNetEncoder(config) + + self.config = config + self.num_labels = config.num_labels + last_block_out_channels = config.block_configs[-1][-1][2] + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.last_convolution = nn.Conv2d( + in_channels=make_divisible(last_block_out_channels * config.scale, config.divisor), + out_channels=config.class_expand, + kernel_size=1, + stride=1, + padding=0, + bias=False, + ) + self.act_fn = ACT2FN[config.hidden_act] + self.hidden_dropout_prob = config.hidden_dropout_prob + + self.flatten = nn.Flatten(start_dim=1, end_dim=-1) + self.head = nn.Linear(config.class_expand, config.num_labels) if config.num_labels > 0 else nn.Identity() + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithNoAttention: + r""" + Examples: + + ```python + >>> import requests + >>> from PIL import Image + >>> from transformers import AutoModelForImageClassification, AutoImageProcessor + + >>> model_path = "PaddlePaddle/PP-LCNet_x1_0_table_cls_safetensors" + >>> model = AutoModelForImageClassification.from_pretrained(model_path) + >>> image_processor = AutoImageProcessor.from_pretrained(model_path) + + >>> url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/img_rot180_demo.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> predicted_label = outputs.last_hidden_state.argmax(-1).item() + >>> print(model.config.id2label[predicted_label]) + wireless_table + ```""" + outputs = self.encoder(pixel_values, **kwargs) + + last_hidden_state = self.avg_pool(outputs.last_hidden_state) + + last_hidden_state = self.last_convolution(last_hidden_state) + last_hidden_state = self.act_fn(last_hidden_state) + last_hidden_state = last_hidden_state * (1 - self.hidden_dropout_prob) + + last_hidden_state = self.flatten(last_hidden_state) + last_hidden_state = self.head(last_hidden_state) + + return BaseModelOutputWithNoAttention(last_hidden_state=last_hidden_state, hidden_states=outputs.hidden_states) + + +__all__ = [ + "PPLCNetBackbone", + "PPLCNetForImageClassification", + "PPLCNetImageProcessor", + "PPLCNetConfig", + "PPLCNetPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bfde11d84f54b721013ddd890c3ec6621e7a0e07 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_lcnet_v3 import * + from .modeling_pp_lcnet_v3 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/configuration_pp_lcnet_v3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/configuration_pp_lcnet_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..f3fc81756c061582fdf2c4c09460b4e20205a548 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/configuration_pp_lcnet_v3.py @@ -0,0 +1,108 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_lcnet_v3/modular_pp_lcnet_v3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_lcnet_v3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import BackboneConfigMixin +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="PaddlePaddle/Not_yet_released") +@strict +class PPLCNetV3Config(BackboneConfigMixin, PreTrainedConfig): + r""" + scale (`float`, *optional*, defaults to 1.0): + The scaling factor for the model's channel dimensions, used to adjust the model size and computational cost + without changing the overall architecture (e.g., 0.25, 0.5, 1.0, 1.5). + block_configs (`list[list[tuple]]`, *optional*, defaults to `None`): + Configuration for each block in each stage. Each tuple contains: + (kernel_size, in_channels, out_channels, stride, use_squeeze_excitation). + If `None`, uses the default PP-LCNet configuration. + stem_channels (`int`, *optional*, defaults to 16): + The number of output channels for the stem layer. + stem_stride (`int`, *optional*, defaults to 2): + The stride for the stem convolution layer. + reduction (`int`, *optional*, defaults to 4): + The reduction factor for feature channel dimensions in the squeeze-and-excitation (SE) blocks, used to + reduce the number of model parameters and computational complexity while maintaining feature representability. + divisor (`int`, *optional*, defaults to 8): + The divisor used to ensure that various model parameters (e.g., channel dimensions, kernel sizes) are + multiples of this value, promoting efficient model implementation and resource utilization. + conv_symmetric_num (`int`, *optional*, defaults to `4`): + The number of kxk convolution branches in the learnable reparameterization layer, used to enhance feature + extraction capability through multi-branch architecture during training while enabling efficient inference + via structural reparameterization. + """ + + model_type = "pp_lcnet_v3" + + scale: float | int = 1.0 + block_configs: list | None = None + stem_channels: int = 16 + stem_stride: int = 2 + reduction: int = 4 + divisor: int = 8 + hidden_act: str = "hardswish" + _out_features: list[str] | None = None + _out_indices: list[int] | None = None + + conv_symmetric_num: int = 4 + + def __post_init__(self, **kwargs): + # Default block configs for PP-LCNetV3 + # Each tuple: (kernel_size, in_channels, out_channels, stride, use_squeeze_excitation) + self.block_configs = ( + [ + # Stage 1 (blocks2) + [[3, 16, 32, 1, False]], + # Stage 2 (blocks3) + [[3, 32, 64, 2, False], [3, 64, 64, 1, False]], + # Stage 3 (blocks4) + [[3, 64, 128, 2, False], [3, 128, 128, 1, False]], + # Stage 4 (blocks5) + [ + [3, 128, 256, 2, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + ], + # Stage 5 (blocks6) + [[5, 256, 512, 2, True], [5, 512, 512, 1, True], [5, 512, 512, 1, False], [5, 512, 512, 1, False]], + ] + if self.block_configs is None + else self.block_configs + ) + self.depths = [len(blocks) for blocks in self.block_configs] + self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.block_configs) + 1)] + self.set_output_features_output_indices( + out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None) + ) + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if len(self.block_configs) != 5: + raise ValueError(f"block_configs must have 5 stages, but got {len(self.block_configs)}") + + +__all__ = ["PPLCNetV3Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/modeling_pp_lcnet_v3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/modeling_pp_lcnet_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..22e0dff2bad3853d0a32953cb737b83ef7b8e816 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/modeling_pp_lcnet_v3.py @@ -0,0 +1,407 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_lcnet_v3/modular_pp_lcnet_v3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_lcnet_v3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn +from torch import Tensor + +from ...activations import ACT2FN +from ...backbone_utils import BackboneMixin, filter_output_hidden_states +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BackboneOutput, BaseModelOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pp_lcnet_v3 import PPLCNetV3Config + + +class PPLCNetV3ConvLayer(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + stride: int = 1, + activation: str = "hardswish", + groups: int = 1, + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + bias=False, + groups=groups, + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class PPLCNetV3LearnableAffineBlock(nn.Module): + def __init__(self, scale_value: float = 1.0, bias_value: float = 0.0): + super().__init__() + self.scale = nn.Parameter(torch.tensor([scale_value]), requires_grad=True) + self.bias = nn.Parameter(torch.tensor([bias_value]), requires_grad=True) + + def forward(self, hidden_state: Tensor) -> Tensor: + hidden_state = self.scale * hidden_state + self.bias + return hidden_state + + +class PPLCNetV3ActLearnableAffineBlock(nn.Module): + """ + Activation block with a trainable affine transformation applied after the non-linear activation. + """ + + def __init__(self, activation="hardswish"): + super().__init__() + self.act = ACT2FN[activation] + self.lab = PPLCNetV3LearnableAffineBlock() + + def forward(self, hidden_state: torch.Tensor): + return self.lab(self.act(hidden_state)) + + +class PPLCNetV3LearnableRepLayer(nn.Module): + """ + Learnable Reparameterization Layer (RepLayer) that fuses multiple convolution branches + (kxk and 1x1) with an optional identity branch. This layer enables structural reparameterization + for efficient inference while maintaining training flexibility. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + activation: str, + stride: int, + num_conv_branches: int, + groups: int = 1, + ): + super().__init__() + self.groups = groups + self.stride = stride + self.kernel_size = kernel_size + self.in_channels = in_channels + self.out_channels = out_channels + self.num_conv_branches = num_conv_branches + self.padding = (kernel_size - 1) // 2 + + self.identity = ( + nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None + ) + + self.conv_symmetric = nn.ModuleList( + [ + PPLCNetV3ConvLayer( + in_channels, + out_channels, + kernel_size, + stride, + groups=groups, + activation=None, + ) + for _ in range(self.num_conv_branches) + ] + ) + + self.conv_small_symmetric = ( + PPLCNetV3ConvLayer(in_channels, out_channels, 1, stride, groups=groups, activation=None) + if kernel_size > 1 + else None + ) + + self.lab = PPLCNetV3LearnableAffineBlock() + self.act = PPLCNetV3ActLearnableAffineBlock(activation=activation) + + def forward(self, hidden_state: torch.Tensor): + output = None + + if self.identity is not None: + output = self.identity(hidden_state) + + if self.conv_small_symmetric is not None: + residual = self.conv_small_symmetric(hidden_state) + output = residual if output is None else output + residual + + for conv in self.conv_symmetric: + residual = conv(hidden_state) + output = residual if output is None else output + residual + + hidden_state = self.lab(output) + if self.stride != 2: + hidden_state = self.act(hidden_state) + return hidden_state + + +class PPLCNetV3SqueezeExcitationModule(nn.Module): + """ + Squeeze-and-Excitation (SE) Module: Adaptive feature recalibration + Enhances the model's ability to focus on important channels by learning channel-wise attention weights. + """ + + def __init__(self, channel, reduction=4): + super().__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + + self.convolutions = nn.ModuleList() + for in_channels, out_channels, activation in [ + [channel, channel // reduction, nn.ReLU()], + [channel // reduction, channel, nn.Hardsigmoid()], + ]: + self.convolutions.append( + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + bias=True, + ) + ) + self.convolutions.append(activation) + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.avg_pool(hidden_state) + for layer in self.convolutions: + hidden_state = layer(hidden_state) + hidden_state = residual * hidden_state + + return hidden_state + + +class PPLCNetV3DepthwiseSeparableConvLayer(GradientCheckpointingLayer): + """ + Depthwise Separable Convolution Layer: Depthwise Conv -> SE Module (optional) -> Pointwise Conv + Core component of lightweight models (e.g., MobileNet, PP-LCNet) that significantly reduces + the number of parameters and computational cost. + """ + + def __init__( + self, + in_channels, + out_channels, + stride, + kernel_size, + use_squeeze_excitation, + config, + ): + super().__init__() + self.depthwise_convolution = PPLCNetV3LearnableRepLayer( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + groups=in_channels, + num_conv_branches=config.conv_symmetric_num, + activation=config.hidden_act, + ) + self.squeeze_excitation_module = ( + PPLCNetV3SqueezeExcitationModule(in_channels, config.reduction) + if use_squeeze_excitation + else nn.Identity() + ) + self.pointwise_convolution = PPLCNetV3LearnableRepLayer( + in_channels=in_channels, + kernel_size=1, + out_channels=out_channels, + stride=1, + num_conv_branches=config.conv_symmetric_num, + activation=config.hidden_act, + ) + + def forward(self, hidden_state): + hidden_state = self.depthwise_convolution(hidden_state) + hidden_state = self.squeeze_excitation_module(hidden_state) + hidden_state = self.pointwise_convolution(hidden_state) + + return hidden_state + + +def make_divisible(value: int, divisor: int = 8, min_value: int | None = None) -> int: + """ + Ensure that all layers have a channel count that is divisible by `divisor`. + """ + if min_value is None: + min_value = divisor + new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_value < 0.9 * value: + new_value += divisor + return int(new_value) + + +class PPLCNetV3Block(nn.Module): + def __init__(self, config, stage_index): + super().__init__() + self.config = config + + blocks = config.block_configs[stage_index] + + self.layers = nn.ModuleList() + for kernel_size, in_channels, out_channels, stride, use_squeeze_excitation in blocks: + scaled_in_channels = make_divisible(in_channels * config.scale, config.divisor) + scaled_out_channels = make_divisible(out_channels * config.scale, config.divisor) + + depthwise_block = PPLCNetV3DepthwiseSeparableConvLayer( + in_channels=scaled_in_channels, + out_channels=scaled_out_channels, + kernel_size=kernel_size, + stride=stride, + use_squeeze_excitation=use_squeeze_excitation, + config=config, + ) + self.layers.append(depthwise_block) + + def forward(self, hidden_states): + for layer in self.layers: + hidden_states = layer(hidden_states) + return hidden_states + + +@auto_docstring +class PPLCNetV3PreTrainedModel(PreTrainedModel): + """ + An abstract base class for PP-LCNet models that inherits from Hugging Face PreTrainedModel. + Provides common functionality for weight initialization and loading. + """ + + config: PPLCNetV3Config + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ("image",) + _can_compile_fullgraph = True + supports_gradient_checkpointing = True + _no_split_modules = ["PPLCNetV3DepthwiseSeparableConvLayer"] + _can_record_outputs = { + "hidden_states": PPLCNetV3Block, + } + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, (PPLCNetV3LearnableAffineBlock)): + nn.init.ones_(module.scale) + nn.init.zeros_(module.bias) + + +@auto_docstring( + custom_intro=""" + PPLCNetV3 backbone model for feature extraction. + """ +) +class PPLCNetV3Backbone(BackboneMixin, PPLCNetV3PreTrainedModel): + has_attentions = False + + def __init__(self, config: PPLCNetV3Config): + super().__init__(config) + num_features = [config.stem_channels] + for block in config.block_configs: + num_features.append(int(block[-1][2] * config.scale)) + self.num_features = num_features + self.encoder = PPLCNetV3Encoder(config) + + self.post_init() + + @can_return_tuple + @filter_output_hidden_states + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> BackboneOutput: + r""" + Examples: + + ```python + >>> from transformers import PPLCNetV3Config, PPLCNetV3Backbone + >>> import torch + + >>> config = PPLCNetV3Config() + >>> model = PPLCNetV3Backbone(config) + + >>> pixel_values = torch.randn(1, 3, 224, 224) + + >>> with torch.no_grad(): + ... outputs = model(pixel_values) + + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + ```""" + kwargs["output_hidden_states"] = True # required to extract layers for the stages + hidden_states = self.encoder(pixel_values, **kwargs).hidden_states + + feature_maps = () + for idx, stage in enumerate(self.stage_names): + if stage in self.out_features: + feature_maps += (hidden_states[idx],) + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=hidden_states, + ) + + +class PPLCNetV3Encoder(PPLCNetV3PreTrainedModel): + def __init__(self, config: PPLCNetV3Config): + super().__init__(config) + self.config = config + + # stem + self.convolution = PPLCNetV3ConvLayer( + in_channels=3, + kernel_size=3, + out_channels=make_divisible(config.stem_channels * config.scale, config.divisor), + stride=config.stem_stride, + activation=None, + ) + # stages + self.blocks = nn.ModuleList([]) + for stage_index in range(len(config.block_configs)): + block = PPLCNetV3Block(config, stage_index) + self.blocks.append(block) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward(self, pixel_values: torch.Tensor, **kwargs) -> BaseModelOutputWithNoAttention: + hidden_state = self.convolution(pixel_values) + for block in self.blocks: + hidden_state = block(hidden_state) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state) + + +__all__ = ["PPLCNetV3Backbone", "PPLCNetV3PreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/modular_pp_lcnet_v3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/modular_pp_lcnet_v3.py new file mode 100644 index 0000000000000000000000000000000000000000..a339afd90874236c1cafda852608f9a3b6753a9e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_lcnet_v3/modular_pp_lcnet_v3.py @@ -0,0 +1,267 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn +from huggingface_hub.dataclasses import strict + +from ...activations import ACT2FN +from ...configuration_utils import PreTrainedConfig +from ...modeling_utils import PreTrainedModel +from ...utils import ( + auto_docstring, +) +from ..hgnet_v2.modeling_hgnet_v2 import HGNetV2LearnableAffineBlock +from ..mobilenet_v2.modeling_mobilenet_v2 import make_divisible +from ..pp_lcnet.configuration_pp_lcnet import PPLCNetConfig +from ..pp_lcnet.modeling_pp_lcnet import ( + PPLCNetBackbone, + PPLCNetConvLayer, + PPLCNetDepthwiseSeparableConvLayer, + PPLCNetEncoder, + PPLCNetPreTrainedModel, +) + + +@auto_docstring(checkpoint="PaddlePaddle/Not_yet_released") +@strict +class PPLCNetV3Config(PPLCNetConfig): + r""" + scale (`float`, *optional*, defaults to 1.0): + The scaling factor for the model's channel dimensions, used to adjust the model size and computational cost + without changing the overall architecture (e.g., 0.25, 0.5, 1.0, 1.5). + block_configs (`list[list[tuple]]`, *optional*, defaults to `None`): + Configuration for each block in each stage. Each tuple contains: + (kernel_size, in_channels, out_channels, stride, use_squeeze_excitation). + If `None`, uses the default PP-LCNet configuration. + stem_channels (`int`, *optional*, defaults to 16): + The number of output channels for the stem layer. + stem_stride (`int`, *optional*, defaults to 2): + The stride for the stem convolution layer. + reduction (`int`, *optional*, defaults to 4): + The reduction factor for feature channel dimensions in the squeeze-and-excitation (SE) blocks, used to + reduce the number of model parameters and computational complexity while maintaining feature representability. + divisor (`int`, *optional*, defaults to 8): + The divisor used to ensure that various model parameters (e.g., channel dimensions, kernel sizes) are + multiples of this value, promoting efficient model implementation and resource utilization. + conv_symmetric_num (`int`, *optional*, defaults to `4`): + The number of kxk convolution branches in the learnable reparameterization layer, used to enhance feature + extraction capability through multi-branch architecture during training while enabling efficient inference + via structural reparameterization. + """ + + model_type = "pp_lcnet_v3" + + conv_symmetric_num: int = 4 + hidden_dropout_prob = AttributeError() + class_expand = AttributeError() + + def __post_init__(self, **kwargs): + # Default block configs for PP-LCNetV3 + # Each tuple: (kernel_size, in_channels, out_channels, stride, use_squeeze_excitation) + self.block_configs = ( + [ + # Stage 1 (blocks2) + [[3, 16, 32, 1, False]], + # Stage 2 (blocks3) + [[3, 32, 64, 2, False], [3, 64, 64, 1, False]], + # Stage 3 (blocks4) + [[3, 64, 128, 2, False], [3, 128, 128, 1, False]], + # Stage 4 (blocks5) + [ + [3, 128, 256, 2, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + [5, 256, 256, 1, False], + ], + # Stage 5 (blocks6) + [[5, 256, 512, 2, True], [5, 512, 512, 1, True], [5, 512, 512, 1, False], [5, 512, 512, 1, False]], + ] + if self.block_configs is None + else self.block_configs + ) + self.depths = [len(blocks) for blocks in self.block_configs] + self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.block_configs) + 1)] + self.set_output_features_output_indices( + out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None) + ) + PreTrainedConfig.__post_init__(**kwargs) + + +class PPLCNetV3ConvLayer(PPLCNetConvLayer): + pass + + +class PPLCNetV3LearnableAffineBlock(HGNetV2LearnableAffineBlock): + pass + + +class PPLCNetV3ActLearnableAffineBlock(nn.Module): + """ + Activation block with a trainable affine transformation applied after the non-linear activation. + """ + + def __init__(self, activation="hardswish"): + super().__init__() + self.act = ACT2FN[activation] + self.lab = PPLCNetV3LearnableAffineBlock() + + def forward(self, hidden_state: torch.Tensor): + return self.lab(self.act(hidden_state)) + + +class PPLCNetV3LearnableRepLayer(nn.Module): + """ + Learnable Reparameterization Layer (RepLayer) that fuses multiple convolution branches + (kxk and 1x1) with an optional identity branch. This layer enables structural reparameterization + for efficient inference while maintaining training flexibility. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + activation: str, + stride: int, + num_conv_branches: int, + groups: int = 1, + ): + super().__init__() + self.groups = groups + self.stride = stride + self.kernel_size = kernel_size + self.in_channels = in_channels + self.out_channels = out_channels + self.num_conv_branches = num_conv_branches + self.padding = (kernel_size - 1) // 2 + + self.identity = ( + nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None + ) + + self.conv_symmetric = nn.ModuleList( + [ + PPLCNetV3ConvLayer( + in_channels, + out_channels, + kernel_size, + stride, + groups=groups, + activation=None, + ) + for _ in range(self.num_conv_branches) + ] + ) + + self.conv_small_symmetric = ( + PPLCNetV3ConvLayer(in_channels, out_channels, 1, stride, groups=groups, activation=None) + if kernel_size > 1 + else None + ) + + self.lab = PPLCNetV3LearnableAffineBlock() + self.act = PPLCNetV3ActLearnableAffineBlock(activation=activation) + + def forward(self, hidden_state: torch.Tensor): + output = None + + if self.identity is not None: + output = self.identity(hidden_state) + + if self.conv_small_symmetric is not None: + residual = self.conv_small_symmetric(hidden_state) + output = residual if output is None else output + residual + + for conv in self.conv_symmetric: + residual = conv(hidden_state) + output = residual if output is None else output + residual + + hidden_state = self.lab(output) + if self.stride != 2: + hidden_state = self.act(hidden_state) + return hidden_state + + +class PPLCNetV3DepthwiseSeparableConvLayer(PPLCNetDepthwiseSeparableConvLayer): + """ + Depthwise Separable Convolution Layer: Depthwise Conv -> SE Module (optional) -> Pointwise Conv + Core component of lightweight models (e.g., MobileNet, PP-LCNet) that significantly reduces + the number of parameters and computational cost. + """ + + def __init__( + self, + in_channels, + out_channels, + stride, + kernel_size, + use_squeeze_excitation, + config, + ): + super().__init__() + self.depthwise_convolution = PPLCNetV3LearnableRepLayer( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + groups=in_channels, + num_conv_branches=config.conv_symmetric_num, + activation=config.hidden_act, + ) + self.pointwise_convolution = PPLCNetV3LearnableRepLayer( + in_channels=in_channels, + kernel_size=1, + out_channels=out_channels, + stride=1, + num_conv_branches=config.conv_symmetric_num, + activation=config.hidden_act, + ) + + +class PPLCNetV3PreTrainedModel(PPLCNetPreTrainedModel): + @torch.no_grad() + def _init_weights(self, module): + PreTrainedModel._init_weights(module) + if isinstance(module, (PPLCNetV3LearnableAffineBlock)): + nn.init.ones_(module.scale) + nn.init.zeros_(module.bias) + + +class PPLCNetV3Backbone(PPLCNetBackbone): + pass + + +class PPLCNetV3Encoder(PPLCNetEncoder): + def __init__(self, config: PPLCNetV3Config): + super().__init__(config) + self.config = config + + # stem + self.convolution = PPLCNetV3ConvLayer( + in_channels=3, + kernel_size=3, + out_channels=make_divisible(config.stem_channels * config.scale, config.divisor), + stride=config.stem_stride, + activation=None, + ) + + +__all__ = [ + "PPLCNetV3Backbone", + "PPLCNetV3Config", + "PPLCNetV3PreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3ff24cae580a5ab531141519a64ec4d781c1437c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_ocrv5_mobile_det import * + from .modeling_pp_ocrv5_mobile_det import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/configuration_pp_ocrv5_mobile_det.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/configuration_pp_ocrv5_mobile_det.py new file mode 100644 index 0000000000000000000000000000000000000000..131aa75a532535009eae8d4aec1c88614a5ae013 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/configuration_pp_ocrv5_mobile_det.py @@ -0,0 +1,76 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_mobile_det/modular_pp_ocrv5_mobile_det.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_mobile_det.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PaddlePaddle/PP-OCRv5_mobile_det_safetensors") +@strict +class PPOCRV5MobileDetConfig(PreTrainedConfig): + r""" + reduction (`int`, *optional*, defaults to 4): + The reduction factor for feature channel dimensions, used to reduce the number of model parameters and + computational complexity while maintaining feature representability. + neck_out_channels (`int`, *optional*, defaults to 96): + The number of output channels from the neck network, which is responsible for feature fusion and + refinement before passing features to the head network. + interpolate_mode (`str`, *optional*, defaults to `"nearest"`): + The interpolation mode used for upsampling or downsampling feature maps in the neck network. Supported + modes include `"nearest"` (nearest neighbor interpolation) and `"bilinear"`. + kernel_list (`List[int]`, *optional*, defaults to `[3, 2, 2]`): + The list of kernel sizes for convolutional layers in the head network, used for multi-scale feature + extraction to detect text regions of different sizes. + layer_list_out_channels (`List[int]`, *optional*, defaults to `[12, 18, 42, 360]`): + The list of output channels for each backbone stage, used to configure the input channels of the RSE layers + in the neck network for multi-scale feature fusion. + """ + + model_type = "pp_ocrv5_mobile_det" + sub_configs = {"backbone_config": AutoConfig} + + backbone_config: dict | PreTrainedConfig | None = None + reduction: int = 4 + neck_out_channels: int = 96 + interpolate_mode: str = "nearest" + kernel_list: list[int] | tuple[int, ...] = (3, 2, 2) + layer_list_out_channels: list[int] | tuple[int, ...] = (12, 18, 42, 360) + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="pp_lcnet_v3", + default_config_kwargs={ + "scale": 0.75, + "out_features": ["stage2", "stage3", "stage4", "stage5"], + "out_indices": [2, 3, 4, 5], + "divisor": 16, + }, + **kwargs, + ) + super().__post_init__(**kwargs) + + +__all__ = ["PPOCRV5MobileDetConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/modeling_pp_ocrv5_mobile_det.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/modeling_pp_ocrv5_mobile_det.py new file mode 100644 index 0000000000000000000000000000000000000000..748f108513c769c7864ec9b508d42637c9578da8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/modeling_pp_ocrv5_mobile_det.py @@ -0,0 +1,320 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_mobile_det/modular_pp_ocrv5_mobile_det.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_mobile_det.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...activations import ACT2FN +from ...backbone_utils import load_backbone +from ...modeling_outputs import BaseModelOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pp_ocrv5_mobile_det import PPOCRV5MobileDetConfig + + +@auto_docstring +class PPOCRV5MobileDetPreTrainedModel(PreTrainedModel): + """ + Base class for all PPOCRV5 Server Det pre-trained models. Handles model initialization, + configuration, and loading of pre-trained weights, following the Transformers library conventions. + """ + + config: PPOCRV5MobileDetConfig + base_model_prefix = "pp_ocrv5_mobile_det" + main_input_name = "pixel_values" + input_modalities = ("image",) + _can_compile_fullgraph = True + + +class PPOCRV5MobileDetSqueezeExcitationModule(nn.Module): + """ + Simplified Squeeze-and-Excitation (SE) Module for the neck network. + Applies channel-wise recalibration with a clamped activation to stabilize training. + """ + + def __init__(self, in_channels, reduction, activation="relu"): + super().__init__() + + self.avg_pool = nn.AdaptiveAvgPool2d(output_size=1) + self.conv1 = nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels // reduction, + kernel_size=1, + stride=1, + padding=0, + ) + self.conv2 = nn.Conv2d( + in_channels=in_channels // reduction, + out_channels=in_channels, + kernel_size=1, + stride=1, + padding=0, + ) + self.act_fn = ACT2FN[activation] + + def forward(self, hidden_states): + residual = hidden_states + hidden_states = self.avg_pool(hidden_states) + hidden_states = self.conv2(self.act_fn(self.conv1(hidden_states))) + hidden_states = torch.clamp(0.2 * hidden_states + 0.5, min=0.0, max=1.0) + return residual * hidden_states + + +class PPOCRV5MobileDetResidualSqueezeExcitationLayer(nn.Module): + """ + Residual Squeeze-and-Excitation (RSE) Layer for the neck network. + Combines a 1x1/3x3 convolution with an SE Module. + """ + + def __init__(self, in_channels, out_channels, kernel_size, reduction): + super().__init__() + self.in_conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + padding=int(kernel_size // 2), + bias=False, + ) + self.squeeze_excitation_block = PPOCRV5MobileDetSqueezeExcitationModule(out_channels, reduction) + + def forward(self, hidden_states): + hidden_states = self.in_conv(hidden_states) + hidden_states = hidden_states + self.squeeze_excitation_block(hidden_states) + + return hidden_states + + +class PPOCRV5MobileDetNeck(nn.Module): + """ + Neck network for PPOCRV5 Mobile Det, responsible for multi-scale feature fusion. + Uses Residual Squeeze-and-Excitation layers to process backbone features and upsampling to fuse features at the same spatial scale, + then concatenates the fused features for input to the head network. + """ + + def __init__(self, config: PPOCRV5MobileDetConfig): + super().__init__() + self.interpolate_mode = config.interpolate_mode + + self.insert_conv = nn.ModuleList() + self.input_conv = nn.ModuleList() + for i in range(len(config.layer_list_out_channels)): + self.insert_conv.append( + PPOCRV5MobileDetResidualSqueezeExcitationLayer( + config.layer_list_out_channels[i], + config.neck_out_channels, + 1, + config.reduction, + ) + ) + self.input_conv.append( + PPOCRV5MobileDetResidualSqueezeExcitationLayer( + config.neck_out_channels, config.neck_out_channels // 4, 3, config.reduction + ) + ) + + def forward(self, feature_maps): + fused = [] + for conv, feature in zip(self.insert_conv, feature_maps): # [p2, p3, p4, p5] + hidden_states = conv(feature) + fused.append(hidden_states) + + for i in range(2, -1, -1): # p4 -> p3-> p2 + fused[i] = fused[i] + F.interpolate(fused[i + 1], scale_factor=2, mode=self.interpolate_mode) + + features = [] + for conv, feat in zip(self.input_conv, [fused[0], fused[1], fused[2], fused[3]]): + features.append(conv(feat)) + + processed = [] + upsample_scales = [1, 2, 4, 8] # p2, p3, p4, p5 + for feat, scale in zip(features, upsample_scales): + if scale != 1: + hidden_states = F.interpolate(feat, scale_factor=scale, mode=self.interpolate_mode) + else: + hidden_states = feat + processed.append(hidden_states) + + fused_feature_map = torch.cat(processed[::-1], dim=1) # [p5, p4, p3, p2] + return fused_feature_map + + +class PPOCRV5MobileDetConvBatchnormLayer(nn.Module): + """ + A basic wrapper for Convolution-BatchNorm-Activation, typically used for head components. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: int | str = 1, + groups: int = 1, + activation: str = "relu", + bias: bool = False, + convolution_transpose: bool = False, + ): + super().__init__() + if convolution_transpose: + self.convolution = nn.ConvTranspose2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + ) + else: + self.convolution = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + bias=bias, + ) + + self.norm = nn.BatchNorm2d(out_channels) + self.act_fn = nn.Identity() if activation is None else ACT2FN[activation] + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.convolution(hidden_states) + hidden_states = self.norm(hidden_states) + hidden_states = self.act_fn(hidden_states) + return hidden_states + + +class PPOCRV5MobileDetHead(nn.Module): + """ + Standard segmentation head for generating probability maps. It uses transposed + convolution to upsample the feature map back to the original image size. + """ + + def __init__( + self, + config: PPOCRV5MobileDetConfig, + ): + super().__init__() + + in_channels = config.neck_out_channels + kernel_list = config.kernel_list + self.conv_down = PPOCRV5MobileDetConvBatchnormLayer( + in_channels=in_channels, + out_channels=in_channels // 4, + kernel_size=kernel_list[0], + padding=int(kernel_list[0] // 2), + ) + self.conv_up = PPOCRV5MobileDetConvBatchnormLayer( + in_channels=in_channels // 4, + out_channels=in_channels // 4, + kernel_size=kernel_list[1], + stride=2, + convolution_transpose=True, + ) + + self.conv_final = nn.ConvTranspose2d( + in_channels=in_channels // 4, + out_channels=1, + kernel_size=kernel_list[2], + stride=2, + ) + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + hidden_states = self.conv_down(hidden_states) + hidden_states = self.conv_up(hidden_states) + hidden_states = self.conv_final(hidden_states) + hidden_states = torch.sigmoid(hidden_states) + return hidden_states + + +@auto_docstring( + custom_intro=""" + Core PP-OCRv5_mobile_det, consisting of Backbone, Neck, and Head networks. + Generates binary text segmentation maps for text detection tasks. + """ +) +class PPOCRV5MobileDetModel(PPOCRV5MobileDetPreTrainedModel): + def __init__(self, config: PPOCRV5MobileDetConfig): + super().__init__(config) + + self.config = config + self.backbone = load_backbone(config) + out_channels = [self.backbone.num_features[i] for i in self.backbone.out_indices] + self.layer = nn.ModuleList() + for idx, out_channel in enumerate(out_channels): + self.layer.append(nn.Conv2d(out_channel, config.layer_list_out_channels[idx], 1, 1, 0)) + + self.neck = PPOCRV5MobileDetNeck(config) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + hidden_states: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.backbone(hidden_states, **kwargs) + feature_maps = outputs.feature_maps + processed_features = [] + for i in range(len(feature_maps)): + processed_features.append(self.layer[i](feature_maps[i])) + hidden_states = self.neck(processed_features) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=outputs.hidden_states) + + +@auto_docstring( + custom_intro=""" + PPOCRV5 Mobile Det model for object (text) detection tasks. Wraps the core PPOCRV5MobileDetModel + and returns outputs compatible with the Transformers object detection API. + """ +) +class PPOCRV5MobileDetForObjectDetection(PPOCRV5MobileDetPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: PPOCRV5MobileDetConfig): + super().__init__(config) + self.model = PPOCRV5MobileDetModel(config) + self.head = PPOCRV5MobileDetHead(config) + self.post_init() + + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.model(pixel_values, **kwargs) + logits = self.head(outputs.last_hidden_state) + + return BaseModelOutputWithNoAttention( + last_hidden_state=logits, + hidden_states=outputs.hidden_states, + ) + + +__all__ = ["PPOCRV5MobileDetForObjectDetection", "PPOCRV5MobileDetModel", "PPOCRV5MobileDetPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/modular_pp_ocrv5_mobile_det.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/modular_pp_ocrv5_mobile_det.py new file mode 100644 index 0000000000000000000000000000000000000000..a2514779eb83269c96a27582b50412a1557fdd43 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_det/modular_pp_ocrv5_mobile_det.py @@ -0,0 +1,270 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict + +from ...activations import ACT2FN +from ...backbone_utils import consolidate_backbone_kwargs_to_config, load_backbone +from ...configuration_utils import PreTrainedConfig +from ...modeling_outputs import BaseModelOutputWithNoAttention +from ...processing_utils import Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + logging, +) +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoConfig +from ..pp_ocrv5_server_det.modeling_pp_ocrv5_server_det import ( + PPOCRV5ServerDetForObjectDetection, + PPOCRV5ServerDetPreTrainedModel, + PPOCRV5ServerDetSegmentationHead, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PP-OCRv5_mobile_det_safetensors") +@strict +class PPOCRV5MobileDetConfig(PreTrainedConfig): + r""" + reduction (`int`, *optional*, defaults to 4): + The reduction factor for feature channel dimensions, used to reduce the number of model parameters and + computational complexity while maintaining feature representability. + neck_out_channels (`int`, *optional*, defaults to 96): + The number of output channels from the neck network, which is responsible for feature fusion and + refinement before passing features to the head network. + interpolate_mode (`str`, *optional*, defaults to `"nearest"`): + The interpolation mode used for upsampling or downsampling feature maps in the neck network. Supported + modes include `"nearest"` (nearest neighbor interpolation) and `"bilinear"`. + kernel_list (`List[int]`, *optional*, defaults to `[3, 2, 2]`): + The list of kernel sizes for convolutional layers in the head network, used for multi-scale feature + extraction to detect text regions of different sizes. + layer_list_out_channels (`List[int]`, *optional*, defaults to `[12, 18, 42, 360]`): + The list of output channels for each backbone stage, used to configure the input channels of the RSE layers + in the neck network for multi-scale feature fusion. + """ + + model_type = "pp_ocrv5_mobile_det" + sub_configs = {"backbone_config": AutoConfig} + + backbone_config: dict | PreTrainedConfig | None = None + reduction: int = 4 + neck_out_channels: int = 96 + interpolate_mode: str = "nearest" + kernel_list: list[int] | tuple[int, ...] = (3, 2, 2) + layer_list_out_channels: list[int] | tuple[int, ...] = (12, 18, 42, 360) + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="pp_lcnet_v3", + default_config_kwargs={ + "scale": 0.75, + "out_features": ["stage2", "stage3", "stage4", "stage5"], + "out_indices": [2, 3, 4, 5], + "divisor": 16, + }, + **kwargs, + ) + super().__post_init__(**kwargs) + + +@auto_docstring +class PPOCRV5MobileDetPreTrainedModel(PPOCRV5ServerDetPreTrainedModel): + pass + + +class PPOCRV5MobileDetSqueezeExcitationModule(nn.Module): + """ + Simplified Squeeze-and-Excitation (SE) Module for the neck network. + Applies channel-wise recalibration with a clamped activation to stabilize training. + """ + + def __init__(self, in_channels, reduction, activation="relu"): + super().__init__() + + self.avg_pool = nn.AdaptiveAvgPool2d(output_size=1) + self.conv1 = nn.Conv2d( + in_channels=in_channels, + out_channels=in_channels // reduction, + kernel_size=1, + stride=1, + padding=0, + ) + self.conv2 = nn.Conv2d( + in_channels=in_channels // reduction, + out_channels=in_channels, + kernel_size=1, + stride=1, + padding=0, + ) + self.act_fn = ACT2FN[activation] + + def forward(self, hidden_states): + residual = hidden_states + hidden_states = self.avg_pool(hidden_states) + hidden_states = self.conv2(self.act_fn(self.conv1(hidden_states))) + hidden_states = torch.clamp(0.2 * hidden_states + 0.5, min=0.0, max=1.0) + return residual * hidden_states + + +class PPOCRV5MobileDetResidualSqueezeExcitationLayer(nn.Module): + """ + Residual Squeeze-and-Excitation (RSE) Layer for the neck network. + Combines a 1x1/3x3 convolution with an SE Module. + """ + + def __init__(self, in_channels, out_channels, kernel_size, reduction): + super().__init__() + self.in_conv = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + padding=int(kernel_size // 2), + bias=False, + ) + self.squeeze_excitation_block = PPOCRV5MobileDetSqueezeExcitationModule(out_channels, reduction) + + def forward(self, hidden_states): + hidden_states = self.in_conv(hidden_states) + hidden_states = hidden_states + self.squeeze_excitation_block(hidden_states) + + return hidden_states + + +class PPOCRV5MobileDetNeck(nn.Module): + """ + Neck network for PPOCRV5 Mobile Det, responsible for multi-scale feature fusion. + Uses Residual Squeeze-and-Excitation layers to process backbone features and upsampling to fuse features at the same spatial scale, + then concatenates the fused features for input to the head network. + """ + + def __init__(self, config: PPOCRV5MobileDetConfig): + super().__init__() + self.interpolate_mode = config.interpolate_mode + + self.insert_conv = nn.ModuleList() + self.input_conv = nn.ModuleList() + for i in range(len(config.layer_list_out_channels)): + self.insert_conv.append( + PPOCRV5MobileDetResidualSqueezeExcitationLayer( + config.layer_list_out_channels[i], + config.neck_out_channels, + 1, + config.reduction, + ) + ) + self.input_conv.append( + PPOCRV5MobileDetResidualSqueezeExcitationLayer( + config.neck_out_channels, config.neck_out_channels // 4, 3, config.reduction + ) + ) + + def forward(self, feature_maps): + fused = [] + for conv, feature in zip(self.insert_conv, feature_maps): # [p2, p3, p4, p5] + hidden_states = conv(feature) + fused.append(hidden_states) + + for i in range(2, -1, -1): # p4 -> p3-> p2 + fused[i] = fused[i] + F.interpolate(fused[i + 1], scale_factor=2, mode=self.interpolate_mode) + + features = [] + for conv, feat in zip(self.input_conv, [fused[0], fused[1], fused[2], fused[3]]): + features.append(conv(feat)) + + processed = [] + upsample_scales = [1, 2, 4, 8] # p2, p3, p4, p5 + for feat, scale in zip(features, upsample_scales): + if scale != 1: + hidden_states = F.interpolate(feat, scale_factor=scale, mode=self.interpolate_mode) + else: + hidden_states = feat + processed.append(hidden_states) + + fused_feature_map = torch.cat(processed[::-1], dim=1) # [p5, p4, p3, p2] + return fused_feature_map + + +class PPOCRV5MobileDetHead(PPOCRV5ServerDetSegmentationHead): + # MobileDet does not return residual features + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + hidden_states = self.conv_down(hidden_states) + hidden_states = self.conv_up(hidden_states) + hidden_states = self.conv_final(hidden_states) + hidden_states = torch.sigmoid(hidden_states) + return hidden_states + + +@auto_docstring( + custom_intro=""" + Core PP-OCRv5_mobile_det, consisting of Backbone, Neck, and Head networks. + Generates binary text segmentation maps for text detection tasks. + """ +) +class PPOCRV5MobileDetModel(PPOCRV5MobileDetPreTrainedModel): + def __init__(self, config: PPOCRV5MobileDetConfig): + super().__init__(config) + + self.config = config + self.backbone = load_backbone(config) + out_channels = [self.backbone.num_features[i] for i in self.backbone.out_indices] + self.layer = nn.ModuleList() + for idx, out_channel in enumerate(out_channels): + self.layer.append(nn.Conv2d(out_channel, config.layer_list_out_channels[idx], 1, 1, 0)) + + self.neck = PPOCRV5MobileDetNeck(config) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + hidden_states: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.backbone(hidden_states, **kwargs) + feature_maps = outputs.feature_maps + processed_features = [] + for i in range(len(feature_maps)): + processed_features.append(self.layer[i](feature_maps[i])) + hidden_states = self.neck(processed_features) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=outputs.hidden_states) + + +@auto_docstring( + custom_intro=""" + PPOCRV5 Mobile Det model for object (text) detection tasks. Wraps the core PPOCRV5MobileDetModel + and returns outputs compatible with the Transformers object detection API. + """ +) +class PPOCRV5MobileDetForObjectDetection(PPOCRV5ServerDetForObjectDetection): + pass + + +__all__ = [ + "PPOCRV5MobileDetForObjectDetection", + "PPOCRV5MobileDetConfig", + "PPOCRV5MobileDetModel", + "PPOCRV5MobileDetPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0337191536f1f067043ff9f9e12f5e6d7afb1145 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_ocrv5_mobile_rec import * + from .modeling_pp_ocrv5_mobile_rec import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/configuration_pp_ocrv5_mobile_rec.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/configuration_pp_ocrv5_mobile_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..82a801c079dc34aced61d03bc6b88cfb8dc60c6d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/configuration_pp_ocrv5_mobile_rec.py @@ -0,0 +1,89 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_mobile_rec/modular_pp_ocrv5_mobile_rec.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_mobile_rec.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PaddlePaddle/PP-OCRv5_mobile_rec_safetensors") +@strict +class PPOCRV5MobileRecConfig(PreTrainedConfig): + r""" + head_out_channels (`int`, *optional*, defaults to 18385): + The number of output channels from the PPOCRV5MobileRecHead, responsible for final classification. + """ + + model_type = "pp_ocrv5_mobile_rec" + sub_configs = {"backbone_config": AutoConfig} + + hidden_act: str = "silu" + backbone_config: dict | PreTrainedConfig | None = None + hidden_size: int = 120 + mlp_ratio: float = 2.0 + depth: int = 2 + head_out_channels: int = 18385 + conv_kernel_size: list | None = None + qkv_bias: bool = True + num_attention_heads: int = 8 + attention_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + + def __post_init__(self, **kwargs): + if self.conv_kernel_size is None: + self.conv_kernel_size = [1, 3] + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="pp_lcnet_v3", + default_config_kwargs={ + "scale": 0.75, + "out_features": ["stage2", "stage3", "stage4", "stage5"], + "out_indices": [2, 3, 4, 5], + "divisor": 16, + }, + **kwargs, + ) + if self.conv_kernel_size is None: + self.conv_kernel_size = [1, 3] + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [0, 1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [1.0, 1.0, 1.0, 1.0, 1.0], + "out_features": ["stage1", "stage2", "stage3", "stage4"], + "stage_downsample": [True, True, True, True], + "stem_strides": [2, 1, 1, 1, 1], + "stage_downsample_strides": [[2, 1], [1, 2], [2, 1], [2, 1]], + }, + **kwargs, + ) + super().__post_init__(**kwargs) + + +__all__ = ["PPOCRV5MobileRecConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/modeling_pp_ocrv5_mobile_rec.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/modeling_pp_ocrv5_mobile_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..376a92670da26b8715bb5611de61b63f74aaef75 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/modeling_pp_ocrv5_mobile_rec.py @@ -0,0 +1,407 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_mobile_rec/modular_pp_ocrv5_mobile_rec.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_mobile_rec.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections.abc import Callable +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ...activations import ACT2FN +from ...backbone_utils import load_backbone +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithNoAttention +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pp_ocrv5_mobile_rec import PPOCRV5MobileRecConfig + + +class PPOCRV5MobileRecAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.is_causal = False + self.attention_dropout = config.attention_dropout + + # small tweak here compared to CLIP, no bias here + self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=False) + + if config.qkv_bias: + q_bias = nn.Parameter(torch.zeros(self.embed_dim)) + v_bias = nn.Parameter(torch.zeros(self.embed_dim)) + else: + q_bias = None + v_bias = None + + if q_bias is not None: + qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias)) + self.qkv.bias = nn.Parameter(qkv_bias) + + self.projection = nn.Linear(self.embed_dim, self.embed_dim) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, # Not used but kept for signature matching in downstream modules + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + bsz, tgt_len, embed_dim = hidden_states.size() + + mixed_qkv = self.qkv(hidden_states) + + mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads).permute( + 2, 0, 3, 1, 4 + ) + query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2] + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=None, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scale, + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous() + attn_output = self.projection(attn_output) + + return attn_output, attn_weights + + +class PPOCRV5MobileRecMLP(nn.Module): + def __init__(self, config, in_features, hidden_features=None, out_features=None, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.activation = ACT2FN[config.hidden_act] + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, hidden_state): + hidden_state = self.fc1(hidden_state) + hidden_state = self.activation(hidden_state) + hidden_state = self.drop(hidden_state) + hidden_state = self.fc2(hidden_state) + hidden_state = self.drop(hidden_state) + return hidden_state + + +class PPOCRV5MobileRecBlock(GradientCheckpointingLayer): + def __init__(self, config): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = PPOCRV5MobileRecAttention(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = PPOCRV5MobileRecMLP( + config=config, + in_features=self.embed_dim, + hidden_features=int(self.embed_dim * config.mlp_ratio), + ) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +# Adapted from transformers.models.siglip.modeling_siglip.eager_attention_forward -> BLIP doesn't cast attn weights to fp32 +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PPOCRV5MobileRecConvLayer(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: tuple[int, int] = (3, 3), + stride: int = 1, + activation: str = "silu", + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size[0] // 2, kernel_size[1] // 2), + bias=False, + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +@auto_docstring +class PPOCRV5MobileRecPreTrainedModel(PreTrainedModel): + config: PPOCRV5MobileRecConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PPOCRV5MobileRecBlock"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PPOCRV5MobileRecBlock, + } + main_input_name = "pixel_values" + input_modalities = ("image",) + + +def make_divisible(value: int, divisor: int = 8, min_value: int | None = None) -> int: + """ + Ensure that all layers have a channel count that is divisible by `divisor`. + """ + if min_value is None: + min_value = divisor + new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_value < 0.9 * value: + new_value += divisor + return int(new_value) + + +class PPOCRV5MobileRecEncoderWithSVTR(PPOCRV5MobileRecPreTrainedModel): + """ + SVTR: Scene Text Recognition with a Single Visual Model + https://www.paddleocr.ai/v2.10.0/en/algorithm/text_recognition/algorithm_rec_svtr.html + """ + + def __init__( + self, + config, + ): + super().__init__(config) + in_channels = make_divisible( # noqa + config.backbone_config.block_configs[-1][-1][2] * config.backbone_config.scale, + config.backbone_config.divisor, + ) + hidden_size = config.hidden_size + + self.conv_block = nn.ModuleList( + [ + PPOCRV5MobileRecConvLayer( + in_channels=in_channels, + out_channels=in_channels // 8, + kernel_size=config.conv_kernel_size, + ), + PPOCRV5MobileRecConvLayer(in_channels=in_channels // 8, out_channels=hidden_size, kernel_size=(1, 1)), + PPOCRV5MobileRecConvLayer(in_channels=hidden_size, out_channels=in_channels, kernel_size=(1, 1)), + PPOCRV5MobileRecConvLayer( + in_channels=2 * in_channels, + out_channels=in_channels // 8, + kernel_size=config.conv_kernel_size, + ), + PPOCRV5MobileRecConvLayer(in_channels=in_channels // 8, out_channels=hidden_size, kernel_size=(1, 1)), + ] + ) + self.svtr_block = nn.ModuleList() + for _ in range(config.depth): + self.svtr_block.append(PPOCRV5MobileRecBlock(config=config)) + + self.norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward(self, hidden_states: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]): + residual = hidden_states + + hidden_states = self.conv_block[0](hidden_states) + hidden_states = self.conv_block[1](hidden_states) + + batch_size, channels, height, width = hidden_states.shape + hidden_states = hidden_states.flatten(2).transpose(1, 2) + for block in self.svtr_block: + hidden_states = block(hidden_states) + + hidden_states = self.norm(hidden_states) + hidden_states = hidden_states.view(batch_size, height, width, channels).permute(0, 3, 1, 2) + hidden_states = self.conv_block[2](hidden_states) + hidden_states = self.conv_block[3](torch.cat((residual, hidden_states), dim=1)) + hidden_states = self.conv_block[4](hidden_states) + hidden_states = hidden_states.squeeze(2).transpose(1, 2) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states) + + +@auto_docstring(custom_intro="PPOCRV5MobileRec model, consisting of Backbone and Head networks.") +class PPOCRV5MobileRecModel(PPOCRV5MobileRecPreTrainedModel): + def __init__(self, config: PPOCRV5MobileRecConfig): + super().__init__(config) + self.backbone = load_backbone(config) + + self.gradient_checkpointing = False + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.backbone(pixel_values, **kwargs) + hidden_state = outputs.feature_maps[-1] + hidden_state = F.avg_pool2d(hidden_state, (3, 2)) + + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_state, + hidden_states=outputs.hidden_states, + ) + + +class PPOCRV5MobileRecHead(nn.Module): + def __init__(self, config): + super().__init__() + + self.encoder = PPOCRV5MobileRecEncoderWithSVTR(config) + self.head = nn.Linear(config.hidden_size, config.head_out_channels) + + def forward(self, hidden_states: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]): + outputs = self.encoder(hidden_states, **kwargs) + hidden_states = self.head(outputs.last_hidden_state) + hidden_states = F.softmax(hidden_states, dim=2, dtype=torch.float32).to(hidden_states.dtype) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=outputs.hidden_states) + + +@auto_docstring +@dataclass +class PPOCRV5MobileRecForTextRecognitionOutput(BaseModelOutputWithNoAttention): + r""" + head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Hidden-states of the PPOCRV5MobileRecHead at the output of each layer plus the optional initial embedding outputs. + """ + + head_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring(custom_intro="PPOCRV5MobileRec model for text recognition tasks.") +class PPOCRV5MobileRecForTextRecognition(PPOCRV5MobileRecPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: PPOCRV5MobileRecConfig): + super().__init__(config) + self.model = PPOCRV5MobileRecModel(config) + self.head = PPOCRV5MobileRecHead(config) + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.model(pixel_values, **kwargs) + head_outputs = self.head(outputs.last_hidden_state, **kwargs) + + return PPOCRV5MobileRecForTextRecognitionOutput( + last_hidden_state=head_outputs.last_hidden_state, + hidden_states=outputs.hidden_states, + head_hidden_states=head_outputs.hidden_states, + ) + + +__all__ = [ + "PPOCRV5MobileRecForTextRecognition", + "PPOCRV5MobileRecModel", + "PPOCRV5MobileRecPreTrainedModel", + "PPOCRV5MobileRecEncoderWithSVTR", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/modular_pp_ocrv5_mobile_rec.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/modular_pp_ocrv5_mobile_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..6393493ea604ba76e16b39954c8b39a2c0f86eae --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_mobile_rec/modular_pp_ocrv5_mobile_rec.py @@ -0,0 +1,83 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...utils import ( + auto_docstring, + logging, +) +from ..pp_lcnet_v3.modeling_pp_lcnet_v3 import make_divisible +from ..pp_ocrv5_server_rec.configuration_pp_ocrv5_server_rec import PPOCRV5ServerRecConfig +from ..pp_ocrv5_server_rec.modeling_pp_ocrv5_server_rec import ( + PPOCRV5ServerRecEncoderWithSVTR, + PPOCRV5ServerRecForTextRecognition, + PPOCRV5ServerRecModel, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PP-OCRv5_mobile_rec_safetensors") +@strict +class PPOCRV5MobileRecConfig(PPOCRV5ServerRecConfig): + def __post_init__(self, **kwargs): + if self.conv_kernel_size is None: + self.conv_kernel_size = [1, 3] + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="pp_lcnet_v3", + default_config_kwargs={ + "scale": 0.75, + "out_features": ["stage2", "stage3", "stage4", "stage5"], + "out_indices": [2, 3, 4, 5], + "divisor": 16, + }, + **kwargs, + ) + super().__post_init__(**kwargs) + + +class PPOCRV5MobileRecEncoderWithSVTR(PPOCRV5ServerRecEncoderWithSVTR): + def __init__( + self, + config, + ): + super().__init__(config) + in_channels = make_divisible( # noqa + config.backbone_config.block_configs[-1][-1][2] * config.backbone_config.scale, + config.backbone_config.divisor, + ) + self.post_init() + + +class PPOCRV5MobileRecModel(PPOCRV5ServerRecModel): + pass + + +@auto_docstring(custom_intro="PPOCRV5MobileRec model for text recognition tasks.") +class PPOCRV5MobileRecForTextRecognition(PPOCRV5ServerRecForTextRecognition): + pass + + +__all__ = [ + "PPOCRV5MobileRecForTextRecognition", + "PPOCRV5MobileRecConfig", + "PPOCRV5MobileRecModel", + "PPOCRV5MobileRecPreTrainedModel", # noqa: F822 + "PPOCRV5MobileRecEncoderWithSVTR", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..28c0dd6a40a4c2fe97596cfa97c5f9debd19a01e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_ocrv5_server_det import * + from .image_processing_pp_ocrv5_server_det import * + from .modeling_pp_ocrv5_server_det import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/configuration_pp_ocrv5_server_det.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/configuration_pp_ocrv5_server_det.py new file mode 100644 index 0000000000000000000000000000000000000000..1a3d6f0e24e28629e5f4270fc630581c6889b441 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/configuration_pp_ocrv5_server_det.py @@ -0,0 +1,87 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_server_det.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PaddlePaddle/PP-OCRv5_server_det_safetensors") +@strict +class PPOCRV5ServerDetConfig(PreTrainedConfig): + r""" + interpolate_mode (`str`, *optional*, defaults to `"nearest"`): + The interpolation mode used for upsampling or downsampling feature maps in the neck network. + neck_out_channels (`int`, *optional*, defaults to 256): + The number of output channels from the neck network, responsible for feature fusion and refinement. + reduce_factor (`int`, *optional*, defaults to 2): + The channel reduction factor used in the neck blocks to balance performance and complexity. + intraclass_block_number (`int`, *optional*, defaults to 4): + The number of Intra-Class Block modules used for enhancing feature representation. + intraclass_block_config (`dict`, *optional*, defaults to `None`): + Configuration for the Intra-Class Block modules, if any, used for enhancing feature representation. + scale_factor (`int`, *optional*, defaults to 2): + The scaling factor used for spatial resolution adjustments in the feature maps. + scale_factor_list (`list[int]`, *optional*, defaults to `None`): + A list of scaling factors used for spatial resolution adjustments in the feature maps. + kernel_list (`list[int]`, *optional*, defaults to `[3, 2, 2]`): + The list of kernel sizes for convolutional layers in the head network for multi-scale feature extraction. + """ + + sub_configs = {"backbone_config": AutoConfig} + model_type = "pp_ocrv5_server_det" + + interpolate_mode: str = "nearest" + backbone_config: dict | PreTrainedConfig | None = None + neck_out_channels: int = 256 + reduce_factor: int = 2 + intraclass_block_number: int = 4 + intraclass_block_config: dict | None = None + scale_factor: int = 2 + scale_factor_list: list | None = None + hidden_act: str = "relu" + kernel_list: list | None = None + id2label: dict[int, str] | dict[str, str] | None = None + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [0, 1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [0, 0.05, 0.05, 0.05, 0.05], + "out_features": ["stage1", "stage2", "stage3", "stage4"], + }, + **kwargs, + ) + + # For object detection pipeline compatibility: single class "text" + self.id2label = {0: "text"} if self.id2label is None else self.id2label + super().__post_init__(**kwargs) + + +__all__ = ["PPOCRV5ServerDetConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/image_processing_pp_ocrv5_server_det.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/image_processing_pp_ocrv5_server_det.py new file mode 100644 index 0000000000000000000000000000000000000000..84cafe5d9c80056f791f5a9919f39e5fa9d60d28 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/image_processing_pp_ocrv5_server_det.py @@ -0,0 +1,418 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_server_det.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import numpy as np +import torch +import torchvision.transforms.v2.functional as tvF + +from ...feature_extraction_utils import BatchFeature +from ...image_processing_backends import TorchvisionBackend +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs +from ...utils import auto_docstring, is_cv2_available, requires_backends +from ...utils.generic import TensorType +from ...utils.import_utils import requires + + +if is_cv2_available(): + import cv2 + + +class PPOCRV5ServerDetImageProcessorKwargs(ImagesKwargs, total=False): + r""" + limit_side_len (`int`, *optional*, defaults to `960`): + Maximum or minimum side length. + limit_type (`str`, *optional*, defaults to `max`): + Resizing strategy: "max", "min", or "resize_long". + max_side_limit (`int`, *optional* defaults to `4000`): + Maximum allowed side length. + """ + + limit_side_len: int + limit_type: str + max_side_limit: int + + +@auto_docstring +@requires(backends=("torch",)) +class PPOCRV5ServerDetImageProcessor(TorchvisionBackend): + resample = 2 + image_mean = [0.406, 0.456, 0.485] + image_std = [0.225, 0.224, 0.229] + size = {"height": 960, "width": 960} + do_resize = True + do_rescale = True + do_normalize = True + limit_side_len = 960 + limit_type = "max" + max_side_limit = 4000 + valid_kwargs = PPOCRV5ServerDetImageProcessorKwargs + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + limit_side_len: int, + limit_type: str, + max_side_limit: int, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + target_sizes = [] + + # Group images by their original spatial shape to enable batched resizing (optimization for efficiency) + # [Key Change] Unlike the original implementation, we now track target shapes for each original shape group + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + # Store resized image batches mapped to their original shape keys + resized_images_grouped = {} + # [Key Change] Core addition: Mapping from original image shape to target resize shape + # This dict ensures consistent target shape handling across all subsequent operations (resize/processing) + target_shape_per_shape = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + resize_size, target_shape = self.get_image_size( + stacked_images[0], limit_side_len, limit_type, max_side_limit + ) + target_shape_per_shape[shape] = target_shape + stacked_images = self.resize(image=stacked_images.float(), size=resize_size, resample=resample) + resized_images_grouped[shape] = stacked_images + + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + if do_resize: + target_sizes = [target_shape_per_shape[grouped_images_index[i][0]] for i in range(len(images))] + + # Group images by size for further processing + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + # BGR to RGB conversion + stacked_images = stacked_images[:, [2, 1, 0], :, :] + processed_images_grouped[shape] = stacked_images + + pixel_values = reorder_images(processed_images_grouped, grouped_images_index) + + return BatchFeature( + data={"pixel_values": pixel_values, "target_sizes": target_sizes}, + tensor_type=return_tensors, + ) + + def _unclip(self, contour_box, unclip_ratio): + """ + Expands (dilates) a detected text bounding box to recover the full text region. + + Args: + contour_box (np.ndarray): Input contour of shape (N, 2), where N is the number of points. + unclip_ratio (float): Expansion ratio, typically greater than 1.0. + + Returns: + np.ndarray: Expanded contour of shape (M, 2). + """ + # --- 1. Parameter calculation --- + polygon = contour_box.reshape(-1, 2).astype(np.float32) + perimeter = cv2.arcLength(polygon, True) + area = cv2.contourArea(polygon) + offset_distance = area * unclip_ratio / perimeter + + # --- 2. Determine polygon orientation and edge normals --- + x, y = polygon[:, 0], polygon[:, 1] + is_counter_clockwise = (x @ np.roll(y, -1) - y @ np.roll(x, -1)) > 0.0 + + edges = np.roll(polygon, -1, axis=0) - polygon + edge_lengths = np.linalg.norm(edges, axis=1, keepdims=True) + edge_directions = edges / np.maximum(edge_lengths, 1e-6) + + if is_counter_clockwise: + normals = np.stack([edge_directions[:, 1], -edge_directions[:, 0]], axis=1) + else: + normals = np.stack([-edge_directions[:, 1], edge_directions[:, 0]], axis=1) + + # --- 3. Calculate new vertices from intersecting shifted edge lines --- + shifted_points = polygon + offset_distance * normals + + prev_shifted_points = np.roll(shifted_points, 1, axis=0) + prev_edge_directions = np.roll(edge_directions, 1, axis=0) + + cross_product = ( + prev_edge_directions[:, 0] * edge_directions[:, 1] - prev_edge_directions[:, 1] * edge_directions[:, 0] + ) + + is_parallel_mask = np.abs(cross_product) < 1e-6 + cross_product_safe = np.where(is_parallel_mask, 1.0, cross_product) + + vec_to_current = shifted_points - prev_shifted_points + intersection_param = ( + vec_to_current[:, 0] * edge_directions[:, 1] - vec_to_current[:, 1] * edge_directions[:, 0] + ) / cross_product_safe + + new_vertices = prev_shifted_points + prev_edge_directions * intersection_param[:, None] + + # --- 4. Handle near-parallel adjacent edges with a fallback --- + if np.any(is_parallel_mask): + prev_normals = np.roll(normals, 1, axis=0) + fallback_points = polygon + 0.5 * offset_distance * (prev_normals + normals) + new_vertices[is_parallel_mask] = fallback_points[is_parallel_mask] + + return np.array([new_vertices.astype(np.float32)]) + + def _get_mini_boxes(self, contour): + """ + Computes the minimum-area bounding rectangle for a given contour and returns + its four corners in a consistent order (top-left, bottom-left, bottom-right, top-right). + + Args: + contour (np.ndarray): Input contour of shape (N, 1, 2). + + Returns: + tuple: + - box (list): List of four corner points in order. + - short_side_length (float): Length of the shorter side of the bounding rectangle. + """ + bounding_box = cv2.minAreaRect(contour) + points = sorted(cv2.boxPoints(bounding_box), key=lambda x: x[0]) + + index_1, index_2, index_3, index_4 = 0, 1, 2, 3 + if points[1][1] > points[0][1]: + index_1 = 0 + index_4 = 1 + else: + index_1 = 1 + index_4 = 0 + if points[3][1] > points[2][1]: + index_2 = 2 + index_3 = 3 + else: + index_2 = 3 + index_3 = 2 + + box = [points[index_1], points[index_2], points[index_3], points[index_4]] + return box, min(bounding_box[1]) + + def _get_box_score(self, bitmap: np.ndarray, polygon_bounding_box: np.ndarray): + """ + Computes the mean score of a bounding box region in the prediction map using + a fast approach with axis-aligned bounding boxes. + + Args: + bitmap (np.ndarray): Binary or float prediction map of shape (H, W). + polygon_bounding_box (np.ndarray): Bounding box polygon of shape (N, 2). + + Returns: + float: Mean score within the bounding box region. + """ + height, width = bitmap.shape[:2] + box = polygon_bounding_box.copy() + xmin = max(0, min(math.floor(box[:, 0].min()), width - 1)) + xmax = max(0, min(math.ceil(box[:, 0].max()), width - 1)) + ymin = max(0, min(math.floor(box[:, 1].min()), height - 1)) + ymax = max(0, min(math.ceil(box[:, 1].max()), height - 1)) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + box[:, 0] = box[:, 0] - xmin + box[:, 1] = box[:, 1] - ymin + cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1) + return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0] + + def _boxes_from_bitmap( + self, + prediction: np.ndarray, + bitmap: np.ndarray, + dest_width: int, + dest_height: int, + box_threshold: float, + unclip_ratio: float, + min_size: int, + max_candidates: int, + ): + """ + Extracts axis-aligned or rotated bounding boxes from a binary segmentation map. + + Args: + prediction (np.ndarray): Raw prediction map of shape (H, W). + bitmap (np.ndarray): Binarized segmentation map of shape (H, W). + dest_width (int): Original image width for scaling back. + dest_height (int): Original image height for scaling back. + box_threshold (float): Score threshold for filtering low-confidence boxes. + unclip_ratio (float): Expansion ratio for contour unclipping. + min_size (int): Minimum side length of valid boxes. + max_candidates (int): Maximum number of contours to process. + + Returns: + tuple: + - boxes (np.ndarray): Array of boxes, each of shape (4, 2). + - scores (list): List of corresponding scores. + """ + + height, width = bitmap.shape + width_scale = dest_width / width + height_scale = dest_height / height + + outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + + contours = outs[1] if len(outs) == 3 else outs[0] + + num_contours = min(len(contours), max_candidates) + + boxes = [] + scores = [] + for index in range(num_contours): + contour = contours[index] + points, short_side_length = self._get_mini_boxes(contour) + if short_side_length < min_size: + continue + points = np.array(points) + score = self._get_box_score(prediction, points.reshape(-1, 2)) + if box_threshold > score: + continue + box = self._unclip(points, unclip_ratio).reshape(-1, 1, 2) + box, short_side_length = self._get_mini_boxes(box) + if short_side_length < min_size + 2: + continue + + box = np.array(box) + for i in range(box.shape[0]): + box[i, 0] = max(0, min(round(box[i, 0] * width_scale), dest_width)) + box[i, 1] = max(0, min(round(box[i, 1] * height_scale), dest_height)) + + boxes.append(box.astype(np.int16)) + scores.append(score) + return np.array(boxes, dtype=np.int16), scores + + def get_image_size( + self, + image: "torch.Tensor", + limit_side_len: int, + limit_type: str, + max_side_limit: int, + ): + """ + Computes the target size for resizing an image while preserving aspect ratio. + + Args: + image (torch.Tensor): Input image. + limit_side_len (int): Maximum or minimum side length. + limit_type (str): Resizing strategy: "max", "min", or "resize_long". + max_side_limit (int): Maximum allowed side length. + + Returns: + tuple: + - SizeDict: Target size. + - torch.Tensor: Original size. + """ + _, height, width = image.shape + height, width = int(height), int(width) + + if limit_type == "max": + if max(height, width) > limit_side_len: + ratio = float(limit_side_len) / max(height, width) + else: + ratio = 1.0 + elif limit_type == "min": + if min(height, width) < limit_side_len: + ratio = float(limit_side_len) / min(height, width) + else: + ratio = 1.0 + elif limit_type == "resize_long": + ratio = float(limit_side_len) / max(height, width) + else: + raise ValueError(f"PPOCRV5ServerDetImageProcessor does not support limit type: {limit_type}") + + resize_height = int(height * ratio) + resize_width = int(width * ratio) + + if max_side_limit is not None and max(resize_height, resize_width) > max_side_limit: + ratio = float(max_side_limit) / max(resize_height, resize_width) + resize_height = int(resize_height * ratio) + resize_width = int(resize_width * ratio) + + resize_height = max(int(round(resize_height / 32) * 32), 32) + resize_width = max(int(round(resize_width / 32) * 32), 32) + + return SizeDict(height=resize_height, width=resize_width), torch.tensor( + [height, width], dtype=torch.float32, device=image.device + ) + + def post_process_object_detection( + self, + predictions, + threshold: float = 0.3, + target_sizes: list[tuple[int, int]] | TensorType | None = None, + box_threshold: float = 0.6, + max_candidates: int = 1000, + min_size: int = 3, + unclip_ratio: float = 1.5, + ): + """ + Converts model outputs into detected text boxes in corners format (xmin, ymin, xmax, ymax). + + Args: + predictions: Model outputs with `logits` attribute (probability maps of shape `(batch_size, 1, H, W)`). + threshold (float): Binarization threshold. + target_sizes: Original image sizes (height, width) per image. + box_threshold (float): Box score threshold. + max_candidates (int): Maximum number of boxes. + min_size (int): Minimum box size. + unclip_ratio (float): Expansion ratio. + + Returns: + list[dict]: List of detection results per image. Each dict contains: + - "boxes": `torch.Tensor` of shape `(N, 4)` in corners format (xmin, ymin, xmax, ymax) + - "scores": `torch.Tensor` of shape `(N,)` + - "labels": `torch.Tensor` of shape `(N,)` (class id 0 for text) + """ + requires_backends(self, ["torch", "cv2"]) + if target_sizes is None: + raise ValueError("target_sizes must be provided for post_process_object_detection") + + device = predictions.last_hidden_state.device + results = [] + for prediction, size in zip(predictions.last_hidden_state, target_sizes): + prediction = prediction[0, :, :].cpu().detach().numpy() + size = size.cpu().detach().numpy() + src_height, src_width = size + mask = prediction > threshold + boxes, scores = self._boxes_from_bitmap( + prediction, mask, src_width, src_height, box_threshold, unclip_ratio, min_size, max_candidates + ) + + results.append( + { + "boxes": torch.from_numpy(boxes).to(device), + "scores": torch.tensor(scores, dtype=torch.float32, device=device), + "labels": torch.zeros(len(scores), dtype=torch.long, device=device), # Single class: text + } + ) + return results + + +__all__ = ["PPOCRV5ServerDetImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/modeling_pp_ocrv5_server_det.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/modeling_pp_ocrv5_server_det.py new file mode 100644 index 0000000000000000000000000000000000000000..2dbc56b2d711ea9657f85afe11d8c55d697a4a10 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/modeling_pp_ocrv5_server_det.py @@ -0,0 +1,448 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_server_det.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...activations import ACT2FN +from ...backbone_utils import load_backbone +from ...modeling_outputs import BaseModelOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from .configuration_pp_ocrv5_server_det import PPOCRV5ServerDetConfig + + +class PPOCRV5ServerDetIntraclassBlock(nn.Module): + """ + Intra-Class Relationship Block. It uses multi-scale convolution (7x7, 5x5, 3x3) + and asymmetric kernels (e.g., 7x1, 1x7) to capture long-range spatial dependencies + within text regions. + """ + + def __init__( + self, + intraclass_block_config: dict | None = None, + in_channels: int = 96, + reduce_factor: int = 4, + ): + super().__init__() + + reduced_channels = in_channels // reduce_factor + + self.conv_reduce_channel = nn.Conv2d(in_channels, reduced_channels, *intraclass_block_config["reduce_channel"]) + + self.vertical_long_to_small_conv_longratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["vertical_long_to_small_conv_longratio"] + ) + self.vertical_long_to_small_conv_midratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["vertical_long_to_small_conv_midratio"] + ) + self.vertical_long_to_small_conv_shortratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["vertical_long_to_small_conv_shortratio"] + ) + + self.horizontal_small_to_long_conv_longratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["horizontal_small_to_long_conv_longratio"] + ) + self.horizontal_small_to_long_conv_midratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["horizontal_small_to_long_conv_midratio"] + ) + self.horizontal_small_to_long_conv_shortratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["horizontal_small_to_long_conv_shortratio"] + ) + + self.symmetric_conv_long_longratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["symmetric_conv_long_longratio"] + ) + self.symmetric_conv_long_midratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["symmetric_conv_long_midratio"] + ) + self.symmetric_conv_long_shortratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["symmetric_conv_long_shortratio"] + ) + + self.conv_final = PPOCRV5ServerDetConvBatchnormLayer( + in_channels=reduced_channels, + out_channels=in_channels, + kernel_size=intraclass_block_config["return_channel"][0], + stride=intraclass_block_config["return_channel"][1], + padding=intraclass_block_config["return_channel"][2], + bias=True, + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + residual = hidden_states + hidden_states = self.conv_reduce_channel(hidden_states) + + hidden_states = ( + self.symmetric_conv_long_longratio(hidden_states) + + self.vertical_long_to_small_conv_longratio(hidden_states) + + self.horizontal_small_to_long_conv_longratio(hidden_states) + ) + hidden_states = ( + self.symmetric_conv_long_midratio(hidden_states) + + self.vertical_long_to_small_conv_midratio(hidden_states) + + self.horizontal_small_to_long_conv_midratio(hidden_states) + ) + hidden_states = ( + self.symmetric_conv_long_shortratio(hidden_states) + + self.vertical_long_to_small_conv_shortratio(hidden_states) + + self.horizontal_small_to_long_conv_shortratio(hidden_states) + ) + + hidden_states = self.conv_final(hidden_states) + + return residual + hidden_states + + +class PPOCRV5ServerDetNeck(nn.Module): + """ + Large Kernel Path Aggregation Network (Neck) for PPOCRV5 Server Detection. + Fuses multi-scale features from backbone stages (stage 2 to stage 5) via top-down and bottom-up paths, + enhanced with large kernel convolution for better spatial dependency modeling. + """ + + def __init__(self, config): + super().__init__() + self.interpolate_mode = config.interpolate_mode + self.scale_factor_list = config.scale_factor_list + self.num_backbone_stages = len(config.backbone_config.stage_out_channels) + + self.input_channel_adjustment_convolution = nn.ModuleList() + self.input_feature_projection_convolution = nn.ModuleList() + self.path_aggregation_head_convolution = nn.ModuleList() + self.path_aggregation_lateral_convolution = nn.ModuleList() + + backbone_stage_output_channels = config.backbone_config.stage_out_channels + + for backbone_stage_index in range(len(backbone_stage_output_channels)): + channel_adjustment_convolution = nn.Conv2d( + in_channels=backbone_stage_output_channels[backbone_stage_index], + out_channels=config.neck_out_channels, + kernel_size=1, + bias=False, + ) + self.input_channel_adjustment_convolution.append(channel_adjustment_convolution) + + feature_projection_convolution = nn.Conv2d( + in_channels=config.neck_out_channels, + out_channels=config.neck_out_channels // 4, + kernel_size=9, + padding=4, + bias=False, + ) + self.input_feature_projection_convolution.append(feature_projection_convolution) + + if backbone_stage_index > 0: + pan_head_convolution = nn.Conv2d( + in_channels=config.neck_out_channels // 4, + out_channels=config.neck_out_channels // 4, + kernel_size=3, + padding=1, + stride=2, + bias=False, + ) + self.path_aggregation_head_convolution.append(pan_head_convolution) + + pan_lateral_convolution = nn.Conv2d( + in_channels=config.neck_out_channels // 4, + out_channels=config.neck_out_channels // 4, + kernel_size=9, + padding=4, + bias=False, + ) + self.path_aggregation_lateral_convolution.append(pan_lateral_convolution) + + self.intraclass_blocks = nn.ModuleList() + for _ in range(config.intraclass_block_number): + self.intraclass_blocks.append( + PPOCRV5ServerDetIntraclassBlock( + config.intraclass_block_config, config.neck_out_channels // 4, reduce_factor=config.reduce_factor + ) + ) + + def forward(self, backbone_stage_feature_maps: list[torch.Tensor], **kwargs) -> torch.Tensor: + channel_adjusted = [] + for i, feature_map in enumerate(backbone_stage_feature_maps): + hidden_states = self.input_channel_adjustment_convolution[i](feature_map) + channel_adjusted.append(hidden_states) + + top_down = [None] * self.num_backbone_stages + top_down[3] = channel_adjusted[3] + for i in range(self.num_backbone_stages - 2, -1, -1): + top_down[i] = channel_adjusted[i] + F.interpolate( + top_down[i + 1], scale_factor=2, mode=self.interpolate_mode + ) + + projected = [] + for i in range(self.num_backbone_stages): + hidden_states = top_down[i] if i < self.num_backbone_stages - 1 else channel_adjusted[-1] + hidden_states = self.input_feature_projection_convolution[i](hidden_states) + projected.append(hidden_states) + + bottom_up = [None] * self.num_backbone_stages + bottom_up[0] = projected[0] + for i in range(1, self.num_backbone_stages): + bottom_up[i] = projected[i] + self.path_aggregation_head_convolution[i - 1](bottom_up[i - 1]) + + lateral_refined = [] + for i in range(self.num_backbone_stages): + hidden_states = projected[0] if i == 0 else bottom_up[i] + hidden_states = self.path_aggregation_lateral_convolution[i](hidden_states) + lateral_refined.append(hidden_states) + + intraclass_refined = [block(feature) for block, feature in zip(self.intraclass_blocks, lateral_refined)] + + upsampled = [] + for feature, scale_factor in zip(intraclass_refined, self.scale_factor_list): + if scale_factor > 1: + hidden_states = F.interpolate(feature, scale_factor=scale_factor, mode=self.interpolate_mode) + else: + hidden_states = feature + upsampled.append(hidden_states) + + upsampled = [ + F.interpolate(feature, scale_factor=scale_factor, mode=self.interpolate_mode) + if scale_factor > 1 + else feature + for feature, scale_factor in zip(intraclass_refined, self.scale_factor_list) + ] + + return torch.cat(upsampled[::-1], dim=1) + + +class PPOCRV5ServerDetConvBatchnormLayer(nn.Module): + """ + A basic wrapper for Convolution-BatchNorm-Activation, typically used for head components. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: int | str = 1, + groups: int = 1, + activation: str = "relu", + bias: bool = False, + convolution_transpose: bool = False, + ): + super().__init__() + if convolution_transpose: + self.convolution = nn.ConvTranspose2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + ) + else: + self.convolution = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + bias=bias, + ) + + self.norm = nn.BatchNorm2d(out_channels) + self.act_fn = nn.Identity() if activation is None else ACT2FN[activation] + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.convolution(hidden_states) + hidden_states = self.norm(hidden_states) + hidden_states = self.act_fn(hidden_states) + return hidden_states + + +class PPOCRV5ServerDetSegmentationHead(nn.Module): + """ + Standard segmentation head for generating probability maps. It uses transposed + convolution to upsample the feature map back to the original image size. + """ + + def __init__( + self, + config: PPOCRV5ServerDetConfig, + ): + super().__init__() + + in_channels = config.neck_out_channels + kernel_list = config.kernel_list + self.conv_down = PPOCRV5ServerDetConvBatchnormLayer( + in_channels=in_channels, + out_channels=in_channels // 4, + kernel_size=kernel_list[0], + padding=int(kernel_list[0] // 2), + ) + self.conv_up = PPOCRV5ServerDetConvBatchnormLayer( + in_channels=in_channels // 4, + out_channels=in_channels // 4, + kernel_size=kernel_list[1], + stride=2, + convolution_transpose=True, + ) + + self.conv_final = nn.ConvTranspose2d( + in_channels=in_channels // 4, + out_channels=1, + kernel_size=kernel_list[2], + stride=2, + ) + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + hidden_states = self.conv_down(hidden_states) + hidden_states = self.conv_up(hidden_states) + feature = hidden_states + hidden_states = self.conv_final(hidden_states) + hidden_states = torch.sigmoid(hidden_states) + return hidden_states, feature + + +class PPOCRV5ServerDetLocalModule(nn.Module): + """ + Local Refinement Module that refines the initial probability map by + concatenating it with higher-resolution features. + """ + + def __init__(self, in_channels: int, out_channels: int, hidden_act: str): + super().__init__() + self.convolution_backbone = PPOCRV5ServerDetConvBatchnormLayer( + in_channels=in_channels + 1, + out_channels=out_channels, + kernel_size=3, + stride=1, + padding=1, + activation=hidden_act, + ) + self.convolution_final = nn.Conv2d( + in_channels=out_channels, + out_channels=1, + kernel_size=1, + stride=1, + padding=0, + ) + + def forward(self, hidden_states: torch.Tensor, init_map: torch.Tensor) -> torch.Tensor: + hidden_states = torch.cat([init_map, hidden_states], dim=1) + # last Conv + hidden_states = self.convolution_backbone(hidden_states) + hidden_states = self.convolution_final(hidden_states) + return hidden_states + + +class PPOCRV5ServerDetHead(nn.Module): + """ + PPOCRV5ServerDetHead implements the Progressive Fusion Head with Local refinement, + the core detection head of PP-OCRv5. + """ + + def __init__(self, config: PPOCRV5ServerDetConfig): + super().__init__() + self.binarize_head = PPOCRV5ServerDetSegmentationHead(config) + self.upsample_convolution = nn.Upsample(scale_factor=config.scale_factor, mode=config.interpolate_mode) + + self.local_refinement_module = PPOCRV5ServerDetLocalModule( + config.neck_out_channels // 4, config.neck_out_channels // 4, config.hidden_act + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states, feature = self.binarize_head(hidden_states) + residual = hidden_states + feature = self.upsample_convolution(feature) + hidden_states = self.local_refinement_module(feature, hidden_states) + hidden_states = torch.sigmoid(hidden_states) + + return 0.5 * (residual + hidden_states) + + +class PPOCRV5ServerDetPreTrainedModel(PreTrainedModel): + """ + Base class for all PPOCRV5 Server Det pre-trained models. Handles model initialization, + configuration, and loading of pre-trained weights, following the Transformers library conventions. + """ + + config: PPOCRV5ServerDetConfig + base_model_prefix = "pp_ocrv5_server_det" + main_input_name = "pixel_values" + input_modalities = ("image",) + _can_compile_fullgraph = True + + +@auto_docstring +class PPOCRV5ServerDetModel(PPOCRV5ServerDetPreTrainedModel): + def __init__(self, config: PPOCRV5ServerDetConfig): + super().__init__(config) + self.backbone = load_backbone(config) + self.neck = PPOCRV5ServerDetNeck(config) + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + backbone_outputs = self.backbone(pixel_values, **kwargs) + hidden_state = backbone_outputs.feature_maps + hidden_state = self.neck(hidden_state) + + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_state, + hidden_states=backbone_outputs.hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + PPOCRV5 Server Det model for object (text) detection tasks. Wraps the core PPOCRV5ServerDetModel + and returns outputs compatible with the Transformers object detection API. + """ +) +class PPOCRV5ServerDetForObjectDetection(PPOCRV5ServerDetPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: PPOCRV5ServerDetConfig): + super().__init__(config) + self.model = PPOCRV5ServerDetModel(config) + self.head = PPOCRV5ServerDetHead(config) + self.post_init() + + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.model(pixel_values, **kwargs) + logits = self.head(outputs.last_hidden_state) + + return BaseModelOutputWithNoAttention( + last_hidden_state=logits, + hidden_states=outputs.hidden_states, + ) + + +__all__ = ["PPOCRV5ServerDetForObjectDetection", "PPOCRV5ServerDetModel", "PPOCRV5ServerDetPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py new file mode 100644 index 0000000000000000000000000000000000000000..1943891143f270bb20a5a8d2396443f0e5bf8dc8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_det/modular_pp_ocrv5_server_det.py @@ -0,0 +1,909 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms.v2.functional as tvF +from huggingface_hub.dataclasses import strict + +from ...activations import ACT2FN +from ...backbone_utils import consolidate_backbone_kwargs_to_config, load_backbone +from ...configuration_utils import PreTrainedConfig +from ...feature_extraction_utils import BatchFeature +from ...image_processing_backends import TorchvisionBackend +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import PILImageResampling, SizeDict +from ...modeling_outputs import BaseModelOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + is_cv2_available, + logging, + requires_backends, +) +from ...utils.generic import TensorType +from ...utils.import_utils import requires +from ..auto import AutoConfig + + +if is_cv2_available(): + import cv2 + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PP-OCRv5_server_det_safetensors") +@strict +class PPOCRV5ServerDetConfig(PreTrainedConfig): + r""" + interpolate_mode (`str`, *optional*, defaults to `"nearest"`): + The interpolation mode used for upsampling or downsampling feature maps in the neck network. + neck_out_channels (`int`, *optional*, defaults to 256): + The number of output channels from the neck network, responsible for feature fusion and refinement. + reduce_factor (`int`, *optional*, defaults to 2): + The channel reduction factor used in the neck blocks to balance performance and complexity. + intraclass_block_number (`int`, *optional*, defaults to 4): + The number of Intra-Class Block modules used for enhancing feature representation. + intraclass_block_config (`dict`, *optional*, defaults to `None`): + Configuration for the Intra-Class Block modules, if any, used for enhancing feature representation. + scale_factor (`int`, *optional*, defaults to 2): + The scaling factor used for spatial resolution adjustments in the feature maps. + scale_factor_list (`list[int]`, *optional*, defaults to `None`): + A list of scaling factors used for spatial resolution adjustments in the feature maps. + kernel_list (`list[int]`, *optional*, defaults to `[3, 2, 2]`): + The list of kernel sizes for convolutional layers in the head network for multi-scale feature extraction. + """ + + sub_configs = {"backbone_config": AutoConfig} + model_type = "pp_ocrv5_server_det" + + interpolate_mode: str = "nearest" + backbone_config: dict | PreTrainedConfig | None = None + neck_out_channels: int = 256 + reduce_factor: int = 2 + intraclass_block_number: int = 4 + intraclass_block_config: dict | None = None + scale_factor: int = 2 + scale_factor_list: list | None = None + hidden_act: str = "relu" + kernel_list: list | None = None + id2label: dict[int, str] | dict[str, str] | None = None + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [0, 1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [0, 0.05, 0.05, 0.05, 0.05], + "out_features": ["stage1", "stage2", "stage3", "stage4"], + }, + **kwargs, + ) + + # For object detection pipeline compatibility: single class "text" + self.id2label = {0: "text"} if self.id2label is None else self.id2label + super().__post_init__(**kwargs) + + +class PPOCRV5ServerDetImageProcessorKwargs(ImagesKwargs, total=False): + r""" + limit_side_len (`int`, *optional*, defaults to `960`): + Maximum or minimum side length. + limit_type (`str`, *optional*, defaults to `max`): + Resizing strategy: "max", "min", or "resize_long". + max_side_limit (`int`, *optional* defaults to `4000`): + Maximum allowed side length. + """ + + limit_side_len: int + limit_type: str + max_side_limit: int + + +@auto_docstring +@requires(backends=("torch",)) +class PPOCRV5ServerDetImageProcessor(TorchvisionBackend): + resample = 2 + image_mean = [0.406, 0.456, 0.485] + image_std = [0.225, 0.224, 0.229] + size = {"height": 960, "width": 960} + do_resize = True + do_rescale = True + do_normalize = True + limit_side_len = 960 + limit_type = "max" + max_side_limit = 4000 + valid_kwargs = PPOCRV5ServerDetImageProcessorKwargs + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + limit_side_len: int, + limit_type: str, + max_side_limit: int, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + target_sizes = [] + + # Group images by their original spatial shape to enable batched resizing (optimization for efficiency) + # [Key Change] Unlike the original implementation, we now track target shapes for each original shape group + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + # Store resized image batches mapped to their original shape keys + resized_images_grouped = {} + # [Key Change] Core addition: Mapping from original image shape to target resize shape + # This dict ensures consistent target shape handling across all subsequent operations (resize/processing) + target_shape_per_shape = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + resize_size, target_shape = self.get_image_size( + stacked_images[0], limit_side_len, limit_type, max_side_limit + ) + target_shape_per_shape[shape] = target_shape + stacked_images = self.resize(image=stacked_images.float(), size=resize_size, resample=resample) + resized_images_grouped[shape] = stacked_images + + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + if do_resize: + target_sizes = [target_shape_per_shape[grouped_images_index[i][0]] for i in range(len(images))] + + # Group images by size for further processing + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + # BGR to RGB conversion + stacked_images = stacked_images[:, [2, 1, 0], :, :] + processed_images_grouped[shape] = stacked_images + + pixel_values = reorder_images(processed_images_grouped, grouped_images_index) + + return BatchFeature( + data={"pixel_values": pixel_values, "target_sizes": target_sizes}, + tensor_type=return_tensors, + ) + + def _unclip(self, contour_box, unclip_ratio): + """ + Expands (dilates) a detected text bounding box to recover the full text region. + + Args: + contour_box (np.ndarray): Input contour of shape (N, 2), where N is the number of points. + unclip_ratio (float): Expansion ratio, typically greater than 1.0. + + Returns: + np.ndarray: Expanded contour of shape (M, 2). + """ + # --- 1. Parameter calculation --- + polygon = contour_box.reshape(-1, 2).astype(np.float32) + perimeter = cv2.arcLength(polygon, True) + area = cv2.contourArea(polygon) + offset_distance = area * unclip_ratio / perimeter + + # --- 2. Determine polygon orientation and edge normals --- + x, y = polygon[:, 0], polygon[:, 1] + is_counter_clockwise = (x @ np.roll(y, -1) - y @ np.roll(x, -1)) > 0.0 + + edges = np.roll(polygon, -1, axis=0) - polygon + edge_lengths = np.linalg.norm(edges, axis=1, keepdims=True) + edge_directions = edges / np.maximum(edge_lengths, 1e-6) + + if is_counter_clockwise: + normals = np.stack([edge_directions[:, 1], -edge_directions[:, 0]], axis=1) + else: + normals = np.stack([-edge_directions[:, 1], edge_directions[:, 0]], axis=1) + + # --- 3. Calculate new vertices from intersecting shifted edge lines --- + shifted_points = polygon + offset_distance * normals + + prev_shifted_points = np.roll(shifted_points, 1, axis=0) + prev_edge_directions = np.roll(edge_directions, 1, axis=0) + + cross_product = ( + prev_edge_directions[:, 0] * edge_directions[:, 1] - prev_edge_directions[:, 1] * edge_directions[:, 0] + ) + + is_parallel_mask = np.abs(cross_product) < 1e-6 + cross_product_safe = np.where(is_parallel_mask, 1.0, cross_product) + + vec_to_current = shifted_points - prev_shifted_points + intersection_param = ( + vec_to_current[:, 0] * edge_directions[:, 1] - vec_to_current[:, 1] * edge_directions[:, 0] + ) / cross_product_safe + + new_vertices = prev_shifted_points + prev_edge_directions * intersection_param[:, None] + + # --- 4. Handle near-parallel adjacent edges with a fallback --- + if np.any(is_parallel_mask): + prev_normals = np.roll(normals, 1, axis=0) + fallback_points = polygon + 0.5 * offset_distance * (prev_normals + normals) + new_vertices[is_parallel_mask] = fallback_points[is_parallel_mask] + + return np.array([new_vertices.astype(np.float32)]) + + def _get_mini_boxes(self, contour): + """ + Computes the minimum-area bounding rectangle for a given contour and returns + its four corners in a consistent order (top-left, bottom-left, bottom-right, top-right). + + Args: + contour (np.ndarray): Input contour of shape (N, 1, 2). + + Returns: + tuple: + - box (list): List of four corner points in order. + - short_side_length (float): Length of the shorter side of the bounding rectangle. + """ + bounding_box = cv2.minAreaRect(contour) + points = sorted(cv2.boxPoints(bounding_box), key=lambda x: x[0]) + + index_1, index_2, index_3, index_4 = 0, 1, 2, 3 + if points[1][1] > points[0][1]: + index_1 = 0 + index_4 = 1 + else: + index_1 = 1 + index_4 = 0 + if points[3][1] > points[2][1]: + index_2 = 2 + index_3 = 3 + else: + index_2 = 3 + index_3 = 2 + + box = [points[index_1], points[index_2], points[index_3], points[index_4]] + return box, min(bounding_box[1]) + + def _get_box_score(self, bitmap: np.ndarray, polygon_bounding_box: np.ndarray): + """ + Computes the mean score of a bounding box region in the prediction map using + a fast approach with axis-aligned bounding boxes. + + Args: + bitmap (np.ndarray): Binary or float prediction map of shape (H, W). + polygon_bounding_box (np.ndarray): Bounding box polygon of shape (N, 2). + + Returns: + float: Mean score within the bounding box region. + """ + height, width = bitmap.shape[:2] + box = polygon_bounding_box.copy() + xmin = max(0, min(math.floor(box[:, 0].min()), width - 1)) + xmax = max(0, min(math.ceil(box[:, 0].max()), width - 1)) + ymin = max(0, min(math.floor(box[:, 1].min()), height - 1)) + ymax = max(0, min(math.ceil(box[:, 1].max()), height - 1)) + + mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) + box[:, 0] = box[:, 0] - xmin + box[:, 1] = box[:, 1] - ymin + cv2.fillPoly(mask, box.reshape(1, -1, 2).astype(np.int32), 1) + return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0] + + def _boxes_from_bitmap( + self, + prediction: np.ndarray, + bitmap: np.ndarray, + dest_width: int, + dest_height: int, + box_threshold: float, + unclip_ratio: float, + min_size: int, + max_candidates: int, + ): + """ + Extracts axis-aligned or rotated bounding boxes from a binary segmentation map. + + Args: + prediction (np.ndarray): Raw prediction map of shape (H, W). + bitmap (np.ndarray): Binarized segmentation map of shape (H, W). + dest_width (int): Original image width for scaling back. + dest_height (int): Original image height for scaling back. + box_threshold (float): Score threshold for filtering low-confidence boxes. + unclip_ratio (float): Expansion ratio for contour unclipping. + min_size (int): Minimum side length of valid boxes. + max_candidates (int): Maximum number of contours to process. + + Returns: + tuple: + - boxes (np.ndarray): Array of boxes, each of shape (4, 2). + - scores (list): List of corresponding scores. + """ + + height, width = bitmap.shape + width_scale = dest_width / width + height_scale = dest_height / height + + outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + + contours = outs[1] if len(outs) == 3 else outs[0] + + num_contours = min(len(contours), max_candidates) + + boxes = [] + scores = [] + for index in range(num_contours): + contour = contours[index] + points, short_side_length = self._get_mini_boxes(contour) + if short_side_length < min_size: + continue + points = np.array(points) + score = self._get_box_score(prediction, points.reshape(-1, 2)) + if box_threshold > score: + continue + box = self._unclip(points, unclip_ratio).reshape(-1, 1, 2) + box, short_side_length = self._get_mini_boxes(box) + if short_side_length < min_size + 2: + continue + + box = np.array(box) + for i in range(box.shape[0]): + box[i, 0] = max(0, min(round(box[i, 0] * width_scale), dest_width)) + box[i, 1] = max(0, min(round(box[i, 1] * height_scale), dest_height)) + + boxes.append(box.astype(np.int16)) + scores.append(score) + return np.array(boxes, dtype=np.int16), scores + + def get_image_size( + self, + image: "torch.Tensor", + limit_side_len: int, + limit_type: str, + max_side_limit: int, + ): + """ + Computes the target size for resizing an image while preserving aspect ratio. + + Args: + image (torch.Tensor): Input image. + limit_side_len (int): Maximum or minimum side length. + limit_type (str): Resizing strategy: "max", "min", or "resize_long". + max_side_limit (int): Maximum allowed side length. + + Returns: + tuple: + - SizeDict: Target size. + - torch.Tensor: Original size. + """ + _, height, width = image.shape + height, width = int(height), int(width) + + if limit_type == "max": + if max(height, width) > limit_side_len: + ratio = float(limit_side_len) / max(height, width) + else: + ratio = 1.0 + elif limit_type == "min": + if min(height, width) < limit_side_len: + ratio = float(limit_side_len) / min(height, width) + else: + ratio = 1.0 + elif limit_type == "resize_long": + ratio = float(limit_side_len) / max(height, width) + else: + raise ValueError(f"PPOCRV5ServerDetImageProcessor does not support limit type: {limit_type}") + + resize_height = int(height * ratio) + resize_width = int(width * ratio) + + if max_side_limit is not None and max(resize_height, resize_width) > max_side_limit: + ratio = float(max_side_limit) / max(resize_height, resize_width) + resize_height = int(resize_height * ratio) + resize_width = int(resize_width * ratio) + + resize_height = max(int(round(resize_height / 32) * 32), 32) + resize_width = max(int(round(resize_width / 32) * 32), 32) + + return SizeDict(height=resize_height, width=resize_width), torch.tensor( + [height, width], dtype=torch.float32, device=image.device + ) + + def post_process_object_detection( + self, + predictions, + threshold: float = 0.3, + target_sizes: list[tuple[int, int]] | TensorType | None = None, + box_threshold: float = 0.6, + max_candidates: int = 1000, + min_size: int = 3, + unclip_ratio: float = 1.5, + ): + """ + Converts model outputs into detected text boxes in corners format (xmin, ymin, xmax, ymax). + + Args: + predictions: Model outputs with `logits` attribute (probability maps of shape `(batch_size, 1, H, W)`). + threshold (float): Binarization threshold. + target_sizes: Original image sizes (height, width) per image. + box_threshold (float): Box score threshold. + max_candidates (int): Maximum number of boxes. + min_size (int): Minimum box size. + unclip_ratio (float): Expansion ratio. + + Returns: + list[dict]: List of detection results per image. Each dict contains: + - "boxes": `torch.Tensor` of shape `(N, 4)` in corners format (xmin, ymin, xmax, ymax) + - "scores": `torch.Tensor` of shape `(N,)` + - "labels": `torch.Tensor` of shape `(N,)` (class id 0 for text) + """ + requires_backends(self, ["torch", "cv2"]) + if target_sizes is None: + raise ValueError("target_sizes must be provided for post_process_object_detection") + + device = predictions.last_hidden_state.device + results = [] + for prediction, size in zip(predictions.last_hidden_state, target_sizes): + prediction = prediction[0, :, :].cpu().detach().numpy() + size = size.cpu().detach().numpy() + src_height, src_width = size + mask = prediction > threshold + boxes, scores = self._boxes_from_bitmap( + prediction, mask, src_width, src_height, box_threshold, unclip_ratio, min_size, max_candidates + ) + + results.append( + { + "boxes": torch.from_numpy(boxes).to(device), + "scores": torch.tensor(scores, dtype=torch.float32, device=device), + "labels": torch.zeros(len(scores), dtype=torch.long, device=device), # Single class: text + } + ) + return results + + +class PPOCRV5ServerDetIntraclassBlock(nn.Module): + """ + Intra-Class Relationship Block. It uses multi-scale convolution (7x7, 5x5, 3x3) + and asymmetric kernels (e.g., 7x1, 1x7) to capture long-range spatial dependencies + within text regions. + """ + + def __init__( + self, + intraclass_block_config: dict | None = None, + in_channels: int = 96, + reduce_factor: int = 4, + ): + super().__init__() + + reduced_channels = in_channels // reduce_factor + + self.conv_reduce_channel = nn.Conv2d(in_channels, reduced_channels, *intraclass_block_config["reduce_channel"]) + + self.vertical_long_to_small_conv_longratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["vertical_long_to_small_conv_longratio"] + ) + self.vertical_long_to_small_conv_midratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["vertical_long_to_small_conv_midratio"] + ) + self.vertical_long_to_small_conv_shortratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["vertical_long_to_small_conv_shortratio"] + ) + + self.horizontal_small_to_long_conv_longratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["horizontal_small_to_long_conv_longratio"] + ) + self.horizontal_small_to_long_conv_midratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["horizontal_small_to_long_conv_midratio"] + ) + self.horizontal_small_to_long_conv_shortratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["horizontal_small_to_long_conv_shortratio"] + ) + + self.symmetric_conv_long_longratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["symmetric_conv_long_longratio"] + ) + self.symmetric_conv_long_midratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["symmetric_conv_long_midratio"] + ) + self.symmetric_conv_long_shortratio = nn.Conv2d( + reduced_channels, reduced_channels, *intraclass_block_config["symmetric_conv_long_shortratio"] + ) + + self.conv_final = PPOCRV5ServerDetConvBatchnormLayer( + in_channels=reduced_channels, + out_channels=in_channels, + kernel_size=intraclass_block_config["return_channel"][0], + stride=intraclass_block_config["return_channel"][1], + padding=intraclass_block_config["return_channel"][2], + bias=True, + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + residual = hidden_states + hidden_states = self.conv_reduce_channel(hidden_states) + + hidden_states = ( + self.symmetric_conv_long_longratio(hidden_states) + + self.vertical_long_to_small_conv_longratio(hidden_states) + + self.horizontal_small_to_long_conv_longratio(hidden_states) + ) + hidden_states = ( + self.symmetric_conv_long_midratio(hidden_states) + + self.vertical_long_to_small_conv_midratio(hidden_states) + + self.horizontal_small_to_long_conv_midratio(hidden_states) + ) + hidden_states = ( + self.symmetric_conv_long_shortratio(hidden_states) + + self.vertical_long_to_small_conv_shortratio(hidden_states) + + self.horizontal_small_to_long_conv_shortratio(hidden_states) + ) + + hidden_states = self.conv_final(hidden_states) + + return residual + hidden_states + + +class PPOCRV5ServerDetNeck(nn.Module): + """ + Large Kernel Path Aggregation Network (Neck) for PPOCRV5 Server Detection. + Fuses multi-scale features from backbone stages (stage 2 to stage 5) via top-down and bottom-up paths, + enhanced with large kernel convolution for better spatial dependency modeling. + """ + + def __init__(self, config): + super().__init__() + self.interpolate_mode = config.interpolate_mode + self.scale_factor_list = config.scale_factor_list + self.num_backbone_stages = len(config.backbone_config.stage_out_channels) + + self.input_channel_adjustment_convolution = nn.ModuleList() + self.input_feature_projection_convolution = nn.ModuleList() + self.path_aggregation_head_convolution = nn.ModuleList() + self.path_aggregation_lateral_convolution = nn.ModuleList() + + backbone_stage_output_channels = config.backbone_config.stage_out_channels + + for backbone_stage_index in range(len(backbone_stage_output_channels)): + channel_adjustment_convolution = nn.Conv2d( + in_channels=backbone_stage_output_channels[backbone_stage_index], + out_channels=config.neck_out_channels, + kernel_size=1, + bias=False, + ) + self.input_channel_adjustment_convolution.append(channel_adjustment_convolution) + + feature_projection_convolution = nn.Conv2d( + in_channels=config.neck_out_channels, + out_channels=config.neck_out_channels // 4, + kernel_size=9, + padding=4, + bias=False, + ) + self.input_feature_projection_convolution.append(feature_projection_convolution) + + if backbone_stage_index > 0: + pan_head_convolution = nn.Conv2d( + in_channels=config.neck_out_channels // 4, + out_channels=config.neck_out_channels // 4, + kernel_size=3, + padding=1, + stride=2, + bias=False, + ) + self.path_aggregation_head_convolution.append(pan_head_convolution) + + pan_lateral_convolution = nn.Conv2d( + in_channels=config.neck_out_channels // 4, + out_channels=config.neck_out_channels // 4, + kernel_size=9, + padding=4, + bias=False, + ) + self.path_aggregation_lateral_convolution.append(pan_lateral_convolution) + + self.intraclass_blocks = nn.ModuleList() + for _ in range(config.intraclass_block_number): + self.intraclass_blocks.append( + PPOCRV5ServerDetIntraclassBlock( + config.intraclass_block_config, config.neck_out_channels // 4, reduce_factor=config.reduce_factor + ) + ) + + def forward(self, backbone_stage_feature_maps: list[torch.Tensor], **kwargs) -> torch.Tensor: + channel_adjusted = [] + for i, feature_map in enumerate(backbone_stage_feature_maps): + hidden_states = self.input_channel_adjustment_convolution[i](feature_map) + channel_adjusted.append(hidden_states) + + top_down = [None] * self.num_backbone_stages + top_down[3] = channel_adjusted[3] + for i in range(self.num_backbone_stages - 2, -1, -1): + top_down[i] = channel_adjusted[i] + F.interpolate( + top_down[i + 1], scale_factor=2, mode=self.interpolate_mode + ) + + projected = [] + for i in range(self.num_backbone_stages): + hidden_states = top_down[i] if i < self.num_backbone_stages - 1 else channel_adjusted[-1] + hidden_states = self.input_feature_projection_convolution[i](hidden_states) + projected.append(hidden_states) + + bottom_up = [None] * self.num_backbone_stages + bottom_up[0] = projected[0] + for i in range(1, self.num_backbone_stages): + bottom_up[i] = projected[i] + self.path_aggregation_head_convolution[i - 1](bottom_up[i - 1]) + + lateral_refined = [] + for i in range(self.num_backbone_stages): + hidden_states = projected[0] if i == 0 else bottom_up[i] + hidden_states = self.path_aggregation_lateral_convolution[i](hidden_states) + lateral_refined.append(hidden_states) + + intraclass_refined = [block(feature) for block, feature in zip(self.intraclass_blocks, lateral_refined)] + + upsampled = [] + for feature, scale_factor in zip(intraclass_refined, self.scale_factor_list): + if scale_factor > 1: + hidden_states = F.interpolate(feature, scale_factor=scale_factor, mode=self.interpolate_mode) + else: + hidden_states = feature + upsampled.append(hidden_states) + + upsampled = [ + F.interpolate(feature, scale_factor=scale_factor, mode=self.interpolate_mode) + if scale_factor > 1 + else feature + for feature, scale_factor in zip(intraclass_refined, self.scale_factor_list) + ] + + return torch.cat(upsampled[::-1], dim=1) + + +class PPOCRV5ServerDetConvBatchnormLayer(nn.Module): + """ + A basic wrapper for Convolution-BatchNorm-Activation, typically used for head components. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int, + stride: int = 1, + padding: int | str = 1, + groups: int = 1, + activation: str = "relu", + bias: bool = False, + convolution_transpose: bool = False, + ): + super().__init__() + if convolution_transpose: + self.convolution = nn.ConvTranspose2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + ) + else: + self.convolution = nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + groups=groups, + bias=bias, + ) + + self.norm = nn.BatchNorm2d(out_channels) + self.act_fn = nn.Identity() if activation is None else ACT2FN[activation] + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.convolution(hidden_states) + hidden_states = self.norm(hidden_states) + hidden_states = self.act_fn(hidden_states) + return hidden_states + + +class PPOCRV5ServerDetSegmentationHead(nn.Module): + """ + Standard segmentation head for generating probability maps. It uses transposed + convolution to upsample the feature map back to the original image size. + """ + + def __init__( + self, + config: PPOCRV5ServerDetConfig, + ): + super().__init__() + + in_channels = config.neck_out_channels + kernel_list = config.kernel_list + self.conv_down = PPOCRV5ServerDetConvBatchnormLayer( + in_channels=in_channels, + out_channels=in_channels // 4, + kernel_size=kernel_list[0], + padding=int(kernel_list[0] // 2), + ) + self.conv_up = PPOCRV5ServerDetConvBatchnormLayer( + in_channels=in_channels // 4, + out_channels=in_channels // 4, + kernel_size=kernel_list[1], + stride=2, + convolution_transpose=True, + ) + + self.conv_final = nn.ConvTranspose2d( + in_channels=in_channels // 4, + out_channels=1, + kernel_size=kernel_list[2], + stride=2, + ) + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + hidden_states = self.conv_down(hidden_states) + hidden_states = self.conv_up(hidden_states) + feature = hidden_states + hidden_states = self.conv_final(hidden_states) + hidden_states = torch.sigmoid(hidden_states) + return hidden_states, feature + + +class PPOCRV5ServerDetLocalModule(nn.Module): + """ + Local Refinement Module that refines the initial probability map by + concatenating it with higher-resolution features. + """ + + def __init__(self, in_channels: int, out_channels: int, hidden_act: str): + super().__init__() + self.convolution_backbone = PPOCRV5ServerDetConvBatchnormLayer( + in_channels=in_channels + 1, + out_channels=out_channels, + kernel_size=3, + stride=1, + padding=1, + activation=hidden_act, + ) + self.convolution_final = nn.Conv2d( + in_channels=out_channels, + out_channels=1, + kernel_size=1, + stride=1, + padding=0, + ) + + def forward(self, hidden_states: torch.Tensor, init_map: torch.Tensor) -> torch.Tensor: + hidden_states = torch.cat([init_map, hidden_states], dim=1) + # last Conv + hidden_states = self.convolution_backbone(hidden_states) + hidden_states = self.convolution_final(hidden_states) + return hidden_states + + +class PPOCRV5ServerDetHead(nn.Module): + """ + PPOCRV5ServerDetHead implements the Progressive Fusion Head with Local refinement, + the core detection head of PP-OCRv5. + """ + + def __init__(self, config: PPOCRV5ServerDetConfig): + super().__init__() + self.binarize_head = PPOCRV5ServerDetSegmentationHead(config) + self.upsample_convolution = nn.Upsample(scale_factor=config.scale_factor, mode=config.interpolate_mode) + + self.local_refinement_module = PPOCRV5ServerDetLocalModule( + config.neck_out_channels // 4, config.neck_out_channels // 4, config.hidden_act + ) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states, feature = self.binarize_head(hidden_states) + residual = hidden_states + feature = self.upsample_convolution(feature) + hidden_states = self.local_refinement_module(feature, hidden_states) + hidden_states = torch.sigmoid(hidden_states) + + return 0.5 * (residual + hidden_states) + + +class PPOCRV5ServerDetPreTrainedModel(PreTrainedModel): + """ + Base class for all PPOCRV5 Server Det pre-trained models. Handles model initialization, + configuration, and loading of pre-trained weights, following the Transformers library conventions. + """ + + config: PPOCRV5ServerDetConfig + base_model_prefix = "pp_ocrv5_server_det" + main_input_name = "pixel_values" + input_modalities = ("image",) + _can_compile_fullgraph = True + + +@auto_docstring +class PPOCRV5ServerDetModel(PPOCRV5ServerDetPreTrainedModel): + def __init__(self, config: PPOCRV5ServerDetConfig): + super().__init__(config) + self.backbone = load_backbone(config) + self.neck = PPOCRV5ServerDetNeck(config) + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + backbone_outputs = self.backbone(pixel_values, **kwargs) + hidden_state = backbone_outputs.feature_maps + hidden_state = self.neck(hidden_state) + + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_state, + hidden_states=backbone_outputs.hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + PPOCRV5 Server Det model for object (text) detection tasks. Wraps the core PPOCRV5ServerDetModel + and returns outputs compatible with the Transformers object detection API. + """ +) +class PPOCRV5ServerDetForObjectDetection(PPOCRV5ServerDetPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: PPOCRV5ServerDetConfig): + super().__init__(config) + self.model = PPOCRV5ServerDetModel(config) + self.head = PPOCRV5ServerDetHead(config) + self.post_init() + + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.model(pixel_values, **kwargs) + logits = self.head(outputs.last_hidden_state) + + return BaseModelOutputWithNoAttention( + last_hidden_state=logits, + hidden_states=outputs.hidden_states, + ) + + +__all__ = [ + "PPOCRV5ServerDetForObjectDetection", + "PPOCRV5ServerDetImageProcessor", + "PPOCRV5ServerDetConfig", + "PPOCRV5ServerDetModel", + "PPOCRV5ServerDetPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b09dc0a5d3439eef8bfedf6278db94f79cb6770d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pp_ocrv5_server_rec import * + from .image_processing_pp_ocrv5_server_rec import * + from .modeling_pp_ocrv5_server_rec import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/configuration_pp_ocrv5_server_rec.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/configuration_pp_ocrv5_server_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..b5bffda9c5d75a0f5d2ac8628c5608b9abcd361f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/configuration_pp_ocrv5_server_rec.py @@ -0,0 +1,75 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_server_rec.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PaddlePaddle/PP-OCRv5_server_rec_safetensors") +@strict +class PPOCRV5ServerRecConfig(PreTrainedConfig): + r""" + head_out_channels (`int`, *optional*, defaults to 18385): + The number of output channels from the PPOCRV5ServerRecHead, responsible for final classification. + """ + + model_type = "pp_ocrv5_server_rec" + sub_configs = {"backbone_config": AutoConfig} + + hidden_act: str = "silu" + backbone_config: dict | PreTrainedConfig | None = None + hidden_size: int = 120 + mlp_ratio: float = 2.0 + depth: int = 2 + head_out_channels: int = 18385 + conv_kernel_size: list | None = None + qkv_bias: bool = True + num_attention_heads: int = 8 + attention_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + + def __post_init__(self, **kwargs): + if self.conv_kernel_size is None: + self.conv_kernel_size = [1, 3] + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [0, 1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [1.0, 1.0, 1.0, 1.0, 1.0], + "out_features": ["stage1", "stage2", "stage3", "stage4"], + "stage_downsample": [True, True, True, True], + "stem_strides": [2, 1, 1, 1, 1], + "stage_downsample_strides": [[2, 1], [1, 2], [2, 1], [2, 1]], + }, + **kwargs, + ) + super().__post_init__(**kwargs) + + +__all__ = ["PPOCRV5ServerRecConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/image_processing_pp_ocrv5_server_rec.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/image_processing_pp_ocrv5_server_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..663788f84fa45fda4df943b7541b1934bbef570f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/image_processing_pp_ocrv5_server_rec.py @@ -0,0 +1,188 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_server_rec.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +import torch +import torchvision.transforms.v2.functional as tvF + +from ...feature_extraction_utils import BatchFeature +from ...image_processing_backends import TorchvisionBackend +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs +from ...utils import auto_docstring, requires_backends +from ...utils.constants import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD +from ...utils.generic import TensorType + + +class PPOCRV5ServerRecImageProcessorKwargs(ImagesKwargs, total=False): + r""" + max_image_width (`int`, *optional*, defaults to `3200`): + Maximum image width used during resizing. + character_list (`list`, *optional*, defaults to `[]`): + Vocabulary list used for text recognition. + """ + + max_image_width: int + character_list: str + + +@auto_docstring +class PPOCRV5ServerRecImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 48, "width": 320} + pad_size = {"height": 48, "width": 320} + do_resize = True + do_rescale = True + do_convert_rgb = True + do_normalize = True + do_pad = True + max_image_width = 3200 + character_list = [] + valid_kwargs = PPOCRV5ServerRecImageProcessorKwargs + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + + # [Key Change] Use get_target_size to calculate target_size for resizing. + shape_list = list(grouped_images.keys()) + target_size = self.get_target_size(shape_list) + + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images.float(), size=target_size, resample=resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad and target_size.width < pad_size.width: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + def get_target_size(self, shape_list: list[torch.Size]): + """ + Calculate the width and height from the widest image in the batch. + """ + max_width = -1 + max_height = -1 + for height, width in shape_list: + # We need the width and height of the widest image in the batch + if width > max_width: + max_width = width + max_height = height + + default_height, default_width = self.size["height"], self.size["width"] + ratio = max(max_width / max_height, default_width / default_height) + + target_width = int(default_height * ratio) + target_height = default_height + + if target_width > self.max_image_width: + target_width = self.max_image_width + else: + ratio = max_width / float(max_height) + if target_width >= math.ceil(default_height * ratio): + target_width = int(math.ceil(default_height * ratio)) + + return SizeDict(height=target_height, width=target_width) + + def post_process_text_recognition( + self, + predictions, + ) -> list[dict[str, str | float]]: + """ + Post-processes raw model logits to decode the recognized text and its confidence score. + + Args: + predictions: Model outputs with `logits` attribute (probability maps of shape `(batch_size, height, vocab_size)`). + + Returns: + A list of dictionaries, where each dictionary corresponds to an image in the batch. + Each dictionary contains: + - "text" (str): The decoded text string. + - "score" (float): The average confidence score of the characters in the decoded text. + """ + requires_backends(self, ["torch"]) + + logits = predictions.last_hidden_state + batch_size = logits.shape[0] + + preds_prob, preds_idx = logits.max(dim=-1) + results = [] + for idx in range(batch_size): + selection = torch.ones(len(preds_idx[idx]), dtype=torch.bool, device=preds_idx.device) + + # remove_duplicate + selection[1:] = preds_idx[idx][1:] != preds_idx[idx][:-1] + # ignore blank token + selection &= preds_idx[idx] != 0 + + character_list = [] + for text_id in preds_idx[idx][selection]: + character_list.append(self.character_list[text_id]) + + results.append( + { + "text": "".join(character_list), + "score": preds_prob[idx][selection].mean().item(), + } + ) + + return results + + +__all__ = ["PPOCRV5ServerRecImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/modeling_pp_ocrv5_server_rec.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/modeling_pp_ocrv5_server_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..9499373344aec4b54691458dde31fcd1223e6c2e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/modeling_pp_ocrv5_server_rec.py @@ -0,0 +1,390 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_pp_ocrv5_server_rec.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ...activations import ACT2FN +from ...backbone_utils import load_backbone +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithNoAttention +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_pp_ocrv5_server_rec import PPOCRV5ServerRecConfig + + +class PPOCRV5ServerRecBlock(GradientCheckpointingLayer): + def __init__(self, config): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = PPOCRV5ServerRecAttention(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = PPOCRV5ServerRecMLP( + config=config, + in_features=self.embed_dim, + hidden_features=int(self.embed_dim * config.mlp_ratio), + ) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +# Adapted from transformers.models.siglip.modeling_siglip.eager_attention_forward -> BLIP doesn't cast attn weights to fp32 +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class PPOCRV5ServerRecAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.is_causal = False + self.attention_dropout = config.attention_dropout + + # small tweak here compared to CLIP, no bias here + self.qkv = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=False) + + if config.qkv_bias: + q_bias = nn.Parameter(torch.zeros(self.embed_dim)) + v_bias = nn.Parameter(torch.zeros(self.embed_dim)) + else: + q_bias = None + v_bias = None + + if q_bias is not None: + qkv_bias = torch.cat((q_bias, torch.zeros_like(v_bias, requires_grad=False), v_bias)) + self.qkv.bias = nn.Parameter(qkv_bias) + + self.projection = nn.Linear(self.embed_dim, self.embed_dim) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, # Not used but kept for signature matching in downstream modules + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + bsz, tgt_len, embed_dim = hidden_states.size() + + mixed_qkv = self.qkv(hidden_states) + + mixed_qkv = mixed_qkv.reshape(bsz, tgt_len, 3, self.num_heads, embed_dim // self.num_heads).permute( + 2, 0, 3, 1, 4 + ) + query_states, key_states, value_states = mixed_qkv[0], mixed_qkv[1], mixed_qkv[2] + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=None, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scale, + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous() + attn_output = self.projection(attn_output) + + return attn_output, attn_weights + + +class PPOCRV5ServerRecConvLayer(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: tuple[int, int] = (3, 3), + stride: int = 1, + activation: str = "silu", + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size[0] // 2, kernel_size[1] // 2), + bias=False, + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +@auto_docstring +class PPOCRV5ServerRecPreTrainedModel(PreTrainedModel): + config: PPOCRV5ServerRecConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["PPOCRV5ServerRecBlock"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": PPOCRV5ServerRecBlock, + } + main_input_name = "pixel_values" + input_modalities = ("image",) + + +class PPOCRV5ServerRecHead(nn.Module): + def __init__(self, config): + super().__init__() + + self.encoder = PPOCRV5ServerRecEncoderWithSVTR(config) + self.head = nn.Linear(config.hidden_size, config.head_out_channels) + + def forward(self, hidden_states: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]): + outputs = self.encoder(hidden_states, **kwargs) + hidden_states = self.head(outputs.last_hidden_state) + hidden_states = F.softmax(hidden_states, dim=2, dtype=torch.float32).to(hidden_states.dtype) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=outputs.hidden_states) + + +class PPOCRV5ServerRecMLP(nn.Module): + def __init__(self, config, in_features, hidden_features=None, out_features=None, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.activation = ACT2FN[config.hidden_act] + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, hidden_state): + hidden_state = self.fc1(hidden_state) + hidden_state = self.activation(hidden_state) + hidden_state = self.drop(hidden_state) + hidden_state = self.fc2(hidden_state) + hidden_state = self.drop(hidden_state) + return hidden_state + + +class PPOCRV5ServerRecEncoderWithSVTR(PPOCRV5ServerRecPreTrainedModel): + """ + SVTR: Scene Text Recognition with a Single Visual Model + https://www.paddleocr.ai/v2.10.0/en/algorithm/text_recognition/algorithm_rec_svtr.html + """ + + def __init__( + self, + config, + ): + super().__init__(config) + in_channels = config.backbone_config.stage_out_channels[-1] + hidden_size = config.hidden_size + + self.conv_block = nn.ModuleList( + [ + PPOCRV5ServerRecConvLayer( + in_channels=in_channels, + out_channels=in_channels // 8, + kernel_size=config.conv_kernel_size, + ), + PPOCRV5ServerRecConvLayer(in_channels=in_channels // 8, out_channels=hidden_size, kernel_size=(1, 1)), + PPOCRV5ServerRecConvLayer(in_channels=hidden_size, out_channels=in_channels, kernel_size=(1, 1)), + PPOCRV5ServerRecConvLayer( + in_channels=2 * in_channels, + out_channels=in_channels // 8, + kernel_size=config.conv_kernel_size, + ), + PPOCRV5ServerRecConvLayer(in_channels=in_channels // 8, out_channels=hidden_size, kernel_size=(1, 1)), + ] + ) + self.svtr_block = nn.ModuleList() + for _ in range(config.depth): + self.svtr_block.append(PPOCRV5ServerRecBlock(config=config)) + + self.norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward(self, hidden_states: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]): + residual = hidden_states + + hidden_states = self.conv_block[0](hidden_states) + hidden_states = self.conv_block[1](hidden_states) + + batch_size, channels, height, width = hidden_states.shape + hidden_states = hidden_states.flatten(2).transpose(1, 2) + for block in self.svtr_block: + hidden_states = block(hidden_states) + + hidden_states = self.norm(hidden_states) + hidden_states = hidden_states.view(batch_size, height, width, channels).permute(0, 3, 1, 2) + hidden_states = self.conv_block[2](hidden_states) + hidden_states = self.conv_block[3](torch.cat((residual, hidden_states), dim=1)) + hidden_states = self.conv_block[4](hidden_states) + hidden_states = hidden_states.squeeze(2).transpose(1, 2) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states) + + +@auto_docstring(custom_intro="PPOCRV5ServerRec model, consisting of Backbone and Head networks.") +class PPOCRV5ServerRecModel(PPOCRV5ServerRecPreTrainedModel): + def __init__(self, config: PPOCRV5ServerRecConfig): + super().__init__(config) + self.backbone = load_backbone(config) + + self.gradient_checkpointing = False + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.backbone(pixel_values, **kwargs) + hidden_state = outputs.feature_maps[-1] + hidden_state = F.avg_pool2d(hidden_state, (3, 2)) + + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_state, + hidden_states=outputs.hidden_states, + ) + + +@auto_docstring +@dataclass +class PPOCRV5ServerRecForTextRecognitionOutput(BaseModelOutputWithNoAttention): + r""" + head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Hidden-states of the PPOCRV5ServerRecHead at the output of each layer plus the optional initial embedding outputs. + """ + + head_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring(custom_intro="PPOCRV5ServerRec model for text recognition tasks.") +class PPOCRV5ServerRecForTextRecognition(PPOCRV5ServerRecPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: PPOCRV5ServerRecConfig): + super().__init__(config) + self.model = PPOCRV5ServerRecModel(config) + self.head = PPOCRV5ServerRecHead(config) + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.model(pixel_values, **kwargs) + head_outputs = self.head(outputs.last_hidden_state, **kwargs) + + return PPOCRV5ServerRecForTextRecognitionOutput( + last_hidden_state=head_outputs.last_hidden_state, + hidden_states=outputs.hidden_states, + head_hidden_states=head_outputs.hidden_states, + ) + + +__all__ = [ + "PPOCRV5ServerRecForTextRecognition", + "PPOCRV5ServerRecModel", + "PPOCRV5ServerRecPreTrainedModel", + "PPOCRV5ServerRecEncoderWithSVTR", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py new file mode 100644 index 0000000000000000000000000000000000000000..f0e4a973dbfb52ec5183ab18424339407ef25e3d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py @@ -0,0 +1,469 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms.v2.functional as tvF +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config, load_backbone +from ...configuration_utils import PreTrainedConfig +from ...feature_extraction_utils import BatchFeature +from ...image_processing_backends import TorchvisionBackend +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + PILImageResampling, + SizeDict, +) +from ...modeling_outputs import BaseModelOutputWithNoAttention +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + logging, + requires_backends, +) +from ...utils.constants import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, +) +from ...utils.generic import TensorType, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoConfig +from ..blip_2.modeling_blip_2 import Blip2Attention +from ..clip.modeling_clip import CLIPEncoderLayer +from ..focalnet.modeling_focalnet import FocalNetMlp +from ..paddleocr_vl.modeling_paddleocr_vl import PaddleOCRVLPreTrainedModel +from ..resnet.modeling_resnet import ResNetConvLayer + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/PP-OCRv5_server_rec_safetensors") +@strict +class PPOCRV5ServerRecConfig(PreTrainedConfig): + r""" + head_out_channels (`int`, *optional*, defaults to 18385): + The number of output channels from the PPOCRV5ServerRecHead, responsible for final classification. + """ + + model_type = "pp_ocrv5_server_rec" + sub_configs = {"backbone_config": AutoConfig} + + hidden_act: str = "silu" + backbone_config: dict | PreTrainedConfig | None = None + hidden_size: int = 120 + mlp_ratio: float = 2.0 + depth: int = 2 + head_out_channels: int = 18385 + conv_kernel_size: list | None = None + qkv_bias: bool = True + num_attention_heads: int = 8 + attention_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + + def __post_init__(self, **kwargs): + if self.conv_kernel_size is None: + self.conv_kernel_size = [1, 3] + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="hgnet_v2", + default_config_kwargs={ + "arch": "L", + "return_idx": [0, 1, 2, 3], + "freeze_stem_only": True, + "freeze_at": 0, + "freeze_norm": True, + "lr_mult_list": [1.0, 1.0, 1.0, 1.0, 1.0], + "out_features": ["stage1", "stage2", "stage3", "stage4"], + "stage_downsample": [True, True, True, True], + "stem_strides": [2, 1, 1, 1, 1], + "stage_downsample_strides": [[2, 1], [1, 2], [2, 1], [2, 1]], + }, + **kwargs, + ) + super().__post_init__(**kwargs) + + +class PPOCRV5ServerRecImageProcessorKwargs(ImagesKwargs, total=False): + r""" + max_image_width (`int`, *optional*, defaults to `3200`): + Maximum image width used during resizing. + character_list (`list`, *optional*, defaults to `[]`): + Vocabulary list used for text recognition. + """ + + max_image_width: int + character_list: str + + +@auto_docstring +class PPOCRV5ServerRecImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 48, "width": 320} + pad_size = {"height": 48, "width": 320} + do_resize = True + do_rescale = True + do_convert_rgb = True + do_normalize = True + do_pad = True + max_image_width = 3200 + character_list = [] + valid_kwargs = PPOCRV5ServerRecImageProcessorKwargs + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + + # [Key Change] Use get_target_size to calculate target_size for resizing. + shape_list = list(grouped_images.keys()) + target_size = self.get_target_size(shape_list) + + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images.float(), size=target_size, resample=resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad and target_size.width < pad_size.width: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + def get_target_size(self, shape_list: list[torch.Size]): + """ + Calculate the width and height from the widest image in the batch. + """ + max_width = -1 + max_height = -1 + for height, width in shape_list: + # We need the width and height of the widest image in the batch + if width > max_width: + max_width = width + max_height = height + + default_height, default_width = self.size["height"], self.size["width"] + ratio = max(max_width / max_height, default_width / default_height) + + target_width = int(default_height * ratio) + target_height = default_height + + if target_width > self.max_image_width: + target_width = self.max_image_width + else: + ratio = max_width / float(max_height) + if target_width >= math.ceil(default_height * ratio): + target_width = int(math.ceil(default_height * ratio)) + + return SizeDict(height=target_height, width=target_width) + + def post_process_text_recognition( + self, + predictions, + ) -> list[dict[str, str | float]]: + """ + Post-processes raw model logits to decode the recognized text and its confidence score. + + Args: + predictions: Model outputs with `logits` attribute (probability maps of shape `(batch_size, height, vocab_size)`). + + Returns: + A list of dictionaries, where each dictionary corresponds to an image in the batch. + Each dictionary contains: + - "text" (str): The decoded text string. + - "score" (float): The average confidence score of the characters in the decoded text. + """ + requires_backends(self, ["torch"]) + + logits = predictions.last_hidden_state + batch_size = logits.shape[0] + + preds_prob, preds_idx = logits.max(dim=-1) + results = [] + for idx in range(batch_size): + selection = torch.ones(len(preds_idx[idx]), dtype=torch.bool, device=preds_idx.device) + + # remove_duplicate + selection[1:] = preds_idx[idx][1:] != preds_idx[idx][:-1] + # ignore blank token + selection &= preds_idx[idx] != 0 + + character_list = [] + for text_id in preds_idx[idx][selection]: + character_list.append(self.character_list[text_id]) + + results.append( + { + "text": "".join(character_list), + "score": preds_prob[idx][selection].mean().item(), + } + ) + + return results + + +class PPOCRV5ServerRecBlock(CLIPEncoderLayer): + def __init__(self, config): + super().__init__() + self.mlp = PPOCRV5ServerRecMLP( + config=config, + in_features=self.embed_dim, + hidden_features=int(self.embed_dim * config.mlp_ratio), + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + super().forward(hidden_states, attention_mask, **kwargs) + + +class PPOCRV5ServerRecAttention(Blip2Attention): + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, # Not used but kept for signature matching in downstream modules + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + super().forward(hidden_states, **kwargs) + + +class PPOCRV5ServerRecConvLayer(ResNetConvLayer): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: tuple[int, int] = (3, 3), + stride: int = 1, + activation: str = "silu", + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size[0] // 2, kernel_size[1] // 2), + bias=False, + ) + + +class PPOCRV5ServerRecPreTrainedModel(PaddleOCRVLPreTrainedModel): + _no_split_modules = ["PPOCRV5ServerRecBlock"] + main_input_name = "pixel_values" + input_modalities = ("image",) + _can_record_outputs = { + "hidden_states": PPOCRV5ServerRecBlock, + } + + def _init_weights(self, module): + raise AttributeError("No override needed here") + + +class PPOCRV5ServerRecHead(nn.Module): + def __init__(self, config): + super().__init__() + + self.encoder = PPOCRV5ServerRecEncoderWithSVTR(config) + self.head = nn.Linear(config.hidden_size, config.head_out_channels) + + def forward(self, hidden_states: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]): + outputs = self.encoder(hidden_states, **kwargs) + hidden_states = self.head(outputs.last_hidden_state) + hidden_states = F.softmax(hidden_states, dim=2, dtype=torch.float32).to(hidden_states.dtype) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=outputs.hidden_states) + + +class PPOCRV5ServerRecMLP(FocalNetMlp): + pass + + +class PPOCRV5ServerRecEncoderWithSVTR(PPOCRV5ServerRecPreTrainedModel): + """ + SVTR: Scene Text Recognition with a Single Visual Model + https://www.paddleocr.ai/v2.10.0/en/algorithm/text_recognition/algorithm_rec_svtr.html + """ + + def __init__( + self, + config, + ): + super().__init__(config) + in_channels = config.backbone_config.stage_out_channels[-1] + hidden_size = config.hidden_size + + self.conv_block = nn.ModuleList( + [ + PPOCRV5ServerRecConvLayer( + in_channels=in_channels, + out_channels=in_channels // 8, + kernel_size=config.conv_kernel_size, + ), + PPOCRV5ServerRecConvLayer(in_channels=in_channels // 8, out_channels=hidden_size, kernel_size=(1, 1)), + PPOCRV5ServerRecConvLayer(in_channels=hidden_size, out_channels=in_channels, kernel_size=(1, 1)), + PPOCRV5ServerRecConvLayer( + in_channels=2 * in_channels, + out_channels=in_channels // 8, + kernel_size=config.conv_kernel_size, + ), + PPOCRV5ServerRecConvLayer(in_channels=in_channels // 8, out_channels=hidden_size, kernel_size=(1, 1)), + ] + ) + self.svtr_block = nn.ModuleList() + for _ in range(config.depth): + self.svtr_block.append(PPOCRV5ServerRecBlock(config=config)) + + self.norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward(self, hidden_states: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs]): + residual = hidden_states + + hidden_states = self.conv_block[0](hidden_states) + hidden_states = self.conv_block[1](hidden_states) + + batch_size, channels, height, width = hidden_states.shape + hidden_states = hidden_states.flatten(2).transpose(1, 2) + for block in self.svtr_block: + hidden_states = block(hidden_states) + + hidden_states = self.norm(hidden_states) + hidden_states = hidden_states.view(batch_size, height, width, channels).permute(0, 3, 1, 2) + hidden_states = self.conv_block[2](hidden_states) + hidden_states = self.conv_block[3](torch.cat((residual, hidden_states), dim=1)) + hidden_states = self.conv_block[4](hidden_states) + hidden_states = hidden_states.squeeze(2).transpose(1, 2) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states) + + +@auto_docstring(custom_intro="PPOCRV5ServerRec model, consisting of Backbone and Head networks.") +class PPOCRV5ServerRecModel(PPOCRV5ServerRecPreTrainedModel): + def __init__(self, config: PPOCRV5ServerRecConfig): + super().__init__(config) + self.backbone = load_backbone(config) + + self.gradient_checkpointing = False + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.backbone(pixel_values, **kwargs) + hidden_state = outputs.feature_maps[-1] + hidden_state = F.avg_pool2d(hidden_state, (3, 2)) + + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_state, + hidden_states=outputs.hidden_states, + ) + + +@auto_docstring +@dataclass +class PPOCRV5ServerRecForTextRecognitionOutput(BaseModelOutputWithNoAttention): + r""" + head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Hidden-states of the PPOCRV5ServerRecHead at the output of each layer plus the optional initial embedding outputs. + """ + + head_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring(custom_intro="PPOCRV5ServerRec model for text recognition tasks.") +class PPOCRV5ServerRecForTextRecognition(PPOCRV5ServerRecPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: PPOCRV5ServerRecConfig): + super().__init__(config) + self.model = PPOCRV5ServerRecModel(config) + self.head = PPOCRV5ServerRecHead(config) + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.model(pixel_values, **kwargs) + head_outputs = self.head(outputs.last_hidden_state, **kwargs) + + return PPOCRV5ServerRecForTextRecognitionOutput( + last_hidden_state=head_outputs.last_hidden_state, + hidden_states=outputs.hidden_states, + head_hidden_states=head_outputs.hidden_states, + ) + + +__all__ = [ + "PPOCRV5ServerRecForTextRecognition", + "PPOCRV5ServerRecImageProcessor", + "PPOCRV5ServerRecConfig", + "PPOCRV5ServerRecModel", + "PPOCRV5ServerRecPreTrainedModel", + "PPOCRV5ServerRecEncoderWithSVTR", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eb0b1324b316d8c950a5b379c781f28c67db88f0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/__init__.py @@ -0,0 +1,32 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_prompt_depth_anything import PromptDepthAnythingConfig + from .image_processing_pil_prompt_depth_anything import PromptDepthAnythingImageProcessorPil + from .image_processing_prompt_depth_anything import PromptDepthAnythingImageProcessor + from .modeling_prompt_depth_anything import ( + PromptDepthAnythingForDepthEstimation, + PromptDepthAnythingPreTrainedModel, + ) +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/configuration_prompt_depth_anything.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/configuration_prompt_depth_anything.py new file mode 100644 index 0000000000000000000000000000000000000000..b6577a84e3b35a17b71cb48ae98cab8d2337c4e5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/configuration_prompt_depth_anything.py @@ -0,0 +1,103 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_prompt_depth_anything.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto.configuration_auto import AutoConfig + + +@auto_docstring(checkpoint="LiheYoung/depth-anything-small-hf") +@strict +class PromptDepthAnythingConfig(PreTrainedConfig): + r""" + reassemble_hidden_size (`int`, *optional*, defaults to 384): + The number of input channels of the reassemble layers. + reassemble_factors (`list[int]`, *optional*, defaults to `[4, 2, 1, 0.5]`): + The up/downsampling factors of the reassemble layers. + neck_hidden_sizes (`list[str]`, *optional*, defaults to `[48, 96, 192, 384]`): + The hidden sizes to project to for the feature maps of the backbone. + fusion_hidden_size (`int`, *optional*, defaults to 64): + The number of channels before fusion. + head_in_index (`int`, *optional*, defaults to -1): + The index of the features to use in the depth estimation head. + head_hidden_size (`int`, *optional*, defaults to 32): + The number of output channels in the second convolution of the depth estimation head. + depth_estimation_type (`str`, *optional*, defaults to `"relative"`): + The type of depth estimation to use. Can be one of `["relative", "metric"]`. + max_depth (`float`, *optional*): + The maximum depth to use for the "metric" depth estimation head. 20 should be used for indoor models + and 80 for outdoor models. For "relative" depth estimation, this value is ignored. + + Example: + + ```python + >>> from transformers import PromptDepthAnythingConfig, PromptDepthAnythingForDepthEstimation + + >>> # Initializing a PromptDepthAnything small style configuration + >>> configuration = PromptDepthAnythingConfig() + + >>> # Initializing a model from the PromptDepthAnything small style configuration + >>> model = PromptDepthAnythingForDepthEstimation(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "prompt_depth_anything" + sub_configs = {"backbone_config": AutoConfig} + + backbone_config: dict | PreTrainedConfig | None = None + patch_size: int | list[int] | tuple[int, int] = 14 + initializer_range: float = 0.02 + reassemble_hidden_size: int = 384 + reassemble_factors: list[int | float] | tuple[int | float, ...] = (4, 2, 1, 0.5) + neck_hidden_sizes: list[int] | tuple[int, ...] = (48, 96, 192, 384) + fusion_hidden_size: int = 64 + head_in_index: int = -1 + head_hidden_size: int = 32 + depth_estimation_type: str = "relative" + max_depth: int | None = None + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="dinov2", + default_config_kwargs={ + "image_size": 518, + "hidden_size": 384, + "num_attention_heads": 6, + "out_indices": [9, 10, 11, 12], + "reshape_hidden_states": False, + }, + **kwargs, + ) + + self.max_depth = self.max_depth if self.max_depth else 1 + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.depth_estimation_type not in ["relative", "metric"]: + raise ValueError("depth_estimation_type must be one of ['relative', 'metric']") + + +__all__ = ["PromptDepthAnythingConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/image_processing_pil_prompt_depth_anything.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/image_processing_pil_prompt_depth_anything.py new file mode 100644 index 0000000000000000000000000000000000000000..26f31249462aa3bb4b6c5c6e68aaa1a7bc50d3f9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/image_processing_pil_prompt_depth_anything.py @@ -0,0 +1,333 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for PromptDepthAnything.""" + +import math +from typing import TYPE_CHECKING + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import PaddingMode +from ...image_transforms import pad as np_pad +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + get_image_size, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires + + +if TYPE_CHECKING: + from ...modeling_outputs import DepthEstimatorOutput + + +def _constrain_to_multiple_of(val, multiple, min_val=0, max_val=None): + """Constrain a value to be a multiple of another value.""" + x = round(val / multiple) * multiple + + if max_val is not None and x > max_val: + x = math.floor(val / multiple) * multiple + + if x < min_val: + x = math.ceil(val / multiple) * multiple + + return x + + +def _get_resize_output_image_size( + input_image: np.ndarray, output_size: tuple[int, int], keep_aspect_ratio: bool, multiple: int +) -> tuple[int, int]: + """Get the output size for resizing an image.""" + input_height, input_width = get_image_size(input_image, channel_dim=ChannelDimension.FIRST) + output_height, output_width = output_size + + # determine new height and width + scale_height = output_height / input_height + scale_width = output_width / input_width + + if keep_aspect_ratio: + # scale as little as possible + if abs(1 - scale_width) < abs(1 - scale_height): + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + + new_height = _constrain_to_multiple_of(scale_height * input_height, multiple=multiple) + new_width = _constrain_to_multiple_of(scale_width * input_width, multiple=multiple) + + return (new_height, new_width) + + +# Adapted from transformers.models.prompt_depth_anything.image_processing_prompt_depth_anything.PromptDepthAnythingImageProcessorKwargs +class PromptDepthAnythingImageProcessorKwargs(ImagesKwargs, total=False): + r""" + keep_aspect_ratio (`bool`, *optional*): + If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. + ensure_multiple_of (`int`, *optional*): + If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. + prompt_scale_to_meter (`float`, *optional*): + Scale factor to convert the prompt depth to meters. + size_divisor (`int`, *optional*): + If `do_pad` is `True`, pads the image dimensions to be divisible by this value. + """ + + keep_aspect_ratio: bool + ensure_multiple_of: int + size_divisor: int + prompt_scale_to_meter: float + + +@auto_docstring +class PromptDepthAnythingImageProcessorPil(PilBackend): + model_input_names = ["pixel_values", "prompt_depth"] + + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 384, "width": 384} + do_resize = True + do_rescale = True + do_normalize = True + keep_aspect_ratio = False + ensure_multiple_of = 1 + do_pad = False + size_divisor = None + prompt_scale_to_meter = 0.001 + valid_kwargs = PromptDepthAnythingImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[PromptDepthAnythingImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + prompt_depth: ImageInput | None = None, + **kwargs: Unpack[PromptDepthAnythingImageProcessorKwargs], + ) -> BatchFeature: + r""" + prompt_depth (`ImageInput`, *optional*): + Prompt depth to preprocess. + """ + return super().preprocess(images, prompt_depth, **kwargs) + + def resize_with_aspect_ratio( + self, + image: np.ndarray, + size: SizeDict, + keep_aspect_ratio: bool = False, + ensure_multiple_of: int = 1, + resample: PILImageResampling | None = None, + ) -> np.ndarray: + """ + Resize an image to target size while optionally maintaining aspect ratio and ensuring dimensions are multiples. + """ + if resample is None: + resample = PILImageResampling.BICUBIC + + # Custom resize with aspect ratio preservation and ensure_multiple_of constraint + output_size = _get_resize_output_image_size( + image, + output_size=(size.height, size.width), + keep_aspect_ratio=keep_aspect_ratio, + multiple=ensure_multiple_of, + ) + + # Standard resize method with calculated output size + return self.resize(image=image, size=SizeDict(height=output_size[0], width=output_size[1]), resample=resample) + + def pad_image(self, image: np.ndarray, size_divisor: int) -> np.ndarray: + """ + Center pad an image to be a multiple of size_divisor. + """ + + def _get_pad(size, size_divisor): + new_size = math.ceil(size / size_divisor) * size_divisor + pad_size = new_size - size + pad_size_left = pad_size // 2 + pad_size_right = pad_size - pad_size_left + return pad_size_left, pad_size_right + + height, width = get_image_size(image, channel_dim=ChannelDimension.FIRST) + + # Match slow processor and PyTorch convention: width->left/right, height->top/bottom + pad_size_left, pad_size_right = _get_pad(width, size_divisor) + pad_size_top, pad_size_bottom = _get_pad(height, size_divisor) + + # NumPy padding: ((pad_top, pad_bottom), (pad_left, pad_right)) + padding = ((pad_size_top, pad_size_bottom), (pad_size_left, pad_size_right)) + padded_image = np_pad( + image, + padding, + mode=PaddingMode.CONSTANT, + constant_values=0, + data_format=ChannelDimension.FIRST, + input_data_format=ChannelDimension.FIRST, + ) + + return padded_image + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + prompt_depth: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + device: str | None = None, + return_tensors: str | TensorType | None = None, + prompt_scale_to_meter: float | None = None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess image-like inputs, including the main images and optional prompt depth. + """ + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=False, input_data_format=input_data_format, device=device + ) # always use do_convert_rgb=False rather than defining it as a param to match slow processor + + # Process images with the standard pipeline + pixel_values = self._preprocess(images, return_tensors=return_tensors, **kwargs) + + data = {"pixel_values": pixel_values} + + # Process prompt depth if provided + if prompt_depth is not None: + processed_prompt_depths = self._prepare_image_like_inputs( + images=prompt_depth, + do_convert_rgb=False, # Depth maps should not be converted + input_data_format=input_data_format, + device=device, + expected_ndims=2, + ) + + # Validate prompt_depths has same length as images as in slow processor + if len(processed_prompt_depths) != len(images): + raise ValueError( + f"Number of prompt depth images ({len(processed_prompt_depths)}) does not match number of input images ({len(images)})" + ) + + if prompt_scale_to_meter is None: + prompt_scale_to_meter = self.prompt_scale_to_meter + + final_prompt_depths = [] + for depth in processed_prompt_depths: + depth = depth * prompt_scale_to_meter + + # Handle case where depth is constant (min == max) + if depth.min() == depth.max(): + depth[0, 0] = depth[0, 0] + 1e-6 # Add small variation to avoid numerical issues + + if depth.ndim == 2: # Add channel dimension if needed + depth = np.expand_dims(depth, 0) # [H, W] -> [1, H, W] (channels first) + + depth = depth.astype(np.float32) # Convert to float32 to match slow processor + final_prompt_depths.append(depth) + + data["prompt_depth"] = final_prompt_depths + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: PILImageResampling | None, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + return_tensors: str | TensorType | None, + keep_aspect_ratio: bool | None = None, + ensure_multiple_of: int | None = None, + size_divisor: int | None = None, + **kwargs, + ) -> list[np.ndarray]: + """ + Override the base _preprocess method to handle custom PromptDepthAnything parameters. + """ + processed_images = [] + for image in images: + if do_resize: + image = self.resize_with_aspect_ratio( + image=image, + size=size, + keep_aspect_ratio=keep_aspect_ratio, + ensure_multiple_of=ensure_multiple_of, + resample=resample, + ) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + if do_pad and size_divisor is not None: + image = self.pad_image(image, size_divisor) + processed_images.append(image) + + return processed_images + + @requires(backends=("torch",)) + def post_process_depth_estimation( + self, outputs: "DepthEstimatorOutput", target_sizes: TensorType | list[tuple[int, int]] | None | None = None + ) -> list[dict[str, TensorType]]: + """ + Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images. + Only supports PyTorch. + + Args: + outputs ([`DepthEstimatorOutput`]): + Raw outputs of the model. + target_sizes (`TensorType` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + (height, width) of each image in the batch. If left to None, predictions will not be resized. + + Returns: + `list[dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth + predictions. + """ + import torch + + predicted_depth = outputs.predicted_depth + + if (target_sizes is not None) and (len(predicted_depth) != len(target_sizes)): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the predicted depth" + ) + + results = [] + target_sizes = [None] * len(predicted_depth) if target_sizes is None else target_sizes + for depth, target_size in zip(predicted_depth, target_sizes): + if target_size is not None: + depth = torch.nn.functional.interpolate( + depth.unsqueeze(0).unsqueeze(1), size=target_size, mode="bicubic", align_corners=False + ).squeeze() + + results.append({"predicted_depth": depth}) + + return results + + +__all__ = ["PromptDepthAnythingImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/image_processing_prompt_depth_anything.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/image_processing_prompt_depth_anything.py new file mode 100644 index 0000000000000000000000000000000000000000..5d8bda90826970b1e5b005bbda2ab0a13a207838 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/image_processing_prompt_depth_anything.py @@ -0,0 +1,357 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for PromptDepthAnything.""" + +import math +from typing import TYPE_CHECKING, Union + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TensorType, + auto_docstring, + requires_backends, +) + + +if TYPE_CHECKING: + from ...modeling_outputs import DepthEstimatorOutput + +import torch +from torchvision.transforms.v2 import functional as tvF + + +class PromptDepthAnythingImageProcessorKwargs(ImagesKwargs, total=False): + r""" + keep_aspect_ratio (`bool`, *optional*): + If `True`, the image is resized to the largest possible size such that the aspect ratio is preserved. + ensure_multiple_of (`int`, *optional*): + If `do_resize` is `True`, the image is resized to a size that is a multiple of this value. + prompt_scale_to_meter (`float`, *optional*): + Scale factor to convert the prompt depth to meters. + size_divisor (`int`, *optional*): + If `do_pad` is `True`, pads the image dimensions to be divisible by this value. + """ + + keep_aspect_ratio: bool + ensure_multiple_of: int + size_divisor: int + prompt_scale_to_meter: float + + +def _constrain_to_multiple_of(val, multiple, min_val=0, max_val=None): + """Constrain a value to be a multiple of another value.""" + x = round(val / multiple) * multiple + + if max_val is not None and x > max_val: + x = math.floor(val / multiple) * multiple + + if x < min_val: + x = math.ceil(val / multiple) * multiple + + return x + + +def _get_resize_output_image_size( + input_image: "torch.Tensor", + output_size: tuple[int, int], + keep_aspect_ratio: bool, + multiple: int, +) -> tuple[int, int]: + """Get the output size for resizing an image.""" + input_height, input_width = input_image.shape[-2:] + output_height, output_width = output_size + + # determine new height and width + scale_height = output_height / input_height + scale_width = output_width / input_width + + if keep_aspect_ratio: + # scale as little as possible + if abs(1 - scale_width) < abs(1 - scale_height): + # fit width + scale_height = scale_width + else: + # fit height + scale_width = scale_height + + new_height = _constrain_to_multiple_of(scale_height * input_height, multiple=multiple) + new_width = _constrain_to_multiple_of(scale_width * input_width, multiple=multiple) + + return (new_height, new_width) + + +@auto_docstring +class PromptDepthAnythingImageProcessor(TorchvisionBackend): + model_input_names = ["pixel_values", "prompt_depth"] + + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 384, "width": 384} + do_resize = True + do_rescale = True + do_normalize = True + keep_aspect_ratio = False + ensure_multiple_of = 1 + do_pad = False + size_divisor = None + prompt_scale_to_meter = 0.001 + valid_kwargs = PromptDepthAnythingImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[PromptDepthAnythingImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + prompt_depth: ImageInput | None = None, + **kwargs: Unpack[PromptDepthAnythingImageProcessorKwargs], + ) -> BatchFeature: + r""" + prompt_depth (`ImageInput`, *optional*): + Prompt depth to preprocess. + """ + return super().preprocess(images, prompt_depth, **kwargs) + + def resize_with_aspect_ratio( + self, + image: "torch.Tensor", + size: SizeDict, + keep_aspect_ratio: bool = False, + ensure_multiple_of: int = 1, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + ) -> "torch.Tensor": + """ + Resize an image to target size while optionally maintaining aspect ratio and ensuring dimensions are multiples. + """ + # Set default resample to BICUBIC to match the slow processor (causes slight numerical differences otherwise) + if resample is None: + resample = PILImageResampling.BICUBIC + + # Custom resize with aspect ratio preservation and ensure_multiple_of constraint + output_size = _get_resize_output_image_size( + image, + output_size=(size.height, size.width), + keep_aspect_ratio=keep_aspect_ratio, + multiple=ensure_multiple_of, + ) + + # Standard resize method with calculated output size + return self.resize( + image=image, + size=SizeDict(height=output_size[0], width=output_size[1]), + resample=resample, + ) + + def pad_image( + self, + image: "torch.Tensor", + size_divisor: int, + ) -> "torch.Tensor": + """ + Center pad an image to be a multiple of size_divisor. + """ + + def _get_pad(size, size_divisor): + new_size = math.ceil(size / size_divisor) * size_divisor + pad_size = new_size - size + pad_size_left = pad_size // 2 + pad_size_right = pad_size - pad_size_left + return pad_size_left, pad_size_right + + height, width = image.shape[-2:] + + # Match slow processor and PyTorch convention: width->left/right, height->top/bottom + pad_size_left, pad_size_right = _get_pad(width, size_divisor) + pad_size_top, pad_size_bottom = _get_pad(height, size_divisor) + + # Use torchvision padding for fast processing + # /!\ NB: torchvision tvF.pad expects (left, top, right, bottom) for the last two dims (W then H) + # Source: https://docs.pytorch.org/vision/main/generated/torchvision.transforms.Pad.html + # So: (left=width_pad, top=height_pad, right=width_pad, bottom=height_pad) + padding = [pad_size_left, pad_size_top, pad_size_right, pad_size_bottom] + padded_image = tvF.pad(image, padding=padding) + + return padded_image + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + prompt_depth: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + device: Union[str, "torch.device"] | None = None, + return_tensors: str | TensorType | None = None, + prompt_scale_to_meter: float | None = None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess image-like inputs, including the main images and optional prompt depth. + """ + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=False, input_data_format=input_data_format, device=device + ) # always use do_convert_rgb=False rather than defining it as a param to match slow processor + + # Process images with the standard pipeline + pixel_values = self._preprocess(images, return_tensors=return_tensors, **kwargs) + + data = {"pixel_values": pixel_values} + + # Process prompt depth if provided + if prompt_depth is not None: + processed_prompt_depths = self._prepare_image_like_inputs( + images=prompt_depth, + do_convert_rgb=False, # Depth maps should not be converted + input_data_format=input_data_format, + device=images[0].device if images else device, + expected_ndims=2, + ) + + # Validate prompt_depths has same length as images as in slow processor + if len(processed_prompt_depths) != len(images): + raise ValueError( + f"Number of prompt depth images ({len(processed_prompt_depths)}) does not match number of input images ({len(images)})" + ) + + if prompt_scale_to_meter is None: + prompt_scale_to_meter = self.prompt_scale_to_meter + + final_prompt_depths = [] + for depth in processed_prompt_depths: + depth = depth * prompt_scale_to_meter + + # Handle case where depth is constant (min == max) + if depth.min() == depth.max(): + depth[0, 0] = depth[0, 0] + 1e-6 # Add small variation to avoid numerical issues + + if depth.ndim == 2: # Add channel dimension if needed + depth = depth.unsqueeze(0) # [H, W] -> [1, H, W] (channels first) + + depth = depth.float() # Convert to float32 to match slow processor + final_prompt_depths.append(depth) + + data["prompt_depth"] = final_prompt_depths + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + keep_aspect_ratio: bool | None = None, + ensure_multiple_of: int | None = None, + size_divisor: int | None = None, + **kwargs, + ) -> list["torch.Tensor"]: + """ + Override the base _preprocess method to handle custom PromptDepthAnything parameters. + """ + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize_with_aspect_ratio( + image=stacked_images, + size=size, + keep_aspect_ratio=keep_aspect_ratio, + ensure_multiple_of=ensure_multiple_of, + resample=resample, + ) + resized_images_grouped[shape] = stacked_images + + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + + for shape, stacked_images in grouped_images.items(): + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + + if do_pad and size_divisor is not None: + stacked_images = self.pad_image(stacked_images, size_divisor) + + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return processed_images + + def post_process_depth_estimation( + self, + outputs: "DepthEstimatorOutput", + target_sizes: TensorType | list[tuple[int, int]] | None | None = None, + ) -> list[dict[str, TensorType]]: + """ + Converts the raw output of [`DepthEstimatorOutput`] into final depth predictions and depth PIL images. + Only supports PyTorch. + + Args: + outputs ([`DepthEstimatorOutput`]): + Raw outputs of the model. + target_sizes (`TensorType` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + (height, width) of each image in the batch. If left to None, predictions will not be resized. + + Returns: + `list[dict[str, TensorType]]`: A list of dictionaries of tensors representing the processed depth + predictions. + """ + requires_backends(self, "torch") + + predicted_depth = outputs.predicted_depth + + if (target_sizes is not None) and (len(predicted_depth) != len(target_sizes)): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the predicted depth" + ) + + results = [] + target_sizes = [None] * len(predicted_depth) if target_sizes is None else target_sizes + for depth, target_size in zip(predicted_depth, target_sizes): + if target_size is not None: + depth = torch.nn.functional.interpolate( + depth.unsqueeze(0).unsqueeze(1), size=target_size, mode="bicubic", align_corners=False + ).squeeze() + + results.append({"predicted_depth": depth}) + + return results + + +__all__ = ["PromptDepthAnythingImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/modeling_prompt_depth_anything.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/modeling_prompt_depth_anything.py new file mode 100644 index 0000000000000000000000000000000000000000..9faecd40f0643f1a513d8e73af7374e7b905f210 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/modeling_prompt_depth_anything.py @@ -0,0 +1,480 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_prompt_depth_anything.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn + +from ...backbone_utils import load_backbone +from ...modeling_outputs import DepthEstimatorOutput +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import merge_with_config_defaults, torch_int +from ...utils.output_capturing import capture_outputs +from .configuration_prompt_depth_anything import PromptDepthAnythingConfig + + +class PromptDepthAnythingLayer(nn.Module): + def __init__(self, config: PromptDepthAnythingConfig): + super().__init__() + self.convolution1 = nn.Conv2d( + 1, + config.fusion_hidden_size, + kernel_size=3, + stride=1, + padding=1, + bias=True, + ) + self.activation1 = nn.ReLU() + + self.convolution2 = nn.Conv2d( + config.fusion_hidden_size, + config.fusion_hidden_size, + kernel_size=3, + stride=1, + padding=1, + bias=True, + ) + self.activation2 = nn.ReLU() + + self.convolution3 = nn.Conv2d( + config.fusion_hidden_size, + config.fusion_hidden_size, + kernel_size=3, + stride=1, + padding=1, + bias=True, + ) + + def forward(self, prompt_depth: torch.Tensor) -> torch.Tensor: + hidden_state = self.convolution1(prompt_depth) + hidden_state = self.activation1(hidden_state) + hidden_state = self.convolution2(hidden_state) + hidden_state = self.activation2(hidden_state) + hidden_state = self.convolution3(hidden_state) + return hidden_state + + +class PromptDepthAnythingPreActResidualLayer(nn.Module): + """ + ResidualConvUnit, pre-activate residual unit. + + Args: + config (`[PromptDepthAnythingConfig]`): + Model configuration class defining the model architecture. + """ + + def __init__(self, config): + super().__init__() + + self.activation1 = nn.ReLU() + self.convolution1 = nn.Conv2d( + config.fusion_hidden_size, + config.fusion_hidden_size, + kernel_size=3, + stride=1, + padding=1, + bias=True, + ) + + self.activation2 = nn.ReLU() + self.convolution2 = nn.Conv2d( + config.fusion_hidden_size, + config.fusion_hidden_size, + kernel_size=3, + stride=1, + padding=1, + bias=True, + ) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + residual = hidden_state + hidden_state = self.activation1(hidden_state) + hidden_state = self.convolution1(hidden_state) + hidden_state = self.activation2(hidden_state) + hidden_state = self.convolution2(hidden_state) + + return hidden_state + residual + + +class PromptDepthAnythingFeatureFusionLayer(nn.Module): + """Feature fusion layer, merges feature maps from different stages. + + Args: + config (`[PromptDepthAnythingConfig]`): + Model configuration class defining the model architecture. + """ + + def __init__(self, config: PromptDepthAnythingConfig): + super().__init__() + + self.projection = nn.Conv2d(config.fusion_hidden_size, config.fusion_hidden_size, kernel_size=1, bias=True) + + self.residual_layer1 = PromptDepthAnythingPreActResidualLayer(config) + self.residual_layer2 = PromptDepthAnythingPreActResidualLayer(config) + self.prompt_depth_layer = PromptDepthAnythingLayer(config) + + def forward(self, hidden_state, residual=None, size=None, prompt_depth=None): + if residual is not None: + if hidden_state.shape != residual.shape: + residual = nn.functional.interpolate( + residual, size=hidden_state.shape[2:], mode="bilinear", align_corners=False + ) + hidden_state = hidden_state + self.residual_layer1(residual) + + hidden_state = self.residual_layer2(hidden_state) + + if prompt_depth is not None: + prompt_depth = nn.functional.interpolate( + prompt_depth, size=hidden_state.shape[2:], mode="bilinear", align_corners=False + ) + res = self.prompt_depth_layer(prompt_depth) + hidden_state = hidden_state + res + + modifier = {"scale_factor": 2} if size is None else {"size": size} + + hidden_state = nn.functional.interpolate( + hidden_state, + **modifier, + mode="bilinear", + align_corners=True, + ) + hidden_state = self.projection(hidden_state) + + return hidden_state + + +class PromptDepthAnythingFeatureFusionStage(nn.Module): + def __init__(self, config: PromptDepthAnythingConfig): + super().__init__() + self.layers = nn.ModuleList() + for _ in range(len(config.neck_hidden_sizes)): + self.layers.append(PromptDepthAnythingFeatureFusionLayer(config)) + + def forward(self, hidden_states, size=None, prompt_depth=None): + # reversing the hidden_states, we start from the last + hidden_states = hidden_states[::-1] + + fused_hidden_states = [] + fused_hidden_state = None + + for idx, (hidden_state, layer) in enumerate(zip(hidden_states, self.layers)): + size = hidden_states[idx + 1].shape[2:] if idx != (len(hidden_states) - 1) else None + + if fused_hidden_state is None: + # first layer only uses the last hidden_state + fused_hidden_state = layer(hidden_state, size=size, prompt_depth=prompt_depth) + else: + fused_hidden_state = layer(fused_hidden_state, hidden_state, size=size, prompt_depth=prompt_depth) + + fused_hidden_states.append(fused_hidden_state) + + return fused_hidden_states + + +class PromptDepthAnythingDepthEstimationHead(nn.Module): + """ + Output head consisting of 3 convolutional layers. It progressively halves the feature dimension and upsamples + the predictions to the input resolution after the first convolutional layer (details can be found in the DPT paper's + supplementary material). The final activation function is either ReLU or Sigmoid, depending on the depth estimation + type (relative or metric). For metric depth estimation, the output is scaled by the maximum depth used during pretraining. + """ + + def __init__(self, config): + super().__init__() + + self.head_in_index = config.head_in_index + self.patch_size = config.patch_size + + features = config.fusion_hidden_size + self.conv1 = nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1) + self.conv2 = nn.Conv2d(features // 2, config.head_hidden_size, kernel_size=3, stride=1, padding=1) + self.activation1 = nn.ReLU() + self.conv3 = nn.Conv2d(config.head_hidden_size, 1, kernel_size=1, stride=1, padding=0) + if config.depth_estimation_type == "relative": + self.activation2 = nn.ReLU() + elif config.depth_estimation_type == "metric": + self.activation2 = nn.Sigmoid() + else: + raise ValueError(f"Unknown depth estimation type: {config.depth_estimation_type}") + self.max_depth = config.max_depth + + def forward(self, hidden_states: list[torch.Tensor], patch_height: int, patch_width: int) -> torch.Tensor: + hidden_states = hidden_states[-1] + + predicted_depth = self.conv1(hidden_states) + target_height = torch_int(patch_height * self.patch_size) + target_width = torch_int(patch_width * self.patch_size) + predicted_depth = nn.functional.interpolate( + predicted_depth, + (target_height, target_width), + mode="bilinear", + align_corners=True, + ) + predicted_depth = self.conv2(predicted_depth) + predicted_depth = self.activation1(predicted_depth) + predicted_depth = self.conv3(predicted_depth) + predicted_depth = self.activation2(predicted_depth) + # (batch_size, 1, height, width) -> (batch_size, height, width), which + # keeps the same behavior as Depth Anything v1 & v2 + predicted_depth = predicted_depth.squeeze(dim=1) + + return predicted_depth + + +@auto_docstring +class PromptDepthAnythingPreTrainedModel(PreTrainedModel): + config: PromptDepthAnythingConfig + base_model_prefix = "prompt_depth_anything" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + + +class PromptDepthAnythingReassembleLayer(nn.Module): + def __init__(self, config: PromptDepthAnythingConfig, channels: int, factor: int): + super().__init__() + self.projection = nn.Conv2d(in_channels=config.reassemble_hidden_size, out_channels=channels, kernel_size=1) + + # up/down sampling depending on factor + if factor > 1: + self.resize = nn.ConvTranspose2d(channels, channels, kernel_size=factor, stride=factor, padding=0) + elif factor == 1: + self.resize = nn.Identity() + elif factor < 1: + # so should downsample + stride = torch_int(1 / factor) + self.resize = nn.Conv2d(channels, channels, kernel_size=3, stride=stride, padding=1) + + def forward(self, hidden_state): + hidden_state = self.projection(hidden_state) + hidden_state = self.resize(hidden_state) + + return hidden_state + + +class PromptDepthAnythingReassembleStage(nn.Module): + """ + This class reassembles the hidden states of the backbone into image-like feature representations at various + resolutions. + + This happens in 3 stages: + 1. Take the patch embeddings and reshape them to image-like feature representations. + 2. Project the channel dimension of the hidden states according to `config.neck_hidden_sizes`. + 3. Resizing the spatial dimensions (height, width). + + Args: + config (`[PromptDepthAnythingConfig]`): + Model configuration class defining the model architecture. + """ + + def __init__(self, config): + super().__init__() + + self.config = config + self.layers = nn.ModuleList() + for channels, factor in zip(config.neck_hidden_sizes, config.reassemble_factors): + self.layers.append(PromptDepthAnythingReassembleLayer(config, channels=channels, factor=factor)) + + def forward(self, hidden_states: list[torch.Tensor], patch_height=None, patch_width=None) -> list[torch.Tensor]: + """ + Args: + hidden_states (`list[torch.FloatTensor]`, each of shape `(batch_size, sequence_length + 1, hidden_size)`): + List of hidden states from the backbone. + """ + out = [] + + for i, hidden_state in enumerate(hidden_states): + # reshape to (batch_size, num_channels, height, width) + hidden_state = hidden_state[:, 1:] + batch_size, _, num_channels = hidden_state.shape + hidden_state = hidden_state.reshape(batch_size, patch_height, patch_width, num_channels) + hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous() + hidden_state = self.layers[i](hidden_state) + out.append(hidden_state) + + return out + + +class PromptDepthAnythingNeck(nn.Module): + """ + PromptDepthAnythingNeck. A neck is a module that is normally used between the backbone and the head. It takes a list of tensors as + input and produces another list of tensors as output. For PromptDepthAnything, it includes 2 stages: + + * PromptDepthAnythingReassembleStage + * PromptDepthAnythingFeatureFusionStage. + + Args: + config (dict): config dict. + """ + + def __init__(self, config): + super().__init__() + self.config = config + + self.reassemble_stage = PromptDepthAnythingReassembleStage(config) + + self.convs = nn.ModuleList() + for channel in config.neck_hidden_sizes: + self.convs.append(nn.Conv2d(channel, config.fusion_hidden_size, kernel_size=3, padding=1, bias=False)) + + # fusion + self.fusion_stage = PromptDepthAnythingFeatureFusionStage(config) + + def forward( + self, + hidden_states: list[torch.Tensor], + patch_height: int | None = None, + patch_width: int | None = None, + prompt_depth: torch.Tensor | None = None, + ) -> list[torch.Tensor]: + """ + Args: + hidden_states (`list[torch.FloatTensor]`, each of shape `(batch_size, sequence_length, hidden_size)` or `(batch_size, hidden_size, height, width)`): + List of hidden states from the backbone. + """ + if not isinstance(hidden_states, (tuple, list)): + raise TypeError("hidden_states should be a tuple or list of tensors") + + if len(hidden_states) != len(self.config.neck_hidden_sizes): + raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.") + + # postprocess hidden states + hidden_states = self.reassemble_stage(hidden_states, patch_height, patch_width) + + features = [self.convs[i](feature) for i, feature in enumerate(hidden_states)] + + # fusion blocks + output = self.fusion_stage(features, prompt_depth=prompt_depth) + + return output + + +@auto_docstring( + custom_intro=""" + Prompt Depth Anything Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. + """ +) +class PromptDepthAnythingForDepthEstimation(PromptDepthAnythingPreTrainedModel): + _no_split_modules = ["DPTViTEmbeddings"] + + def __init__(self, config): + super().__init__(config) + + self.backbone = load_backbone(config) + self.neck = PromptDepthAnythingNeck(config) + self.head = PromptDepthAnythingDepthEstimationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + prompt_depth: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | DepthEstimatorOutput: + r""" + prompt_depth (`torch.FloatTensor` of shape `(batch_size, 1, height, width)`, *optional*): + Prompt depth is the sparse or low-resolution depth obtained from multi-view geometry or a + low-resolution depth sensor. It generally has shape (height, width), where height + and width can be smaller than those of the images. It is optional and can be None, which means no prompt depth + will be used. If it is None, the output will be a monocular relative depth. + The values are recommended to be in meters, but this is not necessary. + + Example: + + ```python + >>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation + >>> import torch + >>> import numpy as np + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/image.jpg?raw=true" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> image_processor = AutoImageProcessor.from_pretrained("depth-anything/prompt-depth-anything-vits-hf") + >>> model = AutoModelForDepthEstimation.from_pretrained("depth-anything/prompt-depth-anything-vits-hf") + + >>> prompt_depth_url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/arkit_depth.png?raw=true" + >>> with httpx.stream("GET", prompt_depth_url) as response: + ... prompt_depth = Image.open(BytesIO(response.read())) + + >>> # prepare image for the model + >>> inputs = image_processor(images=image, return_tensors="pt", prompt_depth=prompt_depth) + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> # interpolate to original size + >>> post_processed_output = image_processor.post_process_depth_estimation( + ... outputs, + ... target_sizes=[(image.height, image.width)], + ... ) + + >>> # visualize the prediction + >>> predicted_depth = post_processed_output[0]["predicted_depth"] + >>> depth = predicted_depth * 1000. + >>> depth = depth.detach().cpu().numpy() + >>> depth = Image.fromarray(depth.astype("uint16")) # mm + ``` + """ + loss = None + if labels is not None: + raise NotImplementedError("Training is not implemented yet") + + outputs = self.backbone.forward_with_filtered_kwargs(pixel_values, **kwargs) + hidden_states = outputs.feature_maps + + _, _, height, width = pixel_values.shape + patch_size = self.config.patch_size + patch_height = height // patch_size + patch_width = width // patch_size + + if prompt_depth is not None: + batch_size = prompt_depth.shape[0] + depth_min = torch.min(prompt_depth.reshape(batch_size, -1), dim=1).values + depth_max = torch.max(prompt_depth.reshape(batch_size, -1), dim=1).values + depth_min, depth_max = depth_min.view(batch_size, 1, 1, 1), depth_max.view(batch_size, 1, 1, 1) + prompt_depth = (prompt_depth - depth_min) / (depth_max - depth_min) + + hidden_states = self.neck(hidden_states, patch_height, patch_width, prompt_depth=prompt_depth) + + predicted_depth = self.head(hidden_states, patch_height, patch_width) + if prompt_depth is not None: + depth_min = depth_min.squeeze(1).to(predicted_depth.device) + depth_max = depth_max.squeeze(1).to(predicted_depth.device) + predicted_depth = predicted_depth * (depth_max - depth_min) + depth_min + + return DepthEstimatorOutput( + loss=loss, + predicted_depth=predicted_depth, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = ["PromptDepthAnythingForDepthEstimation", "PromptDepthAnythingPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py new file mode 100644 index 0000000000000000000000000000000000000000..6658a240230b4d31eb948186377f62815c58696c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prompt_depth_anything/modular_prompt_depth_anything.py @@ -0,0 +1,328 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn + +from ...modeling_outputs import DepthEstimatorOutput +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import merge_with_config_defaults, torch_int +from ...utils.output_capturing import capture_outputs +from ..depth_anything.configuration_depth_anything import DepthAnythingConfig +from ..depth_anything.modeling_depth_anything import ( + DepthAnythingDepthEstimationHead, + DepthAnythingFeatureFusionLayer, + DepthAnythingFeatureFusionStage, + DepthAnythingForDepthEstimation, + DepthAnythingNeck, + DepthAnythingReassembleStage, +) + + +class PromptDepthAnythingConfig(DepthAnythingConfig): + pass + + +class PromptDepthAnythingLayer(nn.Module): + def __init__(self, config: PromptDepthAnythingConfig): + super().__init__() + self.convolution1 = nn.Conv2d( + 1, + config.fusion_hidden_size, + kernel_size=3, + stride=1, + padding=1, + bias=True, + ) + self.activation1 = nn.ReLU() + + self.convolution2 = nn.Conv2d( + config.fusion_hidden_size, + config.fusion_hidden_size, + kernel_size=3, + stride=1, + padding=1, + bias=True, + ) + self.activation2 = nn.ReLU() + + self.convolution3 = nn.Conv2d( + config.fusion_hidden_size, + config.fusion_hidden_size, + kernel_size=3, + stride=1, + padding=1, + bias=True, + ) + + def forward(self, prompt_depth: torch.Tensor) -> torch.Tensor: + hidden_state = self.convolution1(prompt_depth) + hidden_state = self.activation1(hidden_state) + hidden_state = self.convolution2(hidden_state) + hidden_state = self.activation2(hidden_state) + hidden_state = self.convolution3(hidden_state) + return hidden_state + + +class PromptDepthAnythingFeatureFusionLayer(DepthAnythingFeatureFusionLayer): + def __init__(self, config: PromptDepthAnythingConfig): + super().__init__(config) + self.prompt_depth_layer = PromptDepthAnythingLayer(config) + + def forward(self, hidden_state, residual=None, size=None, prompt_depth=None): + if residual is not None: + if hidden_state.shape != residual.shape: + residual = nn.functional.interpolate( + residual, size=hidden_state.shape[2:], mode="bilinear", align_corners=False + ) + hidden_state = hidden_state + self.residual_layer1(residual) + + hidden_state = self.residual_layer2(hidden_state) + + if prompt_depth is not None: + prompt_depth = nn.functional.interpolate( + prompt_depth, size=hidden_state.shape[2:], mode="bilinear", align_corners=False + ) + res = self.prompt_depth_layer(prompt_depth) + hidden_state = hidden_state + res + + modifier = {"scale_factor": 2} if size is None else {"size": size} + + hidden_state = nn.functional.interpolate( + hidden_state, + **modifier, + mode="bilinear", + align_corners=True, + ) + hidden_state = self.projection(hidden_state) + + return hidden_state + + +class PromptDepthAnythingFeatureFusionStage(DepthAnythingFeatureFusionStage): + def forward(self, hidden_states, size=None, prompt_depth=None): + # reversing the hidden_states, we start from the last + hidden_states = hidden_states[::-1] + + fused_hidden_states = [] + fused_hidden_state = None + + for idx, (hidden_state, layer) in enumerate(zip(hidden_states, self.layers)): + size = hidden_states[idx + 1].shape[2:] if idx != (len(hidden_states) - 1) else None + + if fused_hidden_state is None: + # first layer only uses the last hidden_state + fused_hidden_state = layer(hidden_state, size=size, prompt_depth=prompt_depth) + else: + fused_hidden_state = layer(fused_hidden_state, hidden_state, size=size, prompt_depth=prompt_depth) + + fused_hidden_states.append(fused_hidden_state) + + return fused_hidden_states + + +class PromptDepthAnythingDepthEstimationHead(DepthAnythingDepthEstimationHead): + def forward(self, hidden_states: list[torch.Tensor], patch_height: int, patch_width: int) -> torch.Tensor: + hidden_states = hidden_states[-1] + + predicted_depth = self.conv1(hidden_states) + target_height = torch_int(patch_height * self.patch_size) + target_width = torch_int(patch_width * self.patch_size) + predicted_depth = nn.functional.interpolate( + predicted_depth, + (target_height, target_width), + mode="bilinear", + align_corners=True, + ) + predicted_depth = self.conv2(predicted_depth) + predicted_depth = self.activation1(predicted_depth) + predicted_depth = self.conv3(predicted_depth) + predicted_depth = self.activation2(predicted_depth) + # (batch_size, 1, height, width) -> (batch_size, height, width), which + # keeps the same behavior as Depth Anything v1 & v2 + predicted_depth = predicted_depth.squeeze(dim=1) + + return predicted_depth + + +@auto_docstring +class PromptDepthAnythingPreTrainedModel(PreTrainedModel): + config: PromptDepthAnythingConfig + base_model_prefix = "prompt_depth_anything" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + + +class PromptDepthAnythingReassembleLayer(nn.Module): + def __init__(self, config: PromptDepthAnythingConfig, channels: int, factor: int): + super().__init__() + self.projection = nn.Conv2d(in_channels=config.reassemble_hidden_size, out_channels=channels, kernel_size=1) + + # up/down sampling depending on factor + if factor > 1: + self.resize = nn.ConvTranspose2d(channels, channels, kernel_size=factor, stride=factor, padding=0) + elif factor == 1: + self.resize = nn.Identity() + elif factor < 1: + # so should downsample + stride = torch_int(1 / factor) + self.resize = nn.Conv2d(channels, channels, kernel_size=3, stride=stride, padding=1) + + def forward(self, hidden_state): + hidden_state = self.projection(hidden_state) + hidden_state = self.resize(hidden_state) + + return hidden_state + + +class PromptDepthAnythingReassembleStage(DepthAnythingReassembleStage): + pass + + +class PromptDepthAnythingNeck(DepthAnythingNeck): + def forward( + self, + hidden_states: list[torch.Tensor], + patch_height: int | None = None, + patch_width: int | None = None, + prompt_depth: torch.Tensor | None = None, + ) -> list[torch.Tensor]: + """ + Args: + hidden_states (`list[torch.FloatTensor]`, each of shape `(batch_size, sequence_length, hidden_size)` or `(batch_size, hidden_size, height, width)`): + List of hidden states from the backbone. + """ + if not isinstance(hidden_states, (tuple, list)): + raise TypeError("hidden_states should be a tuple or list of tensors") + + if len(hidden_states) != len(self.config.neck_hidden_sizes): + raise ValueError("The number of hidden states should be equal to the number of neck hidden sizes.") + + # postprocess hidden states + hidden_states = self.reassemble_stage(hidden_states, patch_height, patch_width) + + features = [self.convs[i](feature) for i, feature in enumerate(hidden_states)] + + # fusion blocks + output = self.fusion_stage(features, prompt_depth=prompt_depth) + + return output + + +@auto_docstring( + custom_intro=""" + Prompt Depth Anything Model with a depth estimation head on top (consisting of 3 convolutional layers) e.g. for KITTI, NYUv2. + """ +) +class PromptDepthAnythingForDepthEstimation(DepthAnythingForDepthEstimation): + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + prompt_depth: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | DepthEstimatorOutput: + r""" + prompt_depth (`torch.FloatTensor` of shape `(batch_size, 1, height, width)`, *optional*): + Prompt depth is the sparse or low-resolution depth obtained from multi-view geometry or a + low-resolution depth sensor. It generally has shape (height, width), where height + and width can be smaller than those of the images. It is optional and can be None, which means no prompt depth + will be used. If it is None, the output will be a monocular relative depth. + The values are recommended to be in meters, but this is not necessary. + + Example: + + ```python + >>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation + >>> import torch + >>> import numpy as np + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/image.jpg?raw=true" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> image_processor = AutoImageProcessor.from_pretrained("depth-anything/prompt-depth-anything-vits-hf") + >>> model = AutoModelForDepthEstimation.from_pretrained("depth-anything/prompt-depth-anything-vits-hf") + + >>> prompt_depth_url = "https://github.com/DepthAnything/PromptDA/blob/main/assets/example_images/arkit_depth.png?raw=true" + >>> with httpx.stream("GET", prompt_depth_url) as response: + ... prompt_depth = Image.open(BytesIO(response.read())) + + >>> # prepare image for the model + >>> inputs = image_processor(images=image, return_tensors="pt", prompt_depth=prompt_depth) + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> # interpolate to original size + >>> post_processed_output = image_processor.post_process_depth_estimation( + ... outputs, + ... target_sizes=[(image.height, image.width)], + ... ) + + >>> # visualize the prediction + >>> predicted_depth = post_processed_output[0]["predicted_depth"] + >>> depth = predicted_depth * 1000. + >>> depth = depth.detach().cpu().numpy() + >>> depth = Image.fromarray(depth.astype("uint16")) # mm + ``` + """ + loss = None + if labels is not None: + raise NotImplementedError("Training is not implemented yet") + + outputs = self.backbone.forward_with_filtered_kwargs(pixel_values, **kwargs) + hidden_states = outputs.feature_maps + + _, _, height, width = pixel_values.shape + patch_size = self.config.patch_size + patch_height = height // patch_size + patch_width = width // patch_size + + if prompt_depth is not None: + batch_size = prompt_depth.shape[0] + depth_min = torch.min(prompt_depth.reshape(batch_size, -1), dim=1).values + depth_max = torch.max(prompt_depth.reshape(batch_size, -1), dim=1).values + depth_min, depth_max = depth_min.view(batch_size, 1, 1, 1), depth_max.view(batch_size, 1, 1, 1) + prompt_depth = (prompt_depth - depth_min) / (depth_max - depth_min) + + hidden_states = self.neck(hidden_states, patch_height, patch_width, prompt_depth=prompt_depth) + + predicted_depth = self.head(hidden_states, patch_height, patch_width) + if prompt_depth is not None: + depth_min = depth_min.squeeze(1).to(predicted_depth.device) + depth_max = depth_max.squeeze(1).to(predicted_depth.device) + predicted_depth = predicted_depth * (depth_max - depth_min) + depth_min + + return DepthEstimatorOutput( + loss=loss, + predicted_depth=predicted_depth, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "PromptDepthAnythingConfig", + "PromptDepthAnythingForDepthEstimation", + "PromptDepthAnythingPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7485dfbb530c3e11683c2b7dceaf35bf9edc9086 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_prophetnet import * + from .modeling_prophetnet import * + from .tokenization_prophetnet import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/configuration_prophetnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/configuration_prophetnet.py new file mode 100644 index 0000000000000000000000000000000000000000..ba4d5e48b32c85e73e9dfe8971dfa3fa5768217d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/configuration_prophetnet.py @@ -0,0 +1,89 @@ +# Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""ProphetNet model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="microsoft/prophetnet-large-uncased") +@strict +class ProphetNetConfig(PreTrainedConfig): + r""" + ngram (`int`, *optional*, defaults to 2): + Number of future tokens to predict. Set to 1 to be same as traditional Language model to predict next first + token. + num_buckets (`int`, *optional*, defaults to 32): + The number of buckets to use for each attention layer. This is for relative position calculation. See the + [T5 paper](see https://huggingface.co/papers/1910.10683) for more details. + relative_max_distance (`int`, *optional*, defaults to 128): + Relative distances greater than this number will be put into the last same bucket. This is for relative + position calculation. See the [T5 paper](see https://huggingface.co/papers/1910.10683) for more details. + disable_ngram_loss (`bool`, *optional*, defaults to `False`): + Whether be trained predicting only the next first token. + eps (`float`, *optional*, defaults to 0.0): + Controls the `epsilon` parameter value for label smoothing in the loss calculation. If set to 0, no label + smoothing is performed. + """ + + model_type = "prophetnet" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "num_attention_heads": "num_encoder_attention_heads", + } + + activation_dropout: float | int = 0.1 + activation_function: str = "gelu" + vocab_size: int = 30522 + hidden_size: int = 1024 + encoder_ffn_dim: int = 4096 + num_encoder_layers: int = 12 + num_encoder_attention_heads: int = 16 + decoder_ffn_dim: int = 4096 + num_decoder_layers: int = 12 + num_decoder_attention_heads: int = 16 + attention_dropout: float | int = 0.1 + dropout: float | int = 0.1 + max_position_embeddings: int = 512 + init_std: float = 0.02 + is_encoder_decoder: bool = True + add_cross_attention: bool = True + decoder_start_token_id: int | None = 0 + ngram: int = 2 + num_buckets: int = 32 + relative_max_distance: int = 128 + disable_ngram_loss: bool = False + eps: float = 0.0 + use_cache: bool = True + pad_token_id: int | None = 0 + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 2 + is_decoder: bool = False + tie_word_embeddings: bool = True + + @property + def num_hidden_layers(self) -> int: + return self.num_encoder_layers + + @num_hidden_layers.setter + def num_hidden_layers(self, value): + raise NotImplementedError( + "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and" + " `num_decoder_layers`." + ) + + +__all__ = ["ProphetNetConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/modeling_prophetnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/modeling_prophetnet.py new file mode 100644 index 0000000000000000000000000000000000000000..396a36fa610438547138c802c70d6a9650d520c5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/modeling_prophetnet.py @@ -0,0 +1,1846 @@ +# Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch ProphetNet model, ported from ProphetNet repo(fairsequery_states version).""" + +import copy +import math +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from torch.nn import LayerNorm + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...utils import ModelOutput, auto_docstring, logging +from .configuration_prophetnet import ProphetNetConfig + + +logger = logging.get_logger(__name__) + + +def softmax(hidden_state, dim, onnx_trace=False): + if onnx_trace: + return nn.functional.softmax(hidden_state.float(), dim=dim) + else: + return nn.functional.softmax(hidden_state, dim=dim, dtype=torch.float32) + + +def ngram_attention_bias(sequence_length, ngram, device, dtype): + """ + This function computes the bias for the predict stream + """ + left_block = ( + torch.ones((ngram, sequence_length, sequence_length), device=device, dtype=dtype) * torch.finfo(dtype).min + ) + right_block = left_block.detach().clone() + # create bias + for stream_idx in range(ngram): + right_block[stream_idx].fill_diagonal_(0, wrap=False) + left_block[stream_idx].triu_(-stream_idx + 1) + + left_block[:, :, 0] = 0 + return torch.cat([left_block, right_block], dim=2) + + +def compute_relative_buckets(num_buckets, max_distance, relative_positions, is_bidirectional=False): + """ + This function computes individual parts of the relative position buckets. For more detail, see paper. + """ + inv_relative_positions = -relative_positions + rel_positions_bucket = 0 + + if is_bidirectional: + num_buckets = num_buckets // 2 + rel_positions_bucket = ( + rel_positions_bucket + + torch.lt(inv_relative_positions, torch.zeros_like(inv_relative_positions)).int() * num_buckets + ) + inv_relative_positions = torch.abs(inv_relative_positions) + else: + inv_relative_positions = torch.max(inv_relative_positions, torch.zeros_like(inv_relative_positions)) + + max_exact = num_buckets // 2 + is_small = torch.lt(inv_relative_positions, max_exact) + val_if_large = max_exact + torch.log(inv_relative_positions.float() / max_exact) / math.log( + max_distance / max_exact + ) * (num_buckets - max_exact) + val_if_large = torch.min(val_if_large, torch.ones_like(val_if_large) * (num_buckets - 1)).int() + rel_positions_bucket = rel_positions_bucket + torch.where(is_small, inv_relative_positions.int(), val_if_large) + return rel_positions_bucket + + +def compute_all_stream_relative_buckets(num_buckets, max_distance, position_ids): + """ + This function computes both main and predict relative position buckets. For more detail, see paper. + """ + # main stream + main_stream_relative_positions = position_ids.unsqueeze(1).repeat(1, position_ids.size(-1), 1) + main_stream_relative_positions = main_stream_relative_positions - position_ids.unsqueeze(-1) + + # predicting stream + predicting_stream_relative_positions = torch.cat((position_ids - 1, position_ids), dim=-1).unsqueeze(1) + predicting_stream_relative_positions = predicting_stream_relative_positions.repeat(1, position_ids.size(-1), 1) + predicting_stream_relative_positions = predicting_stream_relative_positions - position_ids.unsqueeze(-1) + + # get both position buckets + main_relative_position_buckets = compute_relative_buckets( + num_buckets, max_distance, main_stream_relative_positions, is_bidirectional=False + ) + predict_relative_position_buckets = compute_relative_buckets( + num_buckets, max_distance, predicting_stream_relative_positions, is_bidirectional=False + ) + return main_relative_position_buckets, predict_relative_position_buckets + + +@auto_docstring( + custom_intro=""" + Base class for sequence-to-sequence language models outputs. + """ +) +@dataclass +class ProphetNetSeq2SeqLMOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss. + logits (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, config.vocab_size)`): + Prediction scores of the main stream language modeling head (scores for each vocabulary token before + SoftMax). + logits_ngram (`torch.FloatTensor` of shape `(batch_size, ngram * decoder_sequence_length, config.vocab_size)`): + Prediction scores of the predict stream language modeling head (scores for each vocabulary token before + SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be + used (see `past_key_values` input) to speed up sequential decoding. + decoder_ngram_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, ngram * decoder_sequence_length, hidden_size)`. + + Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding + outputs. + decoder_ngram_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_attn_heads, + decoder_sequence_length, decoder_sequence_length)`. + + Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + logits_ngram: torch.FloatTensor | None = None + past_key_values: Cache | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_ngram_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + decoder_ngram_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential + decoding. + """ +) +@dataclass +class ProphetNetSeq2SeqModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`): + Sequence of main stream hidden-states at the output of the last layer of the decoder of the model. + + If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, + hidden_size)` is output. + last_hidden_state_ngram (`torch.FloatTensor` of shape `(batch_size,ngram * decoder_sequence_length, config.vocab_size)`, *optional*): + Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model. + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be + used (see `past_key_values` input) to speed up sequential decoding. + decoder_ngram_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, ngram * decoder_sequence_length, hidden_size)`. + + Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding + outputs. + decoder_ngram_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_attn_heads, + decoder_sequence_length, decoder_sequence_length)`. + + Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the + weighted average in the + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + """ + + last_hidden_state: torch.FloatTensor + last_hidden_state_ngram: torch.FloatTensor | None = None + past_key_values: Cache | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_ngram_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + decoder_ngram_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). + """ +) +@dataclass +class ProphetNetDecoderModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`): + Sequence of main stream hidden-states at the output of the last layer of the decoder of the model. + + If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, + hidden_size)` is output. + last_hidden_state_ngram (`torch.FloatTensor` of shape `(batch_size, ngram * decoder_sequence_length, config.vocab_size)`): + Sequence of predict stream hidden-states at the output of the last layer of the decoder of the model. + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be + used (see `past_key_values` input) to speed up sequential decoding. + hidden_states_ngram (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, ngram * decoder_sequence_length, hidden_size)`. + + Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding + outputs. + ngram_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_attn_heads, + decoder_sequence_length, decoder_sequence_length)`. + + Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the + weighted average in the + """ + + last_hidden_state: torch.FloatTensor + last_hidden_state_ngram: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + hidden_states_ngram: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + ngram_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding). + """ +) +@dataclass +class ProphetNetDecoderLMOutput(ModelOutput): + r""" + ngram_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, ngram * decoder_sequence_length, hidden_size)`. + + Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding + outputs. + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss. + logits (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, config.vocab_size)`): + Prediction scores of the main stream language modeling head (scores for each vocabulary token before + SoftMax). + logits_ngram (`torch.FloatTensor` of shape `(batch_size, ngram * decoder_sequence_length, config.vocab_size)`): + Prediction scores of the predict stream language modeling head (scores for each vocabulary token before + SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the attention blocks) of the decoder that can be + used (see `past_key_values` input) to speed up sequential decoding. + hidden_states_ngram (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, ngram * decoder_sequence_length, hidden_size)`. + + Hidden-states of the predict stream of the decoder at the output of each layer plus the initial embedding + outputs. + ngram_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_attn_heads, + decoder_sequence_length, decoder_sequence_length)`. + + Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the + weighted average in the + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + logits_ngram: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + hidden_states_ngram: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + ngram_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring +class ProphetNetPreTrainedModel(PreTrainedModel): + config: ProphetNetConfig + base_model_prefix = "prophetnet" + supports_gradient_checkpointing = True + + def _shift_right(self, input_ids): + decoder_start_token_id = self.config.decoder_start_token_id + pad_token_id = self.config.pad_token_id + + assert decoder_start_token_id is not None, ( + "self.model.config.decoder_start_token_id has to be defined. In ProphetNet it is usually set to the" + " pad_token_id. See ProphetNet docs for more information" + ) + + # shift inputs to the right + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[..., 1:] = input_ids[..., :-1].clone() + shifted_input_ids[..., 0] = decoder_start_token_id + + assert pad_token_id is not None, "self.model.config.pad_token_id has to be defined." + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + assert torch.all(shifted_input_ids >= 0).item(), "Verify that `shifted_input_ids` has only positive values" + + return shifted_input_ids + + +class ProphetNetPositionalEmbeddings(nn.Embedding): + """ + This module learns positional embeddings up to a fixed maximum size. Padding ids are ignored by either offsetting + based on padding_idx or by setting padding_idx to None and ensuring that the appropriate position ids are passed to + the forward function. + """ + + def __init__(self, config: ProphetNetConfig) -> None: + self.max_length = config.max_position_embeddings + super().__init__(config.max_position_embeddings, config.hidden_size, config.pad_token_id) + + def forward(self, inputs_shape, device, attention_mask=None, past_key_values=None, position_ids=None): + assert (position_ids is None) or (self.padding_idx is None), ( + "If position_ids is pre-computed then padding_idx should not be set." + ) + + if position_ids is None: + if past_key_values is not None and past_key_values.get_seq_length() != 0: + # position_ids is the same for every token when decoding a single step + # Without the int() cast, it doesn't work in some cases when exporting to ONNX + prev_num_input_ids = past_key_values.get_seq_length() + num_input_ids = inputs_shape[1] + prev_num_input_ids + position_ids = torch.ones((1, 1), dtype=torch.long, device=device) * ( + int(self.padding_idx + num_input_ids) + ) + else: + if attention_mask is None: + attention_mask = torch.ones(inputs_shape, dtype=torch.long, device=device) + + # retrieve position_ids from input_ids / attention_mask + position_ids = ( + torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask + ).long() + self.padding_idx + + # make sure position_ids are not bigger then max_length + position_ids = position_ids.clamp(0, self.max_length - 1) + + return super().forward(position_ids), position_ids + + def _forward(self, position_ids): + return super().forward(position_ids) + + +class ProphetNetAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: ProphetNetConfig, num_attn_heads: int, layer_idx: int | None = None): + super().__init__() + hidden_size = config.hidden_size + + self.attention_dropout = config.attention_dropout + self.dropout = config.dropout + self.num_attn_heads = num_attn_heads + self.head_dim = hidden_size // num_attn_heads + self.layer_idx = layer_idx + + assert self.head_dim * num_attn_heads == hidden_size, ( + "`config.hidden_size` must be divisible by `config.num_encoder_attention_heads` and" + " `config.num_decoder_attention_heads`" + ) + + self.key_proj = nn.Linear(hidden_size, hidden_size) + self.value_proj = nn.Linear(hidden_size, hidden_size) + self.query_proj = nn.Linear(hidden_size, hidden_size) + + self.out_proj = nn.Linear(hidden_size, hidden_size) + + def forward( + self, + hidden_states, + key_value_states: Tensor | None = None, + attention_mask: Tensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool | None = False, + **kwargs, + ) -> tuple[Tensor, Tensor | None]: + batch_size, tgt_len, hidden_size = hidden_states.size() + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + assert list(hidden_states.size()) == [ + batch_size, + tgt_len, + hidden_size, + ], f"Size of hidden states should be {batch_size, tgt_len, hidden_size}, but is {hidden_states.size()}" + + # previous time steps are cached - no need to recompute key and value if they are static + query_states = self.query_proj(hidden_states) / (self.head_dim**0.5) + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = key_value_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.key_proj(current_states) + value_states = self.value_proj(current_states) + key_states = key_states.view(batch_size, -1, self.num_attn_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(batch_size, -1, self.num_attn_heads, self.head_dim).transpose(1, 2) + + if past_key_values is not None: + # save all key/value_states to cache to be re-used for fast auto-regressive generation + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + query_states = query_states.view(batch_size, tgt_len, self.num_attn_heads, self.head_dim).transpose(1, 2) + src_len = key_states.size(2) + + attn_weights = torch.einsum("bsij,bsjk->bsik", query_states, key_states.transpose(2, 3)) + expected_shape = (batch_size, self.num_attn_heads, tgt_len, src_len) + if attn_weights.size() != expected_shape: + raise ValueError(f"Attention weights should have size {expected_shape}, but is {attn_weights.size()}") + + # This is part of a workaround to get around fork/join parallelism not supporting Optional types. + if attention_mask is not None and attention_mask.dim() == 0: + attention_mask = None + + expected_shape = (batch_size, self.num_attn_heads, 1, src_len) + if attention_mask is not None and attention_mask.size() != expected_shape: + raise ValueError(f"Attention mask should have size {expected_shape}, but is {attention_mask.size()}") + if attention_mask is not None: # don't attend to padding symbols + attn_weights = attn_weights + attention_mask + if output_attentions: + attn_weights_reshaped = attn_weights + else: + attn_weights_reshaped = None + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_probs = nn.functional.dropout( + attn_weights, + p=self.attention_dropout, + training=self.training, + ) + attn_output = torch.einsum("bsij,bsjk->bsik", attn_probs, value_states) + expected_shape = (batch_size, self.num_attn_heads, tgt_len, self.head_dim) + if attn_output.size() != expected_shape: + raise ValueError(f"`attn_output` should have shape {expected_shape}, but is of shape {attn_output.size()}") + + attn_output = attn_output.transpose(1, 2).reshape(batch_size, tgt_len, hidden_size) + attn_output = self.out_proj(attn_output) + + attn_output = nn.functional.dropout(attn_output, p=self.dropout, training=self.training) + return attn_output, attn_weights_reshaped + + +class ProphetNetFeedForward(nn.Module): + """ + This is the residual two feed-forward layer block based on the original Transformer implementation. + """ + + def __init__(self, config: ProphetNetConfig, ffn_dim: int): + super().__init__() + self.activation_fn = ACT2FN[config.activation_function] + self.intermediate = nn.Linear(config.hidden_size, ffn_dim) + self.output = nn.Linear(ffn_dim, config.hidden_size) + self.activation_dropout = config.activation_dropout + self.dropout = config.dropout + + def forward(self, hidden_states): + hidden_states = self.intermediate(hidden_states) + hidden_states = self.activation_fn(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.output(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + return hidden_states + + +class ProphetNetNgramSelfAttention(nn.Module): + def __init__(self, config: ProphetNetConfig, layer_idx=None): + super().__init__() + self.hidden_size = config.hidden_size + + self.num_buckets = config.num_buckets + self.relative_max_distance = config.relative_max_distance + self.num_attn_heads = config.num_decoder_attention_heads + self.dropout = config.dropout + self.attention_dropout = config.attention_dropout + self.head_dim = config.hidden_size // self.num_attn_heads + self.ngram = config.ngram + self.layer_idx = layer_idx + + assert self.head_dim * self.num_attn_heads == config.hidden_size, ( + "config.hidden_size must be divisible by num_attn_heads" + ) + # key, value, query projection + self.key_proj = nn.Linear(config.hidden_size, config.hidden_size) + self.value_proj = nn.Linear(config.hidden_size, config.hidden_size) + self.query_proj = nn.Linear(config.hidden_size, config.hidden_size) + + # out projection + self.out_proj = nn.Linear(config.hidden_size, config.hidden_size) + + # rel position embeddings + self.relative_pos_embeddings = nn.Linear(config.hidden_size, self.num_buckets * self.num_attn_heads) + + # for onnx runtime + self.onnx_trace = False + + def _shape(self, tensor, seq_len, batch_size): + return tensor.view(batch_size, seq_len, self.num_attn_heads, self.head_dim).transpose(1, 2).contiguous() + + def prepare_for_onnx_export_(self): + self.onnx_trace = True + + def forward( + self, + hidden_states, + past_key_values: Cache | None = None, + attention_mask=None, + extended_predict_attention_mask=None, + main_relative_position_buckets=None, + predict_relative_position_buckets=None, + position_ids=None, + **kwargs, + ): + batch_size, ngram_sequence_length, hidden_size = hidden_states.size() + assert list(hidden_states.size()) == [batch_size, ngram_sequence_length, hidden_size], ( + f"`hidden_states` should be of shape {batch_size, ngram_sequence_length, hidden_size}, but is of shape" + f" {hidden_states.shape}" + ) + + # project + query_states = self.query_proj(hidden_states) + key_states = self.key_proj(hidden_states) + value_states = self.value_proj(hidden_states) + + # normalize + query_states = query_states / (self.head_dim**0.5) + + # reshape + query_states = self._shape(query_states, ngram_sequence_length, batch_size) + key_states = self._shape(key_states, -1, batch_size) + value_states = self._shape(value_states, -1, batch_size) + proj_shape = (batch_size, self.num_attn_heads, -1, self.head_dim) + + query_states = query_states.reshape(*proj_shape) + key_states = key_states.reshape(*proj_shape) + value_states = value_states.reshape(*proj_shape) + + # chunk into main stream and predict stream + hidden_states_list = hidden_states.chunk(1 + self.ngram, dim=1) + query_states_list = query_states.chunk(1 + self.ngram, dim=2) + key_states_list = key_states.chunk(1 + self.ngram, dim=2) + value_states_list = value_states.chunk(1 + self.ngram, dim=2) + + main_hidden_states, hidden_states_predict_list = hidden_states_list[0], hidden_states_list[1:] + main_query_states, predict_query_states_list = query_states_list[0], query_states_list[1:] + main_key_states, predict_key_states_list = key_states_list[0], key_states_list[1:] + main_value_states, predict_value_states_list = value_states_list[0], value_states_list[1:] + + # ProphetNet has two separate attention layers, one for self and one for cross attention + # We need to obtain the self attention only for this module, if `EncoderDecoderCache` + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + main_key_states, main_value_states = curr_past_key_values.update( + main_key_states, main_value_states, self.layer_idx + ) + + # get seq_length of main stream only + sequence_length = ngram_sequence_length // (1 + self.ngram) + + # MAIN-STREAM + # main attn weights + # [batch_size, number_heads, sequence_length, head_dimesion] + # x [batch_size, number_heads, head_dimesion, sequence_length] + # -> [batch_size, number_heads, sequence_length, sequence_length] + main_attn_weights = torch.einsum("bntc,bncs->bnts", main_query_states, main_key_states.transpose(2, 3)) + + # retrieve relative position embeddings for each layer -> see paper for more details + main_relative_pos_embeddings = self.get_main_relative_pos_embeddings( + main_hidden_states, main_attn_weights, position_ids, main_relative_position_buckets + ) + + main_attn_weights = main_attn_weights + main_relative_pos_embeddings + + if attention_mask is not None: + main_attn_weights = main_attn_weights + attention_mask + + main_attn_probs = softmax( + main_attn_weights, + dim=-1, + onnx_trace=self.onnx_trace, + ).type_as(main_attn_weights) + + main_attn_probs = nn.functional.dropout(main_attn_probs, p=self.attention_dropout, training=self.training) + # project to attn_output + # [batch_size, number_heads, sequence_length, sequence_length] + # x [batch_size, number_heads, sequence_length, head_dimesion] + # -> [batch_size, number_heads, sequence_length, head_dimesion] + main_attn_output = torch.einsum("bntc,bncs->bnts", main_attn_probs, main_value_states) + # reshape so that num_heads dim is merged into last `head_dim` axis + main_attn_output = main_attn_output.transpose(1, 2).reshape(batch_size, 1, sequence_length, hidden_size) + main_attn_output = self.out_proj(main_attn_output) + + # PREDICT-STREAM + # [batch_size, ngram, number_heads, sequence_length, head_dimesion] + predict_query_states = torch.stack(predict_query_states_list, 1).view( + batch_size, self.ngram, self.num_attn_heads, sequence_length, self.head_dim + ) + + # [batch_size, ngram, number_heads, 2*sequence_length, head_dimesion] + predict_key_states = torch.stack([torch.cat([main_key_states, key], 2) for key in predict_key_states_list], 1) + + # [batch_size, sequence_length, ngram, hidden_size] + predict_hidden_states = torch.stack(hidden_states_predict_list, dim=2) + + # [batch_size, number_heads, ngram, 2*sequence_length, head_dimesion] + predict_value_states = torch.cat( + [torch.cat([main_value_states, v_p], 2).unsqueeze(2) for v_p in predict_value_states_list], 2 + ) + + # [batch_size, ngram, number_heads, sequence_length, head_dimesion] + # x [batch_size, ngram, number_heads, 2*sequence_length, head_dimesion] + # -> [batch_size, ngram, number_heads, sequence_length, 2*sequence_length] + predict_attn_weights = torch.einsum("bnhtc,bnhsc->bnhts", (predict_query_states, predict_key_states)) + + # retrieve relative position embeddings for each layer -> see paper for more details + # [batch_size, ngram, number_heads, sequence_length, predict_relative_pos_embeddings] + predict_relative_pos_embeddings = self.get_predict_relative_pos_embeddings( + predict_hidden_states, predict_attn_weights, position_ids, predict_relative_position_buckets + ) + + # [batch_size, ngram, number_heads, sequence_length, 2*sequence_length] + predict_attn_weights = predict_attn_weights + predict_relative_pos_embeddings + + if extended_predict_attention_mask is not None: + # Permuting Predict attention mask to [batch_size, ngram, number_heads, sequence_length, 2*sequence_length] + extended_predict_attention_mask = extended_predict_attention_mask.permute(0, 2, 1, 3, 4) + extended_predict_attention_mask = extended_predict_attention_mask.to(predict_attn_weights.dtype) + predict_attn_weights = predict_attn_weights + extended_predict_attention_mask + + predict_attn_probs = softmax( + predict_attn_weights, + dim=-1, + onnx_trace=self.onnx_trace, + ).type_as(predict_attn_weights) + + predict_attn_probs = nn.functional.dropout( + predict_attn_probs, p=self.attention_dropout, training=self.training + ) + # project to attention output + # [batch_size, ngram, number_heads, sequence_length, 2*sequence_length] + # x [batch_size, ngram, number_heads, 2*sequence_length, head_dimesion] + # -> [batch_size, ngram, number_heads, sequence_length, head_dimesion] + predict_attn_output = torch.einsum( + "bnhts,bnhsc->bnhtc", (predict_attn_probs, predict_value_states.transpose(1, 2)) + ) + + # reshape so that num_heads dim is merged into last `head_dim` axis + # [batch_size, ngram, number_heads, sequence_length, head_dimesion] -> [batch_size, ngram, sequence_length, hidden_size] + predict_attn_output = predict_attn_output.transpose(2, 3) + predict_attn_output = predict_attn_output.reshape(batch_size, self.ngram, sequence_length, hidden_size) + predict_attn_output = self.out_proj(predict_attn_output) + + # concat to single attn output + # [batch_size, (1+ngram)*sequence_length, hidden_size] + attn_output = torch.cat([main_attn_output, predict_attn_output], 1).view(batch_size, -1, hidden_size) + # reshape into better form for `config.output_attentions` + main_attn_probs = main_attn_probs.view(batch_size, self.num_attn_heads, sequence_length, -1) + + attn_output = nn.functional.dropout(attn_output, p=self.dropout, training=self.training) + + return attn_output, main_attn_probs, predict_attn_probs + + def get_main_relative_pos_embeddings( + self, hidden_states, attn_weights, position_ids, main_relative_position_buckets + ): + # input hidden_states [batch_size, sequence_length, hidden_size] + # input attn_weights [batch_size, num_heads, sequence_length, sequence_length] + # input position_ids [batch_size, sequence_length] or [1,1] + batch_size, num_attn_heads, tgt_len, src_len = attn_weights.shape + attn_weights = attn_weights.view(batch_size, num_attn_heads, tgt_len, src_len) + if main_relative_position_buckets is None: + batch_size, sequence_length = hidden_states.shape[:2] + relative_positions = ( + torch.arange(1, attn_weights.shape[-1] + 1) + .unsqueeze(0) + .unsqueeze(0) + .repeat(batch_size, sequence_length, 1) + .to(position_ids.device) + ) + # [batch_size, sequence_length, sequence_length+1] + relative_positions = relative_positions - position_ids.unsqueeze(0).repeat(batch_size, sequence_length, 1) + main_relative_position_buckets = compute_relative_buckets( + self.num_buckets, self.relative_max_distance, relative_positions, False + ) + + # [batch_size, sequence_length, num_buckets * num_heads] + rel_pos_embeddings = self.relative_pos_embeddings(hidden_states) + rel_pos_embeddings = rel_pos_embeddings.view( + rel_pos_embeddings.shape[:2] + (self.num_buckets, self.num_attn_heads) + ) + rel_pos_embeddings = rel_pos_embeddings.permute(0, 3, 1, 2) + # [batch_size, num_heads, sequence_length, num_buckets] + rel_pos_embeddings = rel_pos_embeddings.reshape(attn_weights.shape[:3] + (-1,)) + + main_relative_position_buckets = main_relative_position_buckets.repeat(1, self.num_attn_heads, 1) + # [batch_size * num_heads * sequence_length, sequence_length] + main_relative_position_buckets = main_relative_position_buckets.view( + -1, main_relative_position_buckets.shape[-1] + ) + main_relative_position_buckets = main_relative_position_buckets.long() + # [batch_size * num_heads * sequence_length, sequence_length] + rel_pos_embeddings = rel_pos_embeddings.reshape(-1, rel_pos_embeddings.size(-1)) + + main_relative_pos_embeddings = torch.gather(rel_pos_embeddings, dim=1, index=main_relative_position_buckets) + main_relative_pos_embeddings = main_relative_pos_embeddings.view(batch_size, num_attn_heads, tgt_len, -1) + return main_relative_pos_embeddings + + def get_predict_relative_pos_embeddings( + self, hidden_states, attn_weights, position_ids, predict_relative_position_buckets + ): + # input hidden_states [batch_size, sequence_length, ngram, hidden_size] + # input attn_weights [batch_size, ngram, num_heads, sequence_length, 2*sequence_length] + # input position_ids [batch_size, sequence_length] or [1,1] + # input predict_relative_position_buckets [batch_size, sequence_length, 2*sequence_length] or None + batch_size, sequence_length = hidden_states.shape[0:2] + + if predict_relative_position_buckets is None: + key_sequence_length = attn_weights.shape[-1] + assert position_ids[0][0] == key_sequence_length - 1, ( + "`position_ids` are incorrect. They should be of the format 1 2 3 4 5 ... (key_sequence_length - 1)" + ) + relative_positions = ( + torch.arange(0, key_sequence_length) + .unsqueeze(0) + .unsqueeze(0) + .repeat(batch_size, sequence_length, 1) + .to(position_ids.device) + ) + + relative_positions = relative_positions - position_ids.unsqueeze(0).repeat(batch_size, sequence_length, 1) + predict_relative_position_buckets = compute_relative_buckets( + self.num_buckets, self.relative_max_distance, relative_positions, False + ) + + # [batch_size, ngram, sequence_length, hidden_size] + hidden_states = hidden_states.transpose(1, 2) + rel_pos_embeddings = self.relative_pos_embeddings(hidden_states) + + # [batch_size, ngram, sequence_length, num_buckets, num_heads] + rel_pos_embeddings = rel_pos_embeddings.view( + hidden_states.shape[:-1] + (self.num_buckets, self.num_attn_heads) + ) + rel_pos_embeddings = rel_pos_embeddings.permute(0, 2, 1, 4, 3) + # [batch_size * ngram * sequence_length * num_heads, num_buckets] + rel_pos_embeddings = rel_pos_embeddings.reshape(-1, self.num_buckets) + # [ngram, batch_size, num_heads * sequence_length, -1] + predict_relative_position_buckets = predict_relative_position_buckets.unsqueeze(0) + predict_relative_position_buckets = predict_relative_position_buckets.repeat( + self.ngram, 1, self.num_attn_heads, 1 + ) + # [ngram * batch_size * num_heads * sequence_length, -1] + predict_relative_position_buckets = predict_relative_position_buckets.view( + -1, predict_relative_position_buckets.size(-1) + ).long() + + predict_relative_pos_embeddings = torch.gather( + rel_pos_embeddings, dim=1, index=predict_relative_position_buckets + ) + + # [batch_size, gram, num_heads, sequence_length, -1] + predict_relative_pos_embeddings = predict_relative_pos_embeddings.view( + batch_size, self.ngram, self.num_attn_heads, sequence_length, -1 + ) + + return predict_relative_pos_embeddings + + +class ProphetNetEncoderLayer(GradientCheckpointingLayer): + """ + Encoder block for Prophetnet + """ + + def __init__(self, config: ProphetNetConfig): + super().__init__() + # 1st residual block + self.self_attn = ProphetNetAttention(config, config.num_encoder_attention_heads) + self.self_attn_layer_norm = LayerNorm(config.hidden_size) + + # 2nd residual block + self.feed_forward = ProphetNetFeedForward(config, config.encoder_ffn_dim) + self.feed_forward_layer_norm = LayerNorm(config.hidden_size) + + def forward( + self, + hidden_states, + attention_mask, + output_attentions: bool = False, + ): + # 1st residual block + attention_output, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.self_attn_layer_norm(attention_output + hidden_states) + + # 2nd residual block + feed_forward_output = self.feed_forward(hidden_states) + hidden_states = self.feed_forward_layer_norm(feed_forward_output + hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class ProphetNetDecoderLayer(GradientCheckpointingLayer): + """ + Decoder block for Prophetnet + """ + + def __init__(self, config: ProphetNetConfig, layer_idx=None): + super().__init__() + # 1st residual block + self.self_attn = ProphetNetNgramSelfAttention(config, layer_idx=layer_idx) + self.self_attn_layer_norm = LayerNorm(config.hidden_size) + + # 2nd residual block + if config.add_cross_attention: + self.cross_attn = ProphetNetAttention(config, config.num_decoder_attention_heads, layer_idx=layer_idx) + self.cross_attn_layer_norm = LayerNorm(config.hidden_size) + + # 3rd residual block + self.feed_forward = ProphetNetFeedForward(config, config.decoder_ffn_dim) + self.feed_forward_layer_norm = LayerNorm(config.hidden_size) + + def forward( + self, + hidden_states, + attention_mask=None, + encoder_hidden_states=None, + encoder_attn_mask=None, + extended_predict_attention_mask=None, + main_relative_position_buckets=None, + predict_relative_position_buckets=None, + position_ids=None, + past_key_values=None, + use_cache: bool | None = True, + output_attentions: bool | None = False, + **kwargs, + ): + # 1st residual block + ngram_attention_output, self_attn_weights, self_attn_weights_ngram = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + extended_predict_attention_mask=extended_predict_attention_mask, + main_relative_position_buckets=main_relative_position_buckets, + predict_relative_position_buckets=predict_relative_position_buckets, + position_ids=position_ids, + ) + hidden_states = self.self_attn_layer_norm(hidden_states + ngram_attention_output) + + cross_attn_weights = None + if encoder_hidden_states is not None: + # 2nd residual block + attention_output, cross_attn_weights = self.cross_attn( + hidden_states=hidden_states, + key_value_states=encoder_hidden_states, + attention_mask=encoder_attn_mask, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + hidden_states = self.cross_attn_layer_norm(attention_output + hidden_states) + + # 3rd residual block + feed_forward_output = self.feed_forward(hidden_states) + hidden_states = self.feed_forward_layer_norm(feed_forward_output + hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, self_attn_weights_ngram, cross_attn_weights) + + return outputs + + +@auto_docstring( + custom_intro=""" + The standalone encoder part of the ProphetNetModel. + """ +) +class ProphetNetEncoder(ProphetNetPreTrainedModel): + def __init__(self, config: ProphetNetConfig): + super().__init__(config) + + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = ProphetNetPositionalEmbeddings(config) + self.embeddings_layer_norm = LayerNorm(config.hidden_size) + + self.layers = nn.ModuleList([ProphetNetEncoderLayer(config) for _ in range(config.num_encoder_layers)]) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.word_embeddings + + def set_input_embeddings(self, value): + self.word_embeddings = value + + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, ProphetNetEncoder + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased") + >>> model = ProphetNetEncoder.from_pretrained("patrickvonplaten/prophetnet-large-uncased-standalone") + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + ```""" + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_ids is None and inputs_embeds is None: + raise ValueError("Either input_ids or inputs_embeds has to be passed.") + elif input_ids is not None and inputs_embeds is not None: + raise ValueError("Make sure to only pass input_ids or inputs_embeds.") + elif input_ids is not None and inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + # prepare attention mask + if attention_mask is not None: + extended_attention_mask = ( + 1.0 - attention_mask[:, None, None, :].repeat(1, self.config.num_encoder_attention_heads, 1, 1) + ) * torch.finfo(self.dtype).min + extended_attention_mask = extended_attention_mask.to(inputs_embeds.dtype) + else: + extended_attention_mask = None + + position_embeddings, position_ids = self.position_embeddings(inputs_embeds.shape[:2], inputs_embeds.device) + + hidden_states = inputs_embeds + position_embeddings + hidden_states = self.embeddings_layer_norm(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.config.dropout, training=self.training) + + encoder_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_hidden_states = encoder_hidden_states + (hidden_states,) + + layer_outputs = encoder_layer( + hidden_states, + attention_mask=extended_attention_mask, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_hidden_states = encoder_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_hidden_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_hidden_states, attentions=all_attentions + ) + + +@auto_docstring( + custom_intro=""" + The standalone decoder part of the ProphetNetModel. + """ +) +class ProphetNetDecoder(ProphetNetPreTrainedModel): + def __init__(self, config: ProphetNetConfig): + super().__init__(config) + + self.ngram = config.ngram + self.num_buckets = config.num_buckets + self.relative_max_distance = config.relative_max_distance + self.dropout = config.dropout + self.max_target_positions = config.max_position_embeddings + + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = ProphetNetPositionalEmbeddings(config) + + self.ngram_embeddings = nn.Embedding(self.ngram, config.hidden_size, None) + self.layers = nn.ModuleList( + [ProphetNetDecoderLayer(config, layer_idx=i) for i in range(config.num_decoder_layers)] + ) + self.embeddings_layer_norm = LayerNorm(config.hidden_size) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.word_embeddings + + def set_input_embeddings(self, value): + self.word_embeddings = value + + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | ProphetNetDecoderModelOutput: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, ProphetNetDecoder + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased") + >>> model = ProphetNetDecoder.from_pretrained("microsoft/prophetnet-large-uncased", add_cross_attention=False) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + ```""" + use_cache = use_cache if use_cache is not None else self.config.use_cache + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_ids is None and inputs_embeds is None: + raise ValueError("Either `decoder_input_ids` or `decoder_inputs_embeds` has to be passed.") + elif input_ids is not None and inputs_embeds is not None: + raise ValueError("Make sure to only pass `decoder_input_ids` or `decoder_inputs_embeds`.") + elif input_ids is not None and inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + batch_size, sequence_length = inputs_embeds.shape[:2] + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if use_cache and past_key_values is None: + past_key_values = ( + EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + if encoder_hidden_states is not None or self.config.is_encoder_decoder + else DynamicCache(config=self.config) + ) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + main_stream_pos_embed, position_ids = self.position_embeddings( + (batch_size, sequence_length), + device=inputs_embeds.device, + past_key_values=past_key_values, + ) + + if past_key_values_length != 0: + main_relative_position_buckets, predict_relative_position_buckets = None, None + else: + ( + main_relative_position_buckets, + predict_relative_position_buckets, + ) = self.compute_buffered_relative_buckets(position_ids) + predicting_stream_pos_embed = self.position_embeddings._forward(position_ids + 1) + + # add position embeddings + hidden_states = inputs_embeds + main_stream_pos_embed + + ngram_embeddings = self.ngram_embeddings.weight + + # prepare attention mask + if past_key_values_length != 0: + assert hidden_states.size(1) == 1, ( + "At the moment `use_cache` is only supported for `decoder_input_ids` of length 1" + ) + + ngram_hidden_states = [ + (ngram_embeddings[ngram - 1] + predicting_stream_pos_embed).repeat(batch_size, 1, 1) + for ngram in range(self.ngram) + ] + extended_attention_mask = None + extended_predict_attention_mask = None + else: + ngram_hidden_states = [ + (ngram_embeddings[ngram - 1] + predicting_stream_pos_embed) for ngram in range(self.ngram) + ] + extended_attention_mask = self.prepare_attention_mask(hidden_states, attention_mask) + extended_predict_attention_mask = self.prepare_predict_attention_mask(hidden_states, attention_mask) + + # prepare encoder attention mask + if encoder_attention_mask is not None: + extended_encoder_attention_mask = ( + 1.0 - encoder_attention_mask[:, None, None, :].repeat(1, self.config.num_decoder_attention_heads, 1, 1) + ) * torch.finfo(self.dtype).min + extended_encoder_attention_mask = extended_encoder_attention_mask.to(inputs_embeds.dtype) + else: + extended_encoder_attention_mask = None + + hidden_states = torch.cat([hidden_states] + ngram_hidden_states, 1) + + if self.embeddings_layer_norm: + hidden_states = self.embeddings_layer_norm(hidden_states) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # init attentions, hidden_states and cache with empty tuples + all_main_stream_hidden_states = () if output_hidden_states else None + all_ngram_stream_hidden_states = () if output_hidden_states and self.config.ngram > 0 else None + + all_main_stream_attns = () if output_attentions else None + all_ngram_stream_attns = () if output_attentions else None + all_cross_attns = () if output_attentions and self.config.add_cross_attention else None + + for idx, decoder_layer in enumerate(self.layers): + if output_hidden_states: + # grad cannot be kept because tensor is sliced + all_main_stream_hidden_states += (hidden_states[:, :sequence_length],) + if self.config.ngram > 0: + all_ngram_stream_hidden_states += (hidden_states[:, sequence_length:],) + + layer_outputs = decoder_layer( + hidden_states, + extended_attention_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attn_mask=extended_encoder_attention_mask, + extended_predict_attention_mask=extended_predict_attention_mask, + main_relative_position_buckets=main_relative_position_buckets, + predict_relative_position_buckets=predict_relative_position_buckets, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + if output_attentions: + all_main_stream_attns += (layer_outputs[1],) + all_ngram_stream_attns += (layer_outputs[2],) + + if self.config.add_cross_attention: + all_cross_attns += (layer_outputs[3],) + + if output_hidden_states: + all_main_stream_hidden_states += (hidden_states[:, :sequence_length],) + if self.config.ngram > 0: + all_ngram_stream_hidden_states += (hidden_states[:, sequence_length:],) + + # split last_hidden_state for return + last_hidden_state = hidden_states[:, :sequence_length] + last_hidden_state_ngram = hidden_states[:, sequence_length:] if self.config.ngram > 0 else None + + if not return_dict: + return tuple( + v + for v in [ + last_hidden_state, + last_hidden_state_ngram, + past_key_values, + all_main_stream_hidden_states, + all_ngram_stream_hidden_states, + all_main_stream_attns, + all_ngram_stream_attns, + all_cross_attns, + ] + if v is not None + ) + return ProphetNetDecoderModelOutput( + last_hidden_state=last_hidden_state, + last_hidden_state_ngram=last_hidden_state_ngram, + past_key_values=past_key_values, + hidden_states=all_main_stream_hidden_states, + hidden_states_ngram=all_ngram_stream_hidden_states, + attentions=all_main_stream_attns, + ngram_attentions=all_ngram_stream_attns, + cross_attentions=all_cross_attns, + ) + + def compute_buffered_relative_buckets(self, position_ids): + batch_size, sequence_length = position_ids.shape + + position_ids = torch.arange(1, self.max_target_positions).to(position_ids.device).repeat(1, 1) + main_relative_buckets, predict_relative_buckets = compute_all_stream_relative_buckets( + self.num_buckets, self.relative_max_distance, position_ids + ) + + # buffer relative buckets + main_relative_buckets = main_relative_buckets[:, :sequence_length, :sequence_length].repeat(batch_size, 1, 1) + predict_relative_buckets = torch.cat( + [ + predict_relative_buckets[:, :sequence_length, :sequence_length], + predict_relative_buckets[ + :, :sequence_length, self.max_target_positions : self.max_target_positions + sequence_length + ], + ], + 2, + ).repeat(batch_size, 1, 1) + + return main_relative_buckets, predict_relative_buckets + + def prepare_attention_mask(self, hidden_states, attention_mask): + batch_size, seq_length = hidden_states.shape[:2] + + # get causal mask + causal_mask = torch.full( + (seq_length, seq_length), + torch.finfo(hidden_states.dtype).min, + dtype=hidden_states.dtype, + device=hidden_states.device, + ) + causal_mask = torch.triu(causal_mask, 1) + + extended_causal_mask = causal_mask[:seq_length, :seq_length][None, None, :, :].expand( + (batch_size, self.config.num_decoder_attention_heads) + causal_mask.shape + ) + + # add usual attention mask + if attention_mask is not None: + extended_attention_mask = (1.0 - attention_mask[:, None, None, :]) * torch.finfo(self.dtype).min + extended_attention_mask = extended_causal_mask + extended_attention_mask + else: + extended_attention_mask = extended_causal_mask + return extended_attention_mask.to(hidden_states.dtype) + + def prepare_predict_attention_mask(self, hidden_states, attention_mask): + batch_size, seq_length = hidden_states.shape[:2] + + # get causal mask + predict_causal_mask = ngram_attention_bias( + self.max_target_positions, self.ngram, hidden_states.device, hidden_states.dtype + ) + predict_causal_mask = torch.cat( + [ + predict_causal_mask[:, :seq_length, :seq_length], + predict_causal_mask[ + :, :seq_length, self.max_target_positions : self.max_target_positions + seq_length + ], + ], + dim=-1, + ) + extended_predict_causal_mask = predict_causal_mask[None, None, :, :, :].expand( + (batch_size, self.config.num_decoder_attention_heads) + predict_causal_mask.shape + ) + + # add usual attention mask + if attention_mask is not None: + extended_attention_mask = (1.0 - attention_mask[:, None, None, None, :]) * torch.finfo(self.dtype).min + extended_attention_mask = extended_attention_mask.expand( + (batch_size, self.config.num_decoder_attention_heads, self.ngram, seq_length, seq_length) + ) + # predicted stream attention_mask should always be 0 + extended_attention_mask = torch.cat( + [extended_attention_mask, torch.zeros_like(extended_attention_mask)], dim=-1 + ) + extended_predict_attention_mask = extended_predict_causal_mask + extended_attention_mask + else: + extended_predict_attention_mask = extended_predict_causal_mask + return extended_predict_attention_mask.to(hidden_states.dtype) + + +@auto_docstring +class ProphetNetModel(ProphetNetPreTrainedModel): + _tied_weights_keys = { + "encoder.word_embeddings.weight": "word_embeddings.weight", + "decoder.word_embeddings.weight": "word_embeddings.weight", + } + + def __init__(self, config: ProphetNetConfig): + super().__init__(config) + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + + encoder_config = copy.deepcopy(config) + encoder_config.use_cache = False + self.encoder = ProphetNetEncoder(encoder_config) + + decoder_config = copy.deepcopy(config) + decoder_config.is_decoder = True + self.decoder = ProphetNetDecoder(decoder_config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.word_embeddings + + def set_input_embeddings(self, value): + self.word_embeddings = value + self.encoder.word_embeddings = self.word_embeddings + self.decoder.word_embeddings = self.word_embeddings + + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.Tensor | None = None, + decoder_attention_mask: torch.BoolTensor | None = None, + encoder_outputs: tuple | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + decoder_inputs_embeds: torch.Tensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | ProphetNetSeq2SeqModelOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + ProphetNet uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + Example: + + ```python + >>> from transformers import AutoTokenizer, ProphetNetModel + + >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased") + >>> model = ProphetNetModel.from_pretrained("microsoft/prophetnet-large-uncased") + + >>> input_ids = tokenizer( + ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" + ... ).input_ids # Batch size 1 + >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 + >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) + + >>> last_hidden_states = outputs.last_hidden_state # main stream hidden states + >>> last_hidden_states_ngram = outputs.last_hidden_state_ngram # predict hidden states + ```""" + use_cache = use_cache if use_cache is not None else self.config.use_cache + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + use_cache=use_cache, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + return ProphetNetSeq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + last_hidden_state_ngram=decoder_outputs.last_hidden_state_ngram, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_ngram_hidden_states=decoder_outputs.hidden_states_ngram, + decoder_attentions=decoder_outputs.attentions, + decoder_ngram_attentions=decoder_outputs.ngram_attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + The ProphetNet Model with a language modeling head. Can be used for sequence generation tasks. + """ +) +class ProphetNetForConditionalGeneration(ProphetNetPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "lm_head.weight": "prophetnet.word_embeddings.weight", + } + + def __init__(self, config: ProphetNetConfig): + super().__init__(config) + self.prophetnet = ProphetNetModel(config) + self.padding_idx = config.pad_token_id + self.disable_ngram_loss = config.disable_ngram_loss + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.prophetnet.word_embeddings + + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.Tensor | None = None, + decoder_attention_mask: torch.BoolTensor | None = None, + encoder_outputs: torch.Tensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + decoder_inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | ProphetNetSeq2SeqLMOutput: + r""" + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + ProphetNet uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If + `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see + `past_key_values`). + decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ..., + config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for + labels in `[0, ..., config.vocab_size]` + + Example: + + ```python + >>> from transformers import AutoTokenizer, ProphetNetForConditionalGeneration + + >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased") + >>> model = ProphetNetForConditionalGeneration.from_pretrained("microsoft/prophetnet-large-uncased") + + >>> input_ids = tokenizer( + ... "Studies have been shown that owning a dog is good for you", return_tensors="pt" + ... ).input_ids # Batch size 1 + >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 + >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids) + + >>> logits_next_token = outputs.logits # logits to predict next token as usual + >>> logits_ngram_next_tokens = outputs.logits_ngram # logits to predict 2nd, 3rd, ... next tokens + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: + # get decoder inputs from shifting lm labels to the right + decoder_input_ids = self._shift_right(labels) + + outputs = self.prophetnet( + input_ids=input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + decoder_attention_mask=decoder_attention_mask, + encoder_outputs=encoder_outputs, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + batch_size, sequence_length = ( + decoder_input_ids.shape if decoder_input_ids is not None else decoder_inputs_embeds.shape[:2] + ) + + predicting_streams = outputs[1].view(batch_size, self.config.ngram, sequence_length, -1) + predict_logits = self.lm_head(predicting_streams) + + logits = predict_logits[:, 0] + logits_ngram = predict_logits[:, 1:] if self.config.ngram > 1 else None + + # To use .view in loss computation, make sure that logits is contiguous. + if not logits.is_contiguous(): + logits = logits.contiguous() + + loss = None + if labels is not None: + loss = self._compute_loss(predict_logits, labels) + + if not return_dict: + all_logits = tuple(v for v in [logits, logits_ngram] if v is not None) + return (loss,) + all_logits + outputs[2:] if loss is not None else all_logits + outputs[2:] + else: + return ProphetNetSeq2SeqLMOutput( + loss=loss, + logits=logits, + logits_ngram=logits_ngram, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_ngram_hidden_states=outputs.decoder_ngram_hidden_states, + decoder_attentions=outputs.decoder_attentions, + decoder_ngram_attentions=outputs.decoder_ngram_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def _compute_loss(self, logits, labels, ignore_index=-100): + expend_targets = labels.new_zeros(self.config.ngram, labels.size(0), labels.size(1)).fill_(ignore_index) + + for i in range(self.config.ngram): + if i > 0 and self.disable_ngram_loss: + break + expend_targets[i, :, :] = labels + + logits = logits.transpose(0, 1).contiguous() + lprobs = nn.functional.log_softmax( + logits.view(-1, logits.size(-1)), + dim=-1, + dtype=torch.float32, + ) + + loss = nn.functional.nll_loss(lprobs, expend_targets.view(-1), reduction="mean") + + if self.config.eps > 0.0: + smooth_loss = -lprobs.sum(dim=-1, keepdim=True) + non_masked_tokens = expend_targets.ne(ignore_index).view(-1) + smooth_loss = smooth_loss[non_masked_tokens] + smooth_loss = smooth_loss.mean() + + eps_i = self.config.eps / lprobs.size(-1) + loss = (1.0 - self.config.eps) * loss + eps_i * smooth_loss + + return loss + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return self._shift_right(labels) + + def get_encoder(self, modality=None): + if modality is None: + return self.prophetnet.encoder + else: + return super().get_encoder(modality=modality) + + +@auto_docstring( + custom_intro=""" + The standalone decoder part of the ProphetNetModel with a lm head on top. The model can be used for causal + """ +) +class ProphetNetForCausalLM(ProphetNetPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "lm_head.weight": "prophetnet.word_embeddings.weight", + "prophetnet.decoder.word_embeddings.weight": "prophetnet.word_embeddings.weight", + } + + def __init__(self, config: ProphetNetConfig): + # set config for CLM + config = copy.deepcopy(config) + config.is_decoder = True + config.is_encoder_decoder = False + super().__init__(config) + self.prophetnet = ProphetNetDecoderWrapper(config) + + self.padding_idx = config.pad_token_id + self.disable_ngram_loss = config.disable_ngram_loss + + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.prophetnet.decoder.word_embeddings + + def set_input_embeddings(self, value): + self.prophetnet.decoder.word_embeddings = value + + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | ProphetNetDecoderLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]` + + Example: + + ```python + >>> from transformers import AutoTokenizer, ProphetNetForCausalLM + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased") + >>> model = ProphetNetForCausalLM.from_pretrained("microsoft/prophetnet-large-uncased") + >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + + >>> # Model can also be used with EncoderDecoder framework + >>> from transformers import BertTokenizer, EncoderDecoderModel, AutoTokenizer + >>> import torch + + >>> tokenizer_enc = BertTokenizer.from_pretrained("google-bert/bert-large-uncased") + >>> tokenizer_dec = AutoTokenizer.from_pretrained("microsoft/prophetnet-large-uncased") + >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained( + ... "google-bert/bert-large-uncased", "microsoft/prophetnet-large-uncased" + ... ) + + >>> ARTICLE = ( + ... "the us state department said wednesday it had received no " + ... "formal word from bolivia that it was expelling the us ambassador there " + ... "but said the charges made against him are `` baseless ." + ... ) + >>> input_ids = tokenizer_enc(ARTICLE, return_tensors="pt").input_ids + >>> labels = tokenizer_dec( + ... "us rejects charges against its ambassador in bolivia", return_tensors="pt" + ... ).input_ids + >>> outputs = model(input_ids=input_ids, decoder_input_ids=labels[:, :-1], labels=labels[:, 1:]) + + >>> loss = outputs.loss + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + outputs = self.prophetnet.decoder( + input_ids=input_ids, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + batch_size, sequence_length = input_ids.shape if input_ids is not None else inputs_embeds.shape[:2] + + predicting_streams = outputs[1].view(batch_size, self.config.ngram, sequence_length, -1) + predict_logits = self.lm_head(predicting_streams) + + logits = predict_logits[:, 0] + logits_ngram = predict_logits[:, 1:] if self.config.ngram > 1 else None + + loss = None + if labels is not None: + loss = self._compute_loss(predict_logits, labels) + + if not return_dict: + all_logits = tuple(v for v in [logits, logits_ngram] if v is not None) + return (loss,) + all_logits + outputs[2:] if loss is not None else all_logits + outputs[2:] + else: + return ProphetNetDecoderLMOutput( + loss=loss, + logits=logits, + logits_ngram=logits_ngram, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + hidden_states_ngram=outputs.hidden_states_ngram, + attentions=outputs.attentions, + ngram_attentions=outputs.ngram_attentions, + cross_attentions=outputs.cross_attentions, + ) + + def _compute_loss(self, logits, labels, ignore_index=-100): + expend_targets = labels.new_zeros(self.config.ngram, labels.size(0), labels.size(1)).fill_(ignore_index) + + for i in range(self.config.ngram): + if i > 0 and self.disable_ngram_loss: + break + expend_targets[i, :, :] = labels + + logits = logits.transpose(0, 1).contiguous() + lprobs = nn.functional.log_softmax( + logits.view(-1, logits.size(-1)), + dim=-1, + dtype=torch.float32, + ) + + loss = nn.functional.nll_loss(lprobs, expend_targets.view(-1), reduction="mean") + + if self.config.eps > 0.0: + smooth_loss = -lprobs.sum(dim=-1, keepdim=True) + non_masked_tokens = expend_targets.ne(ignore_index).view(-1) + smooth_loss = smooth_loss[non_masked_tokens] + smooth_loss = smooth_loss.mean() + + eps_i = self.config.eps / lprobs.size(-1) + loss = (1.0 - self.config.eps) * loss + eps_i * smooth_loss + + return loss + + +class ProphetNetDecoderWrapper(ProphetNetPreTrainedModel): + """ + This is a wrapper class, so that [`ProphetNetForCausalLM`] can correctly be loaded from pretrained prophetnet + classes. + """ + + _tied_weights_keys = { + "decoder.word_embeddings.weight": "word_embeddings.weight", + } + + def __init__(self, config: ProphetNetConfig): + super().__init__(config) + + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.decoder = ProphetNetDecoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def forward(self, *args, **kwargs): + return self.decoder(*args, **kwargs) + + +__all__ = [ + "ProphetNetDecoder", + "ProphetNetEncoder", + "ProphetNetForCausalLM", + "ProphetNetForConditionalGeneration", + "ProphetNetModel", + "ProphetNetPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/tokenization_prophetnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/tokenization_prophetnet.py new file mode 100644 index 0000000000000000000000000000000000000000..b19c414e72d11e3b54b248fc2be7d16093eb419d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/prophetnet/tokenization_prophetnet.py @@ -0,0 +1,475 @@ +# Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import collections +import os +import unicodedata +from collections.abc import Iterable + +from ...tokenization_python import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "prophetnet.tokenizer"} + + +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a piece of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +class BasicTokenizer: + """ + Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). + + Args: + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + never_split (`Iterable`, *optional*): + Collection of tokens which will never be split during tokenization. Only has an effect when + `do_basic_tokenize=True` + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. + + This should likely be deactivated for Japanese (see this + [issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original BERT). + do_split_on_punc (`bool`, *optional*, defaults to `True`): + In some instances we want to skip the basic punctuation splitting so that later tokenization can capture + the full context of the words, such as contractions. + """ + + def __init__( + self, + do_lower_case=True, + never_split=None, + tokenize_chinese_chars=True, + strip_accents=None, + do_split_on_punc=True, + ): + if never_split is None: + never_split = [] + self.do_lower_case = do_lower_case + self.never_split = set(never_split) + self.tokenize_chinese_chars = tokenize_chinese_chars + self.strip_accents = strip_accents + self.do_split_on_punc = do_split_on_punc + + def tokenize(self, text, never_split=None): + """ + Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer. + + Args: + never_split (`List[str]`, *optional*) + Kept for backward compatibility purposes. Now implemented directly at the base class level (see + [`PreTrainedTokenizer.tokenize`]) List of token not to split. + """ + # union() returns a new set by concatenating the two sets. + never_split = self.never_split.union(set(never_split)) if never_split else self.never_split + text = self._clean_text(text) + + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + if self.tokenize_chinese_chars: + text = self._tokenize_chinese_chars(text) + # prevents treating the same character with different unicode codepoints as different characters + unicode_normalized_text = unicodedata.normalize("NFC", text) + orig_tokens = whitespace_tokenize(unicode_normalized_text) + split_tokens = [] + for token in orig_tokens: + if token not in never_split: + if self.do_lower_case: + token = token.lower() + if self.strip_accents is not False: + token = self._run_strip_accents(token) + elif self.strip_accents: + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token, never_split)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text, never_split=None): + """Splits punctuation on a piece of text.""" + if not self.do_split_on_punc or (never_split is not None and text in never_split): + return [text] + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ( + (cp >= 0x4E00 and cp <= 0x9FFF) + or (cp >= 0x3400 and cp <= 0x4DBF) + or (cp >= 0x20000 and cp <= 0x2A6DF) + or (cp >= 0x2A700 and cp <= 0x2B73F) + or (cp >= 0x2B740 and cp <= 0x2B81F) + or (cp >= 0x2B820 and cp <= 0x2CEAF) + or (cp >= 0xF900 and cp <= 0xFAFF) + or (cp >= 0x2F800 and cp <= 0x2FA1F) + ): + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xFFFD or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +class WordpieceTokenizer: + """Runs WordPiece tokenization.""" + + def __init__(self, vocab, unk_token, max_input_chars_per_word=100): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + + def tokenize(self, text): + """ + Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform + tokenization using the given vocabulary. + + For example, `input = "unaffable"` will return as output `["un", "##aff", "##able"]`. + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through *BasicTokenizer*. + + Returns: + A list of wordpiece tokens. + """ + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens + + +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + with open(vocab_file, "r", encoding="utf-8") as reader: + tokens = reader.readlines() + for index, token in enumerate(tokens): + token = token.rstrip("\n") + vocab[token] = index + return vocab + + +class ProphetNetTokenizer(PreTrainedTokenizer): + r""" + Construct a ProphetNetTokenizer. Based on WordPiece. + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + File containing the vocabulary. + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + do_basic_tokenize (`bool`, *optional*, defaults to `True`): + Whether or not to do basic tokenization before WordPiece. + never_split (`Iterable`, *optional*): + Collection of tokens which will never be split during tokenization. Only has an effect when + `do_basic_tokenize=True` + unk_token (`str`, *optional*, defaults to `"[UNK]"`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + sep_token (`str`, *optional*, defaults to `"[SEP]"`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + x_sep_token (`str`, *optional*, defaults to `"[X_SEP]"`): + Special second separator token, which can be generated by [`ProphetNetForConditionalGeneration`]. It is + used to separate bullet-point like sentences in summarization, *e.g.*. + pad_token (`str`, *optional*, defaults to `"[PAD]"`): + The token used for padding, for example when batching sequences of different lengths. + mask_token (`str`, *optional*, defaults to `"[MASK]"`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. + + This should likely be deactivated for Japanese (see this + [issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original BERT). + clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`): + Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like + extra spaces. + """ + + vocab_files_names = VOCAB_FILES_NAMES + + # first name has to correspond to main model input name + # to make sure `tokenizer.pad(...)` works correctly + # `ProphetNet` doesn't have `token_type_ids` as argument. + model_input_names: list[str] = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file: str, + do_lower_case: bool | None = True, + do_basic_tokenize: bool | None = True, + never_split: Iterable | None = None, + unk_token: str | None = "[UNK]", + sep_token: str | None = "[SEP]", + x_sep_token: str | None = "[X_SEP]", + pad_token: str | None = "[PAD]", + mask_token: str | None = "[MASK]", + tokenize_chinese_chars: bool | None = True, + strip_accents: bool | None = None, + clean_up_tokenization_spaces: bool = True, + **kwargs, + ): + if not os.path.isfile(vocab_file): + raise ValueError( + f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained" + " model use `tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" + ) + self.vocab = load_vocab(vocab_file) + self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) + self.do_basic_tokenize = do_basic_tokenize + if do_basic_tokenize: + self.basic_tokenizer = BasicTokenizer( + do_lower_case=do_lower_case, + never_split=never_split, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + ) + self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token)) + + super().__init__( + do_lower_case=do_lower_case, + do_basic_tokenize=do_basic_tokenize, + never_split=never_split, + unk_token=unk_token, + sep_token=sep_token, + x_sep_token=x_sep_token, + pad_token=pad_token, + mask_token=mask_token, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + @property + def vocab_size(self): + return len(self.vocab) + + def get_vocab(self): + return dict(self.vocab, **self.added_tokens_encoder) + + def _tokenize(self, text): + split_tokens = [] + if self.do_basic_tokenize: + for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens): + # If the token is part of the never_split set + if token in self.basic_tokenizer.never_split: + split_tokens.append(token) + else: + split_tokens += self.wordpiece_tokenizer.tokenize(token) + else: + split_tokens = self.wordpiece_tokenizer.tokenize(text) + return split_tokens + + def _convert_token_to_id(self, token: str): + """Converts a token (str) in an id using the vocab.""" + return self.vocab.get(token, self.vocab.get(self.unk_token)) + + def _convert_id_to_token(self, index: int): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.ids_to_tokens.get(index, self.unk_token) + + def convert_tokens_to_string(self, tokens: str): + """Converts a sequence of tokens (string) in a single string.""" + out_string = " ".join(tokens).replace(" ##", "").strip() + return out_string + + def get_special_tokens_mask( + self, + token_ids_0: list[int], + token_ids_1: list[int] | None = None, + already_has_special_tokens: bool | None = False, + ) -> list[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + if token_ids_1 is None: + return ([0] * len(token_ids_0)) + [1] + return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + index = 0 + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + else: + vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory + with open(vocab_file, "w", encoding="utf-8") as writer: + for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning( + f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." + " Please check that the vocabulary is not corrupted!" + ) + index = token_index + writer.write(token + "\n") + index += 1 + return (vocab_file,) + + def build_inputs_with_special_tokens( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + if token_ids_1 is None: + return token_ids_0 + [self.sep_token_id] + sep = [self.sep_token_id] + return token_ids_0 + sep + token_ids_1 + sep + + +__all__ = ["ProphetNetTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..126ff90f035affc1b6c96ad477e6841f0f656f5a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pvt import * + from .image_processing_pil_pvt import * + from .image_processing_pvt import * + from .modeling_pvt import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/configuration_pvt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/configuration_pvt.py new file mode 100644 index 0000000000000000000000000000000000000000..f38937c8ae4aca7ba63da2c2e70ea68841ca19ee --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/configuration_pvt.py @@ -0,0 +1,83 @@ +# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, +# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Pvt model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="Xrenya/pvt-tiny-224") +@strict +class PvtConfig(PreTrainedConfig): + r""" + num_encoder_blocks (`int`, *optional*, defaults to 4): + The number of encoder blocks (i.e. stages in the Mix Transformer encoder). + depths (`list[int]`, *optional*, defaults to `[2, 2, 2, 2]`): + The number of layers in each encoder block. + sequence_reduction_ratios (`list[int]`, *optional*, defaults to `[8, 4, 2, 1]`): + Sequence reduction ratios in each encoder block. + patch_sizes (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`): + Patch size before each encoder block. + strides (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`): + Stride before each encoder block. + num_attention_heads (`list[int]`, *optional*, defaults to `[1, 2, 5, 8]`): + Number of attention heads for each attention layer in each block of the Transformer encoder. + mlp_ratios (`list[int]`, *optional*, defaults to `[8, 8, 4, 4]`): + Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the + encoder blocks. + num_labels ('int', *optional*, defaults to 1000): + The number of classes. + + Example: + + ```python + >>> from transformers import PvtModel, PvtConfig + + >>> # Initializing a PVT Xrenya/pvt-tiny-224 style configuration + >>> configuration = PvtConfig() + + >>> # Initializing a model from the Xrenya/pvt-tiny-224 style configuration + >>> model = PvtModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pvt" + + image_size: int | list[int] | tuple[int, int] = 224 + num_channels: int = 3 + num_encoder_blocks: int = 4 + depths: list[int] | tuple[int, ...] = (2, 2, 2, 2) + sequence_reduction_ratios: list[int] | tuple[int, ...] = (8, 4, 2, 1) + hidden_sizes: list[int] | tuple[int, ...] = (64, 128, 320, 512) + patch_sizes: list[int] | tuple[int, ...] = (4, 2, 2, 2) + strides: list[int] | tuple[int, ...] = (4, 2, 2, 2) + num_attention_heads: list[int] | tuple[int, ...] = (1, 2, 5, 8) + mlp_ratios: list[int] | tuple[int, ...] = (8, 8, 4, 4) + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.0 + attention_probs_dropout_prob: float | int = 0.0 + initializer_range: float = 0.02 + drop_path_rate: float | int = 0.0 + layer_norm_eps: float = 1e-6 + qkv_bias: bool = True + num_labels: int = 1000 + + +__all__ = ["PvtConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/image_processing_pil_pvt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/image_processing_pil_pvt.py new file mode 100644 index 0000000000000000000000000000000000000000..6ca5c75e2c098908b88cec2f31331e5a80421c7b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/image_processing_pil_pvt.py @@ -0,0 +1,33 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for PVT.""" + +from ...image_processing_backends import PilBackend +from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling +from ...utils import auto_docstring + + +@auto_docstring +class PvtImageProcessorPil(PilBackend): + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 224, "width": 224} + default_to_square = True + do_resize = True + do_rescale = True + do_normalize = True + + +__all__ = ["PvtImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/image_processing_pvt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/image_processing_pvt.py new file mode 100644 index 0000000000000000000000000000000000000000..a82fa38c004c4a7a013dec1ac71041151554763f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/image_processing_pvt.py @@ -0,0 +1,33 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for PVT.""" + +from ...image_processing_backends import TorchvisionBackend +from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling +from ...utils import auto_docstring + + +@auto_docstring +class PvtImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 224, "width": 224} + default_to_square = True + do_resize = True + do_rescale = True + do_normalize = True + + +__all__ = ["PvtImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/modeling_pvt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/modeling_pvt.py new file mode 100644 index 0000000000000000000000000000000000000000..77be8470212ddc825b2e317b59ab030adab595bc --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt/modeling_pvt.py @@ -0,0 +1,551 @@ +# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, +# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PVT model.""" + +import collections +import math +from collections.abc import Iterable + +import torch +import torch.nn.functional as F +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, logging +from .configuration_pvt import PvtConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.convnext.modeling_convnext.ConvNextDropPath with ConvNext->Pvt +class PvtDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float | None = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return f"p={self.drop_prob}" + + +class PvtPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__( + self, + config: PvtConfig, + image_size: int | Iterable[int], + patch_size: int | Iterable[int], + stride: int, + num_channels: int, + hidden_size: int, + cls_token: bool = False, + ): + super().__init__() + self.config = config + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.position_embeddings = nn.Parameter( + torch.randn(1, num_patches + 1 if cls_token else num_patches, hidden_size) + ) + self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size)) if cls_token else None + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=stride, stride=patch_size) + self.layer_norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(p=config.hidden_dropout_prob) + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + num_patches = height * width + + # always interpolate when tracing to ensure the exported model works for dynamic input shapes + if not torch.jit.is_tracing() and num_patches == self.config.image_size * self.config.image_size: + return self.position_embeddings + embeddings = embeddings.reshape(1, height, width, -1).permute(0, 3, 1, 2) + interpolated_embeddings = F.interpolate(embeddings, size=(height, width), mode="bilinear") + interpolated_embeddings = interpolated_embeddings.reshape(1, -1, height * width).permute(0, 2, 1) + return interpolated_embeddings + + def forward(self, pixel_values: torch.Tensor) -> tuple[torch.Tensor, int, int]: + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + patch_embed = self.projection(pixel_values) + *_, height, width = patch_embed.shape + patch_embed = patch_embed.flatten(2).transpose(1, 2) + embeddings = self.layer_norm(patch_embed) + if self.cls_token is not None: + cls_token = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_token, embeddings), dim=1) + position_embeddings = self.interpolate_pos_encoding(self.position_embeddings[:, 1:], height, width) + position_embeddings = torch.cat((self.position_embeddings[:, :1], position_embeddings), dim=1) + else: + position_embeddings = self.interpolate_pos_encoding(self.position_embeddings, height, width) + embeddings = self.dropout(embeddings + position_embeddings) + + return embeddings, height, width + + +class PvtSelfOutput(nn.Module): + def __init__(self, config: PvtConfig, hidden_size: int): + super().__init__() + self.dense = nn.Linear(hidden_size, hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class PvtEfficientSelfAttention(nn.Module): + """Efficient self-attention mechanism with reduction of the sequence [PvT paper](https://huggingface.co/papers/2102.12122).""" + + def __init__( + self, config: PvtConfig, hidden_size: int, num_attention_heads: int, sequences_reduction_ratio: float + ): + super().__init__() + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + + if self.hidden_size % self.num_attention_heads != 0: + raise ValueError( + f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention " + f"heads ({self.num_attention_heads})" + ) + + self.attention_head_size = int(self.hidden_size / self.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.sequences_reduction_ratio = sequences_reduction_ratio + if sequences_reduction_ratio > 1: + self.sequence_reduction = nn.Conv2d( + hidden_size, hidden_size, kernel_size=sequences_reduction_ratio, stride=sequences_reduction_ratio + ) + self.layer_norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + + def transpose_for_scores(self, hidden_states: int) -> torch.Tensor: + new_shape = hidden_states.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + hidden_states = hidden_states.view(new_shape) + return hidden_states.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + height: int, + width: int, + output_attentions: bool = False, + ) -> tuple[torch.Tensor]: + query_layer = self.transpose_for_scores(self.query(hidden_states)) + + if self.sequences_reduction_ratio > 1: + batch_size, seq_len, num_channels = hidden_states.shape + # Reshape to (batch_size, num_channels, height, width) + hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) + # Apply sequence reduction + hidden_states = self.sequence_reduction(hidden_states) + # Reshape back to (batch_size, seq_len, num_channels) + hidden_states = hidden_states.reshape(batch_size, num_channels, -1).permute(0, 2, 1) + hidden_states = self.layer_norm(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class PvtAttention(nn.Module): + def __init__( + self, config: PvtConfig, hidden_size: int, num_attention_heads: int, sequences_reduction_ratio: float + ): + super().__init__() + self.self = PvtEfficientSelfAttention( + config, + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + sequences_reduction_ratio=sequences_reduction_ratio, + ) + self.output = PvtSelfOutput(config, hidden_size=hidden_size) + + def forward( + self, hidden_states: torch.Tensor, height: int, width: int, output_attentions: bool = False + ) -> tuple[torch.Tensor]: + self_outputs = self.self(hidden_states, height, width, output_attentions) + + attention_output = self.output(self_outputs[0]) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class PvtFFN(nn.Module): + def __init__( + self, + config: PvtConfig, + in_features: int, + hidden_features: int | None = None, + out_features: int | None = None, + ): + super().__init__() + out_features = out_features if out_features is not None else in_features + self.dense1 = nn.Linear(in_features, hidden_features) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + self.dense2 = nn.Linear(hidden_features, out_features) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense1(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.dense2(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class PvtLayer(nn.Module): + def __init__( + self, + config: PvtConfig, + hidden_size: int, + num_attention_heads: int, + drop_path: float, + sequences_reduction_ratio: float, + mlp_ratio: float, + ): + super().__init__() + self.layer_norm_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + self.attention = PvtAttention( + config=config, + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + sequences_reduction_ratio=sequences_reduction_ratio, + ) + self.drop_path = PvtDropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.layer_norm_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + mlp_hidden_size = int(hidden_size * mlp_ratio) + self.mlp = PvtFFN(config=config, in_features=hidden_size, hidden_features=mlp_hidden_size) + + def forward(self, hidden_states: torch.Tensor, height: int, width: int, output_attentions: bool = False): + self_attention_outputs = self.attention( + hidden_states=self.layer_norm_1(hidden_states), + height=height, + width=width, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] + + attention_output = self.drop_path(attention_output) + hidden_states = attention_output + hidden_states + + mlp_output = self.mlp(self.layer_norm_2(hidden_states)) + + mlp_output = self.drop_path(mlp_output) + layer_output = hidden_states + mlp_output + + outputs = (layer_output,) + outputs + + return outputs + + +class PvtEncoder(nn.Module): + def __init__(self, config: PvtConfig): + super().__init__() + self.config = config + + # stochastic depth decay rule + drop_path_decays = torch.linspace(0, config.drop_path_rate, sum(config.depths), device="cpu").tolist() + + # patch embeddings + embeddings = [] + + for i in range(config.num_encoder_blocks): + embeddings.append( + PvtPatchEmbeddings( + config=config, + image_size=config.image_size if i == 0 else self.config.image_size // (2 ** (i + 1)), + patch_size=config.patch_sizes[i], + stride=config.strides[i], + num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1], + hidden_size=config.hidden_sizes[i], + cls_token=i == config.num_encoder_blocks - 1, + ) + ) + self.patch_embeddings = nn.ModuleList(embeddings) + + # Transformer blocks + blocks = [] + cur = 0 + for i in range(config.num_encoder_blocks): + # each block consists of layers + layers = [] + if i != 0: + cur += config.depths[i - 1] + for j in range(config.depths[i]): + layers.append( + PvtLayer( + config=config, + hidden_size=config.hidden_sizes[i], + num_attention_heads=config.num_attention_heads[i], + drop_path=drop_path_decays[cur + j], + sequences_reduction_ratio=config.sequence_reduction_ratios[i], + mlp_ratio=config.mlp_ratios[i], + ) + ) + blocks.append(nn.ModuleList(layers)) + + self.block = nn.ModuleList(blocks) + + # Layer norms + self.layer_norm = nn.LayerNorm(config.hidden_sizes[-1], eps=config.layer_norm_eps) + + def forward( + self, + pixel_values: torch.FloatTensor, + output_attentions: bool | None = False, + output_hidden_states: bool | None = False, + return_dict: bool | None = True, + ) -> tuple | BaseModelOutput: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + batch_size = pixel_values.shape[0] + num_blocks = len(self.block) + hidden_states = pixel_values + for idx, (embedding_layer, block_layer) in enumerate(zip(self.patch_embeddings, self.block)): + # first, obtain patch embeddings + hidden_states, height, width = embedding_layer(hidden_states) + # second, send embeddings through blocks + for block in block_layer: + layer_outputs = block(hidden_states, height, width, output_attentions) + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + if idx != num_blocks - 1: + hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous() + hidden_states = self.layer_norm(hidden_states) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +@auto_docstring +class PvtPreTrainedModel(PreTrainedModel): + config: PvtConfig + base_model_prefix = "pvt" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = [] + + @torch.no_grad() + def _init_weights(self, module: nn.Module) -> None: + """Initialize the weights""" + std = self.config.initializer_range + if isinstance(module, (nn.Linear, nn.Conv2d)): + init.trunc_normal_(module.weight, mean=0.0, std=std) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, PvtPatchEmbeddings): + init.trunc_normal_(module.position_embeddings, mean=0.0, std=std) + if module.cls_token is not None: + init.trunc_normal_(module.cls_token, mean=0.0, std=std) + + +@auto_docstring +class PvtModel(PvtPreTrainedModel): + def __init__(self, config: PvtConfig): + super().__init__(config) + self.config = config + + # hierarchical Transformer encoder + self.encoder = PvtEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + encoder_outputs = self.encoder( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + if not return_dict: + return (sequence_output,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + Pvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of + the [CLS] token) e.g. for ImageNet. + """ +) +class PvtForImageClassification(PvtPreTrainedModel): + def __init__(self, config: PvtConfig) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.pvt = PvtModel(config) + + # Classifier head + self.classifier = ( + nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity() + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor | None, + labels: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | ImageClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.pvt( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.classifier(sequence_output[:, 0, :]) + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = ["PvtForImageClassification", "PvtModel", "PvtPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e3cb83f130dd42eb08943d5d4e08ed9321e6f076 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_pvt_v2 import * + from .modeling_pvt_v2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/configuration_pvt_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/configuration_pvt_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..1c73fa158b5d38e702503930c1833f61db8344b8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/configuration_pvt_v2.py @@ -0,0 +1,93 @@ +# Copyright 2024 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, +# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Pvt V2 model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import BackboneConfigMixin +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="OpenGVLab/pvt_v2_b0") +@strict +class PvtV2Config(BackboneConfigMixin, PreTrainedConfig): + r""" + num_encoder_blocks (`[int]`, *optional*, defaults to 4): + The number of encoder blocks (i.e. stages in the Mix Transformer encoder). + sr_ratios (`list[int]`, *optional*, defaults to `[8, 4, 2, 1]`): + Spatial reduction ratios in each encoder block. + patch_sizes (`list[int]`, *optional*, defaults to `[7, 3, 3, 3]`): + Patch size for overlapping patch embedding before each encoder block. + strides (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`): + Stride for overlapping patch embedding before each encoder block. + num_attention_heads (`list[int]`, *optional*, defaults to `[1, 2, 5, 8]`): + Number of attention heads for each attention layer in each block of the Transformer encoder. + mlp_ratios (`list[int]`, *optional*, defaults to `[8, 8, 4, 4]`): + Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the + encoder blocks. + linear_attention (`bool`, *optional*, defaults to `False`): + Use linear attention complexity. If set to True, `sr_ratio` is ignored and average pooling is used for + dimensionality reduction in the attention layers rather than strided convolution. + + Example: + + ```python + >>> from transformers import PvtV2Model, PvtV2Config + + >>> # Initializing a pvt_v2_b0 style configuration + >>> configuration = PvtV2Config() + + >>> # Initializing a model from the OpenGVLab/pvt_v2_b0 style configuration + >>> model = PvtV2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "pvt_v2" + + image_size: int | list[int] | tuple[int, int] | dict = 224 + num_channels: int = 3 + num_encoder_blocks: int = 4 + depths: list[int] | tuple[int, ...] = (2, 2, 2, 2) + sr_ratios: list[int] | tuple[int, ...] = (8, 4, 2, 1) + hidden_sizes: list[int] | tuple[int, ...] = (32, 64, 160, 256) + patch_sizes: list[int] | tuple[int, ...] = (7, 3, 3, 3) + strides: list[int] | tuple[int, ...] = (4, 2, 2, 2) + num_attention_heads: list[int] | tuple[int, ...] = (1, 2, 5, 8) + mlp_ratios: list[int] | tuple[int, ...] = (8, 8, 4, 4) + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.0 + attention_probs_dropout_prob: float | int = 0.0 + initializer_range: float = 0.02 + drop_path_rate: float | int = 0.0 + layer_norm_eps: float = 1e-6 + qkv_bias: bool = True + linear_attention: bool = False + _out_features: list[str] | None = None + _out_indices: list[int] | None = None + + def __post_init__(self, **kwargs): + self.image_size = (self.image_size, self.image_size) if isinstance(self.image_size, int) else self.image_size + self.stage_names = [f"stage{idx}" for idx in range(1, len(self.depths) + 1)] + self.set_output_features_output_indices( + out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None) + ) + super().__post_init__(**kwargs) + + +__all__ = ["PvtV2Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/modeling_pvt_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/modeling_pvt_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..4ecf3a92b06d0186f53adbe728719c5380121b0f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/pvt_v2/modeling_pvt_v2.py @@ -0,0 +1,588 @@ +# Copyright 2024 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, +# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch PVTv2 model.""" + +import math + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...backbone_utils import BackboneMixin, filter_output_hidden_states +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BackboneOutput, BaseModelOutput, ImageClassifierOutput +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, logging +from ...utils.generic import can_return_tuple +from .configuration_pvt_v2 import PvtV2Config + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.convnext.modeling_convnext.ConvNextDropPath with ConvNext->Pvt +class PvtV2DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float | None = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return f"p={self.drop_prob}" + + +class PvtV2OverlapPatchEmbeddings(nn.Module): + """Image to Patch Embedding""" + + def __init__(self, config: PvtV2Config, layer_idx: int): + super().__init__() + patch_size = config.patch_sizes[layer_idx] + patch_size = (patch_size, patch_size) if isinstance(patch_size, int) else patch_size + stride = config.strides[layer_idx] + num_channels = config.num_channels if layer_idx == 0 else config.hidden_sizes[layer_idx - 1] + hidden_size = config.hidden_sizes[layer_idx] + self.patch_size = patch_size + self.proj = nn.Conv2d( + num_channels, + hidden_size, + kernel_size=patch_size, + stride=stride, + padding=(patch_size[0] // 2, patch_size[1] // 2), + ) + self.layer_norm = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + + def forward(self, pixel_values): + embeddings = self.proj(pixel_values) + _, _, height, width = embeddings.shape + embeddings = embeddings.flatten(2).transpose(1, 2) + embeddings = self.layer_norm(embeddings) + return embeddings, height, width + + +class PvtV2DepthWiseConv(nn.Module): + """ + Depth-wise (DW) convolution to infuse positional information using zero-padding. Depth-wise convolutions + have an equal number of groups to the number of input channels, meaning one filter per input channel. This + reduces the overall parameters and compute costs since the key purpose of this layer is position encoding. + """ + + def __init__(self, config: PvtV2Config, dim: int = 768): + super().__init__() + self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) + + def forward(self, hidden_states, height, width): + batch_size, seq_len, num_channels = hidden_states.shape + hidden_states = hidden_states.transpose(1, 2).view(batch_size, num_channels, height, width) + hidden_states = self.dwconv(hidden_states) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + + return hidden_states + + +class PvtV2SelfAttention(nn.Module): + """Efficient self-attention mechanism.""" + + def __init__(self, config: PvtV2Config, hidden_size: int, num_attention_heads: int, spatial_reduction_ratio: int): + super().__init__() + self.linear_attention = config.linear_attention + + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + + if self.hidden_size % self.num_attention_heads != 0: + raise ValueError( + f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention " + f"heads ({self.num_attention_heads})" + ) + + self.attention_head_size = int(self.hidden_size / self.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.key = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.value = nn.Linear(self.hidden_size, self.all_head_size, bias=config.qkv_bias) + self.attn_drop = nn.Dropout(config.attention_probs_dropout_prob) + self.proj = nn.Linear(self.hidden_size, self.hidden_size) + self.proj_drop = nn.Dropout(config.hidden_dropout_prob) + + self.spatial_reduction_ratio = spatial_reduction_ratio + if self.linear_attention: + self.pool = nn.AdaptiveAvgPool2d(7) + self.spatial_reduction = nn.Conv2d(self.hidden_size, self.hidden_size, kernel_size=1, stride=1) + self.layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + self.act = nn.GELU() + elif spatial_reduction_ratio > 1: + self.spatial_reduction = nn.Conv2d( + self.hidden_size, self.hidden_size, kernel_size=spatial_reduction_ratio, stride=spatial_reduction_ratio + ) + self.layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def transpose_for_scores(self, hidden_states) -> torch.Tensor: + new_shape = hidden_states.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + hidden_states = hidden_states.view(new_shape) + return hidden_states.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + height: int, + width: int, + output_attentions: bool = False, + ) -> tuple[torch.Tensor]: + batch_size, seq_len, num_channels = hidden_states.shape + query_layer = self.transpose_for_scores(self.query(hidden_states)) + + if self.linear_attention: + hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) + hidden_states = ( + self.spatial_reduction(self.pool(hidden_states)).reshape(batch_size, num_channels, -1).permute(0, 2, 1) + ) + hidden_states = self.act(self.layer_norm(hidden_states)) + elif self.spatial_reduction_ratio > 1: + hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) + hidden_states = ( + self.spatial_reduction(hidden_states).reshape(batch_size, num_channels, -1).permute(0, 2, 1) + ) + hidden_states = self.layer_norm(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.attn_drop(attention_probs) + context_layer = (attention_probs @ value_layer).transpose(1, 2).reshape(batch_size, seq_len, num_channels) + context_layer = self.proj(context_layer) + context_layer = self.proj_drop(context_layer) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class PvtV2ConvFeedForwardNetwork(nn.Module): + def __init__( + self, + config: PvtV2Config, + in_features: int, + hidden_features: int | None = None, + out_features: int | None = None, + ): + super().__init__() + out_features = out_features if out_features is not None else in_features + self.dense1 = nn.Linear(in_features, hidden_features) + self.dwconv = PvtV2DepthWiseConv(config, hidden_features) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + self.dense2 = nn.Linear(hidden_features, out_features) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.relu = nn.ReLU() if config.linear_attention else nn.Identity() + + def forward(self, hidden_states: torch.Tensor, height, width) -> torch.Tensor: + hidden_states = self.dense1(hidden_states) + hidden_states = self.relu(hidden_states) + hidden_states = self.dwconv(hidden_states, height, width) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.dense2(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class PvtV2BlockLayer(nn.Module): + def __init__(self, config: PvtV2Config, layer_idx: int, drop_path: float = 0.0): + super().__init__() + hidden_size: int = config.hidden_sizes[layer_idx] + num_attention_heads: int = config.num_attention_heads[layer_idx] + spatial_reduction_ratio: int = config.sr_ratios[layer_idx] + mlp_ratio: float = config.mlp_ratios[layer_idx] + self.layer_norm_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + self.attention = PvtV2SelfAttention( + config=config, + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + spatial_reduction_ratio=spatial_reduction_ratio, + ) + self.drop_path = PvtV2DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.layer_norm_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + mlp_hidden_size = int(hidden_size * mlp_ratio) + self.mlp = PvtV2ConvFeedForwardNetwork(config=config, in_features=hidden_size, hidden_features=mlp_hidden_size) + + def forward(self, hidden_states: torch.Tensor, height: int, width: int, output_attentions: bool = False): + self_attention_outputs = self.attention( + hidden_states=self.layer_norm_1(hidden_states), + height=height, + width=width, + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] + + attention_output = self.drop_path(attention_output) + hidden_states = attention_output + hidden_states + + mlp_output = self.mlp(self.layer_norm_2(hidden_states), height, width) + + mlp_output = self.drop_path(mlp_output) + layer_output = hidden_states + mlp_output + + outputs = (layer_output,) + outputs + + return outputs + + +class PvtV2EncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: PvtV2Config, layer_idx: int): + super().__init__() + self.patch_embedding = PvtV2OverlapPatchEmbeddings( + config=config, + layer_idx=layer_idx, + ) + # Transformer block + # stochastic depth decay rule + drop_path_decays = torch.linspace(0, config.drop_path_rate, sum(config.depths), device="cpu").tolist() + block_layers = [] + for block_idx in range(config.depths[layer_idx]): + block_layers.append( + PvtV2BlockLayer( + config=config, + layer_idx=layer_idx, + drop_path=drop_path_decays[sum(config.depths[:layer_idx]) + block_idx], + ) + ) + self.blocks = nn.ModuleList(block_layers) + + # Layer norm + self.layer_norm = nn.LayerNorm(config.hidden_sizes[layer_idx], eps=config.layer_norm_eps) + + def forward(self, hidden_states, output_attentions): + all_self_attentions = () if output_attentions else None + # first, obtain patch embeddings + hidden_states, height, width = self.patch_embedding(hidden_states) + # second, send embeddings through blocks + for block in self.blocks: + layer_outputs = block(hidden_states, height, width, output_attentions) + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions += (layer_outputs[1],) + # third, apply layer norm + hidden_states = self.layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (all_self_attentions,) + + return outputs, height, width + + +class PvtV2Encoder(nn.Module): + def __init__(self, config: PvtV2Config): + super().__init__() + self.config = config + self.gradient_checkpointing = False + + # encoder layers + self.layers = nn.ModuleList([PvtV2EncoderLayer(config, i) for i in range(config.num_encoder_blocks)]) + + def forward( + self, + pixel_values: torch.FloatTensor, + output_attentions: bool | None = False, + output_hidden_states: bool | None = False, + return_dict: bool | None = True, + ) -> tuple | BaseModelOutput: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + batch_size = pixel_values.shape[0] + hidden_states = pixel_values + for idx, layer in enumerate(self.layers): + layer_output = layer(hidden_states, output_attentions) + outputs, height, width = layer_output + hidden_states = outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (outputs[1],) + # reshape back to (batch_size, num_channels, height, width) + hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous() + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +@auto_docstring +class PvtV2PreTrainedModel(PreTrainedModel): + config: PvtV2Config + base_model_prefix = "pvt_v2" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + + @torch.no_grad() + def _init_weights(self, module: nn.Linear | nn.Conv2d | nn.LayerNorm) -> None: + """Initialize the weights""" + if isinstance(module, nn.Linear): + init.trunc_normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, nn.Conv2d): + fan_out = module.kernel_size[0] * module.kernel_size[1] * module.out_channels + fan_out //= module.groups + init.normal_(module.weight, 0, math.sqrt(2.0 / fan_out)) + if module.bias is not None: + init.zeros_(module.bias) + + +@auto_docstring +class PvtV2Model(PvtV2PreTrainedModel): + def __init__(self, config: PvtV2Config): + super().__init__(config) + self.config = config + + # hierarchical Transformer encoder + self.encoder = PvtV2Encoder(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + encoder_outputs = self.encoder( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + if not return_dict: + return (sequence_output,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + Pvt-v2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state + of the [CLS] token) e.g. for ImageNet. + """ +) +class PvtV2ForImageClassification(PvtV2PreTrainedModel): + def __init__(self, config: PvtV2Config) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.pvt_v2 = PvtV2Model(config) + + # Classifier head + self.classifier = ( + nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity() + ) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor | None, + labels: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | ImageClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.pvt_v2( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + # convert last hidden states to (batch_size, height*width, hidden_size) + batch_size = sequence_output.shape[0] + # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels) + sequence_output = sequence_output.permute(0, 2, 3, 1) + sequence_output = sequence_output.reshape(batch_size, -1, self.config.hidden_sizes[-1]) + + # global average pooling + sequence_output = sequence_output.mean(dim=1) + + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + PVTv2 backbone, to be used with frameworks like DETR and MaskFormer. + """ +) +class PvtV2Backbone(BackboneMixin, PvtV2Model): + def __init__(self, config: PvtV2Config): + super().__init__(config) + self.num_features = config.hidden_sizes + + @can_return_tuple + @filter_output_hidden_states + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> BackboneOutput: + r""" + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoBackbone + >>> import torch + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> processor = AutoImageProcessor.from_pretrained("OpenGVLab/pvt_v2_b0") + >>> model = AutoBackbone.from_pretrained( + ... "OpenGVLab/pvt_v2_b0", out_features=["stage1", "stage2", "stage3", "stage4"] + ... ) + + >>> inputs = processor(image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + [1, 256, 7, 7] + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + outputs = self.encoder( + pixel_values=pixel_values, + output_attentions=output_attentions, + output_hidden_states=True, + return_dict=return_dict, + ) + + hidden_states = outputs.hidden_states + + feature_maps = () + for idx, stage in enumerate(self.stage_names): + if stage in self.out_features: + feature_maps += (hidden_states[idx],) + + if not return_dict: + output = (feature_maps,) + if output_hidden_states: + output += (outputs.hidden_states,) + return output + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=outputs.hidden_states if output_hidden_states else None, + attentions=None, + ) + + +__all__ = ["PvtV2ForImageClassification", "PvtV2Model", "PvtV2PreTrainedModel", "PvtV2Backbone"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..09d051de068670c66e514bb886a401852a537307 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_qianfan_ocr import * + from .modeling_qianfan_ocr import * + from .processing_qianfan_ocr import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/configuration_qianfan_ocr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/configuration_qianfan_ocr.py new file mode 100644 index 0000000000000000000000000000000000000000..693b1eecd5ed862b96ca559321be3dd41a80d5fb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/configuration_qianfan_ocr.py @@ -0,0 +1,143 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qianfan_ocr/modular_qianfan_ocr.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qianfan_ocr.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="baidu/Qianfan-OCR") +@strict +class QianfanOCRVisionConfig(PreTrainedConfig): + r""" + projection_dropout (`float`, *optional*, defaults to 0.0): + Dropout probability for the projection layer. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The type of normalization to use in the encoder. Can be `"layer_norm"` or `"rms_norm"`. + use_mask_token (`bool`, *optional*, defaults to `False`): + Whether to use a mask token for masked image modeling. + use_mean_pooling (`bool`, *optional*, defaults to `True`): + Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the + CLS token, before applying the classification head. + drop_path_rate (`float`, *optional*, defaults to 0.1): + Dropout rate for stochastic depth. + + Example: + + ```python + >>> # Initializing a QianfanOCR vision style configuration + >>> configuration = QianfanOCRVisionConfig() + + >>> # Initializing a model from the configuration + >>> model = QianfanOCRVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qianfan_ocr_vision" + base_config_key = "vision_config" + + hidden_size: int = 1024 + num_hidden_layers: int = 24 + num_attention_heads: int = 16 + + attention_bias: bool = True + use_qk_norm: bool = False + intermediate_size: int = 4096 + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.0 + attention_dropout: float | int = 0.0 + projection_dropout: float | int = 0.0 + initializer_range: float = 0.02 + norm_type: str = "layer_norm" + layer_norm_eps: float = 1e-06 + image_size: int | list[int] | tuple[int, ...] = (448, 448) + patch_size: int | list[int] | tuple[int, ...] = (14, 14) + num_channels: int = 3 + use_mask_token: bool = False + use_absolute_position_embeddings: bool = True + layer_scale_init_value: float = 0.1 + use_mean_pooling: bool = True + drop_path_rate: float = 0.1 + + def __post_init__(self, **kwargs): + self.image_size = ( + self.image_size if isinstance(self.image_size, (list, tuple)) else (self.image_size, self.image_size) + ) + self.patch_size = ( + self.patch_size if isinstance(self.patch_size, (list, tuple)) else (self.patch_size, self.patch_size) + ) + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="baidu/Qianfan-OCR") +@strict +class QianfanOCRConfig(PreTrainedConfig): + r""" + downsample_ratio (`float`, *optional*, defaults to 0.5): + Factor by which to downsample the image. + + Example: + + ```python + >>> # Initializing a QianfanOCR style configuration + >>> configuration = QianfanOCRConfig() + + >>> # Initializing a model from the configuration + >>> model = QianfanOCRForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qianfan_ocr" + sub_configs = {"text_config": AutoConfig, "vision_config": QianfanOCRVisionConfig} + + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + image_token_id: int = 151667 + image_seq_length: int = 256 + downsample_ratio: float = 0.5 + projector_hidden_act: str = "gelu" + vision_feature_layer: int | list[int] = -1 + vision_feature_select_strategy: str = "default" + + tie_word_embeddings: bool = False + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = QianfanOCRVisionConfig(**self.vision_config) + elif self.vision_config is None: + self.vision_config = QianfanOCRVisionConfig() + + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "qwen3") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["qwen3"]() + + super().__post_init__(**kwargs) + + +__all__ = ["QianfanOCRVisionConfig", "QianfanOCRConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/modeling_qianfan_ocr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/modeling_qianfan_ocr.py new file mode 100644 index 0000000000000000000000000000000000000000..9082d11f2a3fbc90e4458eb5d6f50ffb0d3676ed --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/modeling_qianfan_ocr.py @@ -0,0 +1,885 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qianfan_ocr/modular_qianfan_ocr.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qianfan_ocr.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import collections.abc +from collections.abc import Callable +from dataclasses import dataclass + +import torch +import torch.nn as nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, torch_compilable_check, torch_int +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from .configuration_qianfan_ocr import QianfanOCRConfig, QianfanOCRVisionConfig + + +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +class QianfanOCRDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float | None = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return f"p={self.drop_prob}" + + +@use_kernel_forward_from_hub("RMSNorm") +class QianfanOCRVisionRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + QianfanOCRVisionRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float | int = 0.0, + **kwargs, +): + key_states = key + value_states = value + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + # No upcasting of the attention weights to float32 in this implementation + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class QianfanOCRVisionAttention(nn.Module): + """Attention Class for QianfanOCR Vision Encoder""" + + def __init__(self, config: QianfanOCRVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + proj_dropout = config.projection_dropout + qk_norm = config.use_qk_norm + + # Needed for flash attention + self.is_causal = False + + self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias) + self.k_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias) + self.v_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=config.attention_bias) + self.projection_layer = nn.Linear(self.embed_dim, self.embed_dim) + self.projection_dropout = nn.Dropout(proj_dropout) if proj_dropout > 0 else nn.Identity() + + self.q_norm = QianfanOCRVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity() + self.k_norm = QianfanOCRVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ): + batch_size, seq_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = self.q_norm(query_states) + key_states = self.k_norm(key_states) + + query_states = query_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scale, + is_causal=False, + **kwargs, + ) + attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim) + + output = self.projection_layer(attn_output) + output = self.projection_dropout(output) + + return output, attn_weights + + +class QianfanOCRVisionMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +NORM2FN = {"layer_norm": nn.LayerNorm, "rms_norm": QianfanOCRVisionRMSNorm} + + +class QianfanOCRVisionLayer(GradientCheckpointingLayer): + """Vision transformer layer with stochastic depth (DropPath) support.""" + + def __init__(self, config: QianfanOCRVisionConfig, drop_path_rate: float = 0.0) -> None: + super().__init__() + self.attention = QianfanOCRVisionAttention(config) + self.mlp = QianfanOCRVisionMLP(config) + # QianfanOCR uses different layernorm implementations for different models + self.layernorm_before = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps) + + init_values = config.layer_scale_init_value + self.lambda_1 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True) + self.lambda_2 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.drop_path1 = nn.Identity() if drop_path_rate <= 0.0 else QianfanOCRDropPath(drop_path_rate) + self.drop_path2 = nn.Identity() if drop_path_rate <= 0.0 else QianfanOCRDropPath(drop_path_rate) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.layernorm_before(hidden_states) + # Self Attention + hidden_states, _ = self.attention(hidden_states, **kwargs) + hidden_states = self.lambda_1 * hidden_states + hidden_states = self.drop_path1(hidden_states) + hidden_states = hidden_states + residual + + residual = hidden_states + hidden_states = self.layernorm_after(hidden_states) + # Fully Connected + hidden_states = self.mlp(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.lambda_2 * hidden_states + hidden_states = self.drop_path2(hidden_states) + residual + + return hidden_states + + +class QianfanOCRVisionPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + self.patch_shape = patch_shape + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + + embeddings = self.projection(pixel_values.to(self.projection.weight.dtype)) + embeddings = embeddings.flatten(2).transpose(1, 2) + + return embeddings + + +# Based on timm implementation, which can be found here: +# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py +class QianfanOCRVisionEmbeddings(nn.Module): + """ + Construct the CLS token, position and patch embeddings. Optionally, also the mask token. + + """ + + def __init__(self, config: QianfanOCRVisionConfig) -> None: + super().__init__() + + self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) + if config.use_mask_token: + self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size)) + else: + self.mask_token = None + self.patch_embeddings = QianfanOCRVisionPatchEmbeddings(config) + self.patch_size = config.patch_size + self.image_size = ( + config.image_size + if isinstance(config.image_size, collections.abc.Iterable) + else (config.image_size, config.image_size) + ) + num_patches = self.patch_embeddings.num_patches + if config.use_absolute_position_embeddings: + self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size)) + else: + self.position_embeddings = None + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support torch.jit tracing. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + + num_patches = embeddings.shape[1] - 1 + num_positions = self.position_embeddings.shape[1] - 1 + + # always interpolate when tracing to ensure the exported model works for dynamic input shapes + if not torch.jit.is_tracing() and num_patches == num_positions and height == width: + return self.position_embeddings + + class_pos_embed = self.position_embeddings[:, :1] + patch_pos_embed = self.position_embeddings[:, 1:] + + dim = embeddings.shape[-1] + + new_height = height // self.patch_size[0] + new_width = width // self.patch_size[1] + + sqrt_num_positions = torch_int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(new_height, new_width), + mode="bicubic", + align_corners=False, + ) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + + return torch.cat((class_pos_embed, patch_pos_embed), dim=1) + + def forward( + self, + pixel_values: torch.Tensor, + bool_masked_pos: torch.BoolTensor | None = None, + ) -> torch.Tensor: + _, _, height, width = pixel_values.shape + embeddings = self.patch_embeddings(pixel_values) + batch_size, seq_len, _ = embeddings.size() + + if bool_masked_pos is not None: + mask_tokens = self.mask_token.expand(batch_size, seq_len, -1) + # replace the masked visual tokens by mask_tokens + w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens) + embeddings = embeddings * (1 - w) + mask_tokens * w + + cls_tokens = self.cls_token.expand(batch_size, -1, -1) + embeddings = torch.cat((cls_tokens, embeddings), dim=1) + + if self.position_embeddings is not None: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + + embeddings = self.dropout(embeddings) + + return embeddings + + +class QianfanOCRVisionModelOutputWithPooling(BaseModelOutputWithPooling): + r""" + pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): + Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if + *config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token + will be returned. + """ + + pass + + +@auto_docstring +class QianfanOCRVisionPreTrainedModel(PreTrainedModel): + config: QianfanOCRVisionConfig + base_model_prefix = "vision_model" + main_input_name = "pixel_values" + input_modalities = ("image", "video") + supports_gradient_checkpointing = True + _no_split_modules = ["QianfanOCRVisionLayer"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": QianfanOCRVisionLayer, + "attentions": QianfanOCRVisionAttention, + } + config_class = QianfanOCRVisionConfig + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, QianfanOCRVisionEmbeddings): + init.zeros_(module.cls_token) + if module.mask_token is not None: + init.zeros_(module.mask_token) + if module.position_embeddings is not None: + init.zeros_(module.position_embeddings) + elif isinstance(module, QianfanOCRVisionLayer): + init.constant_(module.lambda_1, self.config.layer_scale_init_value) + init.constant_(module.lambda_2, self.config.layer_scale_init_value) + + +@auto_docstring +class QianfanOCRVisionModel(QianfanOCRVisionPreTrainedModel): + def __init__(self, config: QianfanOCRVisionConfig) -> None: + super().__init__(config) + self.config = config + + self.embeddings = QianfanOCRVisionEmbeddings(config) + + self.layernorm = ( + nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + ) + dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers, device="cpu")] + self.layers = nn.ModuleList( + [QianfanOCRVisionLayer(config, drop_path_rate=dpr[i]) for i in range(config.num_hidden_layers)] + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.patch_embeddings + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + bool_masked_pos: torch.BoolTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | QianfanOCRVisionModelOutputWithPooling: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + """ + hidden_states = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos) + for layer_module in self.layers: + hidden_states = layer_module(hidden_states, **kwargs) + hidden_states = self.layernorm(hidden_states) + + return QianfanOCRVisionModelOutputWithPooling( + last_hidden_state=hidden_states, + ) + + +class QianfanOCRMultiModalProjector(nn.Module): + def __init__(self, config: QianfanOCRConfig): + super().__init__() + self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2) + self.linear_1 = nn.Linear( + config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2, config.text_config.hidden_size + ) + self.act = ACT2FN[config.projector_hidden_act] + self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size) + + def forward(self, image_features): + hidden_states = self.layer_norm(image_features) + hidden_states = self.linear_1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.linear_2(hidden_states) + return hidden_states + + +@auto_docstring +class QianfanOCRPreTrainedModel(PreTrainedModel): + config: QianfanOCRConfig + base_model_prefix = "model" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + _skip_keys_device_placement = "past_key_values" + + _supports_flash_attn = True + _supports_sdpa = True + + _can_compile_fullgraph = True + _supports_flex_attn = True + _supports_attention_backend = True + config_class = QianfanOCRConfig + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for QianfanOCR outputs, with hidden states and attentions. + """ +) +class QianfanOCRModelOutputWithPast(BaseModelOutputWithPast): + r""" + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + image_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + The QianfanOCR model which consists of a vision backbone and a language model, without a language modeling head. + """ +) +class QianfanOCRModel(QianfanOCRPreTrainedModel): + def __init__(self, config: QianfanOCRConfig): + super().__init__(config) + self.vision_tower = AutoModel.from_config(config.vision_config) + + self.multi_modal_projector = QianfanOCRMultiModalProjector(config) + self.language_model = AutoModel.from_config(config.text_config) + self.post_init() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + @merge_with_config_defaults + @can_return_tuple + @auto_docstring( + custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection." + ) + def get_image_features( + self, + pixel_values: torch.FloatTensor, + vision_feature_layer: int | list[int] | list[int] | None = None, + vision_feature_select_strategy: str | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`) + The tensors corresponding to the input images. + vision_feature_layer (`int` or `list[int]`): + Layer index or list of layer indices to extract features from. + """ + pixel_values = pixel_values.to(dtype=self.dtype) # fp16 compatibility + + downsample_ratio = self.config.downsample_ratio + if vision_feature_layer != -1: + kwargs["output_hidden_states"] = True + vision_outputs = self.vision_tower(pixel_values=pixel_values, return_dict=True, **kwargs) + if vision_feature_layer == -1: + vision_features = vision_outputs.last_hidden_state + else: + vision_features = vision_outputs.hidden_states[vision_feature_layer] + if vision_feature_select_strategy == "default": + vision_features = vision_features[:, 1:, :] + + # Calculate dimensions based on vision features + channels = vision_features.shape[1] + feature_size = int(channels**0.5) + batch_size = vision_features.shape[0] + + # Reshape tensor to spatial dimensions + vision_features = vision_features.reshape(batch_size, feature_size, feature_size, -1) + + # Apply downsampling using pixel shuffle + vision_features = self.pixel_shuffle(vision_features, scale_factor=downsample_ratio) + + # Reshape tensor to prepare for projection + vision_features = vision_features.reshape(batch_size, -1, vision_features.shape[-1]) + + # Project features through multi-modal projector + vision_features = self.multi_modal_projector(vision_features) + vision_outputs.pooler_output = vision_features + + return vision_outputs + + def get_placeholder_mask( + self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + + n_image_tokens = special_image_mask.sum() + n_image_features = image_features.shape[0] * image_features.shape[1] + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}", + ) + return special_image_mask + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + vision_feature_layer: int | list[int] | list[int] | None = None, + vision_feature_select_strategy: str | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | QianfanOCRModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + if pixel_values is not None: + image_features = self.get_image_features( + pixel_values=pixel_values, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + return_dict=True, + ).pooler_output + image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype) + special_image_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_features + ) + inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) + + outputs = self.language_model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + **kwargs, + ) + + return QianfanOCRModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=image_features if pixel_values is not None else None, + ) + + def pixel_shuffle(self, vision_features: torch.Tensor, scale_factor: float = 0.5): + """Perform pixel shuffle downsampling on vision features. + + Args: + vision_features (`torch.Tensor`): + Input tensor of shape (batch_size, width, height, channels). + scale_factor (`float`, *optional*, defaults to `0.5`): + Factor by which to downsample. Default is 0.5, which halves the dimensions. + + Returns: + vision_features (`torch.Tensor`): + Downsampled tensor of shape (batch_size, height*scale_factor, width*scale_factor, channels/(scale_factor^2)). + """ + batch_size, width, height, channels = vision_features.size() + + if height % scale_factor != 0 or width % scale_factor != 0: + raise ValueError("Height and width must be divisible by scale_factor for proper downsampling.") + + # Reshape to allow downsampling + vision_features = vision_features.view( + batch_size, width, int(height * scale_factor), int(channels / scale_factor) + ) + # Permute dimensions to align downsampled axis correctly + vision_features = vision_features.permute(0, 2, 1, 3).contiguous() + + # Reshape to achieve final downsampled dimensions + vision_features = vision_features.view( + batch_size, int(height * scale_factor), int(width * scale_factor), int(channels / (scale_factor**2)) + ) + + # Swap height and width back for proper orientation + vision_features = vision_features.permute(0, 2, 1, 3).contiguous() + + return vision_features + + +@auto_docstring( + custom_intro=""" + Base class for QianfanOCR causal language model (or autoregressive) outputs. + """ +) +@dataclass +class QianfanOCRCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + image_hidden_states: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + The QIANFAN_OCR model which consists of a vision backbone and a language model. + """ +) +class QianfanOCRForConditionalGeneration(QianfanOCRPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"} + + def __init__(self, config: QianfanOCRConfig): + super().__init__(config) + self.model = QianfanOCRModel(config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + def get_output_embeddings(self) -> nn.Module: + return self.lm_head + + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + vision_feature_layer: int | list[int] | list[int] | None = None, + vision_feature_select_strategy: str | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + return self.model.get_image_features( + pixel_values=pixel_values, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + **kwargs, + ) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + vision_feature_layer: int | list[int] | list[int] | None = None, + vision_feature_select_strategy: str | None = None, + labels: torch.LongTensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + image_sizes: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | QianfanOCRCausalLMOutputWithPast: + r""" + Example: + ```python + >>> import torch + >>> from transformers import AutoProcessor, AutoModelForImageTextToText + >>> torch_device = "cuda" + >>> processor = AutoProcessor.from_pretrained("baidu/Qianfan-OCR") + >>> model = AutoModelForImageTextToText.from_pretrained( + ... "baidu/Qianfan-OCR", dtype=torch.bfloat16, device_map=torch_device + ... ) + >>> messages = [ + ... { + ... "role": "user", + ... "content": [ + ... {"type": "image", "url": "https://example.com/image.jpg"}, + ... {"type": "text", "text": "Describe this image."}, + ... ], + ... }, + ... ] + >>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device) + >>> generate_ids = model.generate(**inputs, max_new_tokens=200) + >>> print(processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)) + ```""" + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + vision_feature_layer=vision_feature_layer, + vision_feature_select_strategy=vision_feature_select_strategy, + image_sizes=image_sizes, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return QianfanOCRCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + inputs_embeds=None, + pixel_values=None, + attention_mask=None, + logits_to_keep=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + logits_to_keep=logits_to_keep, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + if is_first_iteration or not kwargs.get("use_cache", True): + # Pixel values are used only in the first iteration if available + # In subsequent iterations, they are already merged with text and cached + # NOTE: first iteration doesn't have to be prefill, it can be the first + # iteration with a question and cached system prompt (continue generate from cache) + model_inputs["pixel_values"] = pixel_values + + return model_inputs + + +__all__ = [ + "QianfanOCRVisionPreTrainedModel", + "QianfanOCRVisionModel", + "QianfanOCRPreTrainedModel", + "QianfanOCRModel", + "QianfanOCRForConditionalGeneration", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/modular_qianfan_ocr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/modular_qianfan_ocr.py new file mode 100644 index 0000000000000000000000000000000000000000..882577fec5e7c112c54c29301af293e8fb6e3d83 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/modular_qianfan_ocr.py @@ -0,0 +1,380 @@ +# Copyright 2026 HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn as nn +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput +from ...modeling_outputs import BaseModelOutputWithPooling +from ...processing_utils import ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import TransformersKwargs, auto_docstring +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import CONFIG_MAPPING, AutoConfig +from ..beit.modeling_beit import BeitDropPath +from ..internvl.configuration_internvl import InternVLConfig, InternVLVisionConfig +from ..internvl.modeling_internvl import ( + InternVLCausalLMOutputWithPast, + InternVLForConditionalGeneration, + InternVLModel, + InternVLModelOutputWithPast, + InternVLMultiModalProjector, + InternVLPreTrainedModel, + InternVLVisionAttention, + InternVLVisionEmbeddings, + InternVLVisionLayer, + InternVLVisionMLP, + InternVLVisionModel, + InternVLVisionPreTrainedModel, +) +from ..internvl.processing_internvl import InternVLProcessor + + +@auto_docstring(checkpoint="baidu/Qianfan-OCR") +@strict +class QianfanOCRVisionConfig(InternVLVisionConfig): + r""" + projection_dropout (`float`, *optional*, defaults to 0.0): + Dropout probability for the projection layer. + norm_type (`str`, *optional*, defaults to `"layer_norm"`): + The type of normalization to use in the encoder. Can be `"layer_norm"` or `"rms_norm"`. + use_mask_token (`bool`, *optional*, defaults to `False`): + Whether to use a mask token for masked image modeling. + use_mean_pooling (`bool`, *optional*, defaults to `True`): + Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the + CLS token, before applying the classification head. + drop_path_rate (`float`, *optional*, defaults to 0.1): + Dropout rate for stochastic depth. + + Example: + + ```python + >>> # Initializing a QianfanOCR vision style configuration + >>> configuration = QianfanOCRVisionConfig() + + >>> # Initializing a model from the configuration + >>> model = QianfanOCRVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qianfan_ocr_vision" + base_config_key = "vision_config" + + attention_bias: bool = True + drop_path_rate: float = 0.1 + + +@auto_docstring(checkpoint="baidu/Qianfan-OCR") +@strict +class QianfanOCRConfig(InternVLConfig): + r""" + downsample_ratio (`float`, *optional*, defaults to 0.5): + Factor by which to downsample the image. + + Example: + + ```python + >>> # Initializing a QianfanOCR style configuration + >>> configuration = QianfanOCRConfig() + + >>> # Initializing a model from the configuration + >>> model = QianfanOCRForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qianfan_ocr" + sub_configs = {"text_config": AutoConfig, "vision_config": QianfanOCRVisionConfig} + + tie_word_embeddings: bool = False + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = QianfanOCRVisionConfig(**self.vision_config) + elif self.vision_config is None: + self.vision_config = QianfanOCRVisionConfig() + + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "qwen3") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["qwen3"]() + + PreTrainedConfig.__post_init__(self, **kwargs) + + +class QianfanOCRDropPath(BeitDropPath): + pass + + +class QianfanOCRVisionAttention(InternVLVisionAttention): + pass + + +class QianfanOCRVisionMLP(InternVLVisionMLP): + pass + + +class QianfanOCRVisionLayer(InternVLVisionLayer): + """Vision transformer layer with stochastic depth (DropPath) support.""" + + def __init__(self, config: QianfanOCRVisionConfig, drop_path_rate: float = 0.0) -> None: + super().__init__(config) + del self.seq_len_dim + del self.chunk_size_feed_forward + self.drop_path1 = nn.Identity() if drop_path_rate <= 0.0 else QianfanOCRDropPath(drop_path_rate) + self.drop_path2 = nn.Identity() if drop_path_rate <= 0.0 else QianfanOCRDropPath(drop_path_rate) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.layernorm_before(hidden_states) + # Self Attention + hidden_states, _ = self.attention(hidden_states, **kwargs) + hidden_states = self.lambda_1 * hidden_states + hidden_states = self.drop_path1(hidden_states) + hidden_states = hidden_states + residual + + residual = hidden_states + hidden_states = self.layernorm_after(hidden_states) + # Fully Connected + hidden_states = self.mlp(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.lambda_2 * hidden_states + hidden_states = self.drop_path2(hidden_states) + residual + + return hidden_states + + +class QianfanOCRVisionEmbeddings(InternVLVisionEmbeddings): + pass + + +class QianfanOCRVisionModelOutputWithPooling(BaseModelOutputWithPooling): + r""" + pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`): + Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if + *config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token + will be returned. + """ + + pass + + +@auto_docstring +class QianfanOCRVisionPreTrainedModel(InternVLVisionPreTrainedModel): + config_class = QianfanOCRVisionConfig + base_model_prefix = "vision_model" + _no_split_modules = ["QianfanOCRVisionLayer"] + _can_record_outputs = { + "hidden_states": QianfanOCRVisionLayer, + "attentions": QianfanOCRVisionAttention, + } + + +@auto_docstring +class QianfanOCRVisionModel(InternVLVisionModel): + def __init__(self, config: QianfanOCRVisionConfig) -> None: + super().__init__(config) + del self.encoder + dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers, device="cpu")] + self.layers = nn.ModuleList( + [QianfanOCRVisionLayer(config, drop_path_rate=dpr[i]) for i in range(config.num_hidden_layers)] + ) + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + bool_masked_pos: torch.BoolTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | QianfanOCRVisionModelOutputWithPooling: + r""" + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + """ + hidden_states = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos) + for layer_module in self.layers: + hidden_states = layer_module(hidden_states, **kwargs) + hidden_states = self.layernorm(hidden_states) + + return QianfanOCRVisionModelOutputWithPooling( + last_hidden_state=hidden_states, + ) + + +class QianfanOCRMultiModalProjector(InternVLMultiModalProjector): + pass + + +class QianfanOCRPreTrainedModel(InternVLPreTrainedModel): + config_class = QianfanOCRConfig + input_modalities = ("image", "text") + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for QianfanOCR outputs, with hidden states and attentions. + """ +) +class QianfanOCRModelOutputWithPast(InternVLModelOutputWithPast): + r""" + image_hidden_states (`torch.FloatTensor`, *optional*): + A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. + """ + + +class QianfanOCRModel(InternVLModel): + pass + + +class QianfanOCRCausalLMOutputWithPast(InternVLCausalLMOutputWithPast): + pass + + +class QianfanOCRForConditionalGeneration(InternVLForConditionalGeneration): + @can_return_tuple + @auto_docstring + def forward(self, **super_kwargs) -> tuple | QianfanOCRCausalLMOutputWithPast: + r""" + Example: + ```python + >>> import torch + >>> from transformers import AutoProcessor, AutoModelForImageTextToText + >>> torch_device = "cuda" + >>> processor = AutoProcessor.from_pretrained("baidu/Qianfan-OCR") + >>> model = AutoModelForImageTextToText.from_pretrained( + ... "baidu/Qianfan-OCR", dtype=torch.bfloat16, device_map=torch_device + ... ) + >>> messages = [ + ... { + ... "role": "user", + ... "content": [ + ... {"type": "image", "url": "https://example.com/image.jpg"}, + ... {"type": "text", "text": "Describe this image."}, + ... ], + ... }, + ... ] + >>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device) + >>> generate_ids = model.generate(**inputs, max_new_tokens=200) + >>> print(processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)) + ```""" + return super().forward(**super_kwargs) + + +class QianfanOCRProcessor(InternVLProcessor): + def __init__( + self, + image_processor=None, + tokenizer=None, + image_seq_length: int = 256, + chat_template=None, + image_placeholder_token: str = "", + **kwargs, + ): + r""" + image_placeholder_token (`str`, *optional*, defaults to `""`): + The token emitted by the chat template to mark image positions. + It is replaced by the full ``...`` + sequence during processing. + """ + ProcessorMixin.__init__(self, image_processor, tokenizer, chat_template=chat_template, **kwargs) + self.image_seq_length = image_seq_length + self.start_image_token = tokenizer.start_image_token + self.end_image_token = tokenizer.end_image_token + self.start_image_token_id = tokenizer.start_image_token_id + self.end_image_token_id = tokenizer.end_image_token_id + self.image_token = tokenizer.context_image_token + self.image_token_id = tokenizer.context_image_token_id + self.image_ids = [self.image_token_id, self.start_image_token_id, self.end_image_token_id] + self.image_placeholder_token = image_placeholder_token + self.video_token = None + self.video_processor = None + + def _insert_media_placeholders( + self, + text: list[str], + image_pixel_values, + video_pixel_values, + image_num_patches: list[int], + video_num_patches: list[int], + image_num_patches_indices: np.ndarray, + video_num_patches_indices: np.ndarray, + video_patch_indices: np.ndarray, + ): + """ + Processes interleaved text with placeholders, replacing them with appropriate image tokens. + """ + image_index = 0 + processed_text = [] + image_patches = [] + replace_strings = [] + for prompt in text: + new_prompt = prompt + while self.image_placeholder_token in new_prompt: + start_index = image_num_patches_indices[image_index - 1] if image_index > 0 else 0 + end_index = image_num_patches_indices[image_index] + image_patches.append(image_pixel_values[start_index:end_index]) + new_prompt = new_prompt.replace(self.image_placeholder_token, "", 1) + replace_strings.append( + f"{self.start_image_token}{self.image_token * self.image_seq_length * image_num_patches[image_index]}{self.end_image_token}" + ) + image_index += 1 + while "" in new_prompt: + replace_str = replace_strings.pop(0) + new_prompt = new_prompt.replace("", replace_str, 1) + processed_text.append(new_prompt) + return processed_text, image_patches, image_index, 0 + + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + videos=None, + **kwargs, + ) -> BatchFeature: + if videos is not None: + raise ValueError("QianfanOCR does not support video input.") + + return super().__call__(images=images, text=text, videos=None, **kwargs) + + +__all__ = [ + "QianfanOCRVisionConfig", + "QianfanOCRConfig", + "QianfanOCRVisionPreTrainedModel", + "QianfanOCRVisionModel", + "QianfanOCRPreTrainedModel", + "QianfanOCRModel", + "QianfanOCRForConditionalGeneration", + "QianfanOCRProcessor", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/processing_qianfan_ocr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/processing_qianfan_ocr.py new file mode 100644 index 0000000000000000000000000000000000000000..f8d953bed7ac9b1b22f5e14aac64788990da8bbd --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qianfan_ocr/processing_qianfan_ocr.py @@ -0,0 +1,241 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qianfan_ocr/modular_qianfan_ocr.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qianfan_ocr.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np + +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput, concatenate_list, make_flat_list_of_images +from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring + + +class QianfanOCRProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding_side": "left", + "return_mm_token_type_ids": False, + }, + "images_kwargs": { + "crop_to_patches": True, + }, + "videos_kwargs": { + "return_tensors": "pt", + }, + } + + +@auto_docstring +class QianfanOCRProcessor(ProcessorMixin): + def __init__( + self, + image_processor=None, + tokenizer=None, + image_seq_length: int = 256, + chat_template=None, + image_placeholder_token: str = "", + **kwargs, + ): + r""" + image_placeholder_token (`str`, *optional*, defaults to `""`): + The token emitted by the chat template to mark image positions. + It is replaced by the full ``...`` + sequence during processing. + """ + super().__init__(image_processor, tokenizer, chat_template=chat_template, **kwargs) + self.image_seq_length = image_seq_length + self.start_image_token = tokenizer.start_image_token + self.end_image_token = tokenizer.end_image_token + self.start_image_token_id = tokenizer.start_image_token_id + self.end_image_token_id = tokenizer.end_image_token_id + self.image_token = tokenizer.context_image_token + self.image_token_id = tokenizer.context_image_token_id + self.image_ids = [self.image_token_id, self.start_image_token_id, self.end_image_token_id] + self.image_placeholder_token = image_placeholder_token + self.video_token = None + self.video_processor = None + + def _insert_media_placeholders( + self, + text: list[str], + image_pixel_values, + video_pixel_values, + image_num_patches: list[int], + video_num_patches: list[int], + image_num_patches_indices: np.ndarray, + video_num_patches_indices: np.ndarray, + video_patch_indices: np.ndarray, + ): + """ + Processes interleaved text with placeholders, replacing them with appropriate image tokens. + """ + image_index = 0 + processed_text = [] + image_patches = [] + replace_strings = [] + for prompt in text: + new_prompt = prompt + while self.image_placeholder_token in new_prompt: + start_index = image_num_patches_indices[image_index - 1] if image_index > 0 else 0 + end_index = image_num_patches_indices[image_index] + image_patches.append(image_pixel_values[start_index:end_index]) + new_prompt = new_prompt.replace(self.image_placeholder_token, "", 1) + replace_strings.append( + f"{self.start_image_token}{self.image_token * self.image_seq_length * image_num_patches[image_index]}{self.end_image_token}" + ) + image_index += 1 + while "" in new_prompt: + replace_str = replace_strings.pop(0) + new_prompt = new_prompt.replace("", replace_str, 1) + processed_text.append(new_prompt) + return processed_text, image_patches, image_index, 0 + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + videos=None, + **kwargs, + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + """ + if videos is not None: + raise ValueError("QianfanOCR does not support video input.") + if text is None: + raise ValueError("You have to specify text.") + + output_kwargs = self._merge_kwargs( + QianfanOCRProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + if not isinstance(text, (list, tuple)): + text = [text] + + # Process images and videos separately, as videos don't support crop_to_patches + image_num_patches = [] + image_pixel_values = None + image_num_patches_indices = np.array([0]) + if images is not None: + images = self.image_processor.fetch_images(images) + images = make_flat_list_of_images(images) + image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + image_num_patches = image_inputs.pop("num_patches") + image_pixel_values = image_inputs.pop("pixel_values") + image_num_patches_indices = np.cumsum(image_num_patches) + + video_num_patches = [] # per frame + video_pixel_values = None + video_patch_indices = np.array([0]) + video_num_patches_indices = np.array([0]) + if videos is not None: + video_kwargs = output_kwargs["videos_kwargs"] + video_inputs = self.video_processor(videos=videos, **video_kwargs) + video_pixel_values = video_inputs.pop("pixel_values_videos") + + batch_size, num_frames, *_ = video_pixel_values.shape + num_frames_per_video = np.full(batch_size, num_frames) + num_frames = sum(num_frames_per_video) # total + video_patch_indices = np.empty(batch_size + 1, int) + video_patch_indices[0] = 0 + video_patch_indices[1:] = np.cumsum(num_frames_per_video) + video_num_patches = [1] * num_frames + video_num_patches_indices = np.empty(num_frames + 1, int) + video_num_patches_indices[0] = 0 + video_num_patches_indices[1:] = np.cumsum(video_num_patches) + video_pixel_values = video_pixel_values.flatten(0, 1) + + image_videos_inputs = {} + if images is not None or videos is not None: + text, image_video_patches, image_index, video_index = self._insert_media_placeholders( + text, + image_pixel_values, + video_pixel_values, + image_num_patches, + video_num_patches, + image_num_patches_indices, + video_num_patches_indices, + video_patch_indices, + ) + if images is not None and image_index != len(images): + raise ValueError("Number of image placeholders in the prompt does not match the number of images.") + if videos is not None and video_index != len(num_frames_per_video): + raise ValueError("Number of video placeholders in the prompt does not match the number of videos.") + + # Concatenate the interleaved image and video patches (function agnostic to the patches type (list, numpy array, torch tensor)) + image_videos_inputs = {"pixel_values": concatenate_list(image_video_patches)} + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None) + text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) + self._check_special_mm_tokens(text, text_inputs, modalities=["image"]) + + if return_mm_token_type_ids: + text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"]) + return BatchFeature(data={**text_inputs, **image_videos_inputs}, tensor_type=return_tensors) + + def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs): + """ + Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. + + Args: + image_sizes (`list[list[int]]`, *optional*): + The input sizes formatted as (height, width) per each image. + + Returns: + `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided + input modalities, along with other useful data. + """ + + vision_data = {} + if image_sizes is not None: + images_kwargs = QianfanOCRProcessorKwargs._defaults.get("images_kwargs", {}) + images_kwargs.update(kwargs) + + num_image_patches = [ + self.image_processor.get_number_of_image_patches(*image_size, images_kwargs) + for image_size in image_sizes + ] + # Add 2 for BOI and EOI tokens + num_image_tokens = [2 + (self.image_seq_length * num_patches) for num_patches in num_image_patches] + vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) + + return MultiModalData(**vision_data) + + @property + def model_input_names(self): + # Overwritten because QianfanOCR renames video inputs to `pixel_values` before returning + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + return tokenizer_input_names + image_processor_input_names + + +__all__ = ["QianfanOCRProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e447a94c54c86d07eee6c534a6860a2a5a6d0c3e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2024 The Qwen Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_qwen2 import * + from .modeling_qwen2 import * + from .tokenization_qwen2 import * + from .tokenization_qwen2_fast import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/configuration_qwen2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/configuration_qwen2.py new file mode 100644 index 0000000000000000000000000000000000000000..ae41af7b211fbb3e5910936fb3a4a79ed48edd8e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/configuration_qwen2.py @@ -0,0 +1,99 @@ +# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Qwen2 model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="Qwen/Qwen2-7B") +@strict +class Qwen2Config(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Qwen2Model, Qwen2Config + + >>> # Initializing a Qwen2 style configuration + >>> configuration = Qwen2Config() + + >>> # Initializing a model from the Qwen2-7B style configuration + >>> model = Qwen2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2" + keys_to_ignore_at_inference = ["past_key_values"] + + # Default tensor parallel plan for base model `Qwen2` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 151936 + hidden_size: int = 4096 + intermediate_size: int = 22016 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_key_value_heads: int | None = 32 + hidden_act: str = "silu" + max_position_embeddings: int = 32768 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-6 + use_cache: bool = True + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + use_sliding_window: bool = False + sliding_window: int | None = 4096 + max_window_layers: int = 28 + layer_types: list[str] | None = None + attention_dropout: float | int = 0.0 + pad_token_id: int | None = None + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = None + + def __post_init__(self, **kwargs): + self.sliding_window = self.sliding_window if self.use_sliding_window else None + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" + if self.sliding_window is not None and i >= self.max_window_layers + else "full_attention" + for i in range(self.num_hidden_layers) + ] + + super().__post_init__(**kwargs) + + +__all__ = ["Qwen2Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/modeling_qwen2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/modeling_qwen2.py new file mode 100644 index 0000000000000000000000000000000000000000..9263e1d42937db09cf653865cbda215646ea070d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/modeling_qwen2.py @@ -0,0 +1,510 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qwen2/modular_qwen2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qwen2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +from collections.abc import Callable +from typing import Optional + +import torch +from torch import nn + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import ( + GenericForQuestionAnswering, + GenericForSequenceClassification, + GenericForTokenClassification, + GradientCheckpointingLayer, +) +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_qwen2 import Qwen2Config + + +class Qwen2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class Qwen2RotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: Qwen2Config, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: Qwen2Config | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class Qwen2Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: Qwen2Config, layer_idx: int): + super().__init__() + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) + self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, # main diff with Llama + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +@use_kernel_forward_from_hub("RMSNorm") +class Qwen2RMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + Qwen2RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class Qwen2DecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Qwen2Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = Qwen2Attention(config=config, layer_idx=layer_idx) + + self.mlp = Qwen2MLP(config) + self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class Qwen2PreTrainedModel(PreTrainedModel): + config: Qwen2Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["Qwen2DecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": Qwen2DecoderLayer, + "attentions": Qwen2Attention, + } + + +@auto_docstring +class Qwen2Model(Qwen2PreTrainedModel): + def __init__(self, config: Qwen2Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = Qwen2RotaryEmbedding(config=config) + self.gradient_checkpointing = False + self.has_sliding_layers = "sliding_attention" in self.config.layer_types + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + } + # The sliding window alternating layers are not always activated depending on the config + if self.has_sliding_layers: + causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[self.config.layer_types[i]], + position_embeddings=position_embeddings, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +@auto_docstring +class Qwen2ForCausalLM(Qwen2PreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = Qwen2Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, Qwen2ForCausalLM + + >>> model = Qwen2ForCausalLM.from_pretrained("meta-qwen2/Qwen2-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-qwen2/Qwen2-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class Qwen2ForSequenceClassification(GenericForSequenceClassification, Qwen2PreTrainedModel): + pass + + +class Qwen2ForTokenClassification(GenericForTokenClassification, Qwen2PreTrainedModel): + pass + + +class Qwen2ForQuestionAnswering(GenericForQuestionAnswering, Qwen2PreTrainedModel): + base_model_prefix = "transformer" # For BC, where `transformer` was used instead of `model` + + +__all__ = [ + "Qwen2PreTrainedModel", + "Qwen2Model", + "Qwen2ForCausalLM", + "Qwen2RMSNorm", + "Qwen2ForSequenceClassification", + "Qwen2ForTokenClassification", + "Qwen2ForQuestionAnswering", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/modular_qwen2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/modular_qwen2.py new file mode 100644 index 0000000000000000000000000000000000000000..bcc1496d3acf869f3544e2db35126e26949224a9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/modular_qwen2.py @@ -0,0 +1,204 @@ +from collections.abc import Callable + +import torch +from torch import nn + +from ...cache_utils import Cache, DynamicCache +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_outputs import ( + BaseModelOutputWithPast, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..gemma2.modeling_gemma2 import Gemma2RotaryEmbedding +from ..llama.modeling_llama import ( + LlamaAttention, + LlamaDecoderLayer, + LlamaForCausalLM, + LlamaForQuestionAnswering, + LlamaForSequenceClassification, + LlamaForTokenClassification, + LlamaMLP, + LlamaPreTrainedModel, + apply_rotary_pos_emb, + eager_attention_forward, +) +from ..mistral.modeling_mistral import MistralModel +from .configuration_qwen2 import Qwen2Config + + +logger = logging.get_logger(__name__) + + +class Qwen2MLP(LlamaMLP): + def __init__(self, config): + super().__init__(config) + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + + +class Qwen2RotaryEmbedding(Gemma2RotaryEmbedding): + pass + + +class Qwen2Attention(LlamaAttention): + def __init__(self, config: Qwen2Config, layer_idx: int): + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + super().__init__(config, layer_idx) + self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False) + self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, # main diff with Llama + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Qwen2DecoderLayer(LlamaDecoderLayer): + pass + + +class Qwen2PreTrainedModel(LlamaPreTrainedModel): + pass + + +class Qwen2Model(MistralModel): + def __init__(self, config: Qwen2Config): + super().__init__(config) + self.has_sliding_layers = "sliding_attention" in self.config.layer_types + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + } + # The sliding window alternating layers are not always activated depending on the config + if self.has_sliding_layers: + causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[self.config.layer_types[i]], + position_embeddings=position_embeddings, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +class Qwen2ForCausalLM(LlamaForCausalLM): + pass + + +class Qwen2ForSequenceClassification(LlamaForSequenceClassification): + pass + + +class Qwen2ForTokenClassification(LlamaForTokenClassification): + pass + + +class Qwen2ForQuestionAnswering(LlamaForQuestionAnswering): + pass + + +__all__ = [ + "Qwen2PreTrainedModel", + "Qwen2Model", + "Qwen2ForCausalLM", + "Qwen2RMSNorm", # noqa: F822 + "Qwen2ForSequenceClassification", + "Qwen2ForTokenClassification", + "Qwen2ForQuestionAnswering", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/tokenization_qwen2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/tokenization_qwen2.py new file mode 100644 index 0000000000000000000000000000000000000000..dc0646beb1a4af285c69b7474bc578e172b53338 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2/tokenization_qwen2.py @@ -0,0 +1,101 @@ +# Copyright 2024 The Qwen team, Alibaba Group and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for Qwen2.""" + +from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers +from tokenizers.models import BPE + +from ...tokenization_utils_tokenizers import TokenizersBackend +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = { + "vocab_file": "vocab.json", + "merges_file": "merges.txt", + "tokenizer_file": "tokenizer.json", +} + +MAX_MODEL_INPUT_SIZES = {"qwen/qwen-tokenizer": 32768} + +PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""" + + +class Qwen2Tokenizer(TokenizersBackend): + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + model = BPE + + def __init__( + self, + vocab: str | dict[str, int] | None = None, + merges: str | list[str] | None = None, + unk_token: str = "<|endoftext|>", + bos_token=None, + eos_token: str = "<|endoftext|>", + pad_token: str = "<|endoftext|>", + add_prefix_space=None, + **kwargs, + ): + self.add_prefix_space = add_prefix_space if add_prefix_space is not None else False + self._vocab = ( + vocab + if vocab is not None + else { + "<|endoftext|>": 0, + } + ) + self._merges = merges or [] + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + dropout=None, + unk_token=None, + continuing_subword_prefix="", + end_of_word_suffix="", + fuse_unk=False, + byte_fallback=False, + ) + ) + self._tokenizer.decoder = decoders.ByteLevel() + self._tokenizer.normalizer = normalizers.NFC() + self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence( + [ + pre_tokenizers.Split( + Regex(PRETOKENIZE_REGEX), + behavior="isolated", + invert=False, + ), + pre_tokenizers.ByteLevel( + add_prefix_space=self.add_prefix_space, + use_regex=False, + ), + ] + ) + + super().__init__( + unk_token=unk_token, + bos_token=bos_token, + eos_token=eos_token, + pad_token=pad_token, + add_prefix_space=add_prefix_space, + **kwargs, + ) + + self.add_tokens([AddedToken(token, special=True) for token in self.all_special_tokens]) + + +__all__ = ["Qwen2Tokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0d7ddae0da7e1b2c16b95e94e6678f9511cb716f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_qwen2_5_omni import * + from .modeling_qwen2_5_omni import * + from .processing_qwen2_5_omni import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py new file mode 100644 index 0000000000000000000000000000000000000000..1564d2b36de972d685fff5bb5ccd86f8078ab74c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py @@ -0,0 +1,658 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qwen2_5_omni.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniVisionEncoderConfig(PreTrainedConfig): + r""" + window_size (`int`, *optional*, defaults to 11): + Size of windows. + out_hidden_size (`int`, *optional*, defaults to 3584): + The output hidden size of the vision model. + fullatt_block_indexes (`int`, *optional*, defaults to `[7, 15, 23, 31]`): + Indices of layers with full attention + + Example: + + ```python + >>> from transformers import Qwen2_5OmniVisionEncoderConfig, Qwen2_5OmniVisionEncoder + + >>> # Initializing a Qwen2_5OmniVisionEncoderConfig + >>> configuration = Qwen2_5OmniVisionEncoderConfig() + + >>> # Initializing a Qwen2_5OmniVisionEncoder (with random weights) + >>> model = Qwen2_5OmniVisionEncoder(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_vision_encoder" + base_config_key = "vision_config" + + depth: int = 32 + hidden_size: int = 3584 + hidden_act: str = "silu" + intermediate_size: int = 3420 + num_heads: int = 16 + in_channels: int = 3 + patch_size: int | list[int] | tuple[int, int] = 14 + spatial_merge_size: int = 2 + temporal_patch_size: int | list[int] | tuple[int, int] = 2 + window_size: int = 112 + out_hidden_size: int = 3584 + fullatt_block_indexes: list[int] | tuple[int, ...] = (7, 15, 23, 31) + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniAudioEncoderConfig(PreTrainedConfig): + r""" + max_source_positions (`int`, *optional*, defaults to 1500): + The maximum sequence length of log-mel filter-bank features that this model might ever be used with. + n_window (`int`, *optional*, defaults to 100): + The chunk for conv and flash attn in AudioEncoder. + output_dim (`int`, *optional*, defaults to 3584): + The output dimension of AudioEncoder. + + Example: + + ```python + >>> from transformers import Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniAudioEncoder + + >>> # Initializing a Qwen2_5OmniAudioEncoderConfig + >>> configuration = Qwen2_5OmniAudioEncoderConfig() + + >>> # Initializing a Qwen2_5OmniAudioEncoder (with random weights) + >>> model = Qwen2_5OmniAudioEncoder(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_audio_encoder" + attribute_map = {"num_hidden_layers": "encoder_layers"} + + num_mel_bins: int = 128 + encoder_layers: int = 32 + encoder_attention_heads: int = 20 + encoder_ffn_dim: int = 5120 + d_model: int = 1280 + dropout: float | int = 0.0 + attention_dropout: float | int = 0.0 + activation_function: str = "gelu" + activation_dropout: float | int = 0.0 + scale_embedding: bool = False + initializer_range: float = 0.02 + max_source_positions: int = 1500 + + n_window: int = 100 + output_dim: int = 3584 + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniTextConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig + + >>> # Initializing a Qwen2_5OmniAudioEncoder config + >>> audio_config = Qwen2_5OmniAudioEncoderConfig() + + >>> # Initializing a Qwen2_5OmniVisionEncoder config + >>> vision_config = Qwen2_5OmniVisionEncoderConfig() + + >>> # Initializing a Qwen2.5OmniThinker configuration + >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config) + + >>> # Initializing a model from the Qwen-Omni style configuration + >>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_text" + keys_to_ignore_at_inference = ["past_key_values"] + default_theta = 1000000.0 + + # Default tensor parallel plan for base model `Qwen25OmniText` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + ignore_keys_at_rope_validation = {"mrope_section"} + + vocab_size: int = 152064 + hidden_size: int = 3584 + intermediate_size: int = 18944 + num_hidden_layers: int = 28 + num_attention_heads: int = 28 + num_key_value_heads: int | None = 4 + hidden_act: str = "silu" + max_position_embeddings: int = 32768 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-6 + use_cache: bool = True + rope_parameters: RopeParameters | dict | None = None + use_sliding_window: bool = False + sliding_window: int | None = 32768 + max_window_layers: int = 28 + layer_types: list[str] | None = None + attention_dropout: float | int = 0.0 + pad_token_id: int | None = None + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = None + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + self.sliding_window = self.sliding_window if self.use_sliding_window else None + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" + if self.sliding_window is not None and i >= self.max_window_layers + else "full_attention" + for i in range(self.num_hidden_layers) + ] + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniThinkerConfig(PreTrainedConfig): + r""" + position_id_per_seconds (`int`, *optional*, defaults to 25): + The increment of position id per second. + seconds_per_chunk (`int`, *optional*, defaults to 2): + The duration in seconds of the chunk of audio and video data. + audio_start_token_id (`int`, *optional*, defaults to 151647): + The audio start token index to encode the audio prompt. + audio_end_token_id (`int`, *optional*, defaults to 151648): + The audio end token index to encode the audio prompt. + user_token_id (`int, *optional*, defaults to 872): + The user token index to encode the user token. + + Example: + + ```python + >>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig + + >>> # Initializing a Qwen2_5OmniAudioEncoder config + >>> audio_config = Qwen2_5OmniAudioEncoderConfig() + + >>> # Initializing a Qwen2_5OmniVisionEncoder config + >>> vision_config = Qwen2_5OmniVisionEncoderConfig() + + >>> # Initializing a Qwen2_5OmniTextConfig config + >>> text_config = Qwen2_5OmniTextConfig() + + >>> # Initializing a Qwen2.5OmniThinker configuration + >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config, text_config) + + >>> # Initializing a model from the Qwen-Omni style configuration + >>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_thinker" + attribute_map = { + "image_token_id": "image_token_index", + "video_token_id": "video_token_index", + "audio_token_id": "audio_token_index", + } + sub_configs = { + "audio_config": Qwen2_5OmniAudioEncoderConfig, + "vision_config": Qwen2_5OmniVisionEncoderConfig, + "text_config": Qwen2_5OmniTextConfig, + } + + audio_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + audio_token_index: int = 151646 + image_token_index: int = 151655 + video_token_index: int = 151656 + position_id_per_seconds: int = 25 + seconds_per_chunk: int = 2 + audio_start_token_id: int = 151647 + audio_end_token_id: int = 151648 + user_token_id: int = 872 + initializer_range: float = 0.02 + tie_word_embeddings: bool = False + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = Qwen2_5OmniVisionEncoderConfig(**self.vision_config) + elif self.vision_config is None: + self.vision_config = Qwen2_5OmniVisionEncoderConfig() + + if isinstance(self.audio_config, dict): + self.audio_config = Qwen2_5OmniAudioEncoderConfig(**self.audio_config) + elif self.audio_config is None: + self.audio_config = Qwen2_5OmniAudioEncoderConfig() + + if isinstance(self.text_config, dict): + self.text_config = Qwen2_5OmniTextConfig(**self.text_config) + elif self.text_config is None: + self.text_config = Qwen2_5OmniTextConfig() + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniTalkerConfig(PreTrainedConfig): + r""" + tts_text_start_token_id (`int`, *optional*, defaults to 151860): + The tts text start token index to encode the start of tts text. + tts_text_end_token_id (`int`, *optional*, defaults to 151861): + The tts text end token index to encode the end of tts text. + tts_text_pad_token_id (`int`, *optional*, defaults to 151859): + The tts text pad token index to encode the pad of tts text. + tts_codec_start_token_id (`int`, *optional*, defaults to 8293): + The tts codec start token index to encode the start of tts codec. + tts_codec_end_token_id (`int`, *optional*, defaults to 8294): + The tts codec end token index to encode the end of tts codec. + tts_codec_pad_token_id (`int`, *optional*, defaults to 8292): + The tts codec pad token index to encode the pad of tts codec. + tts_codec_mask_token_id (`int`, *optional*, defaults to 8296): + The tts codec mask token index to encode the mask of tts codec. + position_id_per_seconds (`int`, *optional*, defaults to 25): + The increment of position id per second. + seconds_per_chunk (`int`, *optional*, defaults to 2): + The duration in seconds of the chunk of audio and video data. + audio_start_token_id (`int`, *optional*, defaults to 151647): + The audio start token index to encode the audio prompt. + audio_end_token_id (`int`, *optional*, defaults to 151648): + The audio end token index to encode the audio prompt. + + Example: + + ```python + >>> from transformers import Qwen2_5OmniTalkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig + + >>> # Initializing a Qwen2_5OmniAudioEncoder config + >>> audio_config = Qwen2_5OmniAudioEncoderConfig() + + >>> # Initializing a Qwen2 config + >>> text_config = Qwen2Config() + + >>> # Initializing a Qwen2_5Omni configuration + >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, text_config) + + >>> # Initializing a model from the qwen2-audio style configuration + >>> model = Qwen2_5OmniTalkerForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_talker" + default_theta = 1000000.0 + attribute_map = { + "image_token_id": "image_token_index", + "video_token_id": "video_token_index", + "audio_token_id": "audio_token_index", + } + ignore_keys_at_rope_validation = {"mrope_section"} + + audio_token_index: int = 151646 + image_token_index: int = 151655 + video_token_index: int = 151656 + vocab_size: int = 8448 + tts_text_start_token_id: int = 151860 + tts_text_end_token_id: int = 151861 + tts_text_pad_token_id: int = 151859 + tts_codec_start_token_id: int = 8293 + tts_codec_end_token_id: int = 8294 + tts_codec_pad_token_id: int = 8292 + tts_codec_mask_token_id: int = 8296 + vision_start_token_id: int = 151652 + vision_end_token_id: int = 151653 + embedding_size: int = 3584 + hidden_size: int = 3584 + intermediate_size: int = 18944 + num_hidden_layers: int = 28 + num_attention_heads: int = 28 + num_key_value_heads: int = 4 + hidden_act: str = "silu" + max_position_embeddings: int = 32768 + rms_norm_eps: float = 1e-06 + head_dim: int = 128 + use_cache: bool = True + tie_word_embeddings: bool = False + use_sliding_window: bool = False + sliding_window: int | None = 32768 + max_window_layers: int = 28 + attention_dropout: float | int = 0.0 + rope_parameters: RopeParameters | dict | None = None + position_id_per_seconds: int = 25 + seconds_per_chunk: int = 2 + audio_start_token_id: int = 151647 + audio_end_token_id: int = 151648 + initializer_range: float = 0.02 + spatial_merge_size: int = 2 + layer_types: list[str] | None = None + pad_token_id: int | None = None + + def __post_init__(self, **kwargs): + self.sliding_window = self.sliding_window if self.use_sliding_window else None + + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" + if self.sliding_window is not None and i >= self.max_window_layers + else "full_attention" + for i in range(self.num_hidden_layers) + ] + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniDiTConfig(PreTrainedConfig): + r""" + ff_mult (`int`, *optional*, defaults to 2): + The multiplier for the feedforward layer in each transformer block. + emb_dim (`int`, *optional*, defaults to 512): + The dimension of the embedding layer. + block_size (`int`, *optional*, defaults to 64): + Number of tokens (frames) in each processing block. + look_ahead_layers (`list[int]`, *optional*, defaults to `[10]`): + Number of transformer layers that are permitted to attend to future blocks + look_backward_layers (`list[int]`, *optional*, defaults to `[0, 20]`): + Number of transformer layers that attend to past blocks beyond the current block boundary + repeats (`int`, *optional*, defaults to 2): + The number of times the codec embeddings are repeated. + num_embeds (`int`, *optional*, defaults to 8193): + The number of unique embeddings in the codec. + mel_dim (`int`, *optional*, defaults to 80): + The dimension of the mel-spectrogram. + enc_emb_dim (`int`, *optional*, defaults to 192): + The dimension of the pre-trained speaker embedding. + enc_dim (`int`, *optional*, defaults to 128): + The dimension of the encoder output. + enc_channels (`list[int]`, *optional*, defaults to `[256, 256, 256, 256, 768]`): + A list of output channels for each TDNN/SERes2Net layer in the encoder. + enc_kernel_sizes (`list[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`): + A list of kernel sizes for each layer in the encoder. + enc_dilations (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`): + A list of dilations for each layer in the encoder. + enc_attention_channels (`int`, *optional*, defaults to 64): + The number of attention channels in the SqueezeExcitationBlock. + enc_res2net_scale (`int`, *optional*, defaults to 2): + The scale of the Res2Net block in the encoder. + enc_se_channels (`int`, *optional*, defaults to 64): + The number of output channels after squeeze in the SqueezeExcitationBlock. + """ + + model_type = "qwen2_5_omni_dit" + + hidden_size: int = 1024 + num_hidden_layers: int = 22 + num_attention_heads: int = 16 + ff_mult: int = 2 + emb_dim: int = 512 + head_dim: int = 64 + rope_parameters: RopeParameters | dict | None = None + max_position_embeddings: int = 32768 + block_size: int = 24 + look_ahead_layers: list[int] | tuple[int, ...] = (10,) + look_backward_layers: list[int] | tuple[int, ...] = (0, 20) + repeats: int = 2 + num_embeds: int = 8193 + mel_dim: int = 80 + dropout: float | int = 0.1 + enc_emb_dim: int = 192 + enc_dim: int = 128 + enc_channels: list[int] | tuple[int, ...] = (256, 256, 256, 256, 768) + enc_kernel_sizes: list[int] | tuple[int, ...] = (5, 3, 3, 3, 1) + enc_dilations: list[int] | tuple[int, ...] = (1, 2, 3, 4, 1) + enc_attention_channels: int = 64 + enc_res2net_scale: int = 2 + enc_se_channels: int = 64 + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniBigVGANConfig(PreTrainedConfig): + r""" + mel_dim (`int`, *optional*, defaults to 80): + The dimension of the mel-spectrogram. + upsample_initial_channel (`int`, *optional*, defaults to 1536): + The number of channels in the initial upsampling layer. + resblock_kernel_sizes (`list[int]`, *optional*, defaults to `[3, 7, 11]`): + A list of kernel sizes for each residual block. + resblock_dilation_sizes (`list[list[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): + A list of dilation sizes for each residual block. + upsample_rates (`list[int]`, *optional*, defaults to `[5, 3, 2, 2, 2, 2]`): + A list of upsampling rates for each upsampling layer. + upsample_kernel_sizes (`list[int]`, *optional*, defaults to `[11, 7, 4, 4, 4, 4]`): + A list of kernel sizes for each upsampling layer. + """ + + model_type = "qwen2_5_omni_bigvgan" + + mel_dim: int = 80 + upsample_initial_channel: int = 1536 + resblock_kernel_sizes: list[int] | tuple[int, ...] = (3, 7, 11) + resblock_dilation_sizes: list | tuple = ((1, 3, 5), (1, 3, 5), (1, 3, 5)) + upsample_rates: list[int] | tuple[int, ...] = (5, 3, 2, 2, 2, 2) + upsample_kernel_sizes: list[int] | tuple[int, ...] = (11, 7, 4, 4, 4, 4) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniToken2WavConfig(PreTrainedConfig): + r""" + dit_config ([`DiT_Args`], *optional*): + Configuration class for the Diffusion Transformer (DiT) module responsible for generating mel-spectrograms. + bigvgan_config ([`BigVGAN_Args`], *optional*): + Configuration class for the BigVGAN module responsible for converting mel-spectrograms to waveforms. + + Example: + + ```python + >>> from transformers import Qwen2_5OmniToken2WavModel, DiT_Args, BigVGAN_Args + + >>> # Initialize DiT configuration + >>> dit_config = DiT_Args( + ... dim=1024, + ... depth=22, + ... heads=16, + ... ff_mult=2 + ... ) + + >>> # Initialize BigVGAN configuration + >>> bigvgan_config = BigVGAN_Args( + ... mel_dim=80, + ... upsample_rates=[5,3,2,2,2,2] + ... ) + + >>> # Initialize main configuration + >>> config = Qwen2_5OmniToken2WavConfig(dit_config, bigvgan_config) + + >>> # Initialize model with config + >>> model = Qwen2_5OmniToken2Wav(config) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "qwen2_5_omni_token2wav" + sub_configs = { + "dit_config": Qwen2_5OmniDiTConfig, + "bigvgan_config": Qwen2_5OmniBigVGANConfig, + } + + dit_config: dict | PreTrainedConfig | None = None + bigvgan_config: dict | PreTrainedConfig | None = None + + def __post_init__(self, **kwargs): + if self.dit_config is None: + self.dit_config = Qwen2_5OmniDiTConfig() + elif isinstance(self.dit_config, dict): + self.dit_config = Qwen2_5OmniDiTConfig(**self.dit_config) + + if self.bigvgan_config is None: + self.bigvgan_config = Qwen2_5OmniBigVGANConfig() + elif isinstance(self.bigvgan_config, dict): + self.bigvgan_config = Qwen2_5OmniBigVGANConfig(**self.bigvgan_config) + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniConfig(PreTrainedConfig): + r""" + thinker_config (`dict`, *optional*): + Configuration of the underlying thinker sub-model. + talker_config (`dict`, *optional*): + Configuration of the underlying talker sub-model. + token2wav_config (`dict`, *optional*): + Configuration of the underlying codec sub-model. + enable_audio_output (`bool`, *optional*, defaults to `True`): + Whether enable audio output and load talker and token2wav module. + + Example: + + ```python + >>> from transformers import ( + ... Qwen2_5OmniThinkerConfig, + ... Qwen2_5OmniTalkerConfig, + ... Qwen2_5OmniToken2WavConfig, + ... Qwen2_5OmniForConditionalGeneration, + ... Qwen2_5OmniConfig, + ... ) + + >>> # Initializing sub-modules configurations. + >>> thinker_config = Qwen2_5OmniThinkerConfig() + >>> talker_config = Qwen2_5OmniTalkerConfig() + >>> token2wav_config = Qwen2_5OmniToken2WavConfig() + + + >>> # Initializing a module style configuration + >>> configuration = Qwen2_5OmniConfig( + ... thinker_config, talker_config, token2wav_config + ... ) + + >>> # Initializing a model (with random weights) + >>> model = Qwen2_5OmniForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "qwen2_5_omni" + sub_configs = { + "thinker_config": Qwen2_5OmniThinkerConfig, + "talker_config": Qwen2_5OmniTalkerConfig, + "token2wav_config": Qwen2_5OmniToken2WavConfig, + } + + thinker_config: dict | PreTrainedConfig | None = None + talker_config: dict | PreTrainedConfig | None = None + token2wav_config: dict | PreTrainedConfig | None = None + enable_audio_output: bool = True + + def __post_init__(self, **kwargs): + if self.thinker_config is None: + self.thinker_config = Qwen2_5OmniThinkerConfig() + logger.info("thinker_config is None. Initializing thinker model with default values") + elif isinstance(self.thinker_config, dict): + self.thinker_config = Qwen2_5OmniThinkerConfig(**self.thinker_config) + + if self.talker_config is None: + self.talker_config = Qwen2_5OmniTalkerConfig() + logger.info("talker_config is None. Initializing talker model with default values") + elif isinstance(self.talker_config, dict): + self.talker_config = Qwen2_5OmniTalkerConfig(**self.talker_config) + + if self.token2wav_config is None: + self.token2wav_config = Qwen2_5OmniToken2WavConfig() + logger.info("token2wav_config is None. Initializing token2wav model with default values") + elif isinstance(self.token2wav_config, dict): + self.token2wav_config = Qwen2_5OmniToken2WavConfig(**self.token2wav_config) + + super().__post_init__(**kwargs) + + def get_text_config(self, *args, **kwargs): + """ + Returns the config that is meant to be used with text IO. On most models, it is the original config instance + itself. On specific composite models, it is under a set of valid names. + + Args: + decoder (`Optional[bool]`, *optional*, defaults to `False`): + If set to `True`, then only search for decoder config names. + """ + # Overridden for deeply nested config like Qwen2-Omni. We don't have any omni model + # except for Qwen yet. This has to be generalized if more deeply nested configs are + # added. NOTE: currently method used only by vLLM + return self.thinker_config.get_text_config(*args, **kwargs) + + +__all__ = [ + "Qwen2_5OmniConfig", + "Qwen2_5OmniThinkerConfig", + "Qwen2_5OmniTalkerConfig", + "Qwen2_5OmniToken2WavConfig", + "Qwen2_5OmniAudioEncoderConfig", + "Qwen2_5OmniBigVGANConfig", + "Qwen2_5OmniDiTConfig", + "Qwen2_5OmniTextConfig", + "Qwen2_5OmniVisionEncoderConfig", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py new file mode 100644 index 0000000000000000000000000000000000000000..89ff9a04de5dde4e660c8d117f22c7aea0b748aa --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py @@ -0,0 +1,3988 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qwen2_5_omni.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from torch.nn import Parameter + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + check_torch_load_is_safe, + logging, + torch_compilable_check, +) +from ...utils.deprecation import deprecate_kwarg +from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults +from ...utils.hub import cached_file +from ...utils.output_capturing import capture_outputs +from .configuration_qwen2_5_omni import ( + Qwen2_5OmniAudioEncoderConfig, + Qwen2_5OmniBigVGANConfig, + Qwen2_5OmniConfig, + Qwen2_5OmniDiTConfig, + Qwen2_5OmniTalkerConfig, + Qwen2_5OmniTextConfig, + Qwen2_5OmniThinkerConfig, + Qwen2_5OmniToken2WavConfig, + Qwen2_5OmniVisionEncoderConfig, +) + + +logger = logging.get_logger(__name__) + + +def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): + """Generates a 1D Kaiser-windowed sinc filter. + + Args: + cutoff (float): Normalized cutoff frequency (0 to 0.5). + half_width (float): Transition bandwidth. + kernel_size (int): Number of filter taps. + + Returns: + torch.Tensor: A tensor of shape (1, 1, kernel_size) representing the filter. + """ + is_even = kernel_size % 2 == 0 + half_size = kernel_size // 2 + + # Compute Kaiser window parameters + delta_f = 4 * half_width + attenuation = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 + + if attenuation > 50.0: + beta = 0.1102 * (attenuation - 8.7) + elif attenuation >= 21.0: + beta = 0.5842 * (attenuation - 21) ** 0.4 + 0.07886 * (attenuation - 21.0) + else: + beta = 0.0 + + kaiser_window = torch.kaiser_window(kernel_size, beta=beta, periodic=False, dtype=torch.float32) + + # Compute time indices + if is_even: + time_indices = torch.arange(-half_size, half_size) + 0.5 + else: + time_indices = torch.arange(kernel_size) - half_size + + # Compute sinc filter + if cutoff == 0: + return torch.zeros((1, 1, kernel_size), dtype=torch.float32) # Ensures correct shape + + sinc_filter = torch.sinc(2 * cutoff * time_indices) + normalized_filter = 2 * cutoff * kaiser_window * sinc_filter + + # Normalize to ensure sum = 1 (avoid leakage of constant component) + normalized_filter /= normalized_filter.sum() + + return normalized_filter.view(1, 1, kernel_size) + + +@auto_docstring +class Qwen2_5OmniPreTrainedModel(PreTrainedModel): + config: Qwen2_5OmniConfig + base_model_prefix = "model" + input_modalities = ("image", "video", "audio", "text") + supports_gradient_checkpointing = True + _no_split_modules = ["Qwen2_5OmniDecoderLayer", "Qwen2_5OmniVisionBlock"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn = True + _supports_sdpa = True + _can_compile_fullgraph = False + _supports_attention_backend = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, SinusoidsPositionEmbedding): + log_timescale_increment = np.log(module.max_timescale) / (module.channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(module.channels // 2).float()) + scaled_time = torch.arange(module.length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + init.copy_(module.positional_embedding, torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)) + elif isinstance(module, UpSample1d): + filter_tensor = kaiser_sinc_filter1d(0.5 / module.ratio, 0.6 / module.ratio, module.kernel_size) + init.copy_(module.filter, filter_tensor) + elif isinstance(module, DownSample1d): + filter_tensor = kaiser_sinc_filter1d(module.cutoff, module.half_width, module.kernel_size) + init.copy_(module.filter, filter_tensor) + elif isinstance(module, Qwen2_5_VisionRotaryEmbedding): + inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim)) + init.copy_(module.inv_freq, inv_freq) + + +class Qwen2_5OmniPreTrainedModelForConditionalGeneration(Qwen2_5OmniPreTrainedModel): + input_modalities = ("image", "video", "audio", "text") + + def get_llm_pos_ids_for_vision( + self, + start_idx: int, + vision_idx: int, + spatial_merge_size: int, + t_index: list[int], + grid_hs: list[int], + grid_ws: list[int], + ): + llm_pos_ids_list = [] + llm_grid_h = grid_hs[vision_idx] // spatial_merge_size + llm_grid_w = grid_ws[vision_idx] // spatial_merge_size + h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten() + w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten() + t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().long() + _llm_pos_ids = torch.stack([t_index, h_index, w_index]) + llm_pos_ids_list.append(_llm_pos_ids + start_idx) # + 1 ) # 12.09 by malinhan + llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1) + return llm_pos_ids + + def get_chunked_index( + self, token_indices: torch.Tensor, tokens_per_chunk: int, remove_index: int + ) -> list[tuple[int, int]]: + """ + Splits token index list into chunks based on token value ranges. + + Given a list of token indices, returns a list of (start, end) index tuples representing + slices of the list where the token values fall within successive ranges of `t_ntoken_per_chunk`. + + For example, if `t_ntoken_per_chunk` is 1000, the function will create chunks such that: + - the first chunk contains token values < 1000, + - the second chunk contains values >= 1000 and < 2000, and so on. + + Parameters: + token_indices (`torch.Tensor` of shape `(seq_len, )`): A monotonically increasing list of + token index values. + t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold). + remove_index (`int`) An index id to subtract from `token_indices` before chunking + + Returns: + `list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive) + and end (exclusive) indices of a chunk in `token_indices`. + """ + + def _iter(): + i, start_idx = 0, 0 # skip bos token + current_chunk = 1 + while i < len(token_indices): # skip eos token + if token_indices[i] - remove_index >= current_chunk * tokens_per_chunk: + yield (start_idx, i) + start_idx = i + current_chunk += 1 + i += 1 + yield (start_idx, len(token_indices)) + + return list(_iter()) + + def get_rope_index( + self, + input_ids: torch.LongTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + use_audio_in_video: bool = False, + audio_seqlens: torch.LongTensor | None = None, + second_per_grids: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Calculate the 3D rope index based on image and video's temporal, height and width in LLM. + + Explanation: + Each embedding sequence contains vision embedding and text embedding or just contains text embedding. + + For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs. + Examples: + input_ids: [T T T T T], here T is for text. + temporal position_ids: [0, 1, 2, 3, 4] + height position_ids: [0, 1, 2, 3, 4] + width position_ids: [0, 1, 2, 3, 4] + + For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part + and 1D rotary position embedding for text part. + Examples: + Temporal (Time): 3 patches, representing different segments of the video in time. + Height: 2 patches, dividing each frame vertically. + Width: 2 patches, dividing each frame horizontally. + We also have some important parameters: + fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second. + tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity. + temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames. + interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs. + input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision. + vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100] + vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] + vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] + text temporal position_ids: [101, 102, 103, 104, 105] + text height position_ids: [101, 102, 103, 104, 105] + text width position_ids: [101, 102, 103, 104, 105] + Here we calculate the text start position_ids as the max vision position_ids plus 1. + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + use_audio_in_video (`bool`, *optional*): + If set to `True`, use the audio in video. + audio_seqlens (`torch.LongTensor` of shape `(num_audios)`, *optional*): + The length of feature shape of each audio in LLM. + second_per_grids (`torch.LongTensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + + Returns: + position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) + mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) + """ + spatial_merge_size = self.spatial_merge_size + image_token_id = self.config.image_token_id + video_token_id = self.config.video_token_id + audio_token_id = self.config.audio_token_id + vision_start_token_id = self.config.vision_start_token_id + audio_start_token_id = self.config.audio_start_token_id + position_id_per_seconds = self.config.position_id_per_seconds + seconds_per_chunk = self.config.seconds_per_chunk + + mrope_position_deltas = [] + if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): + total_input_ids = input_ids + if attention_mask is not None: + attention_mask = attention_mask == 1 + position_ids = torch.ones( + 3, + input_ids.shape[0], + input_ids.shape[1], + dtype=input_ids.dtype, + device=input_ids.device, + ) + image_idx, video_idx, audio_idx = 0, 0, 0 + for i, input_ids in enumerate(total_input_ids): + if attention_mask is not None: + input_ids = input_ids[attention_mask[i]] + image_nums, video_nums, audio_nums = 0, 0, 0 + vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1) + vision_tokens = input_ids[vision_start_indices + 1] + audio_nums = torch.sum(input_ids == audio_start_token_id) + image_nums = (vision_tokens == image_token_id).sum() + video_nums = ( + (vision_tokens == audio_start_token_id).sum() + if use_audio_in_video + else (vision_tokens == video_token_id).sum() + ) + input_tokens = input_ids.tolist() + llm_pos_ids_list: list = [] + st = 0 + remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums + multimodal_nums = ( + image_nums + audio_nums if use_audio_in_video else image_nums + video_nums + audio_nums + ) + for _ in range(multimodal_nums): + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + if image_token_id in input_tokens and remain_images > 0: + ed_image = input_tokens.index(image_token_id, st) + else: + ed_image = len(input_tokens) + 1 + if video_token_id in input_tokens and remain_videos > 0: + ed_video = input_tokens.index(video_token_id, st) + else: + ed_video = len(input_tokens) + 1 + if audio_token_id in input_tokens and remain_audios > 0: + ed_audio = input_tokens.index(audio_token_id, st) + else: + ed_audio = len(input_tokens) + 1 + min_ed = min(ed_image, ed_video, ed_audio) + if min_ed == ed_audio: + text_len = min_ed - st - 1 + if text_len != 0: + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + bos_len = 1 + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1 + llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx + llm_pos_ids_list.append(llm_pos_ids) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + eos_len = 1 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + st += text_len + bos_len + audio_len + eos_len + audio_idx += 1 + remain_audios -= 1 + + elif min_ed == ed_image: + text_len = min_ed - st - 1 + if text_len != 0: + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + bos_len = 1 + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + grid_t = image_grid_thw[image_idx][0] + grid_hs = image_grid_thw[:, 1] + grid_ws = image_grid_thw[:, 2] + t_index = (torch.arange(grid_t) * 1 * position_id_per_seconds).long() + llm_pos_ids = self.get_llm_pos_ids_for_vision( + st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws + ) + image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2) + llm_pos_ids_list.append(llm_pos_ids) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + eos_len = 1 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + st += text_len + bos_len + image_len + eos_len + image_idx += 1 + remain_images -= 1 + + elif min_ed == ed_video and not use_audio_in_video: + text_len = min_ed - st - 1 + if text_len != 0: + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + bos_len = 1 + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + grid_t = video_grid_thw[video_idx][0] + grid_hs = video_grid_thw[:, 1] + grid_ws = video_grid_thw[:, 2] + t_index = ( + torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds + ).long() + llm_pos_ids = self.get_llm_pos_ids_for_vision( + st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws + ) + video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2) + llm_pos_ids_list.append(llm_pos_ids) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + eos_len = 1 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + st += text_len + bos_len + video_len + eos_len + video_idx += 1 + remain_videos -= 1 + + elif min_ed == ed_video and use_audio_in_video: + text_len = min_ed - st - 2 + if text_len != 0: + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + bos_len = 1 + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1 + audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx + grid_t = video_grid_thw[video_idx][0] + grid_hs = video_grid_thw[:, 1] + grid_ws = video_grid_thw[:, 2] + + t_index = ( + torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds + ).long() + video_llm_pos_ids = self.get_llm_pos_ids_for_vision( + st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws + ) + + t_ntoken_per_chunk = int(position_id_per_seconds * seconds_per_chunk) + video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids[0], t_ntoken_per_chunk, st_idx) + audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids[0], t_ntoken_per_chunk, st_idx) + sub_len = 0 + for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))): + video_chunk_index = video_chunk_indexes[j] if j < len(video_chunk_indexes) else None + audio_chunk_index = audio_chunk_indexes[j] if j < len(audio_chunk_indexes) else None + if video_chunk_index is not None: + sub_len += video_chunk_index[1] - video_chunk_index[0] + + llm_pos_ids_list.append( + video_llm_pos_ids[:, video_chunk_index[0] : video_chunk_index[1]] + ) + if audio_chunk_index is not None: + sub_len += audio_chunk_index[1] - audio_chunk_index[0] + + llm_pos_ids_list.append( + audio_llm_pos_ids[:, audio_chunk_index[0] : audio_chunk_index[1]] + ) + video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + eos_len = 1 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2 + + audio_idx += 1 + video_idx += 1 + remain_videos -= 1 + remain_audios -= 1 + + if st < len(input_tokens): + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + text_len = len(input_tokens) - st + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) + + if attention_mask is not None: + position_ids[..., i, attention_mask[i]] = llm_positions.to(position_ids.device) + else: + position_ids[..., i, :] = llm_positions.to(position_ids.device) + mrope_position_deltas.append(llm_positions.max() + 1 - len(input_ids)) + mrope_position_deltas = torch.tensor(mrope_position_deltas).unsqueeze(1).to(device=input_ids.device) + + return position_ids, mrope_position_deltas + else: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device) + max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0] + mrope_position_deltas = max_position_ids + 1 - torch.sum(attention_mask, dim=-1, keepdim=True) + + return position_ids, mrope_position_deltas + + +############################ +# Start Thinker # +############################ + + +@auto_docstring( + custom_intro=""" + Base class for Qwen2.5OmniThinker causal language model (or autoregressive) outputs. + """ +) +@dataclass +class Qwen2_5OmniThinkerCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`, *optional*): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Qwen2_5OmniAudioAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + config: Qwen2_5OmniAudioEncoderConfig, + ): + super().__init__() + self.embed_dim = config.d_model + self.num_heads = config.encoder_attention_heads + self.dropout = config.attention_dropout + self.head_dim = self.embed_dim // self.num_heads + self.num_key_value_groups = 1 # needed for eager attention + self.config = config + + if (self.head_dim * self.num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {self.num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.attention_dropout = 0.0 + self.is_decoder = False + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + seq_length, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).reshape(seq_length, self.num_heads, -1) + key_states = self.k_proj(hidden_states).reshape(seq_length, self.num_heads, -1) + value_states = self.v_proj(hidden_states).reshape(seq_length, self.num_heads, -1) + + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) + max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + cu_seq_lens_q=cu_seqlens, # pass cu seq lens for FA2 + cu_seq_lens_k=cu_seqlens, + max_length_q=max_seqlen, + max_length_k=max_seqlen, + is_causal=False, + **kwargs, + ) + + attn_output = attn_output.reshape(seq_length, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output + + +class Qwen2_5OmniAudioEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Qwen2_5OmniAudioEncoderConfig): + super().__init__() + self.embed_dim = config.d_model + self.self_attn = Qwen2_5OmniAudioAttention(config) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states = self.self_attn( + hidden_states=hidden_states, + cu_seqlens=cu_seqlens, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + hidden_states = residual + hidden_states + + if hidden_states.dtype == torch.float16: + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + return outputs + + +class SinusoidsPositionEmbedding(nn.Module): + def __init__(self, length, channels, max_timescale=10000): + super().__init__() + self.length = length + self.channels = channels + self.max_timescale = max_timescale + if channels % 2 != 0: + raise ValueError("SinusoidsPositionEmbedding needs even channels input") + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float()) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + self.register_buffer( + "positional_embedding", + torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1), + persistent=False, + ) + + def forward(self, seqlen: int): + return self.positional_embedding[:seqlen, :] + + +@auto_docstring( + custom_intro=""" + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`Qwen2_5OmniAudioEncoderLayer`]. + """ +) +class Qwen2_5OmniAudioEncoder(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniAudioEncoderConfig + main_input_name = "input_features" + input_modalities = "audio" + _no_split_modules = ["Qwen2_5OmniAudioEncoderLayer"] + _supports_sdpa = True + _can_record_outputs = { + "hidden_states": Qwen2_5OmniAudioEncoderLayer, + "attentions": Qwen2_5OmniAudioAttention, + } + + def __init__(self, config: Qwen2_5OmniAudioEncoderConfig): + super().__init__(config) + self.dropout = config.dropout + + embed_dim = config.d_model + self.num_mel_bins = config.num_mel_bins + self.max_source_positions = config.max_source_positions + self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + self.n_window = config.n_window + self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1) + self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1) + self.positional_embedding = SinusoidsPositionEmbedding(self.max_source_positions, embed_dim) + self.audio_bos_eos_token = nn.Embedding(2, config.output_dim) + self.layers = nn.ModuleList([Qwen2_5OmniAudioEncoderLayer(config) for _ in range(config.encoder_layers)]) + self.ln_post = nn.LayerNorm(config.d_model) + self.avg_pooler = nn.AvgPool1d(2, stride=2) + self.proj = nn.Linear(config.d_model, config.output_dim) + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def _freeze_parameters(self): + for param in self.parameters(): + param.requires_grad = False + self._requires_grad = False + + def get_input_embeddings(self) -> nn.Module: + return self.conv1 + + def set_input_embeddings(self, value: nn.Module): + self.conv1 = value + + def _prepare_attention_mask(self, inputs_tensor: torch.Tensor, cu_seqlens: torch.Tensor) -> torch.Tensor: + # Flash Attention 2 doesn't need a 4D mask and relies on `cu_seqlens/max_seqlen` + # NOTE: the created attention masl only approximates the ragged FA2 attention by + # allowing bidirectional attention within `cu_seqlens` blocks, and not attending between + # blocks. Though it will not be a 100% match for FA2's `varlen` path + if is_flash_attention_requested(self.config): + return None + + seq_length = inputs_tensor.shape[0] + attention_mask = torch.full( + [1, 1, seq_length, seq_length], + torch.finfo(inputs_tensor.dtype).min, + device=inputs_tensor.device, + dtype=inputs_tensor.dtype, + ) + for i in range(1, len(cu_seqlens)): + attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 + return attention_mask + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward(self, input_features, feature_lens=None, aftercnn_lens=None, **kwargs: Unpack[TransformersKwargs]): + r""" + feature_lens (`torch.LongTensor` of shape `(batch_size,)`): + mel length + aftercnn_lens (`torch.LongTensor` of shape `(batch_size,)`): + mel length after cnn + """ + chunk_num = torch.ceil(feature_lens / (self.n_window * 2)).long() + + chunk_lengths = torch.full((chunk_num.sum(),), self.n_window * 2, dtype=torch.long, device=feature_lens.device) + tail_chunk_index = F.pad(chunk_num, (1, 0), value=-1).cumsum(0)[1:] + chunk_lengths[tail_chunk_index] = feature_lens % (self.n_window * 2) + chunk_lengths = torch.where(chunk_lengths == 0, self.n_window * 2, chunk_lengths) + + chunk_list = input_features.split(chunk_lengths.tolist(), dim=1) + padded_feature, padded_mask, padded_mask_after_cnn = self.padded_and_mask_function( + chunk_list, chunk_lengths, padding_value=0, padding_side="right" + ) + padded_embed = nn.functional.gelu(self.conv1(padded_feature)) * padded_mask + padded_embed = nn.functional.gelu(self.conv2(padded_embed)).transpose(1, 2) + + padded_embed = padded_embed + self.positional_embedding.positional_embedding[ + : padded_embed.shape[1], : + ].unsqueeze(0).to(padded_embed.dtype) + hidden_states = padded_embed[padded_mask_after_cnn] + cu_seqlens = torch.cat( + ( + torch.zeros(1, device=padded_mask_after_cnn.device, dtype=torch.int32), + padded_mask_after_cnn.sum(1).cumsum(0), + ) + ).to(torch.int32) + attention_mask = self._prepare_attention_mask(hidden_states, cu_seqlens) + + for encoder_layer in self.layers: + layer_outputs = encoder_layer( + hidden_states, + cu_seqlens=cu_seqlens, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = layer_outputs[0] + + hidden_states_list = hidden_states.split(aftercnn_lens.tolist(), dim=0) + token_audio_list = [] + for each_audio_states in hidden_states_list: + each_audio_states = self.avg_pooler(each_audio_states.transpose(0, 1)).transpose_(0, 1) + each_audio_states = self.ln_post(each_audio_states) + each_audio_states = self.proj(each_audio_states) + token_audio_list.append(each_audio_states) + token_audio = torch.cat(token_audio_list, dim=0) + return BaseModelOutputWithPooling(last_hidden_state=token_audio) + + def padded_and_mask_function(self, tensor_list, tensor_len, padding_value=0, padding_side="right"): + """ + Pads a sequence of tensors to their maximum length on indicated `padding_side`. + Then prepares a mask so that pad tokens are not attended to. + """ + max_len = tensor_len.max() + dim = tensor_list[0].shape[0] + padded_tensor = torch.full( + size=(len(tensor_list), dim, max_len), + fill_value=padding_value, + dtype=self.dtype, + device=tensor_list[0].device, + ) + + batch_mask = torch.zeros( + (len(tensor_len), max_len), + dtype=torch.long, + device=padded_tensor.device, + ) + for i, length in enumerate(tensor_len): + batch_mask[i, :length] = 1 + padded_tensor[i, :, :length] = tensor_list[i] + + feature_lens_after_cnn = (tensor_len - 1) // 2 + 1 + max_len_after_cnn = feature_lens_after_cnn.max() + batch_mask_after_cnn = torch.zeros( + (len(tensor_len), max_len_after_cnn), + dtype=torch.long, + device=padded_tensor.device, + ) + for i, length in enumerate(feature_lens_after_cnn): + batch_mask_after_cnn[i, :length] = 1 + return ( + padded_tensor, + batch_mask.unsqueeze(1), + batch_mask_after_cnn.bool(), + ) + + # Ignore copy + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers and the output length of the audio encoder + """ + input_lengths = (input_lengths - 1) // 2 + 1 + output_lengths = (input_lengths - 2) // 2 + 1 + return input_lengths, output_lengths + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor: + orig_dtype = tensor.dtype + tensor = tensor.float() + cos = freqs.cos() + sin = freqs.sin() + cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float() + sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float() + output = (tensor * cos) + (rotate_half(tensor) * sin) + output = output.to(orig_dtype) + return output + + +class Qwen2_5OmniVisionAttention(nn.Module): + def __init__(self, config: Qwen2_5OmniVisionEncoderConfig = None) -> None: + super().__init__() + self.dim = config.hidden_size + self.num_heads = config.num_heads + self.head_dim = self.dim // self.num_heads + self.q = nn.Linear(self.dim, self.dim, bias=True) + self.k = nn.Linear(self.dim, self.dim, bias=True) + self.v = nn.Linear(self.dim, self.dim, bias=True) + self.proj = nn.Linear(self.dim, self.dim) + self.scaling = self.head_dim**-0.5 + self.num_key_value_groups = 1 # needed for eager attention + self.config = config + self.attention_dropout = 0.0 + self.is_causal = False + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + rotary_pos_emb: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + seq_length = hidden_states.shape[0] + query_states = self.q(hidden_states).reshape(seq_length, self.num_heads, -1) + key_states = self.k(hidden_states).reshape(seq_length, self.num_heads, -1) + value_states = self.v(hidden_states).reshape(seq_length, self.num_heads, -1) + query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0) + key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0) + + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config): + # Flash Attention 2: Use cu_seqlens for variable length attention + max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + cu_seq_lens_q=cu_seqlens, + cu_seq_lens_k=cu_seqlens, + max_length_q=max_seqlen, + max_length_k=max_seqlen, + is_causal=False, + **kwargs, + ) + else: + # Other implementations: Process each chunk separately + lengths = cu_seqlens[1:] - cu_seqlens[:-1] + splits = [ + torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states) + ] + + attn_outputs = [ + attention_interface( + self, + q, + k, + v, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + is_causal=False, + **kwargs, + )[0] + for q, k, v in zip(*splits) + ] + attn_output = torch.cat(attn_outputs, dim=1) + + attn_output = attn_output.reshape(seq_length, -1).contiguous() + attn_output = self.proj(attn_output) + return attn_output + + +@use_kernel_forward_from_hub("RMSNorm") +class Qwen2_5OmniRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + Qwen2_5OmniRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class Qwen2_5OmniMLP(nn.Module): + def __init__(self, config, bias: bool = False): + super().__init__() + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_state): + return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) + + +class Qwen2_5OmniVisionBlock(GradientCheckpointingLayer): + def __init__(self, config: Qwen2_5OmniVisionEncoderConfig) -> None: + super().__init__() + self.norm1 = Qwen2_5OmniRMSNorm(config.hidden_size, eps=1e-6) + self.norm2 = Qwen2_5OmniRMSNorm(config.hidden_size, eps=1e-6) + self.attn = Qwen2_5OmniVisionAttention(config=config) + self.mlp = Qwen2_5OmniMLP(config, bias=True) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + rotary_pos_emb: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + r""" + cu_seqlens (`torch.Tensor`): + Cumulative sequence lengths used for packed variable-length attention in Flash Attention kernels. + rotary_pos_emb (`torch.Tensor`, *optional*): + Precomputed rotary positional embeddings applied to the vision attention query/key states. + """ + hidden_states = hidden_states + self.attn( + self.norm1(hidden_states), + cu_seqlens=cu_seqlens, + rotary_pos_emb=rotary_pos_emb, + **kwargs, + ) + hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) + return hidden_states + + +class Qwen2_5_VisionRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, dim: int, theta: float = 10000.0) -> None: + super().__init__() + self.dim = dim + self.theta = theta + inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + def forward(self, seqlen: int) -> torch.Tensor: + seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + freqs = torch.outer(seq, self.inv_freq) + return freqs + + +class Qwen2_5_VisionPatchEmbed(nn.Module): + def __init__( + self, + patch_size: int | list[int] | tuple[int, int] = 14, + temporal_patch_size: int | list[int] | tuple[int, int] = 2, + in_channels: int = 3, + embed_dim: int = 1152, + ) -> None: + super().__init__() + self.patch_size = patch_size + self.temporal_patch_size = temporal_patch_size + self.in_channels = in_channels + self.embed_dim = embed_dim + + kernel_size = [temporal_patch_size, patch_size, patch_size] + self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + target_dtype = self.proj.weight.dtype + hidden_states = hidden_states.view( + -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size + ) + hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim) + return hidden_states + + +class Qwen2_5OmniPatchMerger(nn.Module): + def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None: + super().__init__() + self.hidden_size = context_dim * (spatial_merge_size**2) + self.ln_q = Qwen2_5OmniRMSNorm(context_dim, eps=1e-6) + self.mlp = nn.Sequential( + nn.Linear(self.hidden_size, self.hidden_size), + nn.GELU(), + nn.Linear(self.hidden_size, dim), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.mlp(self.ln_q(x).view(-1, self.hidden_size)) + return x + + +class Qwen2_5OmniVisionEncoder(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniVisionEncoderConfig + _no_split_modules = ["Qwen2_5OmniVisionBlock"] + _input_embed_layer = "patch_embed" + _can_record_outputs = { + "hidden_states": Qwen2_5OmniVisionBlock, + "attentions": Qwen2_5OmniVisionAttention, + } + input_modalities = ("image", "video") + + def __init__(self, config: Qwen2_5OmniVisionEncoderConfig, *inputs, **kwargs) -> None: + super().__init__(config, *inputs, **kwargs) + self.spatial_merge_size = config.spatial_merge_size + self.patch_size = config.patch_size + self.fullatt_block_indexes = config.fullatt_block_indexes + self.window_size = config.window_size + self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size + + self.patch_embed = Qwen2_5_VisionPatchEmbed( + patch_size=config.patch_size, + temporal_patch_size=config.temporal_patch_size, + in_channels=config.in_channels, + embed_dim=config.hidden_size, + ) + + head_dim = config.hidden_size // config.num_heads + self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) + self.blocks = nn.ModuleList([Qwen2_5OmniVisionBlock(config) for _ in range(config.depth)]) + self.merger = Qwen2_5OmniPatchMerger( + dim=config.out_hidden_size, + context_dim=config.hidden_size, + spatial_merge_size=config.spatial_merge_size, + ) + self.gradient_checkpointing = False + + self.post_init() + + def rot_pos_emb(self, grid_thw): + pos_ids = [] + for t, h, w in grid_thw.tolist(): + hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) + hpos_ids = hpos_ids.reshape( + h // self.spatial_merge_size, + self.spatial_merge_size, + w // self.spatial_merge_size, + self.spatial_merge_size, + ) + hpos_ids = hpos_ids.permute(0, 2, 1, 3) + hpos_ids = hpos_ids.flatten() + + wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) + wpos_ids = wpos_ids.reshape( + h // self.spatial_merge_size, + self.spatial_merge_size, + w // self.spatial_merge_size, + self.spatial_merge_size, + ) + wpos_ids = wpos_ids.permute(0, 2, 1, 3) + wpos_ids = wpos_ids.flatten() + pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) + pos_ids = torch.cat(pos_ids, dim=0) + max_grid_size = grid_thw[:, 1:].max() + rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) + rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) + return rotary_pos_emb + + def get_window_index(self, grid_thw): + window_index: list = [] + cu_window_seqlens: list = [0] + window_index_id = 0 + vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size + grid_thw_list = grid_thw.tolist() + + for grid_t, grid_h, grid_w in grid_thw_list: + llm_grid_h, llm_grid_w = ( + grid_h // self.spatial_merge_size, + grid_w // self.spatial_merge_size, + ) + index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) + pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size + pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size + num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size + num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size + index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) + index_padded = index_padded.reshape( + grid_t, + num_windows_h, + vit_merger_window_size, + num_windows_w, + vit_merger_window_size, + ) + index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( + grid_t, + num_windows_h * num_windows_w, + vit_merger_window_size, + vit_merger_window_size, + ) + seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) + index_padded = index_padded.reshape(-1) + index_new = index_padded[index_padded != -100] + window_index.append(index_new + window_index_id) + cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] + cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) + window_index_id += grid_t * llm_grid_h * llm_grid_w + window_index = torch.cat(window_index, dim=0) + + return window_index, cu_window_seqlens + + @merge_with_config_defaults + @capture_outputs + def forward( + self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithPooling: + """ + Args: + hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): + The final hidden states of the model. + grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`): + The temporal, height and width of feature shape of each image in LLM. + + Returns: + `torch.Tensor`: hidden_states. + """ + hidden_states = self.patch_embed(hidden_states) + rotary_pos_emb = self.rot_pos_emb(grid_thw) + + window_index, cu_window_seqlens = self.get_window_index(grid_thw) + cu_window_seqlens = torch.tensor( + cu_window_seqlens, + device=hidden_states.device, + dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) + + seq_len, _ = hidden_states.size() + hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) + hidden_states = hidden_states[window_index, :, :] + hidden_states = hidden_states.reshape(seq_len, -1) + rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) + rotary_pos_emb = rotary_pos_emb[window_index, :, :] + rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) + + cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( + dim=0, + # Select dtype based on the following factors: + # - FA2 requires that cu_seqlens_q must have dtype int32 + # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw + # See https://github.com/huggingface/transformers/pull/34852 for more information + dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) + + # Modification here + for layer_num, blk in enumerate(self.blocks): + if layer_num in self.fullatt_block_indexes: + cu_seqlens_now = cu_seqlens + else: + cu_seqlens_now = cu_window_seqlens + + hidden_states = blk( + hidden_states, + cu_seqlens=cu_seqlens_now, + rotary_pos_emb=rotary_pos_emb, + **kwargs, + ) + + merged_hidden_states = self.merger(hidden_states) + reverse_indices = torch.argsort(window_index) + merged_hidden_states = merged_hidden_states[reverse_indices, :] + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=merged_hidden_states, + ) + + +class Qwen2_5OmniRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: Qwen2_5OmniThinkerConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: Qwen2_5OmniConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + # Ignore copy + def forward(self, x, position_ids): + # In contrast to other models, Qwen2_5Omni has different position ids for the grids + # So we expand the inv_freq to shape (3, ...) + inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1) + position_ids_expanded = position_ids[:, :, None, :].float() # shape (3, bs, 1, positions) + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1): + """Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors (https://qwenlm.github.io/blog/qwen2-vl/). + + Explanation: + Multimodal 3D rotary position embedding is an extension to 1D rotary position embedding. The input embedding + sequence contains vision (images / videos) embedding and text embedding or just contains text embedding. For + vision embedding part, we apply rotary position embedding on temporal, height and width dimension separately. + Here we split the channel dimension to 3 chunks for the temporal, height and width rotary position embedding. + For text embedding part, we just apply 1D rotary position embedding. The three rotary position index (temporal, + height and width) of text embedding is always the same, so the text embedding rotary position embedding has no + difference with modern LLMs. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + mrope_section(`List(int)`): + Multimodal rope section is for channel dimension of temporal, height and width in rope calculation. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + mrope_section = mrope_section * 2 + cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze( + unsqueeze_dim + ) + sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze( + unsqueeze_dim + ) + + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class Qwen2_5OmniAttention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: Qwen2_5OmniConfig, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads) + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.is_causal = True + self.attention_dropout = config.attention_dropout + self.rope_parameters = config.rope_parameters + self.scaling = self.head_dim**-0.5 + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_multimodal_rotary_pos_emb( + query_states, key_states, cos, sin, self.config.rope_parameters["mrope_section"] + ) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, + position_ids=position_ids, # pass positions for FA2 + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Qwen2MLP(nn.Module): + def __init__(self, config, bias: bool = False): + super().__init__() + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_state): + return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) + + +class Qwen2_5OmniDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Qwen2_5OmniTextConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + if config.use_sliding_window and not is_flash_attention_requested(config): + logger.warning_once( + f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " + "unexpected results may be encountered." + ) + self.self_attn = Qwen2_5OmniAttention(config, layer_idx) + + self.mlp = Qwen2MLP(config) + self.input_layernorm = Qwen2_5OmniRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Qwen2_5OmniRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_values (`Cache`, *optional*): cached past key and value projection states + position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): + Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, + with `head_dim` being the embedding dimension of each attention head. + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class Qwen2_5OmniThinkerTextModel(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniTextConfig + input_modalities = ("text",) + _can_record_outputs = { + "hidden_states": Qwen2_5OmniDecoderLayer, + "attentions": Qwen2_5OmniAttention, + } + _no_split_modules = ["Qwen2_5OmniDecoderLayer"] + + def __init__(self, config: Qwen2_5OmniTextConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Qwen2_5OmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Qwen2_5OmniRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.has_sliding_layers = "sliding_attention" in self.config.layer_types + self.rotary_emb = Qwen2_5OmniRotaryEmbedding(config=config) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + # torch.jit.trace() doesn't support cache objects in the output + if use_cache and past_key_values is None and not torch.jit.is_tracing(): + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # the hard coded `3` is for temporal, height and width. + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) + elif position_ids.ndim == 2: + position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) + + # NOTE: we need to pass text position ids for packing. Qwen2-VL uses 3D positions + # where each dim indicates visual spatial positions for temporal/height/width grids. + # There are two scenarios when FA2-like packed masking might be activated. + # 1. User specifically passed packed `position_ids` and no attention mask. + # In this case we expect the useer to create correct position ids for all 3 grids + # and prepend text-only position ids to it. The final tensor will be [4, bs, seq-len] + # 2. User runs forward with no attention mask and no position ids. In this case, position ids + # are prepared by the model (`get_rope_index`) as `[4, bs, seq-len]` tensor. Text-only positions are + # prepended by us when creating positions so that the mask is constructed correctly. NOTE: failing to pass + # text-only positions will cause incorrect mask construction, do not change `prepare_input_for_generation` + if position_ids.ndim == 3 and position_ids.shape[0] == 4: + text_position_ids = position_ids[0] + position_ids = position_ids[1:] + else: + # If inputs are not packed (usual 3D positions), do not prepare mask from position_ids + text_position_ids = None + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": text_position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + } + # The sliding window alternating layers are not always activated depending on the config + if self.has_sliding_layers: + causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for i, decoder_layer in enumerate(self.layers): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[self.config.layer_types[i]], + position_embeddings=position_embeddings, + position_ids=text_position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring( + custom_intro=""" + The Qwen2.5OmniThinker model which consists of a audio backbone and a language model. + """ +) +class Qwen2_5OmniThinkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin): + config: Qwen2_5OmniThinkerConfig + base_model_prefix = "thinker" + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _no_split_modules = ["Qwen2_5OmniAudioEncoder", "Qwen2_5OmniVisionEncoder"] + + def __init__(self, config: Qwen2_5OmniThinkerConfig): + super().__init__(config) + self.audio_tower = Qwen2_5OmniAudioEncoder._from_config(config.audio_config) + self.visual = Qwen2_5OmniVisionEncoder._from_config(config.vision_config) + self.vocab_size = config.text_config.vocab_size + self.model = Qwen2_5OmniThinkerTextModel._from_config(config.text_config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.spatial_merge_size = config.vision_config.spatial_merge_size + self.rope_deltas = None + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + @can_return_tuple + @auto_docstring + def get_video_features( + self, + pixel_values_videos: torch.FloatTensor, + video_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input videos. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + """ + pixel_values_videos = pixel_values_videos.type(self.visual.dtype) + return self.visual(pixel_values_videos, grid_thw=video_grid_thw, **kwargs) + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + pixel_values = pixel_values.type(self.visual.dtype) + return self.visual(pixel_values, grid_thw=image_grid_thw, **kwargs) + + @can_return_tuple + @auto_docstring + def get_audio_features( + self, + input_features: torch.FloatTensor, + feature_attention_mask: torch.LongTensor | None = None, + audio_feature_lengths: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + input_features (`torch.FloatTensor`): + The tensors corresponding to the input audios. + feature_attention_mask (`torch.LongTensor`, *optional*): + Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`: + audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): + The length of feature shape of each audio in LLM. + """ + if feature_attention_mask is not None: + audio_feature_lengths = torch.sum(feature_attention_mask, dim=1) + input_features = input_features.permute(0, 2, 1)[feature_attention_mask.bool()].permute(1, 0) + else: + audio_feature_lengths = None + + audio_feat_lengths, audio_output_lengths = self.audio_tower._get_feat_extract_output_lengths( + audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1) + ) + feature_lens = audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1) + audio_outputs = self.audio_tower( + input_features, + feature_lens=feature_lens, + aftercnn_lens=audio_feat_lengths, + return_dict=True, + **kwargs, + ) + if audio_outputs.last_hidden_state.shape[0] != sum(audio_output_lengths.tolist()): + raise ValueError("length of audio_features should match audio_output_lengths") + + return audio_outputs + + def get_placeholder_mask( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.FloatTensor, + image_features: torch.FloatTensor | None = None, + video_features: torch.FloatTensor | None = None, + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + special_video_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_video_mask = special_video_mask.all(-1) + special_audio_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + ).all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + special_video_mask = input_ids == self.config.video_token_id + special_audio_mask = input_ids == self.config.audio_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if image_features is not None: + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}", + ) + + n_video_tokens = special_video_mask.sum() + special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if video_features is not None: + torch_compilable_check( + inputs_embeds[special_video_mask].numel() == video_features.numel(), + f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}", + ) + + special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + return special_image_mask, special_video_mask, special_audio_mask + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + input_features: torch.FloatTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + feature_attention_mask: torch.Tensor | None = None, + audio_feature_lengths: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + use_audio_in_video: bool | None = None, + video_second_per_grid: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Qwen2_5OmniThinkerCausalLMOutputWithPast: + r""" + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + feature_attention_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*): + Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): + The length of feature shape of each audio in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + use_audio_in_video (`bool`, *optional*): + Whether or not use audio track in video, should same as the parameter in `process_audio_info`. + video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*): + Number of seconds per grid for each video, used for temporal feature mapping. + + Example: + + ```python + >>> from io import BytesIO + >>> from urllib.request import urlopen + >>> import librosa + >>> from qwen_vl_utils import process_vision_info + >>> from transformers import Qwen2_5OmniProcessor, Qwen2_5OmniThinkerForConditionalGeneration + + >>> thinker = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B") + >>> processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B") + + >>> conversations = [ + >>> {'role': 'system', 'content': 'You are a helpful voice chat bot, and please respond to me in a casual conversation manner using random voice.'}, + >>> {"role": "user", "content": [ + >>> {"type": "image", "image_url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, + >>> {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"}, + >>> ]}, + >>> ] + + >>> text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) + >>> audios = [ librosa.load(BytesIO(urlopen( conversations[1]['content'][1]['audio_url'] ).read()), sr=self.processor.feature_extractor.sampling_rate) ] + >>> images, videos = process_vision_info(conversations) + >>> inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True) + + >>> # Generate + >>> inputs['use_audio_in_video'] = `True` or `False` + >>> generation = thinker.generate(**inputs, max_new_tokens=2048) + >>> generate_ids = generation[:, inputs.input_ids.size(1):] + + >>> response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + ```""" + + if inputs_embeds is None: + # 1. Extract the input embeddings + inputs_embeds = self.get_input_embeddings()(input_ids) + + # 2. Merge text , audios , image and video + if input_features is not None: + audio_features = self.get_audio_features( + input_features, + feature_attention_mask=feature_attention_mask, + audio_feature_lengths=audio_feature_lengths, + return_dict=True, + ).last_hidden_state + audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype) + _, _, audio_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds) + inputs_embeds = inputs_embeds.masked_scatter(audio_mask, audio_features) + + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw, return_dict=True).pooler_output + image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) + image_mask, _, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) + + if pixel_values_videos is not None: + video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw, return_dict=True).pooler_output + video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype) + _, video_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) + + if feature_attention_mask is not None: + audio_feature_lengths = torch.sum(feature_attention_mask, dim=1) + else: + audio_feature_lengths = None + + if attention_mask is not None and position_ids is None: + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + if past_key_values_length == 0 or self.rope_deltas is None: + delta0 = (1 - attention_mask).sum(dim=-1).unsqueeze(1) + position_ids, rope_deltas = self.get_rope_index( + input_ids, + image_grid_thw, + video_grid_thw, + attention_mask, + use_audio_in_video, + audio_feature_lengths, + video_second_per_grid, + ) + rope_deltas = rope_deltas - delta0 + self.rope_deltas = rope_deltas + else: + batch_size, seq_length = input_ids.shape + delta = (past_key_values_length + self.rope_deltas).to(input_ids.device) + position_ids = torch.arange(seq_length, device=input_ids.device) + position_ids = position_ids.view(1, -1).expand(batch_size, -1) + position_ids = position_ids.add(delta) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) + + outputs = self.model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.get_text_config().vocab_size + ) + + return Qwen2_5OmniThinkerCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + pixel_values=None, + pixel_values_videos=None, + image_grid_thw=None, + video_grid_thw=None, + input_features=None, + feature_attention_mask=None, + use_audio_in_video=False, + video_second_per_grid=None, + is_first_iteration=False, + **kwargs, + ): + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + use_cache=use_cache, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + input_features=input_features, + feature_attention_mask=feature_attention_mask, + use_audio_in_video=use_audio_in_video, + video_second_per_grid=video_second_per_grid, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + model_inputs["position_ids"] = None + + if not is_first_iteration and use_cache: + model_inputs["pixel_values"] = None + model_inputs["pixel_values_videos"] = None + model_inputs["input_features"] = None + + return model_inputs + + +############################ +# Start Talker # +############################ + + +@auto_docstring( + custom_intro=""" + Base class for Qwen2.5OmniTalker causal language model (or autoregressive) outputs. + """ +) +@dataclass +class Qwen2_5OmniTalkerCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Hidden states from the thinker model that are used as input for the talker model. These represent the encoded + response that the talker model will use to generate speech tokens. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + thinker_reply_part: torch.FloatTensor | None = None + + +@auto_docstring +class Qwen2_5OmniTalkerModel(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniTalkerConfig + input_modalities = ("image", "video", "audio", "text") + _can_record_outputs = { + "hidden_states": Qwen2_5OmniDecoderLayer, + "attentions": Qwen2_5OmniAttention, + } + + _no_split_modules = ["Qwen2_5OmniTalkerDecoderLayer"] + + def __init__(self, config: Qwen2_5OmniTalkerConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.embed_tokens = nn.Embedding(config.vocab_size, config.embedding_size, self.padding_idx) + self.layers = nn.ModuleList( + [Qwen2_5OmniDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Qwen2_5OmniRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.has_sliding_layers = "sliding_attention" in self.config.layer_types + self.rotary_emb = Qwen2_5OmniRotaryEmbedding(config=config) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + # torch.jit.trace() doesn't support cache objects in the output + if use_cache and past_key_values is None and not torch.jit.is_tracing(): + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # the hard coded `3` is for temporal, height and width. + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) + elif position_ids.ndim == 2: + position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) + + # NOTE: we need to pass text position ids for packing. Qwen2-VL uses 3D positions + # where each dim indicates visual spatial positions for temporal/height/width grids. + # There are two scenarios when FA2-like packed masking might be activated. + # 1. User specifically passed packed `position_ids` and no attention mask. + # In this case we expect the useer to create correct position ids for all 3 grids + # and prepend text-only position ids to it. The final tensor will be [4, bs, seq-len] + # 2. User runs forward with no attention mask and no position ids. In this case, position ids + # are prepared by the model (`get_rope_index`) as `[4, bs, seq-len]` tensor. Text-only positions are + # prepended by us when creating positions so that the mask is constructed correctly. NOTE: failing to pass + # text-only positions will cause incorrect mask construction, do not change `prepare_input_for_generation` + if position_ids.ndim == 3 and position_ids.shape[0] == 4: + text_position_ids = position_ids[0] + position_ids = position_ids[1:] + else: + # If inputs are not packed (usual 3D positions), do not prepare mask from position_ids + text_position_ids = None + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": text_position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + } + # The sliding window alternating layers are not always activated depending on the config + if self.has_sliding_layers: + causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for i, decoder_layer in enumerate(self.layers): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[self.config.layer_types[i]], + position_embeddings=position_embeddings, + position_ids=text_position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +class Qwen2_5OmniTalkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin): + config: Qwen2_5OmniTalkerConfig + base_model_prefix = "talker" + output_modalities = ("audio",) + + def __init__(self, config: Qwen2_5OmniTalkerConfig): + super().__init__(config) + + self.thinker_to_talker_proj = nn.Linear(config.embedding_size, config.hidden_size) + + self.model = Qwen2_5OmniTalkerModel(config) + self.codebook_size = config.vocab_size + self.codec_head = nn.Linear(config.hidden_size, self.codebook_size, bias=False) + + self.codec_bos_token = config.tts_codec_start_token_id + self.codec_eos_token = config.tts_codec_end_token_id + self.codec_pad_token = config.tts_codec_pad_token_id + self.codec_mask_token = config.tts_codec_mask_token_id + + self.text_bos_token = config.tts_text_start_token_id + self.text_eos_token = config.tts_text_end_token_id + self.text_pad_token = config.tts_text_pad_token_id + + self.spatial_merge_size = self.config.spatial_merge_size + self.rope_deltas = None + + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + thinker_reply_part: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + use_cache: bool | None = None, + input_text_ids: torch.LongTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + use_audio_in_video: bool | None = None, + audio_feature_lengths: torch.LongTensor | None = None, + video_second_per_grid: torch.LongTensor | None = None, + **kwargs, + ) -> tuple | Qwen2_5OmniTalkerCausalLMOutputWithPast: + r""" + thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Hidden states from the thinker model's output that represent the text reply part to be processed. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + input_text_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Input token IDs for text-only content, used for position calculation in multimodal contexts. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + use_audio_in_video (`bool`, *optional*): + Whether or not use audio track in video, should same as the parameter in `process_audio_info`. + audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): + The length of feature shape of each audio in LLM. + video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*): + Number of seconds per grid for each video, used for temporal feature mapping. + + Example: + + ```python + >>> from io import BytesIO + >>> from urllib.request import urlopen + >>> import librosa + >>> from transformers import AutoProcessor, Qwen2_5OmniTalkerForConditionalGeneration + + >>> model = Qwen2_5OmniTalkerForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B") + >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B") + + >>> prompt = "<|audio_bos|><|AUDIO|><|audio_eos|>Generate the caption in English:" + >>> url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3" + >>> audio, _ = librosa.load(BytesIO(urlopen(url).read()), sr=self.processor.feature_extractor.sampling_rate) + + >>> inputs = processor(text=prompt, audio=audio, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(**inputs, max_length=30) + >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Generate the caption in English: Glass is breaking." + ```""" + + if attention_mask is not None and position_ids is None: + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + if past_key_values_length == 0 or self.rope_deltas is None: + position_ids, rope_deltas = self.get_rope_index( + input_text_ids, + image_grid_thw, + video_grid_thw, + attention_mask, + use_audio_in_video, + audio_feature_lengths, + video_second_per_grid, + ) + + if inputs_embeds is not None: + inputs_embeds[:, -1, :] += self.get_input_embeddings()( + torch.tensor([self.codec_bos_token], dtype=torch.long, device=inputs_embeds.device) + ) + inputs_embeds[:, -2, :] += self.get_input_embeddings()( + torch.tensor([self.codec_pad_token], dtype=torch.long, device=inputs_embeds.device) + ) + self.rope_deltas = rope_deltas + + else: + if inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + batch_size, seq_length = input_ids.shape + + delta = (past_key_values_length + self.rope_deltas).to(input_ids.device) + position_ids = torch.arange(seq_length, device=input_ids.device) + position_ids = position_ids.view(1, -1).expand(batch_size, -1) + position_ids = position_ids.add(delta) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) + + if inputs_embeds is None: + # 1. Inference tokens after second token + codec_embeds = self.get_input_embeddings()(input_ids) + inputs_embeds = codec_embeds + thinker_reply_part[:, :1, :] + if thinker_reply_part.shape[1] > 1: + thinker_reply_part = thinker_reply_part[:, 1:, :] + + talker_lm_input = self.thinker_to_talker_proj(inputs_embeds) + + if attention_mask is not None: + attention_mask = attention_mask.to(inputs_embeds.device) + + outputs = self.model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=talker_lm_input, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs[0] + logits = self.codec_head(hidden_states) + logits = logits.float() + + loss = None + + return Qwen2_5OmniTalkerCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + thinker_reply_part=thinker_reply_part, + ) + + # prepare inputs for talker lm generation + def prepare_inputs_for_generation( + self, + input_ids, + input_text_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + thinker_reply_part=None, + position_ids=None, + use_cache=True, + pixel_values=None, + pixel_values_videos=None, + image_grid_thw=None, + video_grid_thw=None, + input_audio_features=None, + audio_feature_attention_mask=None, + audio_feature_lengths=None, + use_audio_in_video=False, + video_second_per_grid=None, + **kwargs, + ): + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + thinker_reply_part=thinker_reply_part, + input_text_ids=input_text_ids, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + use_audio_in_video=use_audio_in_video, + audio_feature_lengths=audio_feature_lengths, + video_second_per_grid=video_second_per_grid, + **kwargs, + ) + + model_inputs["position_ids"] = None + + return model_inputs + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: dict[str, Any], + is_encoder_decoder: bool = False, + num_new_tokens: int = 1, + ) -> dict[str, Any]: + model_kwargs = super()._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder, num_new_tokens + ) + + if getattr(outputs, "thinker_reply_part", None) is not None: + model_kwargs["thinker_reply_part"] = outputs.thinker_reply_part + + return model_kwargs + + +class Qwen2_5OmniDiTRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: Qwen2_5OmniDiTConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: Qwen2_5OmniDiTConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class TimeDelayNetBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + dilation, + ): + super().__init__() + self.conv = nn.Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + dilation=dilation, + padding="same", + padding_mode="reflect", + ) + self.activation = nn.ReLU() + + def forward(self, hidden_states: torch.Tensor): + return self.activation(self.conv(hidden_states)) + + +class Res2NetBlock(torch.nn.Module): + def __init__(self, in_channels, out_channels, scale=8, kernel_size=3, dilation=1): + super().__init__() + + in_channel = in_channels // scale + hidden_channel = out_channels // scale + + self.blocks = nn.ModuleList( + [ + TimeDelayNetBlock( + in_channel, + hidden_channel, + kernel_size=kernel_size, + dilation=dilation, + ) + for i in range(scale - 1) + ] + ) + self.scale = scale + + def forward(self, hidden_states): + outputs = [] + for i, hidden_part in enumerate(torch.chunk(hidden_states, self.scale, dim=1)): + if i == 0: + output_part = hidden_part + elif i == 1: + output_part = self.blocks[i - 1](hidden_part) + else: + output_part = self.blocks[i - 1](hidden_part + output_part) + outputs.append(output_part) + output = torch.cat(outputs, dim=1) + return output + + +class SqueezeExcitationBlock(nn.Module): + def __init__(self, in_channels, se_channels, out_channels): + super().__init__() + + self.conv1 = nn.Conv1d( + in_channels=in_channels, + out_channels=se_channels, + kernel_size=1, + padding="same", + padding_mode="reflect", + ) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv1d( + in_channels=se_channels, + out_channels=out_channels, + kernel_size=1, + padding="same", + padding_mode="reflect", + ) + self.sigmoid = nn.Sigmoid() + + def forward(self, hidden_states): + hidden_states_mean = hidden_states.mean(dim=2, keepdim=True) + + hidden_states_mean = self.relu(self.conv1(hidden_states_mean)) + hidden_states_mean = self.sigmoid(self.conv2(hidden_states_mean)) + + return hidden_states * hidden_states_mean + + +class AttentiveStatisticsPooling(nn.Module): + """This class implements an attentive statistic pooling layer for each channel. + It returns the concatenated mean and std of the input tensor. + """ + + def __init__(self, channels, attention_channels=128): + super().__init__() + + self.eps = 1e-12 + self.tdnn = TimeDelayNetBlock(channels * 3, attention_channels, 1, 1) + self.tanh = nn.Tanh() + self.conv = nn.Conv1d( + in_channels=attention_channels, + out_channels=channels, + kernel_size=1, + padding="same", + padding_mode="reflect", + ) + + def _length_to_mask(self, length, max_len=None, dtype=None, device=None): + """Creates a binary mask for each sequence. + + Reference: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397/3 + + Arguments + --------- + length : torch.LongTensor + Containing the length of each sequence in the batch. Must be 1D. + max_len : int + Max length for the mask, also the size of the second dimension. + dtype : torch.dtype, default: None + The dtype of the generated mask. + device: torch.device, default: None + The device to put the mask variable. + + Returns + ------- + mask : tensor + The binary mask. + """ + + if max_len is None: + max_len = length.max().long().item() # using arange to generate mask + mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand( + len(length), max_len + ) < length.unsqueeze(1) + + mask = torch.as_tensor(mask, dtype=dtype, device=device) + return mask + + def _compute_statistics(self, x, m, dim=2): + mean = (m * x).sum(dim) + std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(self.eps)) + return mean, std + + def forward(self, hidden_states): + seq_length = hidden_states.shape[-1] + lengths = torch.ones(hidden_states.shape[0], device=hidden_states.device) + + # Make binary mask of shape [N, 1, L] + mask = self._length_to_mask( + lengths * seq_length, max_len=seq_length, dtype=hidden_states.dtype, device=hidden_states.device + ) + mask = mask.unsqueeze(1) + + # Expand the temporal context of the pooling layer by allowing the + # self-attention to look at global properties of the utterance. + total = mask.sum(dim=2, keepdim=True) + + mean, std = self._compute_statistics(hidden_states, mask / total) + mean = mean.unsqueeze(2).repeat(1, 1, seq_length) + std = std.unsqueeze(2).repeat(1, 1, seq_length) + attention = torch.cat([hidden_states, mean, std], dim=1) + + # Apply layers + attention = self.conv(self.tanh(self.tdnn(attention))) + + # Filter out zero-paddings + attention = attention.masked_fill(mask == 0, float("-inf")) + + attention = F.softmax(attention, dim=2) + mean, std = self._compute_statistics(hidden_states, attention) + # Append mean and std of the batch + pooled_stats = torch.cat((mean, std), dim=1) + pooled_stats = pooled_stats.unsqueeze(2) + + return pooled_stats + + +class SqueezeExcitationRes2NetBlock(nn.Module): + """An implementation of building block in ECAPA-TDNN, i.e., + TDNN-Res2Net-TDNN-SqueezeExcitationBlock. + """ + + def __init__( + self, + in_channels, + out_channels, + res2net_scale=8, + se_channels=128, + kernel_size=1, + dilation=1, + ): + super().__init__() + self.out_channels = out_channels + self.tdnn1 = TimeDelayNetBlock( + in_channels, + out_channels, + kernel_size=1, + dilation=1, + ) + self.res2net_block = Res2NetBlock(out_channels, out_channels, res2net_scale, kernel_size, dilation) + self.tdnn2 = TimeDelayNetBlock( + out_channels, + out_channels, + kernel_size=1, + dilation=1, + ) + self.se_block = SqueezeExcitationBlock(out_channels, se_channels, out_channels) + + def forward(self, hidden_state): + residual = hidden_state + + hidden_state = self.tdnn1(hidden_state) + hidden_state = self.res2net_block(hidden_state) + hidden_state = self.tdnn2(hidden_state) + hidden_state = self.se_block(hidden_state) + + return hidden_state + residual + + +class ECAPA_TimeDelayNet(torch.nn.Module): + """An implementation of the speaker embedding model in a paper. + "ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in + TDNN Based Speaker Verification" (https://huggingface.co/papers/2005.07143). + """ + + def __init__(self, config: Qwen2_5OmniDiTConfig): + super().__init__() + if len(config.enc_channels) != len(config.enc_kernel_sizes) or len(config.enc_channels) != len( + config.enc_dilations + ): + raise ValueError("enc_channels, enc_kernel_sizes and enc_dilations should have same length") + self.channels = config.enc_channels + self.blocks = nn.ModuleList() + + # The initial TDNN layer + self.blocks.append( + TimeDelayNetBlock( + config.mel_dim, + config.enc_channels[0], + config.enc_kernel_sizes[0], + config.enc_dilations[0], + ) + ) + + # SE-Res2Net layers + for i in range(1, len(config.enc_channels) - 1): + self.blocks.append( + SqueezeExcitationRes2NetBlock( + config.enc_channels[i - 1], + config.enc_channels[i], + res2net_scale=config.enc_res2net_scale, + se_channels=config.enc_se_channels, + kernel_size=config.enc_kernel_sizes[i], + dilation=config.enc_dilations[i], + ) + ) + + # Multi-layer feature aggregation + self.mfa = TimeDelayNetBlock( + config.enc_channels[-1], + config.enc_channels[-1], + config.enc_kernel_sizes[-1], + config.enc_dilations[-1], + ) + + # Attentive Statistical Pooling + self.asp = AttentiveStatisticsPooling( + config.enc_channels[-1], + attention_channels=config.enc_attention_channels, + ) + + # Final linear transformation + self.fc = nn.Conv1d( + in_channels=config.enc_channels[-1] * 2, + out_channels=config.enc_dim, + kernel_size=1, + padding="same", + padding_mode="reflect", + ) + + def forward(self, hidden_states): + # Minimize transpose for efficiency + hidden_states = hidden_states.transpose(1, 2) + + hidden_states_list = [] + for layer in self.blocks: + hidden_states = layer(hidden_states) + hidden_states_list.append(hidden_states) + + # Multi-layer feature aggregation + hidden_states = torch.cat(hidden_states_list[1:], dim=1) + hidden_states = self.mfa(hidden_states) + + # Attentive Statistical Pooling + hidden_states = self.asp(hidden_states) + + # Final linear transformation + hidden_states = self.fc(hidden_states) + + hidden_states = hidden_states.squeeze(-1) + return hidden_states + + +class DiTInputEmbedding(nn.Module): + def __init__(self, config: Qwen2_5OmniDiTConfig): + super().__init__() + self.proj = nn.Linear( + config.mel_dim + config.enc_dim + config.enc_emb_dim + config.emb_dim, + config.hidden_size, + ) + self.spk_encoder = ECAPA_TimeDelayNet(config) + + def forward( + self, + hidden_states: torch.Tensor, + speaker_embedding: torch.Tensor, + condition_vector: torch.Tensor, + code_embed: torch.Tensor, + drop_audio_cond: bool | None = False, + code_embed_uncond: bool | None = None, + apply_cfg: bool | None = True, + ): + if apply_cfg: + hidden_states = torch.cat([hidden_states, hidden_states], dim=0) + speaker_embedding = torch.cat([speaker_embedding, torch.zeros_like(speaker_embedding)], dim=0) + condition_vector = torch.cat([condition_vector, torch.zeros_like(condition_vector)], dim=0) + code_embed = torch.cat([code_embed, code_embed_uncond], dim=0) + elif drop_audio_cond: # cfg for cond audio + condition_vector = torch.zeros_like(condition_vector) + speaker_embedding = torch.zeros_like(speaker_embedding) + condition_vector = self.spk_encoder(condition_vector).unsqueeze(1).repeat(1, hidden_states.size(1), 1) + hidden_states = self.proj(torch.cat((hidden_states, condition_vector, code_embed, speaker_embedding), dim=-1)) + + return hidden_states + + +# Transformer backbone using DiT blocks +class DiTCodecEmbedding(nn.Module): + def __init__(self, codec_num_embeds, codec_dim, repeats): + super().__init__() + self.repeats = repeats + self.codec_embed = nn.Embedding(codec_num_embeds + 1, codec_dim) + + def forward(self, code, drop_code=False): + if drop_code: + code = torch.zeros_like(code) + code_embed = self.codec_embed(code) + + code_embed = torch.repeat_interleave(code_embed, repeats=self.repeats, dim=1) + return code_embed + + +# AdaLayerNormZero +# return with modulated x for attn input, and params for later mlp modulation +class Qwen2_5_OmniAdaLayerNormZero(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 6) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, hidden_states, emb=None): + emb = self.linear(self.silu(emb)) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1) + + hidden_states = self.norm(hidden_states) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +# AdaLayerNormZero for final layer +# return only with modulated x for attn input, cuz no more mlp modulation +class Qwen2_5_OmniAdaLayerNormZero_Final(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 2) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, hidden_states, emb): + emb = self.linear(self.silu(emb)) + scale, shift = torch.chunk(emb, 2, dim=1) + + hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :] + return hidden_states + + +# FeedForward +class DiTMLP(nn.Module): + def __init__(self, dim, mult=4, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + + self.ff = nn.ModuleList( + [ + nn.Linear(dim, inner_dim), + nn.GELU(approximate="tanh"), + nn.Dropout(dropout), + nn.Linear(inner_dim, dim), + ] + ) + + def forward(self, hidden_states): + for layer in self.ff: + hidden_states = layer(hidden_states) + return hidden_states + + +# Modified from Llama with a different rotate function, will fixed in next release +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + + def rotate_half_codec(x): + # x = rearrange(x, "... (d r) -> ... d r", r=2) + x = x.reshape(*x.shape[:-1], -1, 2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return x.reshape(*x.shape[:-2], -1) + + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half_codec(q) * sin) + k_embed = (k * cos) + (rotate_half_codec(k) * sin) + return q_embed, k_embed + + +class DiTAttention(nn.Module): + def __init__(self, config: Qwen2_5OmniDiTConfig): + super().__init__() + + self.config = config + self.dim = config.hidden_size + self.heads = config.num_attention_heads + self.inner_dim = config.head_dim * config.num_attention_heads + self.dropout = config.dropout + self.is_causal = False + + self.to_q = nn.Linear(config.hidden_size, self.inner_dim) + self.to_k = nn.Linear(config.hidden_size, self.inner_dim) + self.to_v = nn.Linear(config.hidden_size, self.inner_dim) + + self.to_out = nn.ModuleList([nn.Linear(self.inner_dim, config.hidden_size), nn.Dropout(config.dropout)]) + + def forward( + self, + hidden_states, # noised input x + position_embeddings=None, # rotary position embedding for x + attention_mask=None, + ) -> torch.Tensor: + batch_size = hidden_states.shape[0] + + # `sample` projections. + query = self.to_q(hidden_states) + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + + # attention + inner_dim = key.shape[-1] + head_dim = inner_dim // self.heads + query = query.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) + + # apply rotary position embedding + # Due to training process, only first head is applied with RoPE, will be fixed at next release + cos, sin = position_embeddings + query[:, :1], key[:, :1] = apply_rotary_pos_emb(query[:, :1], key[:, :1], cos, sin) + + attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] + attention_weights, _ = attention_interface( + self, + query, + key, + value, + attention_mask=attention_mask, + is_causal=False, + ) + + # mask. e.g. inference got a batch with different target durations, mask out the padding + attention_weights = attention_weights.reshape(batch_size, -1, self.heads * head_dim) + attention_weights = attention_weights.to(query.dtype) + + # linear proj + attention_output = self.to_out[0](attention_weights) + attention_output = self.to_out[1](attention_output) + + return attention_output + + +# time step conditioning embedding +class SinusPositionEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, hidden_states, scale=1000): + device = hidden_states.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) + emb = scale * hidden_states.unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb.type_as(hidden_states) + + +class DiTTimestepEmbedding(nn.Module): + def __init__(self, dim, freq_embed_dim=256): + super().__init__() + self.time_embed = SinusPositionEmbedding(freq_embed_dim) + self.time_mlp = nn.ModuleList([nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)]) + + def forward(self, timestep): + time_hidden = self.time_embed(timestep) + time_hidden = time_hidden.to(timestep.dtype) + for layer in self.time_mlp: + time_hidden = layer(time_hidden) # b d + return time_hidden + + +class DiTDecoderLayer(nn.Module): + def __init__(self, config: Qwen2_5OmniDiTConfig, look_ahead_block=0, look_backward_block=0): + super().__init__() + self.attn_norm = Qwen2_5_OmniAdaLayerNormZero(config.hidden_size) + + self.attn = DiTAttention(config) + self.look_ahead_block = look_ahead_block + self.look_backward_block = look_backward_block + self.ff_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False, eps=1e-6) + self.ff = DiTMLP(dim=config.hidden_size, mult=config.ff_mult, dropout=config.dropout) + + def forward( + self, hidden_states, timestep, position_embeddings=None, block_diff=None + ): # x: noised input, t: time embedding + # pre-norm & modulation for attention input + norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(hidden_states, emb=timestep) + + # attention + attn_output = self.attn( + hidden_states=norm, + position_embeddings=position_embeddings, + attention_mask=(block_diff >= -float(self.look_backward_block)) + & (block_diff <= float(self.look_ahead_block)), + ) + + # process attention output for input x + hidden_states = hidden_states + gate_msa.unsqueeze(1) * attn_output + + norm = self.ff_norm(hidden_states) * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ff_output = self.ff(norm) + hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output + + return hidden_states + + +class SnakeBeta(nn.Module): + """ + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://huggingface.co/papers/2006.08195 + """ + + def __init__(self, in_features, alpha=1.0): + super().__init__() + self.in_features = in_features + + # initialize alpha + self.alpha = Parameter(torch.zeros(in_features) * alpha) + self.beta = Parameter(torch.zeros(in_features) * alpha) + + self.no_div_by_zero = 0.000000001 + + def forward(self, hidden_states): + """ + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + """ + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + beta = self.beta.unsqueeze(0).unsqueeze(-1) + alpha = torch.exp(alpha) + beta = torch.exp(beta) + hidden_states = hidden_states + (1.0 / (beta + self.no_div_by_zero)) * torch.pow( + torch.sin(hidden_states * alpha), 2 + ) + + return hidden_states + + +class UpSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.stride = ratio + self.pad = self.kernel_size // ratio - 1 + self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 + self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 + + filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size) + self.register_buffer("filter", filter, persistent=False) + + def forward(self, hidden_states): + channels = hidden_states.shape[1] + + hidden_states = F.pad(hidden_states, (self.pad, self.pad), mode="replicate") + hidden_states = self.ratio * F.conv_transpose1d( + hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels + ) + hidden_states = hidden_states[..., self.pad_left : -self.pad_right] + + return hidden_states + + +class DownSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + cutoff = 0.5 / ratio + half_width = 0.6 / ratio + self.cutoff = cutoff + self.half_width = half_width + self.kernel_size = kernel_size + + if cutoff < 0.0: + raise ValueError("Minimum cutoff must be larger than zero.") + if cutoff > 0.5: + raise ValueError("A cutoff above 0.5 does not make sense.") + + self.even = kernel_size % 2 == 0 + self.pad_left = kernel_size // 2 - int(self.even) + self.pad_right = kernel_size // 2 + self.stride = ratio + filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) + self.register_buffer("filter", filter, persistent=False) + + def forward(self, hidden_states): + channels = hidden_states.shape[1] + hidden_states = F.pad(hidden_states, (self.pad_left, self.pad_right), mode="replicate") + out = F.conv1d(hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels) + return out + + +class TorchActivation1d(nn.Module): + def __init__( + self, + activation, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12, + ): + super().__init__() + if not callable(activation): + raise TypeError("Activation function must be callable") + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + def forward(self, hidden_states): + hidden_states = self.upsample(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.downsample(hidden_states) + + return hidden_states + + +class AMPBlock(torch.nn.Module): + def __init__( + self, + channels, + kernel_size=3, + dilation=(1, 3, 5), + ): + super().__init__() + + self.convs1 = nn.ModuleList( + [ + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=self._get_padding(kernel_size, dilation[0]), + ), + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=self._get_padding(kernel_size, dilation[1]), + ), + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=self._get_padding(kernel_size, dilation[2]), + ), + ] + ) + + self.convs2 = nn.ModuleList( + [ + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=self._get_padding(kernel_size, 1), + ), + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=self._get_padding(kernel_size, 1), + ), + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=self._get_padding(kernel_size, 1), + ), + ] + ) + + self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers + + self.activations = nn.ModuleList( + [TorchActivation1d(activation=SnakeBeta(channels)) for _ in range(self.num_layers)] + ) + + def _get_padding(self, kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + def forward(self, hidden_states): + acts1, acts2 = self.activations[::2], self.activations[1::2] + for conv1, conv2, act1, act2 in zip(self.convs1, self.convs2, acts1, acts2): + residual = hidden_states + hidden_states = act1(hidden_states) + hidden_states = conv1(hidden_states) + hidden_states = act2(hidden_states) + hidden_states = conv2(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring( + custom_intro=""" + The full Qwen2.5Omni Token2WavBigVGAN model. Which take mel spectrogram as input and predict waveform. + """ +) +class Qwen2_5OmniToken2WavBigVGANModel(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniBigVGANConfig + input_modalities = "audio" + + def __init__(self, config: Qwen2_5OmniBigVGANConfig): + super().__init__(config) + self.num_residual_blocks = len(config.resblock_kernel_sizes) + self.num_upsample_layers = len(config.upsample_rates) + + self.conv_pre = nn.Conv1d(config.mel_dim, config.upsample_initial_channel, 7, 1, padding=3) + + # Removing extra ModuleList breaks official state dict + ups = [ + nn.ModuleList( + [ + nn.ConvTranspose1d( + config.upsample_initial_channel // (2**layer_idx), + config.upsample_initial_channel // (2 ** (layer_idx + 1)), + kernel_size, + stride, + padding=(kernel_size - stride) // 2, + ) + ] + ) + for layer_idx, (stride, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes)) + ] + self.ups = nn.ModuleList(ups) + + self.resblocks = nn.ModuleList( + [ + AMPBlock(config.upsample_initial_channel // (2 ** (layer_idx + 1)), kernel_size, dilation) + for layer_idx in range(self.num_upsample_layers) + for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes) + ] + ) + + self.activation_post = TorchActivation1d( + activation=SnakeBeta(config.upsample_initial_channel // (2**self.num_upsample_layers)) + ) + self.conv_post = nn.Conv1d( + config.upsample_initial_channel // (2**self.num_upsample_layers), 1, 7, 1, padding=3, bias=False + ) + + self.post_init() + + def normalize_spectrogram(self, spectrogram, max_value, min_db): + return torch.clamp((2 * max_value) * ((spectrogram - min_db) / (-min_db)) - max_value, -max_value, max_value) + + def amplitude_to_db(self, amplitude, min_db_level): + min_level = torch.exp( + torch.tensor(min_db_level / 20.0 * np.log(10), device=amplitude.device, dtype=amplitude.dtype) + ) + return 20 * torch.log10(torch.clamp(amplitude, min=min_level)) + + def process_mel_spectrogram(self, mel_spectrogram): + amplitude_spectrum = torch.exp(mel_spectrogram) + decibel_spectrum = self.amplitude_to_db(amplitude_spectrum, -115) - 20 + return self.normalize_spectrogram(decibel_spectrum, 1, -115) + + def forward(self, mel_spectrogram, **kwargs): + processed_spectrogram = self.process_mel_spectrogram(mel_spectrogram) + hidden_representation = self.conv_pre(processed_spectrogram) + + for layer_index in range(self.num_upsample_layers): + hidden_representation = self.ups[layer_index][0](hidden_representation) + residual_output = sum( + self.resblocks[layer_index * self.num_residual_blocks + block_index](hidden_representation) + for block_index in range(self.num_residual_blocks) + ) + residual_output = residual_output / self.num_residual_blocks + hidden_representation = residual_output + + hidden_representation = self.activation_post(hidden_representation) + output_waveform = self.conv_post(hidden_representation) + return torch.clamp(output_waveform, min=-1.0, max=1.0).squeeze().cpu() + + +class RungeKutta4ODESolver: + def __init__(self, function, initial_value): + self.function = function + self.initial_value = initial_value + + self._one_third = 1 / 3 + self._two_thirds = 2 / 3 + + def _rk4_step(self, function, time_start, time_step, time_end, value_start, function_value_start=None): + k1 = function_value_start if function_value_start is not None else function(time_start, value_start) + k2 = function(time_start + time_step * self._one_third, value_start + time_step * k1 * self._one_third) + k3 = function(time_start + time_step * self._two_thirds, value_start + time_step * (k2 - k1 * self._one_third)) + k4 = function(time_end, value_start + time_step * (k1 - k2 + k3)) + return (k1 + 3 * (k2 + k3) + k4) * time_step / 8 + + def _compute_step(self, function, time_start, time_step, time_end, value_start): + function_value_start = function(time_start, value_start) + return self._rk4_step( + function, time_start, time_step, time_end, value_start, function_value_start=function_value_start + ), function_value_start + + def _linear_interpolation(self, time_start, time_end, value_start, value_end, time_point): + if time_point == time_start: + return value_start + if time_point == time_end: + return value_end + weight = (time_point - time_start) / (time_end - time_start) + return value_start + weight * (value_end - value_start) + + def integrate(self, time_points): + solution = torch.empty( + len(time_points), + *self.initial_value.shape, + dtype=self.initial_value.dtype, + device=self.initial_value.device, + ) + solution[0] = self.initial_value + + current_index = 1 + current_value = self.initial_value + for time_start, time_end in zip(time_points[:-1], time_points[1:]): + time_step = time_end - time_start + delta_value, _ = self._compute_step(self.function, time_start, time_step, time_end, current_value) + next_value = current_value + delta_value + + while current_index < len(time_points) and time_end >= time_points[current_index]: + solution[current_index] = self._linear_interpolation( + time_start, time_end, current_value, next_value, time_points[current_index] + ) + current_index += 1 + + current_value = next_value + + return solution + + +@auto_docstring( + custom_intro=""" + The full Qwen2.5Omni Token2WavDiT model. Which take speech tokens as input and predict mel spectrogram. + """ +) +class Qwen2_5OmniToken2WavDiTModel(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniDiTConfig + input_modalities = "audio" + _no_split_modules = ["DiTDecoderLayer"] + + def __init__(self, config: Qwen2_5OmniDiTConfig): + super().__init__(config) + self.mel_dim = config.mel_dim + self.repeats = config.repeats + self.time_embed = DiTTimestepEmbedding(config.hidden_size) + + self.text_embed = DiTCodecEmbedding(config.num_embeds, config.emb_dim, config.repeats) + self.input_embed = DiTInputEmbedding(config) + + self.rotary_embed = Qwen2_5OmniDiTRotaryEmbedding(config=config) + + self.hidden_size = config.hidden_size + self.layers = config.num_hidden_layers + self.block_size = config.block_size + self.num_attention_heads = config.num_attention_heads + + self.transformer_blocks = nn.ModuleList() + for i in range(config.num_hidden_layers): + self.transformer_blocks.append( + DiTDecoderLayer( + config, + look_ahead_block=1 if i in config.look_ahead_layers else 0, + look_backward_block=1 if i in config.look_backward_layers else 0, + ) + ) + + self.norm_out = Qwen2_5_OmniAdaLayerNormZero_Final(config.hidden_size) # final modulation + self.proj_out = nn.Linear(config.hidden_size, config.mel_dim) + + self.post_init() + + def _create_block_diff(self, hidden_states): + batch, seq_len = hidden_states.shape[0], hidden_states.shape[1] + block_indices = torch.arange(seq_len, device=hidden_states.device) // self.block_size # [seq_length] + + block_i = block_indices.unsqueeze(1) # [seq_length, 1] + block_j = block_indices.unsqueeze(0) # [1, seq_length] + block_diff = block_j - block_i # (n, n) + + return block_diff.expand(batch, self.num_attention_heads, seq_len, seq_len) + + def forward( + self, + hidden_states, + condition_vector, + speaker_embedding, + quantized_code, + time_step, + drop_audio_conditioning=False, + drop_code=False, + apply_cfg=True, + **kwargs, + ): + batch_size = hidden_states.shape[0] + if time_step.ndim == 0: + time_step = time_step.repeat(batch_size) + + # Compute embeddings + time_embedding = self.time_embed(time_step) + text_embedding = self.text_embed(quantized_code, drop_code=False if apply_cfg else drop_code) + text_embedding_unconditioned = self.text_embed(quantized_code, drop_code=True) if apply_cfg else None + + hidden_states = self.input_embed( + hidden_states, + speaker_embedding, + condition_vector, + text_embedding, + drop_audio_cond=drop_audio_conditioning, + code_embed_uncond=text_embedding_unconditioned, + apply_cfg=apply_cfg, + ) + + # Compute positional encodings + position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device) + position_ids = position_ids[None, :].repeat(batch_size, 1) + position_embeddings = self.rotary_embed(hidden_states, position_ids) + blockwise_difference = self._create_block_diff(hidden_states) + + # Transformer blocks + for transformer_block in self.transformer_blocks: + hidden_states = transformer_block( + hidden_states, + time_embedding, + position_embeddings=position_embeddings, + block_diff=blockwise_difference, + ) + + hidden_states = self.norm_out(hidden_states, time_embedding) + output = self.proj_out(hidden_states) + + return output + + @torch.no_grad() + def sample( + self, + conditioning_vector, + reference_mel_spectrogram, + quantized_code, + num_steps=10, + guidance_scale=0.5, + sway_coefficient=-1.0, + ): + maximum_duration = quantized_code.shape[1] * self.repeats + batch_size = reference_mel_spectrogram.shape[0] + if batch_size != 1: + raise ValueError("Only batch size = 1 is currently supported") + + if maximum_duration > self.config.max_position_embeddings: + raise ValueError( + f"Requested mel length ({maximum_duration}) exceeds `dit_config.max_position_embeddings` " + f"({self.config.max_position_embeddings}). Provide shorter `quantized_code`." + ) + + initial_state = torch.randn( + [batch_size, maximum_duration, self.mel_dim], + dtype=reference_mel_spectrogram.dtype, + device=quantized_code.device, + ) + conditioning_vector = conditioning_vector.unsqueeze(1).repeat(1, maximum_duration, 1) + + def ode_function(time_step, hidden_states): + if guidance_scale < 1e-5: + prediction = self( + hidden_states=hidden_states, + speaker_embedding=conditioning_vector, + condition_vector=reference_mel_spectrogram, + quantized_code=quantized_code, + time_step=time_step, + drop_audio_conditioning=False, + drop_code=False, + apply_cfg=False, + ) + return prediction + + model_output = self( + hidden_states=hidden_states, + quantized_code=quantized_code, + speaker_embedding=conditioning_vector, + condition_vector=reference_mel_spectrogram, + time_step=time_step, + apply_cfg=True, + ) + guided_prediction, null_prediction = torch.chunk(model_output, 2, dim=0) + return guided_prediction + (guided_prediction - null_prediction) * guidance_scale + + initial_time = 0 + time_embedding = torch.linspace( + initial_time, 1, num_steps, device=quantized_code.device, dtype=conditioning_vector.dtype + ) + + if sway_coefficient is not None: + time_embedding += sway_coefficient * (torch.cos(torch.pi / 2 * time_embedding) - 1 + time_embedding) + + ode_solver = RungeKutta4ODESolver(function=ode_function, initial_value=initial_state) + solution_trajectory = ode_solver.integrate(time_embedding) + + generated_waveform = solution_trajectory[-1] + generated_mel_spectrogram = generated_waveform.permute(0, 2, 1) + return generated_mel_spectrogram + + +@auto_docstring( + custom_intro=""" + The full Qwen2.5Omni Token2Wav model. Consists a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform. + """ +) +class Qwen2_5OmniToken2WavModel(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniToken2WavConfig + base_model_prefix = "model" + input_modalities = "audio" + _no_split_modules = ["Qwen2_5OmniToken2WavDiTModel", "Qwen2_5OmniToken2WavBigVGANModel"] + + def __init__(self, config: Qwen2_5OmniToken2WavConfig): + super().__init__(config) + attn_impl = config._attn_implementation + if is_flash_attention_requested(config): + logger.warning_once( + "Qwen2_5OmniToken2WavModel must inference with fp32, but Flash Attention only supports fp16 and bf16, " + "attention implementation of Qwen2_5OmniToken2WavModel will fallback to sdpa." + ) + attn_impl = "sdpa" + elif config._attn_implementation == "eager": + logger.warning_once( + "Qwen2_5OmniToken2WavModel does not support eager attention implementation, fall back to sdpa" + ) + attn_impl = "sdpa" + self.code2wav_dit_model = Qwen2_5OmniToken2WavDiTModel._from_config( + config.dit_config, attn_implementation=attn_impl + ) + self.code2wav_bigvgan_model = Qwen2_5OmniToken2WavBigVGANModel._from_config( + config.bigvgan_config, attn_implementation=attn_impl + ) + + self.post_init() + + def forward( + self, + code, + conditioning, + reference_mel, + num_steps=10, + guidance_scale=0.5, + sway_coefficient=-1.0, + **kwargs, + ): + """Generates a waveform from input code and conditioning parameters.""" + + mel_spectrogram = self.code2wav_dit_model.sample( + conditioning, + reference_mel, + code, + num_steps=num_steps, + guidance_scale=guidance_scale, + sway_coefficient=sway_coefficient, + ) + + waveform = self.code2wav_bigvgan_model(mel_spectrogram) + + return waveform + + +############################ +# Start Qwen2.5Omni # +############################ + + +@auto_docstring( + custom_intro=""" + The full Qwen2.5Omni model, a multimodal model composed of 3 sub-models: + - [`Qwen2_5OmniThinkerForConditionalGeneration`]: + a causal auto-regressive transformer takes text, audio, image, video as input and predict text tokens. + - [`Qwen2_5OmniTalkerForConditionalGeneration`]: + a causal auto-regressive transformer takes thinker hidden states and response as input and predict speech tokens. + - [`Qwen2_5OmniToken2WavModel`]: + a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform. + """ +) +class Qwen2_5OmniForConditionalGeneration(Qwen2_5OmniPreTrainedModel, GenerationMixin): + config: Qwen2_5OmniConfig + output_modalities = ("audio", "text") + _no_split_modules = [ + "Qwen2_5OmniTalkerForConditionalGeneration", + "Qwen2_5OmniToken2WavModel", + ] + + def __init__(self, config): + super().__init__(config) + + self.thinker = Qwen2_5OmniThinkerForConditionalGeneration(config.thinker_config) + + self.has_talker = config.enable_audio_output + self.speaker_map = {} + if config.enable_audio_output: + self.enable_talker() + self.post_init() + + def enable_talker(self): + self.talker = Qwen2_5OmniTalkerForConditionalGeneration(self.config.talker_config) + self.token2wav = Qwen2_5OmniToken2WavModel(self.config.token2wav_config) + self.token2wav.float() + self.has_talker = True + + def load_speakers(self, path): + check_torch_load_is_safe() + for key, value in torch.load(path, weights_only=True).items(): + self.speaker_map[key] = value + logger.info(f"Speaker {list(self.speaker_map.keys())} loaded") + + def disable_talker(self): + if hasattr(self, "talker"): + del self.talker + if hasattr(self, "token2wav"): + del self.token2wav + self.has_talker = False + + @classmethod + def from_pretrained( + cls, + pretrained_model_name_or_path, + *model_args, + config=None, + cache_dir=None, + ignore_mismatched_sizes=False, + force_download=False, + local_files_only=False, + token=None, + revision="main", + use_safetensors=None, + weights_only=True, + **kwargs, + ): + model = super().from_pretrained( + pretrained_model_name_or_path, + *model_args, + config=config, + cache_dir=cache_dir, + ignore_mismatched_sizes=ignore_mismatched_sizes, + force_download=force_download, + local_files_only=local_files_only, + token=token, + revision=revision, + use_safetensors=use_safetensors, + weights_only=weights_only, + **kwargs, + ) + spk_path = cached_file( + pretrained_model_name_or_path, + "spk_dict.pt", + subfolder=kwargs.pop("subfolder", None), + cache_dir=kwargs.pop("cache_dir", None), + force_download=kwargs.pop("force_download", False), + proxies=kwargs.pop("proxies", None), + local_files_only=kwargs.pop("local_files_only", False), + token=token, + revision=kwargs.pop("revision", None), + ) + if spk_path is None: + raise ValueError(f"""{pretrained_model_name_or_path}/{spk_path} not exists""") + model.load_speakers(spk_path) + + return model + + @torch.no_grad() + @deprecate_kwarg("return_audio", version="v5", new_name="generation_mode") + # TODO: raushan, defaults should be saved in generation config + def generate( + self, + input_ids: torch.Tensor | None = None, + speaker: str = "Chelsie", + use_audio_in_video: bool = False, + thinker_max_new_tokens: int = 1024, + talker_max_new_tokens: int = 4096, + talker_do_sample: bool = True, + talker_top_k: int = 40, + talker_top_p: float = 0.8, + talker_temperature: float = 0.9, + talker_eos_token_id: list[int] = [8292, 8294], + talker_repetition_penalty: float = 1.05, + **kwargs, + ): + r""" + Generate text response and audio from input. + + Args: + input_ids (`Optional[torch.Tensor]`, *optional*): + Input ids, should obtain from processor. + speaker (`str` , defaults to "Chelsie"): + Which speaker should be used in audio response. + use_audio_in_video (`bool`, defaults to False): + Whether or not use audio track in video, should same as the parameter in `process_audio_info`. + generation_mode (`Optional[str]`, *optional*): + Whether or not return response in audio format. When `generation_mode="audio"`, this parameter is same as `config.enable_audio_output`. + kwargs (*optional*): + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model. + - With a *thinker_*, *talker_*, *token2wav_* prefix, they will be input for the `generate` method of the + thinker, talker and token2wav respectively. It has the priority over the keywords without a prefix. + Returns: + When `return_audio=False`: + - **Text** (`torch.Tensor`): Generated text token sequence. + When `return_audio=True`: + - **Text** (`torch.Tensor`): Generated text token sequence. + - **Audio waveform** (`torch.Tensor`): Generated audio waveform. + """ + # check `False` on purpose because the parameter can be `str/bool`. This is needed for BC + generation_mode = kwargs.pop("generation_mode", None) + return_audio = generation_mode != "text" and generation_mode is not False + + if speaker not in self.speaker_map: + raise ValueError(f"{speaker} is not available, available speakers: {self.speaker_map.keys()}") + if return_audio and not self.has_talker: + raise ValueError( + "Cannot use talker when talker module not initialized. Use `enable_talker` method or set enable_talker in config to enable talker." + ) + if return_audio is None: + return_audio = self.has_talker + if input_ids.shape[0] != 1 and return_audio: + raise NotImplementedError("Qwen2.5-Omni currently does not support batched inference with audio output") + + shared_kwargs = {"use_audio_in_video": use_audio_in_video} + thinker_kwargs = { + "max_new_tokens": thinker_max_new_tokens, + } + talker_kwargs = { + "max_new_tokens": talker_max_new_tokens, + "do_sample": talker_do_sample, + "top_k": talker_top_k, + "top_p": talker_top_p, + "temperature": talker_temperature, + "eos_token_id": talker_eos_token_id, + "repetition_penalty": talker_repetition_penalty, + } + token2wav_kwargs = {} + + for key, value in kwargs.items(): + if key.startswith("thinker_"): + thinker_kwargs[key[len("thinker_") :]] = value + elif key.startswith("talker_"): + talker_kwargs[key[len("talker_") :]] = value + elif key.startswith("token2wav_"): + token2wav_kwargs[key[len("token2wav_") :]] = value + # Process special input values + elif key == "feature_attention_mask": + thinker_kwargs[key] = value + talker_kwargs["audio_feature_lengths"] = torch.sum(value, dim=1) + elif key == "input_features" or key == "attention_mask": + thinker_kwargs[key] = value + # Put other key to shared kwargs + else: + shared_kwargs[key] = value + + # Merge kwargs + for key, value in shared_kwargs.items(): + if key not in thinker_kwargs: + thinker_kwargs[key] = value + if key not in talker_kwargs: + talker_kwargs[key] = value + if key not in token2wav_kwargs: + token2wav_kwargs[key] = value + speaker_params = self.speaker_map[speaker] + + # 1. Generate from thinker module + generate_audio = return_audio and self.has_talker + if generate_audio: + thinker_kwargs["output_hidden_states"] = True + thinker_kwargs["return_dict_in_generate"] = True + + thinker_result = self.thinker.generate(input_ids=input_ids, **thinker_kwargs) + + if not generate_audio: + return thinker_result + + # 2. Generate speech tokens from talker module + embeds_to_talker = thinker_result.hidden_states[0][0].clone().to(input_ids.device) + if thinker_kwargs.get("input_features") is not None: + audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index + audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker) + audio_mask_tensor = torch.zeros( + [audio_ids_mask.sum(), embeds_to_talker.shape[-1]], + dtype=embeds_to_talker.dtype, + device=input_ids.device, + ) + embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor) + if thinker_kwargs.get("pixel_values") is not None: + image_ids_mask = input_ids == self.config.thinker_config.image_token_index + image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker) + image_mask_tensor = torch.zeros( + [image_ids_mask.sum(), embeds_to_talker.shape[-1]], + dtype=embeds_to_talker.dtype, + device=input_ids.device, + ) + embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor) + if thinker_kwargs.get("pixel_values_videos") is not None: + video_ids_mask = input_ids == self.config.thinker_config.video_token_index + video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker) + video_mask_tensor = torch.zeros( + [video_ids_mask.sum(), embeds_to_talker.shape[-1]], + dtype=embeds_to_talker.dtype, + device=input_ids.device, + ) + embeds_to_talker.masked_scatter_(video_mask, video_mask_tensor) + + processed_thinker_hidden = ( + (embeds_to_talker,) + thinker_result.hidden_states[0][1:], + ) + thinker_result.hidden_states[1:] + thinker_generate_ids = thinker_result.sequences[:, input_ids.size(1) :].to(input_ids.device) + thinker_token_embeds = [ + token_hidden_states[0].to(input_ids.device) for token_hidden_states in processed_thinker_hidden + ] + thinker_hidden_states = [ + token_hidden_states[-1].to(input_ids.device) for token_hidden_states in processed_thinker_hidden + ] + + talker_text_bos_token = speaker_params["bos_token"] + talker_input_text_ids = torch.cat( + [ + input_ids, + torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=input_ids.device), + thinker_generate_ids[:, :1], + ], + dim=-1, + ) + + talker_input_ids = torch.cat( + [ + torch.full_like(input_ids, fill_value=self.talker.codec_mask_token), + torch.tensor([[self.talker.codec_pad_token]], dtype=torch.long, device=input_ids.device), + torch.tensor([[self.talker.codec_bos_token]], dtype=torch.long, device=input_ids.device), + ], + dim=1, + ) + + thinker_embed_tokens = self.thinker.get_input_embeddings() + thinker_reply_part = torch.cat(thinker_hidden_states[1:], dim=1) + torch.cat(thinker_token_embeds[1:], dim=1) + talker_inputs_embeds = thinker_hidden_states[0] + thinker_token_embeds[0] + talker_text_bos_token = torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=input_ids.device) + talker_text_bos_embed = thinker_embed_tokens(talker_text_bos_token).to(input_ids.device) + talker_inputs_embeds = torch.cat( + [ + talker_inputs_embeds, + talker_text_bos_embed, + thinker_reply_part[:, :1, :], + ], + dim=1, + ) + + eos_token = torch.tensor([[self.talker.text_eos_token]], dtype=torch.long, device=input_ids.device) + eos_embedding = thinker_embed_tokens(eos_token).to(input_ids.device) + + pad_token = torch.tensor([[self.talker.text_pad_token]], dtype=torch.long, device=input_ids.device) + pad_embedding = thinker_embed_tokens(pad_token).to(input_ids.device) + + thinker_reply_part = torch.cat( + [ + thinker_reply_part[:, 1:, :], + eos_embedding, + pad_embedding, + ], + dim=1, + ) + + talker_attention_mask = None + if "attention_mask" in kwargs: + talker_attention_mask = torch.cat( + [kwargs["attention_mask"], kwargs["attention_mask"].new_ones((1, 2))], dim=1 + ).to(input_ids.device) + + talker_result = self.talker.generate( + input_ids=talker_input_ids, + input_text_ids=talker_input_text_ids, + thinker_reply_part=thinker_reply_part, + inputs_embeds=talker_inputs_embeds, + attention_mask=talker_attention_mask, + suppress_tokens=[self.talker.codec_bos_token], + **{k: (v.to(input_ids.device) if torch.is_tensor(v) else v) for k, v in talker_kwargs.items()}, + ) + talker_generate_codes = talker_result[:, talker_input_ids.shape[1] : -1] + + # 3. Generate wavs from code + if self.token2wav.dtype != torch.float: + self.token2wav.float() + + wav = self.token2wav( + talker_generate_codes.to(input_ids.device), + conditioning=speaker_params["cond"].to(input_ids.device).float(), + reference_mel=speaker_params["ref_mel"].to(input_ids.device).float(), + **token2wav_kwargs, + ) + + return thinker_result.sequences, wav.float() + + +__all__ = [ + "Qwen2_5OmniForConditionalGeneration", + "Qwen2_5OmniThinkerTextModel", + "Qwen2_5OmniThinkerForConditionalGeneration", + "Qwen2_5OmniTalkerModel", + "Qwen2_5OmniTalkerForConditionalGeneration", + "Qwen2_5OmniToken2WavDiTModel", + "Qwen2_5OmniToken2WavBigVGANModel", + "Qwen2_5OmniToken2WavModel", + "Qwen2_5OmniPreTrainedModel", + "Qwen2_5OmniPreTrainedModelForConditionalGeneration", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py new file mode 100644 index 0000000000000000000000000000000000000000..2dccae79b4dda967895081920288d6d39afdb1b0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py @@ -0,0 +1,3832 @@ +# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Qwen2.5Omni model (Audio, Image, Video).""" + +import math +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict +from torch import nn +from torch.nn import Parameter + +from ... import initialization as init +from ...cache_utils import Cache +from ...configuration_utils import PreTrainedConfig +from ...generation import GenerationMixin +from ...modeling_outputs import BaseModelOutputWithPooling, ModelOutput +from ...modeling_rope_utils import RopeParameters +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + can_return_tuple, + check_torch_load_is_safe, + logging, + torch_compilable_check, +) +from ...utils.deprecation import deprecate_kwarg +from ...utils.generic import is_flash_attention_requested, merge_with_config_defaults +from ...utils.hub import cached_file +from ...utils.output_capturing import capture_outputs +from ..llama.modeling_llama import LlamaRotaryEmbedding, rotate_half +from ..qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLVisionConfig +from ..qwen2_5_vl.modeling_qwen2_5_vl import ( + Qwen2_5_VisionRotaryEmbedding, + Qwen2_5_VisionTransformerPretrainedModel, + Qwen2_5_VLAttention, + Qwen2_5_VLMLP, + Qwen2_5_VLPreTrainedModel, + Qwen2_5_VLTextModel, + Qwen2_5_VLVisionBlock, + eager_attention_forward, +) +from ..qwen2_audio.configuration_qwen2_audio import Qwen2AudioEncoderConfig +from ..qwen2_audio.modeling_qwen2_audio import Qwen2AudioEncoderLayer +from ..qwen2_vl.modeling_qwen2_vl import Qwen2VLRotaryEmbedding + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniVisionEncoderConfig(Qwen2_5_VLVisionConfig): + r""" + window_size (`int`, *optional*, defaults to 11): + Size of windows. + out_hidden_size (`int`, *optional*, defaults to 3584): + The output hidden size of the vision model. + fullatt_block_indexes (`int`, *optional*, defaults to `[7, 15, 23, 31]`): + Indices of layers with full attention + + Example: + + ```python + >>> from transformers import Qwen2_5OmniVisionEncoderConfig, Qwen2_5OmniVisionEncoder + + >>> # Initializing a Qwen2_5OmniVisionEncoderConfig + >>> configuration = Qwen2_5OmniVisionEncoderConfig() + + >>> # Initializing a Qwen2_5OmniVisionEncoder (with random weights) + >>> model = Qwen2_5OmniVisionEncoder(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_vision_encoder" + + tokens_per_second = AttributeError() + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniAudioEncoderConfig(Qwen2AudioEncoderConfig): + r""" + max_source_positions (`int`, *optional*, defaults to 1500): + The maximum sequence length of log-mel filter-bank features that this model might ever be used with. + n_window (`int`, *optional*, defaults to 100): + The chunk for conv and flash attn in AudioEncoder. + output_dim (`int`, *optional*, defaults to 3584): + The output dimension of AudioEncoder. + + Example: + + ```python + >>> from transformers import Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniAudioEncoder + + >>> # Initializing a Qwen2_5OmniAudioEncoderConfig + >>> configuration = Qwen2_5OmniAudioEncoderConfig() + + >>> # Initializing a Qwen2_5OmniAudioEncoder (with random weights) + >>> model = Qwen2_5OmniAudioEncoder(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_audio_encoder" + + n_window: int = 100 + output_dim: int = 3584 + encoder_layerdrop = AttributeError() + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniTextConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig + + >>> # Initializing a Qwen2_5OmniAudioEncoder config + >>> audio_config = Qwen2_5OmniAudioEncoderConfig() + + >>> # Initializing a Qwen2_5OmniVisionEncoder config + >>> vision_config = Qwen2_5OmniVisionEncoderConfig() + + >>> # Initializing a Qwen2.5OmniThinker configuration + >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config) + + >>> # Initializing a model from the Qwen-Omni style configuration + >>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_text" + keys_to_ignore_at_inference = ["past_key_values"] + default_theta = 1000000.0 + + # Default tensor parallel plan for base model `Qwen25OmniText` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + ignore_keys_at_rope_validation = {"mrope_section"} + + vocab_size: int = 152064 + hidden_size: int = 3584 + intermediate_size: int = 18944 + num_hidden_layers: int = 28 + num_attention_heads: int = 28 + num_key_value_heads: int | None = 4 + hidden_act: str = "silu" + max_position_embeddings: int = 32768 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-6 + use_cache: bool = True + rope_parameters: RopeParameters | dict | None = None + use_sliding_window: bool = False + sliding_window: int | None = 32768 + max_window_layers: int = 28 + layer_types: list[str] | None = None + attention_dropout: float | int = 0.0 + pad_token_id: int | None = None + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = None + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + self.sliding_window = self.sliding_window if self.use_sliding_window else None + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" + if self.sliding_window is not None and i >= self.max_window_layers + else "full_attention" + for i in range(self.num_hidden_layers) + ] + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniThinkerConfig(PreTrainedConfig): + r""" + position_id_per_seconds (`int`, *optional*, defaults to 25): + The increment of position id per second. + seconds_per_chunk (`int`, *optional*, defaults to 2): + The duration in seconds of the chunk of audio and video data. + audio_start_token_id (`int`, *optional*, defaults to 151647): + The audio start token index to encode the audio prompt. + audio_end_token_id (`int`, *optional*, defaults to 151648): + The audio end token index to encode the audio prompt. + user_token_id (`int, *optional*, defaults to 872): + The user token index to encode the user token. + + Example: + + ```python + >>> from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig + + >>> # Initializing a Qwen2_5OmniAudioEncoder config + >>> audio_config = Qwen2_5OmniAudioEncoderConfig() + + >>> # Initializing a Qwen2_5OmniVisionEncoder config + >>> vision_config = Qwen2_5OmniVisionEncoderConfig() + + >>> # Initializing a Qwen2_5OmniTextConfig config + >>> text_config = Qwen2_5OmniTextConfig() + + >>> # Initializing a Qwen2.5OmniThinker configuration + >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, vision_config, text_config) + + >>> # Initializing a model from the Qwen-Omni style configuration + >>> model = Qwen2_5OmniThinkerForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_thinker" + attribute_map = { + "image_token_id": "image_token_index", + "video_token_id": "video_token_index", + "audio_token_id": "audio_token_index", + } + sub_configs = { + "audio_config": Qwen2_5OmniAudioEncoderConfig, + "vision_config": Qwen2_5OmniVisionEncoderConfig, + "text_config": Qwen2_5OmniTextConfig, + } + + audio_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + audio_token_index: int = 151646 + image_token_index: int = 151655 + video_token_index: int = 151656 + position_id_per_seconds: int = 25 + seconds_per_chunk: int = 2 + audio_start_token_id: int = 151647 + audio_end_token_id: int = 151648 + user_token_id: int = 872 + initializer_range: float = 0.02 + tie_word_embeddings: bool = False + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = Qwen2_5OmniVisionEncoderConfig(**self.vision_config) + elif self.vision_config is None: + self.vision_config = Qwen2_5OmniVisionEncoderConfig() + + if isinstance(self.audio_config, dict): + self.audio_config = Qwen2_5OmniAudioEncoderConfig(**self.audio_config) + elif self.audio_config is None: + self.audio_config = Qwen2_5OmniAudioEncoderConfig() + + if isinstance(self.text_config, dict): + self.text_config = Qwen2_5OmniTextConfig(**self.text_config) + elif self.text_config is None: + self.text_config = Qwen2_5OmniTextConfig() + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniTalkerConfig(PreTrainedConfig): + r""" + tts_text_start_token_id (`int`, *optional*, defaults to 151860): + The tts text start token index to encode the start of tts text. + tts_text_end_token_id (`int`, *optional*, defaults to 151861): + The tts text end token index to encode the end of tts text. + tts_text_pad_token_id (`int`, *optional*, defaults to 151859): + The tts text pad token index to encode the pad of tts text. + tts_codec_start_token_id (`int`, *optional*, defaults to 8293): + The tts codec start token index to encode the start of tts codec. + tts_codec_end_token_id (`int`, *optional*, defaults to 8294): + The tts codec end token index to encode the end of tts codec. + tts_codec_pad_token_id (`int`, *optional*, defaults to 8292): + The tts codec pad token index to encode the pad of tts codec. + tts_codec_mask_token_id (`int`, *optional*, defaults to 8296): + The tts codec mask token index to encode the mask of tts codec. + position_id_per_seconds (`int`, *optional*, defaults to 25): + The increment of position id per second. + seconds_per_chunk (`int`, *optional*, defaults to 2): + The duration in seconds of the chunk of audio and video data. + audio_start_token_id (`int`, *optional*, defaults to 151647): + The audio start token index to encode the audio prompt. + audio_end_token_id (`int`, *optional*, defaults to 151648): + The audio end token index to encode the audio prompt. + + Example: + + ```python + >>> from transformers import Qwen2_5OmniTalkerForConditionalGeneration, Qwen2_5OmniThinkerConfig, Qwen2_5OmniAudioEncoderConfig, Qwen2_5OmniVisionEncoderConfig + + >>> # Initializing a Qwen2_5OmniAudioEncoder config + >>> audio_config = Qwen2_5OmniAudioEncoderConfig() + + >>> # Initializing a Qwen2 config + >>> text_config = Qwen2Config() + + >>> # Initializing a Qwen2_5Omni configuration + >>> configuration = Qwen2_5OmniThinkerConfig(audio_config, text_config) + + >>> # Initializing a model from the qwen2-audio style configuration + >>> model = Qwen2_5OmniTalkerForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_omni_talker" + default_theta = 1000000.0 + attribute_map = { + "image_token_id": "image_token_index", + "video_token_id": "video_token_index", + "audio_token_id": "audio_token_index", + } + ignore_keys_at_rope_validation = {"mrope_section"} + + audio_token_index: int = 151646 + image_token_index: int = 151655 + video_token_index: int = 151656 + vocab_size: int = 8448 + tts_text_start_token_id: int = 151860 + tts_text_end_token_id: int = 151861 + tts_text_pad_token_id: int = 151859 + tts_codec_start_token_id: int = 8293 + tts_codec_end_token_id: int = 8294 + tts_codec_pad_token_id: int = 8292 + tts_codec_mask_token_id: int = 8296 + vision_start_token_id: int = 151652 + vision_end_token_id: int = 151653 + embedding_size: int = 3584 + hidden_size: int = 3584 + intermediate_size: int = 18944 + num_hidden_layers: int = 28 + num_attention_heads: int = 28 + num_key_value_heads: int = 4 + hidden_act: str = "silu" + max_position_embeddings: int = 32768 + rms_norm_eps: float = 1e-06 + head_dim: int = 128 + use_cache: bool = True + tie_word_embeddings: bool = False + use_sliding_window: bool = False + sliding_window: int | None = 32768 + max_window_layers: int = 28 + attention_dropout: float | int = 0.0 + rope_parameters: RopeParameters | dict | None = None + position_id_per_seconds: int = 25 + seconds_per_chunk: int = 2 + audio_start_token_id: int = 151647 + audio_end_token_id: int = 151648 + initializer_range: float = 0.02 + spatial_merge_size: int = 2 + layer_types: list[str] | None = None + pad_token_id: int | None = None + + def __post_init__(self, **kwargs): + self.sliding_window = self.sliding_window if self.use_sliding_window else None + + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" + if self.sliding_window is not None and i >= self.max_window_layers + else "full_attention" + for i in range(self.num_hidden_layers) + ] + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniDiTConfig(PreTrainedConfig): + r""" + ff_mult (`int`, *optional*, defaults to 2): + The multiplier for the feedforward layer in each transformer block. + emb_dim (`int`, *optional*, defaults to 512): + The dimension of the embedding layer. + block_size (`int`, *optional*, defaults to 64): + Number of tokens (frames) in each processing block. + look_ahead_layers (`list[int]`, *optional*, defaults to `[10]`): + Number of transformer layers that are permitted to attend to future blocks + look_backward_layers (`list[int]`, *optional*, defaults to `[0, 20]`): + Number of transformer layers that attend to past blocks beyond the current block boundary + repeats (`int`, *optional*, defaults to 2): + The number of times the codec embeddings are repeated. + num_embeds (`int`, *optional*, defaults to 8193): + The number of unique embeddings in the codec. + mel_dim (`int`, *optional*, defaults to 80): + The dimension of the mel-spectrogram. + enc_emb_dim (`int`, *optional*, defaults to 192): + The dimension of the pre-trained speaker embedding. + enc_dim (`int`, *optional*, defaults to 128): + The dimension of the encoder output. + enc_channels (`list[int]`, *optional*, defaults to `[256, 256, 256, 256, 768]`): + A list of output channels for each TDNN/SERes2Net layer in the encoder. + enc_kernel_sizes (`list[int]`, *optional*, defaults to `[5, 3, 3, 3, 1]`): + A list of kernel sizes for each layer in the encoder. + enc_dilations (`list[int]`, *optional*, defaults to `[1, 2, 3, 4, 1]`): + A list of dilations for each layer in the encoder. + enc_attention_channels (`int`, *optional*, defaults to 64): + The number of attention channels in the SqueezeExcitationBlock. + enc_res2net_scale (`int`, *optional*, defaults to 2): + The scale of the Res2Net block in the encoder. + enc_se_channels (`int`, *optional*, defaults to 64): + The number of output channels after squeeze in the SqueezeExcitationBlock. + """ + + model_type = "qwen2_5_omni_dit" + + hidden_size: int = 1024 + num_hidden_layers: int = 22 + num_attention_heads: int = 16 + ff_mult: int = 2 + emb_dim: int = 512 + head_dim: int = 64 + rope_parameters: RopeParameters | dict | None = None + max_position_embeddings: int = 32768 + block_size: int = 24 + look_ahead_layers: list[int] | tuple[int, ...] = (10,) + look_backward_layers: list[int] | tuple[int, ...] = (0, 20) + repeats: int = 2 + num_embeds: int = 8193 + mel_dim: int = 80 + dropout: float | int = 0.1 + enc_emb_dim: int = 192 + enc_dim: int = 128 + enc_channels: list[int] | tuple[int, ...] = (256, 256, 256, 256, 768) + enc_kernel_sizes: list[int] | tuple[int, ...] = (5, 3, 3, 3, 1) + enc_dilations: list[int] | tuple[int, ...] = (1, 2, 3, 4, 1) + enc_attention_channels: int = 64 + enc_res2net_scale: int = 2 + enc_se_channels: int = 64 + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniBigVGANConfig(PreTrainedConfig): + r""" + mel_dim (`int`, *optional*, defaults to 80): + The dimension of the mel-spectrogram. + upsample_initial_channel (`int`, *optional*, defaults to 1536): + The number of channels in the initial upsampling layer. + resblock_kernel_sizes (`list[int]`, *optional*, defaults to `[3, 7, 11]`): + A list of kernel sizes for each residual block. + resblock_dilation_sizes (`list[list[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): + A list of dilation sizes for each residual block. + upsample_rates (`list[int]`, *optional*, defaults to `[5, 3, 2, 2, 2, 2]`): + A list of upsampling rates for each upsampling layer. + upsample_kernel_sizes (`list[int]`, *optional*, defaults to `[11, 7, 4, 4, 4, 4]`): + A list of kernel sizes for each upsampling layer. + """ + + model_type = "qwen2_5_omni_bigvgan" + + mel_dim: int = 80 + upsample_initial_channel: int = 1536 + resblock_kernel_sizes: list[int] | tuple[int, ...] = (3, 7, 11) + resblock_dilation_sizes: list | tuple = ((1, 3, 5), (1, 3, 5), (1, 3, 5)) + upsample_rates: list[int] | tuple[int, ...] = (5, 3, 2, 2, 2, 2) + upsample_kernel_sizes: list[int] | tuple[int, ...] = (11, 7, 4, 4, 4, 4) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniToken2WavConfig(PreTrainedConfig): + r""" + dit_config ([`DiT_Args`], *optional*): + Configuration class for the Diffusion Transformer (DiT) module responsible for generating mel-spectrograms. + bigvgan_config ([`BigVGAN_Args`], *optional*): + Configuration class for the BigVGAN module responsible for converting mel-spectrograms to waveforms. + + Example: + + ```python + >>> from transformers import Qwen2_5OmniToken2WavModel, DiT_Args, BigVGAN_Args + + >>> # Initialize DiT configuration + >>> dit_config = DiT_Args( + ... dim=1024, + ... depth=22, + ... heads=16, + ... ff_mult=2 + ... ) + + >>> # Initialize BigVGAN configuration + >>> bigvgan_config = BigVGAN_Args( + ... mel_dim=80, + ... upsample_rates=[5,3,2,2,2,2] + ... ) + + >>> # Initialize main configuration + >>> config = Qwen2_5OmniToken2WavConfig(dit_config, bigvgan_config) + + >>> # Initialize model with config + >>> model = Qwen2_5OmniToken2Wav(config) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "qwen2_5_omni_token2wav" + sub_configs = { + "dit_config": Qwen2_5OmniDiTConfig, + "bigvgan_config": Qwen2_5OmniBigVGANConfig, + } + + dit_config: dict | PreTrainedConfig | None = None + bigvgan_config: dict | PreTrainedConfig | None = None + + def __post_init__(self, **kwargs): + if self.dit_config is None: + self.dit_config = Qwen2_5OmniDiTConfig() + elif isinstance(self.dit_config, dict): + self.dit_config = Qwen2_5OmniDiTConfig(**self.dit_config) + + if self.bigvgan_config is None: + self.bigvgan_config = Qwen2_5OmniBigVGANConfig() + elif isinstance(self.bigvgan_config, dict): + self.bigvgan_config = Qwen2_5OmniBigVGANConfig(**self.bigvgan_config) + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B") +@strict +class Qwen2_5OmniConfig(PreTrainedConfig): + r""" + thinker_config (`dict`, *optional*): + Configuration of the underlying thinker sub-model. + talker_config (`dict`, *optional*): + Configuration of the underlying talker sub-model. + token2wav_config (`dict`, *optional*): + Configuration of the underlying codec sub-model. + enable_audio_output (`bool`, *optional*, defaults to `True`): + Whether enable audio output and load talker and token2wav module. + + Example: + + ```python + >>> from transformers import ( + ... Qwen2_5OmniThinkerConfig, + ... Qwen2_5OmniTalkerConfig, + ... Qwen2_5OmniToken2WavConfig, + ... Qwen2_5OmniForConditionalGeneration, + ... Qwen2_5OmniConfig, + ... ) + + >>> # Initializing sub-modules configurations. + >>> thinker_config = Qwen2_5OmniThinkerConfig() + >>> talker_config = Qwen2_5OmniTalkerConfig() + >>> token2wav_config = Qwen2_5OmniToken2WavConfig() + + + >>> # Initializing a module style configuration + >>> configuration = Qwen2_5OmniConfig( + ... thinker_config, talker_config, token2wav_config + ... ) + + >>> # Initializing a model (with random weights) + >>> model = Qwen2_5OmniForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "qwen2_5_omni" + sub_configs = { + "thinker_config": Qwen2_5OmniThinkerConfig, + "talker_config": Qwen2_5OmniTalkerConfig, + "token2wav_config": Qwen2_5OmniToken2WavConfig, + } + + thinker_config: dict | PreTrainedConfig | None = None + talker_config: dict | PreTrainedConfig | None = None + token2wav_config: dict | PreTrainedConfig | None = None + enable_audio_output: bool = True + + def __post_init__(self, **kwargs): + if self.thinker_config is None: + self.thinker_config = Qwen2_5OmniThinkerConfig() + logger.info("thinker_config is None. Initializing thinker model with default values") + elif isinstance(self.thinker_config, dict): + self.thinker_config = Qwen2_5OmniThinkerConfig(**self.thinker_config) + + if self.talker_config is None: + self.talker_config = Qwen2_5OmniTalkerConfig() + logger.info("talker_config is None. Initializing talker model with default values") + elif isinstance(self.talker_config, dict): + self.talker_config = Qwen2_5OmniTalkerConfig(**self.talker_config) + + if self.token2wav_config is None: + self.token2wav_config = Qwen2_5OmniToken2WavConfig() + logger.info("token2wav_config is None. Initializing token2wav model with default values") + elif isinstance(self.token2wav_config, dict): + self.token2wav_config = Qwen2_5OmniToken2WavConfig(**self.token2wav_config) + + super().__post_init__(**kwargs) + + def get_text_config(self, *args, **kwargs): + """ + Returns the config that is meant to be used with text IO. On most models, it is the original config instance + itself. On specific composite models, it is under a set of valid names. + + Args: + decoder (`Optional[bool]`, *optional*, defaults to `False`): + If set to `True`, then only search for decoder config names. + """ + # Overridden for deeply nested config like Qwen2-Omni. We don't have any omni model + # except for Qwen yet. This has to be generalized if more deeply nested configs are + # added. NOTE: currently method used only by vLLM + return self.thinker_config.get_text_config(*args, **kwargs) + + +class Qwen2_5OmniPreTrainedModel(Qwen2_5_VLPreTrainedModel): + config: Qwen2_5OmniConfig + input_modalities = ("image", "video", "audio", "text") + _can_compile_fullgraph = False + + def _init_weights(self, module): + PreTrainedModel._init_weights(self, module) + if isinstance(module, SinusoidsPositionEmbedding): + log_timescale_increment = np.log(module.max_timescale) / (module.channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(module.channels // 2).float()) + scaled_time = torch.arange(module.length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + init.copy_(module.positional_embedding, torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)) + elif isinstance(module, UpSample1d): + filter_tensor = kaiser_sinc_filter1d(0.5 / module.ratio, 0.6 / module.ratio, module.kernel_size) + init.copy_(module.filter, filter_tensor) + elif isinstance(module, DownSample1d): + filter_tensor = kaiser_sinc_filter1d(module.cutoff, module.half_width, module.kernel_size) + init.copy_(module.filter, filter_tensor) + elif isinstance(module, Qwen2_5_VisionRotaryEmbedding): + inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim)) + init.copy_(module.inv_freq, inv_freq) + + +class Qwen2_5OmniPreTrainedModelForConditionalGeneration(Qwen2_5OmniPreTrainedModel): + input_modalities = ("image", "video", "audio", "text") + + def get_llm_pos_ids_for_vision( + self, + start_idx: int, + vision_idx: int, + spatial_merge_size: int, + t_index: list[int], + grid_hs: list[int], + grid_ws: list[int], + ): + llm_pos_ids_list = [] + llm_grid_h = grid_hs[vision_idx] // spatial_merge_size + llm_grid_w = grid_ws[vision_idx] // spatial_merge_size + h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(len(t_index), -1, llm_grid_w).flatten() + w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(len(t_index), llm_grid_h, -1).flatten() + t_index = torch.Tensor(t_index).view(-1, 1).expand(-1, llm_grid_h * llm_grid_w).flatten().long() + _llm_pos_ids = torch.stack([t_index, h_index, w_index]) + llm_pos_ids_list.append(_llm_pos_ids + start_idx) # + 1 ) # 12.09 by malinhan + llm_pos_ids = torch.cat(llm_pos_ids_list, dim=1) + return llm_pos_ids + + def get_chunked_index( + self, token_indices: torch.Tensor, tokens_per_chunk: int, remove_index: int + ) -> list[tuple[int, int]]: + """ + Splits token index list into chunks based on token value ranges. + + Given a list of token indices, returns a list of (start, end) index tuples representing + slices of the list where the token values fall within successive ranges of `t_ntoken_per_chunk`. + + For example, if `t_ntoken_per_chunk` is 1000, the function will create chunks such that: + - the first chunk contains token values < 1000, + - the second chunk contains values >= 1000 and < 2000, and so on. + + Parameters: + token_indices (`torch.Tensor` of shape `(seq_len, )`): A monotonically increasing list of + token index values. + t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold). + remove_index (`int`) An index id to subtract from `token_indices` before chunking + + Returns: + `list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive) + and end (exclusive) indices of a chunk in `token_indices`. + """ + + def _iter(): + i, start_idx = 0, 0 # skip bos token + current_chunk = 1 + while i < len(token_indices): # skip eos token + if token_indices[i] - remove_index >= current_chunk * tokens_per_chunk: + yield (start_idx, i) + start_idx = i + current_chunk += 1 + i += 1 + yield (start_idx, len(token_indices)) + + return list(_iter()) + + def get_rope_index( + self, + input_ids: torch.LongTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + use_audio_in_video: bool = False, + audio_seqlens: torch.LongTensor | None = None, + second_per_grids: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Calculate the 3D rope index based on image and video's temporal, height and width in LLM. + + Explanation: + Each embedding sequence contains vision embedding and text embedding or just contains text embedding. + + For pure text embedding sequence, the rotary position embedding has no difference with modern LLMs. + Examples: + input_ids: [T T T T T], here T is for text. + temporal position_ids: [0, 1, 2, 3, 4] + height position_ids: [0, 1, 2, 3, 4] + width position_ids: [0, 1, 2, 3, 4] + + For vision and text embedding sequence, we calculate 3D rotary position embedding for vision part + and 1D rotary position embedding for text part. + Examples: + Temporal (Time): 3 patches, representing different segments of the video in time. + Height: 2 patches, dividing each frame vertically. + Width: 2 patches, dividing each frame horizontally. + We also have some important parameters: + fps (Frames Per Second): The video's frame rate, set to 1. This means one frame is processed each second. + tokens_per_second: This is a crucial parameter. It dictates how many "time-steps" or "temporal tokens" are conceptually packed into a one-second interval of the video. In this case, we have 25 tokens per second. So each second of the video will be represented with 25 separate time points. It essentially defines the temporal granularity. + temporal_patch_size: The number of frames that compose one temporal patch. Here, it's 2 frames. + interval: The step size for the temporal position IDs, calculated as tokens_per_second * temporal_patch_size / fps. In this case, 25 * 2 / 1 = 50. This means that each temporal patch will be have a difference of 50 in the temporal position IDs. + input_ids: [V V V V V V V V V V V V T T T T T], here V is for vision. + vision temporal position_ids: [0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100] + vision height position_ids: [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1] + vision width position_ids: [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1] + text temporal position_ids: [101, 102, 103, 104, 105] + text height position_ids: [101, 102, 103, 104, 105] + text width position_ids: [101, 102, 103, 104, 105] + Here we calculate the text start position_ids as the max vision position_ids plus 1. + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + use_audio_in_video (`bool`, *optional*): + If set to `True`, use the audio in video. + audio_seqlens (`torch.LongTensor` of shape `(num_audios)`, *optional*): + The length of feature shape of each audio in LLM. + second_per_grids (`torch.LongTensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + + Returns: + position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) + mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) + """ + spatial_merge_size = self.spatial_merge_size + image_token_id = self.config.image_token_id + video_token_id = self.config.video_token_id + audio_token_id = self.config.audio_token_id + vision_start_token_id = self.config.vision_start_token_id + audio_start_token_id = self.config.audio_start_token_id + position_id_per_seconds = self.config.position_id_per_seconds + seconds_per_chunk = self.config.seconds_per_chunk + + mrope_position_deltas = [] + if input_ids is not None and (image_grid_thw is not None or video_grid_thw is not None): + total_input_ids = input_ids + if attention_mask is not None: + attention_mask = attention_mask == 1 + position_ids = torch.ones( + 3, + input_ids.shape[0], + input_ids.shape[1], + dtype=input_ids.dtype, + device=input_ids.device, + ) + image_idx, video_idx, audio_idx = 0, 0, 0 + for i, input_ids in enumerate(total_input_ids): + if attention_mask is not None: + input_ids = input_ids[attention_mask[i]] + image_nums, video_nums, audio_nums = 0, 0, 0 + vision_start_indices = torch.argwhere(input_ids == vision_start_token_id).squeeze(1) + vision_tokens = input_ids[vision_start_indices + 1] + audio_nums = torch.sum(input_ids == audio_start_token_id) + image_nums = (vision_tokens == image_token_id).sum() + video_nums = ( + (vision_tokens == audio_start_token_id).sum() + if use_audio_in_video + else (vision_tokens == video_token_id).sum() + ) + input_tokens = input_ids.tolist() + llm_pos_ids_list: list = [] + st = 0 + remain_images, remain_videos, remain_audios = image_nums, video_nums, audio_nums + multimodal_nums = ( + image_nums + audio_nums if use_audio_in_video else image_nums + video_nums + audio_nums + ) + for _ in range(multimodal_nums): + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + if image_token_id in input_tokens and remain_images > 0: + ed_image = input_tokens.index(image_token_id, st) + else: + ed_image = len(input_tokens) + 1 + if video_token_id in input_tokens and remain_videos > 0: + ed_video = input_tokens.index(video_token_id, st) + else: + ed_video = len(input_tokens) + 1 + if audio_token_id in input_tokens and remain_audios > 0: + ed_audio = input_tokens.index(audio_token_id, st) + else: + ed_audio = len(input_tokens) + 1 + min_ed = min(ed_image, ed_video, ed_audio) + if min_ed == ed_audio: + text_len = min_ed - st - 1 + if text_len != 0: + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + bos_len = 1 + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1 + llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx + llm_pos_ids_list.append(llm_pos_ids) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + eos_len = 1 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + st += text_len + bos_len + audio_len + eos_len + audio_idx += 1 + remain_audios -= 1 + + elif min_ed == ed_image: + text_len = min_ed - st - 1 + if text_len != 0: + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + bos_len = 1 + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + grid_t = image_grid_thw[image_idx][0] + grid_hs = image_grid_thw[:, 1] + grid_ws = image_grid_thw[:, 2] + t_index = (torch.arange(grid_t) * 1 * position_id_per_seconds).long() + llm_pos_ids = self.get_llm_pos_ids_for_vision( + st_idx, image_idx, spatial_merge_size, t_index, grid_hs, grid_ws + ) + image_len = image_grid_thw[image_idx].prod() // (spatial_merge_size**2) + llm_pos_ids_list.append(llm_pos_ids) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + eos_len = 1 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + st += text_len + bos_len + image_len + eos_len + image_idx += 1 + remain_images -= 1 + + elif min_ed == ed_video and not use_audio_in_video: + text_len = min_ed - st - 1 + if text_len != 0: + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + bos_len = 1 + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + grid_t = video_grid_thw[video_idx][0] + grid_hs = video_grid_thw[:, 1] + grid_ws = video_grid_thw[:, 2] + t_index = ( + torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds + ).long() + llm_pos_ids = self.get_llm_pos_ids_for_vision( + st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws + ) + video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2) + llm_pos_ids_list.append(llm_pos_ids) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + eos_len = 1 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + st += text_len + bos_len + video_len + eos_len + video_idx += 1 + remain_videos -= 1 + + elif min_ed == ed_video and use_audio_in_video: + text_len = min_ed - st - 2 + if text_len != 0: + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + bos_len = 1 + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + llm_pos_ids_list.append(torch.arange(bos_len).view(1, -1).expand(3, -1) + st_idx) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + audio_len = ((audio_seqlens[audio_idx] - 1) // 2 + 1 - 2) // 2 + 1 + audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx + grid_t = video_grid_thw[video_idx][0] + grid_hs = video_grid_thw[:, 1] + grid_ws = video_grid_thw[:, 2] + + t_index = ( + torch.arange(grid_t) * second_per_grids[video_idx].cpu().float() * position_id_per_seconds + ).long() + video_llm_pos_ids = self.get_llm_pos_ids_for_vision( + st_idx, video_idx, spatial_merge_size, t_index, grid_hs, grid_ws + ) + + t_ntoken_per_chunk = int(position_id_per_seconds * seconds_per_chunk) + video_chunk_indexes = self.get_chunked_index(video_llm_pos_ids[0], t_ntoken_per_chunk, st_idx) + audio_chunk_indexes = self.get_chunked_index(audio_llm_pos_ids[0], t_ntoken_per_chunk, st_idx) + sub_len = 0 + for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))): + video_chunk_index = video_chunk_indexes[j] if j < len(video_chunk_indexes) else None + audio_chunk_index = audio_chunk_indexes[j] if j < len(audio_chunk_indexes) else None + if video_chunk_index is not None: + sub_len += video_chunk_index[1] - video_chunk_index[0] + + llm_pos_ids_list.append( + video_llm_pos_ids[:, video_chunk_index[0] : video_chunk_index[1]] + ) + if audio_chunk_index is not None: + sub_len += audio_chunk_index[1] - audio_chunk_index[0] + + llm_pos_ids_list.append( + audio_llm_pos_ids[:, audio_chunk_index[0] : audio_chunk_index[1]] + ) + video_len = video_grid_thw[video_idx].prod() // (spatial_merge_size**2) + + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + eos_len = 1 + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + llm_pos_ids_list.append(torch.arange(eos_len).view(1, -1).expand(3, -1) + st_idx) + + st += text_len + bos_len * 2 + audio_len + video_len + eos_len * 2 + + audio_idx += 1 + video_idx += 1 + remain_videos -= 1 + remain_audios -= 1 + + if st < len(input_tokens): + st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0 + text_len = len(input_tokens) - st + llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx) + + llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) + + if attention_mask is not None: + position_ids[..., i, attention_mask[i]] = llm_positions.to(position_ids.device) + else: + position_ids[..., i, :] = llm_positions.to(position_ids.device) + mrope_position_deltas.append(llm_positions.max() + 1 - len(input_ids)) + mrope_position_deltas = torch.tensor(mrope_position_deltas).unsqueeze(1).to(device=input_ids.device) + + return position_ids, mrope_position_deltas + else: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1).to(attention_mask.device) + max_position_ids = position_ids.max(0, keepdim=False)[0].max(-1, keepdim=True)[0] + mrope_position_deltas = max_position_ids + 1 - torch.sum(attention_mask, dim=-1, keepdim=True) + + return position_ids, mrope_position_deltas + + +############################ +# Start Thinker # +############################ + + +@auto_docstring( + custom_intro=""" + Base class for Qwen2.5OmniThinker causal language model (or autoregressive) outputs. + """ +) +@dataclass +class Qwen2_5OmniThinkerCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`, *optional*): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + + +class Qwen2_5OmniAudioAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + config: Qwen2_5OmniAudioEncoderConfig, + ): + super().__init__() + self.embed_dim = config.d_model + self.num_heads = config.encoder_attention_heads + self.dropout = config.attention_dropout + self.head_dim = self.embed_dim // self.num_heads + self.num_key_value_groups = 1 # needed for eager attention + self.config = config + + if (self.head_dim * self.num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {self.num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.attention_dropout = 0.0 + self.is_decoder = False + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=True) + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + seq_length, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).reshape(seq_length, self.num_heads, -1) + key_states = self.k_proj(hidden_states).reshape(seq_length, self.num_heads, -1) + value_states = self.v_proj(hidden_states).reshape(seq_length, self.num_heads, -1) + + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) + max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + cu_seq_lens_q=cu_seqlens, # pass cu seq lens for FA2 + cu_seq_lens_k=cu_seqlens, + max_length_q=max_seqlen, + max_length_k=max_seqlen, + is_causal=False, + **kwargs, + ) + + attn_output = attn_output.reshape(seq_length, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output + + +class Qwen2_5OmniAudioEncoderLayer(Qwen2AudioEncoderLayer): + def __init__(self, config: Qwen2_5OmniAudioEncoderConfig): + super().__init__(config) + self.self_attn = Qwen2_5OmniAudioAttention(config) + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states = self.self_attn( + hidden_states=hidden_states, + cu_seqlens=cu_seqlens, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + hidden_states = residual + hidden_states + + if hidden_states.dtype == torch.float16: + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + return outputs + + +class SinusoidsPositionEmbedding(nn.Module): + def __init__(self, length, channels, max_timescale=10000): + super().__init__() + self.length = length + self.channels = channels + self.max_timescale = max_timescale + if channels % 2 != 0: + raise ValueError("SinusoidsPositionEmbedding needs even channels input") + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float()) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + self.register_buffer( + "positional_embedding", + torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1), + persistent=False, + ) + + def forward(self, seqlen: int): + return self.positional_embedding[:seqlen, :] + + +@auto_docstring( + custom_intro=""" + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`Qwen2_5OmniAudioEncoderLayer`]. + """ +) +class Qwen2_5OmniAudioEncoder(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniAudioEncoderConfig + main_input_name = "input_features" + input_modalities = "audio" + _no_split_modules = ["Qwen2_5OmniAudioEncoderLayer"] + _supports_sdpa = True + _can_record_outputs = { + "hidden_states": Qwen2_5OmniAudioEncoderLayer, + "attentions": Qwen2_5OmniAudioAttention, + } + + def __init__(self, config: Qwen2_5OmniAudioEncoderConfig): + super().__init__(config) + self.dropout = config.dropout + + embed_dim = config.d_model + self.num_mel_bins = config.num_mel_bins + self.max_source_positions = config.max_source_positions + self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + self.n_window = config.n_window + self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1) + self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1) + self.positional_embedding = SinusoidsPositionEmbedding(self.max_source_positions, embed_dim) + self.audio_bos_eos_token = nn.Embedding(2, config.output_dim) + self.layers = nn.ModuleList([Qwen2_5OmniAudioEncoderLayer(config) for _ in range(config.encoder_layers)]) + self.ln_post = nn.LayerNorm(config.d_model) + self.avg_pooler = nn.AvgPool1d(2, stride=2) + self.proj = nn.Linear(config.d_model, config.output_dim) + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def _freeze_parameters(self): + for param in self.parameters(): + param.requires_grad = False + self._requires_grad = False + + def get_input_embeddings(self) -> nn.Module: + return self.conv1 + + def set_input_embeddings(self, value: nn.Module): + self.conv1 = value + + def _prepare_attention_mask(self, inputs_tensor: torch.Tensor, cu_seqlens: torch.Tensor) -> torch.Tensor: + # Flash Attention 2 doesn't need a 4D mask and relies on `cu_seqlens/max_seqlen` + # NOTE: the created attention masl only approximates the ragged FA2 attention by + # allowing bidirectional attention within `cu_seqlens` blocks, and not attending between + # blocks. Though it will not be a 100% match for FA2's `varlen` path + if is_flash_attention_requested(self.config): + return None + + seq_length = inputs_tensor.shape[0] + attention_mask = torch.full( + [1, 1, seq_length, seq_length], + torch.finfo(inputs_tensor.dtype).min, + device=inputs_tensor.device, + dtype=inputs_tensor.dtype, + ) + for i in range(1, len(cu_seqlens)): + attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0 + return attention_mask + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward(self, input_features, feature_lens=None, aftercnn_lens=None, **kwargs: Unpack[TransformersKwargs]): + r""" + feature_lens (`torch.LongTensor` of shape `(batch_size,)`): + mel length + aftercnn_lens (`torch.LongTensor` of shape `(batch_size,)`): + mel length after cnn + """ + chunk_num = torch.ceil(feature_lens / (self.n_window * 2)).long() + + chunk_lengths = torch.full((chunk_num.sum(),), self.n_window * 2, dtype=torch.long, device=feature_lens.device) + tail_chunk_index = F.pad(chunk_num, (1, 0), value=-1).cumsum(0)[1:] + chunk_lengths[tail_chunk_index] = feature_lens % (self.n_window * 2) + chunk_lengths = torch.where(chunk_lengths == 0, self.n_window * 2, chunk_lengths) + + chunk_list = input_features.split(chunk_lengths.tolist(), dim=1) + padded_feature, padded_mask, padded_mask_after_cnn = self.padded_and_mask_function( + chunk_list, chunk_lengths, padding_value=0, padding_side="right" + ) + padded_embed = nn.functional.gelu(self.conv1(padded_feature)) * padded_mask + padded_embed = nn.functional.gelu(self.conv2(padded_embed)).transpose(1, 2) + + padded_embed = padded_embed + self.positional_embedding.positional_embedding[ + : padded_embed.shape[1], : + ].unsqueeze(0).to(padded_embed.dtype) + hidden_states = padded_embed[padded_mask_after_cnn] + cu_seqlens = torch.cat( + ( + torch.zeros(1, device=padded_mask_after_cnn.device, dtype=torch.int32), + padded_mask_after_cnn.sum(1).cumsum(0), + ) + ).to(torch.int32) + attention_mask = self._prepare_attention_mask(hidden_states, cu_seqlens) + + for encoder_layer in self.layers: + layer_outputs = encoder_layer( + hidden_states, + cu_seqlens=cu_seqlens, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = layer_outputs[0] + + hidden_states_list = hidden_states.split(aftercnn_lens.tolist(), dim=0) + token_audio_list = [] + for each_audio_states in hidden_states_list: + each_audio_states = self.avg_pooler(each_audio_states.transpose(0, 1)).transpose_(0, 1) + each_audio_states = self.ln_post(each_audio_states) + each_audio_states = self.proj(each_audio_states) + token_audio_list.append(each_audio_states) + token_audio = torch.cat(token_audio_list, dim=0) + return BaseModelOutputWithPooling(last_hidden_state=token_audio) + + def padded_and_mask_function(self, tensor_list, tensor_len, padding_value=0, padding_side="right"): + """ + Pads a sequence of tensors to their maximum length on indicated `padding_side`. + Then prepares a mask so that pad tokens are not attended to. + """ + max_len = tensor_len.max() + dim = tensor_list[0].shape[0] + padded_tensor = torch.full( + size=(len(tensor_list), dim, max_len), + fill_value=padding_value, + dtype=self.dtype, + device=tensor_list[0].device, + ) + + batch_mask = torch.zeros( + (len(tensor_len), max_len), + dtype=torch.long, + device=padded_tensor.device, + ) + for i, length in enumerate(tensor_len): + batch_mask[i, :length] = 1 + padded_tensor[i, :, :length] = tensor_list[i] + + feature_lens_after_cnn = (tensor_len - 1) // 2 + 1 + max_len_after_cnn = feature_lens_after_cnn.max() + batch_mask_after_cnn = torch.zeros( + (len(tensor_len), max_len_after_cnn), + dtype=torch.long, + device=padded_tensor.device, + ) + for i, length in enumerate(feature_lens_after_cnn): + batch_mask_after_cnn[i, :length] = 1 + return ( + padded_tensor, + batch_mask.unsqueeze(1), + batch_mask_after_cnn.bool(), + ) + + # Ignore copy + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers and the output length of the audio encoder + """ + input_lengths = (input_lengths - 1) // 2 + 1 + output_lengths = (input_lengths - 2) // 2 + 1 + return input_lengths, output_lengths + + +def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor: + orig_dtype = tensor.dtype + tensor = tensor.float() + cos = freqs.cos() + sin = freqs.sin() + cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float() + sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float() + output = (tensor * cos) + (rotate_half(tensor) * sin) + output = output.to(orig_dtype) + return output + + +class Qwen2_5OmniVisionAttention(nn.Module): + def __init__(self, config: Qwen2_5OmniVisionEncoderConfig = None) -> None: + super().__init__() + self.dim = config.hidden_size + self.num_heads = config.num_heads + self.head_dim = self.dim // self.num_heads + self.q = nn.Linear(self.dim, self.dim, bias=True) + self.k = nn.Linear(self.dim, self.dim, bias=True) + self.v = nn.Linear(self.dim, self.dim, bias=True) + self.proj = nn.Linear(self.dim, self.dim) + self.scaling = self.head_dim**-0.5 + self.num_key_value_groups = 1 # needed for eager attention + self.config = config + self.attention_dropout = 0.0 + self.is_causal = False + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + rotary_pos_emb: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + seq_length = hidden_states.shape[0] + query_states = self.q(hidden_states).reshape(seq_length, self.num_heads, -1) + key_states = self.k(hidden_states).reshape(seq_length, self.num_heads, -1) + value_states = self.v(hidden_states).reshape(seq_length, self.num_heads, -1) + query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0) + key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0) + + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config): + # Flash Attention 2: Use cu_seqlens for variable length attention + max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + cu_seq_lens_q=cu_seqlens, + cu_seq_lens_k=cu_seqlens, + max_length_q=max_seqlen, + max_length_k=max_seqlen, + is_causal=False, + **kwargs, + ) + else: + # Other implementations: Process each chunk separately + lengths = cu_seqlens[1:] - cu_seqlens[:-1] + splits = [ + torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states) + ] + + attn_outputs = [ + attention_interface( + self, + q, + k, + v, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + is_causal=False, + **kwargs, + )[0] + for q, k, v in zip(*splits) + ] + attn_output = torch.cat(attn_outputs, dim=1) + + attn_output = attn_output.reshape(seq_length, -1).contiguous() + attn_output = self.proj(attn_output) + return attn_output + + +class Qwen2_5OmniVisionBlock(Qwen2_5_VLVisionBlock): + def __init__(self, config: Qwen2_5OmniVisionEncoderConfig) -> None: + super().__init__(config, config._attn_implementation) + self.attn = Qwen2_5OmniVisionAttention(config=config) + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + rotary_pos_emb: torch.Tensor | None = None, + **kwargs, + ) -> torch.Tensor: + hidden_states = hidden_states + self.attn( + self.norm1(hidden_states), + cu_seqlens=cu_seqlens, + rotary_pos_emb=rotary_pos_emb, + **kwargs, + ) + hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) + return hidden_states + + +class Qwen2_5_VisionRotaryEmbedding(Qwen2_5_VisionRotaryEmbedding): + pass + + +class Qwen2_5OmniVisionEncoder(Qwen2_5_VisionTransformerPretrainedModel): + config: Qwen2_5OmniVisionEncoderConfig + input_modalities = ("image", "video") + _no_split_modules = ["Qwen2_5OmniVisionBlock"] + _input_embed_layer = "patch_embed" + _can_record_outputs = { + "hidden_states": Qwen2_5OmniVisionBlock, + "attentions": Qwen2_5OmniVisionAttention, + } + + def __init__(self, config: Qwen2_5OmniVisionEncoderConfig, *inputs, **kwargs) -> None: + super().__init__(config, *inputs, **kwargs) + self.blocks = nn.ModuleList([Qwen2_5OmniVisionBlock(config) for _ in range(config.depth)]) + + @merge_with_config_defaults + @capture_outputs + def forward( + self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithPooling: + """ + Args: + hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): + The final hidden states of the model. + grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`): + The temporal, height and width of feature shape of each image in LLM. + + Returns: + `torch.Tensor`: hidden_states. + """ + hidden_states = self.patch_embed(hidden_states) + rotary_pos_emb = self.rot_pos_emb(grid_thw) + + window_index, cu_window_seqlens = self.get_window_index(grid_thw) + cu_window_seqlens = torch.tensor( + cu_window_seqlens, + device=hidden_states.device, + dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) + + seq_len, _ = hidden_states.size() + hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) + hidden_states = hidden_states[window_index, :, :] + hidden_states = hidden_states.reshape(seq_len, -1) + rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) + rotary_pos_emb = rotary_pos_emb[window_index, :, :] + rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) + + cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( + dim=0, + # Select dtype based on the following factors: + # - FA2 requires that cu_seqlens_q must have dtype int32 + # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw + # See https://github.com/huggingface/transformers/pull/34852 for more information + dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) + + # Modification here + for layer_num, blk in enumerate(self.blocks): + if layer_num in self.fullatt_block_indexes: + cu_seqlens_now = cu_seqlens + else: + cu_seqlens_now = cu_window_seqlens + + hidden_states = blk( + hidden_states, + cu_seqlens=cu_seqlens_now, + rotary_pos_emb=rotary_pos_emb, + **kwargs, + ) + + merged_hidden_states = self.merger(hidden_states) + reverse_indices = torch.argsort(window_index) + merged_hidden_states = merged_hidden_states[reverse_indices, :] + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=merged_hidden_states, + ) + + +class Qwen2_5OmniRotaryEmbedding(Qwen2VLRotaryEmbedding): + def __init__(self, config: Qwen2_5OmniThinkerConfig, device=None): + super().__init__(config, device) + + +# It's same as `Qwen2_5_VLAttention`, but talker model's hidden_size isn't divisible by num_heads. +# Removes the value error as a workaround. +class Qwen2_5OmniAttention(Qwen2_5_VLAttention): + def __init__(self, config: Qwen2_5OmniConfig, layer_idx: int | None = None): + nn.Module.__init__(self) + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads) + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.is_causal = True + self.attention_dropout = config.attention_dropout + self.rope_parameters = config.rope_parameters + self.scaling = self.head_dim**-0.5 + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None + + +class Qwen2MLP(Qwen2_5_VLMLP): + pass + + +class Qwen2_5OmniThinkerTextModel(Qwen2_5_VLTextModel): + config: Qwen2_5OmniTextConfig + _no_split_modules = ["Qwen2_5OmniDecoderLayer"] + + def __init__(self, config: Qwen2_5OmniTextConfig): + super().__init__(config) + + +@auto_docstring( + custom_intro=""" + The Qwen2.5OmniThinker model which consists of a audio backbone and a language model. + """ +) +class Qwen2_5OmniThinkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin): + config: Qwen2_5OmniThinkerConfig + base_model_prefix = "thinker" + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _no_split_modules = ["Qwen2_5OmniAudioEncoder", "Qwen2_5OmniVisionEncoder"] + + def __init__(self, config: Qwen2_5OmniThinkerConfig): + super().__init__(config) + self.audio_tower = Qwen2_5OmniAudioEncoder._from_config(config.audio_config) + self.visual = Qwen2_5OmniVisionEncoder._from_config(config.vision_config) + self.vocab_size = config.text_config.vocab_size + self.model = Qwen2_5OmniThinkerTextModel._from_config(config.text_config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.spatial_merge_size = config.vision_config.spatial_merge_size + self.rope_deltas = None + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + @can_return_tuple + @auto_docstring + def get_video_features( + self, + pixel_values_videos: torch.FloatTensor, + video_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input videos. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + """ + pixel_values_videos = pixel_values_videos.type(self.visual.dtype) + return self.visual(pixel_values_videos, grid_thw=video_grid_thw, **kwargs) + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + pixel_values = pixel_values.type(self.visual.dtype) + return self.visual(pixel_values, grid_thw=image_grid_thw, **kwargs) + + @can_return_tuple + @auto_docstring + def get_audio_features( + self, + input_features: torch.FloatTensor, + feature_attention_mask: torch.LongTensor | None = None, + audio_feature_lengths: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + input_features (`torch.FloatTensor`): + The tensors corresponding to the input audios. + feature_attention_mask (`torch.LongTensor`, *optional*): + Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`: + audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): + The length of feature shape of each audio in LLM. + """ + if feature_attention_mask is not None: + audio_feature_lengths = torch.sum(feature_attention_mask, dim=1) + input_features = input_features.permute(0, 2, 1)[feature_attention_mask.bool()].permute(1, 0) + else: + audio_feature_lengths = None + + audio_feat_lengths, audio_output_lengths = self.audio_tower._get_feat_extract_output_lengths( + audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1) + ) + feature_lens = audio_feature_lengths if audio_feature_lengths is not None else feature_attention_mask.sum(-1) + audio_outputs = self.audio_tower( + input_features, + feature_lens=feature_lens, + aftercnn_lens=audio_feat_lengths, + return_dict=True, + **kwargs, + ) + if audio_outputs.last_hidden_state.shape[0] != sum(audio_output_lengths.tolist()): + raise ValueError("length of audio_features should match audio_output_lengths") + + return audio_outputs + + def get_placeholder_mask( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.FloatTensor, + image_features: torch.FloatTensor | None = None, + video_features: torch.FloatTensor | None = None, + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + special_video_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_video_mask = special_video_mask.all(-1) + special_audio_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + ).all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + special_video_mask = input_ids == self.config.video_token_id + special_audio_mask = input_ids == self.config.audio_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if image_features is not None: + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}", + ) + + n_video_tokens = special_video_mask.sum() + special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if video_features is not None: + torch_compilable_check( + inputs_embeds[special_video_mask].numel() == video_features.numel(), + f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}", + ) + + special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + return special_image_mask, special_video_mask, special_audio_mask + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + input_features: torch.FloatTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + feature_attention_mask: torch.Tensor | None = None, + audio_feature_lengths: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + use_audio_in_video: bool | None = None, + video_second_per_grid: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Qwen2_5OmniThinkerCausalLMOutputWithPast: + r""" + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + feature_attention_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*): + Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): + The length of feature shape of each audio in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + use_audio_in_video (`bool`, *optional*): + Whether or not use audio track in video, should same as the parameter in `process_audio_info`. + video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*): + Number of seconds per grid for each video, used for temporal feature mapping. + + Example: + + ```python + >>> from io import BytesIO + >>> from urllib.request import urlopen + >>> import librosa + >>> from qwen_vl_utils import process_vision_info + >>> from transformers import Qwen2_5OmniProcessor, Qwen2_5OmniThinkerForConditionalGeneration + + >>> thinker = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-Omni-7B") + >>> processor = Qwen2_5OmniProcessor.from_pretrained("Qwen/Qwen2.5-Omni-7B") + + >>> conversations = [ + >>> {'role': 'system', 'content': 'You are a helpful voice chat bot, and please respond to me in a casual conversation manner using random voice.'}, + >>> {"role": "user", "content": [ + >>> {"type": "image", "image_url": "https://www.ilankelman.org/stopsigns/australia.jpg"}, + >>> {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3"}, + >>> ]}, + >>> ] + + >>> text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) + >>> audios = [ librosa.load(BytesIO(urlopen( conversations[1]['content'][1]['audio_url'] ).read()), sr=self.processor.feature_extractor.sampling_rate) ] + >>> images, videos = process_vision_info(conversations) + >>> inputs = processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True) + + >>> # Generate + >>> inputs['use_audio_in_video'] = `True` or `False` + >>> generation = thinker.generate(**inputs, max_new_tokens=2048) + >>> generate_ids = generation[:, inputs.input_ids.size(1):] + + >>> response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + ```""" + + if inputs_embeds is None: + # 1. Extract the input embeddings + inputs_embeds = self.get_input_embeddings()(input_ids) + + # 2. Merge text , audios , image and video + if input_features is not None: + audio_features = self.get_audio_features( + input_features, + feature_attention_mask=feature_attention_mask, + audio_feature_lengths=audio_feature_lengths, + return_dict=True, + ).last_hidden_state + audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype) + _, _, audio_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds) + inputs_embeds = inputs_embeds.masked_scatter(audio_mask, audio_features) + + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw, return_dict=True).pooler_output + image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype) + image_mask, _, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) + + if pixel_values_videos is not None: + video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw, return_dict=True).pooler_output + video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype) + _, video_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) + + if feature_attention_mask is not None: + audio_feature_lengths = torch.sum(feature_attention_mask, dim=1) + else: + audio_feature_lengths = None + + if attention_mask is not None and position_ids is None: + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + if past_key_values_length == 0 or self.rope_deltas is None: + delta0 = (1 - attention_mask).sum(dim=-1).unsqueeze(1) + position_ids, rope_deltas = self.get_rope_index( + input_ids, + image_grid_thw, + video_grid_thw, + attention_mask, + use_audio_in_video, + audio_feature_lengths, + video_second_per_grid, + ) + rope_deltas = rope_deltas - delta0 + self.rope_deltas = rope_deltas + else: + batch_size, seq_length = input_ids.shape + delta = (past_key_values_length + self.rope_deltas).to(input_ids.device) + position_ids = torch.arange(seq_length, device=input_ids.device) + position_ids = position_ids.view(1, -1).expand(batch_size, -1) + position_ids = position_ids.add(delta) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) + + outputs = self.model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs[0] + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.get_text_config().vocab_size + ) + + return Qwen2_5OmniThinkerCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + pixel_values=None, + pixel_values_videos=None, + image_grid_thw=None, + video_grid_thw=None, + input_features=None, + feature_attention_mask=None, + use_audio_in_video=False, + video_second_per_grid=None, + is_first_iteration=False, + **kwargs, + ): + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + use_cache=use_cache, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + input_features=input_features, + feature_attention_mask=feature_attention_mask, + use_audio_in_video=use_audio_in_video, + video_second_per_grid=video_second_per_grid, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + model_inputs["position_ids"] = None + + if not is_first_iteration and use_cache: + model_inputs["pixel_values"] = None + model_inputs["pixel_values_videos"] = None + model_inputs["input_features"] = None + + return model_inputs + + +############################ +# Start Talker # +############################ + + +@auto_docstring( + custom_intro=""" + Base class for Qwen2.5OmniTalker causal language model (or autoregressive) outputs. + """ +) +@dataclass +class Qwen2_5OmniTalkerCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Hidden states from the thinker model that are used as input for the talker model. These represent the encoded + response that the talker model will use to generate speech tokens. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + thinker_reply_part: torch.FloatTensor | None = None + + +class Qwen2_5OmniTalkerModel(Qwen2_5_VLTextModel): + config: Qwen2_5OmniTalkerConfig + input_modalities = ("image", "video", "audio", "text") + + _no_split_modules = ["Qwen2_5OmniTalkerDecoderLayer"] + + def __init__(self, config: Qwen2_5OmniTalkerConfig): + super().__init__(config) + self.embed_tokens = nn.Embedding(config.vocab_size, config.embedding_size, self.padding_idx) + + +class Qwen2_5OmniTalkerForConditionalGeneration(Qwen2_5OmniPreTrainedModelForConditionalGeneration, GenerationMixin): + config: Qwen2_5OmniTalkerConfig + base_model_prefix = "talker" + output_modalities = ("audio",) + + def __init__(self, config: Qwen2_5OmniTalkerConfig): + super().__init__(config) + + self.thinker_to_talker_proj = nn.Linear(config.embedding_size, config.hidden_size) + + self.model = Qwen2_5OmniTalkerModel(config) + self.codebook_size = config.vocab_size + self.codec_head = nn.Linear(config.hidden_size, self.codebook_size, bias=False) + + self.codec_bos_token = config.tts_codec_start_token_id + self.codec_eos_token = config.tts_codec_end_token_id + self.codec_pad_token = config.tts_codec_pad_token_id + self.codec_mask_token = config.tts_codec_mask_token_id + + self.text_bos_token = config.tts_text_start_token_id + self.text_eos_token = config.tts_text_end_token_id + self.text_pad_token = config.tts_text_pad_token_id + + self.spatial_merge_size = self.config.spatial_merge_size + self.rope_deltas = None + + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + thinker_reply_part: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + use_cache: bool | None = None, + input_text_ids: torch.LongTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + use_audio_in_video: bool | None = None, + audio_feature_lengths: torch.LongTensor | None = None, + video_second_per_grid: torch.LongTensor | None = None, + **kwargs, + ) -> tuple | Qwen2_5OmniTalkerCausalLMOutputWithPast: + r""" + thinker_reply_part (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Hidden states from the thinker model's output that represent the text reply part to be processed. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + input_text_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Input token IDs for text-only content, used for position calculation in multimodal contexts. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + use_audio_in_video (`bool`, *optional*): + Whether or not use audio track in video, should same as the parameter in `process_audio_info`. + audio_feature_lengths (`torch.LongTensor` of shape `(num_audios)`, *optional*): + The length of feature shape of each audio in LLM. + video_second_per_grid (`torch.LongTensor` of shape `(num_videos)`, *optional*): + Number of seconds per grid for each video, used for temporal feature mapping. + + Example: + + ```python + >>> from io import BytesIO + >>> from urllib.request import urlopen + >>> import librosa + >>> from transformers import AutoProcessor, Qwen2_5OmniTalkerForConditionalGeneration + + >>> model = Qwen2_5OmniTalkerForConditionalGeneration.from_pretrained("Qwen/Qwen2-Audio-7B") + >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2-Audio-7B") + + >>> prompt = "<|audio_bos|><|AUDIO|><|audio_eos|>Generate the caption in English:" + >>> url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3" + >>> audio, _ = librosa.load(BytesIO(urlopen(url).read()), sr=self.processor.feature_extractor.sampling_rate) + + >>> inputs = processor(text=prompt, audio=audio, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(**inputs, max_length=30) + >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Generate the caption in English: Glass is breaking." + ```""" + + if attention_mask is not None and position_ids is None: + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + if past_key_values_length == 0 or self.rope_deltas is None: + position_ids, rope_deltas = self.get_rope_index( + input_text_ids, + image_grid_thw, + video_grid_thw, + attention_mask, + use_audio_in_video, + audio_feature_lengths, + video_second_per_grid, + ) + + if inputs_embeds is not None: + inputs_embeds[:, -1, :] += self.get_input_embeddings()( + torch.tensor([self.codec_bos_token], dtype=torch.long, device=inputs_embeds.device) + ) + inputs_embeds[:, -2, :] += self.get_input_embeddings()( + torch.tensor([self.codec_pad_token], dtype=torch.long, device=inputs_embeds.device) + ) + self.rope_deltas = rope_deltas + + else: + if inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + batch_size, seq_length = input_ids.shape + + delta = (past_key_values_length + self.rope_deltas).to(input_ids.device) + position_ids = torch.arange(seq_length, device=input_ids.device) + position_ids = position_ids.view(1, -1).expand(batch_size, -1) + position_ids = position_ids.add(delta) + position_ids = position_ids.unsqueeze(0).expand(3, -1, -1) + + if inputs_embeds is None: + # 1. Inference tokens after second token + codec_embeds = self.get_input_embeddings()(input_ids) + inputs_embeds = codec_embeds + thinker_reply_part[:, :1, :] + if thinker_reply_part.shape[1] > 1: + thinker_reply_part = thinker_reply_part[:, 1:, :] + + talker_lm_input = self.thinker_to_talker_proj(inputs_embeds) + + if attention_mask is not None: + attention_mask = attention_mask.to(inputs_embeds.device) + + outputs = self.model( + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=talker_lm_input, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs[0] + logits = self.codec_head(hidden_states) + logits = logits.float() + + loss = None + + return Qwen2_5OmniTalkerCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + thinker_reply_part=thinker_reply_part, + ) + + # prepare inputs for talker lm generation + def prepare_inputs_for_generation( + self, + input_ids, + input_text_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + thinker_reply_part=None, + position_ids=None, + use_cache=True, + pixel_values=None, + pixel_values_videos=None, + image_grid_thw=None, + video_grid_thw=None, + input_audio_features=None, + audio_feature_attention_mask=None, + audio_feature_lengths=None, + use_audio_in_video=False, + video_second_per_grid=None, + **kwargs, + ): + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + thinker_reply_part=thinker_reply_part, + input_text_ids=input_text_ids, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + use_audio_in_video=use_audio_in_video, + audio_feature_lengths=audio_feature_lengths, + video_second_per_grid=video_second_per_grid, + **kwargs, + ) + + model_inputs["position_ids"] = None + + return model_inputs + + def _update_model_kwargs_for_generation( + self, + outputs: ModelOutput, + model_kwargs: dict[str, Any], + is_encoder_decoder: bool = False, + num_new_tokens: int = 1, + ) -> dict[str, Any]: + model_kwargs = super()._update_model_kwargs_for_generation( + outputs, model_kwargs, is_encoder_decoder, num_new_tokens + ) + + if getattr(outputs, "thinker_reply_part", None) is not None: + model_kwargs["thinker_reply_part"] = outputs.thinker_reply_part + + return model_kwargs + + +############################ +# Start Token2Wav # +############################ + + +class Qwen2_5OmniDiTRotaryEmbedding(LlamaRotaryEmbedding): + def __init__(self, config: Qwen2_5OmniDiTConfig, device=None): + super().__init__(config, device=device) + + @staticmethod + def compute_default_rope_parameters( + config: Qwen2_5OmniDiTConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + return super().compute_default_rope_parameters( + config, + device=device, + seq_len=seq_len, + ) + + +# Modified from Llama with a different rotate function, will fixed in next release +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + + def rotate_half_codec(x): + # x = rearrange(x, "... (d r) -> ... d r", r=2) + x = x.reshape(*x.shape[:-1], -1, 2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return x.reshape(*x.shape[:-2], -1) + + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half_codec(q) * sin) + k_embed = (k * cos) + (rotate_half_codec(k) * sin) + return q_embed, k_embed + + +class TimeDelayNetBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + dilation, + ): + super().__init__() + self.conv = nn.Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + dilation=dilation, + padding="same", + padding_mode="reflect", + ) + self.activation = nn.ReLU() + + def forward(self, hidden_states: torch.Tensor): + return self.activation(self.conv(hidden_states)) + + +class Res2NetBlock(torch.nn.Module): + def __init__(self, in_channels, out_channels, scale=8, kernel_size=3, dilation=1): + super().__init__() + + in_channel = in_channels // scale + hidden_channel = out_channels // scale + + self.blocks = nn.ModuleList( + [ + TimeDelayNetBlock( + in_channel, + hidden_channel, + kernel_size=kernel_size, + dilation=dilation, + ) + for i in range(scale - 1) + ] + ) + self.scale = scale + + def forward(self, hidden_states): + outputs = [] + for i, hidden_part in enumerate(torch.chunk(hidden_states, self.scale, dim=1)): + if i == 0: + output_part = hidden_part + elif i == 1: + output_part = self.blocks[i - 1](hidden_part) + else: + output_part = self.blocks[i - 1](hidden_part + output_part) + outputs.append(output_part) + output = torch.cat(outputs, dim=1) + return output + + +class SqueezeExcitationBlock(nn.Module): + def __init__(self, in_channels, se_channels, out_channels): + super().__init__() + + self.conv1 = nn.Conv1d( + in_channels=in_channels, + out_channels=se_channels, + kernel_size=1, + padding="same", + padding_mode="reflect", + ) + self.relu = nn.ReLU(inplace=True) + self.conv2 = nn.Conv1d( + in_channels=se_channels, + out_channels=out_channels, + kernel_size=1, + padding="same", + padding_mode="reflect", + ) + self.sigmoid = nn.Sigmoid() + + def forward(self, hidden_states): + hidden_states_mean = hidden_states.mean(dim=2, keepdim=True) + + hidden_states_mean = self.relu(self.conv1(hidden_states_mean)) + hidden_states_mean = self.sigmoid(self.conv2(hidden_states_mean)) + + return hidden_states * hidden_states_mean + + +class AttentiveStatisticsPooling(nn.Module): + """This class implements an attentive statistic pooling layer for each channel. + It returns the concatenated mean and std of the input tensor. + """ + + def __init__(self, channels, attention_channels=128): + super().__init__() + + self.eps = 1e-12 + self.tdnn = TimeDelayNetBlock(channels * 3, attention_channels, 1, 1) + self.tanh = nn.Tanh() + self.conv = nn.Conv1d( + in_channels=attention_channels, + out_channels=channels, + kernel_size=1, + padding="same", + padding_mode="reflect", + ) + + def _length_to_mask(self, length, max_len=None, dtype=None, device=None): + """Creates a binary mask for each sequence. + + Reference: https://discuss.pytorch.org/t/how-to-generate-variable-length-mask/23397/3 + + Arguments + --------- + length : torch.LongTensor + Containing the length of each sequence in the batch. Must be 1D. + max_len : int + Max length for the mask, also the size of the second dimension. + dtype : torch.dtype, default: None + The dtype of the generated mask. + device: torch.device, default: None + The device to put the mask variable. + + Returns + ------- + mask : tensor + The binary mask. + """ + + if max_len is None: + max_len = length.max().long().item() # using arange to generate mask + mask = torch.arange(max_len, device=length.device, dtype=length.dtype).expand( + len(length), max_len + ) < length.unsqueeze(1) + + mask = torch.as_tensor(mask, dtype=dtype, device=device) + return mask + + def _compute_statistics(self, x, m, dim=2): + mean = (m * x).sum(dim) + std = torch.sqrt((m * (x - mean.unsqueeze(dim)).pow(2)).sum(dim).clamp(self.eps)) + return mean, std + + def forward(self, hidden_states): + seq_length = hidden_states.shape[-1] + lengths = torch.ones(hidden_states.shape[0], device=hidden_states.device) + + # Make binary mask of shape [N, 1, L] + mask = self._length_to_mask( + lengths * seq_length, max_len=seq_length, dtype=hidden_states.dtype, device=hidden_states.device + ) + mask = mask.unsqueeze(1) + + # Expand the temporal context of the pooling layer by allowing the + # self-attention to look at global properties of the utterance. + total = mask.sum(dim=2, keepdim=True) + + mean, std = self._compute_statistics(hidden_states, mask / total) + mean = mean.unsqueeze(2).repeat(1, 1, seq_length) + std = std.unsqueeze(2).repeat(1, 1, seq_length) + attention = torch.cat([hidden_states, mean, std], dim=1) + + # Apply layers + attention = self.conv(self.tanh(self.tdnn(attention))) + + # Filter out zero-paddings + attention = attention.masked_fill(mask == 0, float("-inf")) + + attention = F.softmax(attention, dim=2) + mean, std = self._compute_statistics(hidden_states, attention) + # Append mean and std of the batch + pooled_stats = torch.cat((mean, std), dim=1) + pooled_stats = pooled_stats.unsqueeze(2) + + return pooled_stats + + +class SqueezeExcitationRes2NetBlock(nn.Module): + """An implementation of building block in ECAPA-TDNN, i.e., + TDNN-Res2Net-TDNN-SqueezeExcitationBlock. + """ + + def __init__( + self, + in_channels, + out_channels, + res2net_scale=8, + se_channels=128, + kernel_size=1, + dilation=1, + ): + super().__init__() + self.out_channels = out_channels + self.tdnn1 = TimeDelayNetBlock( + in_channels, + out_channels, + kernel_size=1, + dilation=1, + ) + self.res2net_block = Res2NetBlock(out_channels, out_channels, res2net_scale, kernel_size, dilation) + self.tdnn2 = TimeDelayNetBlock( + out_channels, + out_channels, + kernel_size=1, + dilation=1, + ) + self.se_block = SqueezeExcitationBlock(out_channels, se_channels, out_channels) + + def forward(self, hidden_state): + residual = hidden_state + + hidden_state = self.tdnn1(hidden_state) + hidden_state = self.res2net_block(hidden_state) + hidden_state = self.tdnn2(hidden_state) + hidden_state = self.se_block(hidden_state) + + return hidden_state + residual + + +class ECAPA_TimeDelayNet(torch.nn.Module): + """An implementation of the speaker embedding model in a paper. + "ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in + TDNN Based Speaker Verification" (https://huggingface.co/papers/2005.07143). + """ + + def __init__(self, config: Qwen2_5OmniDiTConfig): + super().__init__() + if len(config.enc_channels) != len(config.enc_kernel_sizes) or len(config.enc_channels) != len( + config.enc_dilations + ): + raise ValueError("enc_channels, enc_kernel_sizes and enc_dilations should have same length") + self.channels = config.enc_channels + self.blocks = nn.ModuleList() + + # The initial TDNN layer + self.blocks.append( + TimeDelayNetBlock( + config.mel_dim, + config.enc_channels[0], + config.enc_kernel_sizes[0], + config.enc_dilations[0], + ) + ) + + # SE-Res2Net layers + for i in range(1, len(config.enc_channels) - 1): + self.blocks.append( + SqueezeExcitationRes2NetBlock( + config.enc_channels[i - 1], + config.enc_channels[i], + res2net_scale=config.enc_res2net_scale, + se_channels=config.enc_se_channels, + kernel_size=config.enc_kernel_sizes[i], + dilation=config.enc_dilations[i], + ) + ) + + # Multi-layer feature aggregation + self.mfa = TimeDelayNetBlock( + config.enc_channels[-1], + config.enc_channels[-1], + config.enc_kernel_sizes[-1], + config.enc_dilations[-1], + ) + + # Attentive Statistical Pooling + self.asp = AttentiveStatisticsPooling( + config.enc_channels[-1], + attention_channels=config.enc_attention_channels, + ) + + # Final linear transformation + self.fc = nn.Conv1d( + in_channels=config.enc_channels[-1] * 2, + out_channels=config.enc_dim, + kernel_size=1, + padding="same", + padding_mode="reflect", + ) + + def forward(self, hidden_states): + # Minimize transpose for efficiency + hidden_states = hidden_states.transpose(1, 2) + + hidden_states_list = [] + for layer in self.blocks: + hidden_states = layer(hidden_states) + hidden_states_list.append(hidden_states) + + # Multi-layer feature aggregation + hidden_states = torch.cat(hidden_states_list[1:], dim=1) + hidden_states = self.mfa(hidden_states) + + # Attentive Statistical Pooling + hidden_states = self.asp(hidden_states) + + # Final linear transformation + hidden_states = self.fc(hidden_states) + + hidden_states = hidden_states.squeeze(-1) + return hidden_states + + +class DiTInputEmbedding(nn.Module): + def __init__(self, config: Qwen2_5OmniDiTConfig): + super().__init__() + self.proj = nn.Linear( + config.mel_dim + config.enc_dim + config.enc_emb_dim + config.emb_dim, + config.hidden_size, + ) + self.spk_encoder = ECAPA_TimeDelayNet(config) + + def forward( + self, + hidden_states: torch.Tensor, + speaker_embedding: torch.Tensor, + condition_vector: torch.Tensor, + code_embed: torch.Tensor, + drop_audio_cond: bool | None = False, + code_embed_uncond: bool | None = None, + apply_cfg: bool | None = True, + ): + if apply_cfg: + hidden_states = torch.cat([hidden_states, hidden_states], dim=0) + speaker_embedding = torch.cat([speaker_embedding, torch.zeros_like(speaker_embedding)], dim=0) + condition_vector = torch.cat([condition_vector, torch.zeros_like(condition_vector)], dim=0) + code_embed = torch.cat([code_embed, code_embed_uncond], dim=0) + elif drop_audio_cond: # cfg for cond audio + condition_vector = torch.zeros_like(condition_vector) + speaker_embedding = torch.zeros_like(speaker_embedding) + condition_vector = self.spk_encoder(condition_vector).unsqueeze(1).repeat(1, hidden_states.size(1), 1) + hidden_states = self.proj(torch.cat((hidden_states, condition_vector, code_embed, speaker_embedding), dim=-1)) + + return hidden_states + + +# Transformer backbone using DiT blocks +class DiTCodecEmbedding(nn.Module): + def __init__(self, codec_num_embeds, codec_dim, repeats): + super().__init__() + self.repeats = repeats + self.codec_embed = nn.Embedding(codec_num_embeds + 1, codec_dim) + + def forward(self, code, drop_code=False): + if drop_code: + code = torch.zeros_like(code) + code_embed = self.codec_embed(code) + + code_embed = torch.repeat_interleave(code_embed, repeats=self.repeats, dim=1) + return code_embed + + +# AdaLayerNormZero +# return with modulated x for attn input, and params for later mlp modulation +class Qwen2_5_OmniAdaLayerNormZero(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 6) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, hidden_states, emb=None): + emb = self.linear(self.silu(emb)) + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1) + + hidden_states = self.norm(hidden_states) * (1 + scale_msa[:, None]) + shift_msa[:, None] + return hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp + + +# AdaLayerNormZero for final layer +# return only with modulated x for attn input, cuz no more mlp modulation +class Qwen2_5_OmniAdaLayerNormZero_Final(nn.Module): + def __init__(self, dim): + super().__init__() + + self.silu = nn.SiLU() + self.linear = nn.Linear(dim, dim * 2) + + self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6) + + def forward(self, hidden_states, emb): + emb = self.linear(self.silu(emb)) + scale, shift = torch.chunk(emb, 2, dim=1) + + hidden_states = self.norm(hidden_states) * (1 + scale)[:, None, :] + shift[:, None, :] + return hidden_states + + +# FeedForward +class DiTMLP(nn.Module): + def __init__(self, dim, mult=4, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + + self.ff = nn.ModuleList( + [ + nn.Linear(dim, inner_dim), + nn.GELU(approximate="tanh"), + nn.Dropout(dropout), + nn.Linear(inner_dim, dim), + ] + ) + + def forward(self, hidden_states): + for layer in self.ff: + hidden_states = layer(hidden_states) + return hidden_states + + +class DiTAttention(nn.Module): + def __init__(self, config: Qwen2_5OmniDiTConfig): + super().__init__() + + self.config = config + self.dim = config.hidden_size + self.heads = config.num_attention_heads + self.inner_dim = config.head_dim * config.num_attention_heads + self.dropout = config.dropout + self.is_causal = False + + self.to_q = nn.Linear(config.hidden_size, self.inner_dim) + self.to_k = nn.Linear(config.hidden_size, self.inner_dim) + self.to_v = nn.Linear(config.hidden_size, self.inner_dim) + + self.to_out = nn.ModuleList([nn.Linear(self.inner_dim, config.hidden_size), nn.Dropout(config.dropout)]) + + def forward( + self, + hidden_states, # noised input x + position_embeddings=None, # rotary position embedding for x + attention_mask=None, + ) -> torch.Tensor: + batch_size = hidden_states.shape[0] + + # `sample` projections. + query = self.to_q(hidden_states) + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + + # attention + inner_dim = key.shape[-1] + head_dim = inner_dim // self.heads + query = query.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, self.heads, head_dim).transpose(1, 2) + + # apply rotary position embedding + # Due to training process, only first head is applied with RoPE, will be fixed at next release + cos, sin = position_embeddings + query[:, :1], key[:, :1] = apply_rotary_pos_emb(query[:, :1], key[:, :1], cos, sin) + + attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] + attention_weights, _ = attention_interface( + self, + query, + key, + value, + attention_mask=attention_mask, + is_causal=False, + ) + + # mask. e.g. inference got a batch with different target durations, mask out the padding + attention_weights = attention_weights.reshape(batch_size, -1, self.heads * head_dim) + attention_weights = attention_weights.to(query.dtype) + + # linear proj + attention_output = self.to_out[0](attention_weights) + attention_output = self.to_out[1](attention_output) + + return attention_output + + +# time step conditioning embedding +class SinusPositionEmbedding(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, hidden_states, scale=1000): + device = hidden_states.device + half_dim = self.dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) + emb = scale * hidden_states.unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat((emb.sin(), emb.cos()), dim=-1) + return emb.type_as(hidden_states) + + +class DiTTimestepEmbedding(nn.Module): + def __init__(self, dim, freq_embed_dim=256): + super().__init__() + self.time_embed = SinusPositionEmbedding(freq_embed_dim) + self.time_mlp = nn.ModuleList([nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim)]) + + def forward(self, timestep): + time_hidden = self.time_embed(timestep) + time_hidden = time_hidden.to(timestep.dtype) + for layer in self.time_mlp: + time_hidden = layer(time_hidden) # b d + return time_hidden + + +class DiTDecoderLayer(nn.Module): + def __init__(self, config: Qwen2_5OmniDiTConfig, look_ahead_block=0, look_backward_block=0): + super().__init__() + self.attn_norm = Qwen2_5_OmniAdaLayerNormZero(config.hidden_size) + + self.attn = DiTAttention(config) + self.look_ahead_block = look_ahead_block + self.look_backward_block = look_backward_block + self.ff_norm = nn.LayerNorm(config.hidden_size, elementwise_affine=False, eps=1e-6) + self.ff = DiTMLP(dim=config.hidden_size, mult=config.ff_mult, dropout=config.dropout) + + def forward( + self, hidden_states, timestep, position_embeddings=None, block_diff=None + ): # x: noised input, t: time embedding + # pre-norm & modulation for attention input + norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(hidden_states, emb=timestep) + + # attention + attn_output = self.attn( + hidden_states=norm, + position_embeddings=position_embeddings, + attention_mask=(block_diff >= -float(self.look_backward_block)) + & (block_diff <= float(self.look_ahead_block)), + ) + + # process attention output for input x + hidden_states = hidden_states + gate_msa.unsqueeze(1) * attn_output + + norm = self.ff_norm(hidden_states) * (1 + scale_mlp[:, None]) + shift_mlp[:, None] + ff_output = self.ff(norm) + hidden_states = hidden_states + gate_mlp.unsqueeze(1) * ff_output + + return hidden_states + + +class SnakeBeta(nn.Module): + """ + A modified Snake function which uses separate parameters for the magnitude of the periodic components + Shape: + - Input: (B, C, T) + - Output: (B, C, T), same shape as the input + Parameters: + - alpha - trainable parameter that controls frequency + - beta - trainable parameter that controls magnitude + References: + - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda: + https://huggingface.co/papers/2006.08195 + """ + + def __init__(self, in_features, alpha=1.0): + super().__init__() + self.in_features = in_features + + # initialize alpha + self.alpha = Parameter(torch.zeros(in_features) * alpha) + self.beta = Parameter(torch.zeros(in_features) * alpha) + + self.no_div_by_zero = 0.000000001 + + def forward(self, hidden_states): + """ + Forward pass of the function. + Applies the function to the input elementwise. + SnakeBeta ∶= x + 1/b * sin^2 (xa) + """ + alpha = self.alpha.unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T] + beta = self.beta.unsqueeze(0).unsqueeze(-1) + alpha = torch.exp(alpha) + beta = torch.exp(beta) + hidden_states = hidden_states + (1.0 / (beta + self.no_div_by_zero)) * torch.pow( + torch.sin(hidden_states * alpha), 2 + ) + + return hidden_states + + +def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): + """Generates a 1D Kaiser-windowed sinc filter. + + Args: + cutoff (float): Normalized cutoff frequency (0 to 0.5). + half_width (float): Transition bandwidth. + kernel_size (int): Number of filter taps. + + Returns: + torch.Tensor: A tensor of shape (1, 1, kernel_size) representing the filter. + """ + is_even = kernel_size % 2 == 0 + half_size = kernel_size // 2 + + # Compute Kaiser window parameters + delta_f = 4 * half_width + attenuation = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 + + if attenuation > 50.0: + beta = 0.1102 * (attenuation - 8.7) + elif attenuation >= 21.0: + beta = 0.5842 * (attenuation - 21) ** 0.4 + 0.07886 * (attenuation - 21.0) + else: + beta = 0.0 + + kaiser_window = torch.kaiser_window(kernel_size, beta=beta, periodic=False, dtype=torch.float32) + + # Compute time indices + if is_even: + time_indices = torch.arange(-half_size, half_size) + 0.5 + else: + time_indices = torch.arange(kernel_size) - half_size + + # Compute sinc filter + if cutoff == 0: + return torch.zeros((1, 1, kernel_size), dtype=torch.float32) # Ensures correct shape + + sinc_filter = torch.sinc(2 * cutoff * time_indices) + normalized_filter = 2 * cutoff * kaiser_window * sinc_filter + + # Normalize to ensure sum = 1 (avoid leakage of constant component) + normalized_filter /= normalized_filter.sum() + + return normalized_filter.view(1, 1, kernel_size) + + +class UpSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + self.ratio = ratio + self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.stride = ratio + self.pad = self.kernel_size // ratio - 1 + self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2 + self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 + + filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio, half_width=0.6 / ratio, kernel_size=self.kernel_size) + self.register_buffer("filter", filter, persistent=False) + + def forward(self, hidden_states): + channels = hidden_states.shape[1] + + hidden_states = F.pad(hidden_states, (self.pad, self.pad), mode="replicate") + hidden_states = self.ratio * F.conv_transpose1d( + hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels + ) + hidden_states = hidden_states[..., self.pad_left : -self.pad_right] + + return hidden_states + + +class DownSample1d(nn.Module): + def __init__(self, ratio=2, kernel_size=None): + super().__init__() + cutoff = 0.5 / ratio + half_width = 0.6 / ratio + self.cutoff = cutoff + self.half_width = half_width + self.kernel_size = kernel_size + + if cutoff < 0.0: + raise ValueError("Minimum cutoff must be larger than zero.") + if cutoff > 0.5: + raise ValueError("A cutoff above 0.5 does not make sense.") + + self.even = kernel_size % 2 == 0 + self.pad_left = kernel_size // 2 - int(self.even) + self.pad_right = kernel_size // 2 + self.stride = ratio + filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) + self.register_buffer("filter", filter, persistent=False) + + def forward(self, hidden_states): + channels = hidden_states.shape[1] + hidden_states = F.pad(hidden_states, (self.pad_left, self.pad_right), mode="replicate") + out = F.conv1d(hidden_states, self.filter.expand(channels, -1, -1), stride=self.stride, groups=channels) + return out + + +class TorchActivation1d(nn.Module): + def __init__( + self, + activation, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12, + ): + super().__init__() + if not callable(activation): + raise TypeError("Activation function must be callable") + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + def forward(self, hidden_states): + hidden_states = self.upsample(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.downsample(hidden_states) + + return hidden_states + + +class AMPBlock(torch.nn.Module): + def __init__( + self, + channels, + kernel_size=3, + dilation=(1, 3, 5), + ): + super().__init__() + + self.convs1 = nn.ModuleList( + [ + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[0], + padding=self._get_padding(kernel_size, dilation[0]), + ), + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[1], + padding=self._get_padding(kernel_size, dilation[1]), + ), + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=dilation[2], + padding=self._get_padding(kernel_size, dilation[2]), + ), + ] + ) + + self.convs2 = nn.ModuleList( + [ + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=self._get_padding(kernel_size, 1), + ), + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=self._get_padding(kernel_size, 1), + ), + nn.Conv1d( + channels, + channels, + kernel_size, + 1, + dilation=1, + padding=self._get_padding(kernel_size, 1), + ), + ] + ) + + self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers + + self.activations = nn.ModuleList( + [TorchActivation1d(activation=SnakeBeta(channels)) for _ in range(self.num_layers)] + ) + + def _get_padding(self, kernel_size, dilation=1): + return int((kernel_size * dilation - dilation) / 2) + + def forward(self, hidden_states): + acts1, acts2 = self.activations[::2], self.activations[1::2] + for conv1, conv2, act1, act2 in zip(self.convs1, self.convs2, acts1, acts2): + residual = hidden_states + hidden_states = act1(hidden_states) + hidden_states = conv1(hidden_states) + hidden_states = act2(hidden_states) + hidden_states = conv2(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring( + custom_intro=""" + The full Qwen2.5Omni Token2WavBigVGAN model. Which take mel spectrogram as input and predict waveform. + """ +) +class Qwen2_5OmniToken2WavBigVGANModel(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniBigVGANConfig + input_modalities = "audio" + + def __init__(self, config: Qwen2_5OmniBigVGANConfig): + super().__init__(config) + self.num_residual_blocks = len(config.resblock_kernel_sizes) + self.num_upsample_layers = len(config.upsample_rates) + + self.conv_pre = nn.Conv1d(config.mel_dim, config.upsample_initial_channel, 7, 1, padding=3) + + # Removing extra ModuleList breaks official state dict + ups = [ + nn.ModuleList( + [ + nn.ConvTranspose1d( + config.upsample_initial_channel // (2**layer_idx), + config.upsample_initial_channel // (2 ** (layer_idx + 1)), + kernel_size, + stride, + padding=(kernel_size - stride) // 2, + ) + ] + ) + for layer_idx, (stride, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes)) + ] + self.ups = nn.ModuleList(ups) + + self.resblocks = nn.ModuleList( + [ + AMPBlock(config.upsample_initial_channel // (2 ** (layer_idx + 1)), kernel_size, dilation) + for layer_idx in range(self.num_upsample_layers) + for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes) + ] + ) + + self.activation_post = TorchActivation1d( + activation=SnakeBeta(config.upsample_initial_channel // (2**self.num_upsample_layers)) + ) + self.conv_post = nn.Conv1d( + config.upsample_initial_channel // (2**self.num_upsample_layers), 1, 7, 1, padding=3, bias=False + ) + + self.post_init() + + def normalize_spectrogram(self, spectrogram, max_value, min_db): + return torch.clamp((2 * max_value) * ((spectrogram - min_db) / (-min_db)) - max_value, -max_value, max_value) + + def amplitude_to_db(self, amplitude, min_db_level): + min_level = torch.exp( + torch.tensor(min_db_level / 20.0 * np.log(10), device=amplitude.device, dtype=amplitude.dtype) + ) + return 20 * torch.log10(torch.clamp(amplitude, min=min_level)) + + def process_mel_spectrogram(self, mel_spectrogram): + amplitude_spectrum = torch.exp(mel_spectrogram) + decibel_spectrum = self.amplitude_to_db(amplitude_spectrum, -115) - 20 + return self.normalize_spectrogram(decibel_spectrum, 1, -115) + + def forward(self, mel_spectrogram, **kwargs): + processed_spectrogram = self.process_mel_spectrogram(mel_spectrogram) + hidden_representation = self.conv_pre(processed_spectrogram) + + for layer_index in range(self.num_upsample_layers): + hidden_representation = self.ups[layer_index][0](hidden_representation) + residual_output = sum( + self.resblocks[layer_index * self.num_residual_blocks + block_index](hidden_representation) + for block_index in range(self.num_residual_blocks) + ) + residual_output = residual_output / self.num_residual_blocks + hidden_representation = residual_output + + hidden_representation = self.activation_post(hidden_representation) + output_waveform = self.conv_post(hidden_representation) + return torch.clamp(output_waveform, min=-1.0, max=1.0).squeeze().cpu() + + +class RungeKutta4ODESolver: + def __init__(self, function, initial_value): + self.function = function + self.initial_value = initial_value + + self._one_third = 1 / 3 + self._two_thirds = 2 / 3 + + def _rk4_step(self, function, time_start, time_step, time_end, value_start, function_value_start=None): + k1 = function_value_start if function_value_start is not None else function(time_start, value_start) + k2 = function(time_start + time_step * self._one_third, value_start + time_step * k1 * self._one_third) + k3 = function(time_start + time_step * self._two_thirds, value_start + time_step * (k2 - k1 * self._one_third)) + k4 = function(time_end, value_start + time_step * (k1 - k2 + k3)) + return (k1 + 3 * (k2 + k3) + k4) * time_step / 8 + + def _compute_step(self, function, time_start, time_step, time_end, value_start): + function_value_start = function(time_start, value_start) + return self._rk4_step( + function, time_start, time_step, time_end, value_start, function_value_start=function_value_start + ), function_value_start + + def _linear_interpolation(self, time_start, time_end, value_start, value_end, time_point): + if time_point == time_start: + return value_start + if time_point == time_end: + return value_end + weight = (time_point - time_start) / (time_end - time_start) + return value_start + weight * (value_end - value_start) + + def integrate(self, time_points): + solution = torch.empty( + len(time_points), + *self.initial_value.shape, + dtype=self.initial_value.dtype, + device=self.initial_value.device, + ) + solution[0] = self.initial_value + + current_index = 1 + current_value = self.initial_value + for time_start, time_end in zip(time_points[:-1], time_points[1:]): + time_step = time_end - time_start + delta_value, _ = self._compute_step(self.function, time_start, time_step, time_end, current_value) + next_value = current_value + delta_value + + while current_index < len(time_points) and time_end >= time_points[current_index]: + solution[current_index] = self._linear_interpolation( + time_start, time_end, current_value, next_value, time_points[current_index] + ) + current_index += 1 + + current_value = next_value + + return solution + + +@auto_docstring( + custom_intro=""" + The full Qwen2.5Omni Token2WavDiT model. Which take speech tokens as input and predict mel spectrogram. + """ +) +class Qwen2_5OmniToken2WavDiTModel(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniDiTConfig + input_modalities = "audio" + _no_split_modules = ["DiTDecoderLayer"] + + def __init__(self, config: Qwen2_5OmniDiTConfig): + super().__init__(config) + self.mel_dim = config.mel_dim + self.repeats = config.repeats + self.time_embed = DiTTimestepEmbedding(config.hidden_size) + + self.text_embed = DiTCodecEmbedding(config.num_embeds, config.emb_dim, config.repeats) + self.input_embed = DiTInputEmbedding(config) + + self.rotary_embed = Qwen2_5OmniDiTRotaryEmbedding(config=config) + + self.hidden_size = config.hidden_size + self.layers = config.num_hidden_layers + self.block_size = config.block_size + self.num_attention_heads = config.num_attention_heads + + self.transformer_blocks = nn.ModuleList() + for i in range(config.num_hidden_layers): + self.transformer_blocks.append( + DiTDecoderLayer( + config, + look_ahead_block=1 if i in config.look_ahead_layers else 0, + look_backward_block=1 if i in config.look_backward_layers else 0, + ) + ) + + self.norm_out = Qwen2_5_OmniAdaLayerNormZero_Final(config.hidden_size) # final modulation + self.proj_out = nn.Linear(config.hidden_size, config.mel_dim) + + self.post_init() + + def _create_block_diff(self, hidden_states): + batch, seq_len = hidden_states.shape[0], hidden_states.shape[1] + block_indices = torch.arange(seq_len, device=hidden_states.device) // self.block_size # [seq_length] + + block_i = block_indices.unsqueeze(1) # [seq_length, 1] + block_j = block_indices.unsqueeze(0) # [1, seq_length] + block_diff = block_j - block_i # (n, n) + + return block_diff.expand(batch, self.num_attention_heads, seq_len, seq_len) + + def forward( + self, + hidden_states, + condition_vector, + speaker_embedding, + quantized_code, + time_step, + drop_audio_conditioning=False, + drop_code=False, + apply_cfg=True, + **kwargs, + ): + batch_size = hidden_states.shape[0] + if time_step.ndim == 0: + time_step = time_step.repeat(batch_size) + + # Compute embeddings + time_embedding = self.time_embed(time_step) + text_embedding = self.text_embed(quantized_code, drop_code=False if apply_cfg else drop_code) + text_embedding_unconditioned = self.text_embed(quantized_code, drop_code=True) if apply_cfg else None + + hidden_states = self.input_embed( + hidden_states, + speaker_embedding, + condition_vector, + text_embedding, + drop_audio_cond=drop_audio_conditioning, + code_embed_uncond=text_embedding_unconditioned, + apply_cfg=apply_cfg, + ) + + # Compute positional encodings + position_ids = torch.arange(hidden_states.shape[1], device=hidden_states.device) + position_ids = position_ids[None, :].repeat(batch_size, 1) + position_embeddings = self.rotary_embed(hidden_states, position_ids) + blockwise_difference = self._create_block_diff(hidden_states) + + # Transformer blocks + for transformer_block in self.transformer_blocks: + hidden_states = transformer_block( + hidden_states, + time_embedding, + position_embeddings=position_embeddings, + block_diff=blockwise_difference, + ) + + hidden_states = self.norm_out(hidden_states, time_embedding) + output = self.proj_out(hidden_states) + + return output + + @torch.no_grad() + def sample( + self, + conditioning_vector, + reference_mel_spectrogram, + quantized_code, + num_steps=10, + guidance_scale=0.5, + sway_coefficient=-1.0, + ): + maximum_duration = quantized_code.shape[1] * self.repeats + batch_size = reference_mel_spectrogram.shape[0] + if batch_size != 1: + raise ValueError("Only batch size = 1 is currently supported") + + if maximum_duration > self.config.max_position_embeddings: + raise ValueError( + f"Requested mel length ({maximum_duration}) exceeds `dit_config.max_position_embeddings` " + f"({self.config.max_position_embeddings}). Provide shorter `quantized_code`." + ) + + initial_state = torch.randn( + [batch_size, maximum_duration, self.mel_dim], + dtype=reference_mel_spectrogram.dtype, + device=quantized_code.device, + ) + conditioning_vector = conditioning_vector.unsqueeze(1).repeat(1, maximum_duration, 1) + + def ode_function(time_step, hidden_states): + if guidance_scale < 1e-5: + prediction = self( + hidden_states=hidden_states, + speaker_embedding=conditioning_vector, + condition_vector=reference_mel_spectrogram, + quantized_code=quantized_code, + time_step=time_step, + drop_audio_conditioning=False, + drop_code=False, + apply_cfg=False, + ) + return prediction + + model_output = self( + hidden_states=hidden_states, + quantized_code=quantized_code, + speaker_embedding=conditioning_vector, + condition_vector=reference_mel_spectrogram, + time_step=time_step, + apply_cfg=True, + ) + guided_prediction, null_prediction = torch.chunk(model_output, 2, dim=0) + return guided_prediction + (guided_prediction - null_prediction) * guidance_scale + + initial_time = 0 + time_embedding = torch.linspace( + initial_time, 1, num_steps, device=quantized_code.device, dtype=conditioning_vector.dtype + ) + + if sway_coefficient is not None: + time_embedding += sway_coefficient * (torch.cos(torch.pi / 2 * time_embedding) - 1 + time_embedding) + + ode_solver = RungeKutta4ODESolver(function=ode_function, initial_value=initial_state) + solution_trajectory = ode_solver.integrate(time_embedding) + + generated_waveform = solution_trajectory[-1] + generated_mel_spectrogram = generated_waveform.permute(0, 2, 1) + return generated_mel_spectrogram + + +@auto_docstring( + custom_intro=""" + The full Qwen2.5Omni Token2Wav model. Consists a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform. + """ +) +class Qwen2_5OmniToken2WavModel(Qwen2_5OmniPreTrainedModel): + config: Qwen2_5OmniToken2WavConfig + base_model_prefix = "model" + input_modalities = "audio" + _no_split_modules = ["Qwen2_5OmniToken2WavDiTModel", "Qwen2_5OmniToken2WavBigVGANModel"] + + def __init__(self, config: Qwen2_5OmniToken2WavConfig): + super().__init__(config) + attn_impl = config._attn_implementation + if is_flash_attention_requested(config): + logger.warning_once( + "Qwen2_5OmniToken2WavModel must inference with fp32, but Flash Attention only supports fp16 and bf16, " + "attention implementation of Qwen2_5OmniToken2WavModel will fallback to sdpa." + ) + attn_impl = "sdpa" + elif config._attn_implementation == "eager": + logger.warning_once( + "Qwen2_5OmniToken2WavModel does not support eager attention implementation, fall back to sdpa" + ) + attn_impl = "sdpa" + self.code2wav_dit_model = Qwen2_5OmniToken2WavDiTModel._from_config( + config.dit_config, attn_implementation=attn_impl + ) + self.code2wav_bigvgan_model = Qwen2_5OmniToken2WavBigVGANModel._from_config( + config.bigvgan_config, attn_implementation=attn_impl + ) + + self.post_init() + + def forward( + self, + code, + conditioning, + reference_mel, + num_steps=10, + guidance_scale=0.5, + sway_coefficient=-1.0, + **kwargs, + ): + """Generates a waveform from input code and conditioning parameters.""" + + mel_spectrogram = self.code2wav_dit_model.sample( + conditioning, + reference_mel, + code, + num_steps=num_steps, + guidance_scale=guidance_scale, + sway_coefficient=sway_coefficient, + ) + + waveform = self.code2wav_bigvgan_model(mel_spectrogram) + + return waveform + + +############################ +# Start Qwen2.5Omni # +############################ + + +@auto_docstring( + custom_intro=""" + The full Qwen2.5Omni model, a multimodal model composed of 3 sub-models: + - [`Qwen2_5OmniThinkerForConditionalGeneration`]: + a causal auto-regressive transformer takes text, audio, image, video as input and predict text tokens. + - [`Qwen2_5OmniTalkerForConditionalGeneration`]: + a causal auto-regressive transformer takes thinker hidden states and response as input and predict speech tokens. + - [`Qwen2_5OmniToken2WavModel`]: + a DiT model take speech tokens as input and predict mel spectrogram and a BigVGAN vocoder take mel spectrogram as input and predict waveform. + """ +) +class Qwen2_5OmniForConditionalGeneration(Qwen2_5OmniPreTrainedModel, GenerationMixin): + config: Qwen2_5OmniConfig + output_modalities = ("audio", "text") + _no_split_modules = [ + "Qwen2_5OmniTalkerForConditionalGeneration", + "Qwen2_5OmniToken2WavModel", + ] + + def __init__(self, config): + super().__init__(config) + + self.thinker = Qwen2_5OmniThinkerForConditionalGeneration(config.thinker_config) + + self.has_talker = config.enable_audio_output + self.speaker_map = {} + if config.enable_audio_output: + self.enable_talker() + self.post_init() + + def enable_talker(self): + self.talker = Qwen2_5OmniTalkerForConditionalGeneration(self.config.talker_config) + self.token2wav = Qwen2_5OmniToken2WavModel(self.config.token2wav_config) + self.token2wav.float() + self.has_talker = True + + def load_speakers(self, path): + check_torch_load_is_safe() + for key, value in torch.load(path, weights_only=True).items(): + self.speaker_map[key] = value + logger.info(f"Speaker {list(self.speaker_map.keys())} loaded") + + def disable_talker(self): + if hasattr(self, "talker"): + del self.talker + if hasattr(self, "token2wav"): + del self.token2wav + self.has_talker = False + + @classmethod + def from_pretrained( + cls, + pretrained_model_name_or_path, + *model_args, + config=None, + cache_dir=None, + ignore_mismatched_sizes=False, + force_download=False, + local_files_only=False, + token=None, + revision="main", + use_safetensors=None, + weights_only=True, + **kwargs, + ): + model = super().from_pretrained( + pretrained_model_name_or_path, + *model_args, + config=config, + cache_dir=cache_dir, + ignore_mismatched_sizes=ignore_mismatched_sizes, + force_download=force_download, + local_files_only=local_files_only, + token=token, + revision=revision, + use_safetensors=use_safetensors, + weights_only=weights_only, + **kwargs, + ) + spk_path = cached_file( + pretrained_model_name_or_path, + "spk_dict.pt", + subfolder=kwargs.pop("subfolder", None), + cache_dir=kwargs.pop("cache_dir", None), + force_download=kwargs.pop("force_download", False), + proxies=kwargs.pop("proxies", None), + local_files_only=kwargs.pop("local_files_only", False), + token=token, + revision=kwargs.pop("revision", None), + ) + if spk_path is None: + raise ValueError(f"""{pretrained_model_name_or_path}/{spk_path} not exists""") + model.load_speakers(spk_path) + + return model + + @torch.no_grad() + @deprecate_kwarg("return_audio", version="v5", new_name="generation_mode") + # TODO: raushan, defaults should be saved in generation config + def generate( + self, + input_ids: torch.Tensor | None = None, + speaker: str = "Chelsie", + use_audio_in_video: bool = False, + thinker_max_new_tokens: int = 1024, + talker_max_new_tokens: int = 4096, + talker_do_sample: bool = True, + talker_top_k: int = 40, + talker_top_p: float = 0.8, + talker_temperature: float = 0.9, + talker_eos_token_id: list[int] = [8292, 8294], + talker_repetition_penalty: float = 1.05, + **kwargs, + ): + r""" + Generate text response and audio from input. + + Args: + input_ids (`Optional[torch.Tensor]`, *optional*): + Input ids, should obtain from processor. + speaker (`str` , defaults to "Chelsie"): + Which speaker should be used in audio response. + use_audio_in_video (`bool`, defaults to False): + Whether or not use audio track in video, should same as the parameter in `process_audio_info`. + generation_mode (`Optional[str]`, *optional*): + Whether or not return response in audio format. When `generation_mode="audio"`, this parameter is same as `config.enable_audio_output`. + kwargs (*optional*): + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model. + - With a *thinker_*, *talker_*, *token2wav_* prefix, they will be input for the `generate` method of the + thinker, talker and token2wav respectively. It has the priority over the keywords without a prefix. + Returns: + When `return_audio=False`: + - **Text** (`torch.Tensor`): Generated text token sequence. + When `return_audio=True`: + - **Text** (`torch.Tensor`): Generated text token sequence. + - **Audio waveform** (`torch.Tensor`): Generated audio waveform. + """ + # check `False` on purpose because the parameter can be `str/bool`. This is needed for BC + generation_mode = kwargs.pop("generation_mode", None) + return_audio = generation_mode != "text" and generation_mode is not False + + if speaker not in self.speaker_map: + raise ValueError(f"{speaker} is not available, available speakers: {self.speaker_map.keys()}") + if return_audio and not self.has_talker: + raise ValueError( + "Cannot use talker when talker module not initialized. Use `enable_talker` method or set enable_talker in config to enable talker." + ) + if return_audio is None: + return_audio = self.has_talker + if input_ids.shape[0] != 1 and return_audio: + raise NotImplementedError("Qwen2.5-Omni currently does not support batched inference with audio output") + + shared_kwargs = {"use_audio_in_video": use_audio_in_video} + thinker_kwargs = { + "max_new_tokens": thinker_max_new_tokens, + } + talker_kwargs = { + "max_new_tokens": talker_max_new_tokens, + "do_sample": talker_do_sample, + "top_k": talker_top_k, + "top_p": talker_top_p, + "temperature": talker_temperature, + "eos_token_id": talker_eos_token_id, + "repetition_penalty": talker_repetition_penalty, + } + token2wav_kwargs = {} + + for key, value in kwargs.items(): + if key.startswith("thinker_"): + thinker_kwargs[key[len("thinker_") :]] = value + elif key.startswith("talker_"): + talker_kwargs[key[len("talker_") :]] = value + elif key.startswith("token2wav_"): + token2wav_kwargs[key[len("token2wav_") :]] = value + # Process special input values + elif key == "feature_attention_mask": + thinker_kwargs[key] = value + talker_kwargs["audio_feature_lengths"] = torch.sum(value, dim=1) + elif key == "input_features" or key == "attention_mask": + thinker_kwargs[key] = value + # Put other key to shared kwargs + else: + shared_kwargs[key] = value + + # Merge kwargs + for key, value in shared_kwargs.items(): + if key not in thinker_kwargs: + thinker_kwargs[key] = value + if key not in talker_kwargs: + talker_kwargs[key] = value + if key not in token2wav_kwargs: + token2wav_kwargs[key] = value + speaker_params = self.speaker_map[speaker] + + # 1. Generate from thinker module + generate_audio = return_audio and self.has_talker + if generate_audio: + thinker_kwargs["output_hidden_states"] = True + thinker_kwargs["return_dict_in_generate"] = True + + thinker_result = self.thinker.generate(input_ids=input_ids, **thinker_kwargs) + + if not generate_audio: + return thinker_result + + # 2. Generate speech tokens from talker module + embeds_to_talker = thinker_result.hidden_states[0][0].clone().to(input_ids.device) + if thinker_kwargs.get("input_features") is not None: + audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index + audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker) + audio_mask_tensor = torch.zeros( + [audio_ids_mask.sum(), embeds_to_talker.shape[-1]], + dtype=embeds_to_talker.dtype, + device=input_ids.device, + ) + embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor) + if thinker_kwargs.get("pixel_values") is not None: + image_ids_mask = input_ids == self.config.thinker_config.image_token_index + image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker) + image_mask_tensor = torch.zeros( + [image_ids_mask.sum(), embeds_to_talker.shape[-1]], + dtype=embeds_to_talker.dtype, + device=input_ids.device, + ) + embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor) + if thinker_kwargs.get("pixel_values_videos") is not None: + video_ids_mask = input_ids == self.config.thinker_config.video_token_index + video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker) + video_mask_tensor = torch.zeros( + [video_ids_mask.sum(), embeds_to_talker.shape[-1]], + dtype=embeds_to_talker.dtype, + device=input_ids.device, + ) + embeds_to_talker.masked_scatter_(video_mask, video_mask_tensor) + + processed_thinker_hidden = ( + (embeds_to_talker,) + thinker_result.hidden_states[0][1:], + ) + thinker_result.hidden_states[1:] + thinker_generate_ids = thinker_result.sequences[:, input_ids.size(1) :].to(input_ids.device) + thinker_token_embeds = [ + token_hidden_states[0].to(input_ids.device) for token_hidden_states in processed_thinker_hidden + ] + thinker_hidden_states = [ + token_hidden_states[-1].to(input_ids.device) for token_hidden_states in processed_thinker_hidden + ] + + talker_text_bos_token = speaker_params["bos_token"] + talker_input_text_ids = torch.cat( + [ + input_ids, + torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=input_ids.device), + thinker_generate_ids[:, :1], + ], + dim=-1, + ) + + talker_input_ids = torch.cat( + [ + torch.full_like(input_ids, fill_value=self.talker.codec_mask_token), + torch.tensor([[self.talker.codec_pad_token]], dtype=torch.long, device=input_ids.device), + torch.tensor([[self.talker.codec_bos_token]], dtype=torch.long, device=input_ids.device), + ], + dim=1, + ) + + thinker_embed_tokens = self.thinker.get_input_embeddings() + thinker_reply_part = torch.cat(thinker_hidden_states[1:], dim=1) + torch.cat(thinker_token_embeds[1:], dim=1) + talker_inputs_embeds = thinker_hidden_states[0] + thinker_token_embeds[0] + talker_text_bos_token = torch.tensor([[talker_text_bos_token]], dtype=torch.long, device=input_ids.device) + talker_text_bos_embed = thinker_embed_tokens(talker_text_bos_token).to(input_ids.device) + talker_inputs_embeds = torch.cat( + [ + talker_inputs_embeds, + talker_text_bos_embed, + thinker_reply_part[:, :1, :], + ], + dim=1, + ) + + eos_token = torch.tensor([[self.talker.text_eos_token]], dtype=torch.long, device=input_ids.device) + eos_embedding = thinker_embed_tokens(eos_token).to(input_ids.device) + + pad_token = torch.tensor([[self.talker.text_pad_token]], dtype=torch.long, device=input_ids.device) + pad_embedding = thinker_embed_tokens(pad_token).to(input_ids.device) + + thinker_reply_part = torch.cat( + [ + thinker_reply_part[:, 1:, :], + eos_embedding, + pad_embedding, + ], + dim=1, + ) + + talker_attention_mask = None + if "attention_mask" in kwargs: + talker_attention_mask = torch.cat( + [kwargs["attention_mask"], kwargs["attention_mask"].new_ones((1, 2))], dim=1 + ).to(input_ids.device) + + talker_result = self.talker.generate( + input_ids=talker_input_ids, + input_text_ids=talker_input_text_ids, + thinker_reply_part=thinker_reply_part, + inputs_embeds=talker_inputs_embeds, + attention_mask=talker_attention_mask, + suppress_tokens=[self.talker.codec_bos_token], + **{k: (v.to(input_ids.device) if torch.is_tensor(v) else v) for k, v in talker_kwargs.items()}, + ) + talker_generate_codes = talker_result[:, talker_input_ids.shape[1] : -1] + + # 3. Generate wavs from code + if self.token2wav.dtype != torch.float: + self.token2wav.float() + + wav = self.token2wav( + talker_generate_codes.to(input_ids.device), + conditioning=speaker_params["cond"].to(input_ids.device).float(), + reference_mel=speaker_params["ref_mel"].to(input_ids.device).float(), + **token2wav_kwargs, + ) + + return thinker_result.sequences, wav.float() + + +__all__ = [ + "Qwen2_5OmniConfig", + "Qwen2_5OmniThinkerConfig", + "Qwen2_5OmniTalkerConfig", + "Qwen2_5OmniToken2WavConfig", + "Qwen2_5OmniAudioEncoderConfig", + "Qwen2_5OmniBigVGANConfig", + "Qwen2_5OmniDiTConfig", + "Qwen2_5OmniTextConfig", + "Qwen2_5OmniVisionEncoderConfig", + "Qwen2_5OmniForConditionalGeneration", + "Qwen2_5OmniThinkerTextModel", + "Qwen2_5OmniThinkerForConditionalGeneration", + "Qwen2_5OmniTalkerModel", + "Qwen2_5OmniTalkerForConditionalGeneration", + "Qwen2_5OmniToken2WavDiTModel", + "Qwen2_5OmniToken2WavBigVGANModel", + "Qwen2_5OmniToken2WavModel", + "Qwen2_5OmniPreTrainedModel", + "Qwen2_5OmniPreTrainedModelForConditionalGeneration", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py new file mode 100644 index 0000000000000000000000000000000000000000..5f5b6584862a740d7c0f8f5430fbc6963a157f33 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py @@ -0,0 +1,375 @@ +# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for Qwen2.5Omni. +""" + +import re + +import numpy as np + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs +from ...tokenization_utils_base import AudioInput, PreTokenizedInput, TextInput +from ...utils import auto_docstring +from ...video_utils import VideoInput + + +# Redefine kwargs for videos because Qwen-Omni uses some kwargs for processing omni +# and does not use them in video processor class +class Qwen2_5_OmniVideosKwargs(VideosKwargs, total=False): + """ + min_pixels (`int`, *optional*): + Minimum number of pixels (height × width) for video frames after resizing. Frames smaller than this + threshold will be upscaled to meet the minimum requirement. + max_pixels (`int`, *optional*): + Maximum number of pixels (height × width) for video frames after resizing. Frames larger than this + threshold will be downscaled to fit within the limit. + patch_size (`int`, *optional*): + The spatial patch size used by the vision encoder. Video frames are divided into patches of this size + in both height and width dimensions. + temporal_patch_size (`int`, *optional*): + The temporal patch size used by the vision encoder. This determines how many consecutive frames are + grouped together as a single temporal patch. + merge_size (`int`, *optional*): + The merge size used for combining spatial patches. Multiple patches are merged together to reduce the + sequence length while maintaining spatial information. + min_frames (`int`, *optional*): + Minimum number of frames to extract from the video. Videos with fewer frames will be padded or repeated + to meet this requirement. + max_frames (`int`, *optional*): + Maximum number of frames to extract from the video. Longer videos will be truncated or sampled to fit + within this limit. + use_audio_in_video (`bool`, *optional*, defaults to `False`): + Whether to incorporate audio information when processing videos. When enabled, audio tokens are + interleaved with video tokens based on temporal alignment, creating a unified multimodal representation. + seconds_per_chunk (`float`, *optional*, defaults to `2.0`): + The duration (in seconds) of each video chunk when splitting long videos. This parameter controls how + videos are divided into temporal segments for processing. + position_id_per_seconds (`int` or `float`, *optional*, defaults to `25`): + The number of position IDs allocated per second of video. This parameter controls the temporal resolution + of position embeddings and is used to align video tokens with audio tokens when `use_audio_in_video=True`. + """ + + min_pixels: int + max_pixels: int + patch_size: int + temporal_patch_size: int + merge_size: int + min_frames: int + max_frames: int + use_audio_in_video: bool + seconds_per_chunk: float + position_id_per_seconds: int | float + + +class Qwen2_5OmniProcessorKwargs(ProcessingKwargs, total=False): + videos_kwargs: Qwen2_5_OmniVideosKwargs + + _defaults = { + "text_kwargs": { + "padding": False, + "padding_side": "left", + }, + "videos_kwargs": { + "seconds_per_chunk": 2.0, + "position_id_per_seconds": 25, + "use_audio_in_video": False, + "size": { + "shortest_edge": 128 * 28 * 28, + "longest_edge": 768 * 28 * 28, + }, + }, + "audio_kwargs": { + "sampling_rate": 16000, + "padding": "max_length", + "return_attention_mask": True, + }, + } + + +@auto_docstring +class Qwen2_5OmniProcessor(ProcessorMixin): + def __init__( + self, image_processor=None, video_processor=None, feature_extractor=None, tokenizer=None, chat_template=None + ): + super().__init__(image_processor, video_processor, feature_extractor, tokenizer, chat_template=chat_template) + self.image_token = self.tokenizer.image_token + self.audio_token = self.tokenizer.audio_token + self.video_token = self.tokenizer.video_token + self.vision_bos_token = self.tokenizer.vision_bos_token + self.vision_eos_token = self.tokenizer.vision_eos_token + self.audio_bos_token = self.tokenizer.audio_bos_token + self.audio_eos_token = self.tokenizer.audio_eos_token + + @auto_docstring + def __call__( + self, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + images: ImageInput | None = None, + videos: VideoInput | None = None, + audio: AudioInput | None = None, + **kwargs: Unpack[Qwen2_5OmniProcessorKwargs], + ) -> BatchFeature: + if text is None: + raise ValueError("You need to specify either a `text` input to process.") + + output_kwargs = self._merge_kwargs( + Qwen2_5OmniProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + seconds_per_chunk = output_kwargs["videos_kwargs"].pop("seconds_per_chunk") + position_id_per_seconds = output_kwargs["videos_kwargs"].pop("position_id_per_seconds") + use_audio_in_video = output_kwargs["videos_kwargs"].pop("use_audio_in_video") + + if audio is not None: + output_kwargs["audio_kwargs"]["padding"] = "max_length" # Support "max_length" padding only here + audio_inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"]) + audio_inputs["feature_attention_mask"] = audio_inputs.pop( + "attention_mask" + ) # rename feature_attention_mask to prevent conflicts later on + audio_inputs["input_features"] = audio_inputs.pop( + "input_features" + ) # rename input_features to prevent conflicts later on + input_lengths = (audio_inputs["feature_attention_mask"].sum(-1) - 1) // 2 + 1 + audio_lengths = iter((input_lengths - 2) // 2 + 1) + else: + audio_inputs = {} + audio_lengths = iter([]) + + if images is not None: + images_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + image_grid_thw = iter(images_inputs["image_grid_thw"]) + else: + images_inputs = {} + image_grid_thw = iter([]) + + if videos is not None: + videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"]) + + fps = output_kwargs["videos_kwargs"].get("fps", 2.0) + video_grid_thw = videos_inputs["video_grid_thw"] + second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw) + videos_inputs["video_second_per_grid"] = second_per_grid_ts + + video_grid_thw = iter(video_grid_thw) + video_second_per_grid = iter(second_per_grid_ts) + else: + videos_inputs = {} + video_grid_thw = iter([]) + video_second_per_grid = iter([]) + + if not isinstance(text, list): + text = [text] + + if images is not None or videos is not None or audio is not None: + text = self.replace_multimodal_special_tokens( + text, + audio_lengths, + image_grid_thw, + video_grid_thw, + video_second_per_grid=video_second_per_grid, + use_audio_in_video=use_audio_in_video, + position_id_per_seconds=position_id_per_seconds, + seconds_per_chunk=seconds_per_chunk, + ) + + texts_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) + + return BatchFeature( + data={**texts_inputs, **images_inputs, **videos_inputs, **audio_inputs}, + tensor_type=kwargs.get("return_tensors"), + ) + + def replace_multimodal_special_tokens( + self, + text, + audio_lengths, + image_grid_thw, + video_grid_thw, + video_second_per_grid, + use_audio_in_video, + position_id_per_seconds, + seconds_per_chunk, + ): + # Extend mm token length + merge_length_image = self.image_processor.merge_size**2 + merge_length_video = self.video_processor.merge_size**2 + + processed_text = [] + for sample in text: + positions = [] + special_tokens = [re.escape(tok) for tok in [self.audio_token, self.image_token, self.video_token]] + pattern = "|".join(special_tokens) + positions = sorted([(match.start(), match.group()) for match in re.finditer(pattern, sample)]) + positions.sort(key=lambda x: x[0]) + + for _, special_token in positions: + if special_token == self.audio_token: + sample = sample.replace(self.audio_token, "<|audio_placeholder|>" * next(audio_lengths), 1) + elif special_token == self.image_token: + image_seq_length = next(image_grid_thw).prod() // merge_length_image + sample = sample.replace(self.image_token, "<|image_placeholder|>" * image_seq_length, 1) + elif special_token == self.video_token: + if not use_audio_in_video: + video_seq_length = next(video_grid_thw).prod() // merge_length_video + sample = sample.replace(self.video_token, "<|video_placeholder|>" * video_seq_length, 1) + else: + audio_token_indices = np.arange(next(audio_lengths)) + curr_video_grid_thw = next(video_grid_thw) + height = curr_video_grid_thw[1] // self.video_processor.merge_size + width = curr_video_grid_thw[2] // self.video_processor.merge_size + video_token_indices = np.arange(curr_video_grid_thw[0]).reshape(-1, 1, 1) + video_token_indices = np.broadcast_to( + video_token_indices, (video_token_indices.shape[0], height, width) + ).reshape(-1) + video_token_indices = ( + video_token_indices * next(video_second_per_grid) * position_id_per_seconds + ) + + tokens_per_chunk = int(position_id_per_seconds * seconds_per_chunk) + video_chunk_indexes = self.get_chunked_index(video_token_indices, tokens_per_chunk) + audio_chunk_indexes = self.get_chunked_index(audio_token_indices, tokens_per_chunk) + + placeholder_string = self.vision_bos_token + self.audio_bos_token + for j in range(max(len(video_chunk_indexes), len(audio_chunk_indexes))): + if j < len(video_chunk_indexes): + video_seq_length = video_chunk_indexes[j][1] - video_chunk_indexes[j][0] + placeholder_string += "<|video_placeholder|>" * video_seq_length + if j < len(audio_chunk_indexes): + audio_seq_length = audio_chunk_indexes[j][1] - audio_chunk_indexes[j][0] + placeholder_string += "<|audio_placeholder|>" * audio_seq_length + placeholder_string += self.audio_eos_token + self.vision_eos_token + sample = sample.replace( + self.vision_bos_token + self.video_token + self.vision_eos_token, + placeholder_string, + 1, + ) + + sample = sample.replace("<|audio_placeholder|>", self.audio_token) + sample = sample.replace("<|image_placeholder|>", self.image_token) + sample = sample.replace("<|video_placeholder|>", self.video_token) + processed_text.append(sample) + return processed_text + + def get_chunked_index(self, token_indices: np.ndarray, tokens_per_chunk: int) -> list[tuple[int, int]]: + """ + Splits token index list into chunks based on token value ranges. + + Given a list of token indices, returns a list of (start, end) index tuples representing + slices of the list where the token values fall within successive ranges of `t_ntoken_per_chunk`. + + For example, if `t_ntoken_per_chunk` is 1000, the function will create chunks such that: + - the first chunk contains token values < 1000, + - the second chunk contains values >= 1000 and < 2000, and so on. + + Parameters: + token_indices (`np.ndarray`): A monotonically increasing list of token index values. + t_ntoken_per_chunk (`int`): Number of tokens per chunk (used as the chunk size threshold). + + Returns: + `list[tuple[int, int]]`: A list of tuples, each representing the start (inclusive) + and end (exclusive) indices of a chunk in `token_indices`. + """ + + def _iter(): + i, start_idx = 0, 0 # skip bos token + current_chunk = 1 + while i < len(token_indices): # skip eos token + if token_indices[i] >= current_chunk * tokens_per_chunk: + yield (start_idx, i) + start_idx = i + current_chunk += 1 + i += 1 + yield (start_idx, len(token_indices)) + + return list(_iter()) + + def post_process_image_text_to_text(self, generated_outputs, skip_special_tokens=True, **kwargs): + """ + Post-process the output of a vlm to decode the text. + + Args: + generated_outputs (`torch.Tensor` or `np.ndarray`): + The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` + or `(sequence_length,)`. + skip_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. + **kwargs: + Additional arguments to be passed to the tokenizer's `batch_decode method`. + + Returns: + `list[str]`: The decoded text. + """ + return self.tokenizer.batch_decode(generated_outputs[0], skip_special_tokens=skip_special_tokens, **kwargs) + + def post_process_multimodal_output( + self, generated_outputs, skip_special_tokens=True, generation_mode=None, **kwargs + ): + """ + Post-process the output of a multimodal model to return the requested modality output. + If the model cannot generated the requested modality, an error will be raised. + + Args: + generated_outputs (`torch.Tensor` or `np.ndarray`): + The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` + or `(sequence_length,)`. + skip_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. + generation_mode (`str`, *optional*): + Generation mode indicated which modality to output and can be one of `["text", "image", "audio"]`. + **kwargs: + Additional arguments to be passed to the tokenizer's `batch_decode method`. + + Returns: + `list[Inion[str, np.ndarray]]`: The decoded text or generated audio. + """ + if generation_mode is None or generation_mode == "text": + return self.post_process_image_text_to_text( + generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs + ) + + elif generation_mode == "audio": + # model supports only bs=1, so we will never get several audio outputs + audio = generated_outputs[1].reshape(-1).detach().cpu().numpy() + return [audio] + + else: + raise ValueError( + f"{self.__class__.__name__} got an unexpected generation_mode={generation_mode}. Supported options are only `text` and `audio" + ) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + feature_extractor_input_names = self.feature_extractor.model_input_names + image_processor_input_names = self.image_processor.model_input_names + video_processor_input_names = self.video_processor.model_input_names + return list( + dict.fromkeys( + tokenizer_input_names + + feature_extractor_input_names + + image_processor_input_names + + video_processor_input_names + + ["feature_attention_mask"] + + ["video_second_per_grid"] + ) + ) + + +__all__ = ["Qwen2_5OmniProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7a9f44a7a05fc65136552948be9acef53e5ea106 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_qwen2_5_vl import * + from .modeling_qwen2_5_vl import * + from .processing_qwen2_5_vl import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/configuration_qwen2_5_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/configuration_qwen2_5_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..385f2ecad05739e8d1d7f9e1567748a2c45b3c5e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/configuration_qwen2_5_vl.py @@ -0,0 +1,209 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qwen2_5_vl.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import inspect + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="Qwen/Qwen2-VL-7B-Instruct") +@strict +class Qwen2_5_VLVisionConfig(PreTrainedConfig): + r""" + tokens_per_second (`int`, *optional*, defaults to 41): + Number of tokens to merge for each second of video. + window_size (`int`, *optional*, defaults to 11): + Size of windows. + out_hidden_size (`int`, *optional*, defaults to 3584): + The output hidden size of the vision model. + fullatt_block_indexes (`int`, *optional*, defaults to `[7, 15, 23, 31]`): + Indices of layers with full attention + """ + + model_type = "qwen2_5_vl_vision" + base_config_key = "vision_config" + + depth: int = 32 + hidden_size: int = 3584 + hidden_act: str = "silu" + intermediate_size: int = 3420 + num_heads: int = 16 + in_channels: int = 3 + patch_size: int | list[int] | tuple[int, int] = 14 + spatial_merge_size: int = 2 + temporal_patch_size: int | list[int] | tuple[int, int] = 2 + tokens_per_second: int = 4 + window_size: int = 112 + out_hidden_size: int = 3584 + fullatt_block_indexes: list[int] | tuple[int, ...] = (7, 15, 23, 31) + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="Qwen/Qwen2-VL-7B-Instruct") +@strict +class Qwen2_5_VLTextConfig(PreTrainedConfig): + r""" + ```python + >>> from transformers import Qwen2_5_VLTextModel, Qwen2_5_VLConfig + + >>> # Initializing a Qwen2_5_VL style configuration + >>> configuration = Qwen2_5_VLConfig() + + >>> # Initializing a model from the Qwen2-VL-7B style configuration + >>> model = Qwen2_5_VLTextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "qwen2_5_vl_text" + base_config_key = "text_config" + keys_to_ignore_at_inference = ["past_key_values"] + default_theta = 1000000.0 + # Default tensor parallel plan for base model `Qwen2_5_VL` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + ignore_keys_at_rope_validation = {"mrope_section"} + + vocab_size: int = 152064 + hidden_size: int = 8192 + intermediate_size: int = 29568 + num_hidden_layers: int = 80 + num_attention_heads: int = 64 + num_key_value_heads: int | None = 8 + hidden_act: str = "silu" + max_position_embeddings: int = 32768 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-05 + use_cache: bool = True + use_sliding_window: bool | None = False + sliding_window: int | None = 4096 + max_window_layers: int | None = 80 + layer_types: list[str] | None = None + attention_dropout: float | int | None = 0.0 + rope_parameters: RopeParameters | dict | None = None + bos_token_id: int | None = 151643 + eos_token_id: int | list[int] | None = 151645 + pad_token_id: int | None = None + + def __post_init__(self, **kwargs): + self.sliding_window = self.sliding_window if self.use_sliding_window else None + + # for backward compatibility + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.layer_types is None: + self.layer_types = [ + "sliding_attention" + if self.sliding_window is not None and i >= self.max_window_layers + else "full_attention" + for i in range(self.num_hidden_layers) + ] + + super().__post_init__(**kwargs) + + def convert_rope_params_to_dict(self, **kwargs): + rope_scaling = kwargs.pop("rope_scaling", None) + self.rope_parameters = rope_scaling or self.rope_parameters + self.rope_parameters = self.rope_parameters if self.rope_parameters is not None else {} + + # Standardize and validate the correctness of rotary position embeddings parameters + self.rope_parameters.setdefault("rope_theta", kwargs.pop("rope_theta", self.default_theta)) + if self.rope_parameters.get("rope_type", self.rope_parameters.get("type")) == "mrope": + self.rope_parameters["rope_type"] = "default" + self.standardize_rope_params() + return kwargs + + +@auto_docstring(checkpoint="Qwen/Qwen2-VL-7B-Instruct") +@strict +class Qwen2_5_VLConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Qwen2_5_VLForConditionalGeneration, Qwen2_5_VLConfig + + >>> # Initializing a Qwen2_5_VL style configuration + >>> configuration = Qwen2_5_VLConfig() + + >>> # Initializing a model from the Qwen2-VL-7B style configuration + >>> model = Qwen2_5_VLForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_5_vl" + sub_configs = {"vision_config": Qwen2_5_VLVisionConfig, "text_config": Qwen2_5_VLTextConfig} + keys_to_ignore_at_inference = ["past_key_values"] + + text_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + image_token_id: int = 151655 + video_token_id: int = 151656 + vision_start_token_id: int = 151652 + vision_end_token_id: int = 151653 + tie_word_embeddings: bool = False + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = self.sub_configs["vision_config"](**self.vision_config) + elif self.vision_config is None: + self.vision_config = self.sub_configs["vision_config"]() + + # Hub configs are saved as flat dicts so we pop some of kwargs to init `TextConfig` + text_params = inspect.signature(self.sub_configs["text_config"].__init__).parameters.keys() + text_params = list(text_params) + ["rope_parameters", "rope_scaling", "rope_theta"] + text_kwargs = {key: kwargs.pop(key) for key in text_params if key in kwargs} + + if isinstance(self.text_config, dict): + self.text_config = self.sub_configs["text_config"](**self.text_config) + elif self.text_config is None: + # Hub configs are saved as flat dicts so we pop some of kwargs to init `TextConfig` + text_kwargs["dtype"] = kwargs.get("torch_dtype", kwargs.get("dtype")) # don't pop the dtype + self.text_config = self.sub_configs["text_config"](**text_kwargs) + + super().__post_init__(**kwargs) + + +__all__ = ["Qwen2_5_VLConfig", "Qwen2_5_VLVisionConfig", "Qwen2_5_VLTextConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..7e7d1538bb0b4d8d38e53d5d61877586f8420426 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py @@ -0,0 +1,1761 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qwen2_5_vl.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import itertools +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check +from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_qwen2_5_vl import Qwen2_5_VLConfig, Qwen2_5_VLTextConfig, Qwen2_5_VLVisionConfig + + +logger = logging.get_logger(__name__) + + +@use_kernel_forward_from_hub("RMSNorm") +class Qwen2_5_VLRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + Qwen2_5_VLRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class Qwen2_5_VLMLP(nn.Module): + def __init__(self, config, bias: bool = False): + super().__init__() + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_state): + return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) + + +class Qwen2_5_VisionPatchEmbed(nn.Module): + def __init__( + self, + patch_size: int | list[int] | tuple[int, int] = 14, + temporal_patch_size: int | list[int] | tuple[int, int] = 2, + in_channels: int = 3, + embed_dim: int = 1152, + ) -> None: + super().__init__() + self.patch_size = patch_size + self.temporal_patch_size = temporal_patch_size + self.in_channels = in_channels + self.embed_dim = embed_dim + + kernel_size = [temporal_patch_size, patch_size, patch_size] + self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + target_dtype = self.proj.weight.dtype + hidden_states = hidden_states.view( + -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size + ) + hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim) + return hidden_states + + +class Qwen2_5_VisionRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, dim: int, theta: float = 10000.0) -> None: + super().__init__() + self.dim = dim + self.theta = theta + inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + + def forward(self, seqlen: int) -> torch.Tensor: + seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype) + freqs = torch.outer(seq, self.inv_freq) + return freqs + + +class Qwen2_5_VLPatchMerger(nn.Module): + def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None: + super().__init__() + self.hidden_size = context_dim * (spatial_merge_size**2) + self.ln_q = Qwen2_5_VLRMSNorm(context_dim, eps=1e-6) + self.mlp = nn.Sequential( + nn.Linear(self.hidden_size, self.hidden_size), + nn.GELU(), + nn.Linear(self.hidden_size, dim), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.mlp(self.ln_q(x).view(-1, self.hidden_size)) + return x + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb_vision( + q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor]: + orig_q_dtype = q.dtype + orig_k_dtype = k.dtype + q, k = q.float(), k.float() + cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float() + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + q_embed = q_embed.to(orig_q_dtype) + k_embed = k_embed.to(orig_k_dtype) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Qwen2_5_VLVisionAttention(nn.Module): + def __init__(self, config: Qwen2_5_VLVisionConfig) -> None: + super().__init__() + self.dim = config.hidden_size + self.num_heads = config.num_heads + self.head_dim = self.dim // self.num_heads + self.num_key_value_groups = 1 # needed for eager attention + self.qkv = nn.Linear(self.dim, self.dim * 3, bias=True) + self.proj = nn.Linear(self.dim, self.dim) + self.scaling = self.head_dim**-0.5 + self.config = config + self.attention_dropout = 0.0 + self.is_causal = False + + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + rotary_pos_emb: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs, + ) -> torch.Tensor: + seq_length = hidden_states.shape[0] + query_states, key_states, value_states = ( + self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0) + ) + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin) + + query_states = query_states.transpose(0, 1).unsqueeze(0) + key_states = key_states.transpose(0, 1).unsqueeze(0) + value_states = value_states.transpose(0, 1).unsqueeze(0) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config): + # Flash Attention: Use cu_seqlens for variable length attention + max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() + attn_output, _ = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + cu_seq_lens_q=cu_seqlens, + cu_seq_lens_k=cu_seqlens, + max_length_q=max_seqlen, + max_length_k=max_seqlen, + is_causal=False, + **kwargs, + ) + else: + # Other implementations: Process each chunk separately + lengths = cu_seqlens[1:] - cu_seqlens[:-1] + splits = [ + torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states) + ] + + attn_outputs = [ + attention_interface( + self, + q, + k, + v, + attention_mask=None, + scaling=self.scaling, + dropout=0.0 if not self.training else self.attention_dropout, + is_causal=False, + **kwargs, + )[0] + for q, k, v in zip(*splits) + ] + attn_output = torch.cat(attn_outputs, dim=1) + + attn_output = attn_output.reshape(seq_length, -1).contiguous() + attn_output = self.proj(attn_output) + return attn_output + + +class Qwen2_5_VLVisionBlock(GradientCheckpointingLayer): + def __init__(self, config, attn_implementation: str = "sdpa") -> None: + super().__init__() + self.norm1 = Qwen2_5_VLRMSNorm(config.hidden_size, eps=1e-6) + self.norm2 = Qwen2_5_VLRMSNorm(config.hidden_size, eps=1e-6) + self.attn = Qwen2_5_VLVisionAttention(config=config) + self.mlp = Qwen2_5_VLMLP(config, bias=True) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + rotary_pos_emb: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs, + ) -> torch.Tensor: + r""" + cu_seqlens (`torch.Tensor`): + Cumulative sequence lengths used for packed variable-length attention in Flash Attention kernels. + rotary_pos_emb (`torch.Tensor`, *optional*): + Precomputed rotary positional embeddings applied to the vision attention query/key states. + """ + hidden_states = hidden_states + self.attn( + self.norm1(hidden_states), + cu_seqlens=cu_seqlens, + rotary_pos_emb=rotary_pos_emb, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) + return hidden_states + + +@auto_docstring +class Qwen2_5_VLPreTrainedModel(PreTrainedModel): + config: Qwen2_5_VLConfig + base_model_prefix = "model" + input_modalities = ("image", "video", "text") + supports_gradient_checkpointing = True + _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn = True + _supports_sdpa = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Qwen2_5_VisionRotaryEmbedding): + inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim)) + init.copy_(module.inv_freq, inv_freq) + + +class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel): + config: Qwen2_5_VLVisionConfig + _no_split_modules = ["Qwen2_5_VLVisionBlock"] + _input_embed_layer = "patch_embed" + _can_record_outputs = { + "hidden_states": Qwen2_5_VLVisionBlock, + "attentions": Qwen2_5_VLVisionAttention, + } + + def __init__(self, config, *inputs, **kwargs) -> None: + super().__init__(config, *inputs, **kwargs) + self.spatial_merge_size = config.spatial_merge_size + self.patch_size = config.patch_size + self.fullatt_block_indexes = config.fullatt_block_indexes + self.window_size = config.window_size + self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size + + self.patch_embed = Qwen2_5_VisionPatchEmbed( + patch_size=config.patch_size, + temporal_patch_size=config.temporal_patch_size, + in_channels=config.in_channels, + embed_dim=config.hidden_size, + ) + + head_dim = config.hidden_size // config.num_heads + self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) + + self.blocks = nn.ModuleList([Qwen2_5_VLVisionBlock(config) for _ in range(config.depth)]) + self.merger = Qwen2_5_VLPatchMerger( + dim=config.out_hidden_size, + context_dim=config.hidden_size, + spatial_merge_size=config.spatial_merge_size, + ) + self.gradient_checkpointing = False + + self.post_init() + + def rot_pos_emb(self, grid_thw): + pos_ids = [] + for t, h, w in grid_thw.tolist(): + hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) + hpos_ids = hpos_ids.reshape( + h // self.spatial_merge_size, + self.spatial_merge_size, + w // self.spatial_merge_size, + self.spatial_merge_size, + ) + hpos_ids = hpos_ids.permute(0, 2, 1, 3) + hpos_ids = hpos_ids.flatten() + + wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) + wpos_ids = wpos_ids.reshape( + h // self.spatial_merge_size, + self.spatial_merge_size, + w // self.spatial_merge_size, + self.spatial_merge_size, + ) + wpos_ids = wpos_ids.permute(0, 2, 1, 3) + wpos_ids = wpos_ids.flatten() + pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) + pos_ids = torch.cat(pos_ids, dim=0) + max_grid_size = grid_thw[:, 1:].max() + rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) + rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) + return rotary_pos_emb + + def get_window_index(self, grid_thw): + window_index: list = [] + cu_window_seqlens: list = [0] + window_index_id = 0 + vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size + grid_thw_list = grid_thw.tolist() + + for grid_t, grid_h, grid_w in grid_thw_list: + llm_grid_h, llm_grid_w = ( + grid_h // self.spatial_merge_size, + grid_w // self.spatial_merge_size, + ) + index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) + pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size + pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size + num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size + num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size + index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) + index_padded = index_padded.reshape( + grid_t, + num_windows_h, + vit_merger_window_size, + num_windows_w, + vit_merger_window_size, + ) + index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( + grid_t, + num_windows_h * num_windows_w, + vit_merger_window_size, + vit_merger_window_size, + ) + seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) + index_padded = index_padded.reshape(-1) + index_new = index_padded[index_padded != -100] + window_index.append(index_new + window_index_id) + cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] + cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) + window_index_id += grid_t * llm_grid_h * llm_grid_w + window_index = torch.cat(window_index, dim=0) + + return window_index, cu_window_seqlens + + @merge_with_config_defaults + @capture_outputs + def forward( + self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithPooling: + """ + Args: + hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): + The final hidden states of the model. + grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`): + The temporal, height and width of feature shape of each image in LLM. + + Returns: + `torch.Tensor`: hidden_states. + """ + hidden_states = self.patch_embed(hidden_states) + rotary_pos_emb = self.rot_pos_emb(grid_thw) + window_index, cu_window_seqlens = self.get_window_index(grid_thw) + cu_window_seqlens = torch.tensor( + cu_window_seqlens, + device=hidden_states.device, + dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) + + seq_len, _ = hidden_states.size() + hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) + hidden_states = hidden_states[window_index, :, :] + hidden_states = hidden_states.reshape(seq_len, -1) + rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) + rotary_pos_emb = rotary_pos_emb[window_index, :, :] + rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) + emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) + position_embeddings = (emb.cos(), emb.sin()) + + cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( + dim=0, + # Select dtype based on the following factors: + # - FA2 requires that cu_seqlens_q must have dtype int32 + # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw + # See https://github.com/huggingface/transformers/pull/34852 for more information + dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) + + for layer_num, blk in enumerate(self.blocks): + if layer_num in self.fullatt_block_indexes: + cu_seqlens_now = cu_seqlens + else: + cu_seqlens_now = cu_window_seqlens + + hidden_states = blk( + hidden_states, + cu_seqlens=cu_seqlens_now, + position_embeddings=position_embeddings, + **kwargs, + ) + + merged_hidden_states = self.merger(hidden_states) + reverse_indices = torch.argsort(window_index) + merged_hidden_states = merged_hidden_states[reverse_indices, :] + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=merged_hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + Base class for Llava outputs, with hidden states and attentions. + """ +) +@dataclass +class Qwen2_5_VLModelOutputWithPast(ModelOutput): + r""" + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + last_hidden_state: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + + +class Qwen2_5_VLRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: Qwen2_5_VLConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: Qwen2_5_VLConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + # Ignore copy + def forward(self, x, position_ids): + # In contrast to other models, Qwen2_5_VL has different position ids for the grids + # So we expand the inv_freq to shape (3, ...) + inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1) + position_ids_expanded = position_ids[:, :, None, :].float() # shape (3, bs, 1, positions) + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +class Qwen2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1): + """Applies Rotary Position Embedding with Multimodal Sections to the query and key tensors (https://qwenlm.github.io/blog/qwen2-vl/). + + Explanation: + Multimodal 3D rotary position embedding is an extension to 1D rotary position embedding. The input embedding + sequence contains vision (images / videos) embedding and text embedding or just contains text embedding. For + vision embedding part, we apply rotary position embedding on temporal, height and width dimension separately. + Here we split the channel dimension to 3 chunks for the temporal, height and width rotary position embedding. + For text embedding part, we just apply 1D rotary position embedding. The three rotary position index (temporal, + height and width) of text embedding is always the same, so the text embedding rotary position embedding has no + difference with modern LLMs. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`): + The position indices of the tokens corresponding to the query and key tensors. For example, this can be + used to pass offsetted position ids when working with a KV-cache. + mrope_section(`List(int)`): + Multimodal rope section is for channel dimension of temporal, height and width in rope calculation. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + mrope_section = mrope_section * 2 + cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze( + unsqueeze_dim + ) + sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze( + unsqueeze_dim + ) + + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +class Qwen2_5_VLAttention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer + and "Generating Long Sequences with Sparse Transformers". + """ + + def __init__(self, config: Qwen2_5_VLTextConfig, layer_idx: int | None = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will " + "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.is_causal = True + self.attention_dropout = config.attention_dropout + self.rope_parameters = config.rope_parameters + self.scaling = self.head_dim**-0.5 + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True) + self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False) + self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None + self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + use_cache: bool = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_multimodal_rotary_pos_emb( + query_states, key_states, cos, sin, self.config.rope_parameters["mrope_section"] + ) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, + position_ids=position_ids, # pass positions for FA2 + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, q_len, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Qwen2_5_VLDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Qwen2_5_VLTextConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + if config.use_sliding_window and not is_flash_attention_requested(config): + logger.warning_once( + f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; " + "unexpected results may be encountered." + ) + self.self_attn = Qwen2_5_VLAttention(config, layer_idx) + + self.mlp = Qwen2MLP(config) + self.input_layernorm = Qwen2_5_VLRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = Qwen2_5_VLRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): attention mask of size + `(batch, sequence_length)` where padding elements are indicated by 0. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_values (`Cache`, *optional*): cached past key and value projection states + position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*): + Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`, + with `head_dim` being the embedding dimension of each attention head. + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + + residual = hidden_states + + hidden_states = self.input_layernorm(hidden_states) + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class Qwen2_5_VLTextModel(Qwen2_5_VLPreTrainedModel): + config: Qwen2_5_VLTextConfig + input_modalities = ("text",) + _can_record_outputs = { + "hidden_states": Qwen2_5_VLDecoderLayer, + "attentions": Qwen2_5_VLAttention, + } + + def __init__(self, config: Qwen2_5_VLTextConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [Qwen2_5_VLDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self._attn_implementation = config._attn_implementation + self.norm = Qwen2_5_VLRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.has_sliding_layers = "sliding_attention" in self.config.layer_types + self.rotary_emb = Qwen2_5_VLRotaryEmbedding(config=config) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + # torch.jit.trace() doesn't support cache objects in the output + if use_cache and past_key_values is None and not torch.jit.is_tracing(): + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + # the hard coded `3` is for temporal, height and width. + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.view(1, 1, -1).expand(3, inputs_embeds.shape[0], -1) + elif position_ids.ndim == 2: + position_ids = position_ids[None, ...].expand(3, position_ids.shape[0], -1) + + # NOTE: we need to pass text position ids for packing. Qwen2-VL uses 3D positions + # where each dim indicates visual spatial positions for temporal/height/width grids. + # There are two scenarios when FA2-like packed masking might be activated. + # 1. User specifically passed packed `position_ids` and no attention mask. + # In this case we expect the useer to create correct position ids for all 3 grids + # and prepend text-only position ids to it. The final tensor will be [4, bs, seq-len] + # 2. User runs forward with no attention mask and no position ids. In this case, position ids + # are prepared by the model (`get_rope_index`) as `[4, bs, seq-len]` tensor. Text-only positions are + # prepended by us when creating positions so that the mask is constructed correctly. NOTE: failing to pass + # text-only positions will cause incorrect mask construction, do not change `prepare_input_for_generation` + if position_ids.ndim == 3 and position_ids.shape[0] == 4: + text_position_ids = position_ids[0] + position_ids = position_ids[1:] + else: + # If inputs are not packed (usual 3D positions), do not prepare mask from position_ids + text_position_ids = None + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": text_position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + } + # The sliding window alternating layers are not always activated depending on the config + if self.has_sliding_layers: + causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for i, decoder_layer in enumerate(self.layers): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[self.config.layer_types[i]], + position_embeddings=position_embeddings, + position_ids=text_position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class Qwen2_5_VLModel(Qwen2_5_VLPreTrainedModel): + base_model_prefix = "model" + # Reference: fix gemma3 grad acc #37208 + accepts_loss_kwargs = False + config: Qwen2_5_VLConfig + _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"] + + def __init__(self, config): + super().__init__(config) + self.visual = Qwen2_5_VisionTransformerPretrainedModel._from_config(config.vision_config) + self.language_model = Qwen2_5_VLTextModel._from_config(config.text_config) + self.rope_deltas = None # cache rope_deltas here + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + def get_vision_position_ids( + self, + start_position: int, + grid_thw: list[int, int, int] | torch.Tensor, + temp_merge_size: int = 1, + spatial_merge_size: int = 1, + time_interval: int = 1, + device: str | torch.device | None = None, + ): + """ + Compute 3D positional indices for vision tokens derived from a single image or video input. + + The positions are generated from the input grid defined by temporal (T), height (H), and + width (W) dimensions. Temporal and spatial dimensions can be downscaled according to the + merge sizes used in the vision backbone. The resulting positions are offset by `start_position`. + + Args: + start_position (`int`): + Offset added to all computed positional indices. + grid_thw (`Sequence[int]` or `torch.Tensor` of shape `(3,)`): + The (T, H, W) grid representing the feature layout of the current image or video after patch embedding. + temp_merge_size (`int`, *optional*): + Factor by which the temporal dimension is reduced in the backbone. The temporal grid size is divided + by this value. Defaults to 1. + spatial_merge_size (`int`, *optional*): + Factor by which the spatial dimensions (H and W) are reduced in the backbone. Both H and W are divided + by this value. Defaults to 1. + time_interval (`int`, *optional*): + Spacing factor applied between consecutive temporal position indices.Defaults to 1. + device (`str` or `torch.device`, *optional*): + Device on which the resulting tensor is allocated. If `None`, uses the current default device. + + Returns: + torch.LongTensor of shape (3, sequence_length): + Positional indices for temporal, height, and width dimensions, + flattened into sequence form and offset by `start_position`. + """ + llm_grid_t, llm_grid_h, llm_grid_w = ( + grid_thw[0].item() // temp_merge_size, + grid_thw[1].item() // spatial_merge_size, + grid_thw[2].item() // spatial_merge_size, + ) + + # Add `start_position` after arange for compile + position_temporal = torch.arange(llm_grid_t, device=device) * time_interval + position_width = torch.arange(llm_grid_w, device=device) + start_position + position_height = torch.arange(llm_grid_h, device=device) + start_position + + # Repeat the positions per each grid and per video frame. Repeat patterns are important + # do not modify without checking values! + position_width = position_width.repeat(llm_grid_h * llm_grid_t) + position_height = position_height.repeat_interleave(llm_grid_w).repeat(llm_grid_t) + # Important: add `start_positions` after applying `time_interval`, order matters + position_temporal = position_temporal.repeat_interleave(llm_grid_h * llm_grid_w) + start_position + vision_position_ids = torch.stack([position_temporal, position_height, position_width], dim=0) + + return vision_position_ids + + def get_rope_index( + self, + input_ids: torch.LongTensor, + mm_token_type_ids: torch.IntTensor, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + second_per_grid_ts: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Calculate the 3D rope index based on image and video's sizes. The utility expects a `vision + text` + sequence and will error out otherwise. For pure text sequence, please rely on model's auto-inferred + position ids. In a mixed vision + text sequence, vision tokens use 3D RoPE (temporal, height, width) + while text tokens use standard 1D RoPE. + + Example: + Temporal patches: 3; Height patches: 2; Width patches: 2 + Each vision input results in (temporal x height × width) positions. Here: 3 x 2 × 2 = 12 positions total. + + Temporal position IDs are spaced by: + `interval = tokens_per_second * temporal_patch_size / fps` + + If fps = 1; tokens_per_second = 25; temporal_patch_size = 2, temporal IDs increase by 50 for each temporal patch: + `[0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100]` + + Height IDs repeat per row: `[0, 0, 1, 1, ...]` + Width IDs alternate per column: `[0, 1, 0, 1, ...]` + Text tokens follow standard 1D RoPE and the position IDs grow consequently with a step of `1` + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + mm_token_type_ids (`torch.IntTensor` of shape `(batch_size, sequence_length)`): + Token type ids matching each modality to a different value in the input sequence, i.e. text (0), image (1), video (2). + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + Returns: + position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) + mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) + """ + spatial_merge_size = self.config.vision_config.spatial_merge_size + tokens_per_second = self.config.vision_config.tokens_per_second + + mrope_position_deltas = [] + position_ids = torch.zeros( + 3, + input_ids.shape[0], + input_ids.shape[1], + dtype=input_ids.dtype, + device=input_ids.device, + ) + grid_iters = { + 1: iter(image_grid_thw) if image_grid_thw is not None else None, + 2: iter(video_grid_thw) if video_grid_thw is not None else None, + } + second_per_grid_ts = ( + iter(second_per_grid_ts) if second_per_grid_ts is not None else iter([1] * input_ids.shape[1]) + ) + for batch_idx, current_input_ids in enumerate(input_ids): + input_token_type = mm_token_type_ids[batch_idx] + if attention_mask is not None: + current_input_ids = current_input_ids[attention_mask[batch_idx].bool()] + input_token_type = input_token_type[attention_mask[batch_idx].bool()] + + input_type_group = [] + for key, group in itertools.groupby(enumerate(input_token_type.tolist()), lambda x: x[1]): + group = list(group) + start_index = group[0][0] + end_index = group[-1][0] + 1 + input_type_group.append((key, start_index, end_index)) + + current_pos = 0 + llm_pos_ids_list = [] + for modality_type, start_idx, end_idx in input_type_group: + # text == 0 + if modality_type == 0: + text_len = end_idx - start_idx + llm_pos_ids_list.append( + torch.arange(text_len, device=input_ids.device).view(1, -1).expand(3, -1) + current_pos + ) + current_pos += text_len + # image == 1, video == 2 + else: + grid_thw = next(grid_iters[modality_type]) + # Only apply temporal scaling for videos; still images have no + # temporal dimension to space out (fixes #45325). + if modality_type == 2: + time_interval = tokens_per_second * int(next(second_per_grid_ts)) + else: + time_interval = 1 + vision_position_ids = self.get_vision_position_ids( + current_pos, grid_thw, 1, spatial_merge_size, time_interval, device=input_ids.device + ) + llm_pos_ids_list.append(vision_position_ids) + current_pos += max(grid_thw[1], grid_thw[2]) // spatial_merge_size + llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) + if attention_mask is not None: + position_ids[:, batch_idx, attention_mask[batch_idx].bool()] = llm_positions.to(position_ids.device) + else: + position_ids[:, batch_idx] = llm_positions.to(position_ids.device) + mrope_position_deltas.append(llm_positions.max() + 1 - len(current_input_ids)) + mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1) + return position_ids, mrope_position_deltas + + @can_return_tuple + @auto_docstring + def get_video_features( + self, + pixel_values_videos: torch.FloatTensor, + video_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input videos. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + """ + pixel_values_videos = pixel_values_videos.type(self.visual.dtype) + vision_outputs = self.visual(pixel_values_videos, grid_thw=video_grid_thw, **kwargs) + split_sizes = (video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist() + video_embeds = torch.split(vision_outputs.pooler_output, split_sizes) + vision_outputs.pooler_output = video_embeds + + return vision_outputs + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + pixel_values = pixel_values.type(self.visual.dtype) + vision_outputs = self.visual(pixel_values, grid_thw=image_grid_thw, **kwargs) + split_sizes = (image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist() + image_embeds = torch.split(vision_outputs.pooler_output, split_sizes) + vision_outputs.pooler_output = image_embeds + + return vision_outputs + + def get_placeholder_mask( + self, + input_ids: torch.LongTensor, + inputs_embeds: torch.FloatTensor, + image_features: torch.FloatTensor | None = None, + video_features: torch.FloatTensor | None = None, + ): + """ + Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is + equal to the length of multimodal features. If the lengths are different, an error is raised. + """ + if input_ids is None: + special_image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_image_mask = special_image_mask.all(-1) + special_video_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + special_video_mask = special_video_mask.all(-1) + else: + special_image_mask = input_ids == self.config.image_token_id + special_video_mask = input_ids == self.config.video_token_id + + n_image_tokens = special_image_mask.sum() + special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if image_features is not None: + torch_compilable_check( + inputs_embeds[special_image_mask].numel() == image_features.numel(), + f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}", + ) + + n_video_tokens = special_video_mask.sum() + special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device) + if video_features is not None: + torch_compilable_check( + inputs_embeds[special_video_mask].numel() == video_features.numel(), + f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}", + ) + return special_image_mask, special_video_mask + + def compute_3d_position_ids( + self, + input_ids: torch.Tensor | None, + image_grid_thw: torch.Tensor | None, + video_grid_thw: torch.Tensor | None, + inputs_embeds: torch.Tensor | None, + attention_mask: torch.Tensor | None, + past_key_values: torch.Tensor | None, + second_per_grid_ts: torch.Tensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + ) -> torch.Tensor | None: + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + can_compute_mrope = ( + input_ids is not None + and mm_token_type_ids is not None + and (image_grid_thw is not None or video_grid_thw is not None) + ) + + if can_compute_mrope and (self.rope_deltas is None or past_key_values_length == 0): + position_ids, rope_deltas = self.get_rope_index( + input_ids, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + attention_mask=attention_mask, + second_per_grid_ts=second_per_grid_ts, + mm_token_type_ids=mm_token_type_ids, + ) + self.rope_deltas = rope_deltas + # Use pre-calculated rope-deltas to infer correct 3D position ids during incremental + # generation (past_key_values_length > 0) or when only inputs_embeds is provided (no input_ids + # to recompute from). Skip when input_ids is provided without past_key_values to avoid shape + # mismatches from stale rope_deltas (e.g., training forward pass after generation). + elif self.rope_deltas is not None and (past_key_values_length > 0 or input_ids is None): + batch_size, seq_length, _ = inputs_embeds.shape + if attention_mask is not None: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids = position_ids.masked_fill(attention_mask == 0, 0) + position_ids = position_ids.view(1, batch_size, -1).repeat(3, 1, 1).to(inputs_embeds.device) + else: + position_ids = torch.arange(past_key_values_length, past_key_values_length + seq_length) + position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1).to(inputs_embeds.device) + delta = self.rope_deltas.repeat_interleave(batch_size // self.rope_deltas.shape[0], dim=0) + position_ids = position_ids + delta.to(device=position_ids.device) + else: + # Can't build correct 3D positions. Let the model infer it + position_ids = None + return position_ids + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + pixel_values: torch.Tensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + second_per_grid_ts: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Qwen2_5_VLModelOutputWithPast: + r""" + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + """ + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw).pooler_output + image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) + + if pixel_values_videos is not None: + video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw).pooler_output + video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + _, video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) + + if position_ids is None: + position_ids = self.compute_3d_position_ids( + input_ids=input_ids, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + mm_token_type_ids=mm_token_type_ids, + ) + + outputs = self.language_model( + input_ids=None, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Qwen2_5_VLModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + ) + + +@auto_docstring( + custom_intro=""" + Base class for Qwen2_5_VL causal language model (or autoregressive) outputs. + """ +) +@dataclass +class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + rope_deltas: torch.LongTensor | None = None + + +class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"} + # Reference: fix gemma3 grad acc #37208 + accepts_loss_kwargs = False + + def __init__(self, config): + super().__init__(config) + self.model = Qwen2_5_VLModel(config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + + self.post_init() + + def get_input_embeddings(self): + return self.model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.set_input_embeddings(value) + + @auto_docstring + def get_video_features( + self, + pixel_values_videos: torch.FloatTensor, + video_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input videos. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + """ + return self.model.get_video_features( + pixel_values_videos=pixel_values_videos, video_grid_thw=video_grid_thw, **kwargs + ) + + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + image_grid_thw: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + """ + return self.model.get_image_features(pixel_values=pixel_values, image_grid_thw=image_grid_thw, **kwargs) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + pixel_values: torch.Tensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + second_per_grid_ts: torch.Tensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Qwen2_5_VLCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + + Example: + + ```python + >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration + + >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") + >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") + + >>> messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg", + }, + {"type": "text", "text": "Describe the image."}, + ], + } + ] + + >>> inputs = processor.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_dict=True, + return_tensors="pt" + ) + + >>> # Generate + >>> generated_ids = model.generate(**inputs, max_new_tokens=1024) + >>> generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)] + >>> output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + >>> print(output_text) + ``` + """ + + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + mm_token_type_ids=mm_token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return Qwen2_5_VLCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=outputs.rope_deltas, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + pixel_values=None, + pixel_values_videos=None, + image_grid_thw=None, + video_grid_thw=None, + second_per_grid_ts=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + use_cache=use_cache, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + if not is_first_iteration and use_cache: + model_inputs["pixel_values"] = None + model_inputs["pixel_values_videos"] = None + + return model_inputs + + def _prepare_position_ids_for_generation(self, inputs_tensor, model_kwargs): + # Overwritten -- requires 3D position ids + + text_positions = super()._prepare_position_ids_for_generation(inputs_tensor, model_kwargs) + + # Early exit in case we are continuing generation from past kv + past_length = 0 + if (cache := model_kwargs.get("past_key_values")) is not None: + past_length = cache.get_seq_length() + if past_length != 0 and self.model.rope_deltas is not None: + position_ids = text_positions[None, ...] + self.model.rope_deltas + return position_ids + + # Otherwise compute 3d position ids for vision tokens and concat with text position ids + if "input_ids" in model_kwargs and model_kwargs["input_ids"].shape[1] > 0: + inputs_tensor = model_kwargs["input_ids"] + + is_input_ids = len(inputs_tensor.shape) == 2 and inputs_tensor.dtype in [torch.int, torch.long] + if ( + is_input_ids + and model_kwargs.get("mm_token_type_ids") is not None + and (model_kwargs.get("image_grid_thw") is not None or model_kwargs.get("video_grid_thw") is not None) + ): + model_kwargs = {k: v for k, v in model_kwargs.items() if k != "input_ids"} + vision_positions, rope_deltas = self.model.get_rope_index(inputs_tensor, **model_kwargs) + self.model.rope_deltas = rope_deltas + else: + vision_positions = text_positions.unsqueeze(0).expand(3, -1, -1) + self.model.rope_deltas = torch.zeros( + inputs_tensor.shape[0], 1, dtype=torch.long, device=inputs_tensor.device + ) + + # Concatenate "text + vision" positions into [4, bs, seq-len] + text_positions = text_positions[None, ...] + position_ids = torch.cat([text_positions, vision_positions], dim=0) + + return position_ids + + def _get_image_nums_and_video_nums( + self, + input_ids: torch.LongTensor | None, + inputs_embeds: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Get the number of images and videos for each sample to calculate the separation length of the sample tensor. + These parameters are not passed through the processor to avoid unpredictable impacts from interface modifications. + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Returns: + image_nums (`torch.LongTensor` of shape `(batch_size, num_images_sample)`) + video_nums (`torch.LongTensor` of shape `(batch_size, num_videos_sample)`) + """ + image_token_id = self.config.image_token_id + video_token_id = self.config.video_token_id + vision_start_token_id = self.config.vision_start_token_id + + if inputs_embeds is not None: + vision_start_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(vision_start_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + image_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + video_mask = ( + inputs_embeds + == self.get_input_embeddings()( + torch.tensor(video_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + )[..., 0] + else: + vision_start_mask = input_ids == vision_start_token_id + image_mask = input_ids == image_token_id + video_mask = input_ids == video_token_id + + vision_first_mask = torch.roll(vision_start_mask, shifts=1, dims=1) + image_nums = torch.sum(vision_first_mask & image_mask, dim=1) + video_nums = torch.sum(vision_first_mask & video_mask, dim=1) + + return image_nums, video_nums + + def _expand_inputs_for_generation( + self, + expand_size: int = 1, + is_encoder_decoder: bool = False, + input_ids: torch.LongTensor | None = None, + **model_kwargs, + ) -> tuple[torch.LongTensor, dict[str, Any]]: + # Overwritten -- Support for expanding tensors without a batch size dimension + # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw, second_per_grid_t + # pixel_values.shape[0] is sum(seqlen_images for samples) + # image_grid_thw.shape[0] is sum(num_images for samples) + + if expand_size == 1: + return input_ids, model_kwargs + + visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw", "second_per_grid_ts"] + + def _expand_dict_for_generation_visual(dict_to_expand): + image_grid_thw = model_kwargs.get("image_grid_thw", None) + video_grid_thw = model_kwargs.get("video_grid_thw", None) + image_nums, video_nums = self._get_image_nums_and_video_nums( + input_ids, inputs_embeds=model_kwargs.get("inputs_embeds", None) + ) + + def _repeat_interleave_samples(x, lengths, repeat_times): + samples = torch.split(x, lengths) + repeat_args = [repeat_times] + [1] * (x.dim() - 1) + result = torch.cat([sample.repeat(*repeat_args) for sample in samples], dim=0) + return result + + for key in dict_to_expand: + if key == "pixel_values": + # split images into samples + samples = torch.split(image_grid_thw, list(image_nums)) + # compute the sequence length of images for each sample + lengths = [torch.prod(sample, dim=1).sum() for sample in samples] + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "image_grid_thw": + # get the num of images for each sample + lengths = list(image_nums) + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "pixel_values_videos": + samples = torch.split(video_grid_thw, list(video_nums)) + lengths = [torch.prod(sample, dim=1).sum() for sample in samples] + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "video_grid_thw": + lengths = list(video_nums) + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=lengths, repeat_times=expand_size + ) + elif key == "second_per_grid_ts": + dict_to_expand[key] = _repeat_interleave_samples( + dict_to_expand[key], lengths=list(video_nums), repeat_times=expand_size + ) + return dict_to_expand + + def _expand_dict_for_generation(dict_to_expand): + for key in dict_to_expand: + if key == "position_ids" and dict_to_expand[key].ndim == 3: + dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=1) + elif ( + dict_to_expand[key] is not None + and isinstance(dict_to_expand[key], torch.Tensor) + and key not in visual_keys + ): + dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0) + return dict_to_expand + + model_kwargs = _expand_dict_for_generation_visual(model_kwargs) + + if input_ids is not None: + input_ids = input_ids.repeat_interleave(expand_size, dim=0) + + model_kwargs = _expand_dict_for_generation(model_kwargs) + + if is_encoder_decoder: + if model_kwargs.get("encoder_outputs") is None: + raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.") + model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"]) + + return input_ids, model_kwargs + + +__all__ = ["Qwen2_5_VLForConditionalGeneration", "Qwen2_5_VLModel", "Qwen2_5_VLPreTrainedModel", "Qwen2_5_VLTextModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..038209892b6db4b3fa8d6f76068d6f6362ad64f0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py @@ -0,0 +1,933 @@ +# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Qwen2.5-VL model.""" + +import itertools + +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...configuration_utils import PreTrainedConfig +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPooling +from ...modeling_utils import PreTrainedModel +from ...processing_utils import MultiModalData, ProcessingKwargs, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring, can_return_tuple, logging +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ...video_utils import VideoInput +from ..llama.modeling_llama import LlamaRMSNorm +from ..qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig, Qwen2VLTextConfig +from ..qwen2_vl.modeling_qwen2_vl import ( + PatchEmbed, + PatchMerger, + Qwen2VLCausalLMOutputWithPast, + Qwen2VLForConditionalGeneration, + Qwen2VLModel, + Qwen2VLModelOutputWithPast, + Qwen2VLPreTrainedModel, + TransformersKwargs, + VisionAttention, + VisionRotaryEmbedding, +) +from ..qwen2_vl.processing_qwen2_vl import Qwen2VLProcessor + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="Qwen/Qwen2-VL-7B-Instruct") +@strict +class Qwen2_5_VLVisionConfig(PreTrainedConfig): + r""" + tokens_per_second (`int`, *optional*, defaults to 41): + Number of tokens to merge for each second of video. + window_size (`int`, *optional*, defaults to 11): + Size of windows. + out_hidden_size (`int`, *optional*, defaults to 3584): + The output hidden size of the vision model. + fullatt_block_indexes (`int`, *optional*, defaults to `[7, 15, 23, 31]`): + Indices of layers with full attention + """ + + model_type = "qwen2_5_vl_vision" + base_config_key = "vision_config" + + depth: int = 32 + hidden_size: int = 3584 + hidden_act: str = "silu" + intermediate_size: int = 3420 + num_heads: int = 16 + in_channels: int = 3 + patch_size: int | list[int] | tuple[int, int] = 14 + spatial_merge_size: int = 2 + temporal_patch_size: int | list[int] | tuple[int, int] = 2 + tokens_per_second: int = 4 + window_size: int = 112 + out_hidden_size: int = 3584 + fullatt_block_indexes: list[int] | tuple[int, ...] = (7, 15, 23, 31) + initializer_range: float = 0.02 + + +class Qwen2_5_VLTextConfig(Qwen2VLTextConfig): + pass + + +class Qwen2_5_VLConfig(Qwen2VLConfig): + pass + + +class Qwen2_5_VLRMSNorm(LlamaRMSNorm): + pass + + +class Qwen2_5_VLMLP(nn.Module): + def __init__(self, config, bias: bool = False): + super().__init__() + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, hidden_state): + return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state)) + + +class Qwen2_5_VisionPatchEmbed(PatchEmbed): + pass + + +class Qwen2_5_VisionRotaryEmbedding(VisionRotaryEmbedding): + pass + + +class Qwen2_5_VLPatchMerger(PatchMerger): + def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None: + super().__init__(dim, context_dim, spatial_merge_size) + self.ln_q = Qwen2_5_VLRMSNorm(context_dim, eps=1e-6) + + +class Qwen2_5_VLVisionAttention(VisionAttention): + def __init__(self, config: Qwen2_5_VLVisionConfig) -> None: + super().__init__(config) + self.dim = config.hidden_size + + +class Qwen2_5_VLVisionBlock(GradientCheckpointingLayer): + def __init__(self, config, attn_implementation: str = "sdpa") -> None: + super().__init__() + self.norm1 = Qwen2_5_VLRMSNorm(config.hidden_size, eps=1e-6) + self.norm2 = Qwen2_5_VLRMSNorm(config.hidden_size, eps=1e-6) + self.attn = Qwen2_5_VLVisionAttention(config=config) + self.mlp = Qwen2_5_VLMLP(config, bias=True) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + cu_seqlens: torch.Tensor, + rotary_pos_emb: torch.Tensor | None = None, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs, + ) -> torch.Tensor: + r""" + cu_seqlens (`torch.Tensor`): + Cumulative sequence lengths used for packed variable-length attention in Flash Attention kernels. + rotary_pos_emb (`torch.Tensor`, *optional*): + Precomputed rotary positional embeddings applied to the vision attention query/key states. + """ + hidden_states = hidden_states + self.attn( + self.norm1(hidden_states), + cu_seqlens=cu_seqlens, + rotary_pos_emb=rotary_pos_emb, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) + return hidden_states + + +class Qwen2_5_VLPreTrainedModel(Qwen2VLPreTrainedModel): + def _init_weights(self, module): + PreTrainedModel._init_weights(self, module) + if isinstance(module, Qwen2_5_VisionRotaryEmbedding): + inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim)) + init.copy_(module.inv_freq, inv_freq) + + +class Qwen2_5_VisionTransformerPretrainedModel(Qwen2_5_VLPreTrainedModel): + config: Qwen2_5_VLVisionConfig + _no_split_modules = ["Qwen2_5_VLVisionBlock"] + _input_embed_layer = "patch_embed" + _can_record_outputs = { + "hidden_states": Qwen2_5_VLVisionBlock, + "attentions": Qwen2_5_VLVisionAttention, + } + + def __init__(self, config, *inputs, **kwargs) -> None: + super().__init__(config, *inputs, **kwargs) + self.spatial_merge_size = config.spatial_merge_size + self.patch_size = config.patch_size + self.fullatt_block_indexes = config.fullatt_block_indexes + self.window_size = config.window_size + self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size + + self.patch_embed = Qwen2_5_VisionPatchEmbed( + patch_size=config.patch_size, + temporal_patch_size=config.temporal_patch_size, + in_channels=config.in_channels, + embed_dim=config.hidden_size, + ) + + head_dim = config.hidden_size // config.num_heads + self.rotary_pos_emb = Qwen2_5_VisionRotaryEmbedding(head_dim // 2) + + self.blocks = nn.ModuleList([Qwen2_5_VLVisionBlock(config) for _ in range(config.depth)]) + self.merger = Qwen2_5_VLPatchMerger( + dim=config.out_hidden_size, + context_dim=config.hidden_size, + spatial_merge_size=config.spatial_merge_size, + ) + self.gradient_checkpointing = False + + self.post_init() + + def rot_pos_emb(self, grid_thw): + pos_ids = [] + for t, h, w in grid_thw.tolist(): + hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) + hpos_ids = hpos_ids.reshape( + h // self.spatial_merge_size, + self.spatial_merge_size, + w // self.spatial_merge_size, + self.spatial_merge_size, + ) + hpos_ids = hpos_ids.permute(0, 2, 1, 3) + hpos_ids = hpos_ids.flatten() + + wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) + wpos_ids = wpos_ids.reshape( + h // self.spatial_merge_size, + self.spatial_merge_size, + w // self.spatial_merge_size, + self.spatial_merge_size, + ) + wpos_ids = wpos_ids.permute(0, 2, 1, 3) + wpos_ids = wpos_ids.flatten() + pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) + pos_ids = torch.cat(pos_ids, dim=0) + max_grid_size = grid_thw[:, 1:].max() + rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) + rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) + return rotary_pos_emb + + def get_window_index(self, grid_thw): + window_index: list = [] + cu_window_seqlens: list = [0] + window_index_id = 0 + vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size + grid_thw_list = grid_thw.tolist() + + for grid_t, grid_h, grid_w in grid_thw_list: + llm_grid_h, llm_grid_w = ( + grid_h // self.spatial_merge_size, + grid_w // self.spatial_merge_size, + ) + index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w) + pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size + pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size + num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size + num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size + index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) + index_padded = index_padded.reshape( + grid_t, + num_windows_h, + vit_merger_window_size, + num_windows_w, + vit_merger_window_size, + ) + index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( + grid_t, + num_windows_h * num_windows_w, + vit_merger_window_size, + vit_merger_window_size, + ) + seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) + index_padded = index_padded.reshape(-1) + index_new = index_padded[index_padded != -100] + window_index.append(index_new + window_index_id) + cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] + cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) + window_index_id += grid_t * llm_grid_h * llm_grid_w + window_index = torch.cat(window_index, dim=0) + + return window_index, cu_window_seqlens + + @merge_with_config_defaults + @capture_outputs + def forward( + self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | BaseModelOutputWithPooling: + """ + Args: + hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`): + The final hidden states of the model. + grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`): + The temporal, height and width of feature shape of each image in LLM. + + Returns: + `torch.Tensor`: hidden_states. + """ + hidden_states = self.patch_embed(hidden_states) + rotary_pos_emb = self.rot_pos_emb(grid_thw) + window_index, cu_window_seqlens = self.get_window_index(grid_thw) + cu_window_seqlens = torch.tensor( + cu_window_seqlens, + device=hidden_states.device, + dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) + + seq_len, _ = hidden_states.size() + hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) + hidden_states = hidden_states[window_index, :, :] + hidden_states = hidden_states.reshape(seq_len, -1) + rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1) + rotary_pos_emb = rotary_pos_emb[window_index, :, :] + rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) + emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) + position_embeddings = (emb.cos(), emb.sin()) + + cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( + dim=0, + # Select dtype based on the following factors: + # - FA2 requires that cu_seqlens_q must have dtype int32 + # - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw + # See https://github.com/huggingface/transformers/pull/34852 for more information + dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32, + ) + cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0) + + for layer_num, blk in enumerate(self.blocks): + if layer_num in self.fullatt_block_indexes: + cu_seqlens_now = cu_seqlens + else: + cu_seqlens_now = cu_window_seqlens + + hidden_states = blk( + hidden_states, + cu_seqlens=cu_seqlens_now, + position_embeddings=position_embeddings, + **kwargs, + ) + + merged_hidden_states = self.merger(hidden_states) + reverse_indices = torch.argsort(window_index) + merged_hidden_states = merged_hidden_states[reverse_indices, :] + + return BaseModelOutputWithPooling( + last_hidden_state=hidden_states, + pooler_output=merged_hidden_states, + ) + + +class Qwen2_5_VLModelOutputWithPast(Qwen2VLModelOutputWithPast): + pass + + +class Qwen2_5_VLModel(Qwen2VLModel): + config: Qwen2_5_VLConfig + base_model_prefix = "model" + _no_split_modules = ["Qwen2_5_VLDecoderLayer", "Qwen2_5_VLVisionBlock"] + # Reference: fix gemma3 grad acc #37208 + accepts_loss_kwargs = False + + def __init__(self, config): + super().__init__(config) + self.visual = Qwen2_5_VisionTransformerPretrainedModel._from_config(config.vision_config) + + def get_rope_index( + self, + input_ids: torch.LongTensor, + mm_token_type_ids: torch.IntTensor, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + second_per_grid_ts: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Calculate the 3D rope index based on image and video's sizes. The utility expects a `vision + text` + sequence and will error out otherwise. For pure text sequence, please rely on model's auto-inferred + position ids. In a mixed vision + text sequence, vision tokens use 3D RoPE (temporal, height, width) + while text tokens use standard 1D RoPE. + + Example: + Temporal patches: 3; Height patches: 2; Width patches: 2 + Each vision input results in (temporal x height × width) positions. Here: 3 x 2 × 2 = 12 positions total. + + Temporal position IDs are spaced by: + `interval = tokens_per_second * temporal_patch_size / fps` + + If fps = 1; tokens_per_second = 25; temporal_patch_size = 2, temporal IDs increase by 50 for each temporal patch: + `[0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100]` + + Height IDs repeat per row: `[0, 0, 1, 1, ...]` + Width IDs alternate per column: `[0, 1, 0, 1, ...]` + Text tokens follow standard 1D RoPE and the position IDs grow consequently with a step of `1` + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + mm_token_type_ids (`torch.IntTensor` of shape `(batch_size, sequence_length)`): + Token type ids matching each modality to a different value in the input sequence, i.e. text (0), image (1), video (2). + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + Returns: + position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`) + mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`) + """ + spatial_merge_size = self.config.vision_config.spatial_merge_size + tokens_per_second = self.config.vision_config.tokens_per_second + + mrope_position_deltas = [] + position_ids = torch.zeros( + 3, + input_ids.shape[0], + input_ids.shape[1], + dtype=input_ids.dtype, + device=input_ids.device, + ) + grid_iters = { + 1: iter(image_grid_thw) if image_grid_thw is not None else None, + 2: iter(video_grid_thw) if video_grid_thw is not None else None, + } + second_per_grid_ts = ( + iter(second_per_grid_ts) if second_per_grid_ts is not None else iter([1] * input_ids.shape[1]) + ) + for batch_idx, current_input_ids in enumerate(input_ids): + input_token_type = mm_token_type_ids[batch_idx] + if attention_mask is not None: + current_input_ids = current_input_ids[attention_mask[batch_idx].bool()] + input_token_type = input_token_type[attention_mask[batch_idx].bool()] + + input_type_group = [] + for key, group in itertools.groupby(enumerate(input_token_type.tolist()), lambda x: x[1]): + group = list(group) + start_index = group[0][0] + end_index = group[-1][0] + 1 + input_type_group.append((key, start_index, end_index)) + + current_pos = 0 + llm_pos_ids_list = [] + for modality_type, start_idx, end_idx in input_type_group: + # text == 0 + if modality_type == 0: + text_len = end_idx - start_idx + llm_pos_ids_list.append( + torch.arange(text_len, device=input_ids.device).view(1, -1).expand(3, -1) + current_pos + ) + current_pos += text_len + # image == 1, video == 2 + else: + grid_thw = next(grid_iters[modality_type]) + # Only apply temporal scaling for videos; still images have no + # temporal dimension to space out (fixes #45325). + if modality_type == 2: + time_interval = tokens_per_second * int(next(second_per_grid_ts)) + else: + time_interval = 1 + vision_position_ids = self.get_vision_position_ids( + current_pos, grid_thw, 1, spatial_merge_size, time_interval, device=input_ids.device + ) + llm_pos_ids_list.append(vision_position_ids) + current_pos += max(grid_thw[1], grid_thw[2]) // spatial_merge_size + llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1) + if attention_mask is not None: + position_ids[:, batch_idx, attention_mask[batch_idx].bool()] = llm_positions.to(position_ids.device) + else: + position_ids[:, batch_idx] = llm_positions.to(position_ids.device) + mrope_position_deltas.append(llm_positions.max() + 1 - len(current_input_ids)) + mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1) + return position_ids, mrope_position_deltas + + def compute_3d_position_ids( + self, + input_ids: torch.Tensor | None, + image_grid_thw: torch.Tensor | None, + video_grid_thw: torch.Tensor | None, + inputs_embeds: torch.Tensor | None, + attention_mask: torch.Tensor | None, + past_key_values: torch.Tensor | None, + second_per_grid_ts: torch.Tensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + ) -> torch.Tensor | None: + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + can_compute_mrope = ( + input_ids is not None + and mm_token_type_ids is not None + and (image_grid_thw is not None or video_grid_thw is not None) + ) + + if can_compute_mrope and (self.rope_deltas is None or past_key_values_length == 0): + position_ids, rope_deltas = self.get_rope_index( + input_ids, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + attention_mask=attention_mask, + second_per_grid_ts=second_per_grid_ts, + mm_token_type_ids=mm_token_type_ids, + ) + self.rope_deltas = rope_deltas + # Use pre-calculated rope-deltas to infer correct 3D position ids during incremental + # generation (past_key_values_length > 0) or when only inputs_embeds is provided (no input_ids + # to recompute from). Skip when input_ids is provided without past_key_values to avoid shape + # mismatches from stale rope_deltas (e.g., training forward pass after generation). + elif self.rope_deltas is not None and (past_key_values_length > 0 or input_ids is None): + batch_size, seq_length, _ = inputs_embeds.shape + if attention_mask is not None: + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids = position_ids.masked_fill(attention_mask == 0, 0) + position_ids = position_ids.view(1, batch_size, -1).repeat(3, 1, 1).to(inputs_embeds.device) + else: + position_ids = torch.arange(past_key_values_length, past_key_values_length + seq_length) + position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1).to(inputs_embeds.device) + delta = self.rope_deltas.repeat_interleave(batch_size // self.rope_deltas.shape[0], dim=0) + position_ids = position_ids + delta.to(device=position_ids.device) + else: + # Can't build correct 3D positions. Let the model infer it + position_ids = None + return position_ids + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + pixel_values: torch.Tensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + second_per_grid_ts: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Qwen2_5_VLModelOutputWithPast: + r""" + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + """ + + if inputs_embeds is None: + inputs_embeds = self.get_input_embeddings()(input_ids) + + if pixel_values is not None: + image_embeds = self.get_image_features(pixel_values, image_grid_thw).pooler_output + image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + image_mask, _ = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds) + + if pixel_values_videos is not None: + video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw).pooler_output + video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype) + _, video_mask = self.get_placeholder_mask( + input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds + ) + inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds) + + if position_ids is None: + position_ids = self.compute_3d_position_ids( + input_ids=input_ids, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + mm_token_type_ids=mm_token_type_ids, + ) + + outputs = self.language_model( + input_ids=None, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + return Qwen2_5_VLModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=self.rope_deltas, + ) + + +class Qwen2_5_VLCausalLMOutputWithPast(Qwen2VLCausalLMOutputWithPast): + pass + + +class Qwen2_5_VLForConditionalGeneration(Qwen2VLForConditionalGeneration): + # Reference: fix gemma3 grad acc #37208 + accepts_loss_kwargs = False + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + pixel_values: torch.Tensor | None = None, + pixel_values_videos: torch.FloatTensor | None = None, + image_grid_thw: torch.LongTensor | None = None, + video_grid_thw: torch.LongTensor | None = None, + rope_deltas: torch.LongTensor | None = None, + mm_token_type_ids: torch.IntTensor | None = None, + second_per_grid_ts: torch.Tensor | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Qwen2_5_VLCausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*): + The temporal, height and width of feature shape of each image in LLM. + video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*): + The temporal, height and width of feature shape of each video in LLM. + rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*): + The rope index difference between sequence length and multimodal rope. + second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*): + The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs. + + Example: + + ```python + >>> from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration + + >>> model = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") + >>> processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") + + >>> messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg", + }, + {"type": "text", "text": "Describe the image."}, + ], + } + ] + + >>> inputs = processor.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_dict=True, + return_tensors="pt" + ) + + >>> # Generate + >>> generated_ids = model.generate(**inputs, max_new_tokens=1024) + >>> generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)] + >>> output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + >>> print(output_text) + ``` + """ + + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + mm_token_type_ids=mm_token_type_ids, + position_ids=position_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs[0] + + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return Qwen2_5_VLCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + rope_deltas=outputs.rope_deltas, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + position_ids=None, + use_cache=True, + pixel_values=None, + pixel_values_videos=None, + image_grid_thw=None, + video_grid_thw=None, + second_per_grid_ts=None, + is_first_iteration=False, + **kwargs, + ): + # Overwritten -- in specific circumstances we don't want to forward image inputs to the model + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + position_ids=position_ids, + pixel_values=pixel_values, + pixel_values_videos=pixel_values_videos, + image_grid_thw=image_grid_thw, + video_grid_thw=video_grid_thw, + second_per_grid_ts=second_per_grid_ts, + use_cache=use_cache, + is_first_iteration=is_first_iteration, + **kwargs, + ) + + if not is_first_iteration and use_cache: + model_inputs["pixel_values"] = None + model_inputs["pixel_values_videos"] = None + + return model_inputs + + +class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": False, + "return_mm_token_type_ids": True, + }, + "videos_kwargs": {"return_metadata": True}, + } + + +class Qwen2_5_VLProcessor(Qwen2VLProcessor): + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + video_processor_input_names = self.video_processor.model_input_names + names_from_processor = list( + dict.fromkeys(tokenizer_input_names + image_processor_input_names + video_processor_input_names) + ) + return names_from_processor + ["second_per_grid_ts", "mm_token_type_ids"] + + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + videos: VideoInput | None = None, + **kwargs: Unpack[Qwen2_5_VLProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. + - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. + - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. + - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`. + """ + output_kwargs = self._merge_kwargs( + Qwen2_5_VLProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + image_inputs = videos_inputs = {} + if images is not None: + image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + image_grid_thw = image_inputs["image_grid_thw"] + + if videos is not None: + videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"]) + video_grid_thw = videos_inputs["video_grid_thw"] + + # Get video metadata + if not kwargs.get("return_metadata"): + video_metadata = videos_inputs.pop("video_metadata") + else: + video_metadata = videos_inputs["video_metadata"] + + fps = [metadata.sampled_fps for metadata in video_metadata] + + if isinstance(fps, (int, float)): + second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw) + elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw): + second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps] + else: + raise ValueError( + f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number." + ) + videos_inputs.update({"second_per_grid_ts": second_per_grid_ts}) + + if not isinstance(text, list): + text = [text] + + text = text.copy() # below lines change text in-place + if images is not None: + merge_length = self.image_processor.merge_size**2 + index = 0 + for i in range(len(text)): + while self.image_token in text[i]: + num_image_tokens = image_grid_thw[index].prod() // merge_length + text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) + index += 1 + text[i] = text[i].replace("<|placeholder|>", self.image_token) + + if videos is not None: + merge_length = self.video_processor.merge_size**2 + index = 0 + for i in range(len(text)): + while self.video_token in text[i]: + num_video_tokens = video_grid_thw[index].prod() // merge_length + text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1) + index += 1 + text[i] = text[i].replace("<|placeholder|>", self.video_token) + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None) + text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) + self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"]) + + if return_mm_token_type_ids: + text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"]) + return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors) + + def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs): + """ + Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. + Args: + image_sizes (`list[list[int]]`, *optional*): + The input sizes formatted as (height, width) per each image. + video_sizes (`list[list[int]]`, *optional*): + The input sizes formatted as (num_frames, height, width) per each video. + Returns: + `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided + input modalities, along with other useful data. + """ + + vision_data = {} + if image_sizes is not None: + images_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("images_kwargs", {}) + images_kwargs.update(kwargs) + merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size + + num_image_patches = [ + self.image_processor.get_number_of_image_patches(*image_size, images_kwargs) + for image_size in image_sizes + ] + num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches] + vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) + + if video_sizes is not None: + videos_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("videos_kwargs", {}) + videos_kwargs.update(kwargs) + num_video_patches = [ + self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs) + for video_size in video_sizes + ] + num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches] + vision_data["num_video_tokens"] = num_video_tokens + + return MultiModalData(**vision_data) + + +__all__ = [ + "Qwen2_5_VLConfig", + "Qwen2_5_VLVisionConfig", + "Qwen2_5_VLTextConfig", + "Qwen2_5_VLForConditionalGeneration", + "Qwen2_5_VLModel", + "Qwen2_5_VLPreTrainedModel", + "Qwen2_5_VLProcessor", + "Qwen2_5_VLTextModel", # noqa: F822 +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py new file mode 100644 index 0000000000000000000000000000000000000000..8873eb82557add4b59065cca77dd72d31b9ffc8b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py @@ -0,0 +1,224 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_qwen2_5_vl.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring +from ...video_utils import VideoInput + + +class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": False, + "return_mm_token_type_ids": True, + }, + "videos_kwargs": {"return_metadata": True}, + } + + +@auto_docstring +class Qwen2_5_VLProcessor(ProcessorMixin): + def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs): + self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token + self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token + self.image_token_id = ( + tokenizer.image_token_id + if getattr(tokenizer, "image_token_id", None) + else tokenizer.convert_tokens_to_ids(self.image_token) + ) + self.video_token_id = ( + tokenizer.video_token_id + if getattr(tokenizer, "video_token_id", None) + else tokenizer.convert_tokens_to_ids(self.video_token) + ) + super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template) + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, + videos: VideoInput | None = None, + **kwargs: Unpack[Qwen2_5_VLProcessorKwargs], + ) -> BatchFeature: + r""" + Returns: + [`BatchFeature`]: A [`BatchFeature`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. + - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. + - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. + - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. + - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`. + """ + output_kwargs = self._merge_kwargs( + Qwen2_5_VLProcessorKwargs, + tokenizer_init_kwargs=self.tokenizer.init_kwargs, + **kwargs, + ) + + image_inputs = videos_inputs = {} + if images is not None: + image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) + image_grid_thw = image_inputs["image_grid_thw"] + + if videos is not None: + videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"]) + video_grid_thw = videos_inputs["video_grid_thw"] + + # Get video metadata + if not kwargs.get("return_metadata"): + video_metadata = videos_inputs.pop("video_metadata") + else: + video_metadata = videos_inputs["video_metadata"] + + fps = [metadata.sampled_fps for metadata in video_metadata] + + if isinstance(fps, (int, float)): + second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw) + elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw): + second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps] + else: + raise ValueError( + f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number." + ) + videos_inputs.update({"second_per_grid_ts": second_per_grid_ts}) + + if not isinstance(text, list): + text = [text] + + text = text.copy() # below lines change text in-place + if images is not None: + merge_length = self.image_processor.merge_size**2 + index = 0 + for i in range(len(text)): + while self.image_token in text[i]: + num_image_tokens = image_grid_thw[index].prod() // merge_length + text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) + index += 1 + text[i] = text[i].replace("<|placeholder|>", self.image_token) + + if videos is not None: + merge_length = self.video_processor.merge_size**2 + index = 0 + for i in range(len(text)): + while self.video_token in text[i]: + num_video_tokens = video_grid_thw[index].prod() // merge_length + text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1) + index += 1 + text[i] = text[i].replace("<|placeholder|>", self.video_token) + + return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) + return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None) + text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) + self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"]) + + if return_mm_token_type_ids: + text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"]) + return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors) + + def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs): + """ + Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. + Args: + image_sizes (`list[list[int]]`, *optional*): + The input sizes formatted as (height, width) per each image. + video_sizes (`list[list[int]]`, *optional*): + The input sizes formatted as (num_frames, height, width) per each video. + Returns: + `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided + input modalities, along with other useful data. + """ + + vision_data = {} + if image_sizes is not None: + images_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("images_kwargs", {}) + images_kwargs.update(kwargs) + merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size + + num_image_patches = [ + self.image_processor.get_number_of_image_patches(*image_size, images_kwargs) + for image_size in image_sizes + ] + num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches] + vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) + + if video_sizes is not None: + videos_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("videos_kwargs", {}) + videos_kwargs.update(kwargs) + num_video_patches = [ + self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs) + for video_size in video_sizes + ] + num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches] + vision_data["num_video_tokens"] = num_video_tokens + + return MultiModalData(**vision_data) + + def post_process_image_text_to_text( + self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs + ): + """ + Post-process the output of the model to decode the text. + + Args: + generated_outputs (`torch.Tensor` or `np.ndarray`): + The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` + or `(sequence_length,)`. + skip_special_tokens (`bool`, *optional*, defaults to `True`): + Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. + clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): + Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method. + **kwargs: + Additional arguments to be passed to the tokenizer's `batch_decode method`. + + Returns: + `list[str]`: The decoded text. + """ + return self.tokenizer.batch_decode( + generated_outputs, + skip_special_tokens=skip_special_tokens, + clean_up_tokenization_spaces=clean_up_tokenization_spaces, + **kwargs, + ) + + @property + def model_input_names(self): + tokenizer_input_names = self.tokenizer.model_input_names + image_processor_input_names = self.image_processor.model_input_names + video_processor_input_names = self.video_processor.model_input_names + names_from_processor = list( + dict.fromkeys(tokenizer_input_names + image_processor_input_names + video_processor_input_names) + ) + return names_from_processor + ["second_per_grid_ts", "mm_token_type_ids"] + + +__all__ = ["Qwen2_5_VLProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b126d712bd469e57188316a0b0ba127f31ddfd2e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_qwen2_audio import * + from .modeling_qwen2_audio import * + from .processing_qwen2_audio import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/configuration_qwen2_audio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/configuration_qwen2_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..a617f33e61771ee6aa6404793687e4e5209aaf80 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/configuration_qwen2_audio.py @@ -0,0 +1,123 @@ +# Copyright 2024 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. team. All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Qwen2Audio model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="Qwen/Qwen2-Audio-7B") +@strict +class Qwen2AudioEncoderConfig(PreTrainedConfig): + r""" + max_source_positions (`int`, *optional*, defaults to 1500): + The maximum sequence length of log-mel filter-bank features that this model might ever be used with. + + Example: + + ```python + >>> from transformers import Qwen2AudioEncoderConfig, Qwen2AudioEncoder + + >>> # Initializing a Qwen2AudioEncoderConfig + >>> configuration = Qwen2AudioEncoderConfig() + + >>> # Initializing a Qwen2AudioEncoder (with random weights) + >>> model = Qwen2AudioEncoder(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_audio_encoder" + attribute_map = {"num_hidden_layers": "encoder_layers"} + + num_mel_bins: int = 128 + encoder_layers: int = 32 + encoder_attention_heads: int = 20 + encoder_ffn_dim: int = 5120 + encoder_layerdrop: float | int = 0.0 + d_model: int = 1280 + dropout: float | int = 0.0 + attention_dropout: float | int = 0.0 + activation_function: str = "gelu" + activation_dropout: float | int = 0.0 + scale_embedding: bool = False + initializer_range: float = 0.02 + max_source_positions: int = 1500 + + +@auto_docstring(checkpoint="Qwen/Qwen2-Audio-7B") +@strict +class Qwen2AudioConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Qwen2AudioForConditionalGeneration, Qwen2AudioConfig, Qwen2AudioEncoderConfig, Qwen2Config + + >>> # Initializing a Qwen2AudioEncoder config + >>> audio_config = Qwen2AudioEncoderConfig() + + >>> # Initializing a Qwen2 config + >>> text_config = Qwen2Config() + + >>> # Initializing a Qwen2Audio configuration + >>> configuration = Qwen2AudioConfig(audio_config, text_config) + + >>> # Initializing a model from the qwen2-audio style configuration + >>> model = Qwen2AudioForConditionalGeneration(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "qwen2_audio" + attribute_map = { + "audio_token_id": "audio_token_index", + } + sub_configs = {"text_config": AutoConfig, "audio_config": AutoConfig} + + audio_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + audio_token_index: int = 151646 + + def __post_init__(self, **kwargs): + if isinstance(self.audio_config, dict): + self.audio_config["model_type"] = self.audio_config.get("model_type", "qwen2_audio_encoder") + self.audio_config = CONFIG_MAPPING[self.audio_config["model_type"]](**self.audio_config) + elif self.audio_config is None: + self.audio_config = CONFIG_MAPPING["qwen2_audio_encoder"]( + d_model=1280, + encoder_attention_heads=20, + encoder_ffn_dim=5120, + encoder_layerdrop=0.0, + encoder_layers=32, + num_mel_bins=128, + max_source_positions=1500, + scale_embedding=False, + activation_function="gelu", + ) + + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "qwen2") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["qwen2"]() + + super().__post_init__(**kwargs) + + +__all__ = ["Qwen2AudioConfig", "Qwen2AudioEncoderConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/modeling_qwen2_audio.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/modeling_qwen2_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..cbdf67cfd29d6b82d7d98a81c026803fdd7234ae --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/qwen2_audio/modeling_qwen2_audio.py @@ -0,0 +1,812 @@ +# Copyright 2024 the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Qwen2Audio model.""" + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import torch +from torch import nn + +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, ModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, is_torchdynamo_compiling, logging, torch_compilable_check +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel, AutoModelForCausalLM +from .configuration_qwen2_audio import Qwen2AudioConfig, Qwen2AudioEncoderConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring( + custom_intro=""" + Base class for Qwen2Audio causal language model (or autoregressive) outputs. + """ +) +@dataclass +class Qwen2AudioCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + Pre-computed hidden-states that can be used to speed up auto-regressive (sequential) decoding. There are + two sets of pre-computed hidden-states: key and values states in the self-attention blocks. + The `past_key_values` are returned when `use_cache=True` is passed or when `config.use_cache=True`. + It is a [`~cache_utils.Cache`] instance. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those + that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of + all `input_ids` of shape `(batch_size, sequence_length)`. + attention_mask (`torch.FloatTensor`, *optional*): + Attentions mask, used to update attention mask and position_ids. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + attention_mask: torch.FloatTensor | None = None + + +# Copied from transformers.models.whisper.modeling_whisper.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs, +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Qwen2AudioAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + # Copied from transformers.models.whisper.modeling_whisper.WhisperAttention.__init__ with Whisper->Qwen2Audio + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + layer_idx: int | None = None, + config: Qwen2AudioConfig | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + if layer_idx is None and is_decoder: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + self.layer_idx = layer_idx + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + bsz, tgt_len, _ = hidden_states.size() + + # Scaling is susceptible to floating point arithmetics' inprecisions + # which can lead to different results (this is dependent from model + # to model, e.g. whisper is one such case). We therefore keep the + # original order of scaling to follow the original implementation + # and enforce no scaling (1.0) in the attention call below. + query_states = self._shape(self.q_proj(hidden_states) * self.scaling, tgt_len, bsz) + key_states = self._shape(self.k_proj(hidden_states), -1, bsz) + value_states = self._shape(self.v_proj(hidden_states), -1, bsz) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=1.0, + output_attentions=output_attentions, + **kwargs, + ) + + attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +# Copied from transformers.models.whisper.modeling_whisper.WhisperEncoderLayer with Whisper->Qwen2Audio, WHISPER->QWEN2AUDIO +class Qwen2AudioEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Qwen2AudioConfig): + super().__init__() + self.embed_dim = config.d_model + + self.self_attn = Qwen2AudioAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + config=config, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.final_layer_norm(hidden_states) + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + if hidden_states.dtype == torch.float16: + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +@auto_docstring +class Qwen2AudioPreTrainedModel(PreTrainedModel): + config: Qwen2AudioConfig + base_model_prefix = "model" + input_modalities = ("audio", "text") + supports_gradient_checkpointing = True + _no_split_modules = ["Qwen2AudioAttention"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn = True + _supports_sdpa = True + + +@auto_docstring( + custom_intro=""" + The audio model from Qwen2Audio without any head or projection on top. + """ +) +# Copied from transformers.models.whisper.modeling_whisper.WhisperEncoder with Whisper->Qwen2Audio +class Qwen2AudioEncoder(Qwen2AudioPreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`Qwen2AudioEncoderLayer`]. + + Args: + config: Qwen2AudioEncoderConfig + """ + + # Ignore copy + config: Qwen2AudioEncoderConfig + main_input_name = "input_features" + input_modalities = "audio" + _no_split_modules = ["Qwen2AudioEncoderLayer"] + _can_record_outputs = {"hidden_states": Qwen2AudioEncoderLayer, "attentions": Qwen2AudioAttention} + + def __init__(self, config: Qwen2AudioEncoderConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + embed_dim = config.d_model + self.num_mel_bins = config.num_mel_bins + self.max_source_positions = config.max_source_positions + self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.conv1 = nn.Conv1d(self.num_mel_bins, embed_dim, kernel_size=3, padding=1) + self.conv2 = nn.Conv1d(embed_dim, embed_dim, kernel_size=3, stride=2, padding=1) + + self.embed_positions = nn.Embedding(self.max_source_positions, embed_dim) + self.embed_positions.requires_grad_(False) + + self.layers = nn.ModuleList([Qwen2AudioEncoderLayer(config) for _ in range(config.encoder_layers)]) + self.layer_norm = nn.LayerNorm(config.d_model) + # Ignore copy + self.avg_pooler = nn.AvgPool1d(2, stride=2) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def _freeze_parameters(self): + for param in self.parameters(): + param.requires_grad = False + self._requires_grad = False + + def get_input_embeddings(self) -> nn.Module: + return self.conv1 + + def set_input_embeddings(self, value: nn.Module): + self.conv1 = value + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + input_features, + attention_mask=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + attention_mask (`torch.Tensor`)`, *optional*): + Qwen2Audio does not support masking of the `input_features`, this argument is preserved for compatibility, + but it is not used. By default the silence in the input log mel spectrogram are ignored. + """ + + expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0] + if input_features.shape[-1] != expected_seq_length: + raise ValueError( + f"Qwen2Audio expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}." + ) + + # Ignore copy + input_features = input_features.to(dtype=self.conv1.weight.dtype, device=self.conv1.weight.device) + + inputs_embeds = nn.functional.gelu(self.conv1(input_features)) + inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds)) + + inputs_embeds = inputs_embeds.permute(0, 2, 1) + embed_pos = self.embed_positions.weight + + hidden_states = inputs_embeds + embed_pos + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + for idx, encoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + # Ignore copy + if not to_drop: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + **kwargs, + ) + + # Ignore copy + hidden_states = hidden_states.permute(0, 2, 1) + hidden_states = self.avg_pooler(hidden_states) + hidden_states = hidden_states.permute(0, 2, 1) + + hidden_states = self.layer_norm(hidden_states) + + return BaseModelOutput(last_hidden_state=hidden_states) + + # Ignore copy + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): + """ + Computes the output length of the convolutional layers and the output length of the audio encoder + """ + input_lengths = (input_lengths - 1) // 2 + 1 + output_lengths = (input_lengths - 2) // 2 + 1 + return input_lengths, output_lengths + + +class Qwen2AudioMultiModalProjector(nn.Module): + def __init__(self, config: Qwen2AudioConfig): + super().__init__() + self.linear = nn.Linear(config.audio_config.d_model, config.text_config.hidden_size, bias=True) + + def forward(self, audio_features): + hidden_states = self.linear(audio_features) + return hidden_states + + +@auto_docstring( + custom_intro=""" + The QWEN2AUDIO model which consists of a audio backbone and a language model. + """ +) +class Qwen2AudioForConditionalGeneration(Qwen2AudioPreTrainedModel, GenerationMixin): + def __init__(self, config: Qwen2AudioConfig): + super().__init__(config) + self.audio_tower = AutoModel.from_config(config.audio_config) # Usually a `Qwen2AudioEncoder` instance + + self.multi_modal_projector = Qwen2AudioMultiModalProjector(config) + self.vocab_size = config.text_config.vocab_size + self.language_model = AutoModelForCausalLM.from_config(config.text_config) + + self.pad_token_id = ( + self.config.text_config.pad_token_id if self.config.text_config.pad_token_id is not None else -1 + ) + self._padding_side = "left" # set it to left by default, user can use setter to change padding_sides + self.post_init() + + @property + def padding_side(self): + return self._padding_side + + @padding_side.setter + def padding_side(self, padding_side: str): + if padding_side not in ["left", "right"]: + raise ValueError(f"{padding_side} is not `left` or `right`.") + self._padding_side = padding_side + + def get_input_embeddings(self): + return self.language_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.language_model.set_input_embeddings(value) + + def get_output_embeddings(self): + return self.language_model.get_output_embeddings() + + def set_output_embeddings(self, new_embeddings): + self.language_model.set_output_embeddings(new_embeddings) + + def set_decoder(self, decoder): + self.language_model.set_decoder(decoder) + + def get_decoder(self): + return self.language_model.get_decoder() + + def _merge_input_ids_with_audio_features( + self, audio_features, num_audio_tokens, inputs_embeds, input_ids, attention_mask, labels + ): + """ + Merge input_ids with audio features into final embeddings + + Args: + audio_features (`torch.Tensor` of shape `(num_audios, max_audio_tokens, embed_dim)`): + All audio vectors of all audios in the batch + num_audio_tokens (`torch.LongTensor` of shape `(num_audios)`): + The length of audio embeddings of each audio as stacked in `audio_features` + inputs_embeds (`torch.Tensor` of shape `(batch_size, sequence_length, embed_dim)`): + Token embeddings before merging with audio embeddings + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Input_ids of tokens, possibly filled with audio token + attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Mask to avoid performing attention on padding token indices. + labels (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) + labels need to be recalculated to support training (if provided) + Returns: + final_embedding, final_attention_mask, final_labels, position_ids, final_input_ids + + Explanation: + each audio has variable length embeddings, with length specified by num_audio_tokens + audio_features is concatenation of all audio embed vectors + task: fill each <|AUDIO|> with the correct number of audio embeddings + Example: + X (5 tokens), Y (3 tokens), Z (8 tokens) + X, Y are in the same sequence (in-context learning) + if right padding + input_ids: [ + a b c d e f X g h i j k Y l m + o p q r Z s t u v _ _ _ _ _ _ + ] + input_ids should be: [ + a b c d e f X X X X X g h i j k Y Y Y l m + o p q r Z Z Z Z Z Z Z Z s t u v _ _ _ _ _ + ] + labels should be: [ + a b c d e f _ _ _ _ _ g h i j k _ _ _ l m + o p q r _ _ _ _ _ _ _ _ s t u v _ _ _ _ _ + ] + elif left padding + input_ids: [ + a b c d e f X g h i j k Y l m + _ _ _ _ _ _ o p q r Z s t u v + ] + input_ids should be: [ + a b c d e f X X X X X g h i j k Y Y Y l m + _ _ _ _ _ o p q r Z Z Z Z Z Z Z Z s t u v + ] + labels should be: [ + a b c d e f _ _ _ _ _ g h i j k _ _ _ l m + _ _ _ _ _ o p q r _ _ _ _ _ _ _ _ s t u v + ] + Edge cases: + * If tokens are same but audio token sizes are different, then cannot infer left or right padding + ```python + url1 = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/glass-breaking-151256.mp3" + audio1, _ = librosa.load(BytesIO(urlopen(url1).read()), sr=processor.feature_extractor.sampling_rate) + url2 = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/f2641_0_throatclearing.wav" + audio2, _ = librosa.load(BytesIO(urlopen(url2).read()), sr=processor.feature_extractor.sampling_rate) + prompts = [ + "[INST] <|AUDIO|>\nWhat is that in this audio? [/INST]", + "[INST] <|AUDIO|>\nWhat is that in this audio? [/INST]", + ] + inputs = processor(text=prompts, audio=[audio1, audio2], return_tensors='pt', padding=True).to("cuda") + audio1 has 101 tokens, while audio2 has 72 tokens + ``` + + input_ids: [ + a b c d X g h + i j Y k l m n + ] + where X is 3 tokens while Y is 5, this mean after merge + if left-padding (batched generation) + input_ids should be: [ + _ _ a b c d X X X g h + i j Y Y Y Y Y k l m n + ] + elif (right padding) (training) + input_ids should be: [ + a b c d X X X g h _ _ + i j Y Y Y Y Y k l m n + ] + """ + num_audios, max_audio_tokens, embed_dim = audio_features.shape + audio_features_mask = torch.arange(max_audio_tokens).expand(num_audios, max_audio_tokens).to( + num_audio_tokens.device + ) < num_audio_tokens.unsqueeze(1) + masked_audio_features = audio_features[audio_features_mask].view(-1, embed_dim) + batch_size, sequence_length = input_ids.shape + _left_padding = torch.any(attention_mask[:, 0] == 0) + _right_padding = torch.any(attention_mask[:, -1] == 0) + + left_padding = True + if batch_size > 1: + if _left_padding and not _right_padding: + left_padding = True + elif not _left_padding and _right_padding: + left_padding = False + elif not _left_padding and not _right_padding: + # both side is 1, so cannot tell + left_padding = self.padding_side == "left" + else: + # invalid attention_mask + raise ValueError(f"both side of attention_mask has zero, invalid. {attention_mask}") + + # 1. Create a mask to know where special audio tokens are + special_audio_token_mask = input_ids == self.config.audio_token_id + num_special_audio_tokens = torch.sum(special_audio_token_mask, dim=-1) + + # In case the Audio model or the Language model has been offloaded to CPU, we need to manually + # set the corresponding tensors into their correct target device. + target_device = inputs_embeds.device + attention_mask = attention_mask.to(target_device) + input_ids = input_ids.to(target_device) + num_audio_tokens = num_audio_tokens.to(target_device) + batch_indices, non_audio_indices = torch.where( + (input_ids != self.config.audio_token_id) & (attention_mask == 1) + ) + + # 2. Compute the positions where text should be written + # Calculate new positions for text tokens in merged audio-text sequence. + # `special_audio_token_mask` identifies audio tokens. Each audio token will be replaced by `audio_feat_lengths - 1` text tokens. + # `torch.cumsum` computes how each audio token shifts subsequent text token positions. + token_placeholder_num = torch.zeros_like(input_ids) + token_placeholder_num[special_audio_token_mask] = num_audio_tokens.long() - 1 + token_placeholder_num = token_placeholder_num + 1 + new_token_positions = torch.cumsum(token_placeholder_num, -1) - 1 + max_token_num = token_placeholder_num.sum(-1).max() + nb_audio_pad = max_token_num - 1 - new_token_positions[:, -1] + if left_padding: + new_token_positions += nb_audio_pad[:, None] # offset for left padding + text_to_overwrite = new_token_positions[batch_indices, non_audio_indices] + batch_indices, non_audio_indices, text_to_overwrite = ( + batch_indices.to(target_device), + non_audio_indices.to(target_device), + text_to_overwrite.to(target_device), + ) + + # 3. Create the full embedding, already padded to the maximum position + final_embedding = torch.zeros( + batch_size, max_token_num, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device + ) + final_attention_mask = torch.zeros( + batch_size, max_token_num, dtype=attention_mask.dtype, device=inputs_embeds.device + ) + final_input_ids = torch.full( + (batch_size, max_token_num), self.pad_token_id, dtype=input_ids.dtype, device=inputs_embeds.device + ) + + # 4. Fill the embeddings based on the mask. If we have ["hey" "", + unk_token: str = "", + _spm_precompiled_charsmap: str | None = None, + additional_special_tokens: list | None = None, + **kwargs, + ): + self._vocab = vocab or {} + self._merges = merges or [] + + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + unk_token=str(unk_token), + fuse_unk=True, + byte_fallback=False, + dropout=None, + ) + ) + + if _spm_precompiled_charsmap is not None: + self._tokenizer.normalizer = normalizers.Sequence( + [ + normalizers.Precompiled(_spm_precompiled_charsmap), + normalizers.Replace(pattern=Regex(" {2,}"), content=" "), + ] + ) + + self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="always") + self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="always") + + super().__init__( + eos_token=eos_token, + unk_token=unk_token, + additional_special_tokens=additional_special_tokens or [], + **kwargs, + ) + + super()._post_init() + + +__all__ = ["ReformerTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a49c5ee7f2e424ea688f7e83a97dad4eb38cfc8b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_regnet import * + from .modeling_regnet import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/configuration_regnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/configuration_regnet.py new file mode 100644 index 0000000000000000000000000000000000000000..8d4d3c5ae5d44f8ecb9a84557f6c60b015e3728f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/configuration_regnet.py @@ -0,0 +1,66 @@ +# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RegNet model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="facebook/regnet-y-040") +@strict +class RegNetConfig(PreTrainedConfig): + r""" + groups_width (`int`, *optional*, defaults to 64): + Width of group for each stage. + layer_type (`str`, *optional*, defaults to `"y"`): + The layer to use, it can be either `"x" or `"y"`. An `x` layer is a ResNet's BottleNeck layer with + `reduction` fixed to `1`. While a `y` layer is a `x` but with squeeze and excitation. Please refer to the + paper for a detailed explanation of how these layers were constructed. + downsample_in_first_stage (`bool`, *optional*, defaults to `False`): + If `True`, the first stage will downsample the inputs using a `stride` of 2. + + Example: + ```python + >>> from transformers import RegNetConfig, RegNetModel + + >>> # Initializing a RegNet regnet-y-40 style configuration + >>> configuration = RegNetConfig() + >>> # Initializing a model from the regnet-y-40 style configuration + >>> model = RegNetModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "regnet" + layer_types = ["x", "y"] + + num_channels: int = 3 + embedding_size: int = 32 + hidden_sizes: list[int] | tuple[int, ...] = (128, 192, 512, 1088) + depths: list[int] | tuple[int, ...] = (2, 6, 12, 2) + groups_width: int = 64 + layer_type: str = "y" + hidden_act: str = "relu" + downsample_in_first_stage: bool = True + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.layer_type not in self.layer_types: + raise ValueError(f"layer_type={self.layer_type} is not one of {','.join(self.layer_types)}") + + +__all__ = ["RegNetConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/modeling_regnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/modeling_regnet.py new file mode 100644 index 0000000000000000000000000000000000000000..6c43d79d7894469ac2b4bf0bf4d5b0552d42073b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/regnet/modeling_regnet.py @@ -0,0 +1,384 @@ +# Copyright 2022 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RegNet model.""" + +import math + +import torch +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_outputs import ( + BaseModelOutputWithNoAttention, + BaseModelOutputWithPoolingAndNoAttention, + ImageClassifierOutputWithNoAttention, +) +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, logging +from .configuration_regnet import RegNetConfig + + +logger = logging.get_logger(__name__) + + +class RegNetConvLayer(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + stride: int = 1, + groups: int = 1, + activation: str | None = "relu", + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + groups=groups, + bias=False, + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, hidden_state): + hidden_state = self.convolution(hidden_state) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RegNetEmbeddings(nn.Module): + """ + RegNet Embeddings (stem) composed of a single aggressive convolution. + """ + + def __init__(self, config: RegNetConfig): + super().__init__() + self.embedder = RegNetConvLayer( + config.num_channels, config.embedding_size, kernel_size=3, stride=2, activation=config.hidden_act + ) + self.num_channels = config.num_channels + + def forward(self, pixel_values): + num_channels = pixel_values.shape[1] + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + hidden_state = self.embedder(pixel_values) + return hidden_state + + +# Copied from transformers.models.resnet.modeling_resnet.ResNetShortCut with ResNet->RegNet +class RegNetShortCut(nn.Module): + """ + RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to + downsample the input using `stride=2`. + """ + + def __init__(self, in_channels: int, out_channels: int, stride: int = 2): + super().__init__() + self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + self.normalization = nn.BatchNorm2d(out_channels) + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + return hidden_state + + +class RegNetSELayer(nn.Module): + """ + Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://huggingface.co/papers/1709.01507). + """ + + def __init__(self, in_channels: int, reduced_channels: int): + super().__init__() + + self.pooler = nn.AdaptiveAvgPool2d((1, 1)) + self.attention = nn.Sequential( + nn.Conv2d(in_channels, reduced_channels, kernel_size=1), + nn.ReLU(), + nn.Conv2d(reduced_channels, in_channels, kernel_size=1), + nn.Sigmoid(), + ) + + def forward(self, hidden_state): + # b c h w -> b c 1 1 + pooled = self.pooler(hidden_state) + attention = self.attention(pooled) + hidden_state = hidden_state * attention + return hidden_state + + +class RegNetXLayer(nn.Module): + """ + RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1. + """ + + def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1): + super().__init__() + should_apply_shortcut = in_channels != out_channels or stride != 1 + groups = max(1, out_channels // config.groups_width) + self.shortcut = ( + RegNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity() + ) + self.layer = nn.Sequential( + RegNetConvLayer(in_channels, out_channels, kernel_size=1, activation=config.hidden_act), + RegNetConvLayer(out_channels, out_channels, stride=stride, groups=groups, activation=config.hidden_act), + RegNetConvLayer(out_channels, out_channels, kernel_size=1, activation=None), + ) + self.activation = ACT2FN[config.hidden_act] + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.layer(hidden_state) + residual = self.shortcut(residual) + hidden_state += residual + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RegNetYLayer(nn.Module): + """ + RegNet's Y layer: an X layer with Squeeze and Excitation. + """ + + def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1): + super().__init__() + should_apply_shortcut = in_channels != out_channels or stride != 1 + groups = max(1, out_channels // config.groups_width) + self.shortcut = ( + RegNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity() + ) + self.layer = nn.Sequential( + RegNetConvLayer(in_channels, out_channels, kernel_size=1, activation=config.hidden_act), + RegNetConvLayer(out_channels, out_channels, stride=stride, groups=groups, activation=config.hidden_act), + RegNetSELayer(out_channels, reduced_channels=int(round(in_channels / 4))), + RegNetConvLayer(out_channels, out_channels, kernel_size=1, activation=None), + ) + self.activation = ACT2FN[config.hidden_act] + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.layer(hidden_state) + residual = self.shortcut(residual) + hidden_state += residual + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RegNetStage(nn.Module): + """ + A RegNet stage composed by stacked layers. + """ + + def __init__( + self, + config: RegNetConfig, + in_channels: int, + out_channels: int, + stride: int = 2, + depth: int = 2, + ): + super().__init__() + + layer = RegNetXLayer if config.layer_type == "x" else RegNetYLayer + + self.layers = nn.Sequential( + # downsampling is done in the first layer with stride of 2 + layer( + config, + in_channels, + out_channels, + stride=stride, + ), + *[layer(config, out_channels, out_channels) for _ in range(depth - 1)], + ) + + def forward(self, hidden_state): + hidden_state = self.layers(hidden_state) + return hidden_state + + +class RegNetEncoder(nn.Module): + def __init__(self, config: RegNetConfig): + super().__init__() + self.stages = nn.ModuleList([]) + # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input + self.stages.append( + RegNetStage( + config, + config.embedding_size, + config.hidden_sizes[0], + stride=2 if config.downsample_in_first_stage else 1, + depth=config.depths[0], + ) + ) + in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:]) + for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]): + self.stages.append(RegNetStage(config, in_channels, out_channels, depth=depth)) + + def forward( + self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True + ) -> BaseModelOutputWithNoAttention: + hidden_states = () if output_hidden_states else None + + for stage_module in self.stages: + if output_hidden_states: + hidden_states = hidden_states + (hidden_state,) + + hidden_state = stage_module(hidden_state) + + if output_hidden_states: + hidden_states = hidden_states + (hidden_state,) + + if not return_dict: + return tuple(v for v in [hidden_state, hidden_states] if v is not None) + + return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=hidden_states) + + +@auto_docstring +class RegNetPreTrainedModel(PreTrainedModel): + config: RegNetConfig + base_model_prefix = "regnet" + main_input_name = "pixel_values" + _no_split_modules = ["RegNetYLayer"] + + @torch.no_grad() + def _init_weights(self, module): + if isinstance(module, nn.Conv2d): + init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") + # copied from the `reset_parameters` method of `class Linear(Module)` in `torch`. + elif isinstance(module, nn.Linear): + init.kaiming_uniform_(module.weight, a=math.sqrt(5)) + if module.bias is not None: + fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(module.weight) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + init.uniform_(module.bias, -bound, bound) + elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)): + init.constant_(module.weight, 1) + init.constant_(module.bias, 0) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + +@auto_docstring +# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet +class RegNetModel(RegNetPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + self.embedder = RegNetEmbeddings(config) + self.encoder = RegNetEncoder(config) + self.pooler = nn.AdaptiveAvgPool2d((1, 1)) + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: Tensor, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> BaseModelOutputWithPoolingAndNoAttention: + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + embedding_output = self.embedder(pixel_values) + + encoder_outputs = self.encoder( + embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict + ) + + last_hidden_state = encoder_outputs[0] + + pooled_output = self.pooler(last_hidden_state) + + if not return_dict: + return (last_hidden_state, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndNoAttention( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for + ImageNet. + """ +) +# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet +class RegNetForImageClassification(RegNetPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.regnet = RegNetModel(config) + # classification head + self.classifier = nn.Sequential( + nn.Flatten(), + nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(), + ) + # initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> ImageClassifierOutputWithNoAttention: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.regnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) + + pooled_output = outputs.pooler_output if return_dict else outputs[1] + + logits = self.classifier(pooled_output) + + loss = None + + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[2:] + return (loss,) + output if loss is not None else output + + return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states) + + +__all__ = ["RegNetForImageClassification", "RegNetModel", "RegNetPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..371d23ef13a7ca20e9f3b6afa75cadad488e1fea --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_rembert import * + from .modeling_rembert import * + from .tokenization_rembert import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/configuration_rembert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/configuration_rembert.py new file mode 100644 index 0000000000000000000000000000000000000000..ea6c33b03f4e9fc75f64bba301afd89013d00c14 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/configuration_rembert.py @@ -0,0 +1,72 @@ +# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RemBERT model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="google/rembert") +@strict +class RemBertConfig(PreTrainedConfig): + r""" + input_embedding_size (`int`, *optional*, defaults to 256): + Dimensionality of the input embeddings. + output_embedding_size (`int`, *optional*, defaults to 1664): + Dimensionality of the output embeddings. + + Example: + + ```python + >>> from transformers import RemBertModel, RemBertConfig + + >>> # Initializing a RemBERT rembert style configuration + >>> configuration = RemBertConfig() + + >>> # Initializing a model from the rembert style configuration + >>> model = RemBertModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "rembert" + + vocab_size: int = 250300 + hidden_size: int = 1152 + num_hidden_layers: int = 32 + num_attention_heads: int = 18 + input_embedding_size: int = 256 + output_embedding_size: int = 1664 + intermediate_size: int = 4608 + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.0 + attention_probs_dropout_prob: float | int = 0.0 + classifier_dropout_prob: float | int = 0.1 + max_position_embeddings: int = 512 + type_vocab_size: int = 2 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-12 + use_cache: bool = True + pad_token_id: int | None = 0 + bos_token_id: int | None = 312 + eos_token_id: int | list[int] | None = 313 + is_decoder: bool = False + add_cross_attention: bool = False + tie_word_embeddings: bool = False + + +__all__ = ["RemBertConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/modeling_rembert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/modeling_rembert.py new file mode 100644 index 0000000000000000000000000000000000000000..b21dc813bd9a4a040f983b868ea5288323689525 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/modeling_rembert.py @@ -0,0 +1,1142 @@ +# Copyright 2021 The HuggingFace Team The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RemBERT model.""" + +import math + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import apply_chunking_to_forward +from ...utils import auto_docstring, logging +from .configuration_rembert import RemBertConfig + + +logger = logging.get_logger(__name__) + + +class RemBertEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding( + config.vocab_size, config.input_embedding_size, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.input_embedding_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.input_embedding_size) + + self.LayerNorm = nn.LayerNorm(config.input_embedding_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + past_key_values_length: int = 0, + ) -> torch.Tensor: + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->RemBert +class RemBertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class RemBertSelfAttention(nn.Module): + def __init__(self, config, layer_idx=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.is_decoder = config.is_decoder + self.layer_idx = layer_idx + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool = False, + **kwargs, + ) -> tuple: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.attention_head_size) + query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = False + is_cross_attention = encoder_hidden_states is not None + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_layer from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = encoder_hidden_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_layer = curr_past_key_values.layers[self.layer_idx].keys + value_layer = curr_past_key_values.layers[self.layer_idx].values + else: + kv_shape = (*current_states.shape[:-1], -1, self.attention_head_size) + key_layer = self.key(current_states).view(kv_shape).transpose(1, 2) + value_layer = self.value(current_states).view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + # save all key/value_layer to cache to be re-used for fast auto-regressive generation + key_layer, value_layer = curr_past_key_values.update(key_layer, value_layer, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RemBertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer, attention_probs + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->RemBert +class RemBertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class RemBertAttention(nn.Module): + def __init__(self, config, layer_idx=None): + super().__init__() + self.self = RemBertSelfAttention(config, layer_idx=layer_idx) + self.output = RemBertSelfOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool | None = False, + **kwargs, + ) -> tuple[torch.Tensor]: + self_outputs = self.self( + hidden_states, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->RemBert +class RemBertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RemBert +class RemBertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class RemBertLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_idx=None): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RemBertAttention(config, layer_idx) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError(f"{self} should be used as a decoder model if cross attention is added") + self.crossattention = RemBertAttention(config, layer_idx=layer_idx) + self.intermediate = RemBertIntermediate(config) + self.output = RemBertOutput(config) + + # copied from transformers.models.bert.modeling_bert.BertLayer.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool | None = False, + **kwargs, + ) -> tuple[torch.Tensor]: + self_attention_outputs = self.attention( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + past_key_values=past_key_values, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" + " by setting `config.add_cross_attention=True`" + ) + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:] # add cross attentions if we output attention weights + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + return outputs + + # Copied from transformers.models.bert.modeling_bert.BertLayer.feed_forward_chunk + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class RemBertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + + self.embedding_hidden_mapping_in = nn.Linear(config.input_embedding_size, config.hidden_size) + self.layer = nn.ModuleList([RemBertLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + **kwargs, + ) -> tuple | BaseModelOutputWithPastAndCrossAttentions: + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if use_cache and past_key_values is None: + past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + + hidden_states = self.embedding_hidden_mapping_in(hidden_states) + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module( + hidden_states, + attention_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_values, + output_attentions, + ) + + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + past_key_values, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->RemBert +class RemBertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class RemBertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.output_embedding_size) + self.decoder = nn.Linear(config.output_embedding_size, config.vocab_size) + self.activation = ACT2FN[config.hidden_act] + self.LayerNorm = nn.LayerNorm(config.output_embedding_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->RemBert +class RemBertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = RemBertLMPredictionHead(config) + + def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +@auto_docstring +class RemBertPreTrainedModel(PreTrainedModel): + config: RemBertConfig + base_model_prefix = "rembert" + supports_gradient_checkpointing = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, RemBertEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + + +@auto_docstring( + custom_intro=""" + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in [Attention is + all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + """ +) +class RemBertModel(RemBertPreTrainedModel): + def __init__(self, config, add_pooling_layer=True): + r""" + add_pooling_layer (bool, *optional*, defaults to `True`): + Whether to add a pooling layer + """ + super().__init__(config) + self.config = config + + self.embeddings = RemBertEmbeddings(config) + self.encoder = RemBertEncoder(config) + + self.pooler = RemBertPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPoolingAndCrossAttentions: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +@auto_docstring +class RemBertForMaskedLM(RemBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RemBertForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + self.rembert = RemBertModel(config, add_pooling_layer=False) + self.cls = RemBertOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | MaskedLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.rembert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + RemBERT Model with a `language modeling` head on top for CLM fine-tuning. + """ +) +class RemBertForCausalLM(RemBertPreTrainedModel, GenerationMixin): + def __init__(self, config): + super().__init__(config) + + if not config.is_decoder: + logger.warning("If you want to use `RemBertForCausalLM` as a standalone, add `is_decoder=True.`") + + self.rembert = RemBertModel(config, add_pooling_layer=False) + self.cls = RemBertOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | CausalLMOutputWithCrossAttentions: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, RemBertForCausalLM, RemBertConfig + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("google/rembert") + >>> config = RemBertConfig.from_pretrained("google/rembert") + >>> config.is_decoder = True + >>> model = RemBertForCausalLM.from_pretrained("google/rembert", config=config) + + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> prediction_logits = outputs.logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.rembert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.cls(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + RemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the + pooled output) e.g. for GLUE tasks. + """ +) +class RemBertForSequenceClassification(RemBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.rembert = RemBertModel(config) + self.dropout = nn.Dropout(config.classifier_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.FloatTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | SequenceClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.rembert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RemBertForMultipleChoice(RemBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.rembert = RemBertModel(config) + self.dropout = nn.Dropout(config.classifier_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.FloatTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | MultipleChoiceModelOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert *input_ids* indices into associated vectors than the + model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + outputs = self.rembert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + if not return_dict: + output = (reshaped_logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RemBertForTokenClassification(RemBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.rembert = RemBertModel(config, add_pooling_layer=False) + self.dropout = nn.Dropout(config.classifier_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.FloatTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | TokenClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.rembert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RemBertForQuestionAnswering(RemBertPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_labels = config.num_labels + + self.rembert = RemBertModel(config, add_pooling_layer=False) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.FloatTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + start_positions: torch.LongTensor | None = None, + end_positions: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | QuestionAnsweringModelOutput: + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.rembert( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions.clamp_(0, ignored_index) + end_positions.clamp_(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "RemBertForCausalLM", + "RemBertForMaskedLM", + "RemBertForMultipleChoice", + "RemBertForQuestionAnswering", + "RemBertForSequenceClassification", + "RemBertForTokenClassification", + "RemBertLayer", + "RemBertModel", + "RemBertPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/tokenization_rembert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/tokenization_rembert.py new file mode 100644 index 0000000000000000000000000000000000000000..d0366018d340cd54b511d14b4e9085ff5f49c3ee --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rembert/tokenization_rembert.py @@ -0,0 +1,178 @@ +# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for RemBert model.""" + +from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors +from tokenizers.models import Unigram + +from ...tokenization_utils_tokenizers import TokenizersBackend +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"} + + +class RemBertTokenizer(TokenizersBackend): + """ + Construct a "fast" RemBert tokenizer (backed by HuggingFace's *tokenizers* library). Based on + [Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models). This + tokenizer inherits from [`AlbertTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods + + Args: + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + remove_space (`bool`, *optional*, defaults to `True`): + Whether or not to strip the text when tokenizing (removing excess spaces before and after the string). + keep_accents (`bool`, *optional*, defaults to `False`): + Whether or not to keep accents when tokenizing. + bos_token (`str`, *optional*, defaults to `"[CLS]"`): + The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. + + + + When building a sequence using special tokens, this is not the token that is used for the beginning of + sequence. The token used is the `cls_token`. + + + + eos_token (`str`, *optional*, defaults to `"[SEP]"`): + The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token + that is used for the end of sequence. The token used is the `sep_token`. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + sep_token (`str`, *optional*, defaults to `"[SEP]"`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + cls_token (`str`, *optional*, defaults to `"[CLS]"`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + mask_token (`str`, *optional*, defaults to `"[MASK]"`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + model = Unigram + + def __init__( + self, + vocab: str | list[tuple[str, float]] | None = None, + do_lower_case: bool = False, + keep_accents: bool = False, + bos_token: str = "[CLS]", + eos_token: str = "[SEP]", + unk_token: str = "", + sep_token: str = "[SEP]", + pad_token: str = "", + cls_token: str = "[CLS]", + mask_token: str = "[MASK]", + add_prefix_space: bool = True, + remove_space: bool = True, + **kwargs, + ): + self.remove_space = remove_space + self.do_lower_case = do_lower_case + self.keep_accents = keep_accents + + if vocab is not None: + self._vocab_scores = vocab + else: + self._vocab_scores = [ + (str(pad_token), 0.0), + (str(unk_token), 0.0), + (str(cls_token), 0.0), + (str(sep_token), 0.0), + (str(mask_token), 0.0), + ] + + self._tokenizer = Tokenizer( + Unigram( + self._vocab_scores, + unk_id=2, + byte_fallback=False, + ) + ) + + # Build normalizer matching RemBertConverter behavior + # When loading from pretrained, this will be overridden by tokenizer.json config + # When creating from extractor (vocab), this provides equivalent behavior + list_normalizers = [ + normalizers.Replace("``", '"'), + normalizers.Replace("''", '"'), + normalizers.Replace(Regex(" {2,}"), " "), + ] + if not self.keep_accents: + list_normalizers.append(normalizers.NFKD()) + list_normalizers.append(normalizers.StripAccents()) + if self.do_lower_case: + list_normalizers.append(normalizers.Lowercase()) + + # Add Precompiled equivalent (newline conversion + NFKC normalization) + list_normalizers.extend( + [ + normalizers.Replace(Regex(r"[\n\r\t]"), " "), # Precompiled converts newlines/tabs to spaces + normalizers.NFKC(), # Precompiled does NFKC normalization + ] + ) + + self._tokenizer.normalizer = normalizers.Sequence(list_normalizers) + + prepend_scheme = "always" if add_prefix_space else "never" + # Remove WhitespaceSplit - should only have Metaspace (matches SpmConverter) + self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme=prepend_scheme) + + self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme=prepend_scheme) + super().__init__( + add_prefix_space=add_prefix_space, + do_lower_case=do_lower_case, + keep_accents=keep_accents, + bos_token=bos_token, + eos_token=eos_token, + sep_token=sep_token, + cls_token=cls_token, + unk_token=unk_token, + pad_token=pad_token, + mask_token=mask_token, + remove_space=remove_space, + **kwargs, + ) + + # Set post_processor after super().__init__() so we have token IDs available + # This matches RemBertConverter.post_processor() + cls_token_str = str(cls_token) + sep_token_str = str(sep_token) + cls_token_id = self.convert_tokens_to_ids(cls_token_str) + sep_token_id = self.convert_tokens_to_ids(sep_token_str) + + self._tokenizer.post_processor = processors.TemplateProcessing( + single=f"{cls_token_str}:0 $A:0 {sep_token_str}:0", + pair=f"{cls_token_str}:0 $A:0 {sep_token_str}:0 $B:1 {sep_token_str}:1", + special_tokens=[ + (cls_token_str, cls_token_id), + (sep_token_str, sep_token_id), + ], + ) + + super()._post_init() + + +__all__ = ["RemBertTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..db16908bff31bed48a5627ae7a424d58156f098f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_resnet import * + from .modeling_resnet import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/configuration_resnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/configuration_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..b05e086943f598ac735b2a9a63c92900e6ef817f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/configuration_resnet.py @@ -0,0 +1,78 @@ +# Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""ResNet model configuration""" + +from typing import ClassVar + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import BackboneConfigMixin +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="microsoft/resnet-50") +@strict +class ResNetConfig(BackboneConfigMixin, PreTrainedConfig): + r""" + layer_type (`str`, *optional*, defaults to `"bottleneck"`): + The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or + `"bottleneck"` (used for larger models like resnet-50 and above). + downsample_in_first_stage (`bool`, *optional*, defaults to `False`): + If `True`, the first stage will downsample the inputs using a `stride` of 2. + downsample_in_bottleneck (`bool`, *optional*, defaults to `False`): + If `True`, the first conv 1x1 in ResNetBottleNeckLayer will downsample the inputs using a `stride` of 2. + + Example: + ```python + >>> from transformers import ResNetConfig, ResNetModel + + >>> # Initializing a ResNet resnet-50 style configuration + >>> configuration = ResNetConfig() + + >>> # Initializing a model (with random weights) from the resnet-50 style configuration + >>> model = ResNetModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "resnet" + layer_types: ClassVar[list[str]] = ["basic", "bottleneck"] + + num_channels: int = 3 + embedding_size: int = 64 + hidden_sizes: list[int] | tuple[int, ...] | None = (256, 512, 1024, 2048) + depths: list[int] | tuple[int, ...] | None = (3, 4, 6, 3) + layer_type: str = "bottleneck" + hidden_act: str = "relu" + downsample_in_first_stage: bool = False + downsample_in_bottleneck: bool = False + + def __post_init__(self, **kwargs): + self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 1)] + self.set_output_features_output_indices( + out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None) + ) + self.hidden_sizes = list(self.hidden_sizes) + super().__post_init__(**kwargs) + + def validate_layer_type(self): + """Check that `layer_types` is correctly defined.""" + if self.layer_type not in self.layer_types: + raise ValueError(f"layer_type={self.layer_type} is not one of {','.join(self.layer_types)}") + + +__all__ = ["ResNetConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/modeling_resnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/modeling_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..9e4ed8aecfa724ebc2b78ee288973396a581008e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/resnet/modeling_resnet.py @@ -0,0 +1,454 @@ +# Copyright 2022 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch ResNet model.""" + +import math + +import torch +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...backbone_utils import BackboneMixin, filter_output_hidden_states +from ...modeling_outputs import ( + BackboneOutput, + BaseModelOutputWithNoAttention, + BaseModelOutputWithPoolingAndNoAttention, + ImageClassifierOutputWithNoAttention, +) +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, logging +from ...utils.generic import can_return_tuple +from .configuration_resnet import ResNetConfig + + +logger = logging.get_logger(__name__) + + +class ResNetConvLayer(nn.Module): + def __init__( + self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu" + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=False + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class ResNetEmbeddings(nn.Module): + """ + ResNet Embeddings (stem) composed of a single aggressive convolution. + """ + + def __init__(self, config: ResNetConfig): + super().__init__() + self.embedder = ResNetConvLayer( + config.num_channels, config.embedding_size, kernel_size=7, stride=2, activation=config.hidden_act + ) + self.pooler = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.num_channels = config.num_channels + + def forward(self, pixel_values: Tensor) -> Tensor: + num_channels = pixel_values.shape[1] + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + embedding = self.embedder(pixel_values) + embedding = self.pooler(embedding) + return embedding + + +class ResNetShortCut(nn.Module): + """ + ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to + downsample the input using `stride=2`. + """ + + def __init__(self, in_channels: int, out_channels: int, stride: int = 2): + super().__init__() + self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + self.normalization = nn.BatchNorm2d(out_channels) + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + return hidden_state + + +class ResNetBasicLayer(nn.Module): + """ + A classic ResNet's residual layer composed by two `3x3` convolutions. + """ + + def __init__(self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu"): + super().__init__() + should_apply_shortcut = in_channels != out_channels or stride != 1 + self.shortcut = ( + ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity() + ) + self.layer = nn.Sequential( + ResNetConvLayer(in_channels, out_channels, stride=stride), + ResNetConvLayer(out_channels, out_channels, activation=None), + ) + self.activation = ACT2FN[activation] + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.layer(hidden_state) + residual = self.shortcut(residual) + hidden_state += residual + hidden_state = self.activation(hidden_state) + return hidden_state + + +class ResNetBottleNeckLayer(nn.Module): + """ + A classic ResNet's bottleneck layer composed by three `3x3` convolutions. + + The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3` + convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. If + `downsample_in_bottleneck` is true, downsample will be in the first layer instead of the second layer. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + stride: int = 1, + activation: str = "relu", + reduction: int = 4, + downsample_in_bottleneck: bool = False, + ): + super().__init__() + should_apply_shortcut = in_channels != out_channels or stride != 1 + reduces_channels = out_channels // reduction + self.shortcut = ( + ResNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity() + ) + self.layer = nn.Sequential( + ResNetConvLayer( + in_channels, reduces_channels, kernel_size=1, stride=stride if downsample_in_bottleneck else 1 + ), + ResNetConvLayer(reduces_channels, reduces_channels, stride=stride if not downsample_in_bottleneck else 1), + ResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None), + ) + self.activation = ACT2FN[activation] + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.layer(hidden_state) + residual = self.shortcut(residual) + hidden_state += residual + hidden_state = self.activation(hidden_state) + return hidden_state + + +class ResNetStage(nn.Module): + """ + A ResNet stage composed by stacked layers. + """ + + def __init__( + self, + config: ResNetConfig, + in_channels: int, + out_channels: int, + stride: int = 2, + depth: int = 2, + ): + super().__init__() + + layer = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer + + if config.layer_type == "bottleneck": + first_layer = layer( + in_channels, + out_channels, + stride=stride, + activation=config.hidden_act, + downsample_in_bottleneck=config.downsample_in_bottleneck, + ) + else: + first_layer = layer(in_channels, out_channels, stride=stride, activation=config.hidden_act) + self.layers = nn.Sequential( + first_layer, *[layer(out_channels, out_channels, activation=config.hidden_act) for _ in range(depth - 1)] + ) + + def forward(self, input: Tensor) -> Tensor: + hidden_state = input + for layer in self.layers: + hidden_state = layer(hidden_state) + return hidden_state + + +class ResNetEncoder(nn.Module): + def __init__(self, config: ResNetConfig): + super().__init__() + self.stages = nn.ModuleList([]) + # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input + self.stages.append( + ResNetStage( + config, + config.embedding_size, + config.hidden_sizes[0], + stride=2 if config.downsample_in_first_stage else 1, + depth=config.depths[0], + ) + ) + in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:]) + for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]): + self.stages.append(ResNetStage(config, in_channels, out_channels, depth=depth)) + + def forward( + self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True + ) -> BaseModelOutputWithNoAttention: + hidden_states = () if output_hidden_states else None + + for stage_module in self.stages: + if output_hidden_states: + hidden_states = hidden_states + (hidden_state,) + + hidden_state = stage_module(hidden_state) + + if output_hidden_states: + hidden_states = hidden_states + (hidden_state,) + + if not return_dict: + return tuple(v for v in [hidden_state, hidden_states] if v is not None) + + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_state, + hidden_states=hidden_states, + ) + + +@auto_docstring +class ResNetPreTrainedModel(PreTrainedModel): + config: ResNetConfig + base_model_prefix = "resnet" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = ["ResNetConvLayer", "ResNetShortCut"] + + @torch.no_grad() + def _init_weights(self, module): + if isinstance(module, nn.Conv2d): + init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") + # copied from the `reset_parameters` method of `class Linear(Module)` in `torch`. + elif isinstance(module, nn.Linear): + init.kaiming_uniform_(module.weight, a=math.sqrt(5)) + if module.bias is not None: + fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(module.weight) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + init.uniform_(module.bias, -bound, bound) + # We need to check it like that as some Detr models replace the BatchNorm2d by their own + elif "BatchNorm" in module.__class__.__name__: + init.ones_(module.weight) + init.zeros_(module.bias) + init.zeros_(module.running_mean) + init.ones_(module.running_var) + if getattr(module, "num_batches_tracked", None) is not None: + init.zeros_(module.num_batches_tracked) + + +@auto_docstring +class ResNetModel(ResNetPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + self.embedder = ResNetEmbeddings(config) + self.encoder = ResNetEncoder(config) + self.pooler = nn.AdaptiveAvgPool2d((1, 1)) + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: Tensor, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> BaseModelOutputWithPoolingAndNoAttention: + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + embedding_output = self.embedder(pixel_values) + + encoder_outputs = self.encoder( + embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict + ) + + last_hidden_state = encoder_outputs[0] + + pooled_output = self.pooler(last_hidden_state) + + if not return_dict: + return (last_hidden_state, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndNoAttention( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + hidden_states=encoder_outputs.hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for + ImageNet. + """ +) +class ResNetForImageClassification(ResNetPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.resnet = ResNetModel(config) + # classification head + self.classifier = nn.Sequential( + nn.Flatten(), + nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(), + ) + # initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> ImageClassifierOutputWithNoAttention: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.resnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) + + pooled_output = outputs.pooler_output if return_dict else outputs[1] + + logits = self.classifier(pooled_output) + + loss = None + + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[2:] + return (loss,) + output if loss is not None else output + + return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states) + + +@auto_docstring( + custom_intro=""" + ResNet backbone, to be used with frameworks like DETR and MaskFormer. + """ +) +class ResNetBackbone(BackboneMixin, ResNetPreTrainedModel): + has_attentions = False + + def __init__(self, config): + super().__init__(config) + + self.num_features = [config.embedding_size] + config.hidden_sizes + self.embedder = ResNetEmbeddings(config) + self.encoder = ResNetEncoder(config) + + # initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @filter_output_hidden_states + @auto_docstring + def forward( + self, + pixel_values: Tensor, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> BackboneOutput: + r""" + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoBackbone + >>> import torch + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") + >>> model = AutoBackbone.from_pretrained( + ... "microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"] + ... ) + + >>> inputs = processor(image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + [1, 2048, 7, 7] + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + embedding_output = self.embedder(pixel_values) + + outputs = self.encoder(embedding_output, output_hidden_states=True, return_dict=True) + + hidden_states = outputs.hidden_states + + feature_maps = () + for idx, stage in enumerate(self.stage_names): + if stage in self.out_features: + feature_maps += (hidden_states[idx],) + + if not return_dict: + output = (feature_maps,) + if output_hidden_states: + output += (outputs.hidden_states,) + return output + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=outputs.hidden_states if output_hidden_states else None, + attentions=None, + ) + + +__all__ = ["ResNetForImageClassification", "ResNetModel", "ResNetPreTrainedModel", "ResNetBackbone"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2e8011675176b7e122895b6d6913065f89c2229e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_roberta import * + from .modeling_roberta import * + from .tokenization_roberta import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/configuration_roberta.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/configuration_roberta.py new file mode 100644 index 0000000000000000000000000000000000000000..210a1b3698ebd65f91097752f6bad94148edfa2e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/configuration_roberta.py @@ -0,0 +1,66 @@ +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RoBERTa configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="FacebookAI/roberta-base") +@strict +class RobertaConfig(PreTrainedConfig): + r""" + Examples: + + ```python + >>> from transformers import RobertaConfig, RobertaModel + + >>> # Initializing a RoBERTa configuration + >>> configuration = RobertaConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = RobertaModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "roberta" + + vocab_size: int = 50265 + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.1 + attention_probs_dropout_prob: float | int = 0.1 + max_position_embeddings: int = 512 + type_vocab_size: int = 2 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-12 + pad_token_id: int | None = 1 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 2 + use_cache: bool = True + classifier_dropout: float | int | None = None + is_decoder: bool = False + add_cross_attention: bool = False + tie_word_embeddings: bool = True + + +__all__ = ["RobertaConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/modeling_roberta.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/modeling_roberta.py new file mode 100644 index 0000000000000000000000000000000000000000..bf891b7dbfe7a19e7767f954dcb831ae8913d4a2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/modeling_roberta.py @@ -0,0 +1,1271 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/roberta/modular_roberta.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_roberta.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable + +import torch +import torch.nn as nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN, gelu +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import apply_chunking_to_forward +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_roberta import RobertaConfig + + +logger = logging.get_logger(__name__) + + +class RobertaEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + self.register_buffer( + "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False + ) + + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + past_key_values_length: int = 0, + ) -> torch.Tensor: + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, self.padding_idx) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + batch_size, seq_length = input_shape + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0]) + buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1) + buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids) + token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length) + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = inputs_embeds + token_type_embeddings + + position_embeddings = self.position_embeddings(position_ids) + embeddings = embeddings + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + @staticmethod + def create_position_ids_from_inputs_embeds(inputs_embeds, padding_idx): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape) + + @staticmethod + def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class RobertaSelfAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + self.config = config + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.scaling = self.attention_head_size**-0.5 + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.is_decoder = config.is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.attention_head_size) + + # get all proj + query_layer = self.query(hidden_states).view(*hidden_shape).transpose(1, 2) + key_layer = self.key(hidden_states).view(*hidden_shape).transpose(1, 2) + value_layer = self.value(hidden_states).view(*hidden_shape).transpose(1, 2) + + if past_key_values is not None: + # decoder-only roberta can have a simple dynamic cache for example + current_past_key_values = past_key_values + if isinstance(past_key_values, EncoderDecoderCache): + current_past_key_values = past_key_values.self_attention_cache + + # save all key/value_layer to cache to be re-used for fast auto-regressive generation + key_layer, value_layer = current_past_key_values.update(key_layer, value_layer, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_layer, + key_layer, + value_layer, + attention_mask, + dropout=0.0 if not self.training else self.dropout.p, + scaling=self.scaling, + **kwargs, + ) + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + return attn_output, attn_weights + + +class RobertaCrossAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + self.config = config + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.scaling = self.attention_head_size**-0.5 + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.is_causal = is_causal + self.layer_idx = layer_idx + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.FloatTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + past_key_values: EncoderDecoderCache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.attention_head_size) + + # get query proj + query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = past_key_values.is_updated.get(self.layer_idx) if past_key_values is not None else False + if past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].keys + value_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].values + else: + kv_shape = (*encoder_hidden_states.shape[:-1], -1, self.attention_head_size) + key_layer = self.key(encoder_hidden_states).view(kv_shape).transpose(1, 2) + value_layer = self.value(encoder_hidden_states).view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + # save all states to the cache + key_layer, value_layer = past_key_values.cross_attention_cache.update( + key_layer, value_layer, self.layer_idx + ) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_layer, + key_layer, + value_layer, + attention_mask, + dropout=0.0 if not self.training else self.dropout.p, + scaling=self.scaling, + **kwargs, + ) + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + return attn_output, attn_weights + + +class RobertaSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class RobertaAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None, is_cross_attention=False): + super().__init__() + self.is_cross_attention = is_cross_attention + attention_class = RobertaCrossAttention if is_cross_attention else RobertaSelfAttention + self.self = attention_class(config, is_causal=is_causal, layer_idx=layer_idx) + self.output = RobertaSelfOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + attention_mask = attention_mask if not self.is_cross_attention else encoder_attention_mask + attention_output, attn_weights = self.self( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = self.output(attention_output, hidden_states) + return attention_output, attn_weights + + +class RobertaIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class RobertaOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class RobertaLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_idx=None): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaAttention(config, is_causal=config.is_decoder, layer_idx=layer_idx) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError(f"{self} should be used as a decoder model if cross attention is added") + self.crossattention = RobertaAttention( + config, + is_causal=False, + layer_idx=layer_idx, + is_cross_attention=True, + ) + self.intermediate = RobertaIntermediate(config) + self.output = RobertaOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + self_attention_output, _ = self.attention( + hidden_states, + attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = self_attention_output + + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" + " by setting `config.add_cross_attention=True`" + ) + + cross_attention_output, _ = self.crossattention( + self_attention_output, + None, # attention_mask + encoder_hidden_states, + encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = cross_attention_output + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + return layer_output + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +@auto_docstring +class RobertaPreTrainedModel(PreTrainedModel): + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": RobertaLayer, + "attentions": RobertaSelfAttention, + "cross_attentions": RobertaCrossAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, RobertaLMHead): + init.zeros_(module.bias) + elif isinstance(module, RobertaEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + init.zeros_(module.token_type_ids) + + +class RobertaEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([RobertaLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | BaseModelOutputWithPastAndCrossAttentions: + for i, layer_module in enumerate(self.layer): + hidden_states = layer_module( + hidden_states, + attention_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +class RobertaPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +@auto_docstring( + custom_intro=""" + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in [Attention is + all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + """ +) +class RobertaModel(RobertaPreTrainedModel): + _no_split_modules = ["RobertaEmbeddings", "RobertaLayer"] + + def __init__(self, config, add_pooling_layer=True): + r""" + add_pooling_layer (bool, *optional*, defaults to `True`): + Whether to add a pooling layer + """ + super().__init__(config) + self.config = config + self.gradient_checkpointing = False + + self.embeddings = RobertaEmbeddings(config) + self.encoder = RobertaEncoder(config) + + self.pooler = RobertaPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | BaseModelOutputWithPoolingAndCrossAttentions: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if use_cache and past_key_values is None: + past_key_values = ( + EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + if encoder_hidden_states is not None or self.config.is_encoder_decoder + else DynamicCache(config=self.config) + ) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + + attention_mask, encoder_attention_mask = self._create_attention_masks( + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + embedding_output=embedding_output, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + ) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + position_ids=position_ids, + **kwargs, + ) + sequence_output = encoder_outputs.last_hidden_state + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + ) + + def _create_attention_masks( + self, + attention_mask, + encoder_attention_mask, + embedding_output, + encoder_hidden_states, + past_key_values, + ): + if self.config.is_decoder: + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + else: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=attention_mask, + ) + + if encoder_attention_mask is not None: + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + return attention_mask, encoder_attention_mask + + +@auto_docstring( + custom_intro=""" + RoBERTa Model with a `language modeling` head on top for CLM fine-tuning. + """ +) +class RobertaForCausalLM(RobertaPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "lm_head.decoder.weight": "roberta.embeddings.word_embeddings.weight", + "lm_head.decoder.bias": "lm_head.bias", + } + + def __init__(self, config): + super().__init__(config) + + if not config.is_decoder: + logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`") + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.lm_head = RobertaLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + past_key_values: tuple[tuple[torch.FloatTensor]] | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + + Example: + + ```python + >>> from transformers import AutoTokenizer, RobertaForCausalLM, AutoConfig + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base") + >>> config = AutoConfig.from_pretrained("FacebookAI/roberta-base") + >>> config.is_decoder = True + >>> model = RobertaForCausalLM.from_pretrained("FacebookAI/roberta-base", config=config) + + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> prediction_logits = outputs.logits + ```""" + if labels is not None: + use_cache = False + + outputs: BaseModelOutputWithPoolingAndCrossAttentions = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring +class RobertaForMaskedLM(RobertaPreTrainedModel): + _tied_weights_keys = { + "lm_head.decoder.weight": "roberta.embeddings.word_embeddings.weight", + "lm_head.decoder.bias": "lm_head.bias", + } + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.lm_head = RobertaLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MaskedLMOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + return_dict=True, + **kwargs, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(prediction_scores.device) + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + +@auto_docstring( + custom_intro=""" + RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the + pooled output) e.g. for GLUE tasks. + """ +) +class RobertaForSequenceClassification(RobertaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | SequenceClassifierOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RobertaForMultipleChoice(RobertaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.roberta = RobertaModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MultipleChoiceModelOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + """ + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + flat_inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + outputs = self.roberta( + flat_input_ids, + position_ids=flat_position_ids, + token_type_ids=flat_token_type_ids, + attention_mask=flat_attention_mask, + inputs_embeds=flat_inputs_embeds, + return_dict=True, + **kwargs, + ) + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(reshaped_logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RobertaForTokenClassification(RobertaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roberta = RobertaModel(config, add_pooling_layer=False) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | TokenClassifierOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +@auto_docstring +class RobertaForQuestionAnswering(RobertaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + start_positions: torch.LongTensor | None = None, + end_positions: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + """ + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "RobertaForCausalLM", + "RobertaForMaskedLM", + "RobertaForMultipleChoice", + "RobertaForQuestionAnswering", + "RobertaForSequenceClassification", + "RobertaForTokenClassification", + "RobertaModel", + "RobertaPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/modular_roberta.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/modular_roberta.py new file mode 100644 index 0000000000000000000000000000000000000000..a215c8e7a0c731af359d0ab923e674d8530b6eee --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/modular_roberta.py @@ -0,0 +1,771 @@ +# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RoBERTa model.""" + +import torch +import torch.nn as nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import gelu +from ...generation import GenerationMixin +from ...modeling_outputs import ( + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import can_return_tuple +from ..bert.modeling_bert import BertCrossAttention, BertEmbeddings, BertLayer, BertModel, BertSelfAttention +from .configuration_roberta import RobertaConfig + + +logger = logging.get_logger(__name__) + + +class RobertaEmbeddings(BertEmbeddings): + def __init__(self, config): + super().__init__(config) + + del self.pad_token_id + del self.position_embeddings + + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + past_key_values_length: int = 0, + ): + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, self.padding_idx) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + batch_size, seq_length = input_shape + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0]) + buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1) + buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids) + token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length) + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = inputs_embeds + token_type_embeddings + + position_embeddings = self.position_embeddings(position_ids) + embeddings = embeddings + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + @staticmethod + def create_position_ids_from_inputs_embeds(inputs_embeds, padding_idx): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape) + + @staticmethod + def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx + + +class RobertaSelfAttention(BertSelfAttention): + pass + + +class RobertaCrossAttention(BertCrossAttention): + pass + + +class RobertaLayer(BertLayer): + pass + + +@auto_docstring +class RobertaPreTrainedModel(PreTrainedModel): + config_class = RobertaConfig + base_model_prefix = "roberta" + supports_gradient_checkpointing = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": RobertaLayer, + "attentions": RobertaSelfAttention, + "cross_attentions": RobertaCrossAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, RobertaLMHead): + init.zeros_(module.bias) + elif isinstance(module, RobertaEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + init.zeros_(module.token_type_ids) + + +class RobertaModel(BertModel): + def __init__(self, config, add_pooling_layer=True): + super().__init__(self, config) + + +@auto_docstring( + custom_intro=""" + RoBERTa Model with a `language modeling` head on top for CLM fine-tuning. + """ +) +class RobertaForCausalLM(RobertaPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "lm_head.decoder.weight": "roberta.embeddings.word_embeddings.weight", + "lm_head.decoder.bias": "lm_head.bias", + } + + def __init__(self, config): + super().__init__(config) + + if not config.is_decoder: + logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`") + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.lm_head = RobertaLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + past_key_values: tuple[tuple[torch.FloatTensor]] | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + + Example: + + ```python + >>> from transformers import AutoTokenizer, RobertaForCausalLM, AutoConfig + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base") + >>> config = AutoConfig.from_pretrained("FacebookAI/roberta-base") + >>> config.is_decoder = True + >>> model = RobertaForCausalLM.from_pretrained("FacebookAI/roberta-base", config=config) + + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> prediction_logits = outputs.logits + ```""" + if labels is not None: + use_cache = False + + outputs: BaseModelOutputWithPoolingAndCrossAttentions = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring +class RobertaForMaskedLM(RobertaPreTrainedModel): + _tied_weights_keys = { + "lm_head.decoder.weight": "roberta.embeddings.word_embeddings.weight", + "lm_head.decoder.bias": "lm_head.bias", + } + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.lm_head = RobertaLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MaskedLMOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + return_dict=True, + **kwargs, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(prediction_scores.device) + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaLMHead(nn.Module): + """Roberta Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + +@auto_docstring( + custom_intro=""" + RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the + pooled output) e.g. for GLUE tasks. + """ +) +class RobertaForSequenceClassification(RobertaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.classifier = RobertaClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | SequenceClassifierOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RobertaForMultipleChoice(RobertaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.roberta = RobertaModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MultipleChoiceModelOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + """ + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + flat_inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + outputs = self.roberta( + flat_input_ids, + position_ids=flat_position_ids, + token_type_ids=flat_token_type_ids, + attention_mask=flat_attention_mask, + inputs_embeds=flat_inputs_embeds, + return_dict=True, + **kwargs, + ) + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(reshaped_logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RobertaForTokenClassification(RobertaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roberta = RobertaModel(config, add_pooling_layer=False) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | TokenClassifierOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class RobertaClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +@auto_docstring +class RobertaForQuestionAnswering(RobertaPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roberta = RobertaModel(config, add_pooling_layer=False) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + start_positions: torch.LongTensor | None = None, + end_positions: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + """ + outputs = self.roberta( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "RobertaForCausalLM", + "RobertaForMaskedLM", + "RobertaForMultipleChoice", + "RobertaForQuestionAnswering", + "RobertaForSequenceClassification", + "RobertaForTokenClassification", + "RobertaModel", + "RobertaPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/tokenization_roberta.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/tokenization_roberta.py new file mode 100644 index 0000000000000000000000000000000000000000..40b4e78426f6152b9ac8eb2d43b48e757b508f67 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/tokenization_roberta.py @@ -0,0 +1,178 @@ +# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for RoBERTa.""" + +from tokenizers import Tokenizer, decoders, pre_tokenizers, processors +from tokenizers.models import BPE + +from ...tokenization_utils_tokenizers import TokenizersBackend +from ...utils import logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} + + +class RobertaTokenizer(TokenizersBackend): + r""" + Construct a RoBERTa tokenizer (backed by HuggingFace's tokenizers library). Based on Byte-Pair-Encoding. + + This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will + be encoded differently whether it is at the beginning of the sentence (without space) or not: + + ```python + >>> from transformers import RobertaTokenizer + + >>> tokenizer = RobertaTokenizer.from_pretrained("FacebookAI/roberta-base") + >>> tokenizer("Hello world")["input_ids"] + [0, 31414, 232, 2] + + >>> tokenizer(" Hello world")["input_ids"] + [0, 20920, 232, 2] + ``` + + You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you + call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. + + + + When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. + + + + This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab (`str`, `dict` or `list`, *optional*): + Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file. + merges (`str` or `list`, *optional*): + Custom merges list. If not provided, merges are loaded from merges_file. + errors (`str`, *optional*, defaults to `"replace"`): + Paradigm to follow when decoding bytes to UTF-8. See + [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. + bos_token (`str`, *optional*, defaults to `""`): + The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. + + + + When building a sequence using special tokens, this is not the token that is used for the beginning of + sequence. The token used is the `cls_token`. + + + + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + sep_token (`str`, *optional*, defaults to `""`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + cls_token (`str`, *optional*, defaults to `""`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + mask_token (`str`, *optional*, defaults to `""`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + add_prefix_space (`bool`, *optional*, defaults to `False`): + Whether or not to add an initial space to the input. This allows to treat the leading word just as any + other word. (RoBERTa tokenizer detect beginning of words by the preceding space). + trim_offsets (`bool`, *optional*, defaults to `True`): + Whether the post processing step should trim offsets to avoid including whitespaces. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + model = BPE + + def __init__( + self, + vocab: str | dict[str, int] | None = None, + merges: str | list[str] | None = None, + errors: str = "replace", + bos_token: str = "", + eos_token: str = "", + sep_token: str = "", + cls_token: str = "", + unk_token: str = "", + pad_token: str = "", + mask_token: str = "", + add_prefix_space: bool = False, + trim_offsets: bool = True, + **kwargs, + ): + self.add_prefix_space = add_prefix_space + self.trim_offsets = trim_offsets + + if vocab is None: + vocab = { + str(pad_token): 0, + str(unk_token): 1, + str(cls_token): 2, + str(sep_token): 3, + str(mask_token): 4, + } + self._vocab = vocab + + self._merges = merges or [] + + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + dropout=None, + continuing_subword_prefix="", + end_of_word_suffix="", + fuse_unk=False, + ) + ) + + self._tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space) + self._tokenizer.decoder = decoders.ByteLevel() + + super().__init__( + errors=errors, + bos_token=bos_token, + eos_token=eos_token, + sep_token=sep_token, + cls_token=cls_token, + unk_token=unk_token, + pad_token=pad_token, + mask_token=mask_token, + add_prefix_space=add_prefix_space, + trim_offsets=trim_offsets, + **kwargs, + ) + self._tokenizer.post_processor = processors.RobertaProcessing( + sep=(str(sep_token), self.sep_token_id), + cls=(str(cls_token), self.cls_token_id), + add_prefix_space=add_prefix_space, + trim_offsets=trim_offsets, + ) + + +__all__ = ["RobertaTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/tokenization_roberta_old.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/tokenization_roberta_old.py new file mode 100644 index 0000000000000000000000000000000000000000..3dceda4c310fe3b53b97048f91ed024bc6503c1f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta/tokenization_roberta_old.py @@ -0,0 +1,262 @@ +# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Fast Tokenization classes for RoBERTa.""" + +import json + +from tokenizers import processors + +from ...tokenization_utils_base import AddedToken, BatchEncoding +from ...tokenization_utils_tokenizers import PreTrainedTokenizerFast +from ...utils import logging +from .tokenization_roberta import RobertaTokenizer + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} + + +class RobertaTokenizerFast(PreTrainedTokenizerFast): + """ + Construct a "fast" RoBERTa tokenizer (backed by HuggingFace's *tokenizers* library), derived from the GPT-2 + tokenizer, using byte-level Byte-Pair-Encoding. + + This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will + be encoded differently whether it is at the beginning of the sentence (without space) or not: + + ```python + >>> from transformers import RobertaTokenizerFast + + >>> tokenizer = RobertaTokenizerFast.from_pretrained("FacebookAI/roberta-base") + >>> tokenizer("Hello world")["input_ids"] + [0, 31414, 232, 2] + + >>> tokenizer(" Hello world")["input_ids"] + [0, 20920, 232, 2] + ``` + + You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer or when you + call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance. + + + + When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. + + + + This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + Path to the vocabulary file. + merges_file (`str`): + Path to the merges file. + errors (`str`, *optional*, defaults to `"replace"`): + Paradigm to follow when decoding bytes to UTF-8. See + [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. + bos_token (`str`, *optional*, defaults to `""`): + The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. + + + + When building a sequence using special tokens, this is not the token that is used for the beginning of + sequence. The token used is the `cls_token`. + + + + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + sep_token (`str`, *optional*, defaults to `""`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + cls_token (`str`, *optional*, defaults to `""`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + mask_token (`str`, *optional*, defaults to `""`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + add_prefix_space (`bool`, *optional*, defaults to `False`): + Whether or not to add an initial space to the input. This allows to treat the leading word just as any + other word. (RoBERTa tokenizer detect beginning of words by the preceding space). + trim_offsets (`bool`, *optional*, defaults to `True`): + Whether the post processing step should trim offsets to avoid including whitespaces. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + slow_tokenizer_class = RobertaTokenizer + + def __init__( + self, + vocab_file=None, + merges_file=None, + tokenizer_file=None, + errors="replace", + bos_token="", + eos_token="", + sep_token="", + cls_token="", + unk_token="", + pad_token="", + mask_token="", + add_prefix_space=False, + trim_offsets=True, + **kwargs, + ): + mask_token = ( + AddedToken(mask_token, lstrip=True, rstrip=False, normalized=False) + if isinstance(mask_token, str) + else mask_token + ) + super().__init__( + vocab_file, + merges_file, + tokenizer_file=tokenizer_file, + errors=errors, + bos_token=bos_token, + eos_token=eos_token, + sep_token=sep_token, + cls_token=cls_token, + unk_token=unk_token, + pad_token=pad_token, + mask_token=mask_token, + add_prefix_space=add_prefix_space, + trim_offsets=trim_offsets, + **kwargs, + ) + + tokenizer_component = "post_processor" + tokenizer_component_instance = getattr(self.backend_tokenizer, tokenizer_component, None) + if tokenizer_component_instance: + state = json.loads(tokenizer_component_instance.__getstate__()) + + # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` + if "sep" in state: + state["sep"] = tuple(state["sep"]) + if "cls" in state: + state["cls"] = tuple(state["cls"]) + + changes_to_apply = False + + if state.get("add_prefix_space", add_prefix_space) != add_prefix_space: + state["add_prefix_space"] = add_prefix_space + changes_to_apply = True + + if state.get("trim_offsets", trim_offsets) != trim_offsets: + state["trim_offsets"] = trim_offsets + changes_to_apply = True + + if changes_to_apply: + component_class = getattr(processors, state.pop("type")) + new_value = component_class(**state) + setattr(self.backend_tokenizer, tokenizer_component, new_value) + + @property + def mask_token(self) -> str: + """ + `str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not + having been set. + + Roberta tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily + comprise the space before the **. + """ + if self._mask_token is None: + if self.verbose: + logger.error("Using mask_token, but it is not set yet.") + return None + return str(self._mask_token) + + @mask_token.setter + def mask_token(self, value): + """ + Overriding the default behavior of the mask token to have it eat the space before it. + + This is needed to preserve backward compatibility with all the previously used models based on Roberta. + """ + # Mask token behave like a normal word, i.e. include the space before it + # So we set lstrip to True + value = AddedToken(value, lstrip=True, rstrip=False) if isinstance(value, str) else value + self._mask_token = value + + def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding: + is_split_into_words = kwargs.get("is_split_into_words", False) + assert self.add_prefix_space or not is_split_into_words, ( + f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " + "to use it with pretokenized inputs." + ) + + return super()._batch_encode_plus(*args, **kwargs) + + def _encode_plus(self, *args, **kwargs) -> BatchEncoding: + is_split_into_words = kwargs.get("is_split_into_words", False) + + assert self.add_prefix_space or not is_split_into_words, ( + f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " + "to use it with pretokenized inputs." + ) + + return super()._encode_plus(*args, **kwargs) + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + files = self._tokenizer.model.save(save_directory, name=filename_prefix) + return tuple(files) + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id] + if token_ids_1 is None: + return output + + return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id] + + def create_token_type_ids_from_sequences( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not + make use of token type ids, therefore a list of zeros is returned. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of zeros. + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0] + + +__all__ = ["RobertaTokenizerFast"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..369698d84ba093652e98f7c23dacbce2663f2269 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_roberta_prelayernorm import * + from .modeling_roberta_prelayernorm import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py new file mode 100644 index 0000000000000000000000000000000000000000..39cbd96d1b2bc0e4a8f5273f13effd8af233f8f7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/configuration_roberta_prelayernorm.py @@ -0,0 +1,67 @@ +# Copyright 2022 The Google AI Language Team Authors and The HuggingFace Inc. team. +# All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RoBERTa-PreLayerNorm configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@strict +@auto_docstring(checkpoint="andreasmadsen/efficient_mlm_m0.40") +# Copied from transformers.models.roberta.configuration_roberta.RobertaConfig with FacebookAI/roberta-base->andreasmadsen/efficient_mlm_m0.40,RoBERTa->RoBERTa-PreLayerNorm,Roberta->RobertaPreLayerNorm,roberta->roberta-prelayernorm +class RobertaPreLayerNormConfig(PreTrainedConfig): + r""" + Examples: + + ```python + >>> from transformers import RobertaPreLayerNormConfig, RobertaPreLayerNormModel + + >>> # Initializing a RoBERTa-PreLayerNorm configuration + >>> configuration = RobertaPreLayerNormConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = RobertaPreLayerNormModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "roberta-prelayernorm" + + vocab_size: int = 50265 + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.1 + attention_probs_dropout_prob: float | int = 0.1 + max_position_embeddings: int = 512 + type_vocab_size: int = 2 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-12 + pad_token_id: int | None = 1 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 2 + use_cache: bool = True + classifier_dropout: float | int | None = None + is_decoder: bool = False + add_cross_attention: bool = False + tie_word_embeddings: bool = True + + +__all__ = ["RobertaPreLayerNormConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py new file mode 100644 index 0000000000000000000000000000000000000000..299d0565edc73030c5bd1869852ac88009c9ea3d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py @@ -0,0 +1,1312 @@ +# Copyright 2022 The Google AI Language Team Authors and The HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RoBERTa-PreLayerNorm model.""" + +from collections.abc import Callable + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN, gelu +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import apply_chunking_to_forward +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_roberta_prelayernorm import RobertaPreLayerNormConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings with Roberta->RobertaPreLayerNorm +class RobertaPreLayerNormEmbeddings(nn.Module): + """Construct the embeddings from word, position and token_type embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + self.register_buffer( + "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False + ) + + self.padding_idx = config.pad_token_id + self.position_embeddings = nn.Embedding( + config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + past_key_values_length: int = 0, + ) -> torch.Tensor: + if position_ids is None: + if input_ids is not None: + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ) + else: + position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds, self.padding_idx) + + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + batch_size, seq_length = input_shape + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0]) + buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1) + buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids) + token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length) + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = inputs_embeds + token_type_embeddings + + position_embeddings = self.position_embeddings(position_ids) + embeddings = embeddings + position_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + @staticmethod + def create_position_ids_from_inputs_embeds(inputs_embeds, padding_idx): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape) + + @staticmethod + def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->RobertaPreLayerNorm +class RobertaPreLayerNormSelfAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + self.config = config + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.scaling = self.attention_head_size**-0.5 + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.is_decoder = config.is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.attention_head_size) + + # get all proj + query_layer = self.query(hidden_states).view(*hidden_shape).transpose(1, 2) + key_layer = self.key(hidden_states).view(*hidden_shape).transpose(1, 2) + value_layer = self.value(hidden_states).view(*hidden_shape).transpose(1, 2) + + if past_key_values is not None: + # decoder-only bert can have a simple dynamic cache for example + current_past_key_values = past_key_values + if isinstance(past_key_values, EncoderDecoderCache): + current_past_key_values = past_key_values.self_attention_cache + + # save all key/value_layer to cache to be re-used for fast auto-regressive generation + key_layer, value_layer = current_past_key_values.update(key_layer, value_layer, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_layer, + key_layer, + value_layer, + attention_mask, + dropout=0.0 if not self.training else self.dropout.p, + scaling=self.scaling, + **kwargs, + ) + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + return attn_output, attn_weights + + +# Copied from transformers.models.bert.modeling_bert.BertCrossAttention with Bert->RobertaPreLayerNorm +class RobertaPreLayerNormCrossAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + self.config = config + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.scaling = self.attention_head_size**-0.5 + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.is_causal = is_causal + self.layer_idx = layer_idx + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.FloatTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + past_key_values: EncoderDecoderCache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.attention_head_size) + + # get query proj + query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = past_key_values.is_updated.get(self.layer_idx) if past_key_values is not None else False + if past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].keys + value_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].values + else: + kv_shape = (*encoder_hidden_states.shape[:-1], -1, self.attention_head_size) + key_layer = self.key(encoder_hidden_states).view(kv_shape).transpose(1, 2) + value_layer = self.value(encoder_hidden_states).view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + # save all states to the cache + key_layer, value_layer = past_key_values.cross_attention_cache.update( + key_layer, value_layer, self.layer_idx + ) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_layer, + key_layer, + value_layer, + attention_mask, + dropout=0.0 if not self.training else self.dropout.p, + scaling=self.scaling, + **kwargs, + ) + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + return attn_output, attn_weights + + +class RobertaPreLayerNormSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = hidden_states + input_tensor + return hidden_states + + +class RobertaPreLayerNormAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None, is_cross_attention=False): + super().__init__() + self.is_cross_attention = is_cross_attention + attention_class = RobertaPreLayerNormCrossAttention if is_cross_attention else RobertaPreLayerNormSelfAttention + self.self = attention_class(config, is_causal=is_causal, layer_idx=layer_idx) + self.output = RobertaPreLayerNormSelfOutput(config) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: tuple[tuple[torch.FloatTensor]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + hidden_states_pre_layer_norm = self.LayerNorm(hidden_states) + attention_mask = attention_mask if not self.is_cross_attention else encoder_attention_mask + attention_output, attn_weights = self.self( + hidden_states_pre_layer_norm, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = self.output(attention_output, hidden_states) + return attention_output, attn_weights + + +class RobertaPreLayerNormIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.LayerNorm(hidden_states) + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class RobertaPreLayerNormOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = hidden_states + input_tensor + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->RobertaPreLayerNorm +class RobertaPreLayerNormLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_idx=None): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RobertaPreLayerNormAttention(config, is_causal=config.is_decoder, layer_idx=layer_idx) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError(f"{self} should be used as a decoder model if cross attention is added") + self.crossattention = RobertaPreLayerNormAttention( + config, + is_causal=False, + layer_idx=layer_idx, + is_cross_attention=True, + ) + self.intermediate = RobertaPreLayerNormIntermediate(config) + self.output = RobertaPreLayerNormOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + self_attention_output, _ = self.attention( + hidden_states, + attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = self_attention_output + + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" + " by setting `config.add_cross_attention=True`" + ) + + cross_attention_output, _ = self.crossattention( + self_attention_output, + None, # attention_mask + encoder_hidden_states, + encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = cross_attention_output + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + return layer_output + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->RobertaPreLayerNorm +class RobertaPreLayerNormEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList( + [RobertaPreLayerNormLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)] + ) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | BaseModelOutputWithPastAndCrossAttentions: + for i, layer_module in enumerate(self.layer): + hidden_states = layer_module( + hidden_states, + attention_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler +class RobertaPreLayerNormPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +@auto_docstring +class RobertaPreLayerNormPreTrainedModel(PreTrainedModel): + config_class = RobertaPreLayerNormConfig + base_model_prefix = "roberta_prelayernorm" + supports_gradient_checkpointing = True + _no_split_modules = [ + "RobertaPreLayerNormEmbeddings", + "RobertaPreLayerNormSelfAttention", + "RobertaPreLayerNormCrossAttention", + ] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": RobertaPreLayerNormLayer, + "attentions": RobertaPreLayerNormSelfAttention, + "cross_attentions": RobertaPreLayerNormCrossAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, RobertaPreLayerNormLMHead): + init.zeros_(module.bias) + elif isinstance(module, RobertaPreLayerNormEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + init.zeros_(module.token_type_ids) + + +@auto_docstring( + custom_intro=""" + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in *Attention is + all you need*_ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz + Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + + .. _*Attention is all you need*: https://huggingface.co/papers/1706.03762 + """ +) +class RobertaPreLayerNormModel(RobertaPreLayerNormPreTrainedModel): + def __init__(self, config, add_pooling_layer=True): + r""" + add_pooling_layer (bool, *optional*, defaults to `True`): + Whether to add a pooling layer + """ + super().__init__(config) + self.config = config + self.gradient_checkpointing = False + + self.embeddings = RobertaPreLayerNormEmbeddings(config) + self.encoder = RobertaPreLayerNormEncoder(config) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.pooler = RobertaPreLayerNormPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | BaseModelOutputWithPoolingAndCrossAttentions: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + """ + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if use_cache and past_key_values is None: + past_key_values = ( + EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + if encoder_hidden_states is not None or self.config.is_encoder_decoder + else DynamicCache(config=self.config) + ) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + + attention_mask, encoder_attention_mask = self._create_attention_masks( + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + embedding_output=embedding_output, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + ) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + position_ids=position_ids, + **kwargs, + ) + sequence_output = encoder_outputs[0] + sequence_output = self.LayerNorm(sequence_output) + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + ) + + # Copied from transformers.models.bert.modeling_bert.BertModel._create_attention_masks + def _create_attention_masks( + self, + attention_mask, + encoder_attention_mask, + embedding_output, + encoder_hidden_states, + past_key_values, + ): + if self.config.is_decoder: + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + else: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=attention_mask, + ) + + if encoder_attention_mask is not None: + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + return attention_mask, encoder_attention_mask + + +@auto_docstring( + custom_intro=""" + RoBERTa-PreLayerNorm Model with a `language modeling` head on top for CLM fine-tuning. + """ +) +# Copied from transformers.models.roberta.modeling_roberta.RobertaForCausalLM with FacebookAI/roberta-base->andreasmadsen/efficient_mlm_m0.40,ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm, RobertaPreLayerNormTokenizer->RobertaTokenizer +class RobertaPreLayerNormForCausalLM(RobertaPreLayerNormPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "lm_head.decoder.weight": "roberta_prelayernorm.embeddings.word_embeddings.weight", + "lm_head.decoder.bias": "lm_head.bias", + } + + def __init__(self, config): + super().__init__(config) + + if not config.is_decoder: + logger.warning( + "If you want to use `RobertaPreLayerNormLMHeadModel` as a standalone, add `is_decoder=True.`" + ) + + self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False) + self.lm_head = RobertaPreLayerNormLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + past_key_values: tuple[tuple[torch.FloatTensor]] | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + + Example: + + ```python + >>> from transformers import AutoTokenizer, RobertaPreLayerNormForCausalLM, AutoConfig + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("andreasmadsen/efficient_mlm_m0.40") + >>> config = AutoConfig.from_pretrained("andreasmadsen/efficient_mlm_m0.40") + >>> config.is_decoder = True + >>> model = RobertaPreLayerNormForCausalLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40", config=config) + + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> prediction_logits = outputs.logits + ```""" + if labels is not None: + use_cache = False + + outputs: BaseModelOutputWithPoolingAndCrossAttentions = self.roberta_prelayernorm( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + RoBERTa-PreLayerNorm Model with a `language modeling` head on top. + """ +) +class RobertaPreLayerNormForMaskedLM(RobertaPreLayerNormPreTrainedModel): + _tied_weights_keys = { + "lm_head.decoder.weight": "roberta_prelayernorm.embeddings.word_embeddings.weight", + "lm_head.decoder.bias": "lm_head.bias", + } + + # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.__init__ with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RobertaPreLayerNormForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False) + self.lm_head = RobertaPreLayerNormLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head.decoder + + def set_output_embeddings(self, new_embeddings): + self.lm_head.decoder = new_embeddings + + @can_return_tuple + @auto_docstring + # Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM.forward with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MaskedLMOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + outputs = self.roberta_prelayernorm( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + return_dict=True, + **kwargs, + ) + sequence_output = outputs[0] + prediction_scores = self.lm_head(sequence_output) + + masked_lm_loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(prediction_scores.device) + loss_fct = CrossEntropyLoss() + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +# Copied from transformers.models.roberta.modeling_roberta.RobertaLMHead with Roberta->RobertaPreLayerNorm +class RobertaPreLayerNormLMHead(nn.Module): + """RobertaPreLayerNorm Head for masked language modeling.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.decoder = nn.Linear(config.hidden_size, config.vocab_size) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + def forward(self, features, **kwargs): + x = self.dense(features) + x = gelu(x) + x = self.layer_norm(x) + + # project back to size of vocabulary with bias + x = self.decoder(x) + + return x + + +@auto_docstring( + custom_intro=""" + RoBERTa-PreLayerNorm Model transformer with a sequence classification/regression head on top (a linear layer on top + of the pooled output) e.g. for GLUE tasks. + """ +) +class RobertaPreLayerNormForSequenceClassification(RobertaPreLayerNormPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False) + self.classifier = RobertaPreLayerNormClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + # Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.forward with roberta->roberta_prelayernorm + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | SequenceClassifierOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + outputs = self.roberta_prelayernorm( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +# Copied from transformers.models.roberta.modeling_roberta.RobertaForMultipleChoice with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm +class RobertaPreLayerNormForMultipleChoice(RobertaPreLayerNormPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.roberta_prelayernorm = RobertaPreLayerNormModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MultipleChoiceModelOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + """ + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + flat_position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + flat_token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + flat_inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + outputs = self.roberta_prelayernorm( + flat_input_ids, + position_ids=flat_position_ids, + token_type_ids=flat_token_type_ids, + attention_mask=flat_attention_mask, + inputs_embeds=flat_inputs_embeds, + return_dict=True, + **kwargs, + ) + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(reshaped_logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RobertaPreLayerNormForTokenClassification(RobertaPreLayerNormPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + # Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification.forward with roberta->roberta_prelayernorm + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | TokenClassifierOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + outputs = self.roberta_prelayernorm( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + # move labels to correct device + labels = labels.to(logits.device) + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +# Copied from transformers.models.roberta.modeling_roberta.RobertaClassificationHead with Roberta->RobertaPreLayerNorm +class RobertaPreLayerNormClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +@auto_docstring +class RobertaPreLayerNormForQuestionAnswering(RobertaPreLayerNormPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + # Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.forward with roberta->roberta_prelayernorm + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + start_positions: torch.LongTensor | None = None, + end_positions: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput: + r""" + token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + This parameter can only be used when the model is initialized with `type_vocab_size` parameter with value + >= 2. All the value in this tensor should be always < type_vocab_size. + + [What are token type IDs?](../glossary#token-type-ids) + """ + outputs = self.roberta_prelayernorm( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "RobertaPreLayerNormForCausalLM", + "RobertaPreLayerNormForMaskedLM", + "RobertaPreLayerNormForMultipleChoice", + "RobertaPreLayerNormForQuestionAnswering", + "RobertaPreLayerNormForSequenceClassification", + "RobertaPreLayerNormForTokenClassification", + "RobertaPreLayerNormModel", + "RobertaPreLayerNormPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..eb847b03b75587df50ebb17140401da0be4dea22 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_roc_bert import * + from .modeling_roc_bert import * + from .tokenization_roc_bert import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/configuration_roc_bert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/configuration_roc_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..4600e6cab1eb3390fa6f0dda435d7b573f8263d2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/configuration_roc_bert.py @@ -0,0 +1,89 @@ +# Copyright 2022 WeChatAI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RoCBert model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="weiweishi/roc-bert-base-zh") +@strict +class RoCBertConfig(PreTrainedConfig): + r""" + enable_pronunciation (`bool`, *optional*, defaults to `True`): + Whether or not the model use pronunciation embed when training. + enable_shape (`bool`, *optional*, defaults to `True`): + Whether or not the model use shape embed when training. + pronunciation_embed_dim (`int`, *optional*, defaults to 768): + Dimension of the pronunciation_embed. + pronunciation_vocab_size (`int`, *optional*, defaults to 910): + Pronunciation Vocabulary size of the RoCBert model. Defines the number of different tokens that can be + represented by the `input_pronunciation_ids` passed when calling [`RoCBertModel`]. + shape_embed_dim (`int`, *optional*, defaults to 512): + Dimension of the shape_embed. + shape_vocab_size (`int`, *optional*, defaults to 24858): + Shape Vocabulary size of the RoCBert model. Defines the number of different tokens that can be represented + by the `input_shape_ids` passed when calling [`RoCBertModel`]. + concat_input (`bool`, *optional*, defaults to `True`): + Defines the way of merging the shape_embed, pronunciation_embed and word_embed, if the value is true, + output_embed = torch.cat((word_embed, shape_embed, pronunciation_embed), -1), else output_embed = + (word_embed + shape_embed + pronunciation_embed) / 3 + + Example: + + ```python + >>> from transformers import RoCBertModel, RoCBertConfig + + >>> # Initializing a RoCBert weiweishi/roc-bert-base-zh style configuration + >>> configuration = RoCBertConfig() + + >>> # Initializing a model from the weiweishi/roc-bert-base-zh style configuration + >>> model = RoCBertModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "roc_bert" + + vocab_size: int = 30522 + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.1 + attention_probs_dropout_prob: float | int = 0.1 + max_position_embeddings: int = 512 + type_vocab_size: int = 2 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-12 + use_cache: bool = True + pad_token_id: int | None = 0 + classifier_dropout: float | int | None = None + enable_pronunciation: bool = True + enable_shape: bool = True + pronunciation_embed_dim: int = 768 + pronunciation_vocab_size: int = 910 + shape_embed_dim: int = 512 + shape_vocab_size: int = 24858 + concat_input: bool = True + is_decoder: bool = False + add_cross_attention: bool = False + tie_word_embeddings: bool = True + + +__all__ = ["RoCBertConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/modeling_roc_bert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/modeling_roc_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..40bd4dcf5964f936205075237a4b514308cda8ae --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/modeling_roc_bert.py @@ -0,0 +1,1660 @@ +# Copyright 2022 WeChatAI The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RoCBert model.""" + +from collections.abc import Callable + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import apply_chunking_to_forward +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_roc_bert import RoCBertConfig + + +logger = logging.get_logger(__name__) + + +class RoCBertEmbeddings(nn.Module): + """Construct the embeddings from word, position, shape, pronunciation and token_type embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.pronunciation_embed = nn.Embedding( + config.pronunciation_vocab_size, config.pronunciation_embed_dim, padding_idx=config.pad_token_id + ) + self.shape_embed = nn.Embedding( + config.shape_vocab_size, config.shape_embed_dim, padding_idx=config.pad_token_id + ) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size) + + self.enable_pronunciation = config.enable_pronunciation + self.enable_shape = config.enable_shape + + if config.concat_input: + input_dim = config.hidden_size + if self.enable_pronunciation: + pronunciation_dim = config.pronunciation_embed_dim + input_dim += pronunciation_dim + if self.enable_shape: + shape_dim = config.shape_embed_dim + input_dim += shape_dim + self.map_inputs_layer = torch.nn.Linear(input_dim, config.hidden_size) + else: + self.map_inputs_layer = None + + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + self.register_buffer( + "token_type_ids", + torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device), + persistent=False, + ) + + def forward( + self, + input_ids=None, + input_shape_ids=None, + input_pronunciation_ids=None, + token_type_ids=None, + position_ids=None, + inputs_embeds=None, + past_key_values_length=0, + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + batch_size, seq_length = input_shape + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs + # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves + # issue #5664 + if token_type_ids is None: + if hasattr(self, "token_type_ids"): + # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0]) + buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1) + buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids) + token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length) + else: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) + + if self.map_inputs_layer is None: + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embeddings = inputs_embeds + token_type_embeddings + position_embeddings = self.position_embeddings(position_ids) + embeddings = embeddings + position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + + denominator = 1 + embedding_in = torch.clone(embeddings) + if self.enable_shape and input_shape_ids is not None: + embedding_shape = self.shape_embed(input_shape_ids) + embedding_in += embedding_shape + denominator += 1 + if self.enable_pronunciation and input_pronunciation_ids is not None: + embedding_pronunciation = self.pronunciation_embed(input_pronunciation_ids) + embedding_in += embedding_pronunciation + denominator += 1 + + embedding_in /= denominator + return embedding_in + else: + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) # embedding_word + device = inputs_embeds.device + + embedding_in = torch.clone(inputs_embeds) + if self.enable_shape: + if input_shape_ids is None: + input_shape_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + embedding_shape = self.shape_embed(input_shape_ids) + embedding_in = torch.cat((embedding_in, embedding_shape), -1) + if self.enable_pronunciation: + if input_pronunciation_ids is None: + input_pronunciation_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + embedding_pronunciation = self.pronunciation_embed(input_pronunciation_ids) + embedding_in = torch.cat((embedding_in, embedding_pronunciation), -1) + + embedding_in = self.map_inputs_layer(embedding_in) # batch_size * seq_len * hidden_dim + + token_type_embeddings = self.token_type_embeddings(token_type_ids) + embedding_in += token_type_embeddings + position_embeddings = self.position_embeddings(position_ids) + embedding_in += position_embeddings + + embedding_in = self.LayerNorm(embedding_in) + embedding_in = self.dropout(embedding_in) + return embedding_in + + +# Copied from transformers.models.bert.modeling_bert.eager_attention_forward +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->RoCBert +class RoCBertSelfAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + self.config = config + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.scaling = self.attention_head_size**-0.5 + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.is_decoder = config.is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.attention_head_size) + + # get all proj + query_layer = self.query(hidden_states).view(*hidden_shape).transpose(1, 2) + key_layer = self.key(hidden_states).view(*hidden_shape).transpose(1, 2) + value_layer = self.value(hidden_states).view(*hidden_shape).transpose(1, 2) + + if past_key_values is not None: + # decoder-only bert can have a simple dynamic cache for example + current_past_key_values = past_key_values + if isinstance(past_key_values, EncoderDecoderCache): + current_past_key_values = past_key_values.self_attention_cache + + # save all key/value_layer to cache to be re-used for fast auto-regressive generation + key_layer, value_layer = current_past_key_values.update(key_layer, value_layer, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_layer, + key_layer, + value_layer, + attention_mask, + dropout=0.0 if not self.training else self.dropout.p, + scaling=self.scaling, + **kwargs, + ) + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + return attn_output, attn_weights + + +# Copied from transformers.models.bert.modeling_bert.BertCrossAttention with Bert->RoCBert +class RoCBertCrossAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + self.config = config + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.scaling = self.attention_head_size**-0.5 + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.is_causal = is_causal + self.layer_idx = layer_idx + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.FloatTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + past_key_values: EncoderDecoderCache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.attention_head_size) + + # get query proj + query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + + is_updated = past_key_values.is_updated.get(self.layer_idx) if past_key_values is not None else False + if past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].keys + value_layer = past_key_values.cross_attention_cache.layers[self.layer_idx].values + else: + kv_shape = (*encoder_hidden_states.shape[:-1], -1, self.attention_head_size) + key_layer = self.key(encoder_hidden_states).view(kv_shape).transpose(1, 2) + value_layer = self.value(encoder_hidden_states).view(kv_shape).transpose(1, 2) + + if past_key_values is not None: + # save all states to the cache + key_layer, value_layer = past_key_values.cross_attention_cache.update( + key_layer, value_layer, self.layer_idx + ) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + past_key_values.is_updated[self.layer_idx] = True + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_layer, + key_layer, + value_layer, + attention_mask, + dropout=0.0 if not self.training else self.dropout.p, + scaling=self.scaling, + **kwargs, + ) + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + return attn_output, attn_weights + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->RoCBert +class RoCBertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->RoCBert,BERT->ROC_BERT +class RoCBertAttention(nn.Module): + def __init__(self, config, is_causal=False, layer_idx=None, is_cross_attention=False): + super().__init__() + self.is_cross_attention = is_cross_attention + attention_class = RoCBertCrossAttention if is_cross_attention else RoCBertSelfAttention + self.self = attention_class(config, is_causal=is_causal, layer_idx=layer_idx) + self.output = RoCBertSelfOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor]: + attention_mask = attention_mask if not self.is_cross_attention else encoder_attention_mask + attention_output, attn_weights = self.self( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = self.output(attention_output, hidden_states) + return attention_output, attn_weights + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->RoCBert +class RoCBertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RoCBert +class RoCBertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->RoCBert +class RoCBertLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_idx=None): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RoCBertAttention(config, is_causal=config.is_decoder, layer_idx=layer_idx) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError(f"{self} should be used as a decoder model if cross attention is added") + self.crossattention = RoCBertAttention( + config, + is_causal=False, + layer_idx=layer_idx, + is_cross_attention=True, + ) + self.intermediate = RoCBertIntermediate(config) + self.output = RoCBertOutput(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + self_attention_output, _ = self.attention( + hidden_states, + attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = self_attention_output + + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers" + " by setting `config.add_cross_attention=True`" + ) + + cross_attention_output, _ = self.crossattention( + self_attention_output, + None, # attention_mask + encoder_hidden_states, + encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + attention_output = cross_attention_output + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + return layer_output + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->RoCBert +class RoCBertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([RoCBertLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | BaseModelOutputWithPastAndCrossAttentions: + for i, layer_module in enumerate(self.layer): + hidden_states = layer_module( + hidden_states, + attention_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + **kwargs, + ) + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->RoCBert +class RoCBertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +# Copied from transformers.models.bert.modeling_bert.BertPredictionHeadTransform with Bert->RoCBert +class RoCBertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->RoCBert +class RoCBertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = RoCBertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=True) + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->RoCBert +class RoCBertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = RoCBertLMPredictionHead(config) + + def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +@auto_docstring +class RoCBertPreTrainedModel(PreTrainedModel): + config_class = RoCBertConfig + base_model_prefix = "roc_bert" + supports_gradient_checkpointing = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": RoCBertLayer, + "attentions": RoCBertSelfAttention, + "cross_attentions": RoCBertCrossAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + if isinstance(module, RoCBertLMPredictionHead): + init.zeros_(module.bias) + elif isinstance(module, RoCBertEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + init.zeros_(module.token_type_ids) + + +@auto_docstring( + custom_intro=""" + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in [Attention is + all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to be initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + """ +) +class RoCBertModel(RoCBertPreTrainedModel): + def __init__(self, config, add_pooling_layer=True): + r""" + add_pooling_layer (bool, *optional*, defaults to `True`): + Whether to add a pooling layer + """ + super().__init__(config) + self.config = config + self.gradient_checkpointing = False + + self.embeddings = RoCBertEmbeddings(config) + self.encoder = RoCBertEncoder(config) + + self.pooler = RoCBertPooler(config) if add_pooling_layer else None + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.bert.modeling_bert.BertModel.get_input_embeddings + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + # Copied from transformers.models.bert.modeling_bert.BertModel.set_input_embeddings + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def get_pronunciation_embeddings(self): + return self.embeddings.pronunciation_embed + + def set_pronunciation_embeddings(self, value): + self.embeddings.pronunciation_embed = value + + def get_shape_embeddings(self): + return self.embeddings.shape_embed + + def set_shape_embeddings(self, value): + self.embeddings.shape_embed = value + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + input_shape_ids: torch.Tensor | None = None, + input_pronunciation_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | BaseModelOutputWithPoolingAndCrossAttentions: + r""" + input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the shape vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_shape_ids) + input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the pronunciation vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_pronunciation_ids) + """ + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if use_cache and past_key_values is None: + past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + embedding_output = self.embeddings( + input_ids=input_ids, + input_shape_ids=input_shape_ids, + input_pronunciation_ids=input_pronunciation_ids, + position_ids=position_ids, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + + attention_mask, encoder_attention_mask = self._create_attention_masks( + attention_mask=attention_mask, + encoder_attention_mask=encoder_attention_mask, + embedding_output=embedding_output, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + ) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + position_ids=position_ids, + **kwargs, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + ) + + # Copied from transformers.models.bert.modeling_bert.BertModel._create_attention_masks + def _create_attention_masks( + self, + attention_mask, + encoder_attention_mask, + embedding_output, + encoder_hidden_states, + past_key_values, + ): + if self.config.is_decoder: + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + else: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=attention_mask, + ) + + if encoder_attention_mask is not None: + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=embedding_output, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + return attention_mask, encoder_attention_mask + + +@auto_docstring( + custom_intro=""" + RoCBert Model with contrastive loss and masked_lm_loss during the pretraining. + """ +) +class RoCBertForPreTraining(RoCBertPreTrainedModel): + _tied_weights_keys = { + "cls.predictions.decoder.bias": "cls.predictions.bias", + "cls.predictions.decoder.weight": "roc_bert.embeddings.word_embeddings.weight", + } + + def __init__(self, config): + super().__init__(config) + + self.roc_bert = RoCBertModel(config) + self.cls = RoCBertOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.bert.modeling_bert.BertForPreTraining.get_output_embeddings + def get_output_embeddings(self): + return self.cls.predictions.decoder + + # Copied from transformers.models.bert.modeling_bert.BertForPreTraining.set_output_embeddings + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + self.cls.predictions.bias = new_embeddings.bias + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + input_shape_ids: torch.Tensor | None = None, + input_pronunciation_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + attack_input_ids: torch.Tensor | None = None, + attack_input_shape_ids: torch.Tensor | None = None, + attack_input_pronunciation_ids: torch.Tensor | None = None, + attack_attention_mask: torch.Tensor | None = None, + attack_token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + labels_input_ids: torch.Tensor | None = None, + labels_input_shape_ids: torch.Tensor | None = None, + labels_input_pronunciation_ids: torch.Tensor | None = None, + labels_attention_mask: torch.Tensor | None = None, + labels_token_type_ids: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MaskedLMOutput: + r""" + input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the shape vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_shape_ids) + input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the pronunciation vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_pronunciation_ids) + attack_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + attack sample ids for computing the contrastive loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), + the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + attack_input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + attack sample shape ids for computing the contrastive loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), + the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + attack_input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + attack sample pronunciation ids for computing the contrastive loss. Indices should be in `[-100, 0, + ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + attack_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices for the attack sample. Mask values selected in + `[0, 1]`: `1` for tokens that are NOT MASKED, `0` for MASKED tokens. + attack_token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate different portions of the attack inputs. Indices are selected in `[0, 1]`: + `0` corresponds to a sentence A token, `1` corresponds to a sentence B token. + labels_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + target ids for computing the contrastive loss and masked_lm_loss . Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), + the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + labels_input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + target shape ids for computing the contrastive loss and masked_lm_loss . Indices should be in `[-100, + 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + labels_input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + target pronunciation ids for computing the contrastive loss and masked_lm_loss . Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., + config.vocab_size]` + labels_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices for the label sample. Mask values selected in + `[0, 1]`: `1` for tokens that are NOT MASKED, `0` for MASKED tokens. + labels_token_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Segment token indices to indicate different portions of the label inputs. Indices are selected in `[0, 1]`: + `0` corresponds to a sentence A token, `1` corresponds to a sentence B token. + + Example: + + ```python + >>> from transformers import AutoTokenizer, RoCBertForPreTraining + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("weiweishi/roc-bert-base-zh") + >>> model = RoCBertForPreTraining.from_pretrained("weiweishi/roc-bert-base-zh") + + >>> inputs = tokenizer("你好，很高兴认识你", return_tensors="pt") + >>> attack_inputs = {} + >>> for key in list(inputs.keys()): + ... attack_inputs[f"attack_{key}"] = inputs[key] + >>> label_inputs = {} + >>> for key in list(inputs.keys()): + ... label_inputs[f"labels_{key}"] = inputs[key] + + >>> inputs.update(label_inputs) + >>> inputs.update(attack_inputs) + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> logits.shape + torch.Size([1, 11, 21128]) + ``` + """ + outputs = self.roc_bert( + input_ids, + input_shape_ids=input_shape_ids, + input_pronunciation_ids=input_pronunciation_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output, pooled_output = outputs[:2] + prediction_scores = self.cls(sequence_output) + + loss = None + if labels_input_ids is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels_input_ids.view(-1)) + + if attack_input_ids is not None: + batch_size, _ = labels_input_ids.shape + device = labels_input_ids.device + + target_inputs = torch.clone(labels_input_ids) + target_inputs[target_inputs == -100] = self.config.pad_token_id + + labels_output = self.roc_bert( + target_inputs, + input_shape_ids=labels_input_shape_ids, + input_pronunciation_ids=labels_input_pronunciation_ids, + attention_mask=labels_attention_mask, + token_type_ids=labels_token_type_ids, + ) + attack_output = self.roc_bert( + attack_input_ids, + input_shape_ids=attack_input_shape_ids, + input_pronunciation_ids=attack_input_pronunciation_ids, + attention_mask=attack_attention_mask, + token_type_ids=attack_token_type_ids, + ) + + labels_pooled_output = labels_output[1] + attack_pooled_output = attack_output[1] + + pooled_output_norm = torch.nn.functional.normalize(pooled_output, dim=-1) + labels_pooled_output_norm = torch.nn.functional.normalize(labels_pooled_output, dim=-1) + attack_pooled_output_norm = torch.nn.functional.normalize(attack_pooled_output, dim=-1) + + sim_matrix = torch.matmul(pooled_output_norm, attack_pooled_output_norm.T) # batch_size * hidden_dim + sim_matrix_target = torch.matmul(labels_pooled_output_norm, attack_pooled_output_norm.T) + batch_labels = torch.tensor(list(range(batch_size)), device=device) + contrastive_loss = ( + loss_fct(100 * sim_matrix.view(batch_size, -1), batch_labels.view(-1)) + + loss_fct(100 * sim_matrix_target.view(batch_size, -1), batch_labels.view(-1)) + ) / 2 + + loss = contrastive_loss + masked_lm_loss + else: + loss = masked_lm_loss + + return MaskedLMOutput( + loss=loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RoCBertForMaskedLM(RoCBertPreTrainedModel): + _tied_weights_keys = { + "cls.predictions.decoder.bias": "cls.predictions.bias", + "cls.predictions.decoder.weight": "roc_bert.embeddings.word_embeddings.weight", + } + + # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.__init__ with Bert->RoCBert,bert->roc_bert + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RoCBertForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + self.roc_bert = RoCBertModel(config, add_pooling_layer=False) + self.cls = RoCBertOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.get_output_embeddings + def get_output_embeddings(self): + return self.cls.predictions.decoder + + # Copied from transformers.models.bert.modeling_bert.BertForMaskedLM.set_output_embeddings + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + self.cls.predictions.bias = new_embeddings.bias + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + input_shape_ids: torch.Tensor | None = None, + input_pronunciation_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MaskedLMOutput: + r""" + input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the shape vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_shape_ids) + input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the pronunciation vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_pronunciation_ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + ```python + >>> from transformers import AutoTokenizer, RoCBertForMaskedLM + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("weiweishi/roc-bert-base-zh") + >>> model = RoCBertForMaskedLM.from_pretrained("weiweishi/roc-bert-base-zh") + + >>> inputs = tokenizer("法国是首都[MASK].", return_tensors="pt") + + >>> with torch.no_grad(): + ... logits = model(**inputs).logits + + >>> # retrieve index of {mask} + >>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0] + + >>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1) + >>> tokenizer.decode(predicted_token_id) + '.' + ``` + """ + outputs = self.roc_bert( + input_ids, + input_shape_ids=input_shape_ids, + input_pronunciation_ids=input_pronunciation_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + RoCBert Model with a `language modeling` head on top for CLM fine-tuning. + """ +) +class RoCBertForCausalLM(RoCBertPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "cls.predictions.decoder.bias": "cls.predictions.bias", + "cls.predictions.decoder.weight": "roc_bert.embeddings.word_embeddings.weight", + } + + # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.__init__ with BertLMHeadModel->RoCBertForCausalLM,Bert->RoCBert,bert->roc_bert + def __init__(self, config): + super().__init__(config) + + if not config.is_decoder: + logger.warning("If you want to use `RoCRoCBertForCausalLM` as a standalone, add `is_decoder=True.`") + + self.roc_bert = RoCBertModel(config, add_pooling_layer=False) + self.cls = RoCBertOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.get_output_embeddings + def get_output_embeddings(self): + return self.cls.predictions.decoder + + # Copied from transformers.models.bert.modeling_bert.BertLMHeadModel.set_output_embeddings + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + self.cls.predictions.bias = new_embeddings.bias + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + input_shape_ids: torch.Tensor | None = None, + input_pronunciation_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: list[torch.Tensor] | None = None, + labels: torch.Tensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions: + r""" + input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the shape vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_shape_ids) + input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the pronunciation vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_pronunciation_ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, RoCBertForCausalLM, RoCBertConfig + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("weiweishi/roc-bert-base-zh") + >>> config = RoCBertConfig.from_pretrained("weiweishi/roc-bert-base-zh") + >>> config.is_decoder = True + >>> model = RoCBertForCausalLM.from_pretrained("weiweishi/roc-bert-base-zh", config=config) + + >>> inputs = tokenizer("你好，很高兴认识你", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> prediction_logits = outputs.logits + ``` + """ + outputs: BaseModelOutputWithPoolingAndCrossAttentions = self.roc_bert( + input_ids, + input_shape_ids=input_shape_ids, + input_pronunciation_ids=input_pronunciation_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.cls(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + input_shape_ids=None, + input_pronunciation_ids=None, + past_key_values=None, + attention_mask=None, + **model_kwargs, + ): + # Overwritten -- `input_pronunciation_ids` + + model_inputs = super().prepare_inputs_for_generation( + input_ids=input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + **model_kwargs, + ) + + # cut decoder_input_ids if past_key_values is used + if past_key_values is not None: + if input_shape_ids is not None: + model_inputs["input_shape_ids"] = input_shape_ids[:, -1:] + if input_pronunciation_ids is not None: + model_inputs["input_pronunciation_ids"] = input_pronunciation_ids[:, -1:] + + return model_inputs + + +@auto_docstring( + custom_intro=""" + RoCBert Model transformer with a sequence classification/regression head on top (a linear layer on top of + the pooled output) e.g. for GLUE tasks. + """ +) +class RoCBertForSequenceClassification(RoCBertPreTrainedModel): + # Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification.__init__ with Bert->RoCBert,bert->roc_bert + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.config = config + + self.roc_bert = RoCBertModel(config) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + input_shape_ids: torch.Tensor | None = None, + input_pronunciation_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | SequenceClassifierOutput: + r""" + input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the shape vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_shape_ids) + input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the pronunciation vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_pronunciation_ids) + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + outputs = self.roc_bert( + input_ids, + input_shape_ids=input_shape_ids, + input_pronunciation_ids=input_pronunciation_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RoCBertForMultipleChoice(RoCBertPreTrainedModel): + # Copied from transformers.models.bert.modeling_bert.BertForMultipleChoice.__init__ with Bert->RoCBert,bert->roc_bert + def __init__(self, config): + super().__init__(config) + + self.roc_bert = RoCBertModel(config) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + input_shape_ids: torch.Tensor | None = None, + input_pronunciation_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | MultipleChoiceModelOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + input_shape_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the shape vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_shape_ids) + input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the pronunciation vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_pronunciation_ids) + token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + [What are position IDs?](../glossary#position-ids) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert *input_ids* indices into associated vectors than the + model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + """ + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + input_shape_ids = input_shape_ids.view(-1, input_shape_ids.size(-1)) if input_shape_ids is not None else None + input_pronunciation_ids = ( + input_pronunciation_ids.view(-1, input_pronunciation_ids.size(-1)) + if input_pronunciation_ids is not None + else None + ) + attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None + inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + outputs = self.roc_bert( + input_ids, + input_shape_ids=input_shape_ids, + input_pronunciation_ids=input_pronunciation_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + pooled_output = outputs[1] + + pooled_output = self.dropout(pooled_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RoCBertForTokenClassification(RoCBertPreTrainedModel): + # Copied from transformers.models.bert.modeling_bert.BertForTokenClassification.__init__ with Bert->RoCBert,bert->roc_bert + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roc_bert = RoCBertModel(config, add_pooling_layer=False) + classifier_dropout = ( + config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob + ) + self.dropout = nn.Dropout(classifier_dropout) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + input_shape_ids: torch.Tensor | None = None, + input_pronunciation_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | TokenClassifierOutput: + r""" + input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the shape vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_shape_ids) + input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the pronunciation vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_pronunciation_ids) + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + outputs = self.roc_bert( + input_ids, + input_shape_ids=input_shape_ids, + input_pronunciation_ids=input_pronunciation_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RoCBertForQuestionAnswering(RoCBertPreTrainedModel): + # Copied from transformers.models.bert.modeling_bert.BertForQuestionAnswering.__init__ with Bert->RoCBert,bert->roc_bert + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roc_bert = RoCBertModel(config, add_pooling_layer=False) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + input_shape_ids: torch.Tensor | None = None, + input_pronunciation_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + token_type_ids: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + start_positions: torch.Tensor | None = None, + end_positions: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput: + r""" + input_shape_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the shape vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_shape_ids) + input_pronunciation_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the pronunciation vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input_pronunciation_ids) + """ + outputs = self.roc_bert( + input_ids, + input_shape_ids=input_shape_ids, + input_pronunciation_ids=input_pronunciation_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + return_dict=True, + **kwargs, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "RoCBertForCausalLM", + "RoCBertForMaskedLM", + "RoCBertForMultipleChoice", + "RoCBertForPreTraining", + "RoCBertForQuestionAnswering", + "RoCBertForSequenceClassification", + "RoCBertForTokenClassification", + "RoCBertLayer", + "RoCBertModel", + "RoCBertPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/tokenization_roc_bert.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/tokenization_roc_bert.py new file mode 100644 index 0000000000000000000000000000000000000000..3663ee35f21be3b04af3a931a2e32cee8c39bc46 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roc_bert/tokenization_roc_bert.py @@ -0,0 +1,1319 @@ +# Copyright 2022 WeChatAI and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization classes for RoCBert.""" + +import collections +import itertools +import json +import os +import unicodedata + +from ...tokenization_python import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace +from ...tokenization_utils_base import ( + ENCODE_KWARGS_DOCSTRING, + ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING, + BatchEncoding, + EncodedInput, + EncodedInputPair, + PaddingStrategy, + PreTokenizedInput, + PreTokenizedInputPair, + TensorType, + TextInput, + TextInputPair, + TruncationStrategy, +) +from ...utils import add_end_docstrings, logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = { + "vocab_file": "vocab.txt", + "word_shape_file": "word_shape.json", + "word_pronunciation_file": "word_pronunciation.json", +} + + +def load_vocab(vocab_file): + """Loads a vocabulary file into a dictionary.""" + vocab = collections.OrderedDict() + with open(vocab_file, "r", encoding="utf-8") as reader: + tokens = reader.readlines() + for index, token in enumerate(tokens): + token = token.rstrip("\n") + vocab[token] = index + return vocab + + +def whitespace_tokenize(text): + """Runs basic whitespace cleaning and splitting on a piece of text.""" + text = text.strip() + if not text: + return [] + tokens = text.split() + return tokens + + +class RoCBertTokenizer(PreTrainedTokenizer): + r""" + Args: + Construct a RoCBert tokenizer. Based on WordPiece. This tokenizer inherits from [`PreTrainedTokenizer`] which + contains most of the main methods. Users should refer to this superclass for more information regarding those + methods. + vocab_file (`str`): + File containing the vocabulary. + word_shape_file (`str`): + File containing the word => shape info. + word_pronunciation_file (`str`): + File containing the word => pronunciation info. + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + do_basic_tokenize (`bool`, *optional*, defaults to `True`): + Whether or not to do basic tokenization before WordPiece. + never_split (`Iterable`, *optional*): + Collection of tokens which will never be split during tokenization. Only has an effect when + `do_basic_tokenize=True` + unk_token (`str`, *optional*, defaults to `"[UNK]"`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + sep_token (`str`, *optional*, defaults to `"[SEP]"`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + pad_token (`str`, *optional*, defaults to `"[PAD]"`): + The token used for padding, for example when batching sequences of different lengths. + cls_token (`str`, *optional*, defaults to `"[CLS]"`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + mask_token (`str`, *optional*, defaults to `"[MASK]"`): + The token used for masking values. This is the token used when training this model with masked language + modeling. This is the token which the model will try to predict. + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see this + [issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original BERT). + """ + + vocab_files_names = VOCAB_FILES_NAMES + + def __init__( + self, + vocab_file, + word_shape_file, + word_pronunciation_file, + do_lower_case=True, + do_basic_tokenize=True, + never_split=None, + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]", + tokenize_chinese_chars=True, + strip_accents=None, + **kwargs, + ): + for cur_file in [vocab_file, word_shape_file, word_pronunciation_file]: + if cur_file is None or not os.path.isfile(cur_file): + raise ValueError( + f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google " + "pretrained model use `tokenizer = RoCBertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" + ) + + self.vocab = load_vocab(vocab_file) + + with open(word_shape_file, "r", encoding="utf8") as in_file: + self.word_shape = json.load(in_file) + + with open(word_pronunciation_file, "r", encoding="utf8") as in_file: + self.word_pronunciation = json.load(in_file) + + self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()]) + + self.do_basic_tokenize = do_basic_tokenize + if do_basic_tokenize: + self.basic_tokenizer = RoCBertBasicTokenizer( + do_lower_case=do_lower_case, + never_split=never_split, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + ) + self.wordpiece_tokenizer = RoCBertWordpieceTokenizer(vocab=self.vocab, unk_token=str(unk_token)) + super().__init__( + do_lower_case=do_lower_case, + do_basic_tokenize=do_basic_tokenize, + never_split=never_split, + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + cls_token=cls_token, + mask_token=mask_token, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + **kwargs, + ) + + @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) + def __call__( + self, + text: TextInput | list[TextInput], + text_pair: TextInput | list[TextInput] | None = None, + text_target: TextInput | list[TextInput] | None = None, + add_special_tokens: bool = True, + padding: bool | str | PaddingStrategy = False, + truncation: bool | str | TruncationStrategy = None, + max_length: int | None = None, + max_target_length: int | None = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: int | None = None, + padding_side: str | None = None, + return_tensors: str | TensorType | None = None, + return_token_type_ids: bool | None = None, + return_attention_mask: bool | None = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + # Handle text_target for seq2seq tasks + if text_target is not None: + # Tokenize source text + encodings = self.__call__( + text=text, + text_pair=text_pair, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + # Tokenize target text + target_length = max_target_length if max_target_length is not None else max_length + target_encodings = self.__call__( + text=text_target, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation if target_length is not None else False, + max_length=target_length, + stride=0, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_tensors=return_tensors, + return_token_type_ids=False, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=False, + return_special_tokens_mask=False, + return_offsets_mapping=False, + return_length=False, + verbose=verbose, + **kwargs, + ) + # Add labels from target input_ids + encodings["labels"] = target_encodings["input_ids"] + return encodings + + # Detect batch vs single + is_batched = isinstance(text, (list, tuple)) and ( + not is_split_into_words or (len(text) > 0 and isinstance(text[0], (list, tuple))) + ) + + if is_batched: + # Build batch tuples of (text, text_pair) if provided + batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text + return self.batch_encode_plus( + batch_text_or_text_pairs=batch_text_or_text_pairs, # type: ignore[arg-type] + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + else: + return self.encode_plus( + text=text, + text_pair=text_pair, + add_special_tokens=add_special_tokens, + padding=padding, + truncation=truncation, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + def encode_plus( + self, + text: TextInput | PreTokenizedInput | EncodedInput, + text_pair: TextInput | PreTokenizedInput | EncodedInput | None = None, + add_special_tokens: bool = True, + padding: bool | str | PaddingStrategy = False, + truncation: bool | str | TruncationStrategy = None, + max_length: int | None = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: int | None = None, + padding_side: str | None = None, + return_tensors: str | TensorType | None = None, + return_token_type_ids: bool | None = None, + return_attention_mask: bool | None = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + return self._encode_plus( + text=text, + text_pair=text_pair, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + def batch_encode_plus( + self, + batch_text_or_text_pairs: list[TextInput] + | list[TextInputPair] + | list[PreTokenizedInput] + | list[PreTokenizedInputPair] + | list[EncodedInput] + | list[EncodedInputPair], + add_special_tokens: bool = True, + padding: bool | str | PaddingStrategy = False, + truncation: bool | str | TruncationStrategy = None, + max_length: int | None = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: int | None = None, + padding_side: str | None = None, + return_tensors: str | TensorType | None = None, + return_token_type_ids: bool | None = None, + return_attention_mask: bool | None = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + return self._batch_encode_plus( + batch_text_or_text_pairs=batch_text_or_text_pairs, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + is_split_into_words=is_split_into_words, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_tensors=return_tensors, + return_token_type_ids=return_token_type_ids, + return_attention_mask=return_attention_mask, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_offsets_mapping=return_offsets_mapping, + return_length=return_length, + verbose=verbose, + **kwargs, + ) + + @property + def do_lower_case(self): + return self.basic_tokenizer.do_lower_case + + @property + def vocab_size(self): + return len(self.vocab) + + def get_vocab(self): + return dict(self.vocab, **self.added_tokens_encoder) + + def _tokenize(self, text, split_special_tokens=False): + split_tokens = [] + if self.do_basic_tokenize: + for token in self.basic_tokenizer.tokenize( + text, never_split=self.all_special_tokens if not split_special_tokens else None + ): + # If the token is part of the never_split set + if token in self.basic_tokenizer.never_split: + split_tokens.append(token) + else: + split_tokens += self.wordpiece_tokenizer.tokenize(token) + else: + split_tokens = self.wordpiece_tokenizer.tokenize(text) + return split_tokens + + def _encode_plus( + self, + text: TextInput | PreTokenizedInput | EncodedInput, + text_pair: TextInput | PreTokenizedInput | EncodedInput | None = None, + add_special_tokens: bool = True, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, + max_length: int | None = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: int | None = None, + padding_side: str | None = None, + return_tensors: str | TensorType | None = None, + return_token_type_ids: bool | None = None, + return_attention_mask: bool | None = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + def get_input_ids(text): + if isinstance(text, str): + tokens = self.tokenize(text, **kwargs) + tokens_ids = self.convert_tokens_to_ids(tokens) + tokens_shape_ids = self.convert_tokens_to_shape_ids(tokens) + tokens_proun_ids = self.convert_tokens_to_pronunciation_ids(tokens) + return tokens_ids, tokens_shape_ids, tokens_proun_ids + elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str): + if is_split_into_words: + tokens = list( + itertools.chain(*(self.tokenize(t, is_split_into_words=True, **kwargs) for t in text)) + ) + tokens_ids = self.convert_tokens_to_ids(tokens) + tokens_shape_ids = self.convert_tokens_to_shape_ids(tokens) + tokens_proun_ids = self.convert_tokens_to_pronunciation_ids(tokens) + return tokens_ids, tokens_shape_ids, tokens_proun_ids + else: + tokens_ids = self.convert_tokens_to_ids(text) + tokens_shape_ids = self.convert_tokens_to_shape_ids(text) + tokens_proun_ids = self.convert_tokens_to_pronunciation_ids(text) + return tokens_ids, tokens_shape_ids, tokens_proun_ids + elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int): + return text, [0] * len(text), [0] * len(text) # shape and proun id is pad_value + else: + if is_split_into_words: + raise ValueError( + f"Input {text} is not valid. Should be a string or a list/tuple of strings when" + " `is_split_into_words=True`." + ) + else: + raise ValueError( + f"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of" + " integers." + ) + + if return_offsets_mapping: + raise NotImplementedError( + "return_offset_mapping is not available when using Python tokenizers. " + "To use this feature, change your tokenizer to one deriving from " + "transformers.PreTrainedTokenizerFast. " + "More information on available tokenizers at " + "https://github.com/huggingface/transformers/pull/2674" + ) + + first_ids, first_shape_ids, first_proun_ids = get_input_ids(text) + if text_pair is not None: + second_ids, second_shape_ids, second_proun_ids = get_input_ids(text_pair) + else: + second_ids, second_shape_ids, second_proun_ids = None, None, None + + return self.prepare_for_model( + first_ids, + first_shape_ids, + first_proun_ids, + pair_ids=second_ids, + pair_shape_ids=second_shape_ids, + pair_pronunciation_ids=second_proun_ids, + add_special_tokens=add_special_tokens, + padding=padding_strategy.value, + truncation=truncation_strategy.value, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_tensors=return_tensors, + prepend_batch_axis=True, + return_attention_mask=return_attention_mask, + return_token_type_ids=return_token_type_ids, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_length=return_length, + verbose=verbose, + ) + + @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) + def prepare_for_model( + self, + ids: list[int], + shape_ids: list[int], + pronunciation_ids: list[int], + pair_ids: list[int] | None = None, + pair_shape_ids: list[int] | None = None, + pair_pronunciation_ids: list[int] | None = None, + add_special_tokens: bool = True, + padding: bool | str | PaddingStrategy = False, + truncation: bool | str | TruncationStrategy = None, + max_length: int | None = None, + stride: int = 0, + pad_to_multiple_of: int | None = None, + padding_side: str | None = None, + return_tensors: str | TensorType | None = None, + return_token_type_ids: bool | None = None, + return_attention_mask: bool | None = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + prepend_batch_axis: bool = False, + **kwargs, + ) -> BatchEncoding: + """ + Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It + adds special tokens, truncates sequences if overflowing while taking into account the special tokens and + manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *pair_ids* + different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return + overflowing tokens. Such a combination of arguments will raise an error. + + Args: + ids (`List[int]`): + Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and + `convert_tokens_to_id` methods. + shape_ids (`List[int]`): + Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and + `convert_token_to_shape_id` methods. + pronunciation_ids (`List[int]`): + Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and + `convert_token_to_pronunciation_id` methods. + pair_ids (`List[int]`, *optional*): + Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` + and `convert_tokens_to_id` methods. + pair_shape_ids (`List[int]`, *optional*): + Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` + and `convert_token_to_shape_id` methods. + pair_pronunciation_ids (`List[int]`, *optional*): + Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize` + and `convert_token_to_pronunciation_id` methods. + """ + + # Backward compatibility for 'truncation_strategy', 'pad_to_max_length' + padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies( + padding=padding, + truncation=truncation, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + verbose=verbose, + **kwargs, + ) + + pair = bool(pair_ids is not None) + len_ids = len(ids) + len_pair_ids = len(pair_ids) if pair else 0 + + if return_token_type_ids and not add_special_tokens: + raise ValueError( + "Asking to return token_type_ids while setting add_special_tokens to False " + "results in an undefined behavior. Please set add_special_tokens to True or " + "set return_token_type_ids to None." + ) + + if ( + return_overflowing_tokens + and truncation_strategy == TruncationStrategy.LONGEST_FIRST + and pair_ids is not None + ): + raise ValueError( + "Not possible to return overflowing tokens for pair of sequences with the " + "`longest_first`. Please select another truncation strategy than `longest_first`, " + "for instance `only_second` or `only_first`." + ) + + # Load from model defaults + if return_token_type_ids is None: + return_token_type_ids = "token_type_ids" in self.model_input_names + if return_attention_mask is None: + return_attention_mask = "attention_mask" in self.model_input_names + + encoded_inputs = {} + + # Compute the total size of the returned encodings + total_len = len_ids + len_pair_ids + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0) + + # Truncation: Handle max sequence length + overflowing_tokens = [] + if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length: + ids, pair_ids, overflowing_tokens = self.truncate_sequences( + ids, + pair_ids=pair_ids, + num_tokens_to_remove=total_len - max_length, + truncation_strategy=truncation_strategy, + stride=stride, + ) + shape_ids, pair_shape_ids, _ = self.truncate_sequences( + shape_ids, + pair_ids=pair_shape_ids, + num_tokens_to_remove=total_len - max_length, + truncation_strategy=truncation_strategy, + stride=stride, + ) + pronunciation_ids, pair_pronunciation_ids, _ = self.truncate_sequences( + pronunciation_ids, + pair_ids=pair_pronunciation_ids, + num_tokens_to_remove=total_len - max_length, + truncation_strategy=truncation_strategy, + stride=stride, + ) + + if return_overflowing_tokens and not return_tensors and overflowing_tokens: + encoded_inputs["overflowing_tokens"] = overflowing_tokens + encoded_inputs["num_truncated_tokens"] = total_len - max_length if max_length else 0 + + # Add special tokens + if add_special_tokens: + sequence = self.build_inputs_with_special_tokens(ids, pair_ids) + token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids) + input_shape_ids = self.build_inputs_with_special_tokens( + shape_ids, pair_shape_ids, self.word_shape["[UNK]"], self.word_shape["[UNK]"] + ) + input_pronunciation_ids = self.build_inputs_with_special_tokens( + pronunciation_ids, + pair_pronunciation_ids, + self.word_pronunciation["[UNK]"], + self.word_pronunciation["[UNK]"], + ) + else: + sequence = ids + pair_ids if pair_ids else ids + token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair_ids else []) + input_shape_ids = shape_ids + pair_shape_ids if pair_shape_ids else shape_ids + input_pronunciation_ids = ( + pronunciation_ids + pair_pronunciation_ids if pair_pronunciation_ids else pronunciation_ids + ) + + # Build output dictionary + encoded_inputs["input_ids"] = sequence + encoded_inputs["input_shape_ids"] = input_shape_ids + encoded_inputs["input_pronunciation_ids"] = input_pronunciation_ids + if return_token_type_ids: + encoded_inputs["token_type_ids"] = token_type_ids + if return_special_tokens_mask: + if add_special_tokens: + encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(ids, pair_ids) + else: + encoded_inputs["special_tokens_mask"] = [0] * len(sequence) + + # Check lengths + self._eventual_warn_about_too_long_sequence(encoded_inputs["input_ids"], max_length, verbose) + + # Padding + if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask: + encoded_inputs = self.pad( + encoded_inputs, + max_length=max_length, + padding=padding_strategy.value, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_attention_mask=return_attention_mask, + ) + + if return_length: + encoded_inputs["length"] = len(encoded_inputs["input_ids"]) + + batch_outputs = BatchEncoding( + encoded_inputs, tensor_type=return_tensors, prepend_batch_axis=prepend_batch_axis + ) + + return batch_outputs + + def _pad( + self, + encoded_inputs: dict[str, EncodedInput] | BatchEncoding, + max_length: int | None = None, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + pad_to_multiple_of: int | None = None, + padding_side: str | None = None, + return_attention_mask: bool | None = None, + ) -> dict: + # Load from model defaults + if return_attention_mask is None: + return_attention_mask = "attention_mask" in self.model_input_names + + required_input = encoded_inputs[self.model_input_names[0]] + + if padding_strategy == PaddingStrategy.LONGEST: + max_length = len(required_input) + + if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0): + max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of + + needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length + + # Initialize attention mask if not present. + if return_attention_mask and "attention_mask" not in encoded_inputs: + encoded_inputs["attention_mask"] = [1] * len(required_input) + + if needs_to_be_padded: + difference = max_length - len(required_input) + padding_side = padding_side if padding_side is not None else self.padding_side + + if padding_side == "right": + if return_attention_mask: + encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [0] * difference + if "token_type_ids" in encoded_inputs: + encoded_inputs["token_type_ids"] = ( + encoded_inputs["token_type_ids"] + [self.pad_token_type_id] * difference + ) + if "special_tokens_mask" in encoded_inputs: + encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference + for key in ["input_shape_ids", "input_pronunciation_ids"]: + if key in encoded_inputs: + encoded_inputs[key] = encoded_inputs[key] + [self.pad_token_id] * difference + encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference + elif padding_side == "left": + if return_attention_mask: + encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"] + if "token_type_ids" in encoded_inputs: + encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[ + "token_type_ids" + ] + if "special_tokens_mask" in encoded_inputs: + encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"] + for key in ["input_shape_ids", "input_pronunciation_ids"]: + if key in encoded_inputs: + encoded_inputs[key] = [self.pad_token_id] * difference + encoded_inputs[key] + encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input + else: + raise ValueError("Invalid padding strategy:" + str(padding_side)) + + return encoded_inputs + + def _batch_encode_plus( + self, + batch_text_or_text_pairs: list[TextInput] + | list[TextInputPair] + | list[PreTokenizedInput] + | list[PreTokenizedInputPair] + | list[EncodedInput] + | list[EncodedInputPair], + add_special_tokens: bool = True, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, + max_length: int | None = None, + stride: int = 0, + is_split_into_words: bool = False, + pad_to_multiple_of: int | None = None, + padding_side: str | None = None, + return_tensors: str | TensorType | None = None, + return_token_type_ids: bool | None = None, + return_attention_mask: bool | None = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_offsets_mapping: bool = False, + return_length: bool = False, + verbose: bool = True, + **kwargs, + ) -> BatchEncoding: + def get_input_ids(text): + if isinstance(text, str): + tokens = self.tokenize(text, **kwargs) + tokens_ids = self.convert_tokens_to_ids(tokens) + tokens_shape_ids = self.convert_tokens_to_shape_ids(tokens) + tokens_proun_ids = self.convert_tokens_to_pronunciation_ids(tokens) + return tokens_ids, tokens_shape_ids, tokens_proun_ids + elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str): + if is_split_into_words: + tokens = list( + itertools.chain(*(self.tokenize(t, is_split_into_words=True, **kwargs) for t in text)) + ) + tokens_ids = self.convert_tokens_to_ids(tokens) + tokens_shape_ids = self.convert_tokens_to_shape_ids(tokens) + tokens_proun_ids = self.convert_tokens_to_pronunciation_ids(tokens) + return tokens_ids, tokens_shape_ids, tokens_proun_ids + else: + tokens_ids = self.convert_tokens_to_ids(text) + tokens_shape_ids = self.convert_tokens_to_shape_ids(text) + tokens_proun_ids = self.convert_tokens_to_pronunciation_ids(text) + return tokens_ids, tokens_shape_ids, tokens_proun_ids + elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], int): + return text, [0] * len(text), [0] * len(text) # shape and proun id is pad_value + else: + raise ValueError( + "Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers." + ) + + if return_offsets_mapping: + raise NotImplementedError( + "return_offset_mapping is not available when using Python tokenizers. " + "To use this feature, change your tokenizer to one deriving from " + "transformers.PreTrainedTokenizerFast." + ) + + input_ids = [] + input_shape_ids = [] + input_pronunciation_ids = [] + for ids_or_pair_ids in batch_text_or_text_pairs: + if not isinstance(ids_or_pair_ids, (list, tuple)): + ids, pair_ids = ids_or_pair_ids, None + elif is_split_into_words and not isinstance(ids_or_pair_ids[0], (list, tuple)): + ids, pair_ids = ids_or_pair_ids, None + else: + ids, pair_ids = ids_or_pair_ids + + first_ids, first_shape_ids, first_proun_ids = get_input_ids(ids) + if pair_ids is not None: + second_ids, second_shape_ids, second_proun_ids = get_input_ids(pair_ids) + else: + second_ids, second_shape_ids, second_proun_ids = None, None, None + + input_ids.append((first_ids, second_ids)) + input_shape_ids.append((first_shape_ids, second_shape_ids)) + input_pronunciation_ids.append((first_proun_ids, second_proun_ids)) + + batch_outputs = self._batch_prepare_for_model( + input_ids, + batch_shape_ids_pairs=input_shape_ids, + batch_pronunciation_ids_pairs=input_pronunciation_ids, + add_special_tokens=add_special_tokens, + padding_strategy=padding_strategy, + truncation_strategy=truncation_strategy, + max_length=max_length, + stride=stride, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_attention_mask=return_attention_mask, + return_token_type_ids=return_token_type_ids, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_length=return_length, + return_tensors=return_tensors, + verbose=verbose, + ) + + return batch_outputs + + @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING) + def _batch_prepare_for_model( + self, + batch_ids_pairs: list[PreTokenizedInputPair | tuple[list[int], None]], + batch_shape_ids_pairs: list[PreTokenizedInputPair | tuple[list[int], None]], + batch_pronunciation_ids_pairs: list[PreTokenizedInputPair | tuple[list[int], None]], + add_special_tokens: bool = True, + padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, + truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE, + max_length: int | None = None, + stride: int = 0, + pad_to_multiple_of: int | None = None, + padding_side: str | None = None, + return_tensors: str | None = None, + return_token_type_ids: bool | None = None, + return_attention_mask: bool | None = None, + return_overflowing_tokens: bool = False, + return_special_tokens_mask: bool = False, + return_length: bool = False, + verbose: bool = True, + ) -> BatchEncoding: + """ + Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It + adds special tokens, truncates sequences if overflowing while taking into account the special tokens and + manages a moving window (with user defined stride) for overflowing tokens + + Args: + batch_ids_pairs: list of tokenized input ids or input ids pairs + batch_shape_ids_pairs: list of tokenized input shape ids or input shape ids pairs + batch_pronunciation_ids_pairs: list of tokenized input pronunciation ids or input pronunciation ids pairs + """ + + batch_outputs = {} + for i, (first_ids, second_ids) in enumerate(batch_ids_pairs): + first_shape_ids, second_shape_ids = batch_shape_ids_pairs[i] + first_pronunciation_ids, second_pronunciation_ids = batch_pronunciation_ids_pairs[i] + outputs = self.prepare_for_model( + first_ids, + first_shape_ids, + first_pronunciation_ids, + pair_ids=second_ids, + pair_shape_ids=second_shape_ids, + pair_pronunciation_ids=second_pronunciation_ids, + add_special_tokens=add_special_tokens, + padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward + truncation=truncation_strategy.value, + max_length=max_length, + stride=stride, + pad_to_multiple_of=None, # we pad in batch afterward + padding_side=None, # we pad in batch afterward + return_attention_mask=False, # we pad in batch afterward + return_token_type_ids=return_token_type_ids, + return_overflowing_tokens=return_overflowing_tokens, + return_special_tokens_mask=return_special_tokens_mask, + return_length=return_length, + return_tensors=None, # We convert the whole batch to tensors at the end + prepend_batch_axis=False, + verbose=verbose, + ) + + for key, value in outputs.items(): + if key not in batch_outputs: + batch_outputs[key] = [] + batch_outputs[key].append(value) + + batch_outputs = self.pad( + batch_outputs, + padding=padding_strategy.value, + max_length=max_length, + pad_to_multiple_of=pad_to_multiple_of, + padding_side=padding_side, + return_attention_mask=return_attention_mask, + ) + + # Remove overflow-related keys before tensor conversion if return_tensors is set + # Slow tokenizers don't support returning these as tensors + if return_tensors and return_overflowing_tokens: + batch_outputs.pop("overflowing_tokens", None) + batch_outputs.pop("num_truncated_tokens", None) + + batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors) + + return batch_outputs + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.vocab.get(token, self.vocab.get(self.unk_token)) + + def _convert_token_to_shape_id(self, token): + """Converts a token (str) in an shape_id using the shape vocab.""" + return self.word_shape.get(token, self.word_shape.get(self.unk_token)) + + def convert_tokens_to_shape_ids(self, tokens: str | list[str]) -> int | list[int]: + if tokens is None: + return None + + ids = [] + for token in tokens: + ids.append(self._convert_token_to_shape_id(token)) + return ids + + def _convert_token_to_pronunciation_id(self, token): + """Converts a token (str) in an shape_id using the shape vocab.""" + return self.word_pronunciation.get(token, self.word_pronunciation.get(self.unk_token)) + + def convert_tokens_to_pronunciation_ids(self, tokens: str | list[str]) -> int | list[int]: + if tokens is None: + return None + + ids = [] + for token in tokens: + ids.append(self._convert_token_to_pronunciation_id(token)) + return ids + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + return self.ids_to_tokens.get(index, self.unk_token) + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + out_string = " ".join(tokens).replace(" ##", "").strip() + return out_string + + def build_inputs_with_special_tokens( + self, + token_ids_0: list[int], + token_ids_1: list[int] | None = None, + cls_token_id: int | None = None, + sep_token_id: int | None = None, + ) -> list[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A BERT sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + cls = [self.cls_token_id] if cls_token_id is None else [cls_token_id] + sep = [self.sep_token_id] if sep_token_id is None else [sep_token_id] + if token_ids_1 is None: + return cls + token_ids_0 + sep + return cls + token_ids_0 + sep + token_ids_1 + sep + + def get_special_tokens_mask( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None, already_has_special_tokens: bool = False + ) -> list[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`List[int]`): + List of IDs. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + if token_ids_1 is not None: + return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + return [1] + ([0] * len(token_ids_0)) + [1] + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str, str, str]: + index = 0 + if os.path.isdir(save_directory): + vocab_file = os.path.join( + save_directory, + (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"], + ) + word_shape_file = os.path.join( + save_directory, + (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["word_shape_file"], + ) + word_pronunciation_file = os.path.join( + save_directory, + (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["word_pronunciation_file"], + ) + else: + raise ValueError( + f"Can't find a directory at path '{save_directory}'. To load the vocabulary from a Google " + "pretrained model use `tokenizer = RoCBertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`" + ) + + with open(vocab_file, "w", encoding="utf-8") as writer: + for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]): + if index != token_index: + logger.warning( + f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive." + " Please check that the vocabulary is not corrupted!" + ) + index = token_index + writer.write(token + "\n") + index += 1 + + with open(word_shape_file, "w", encoding="utf8") as writer: + json.dump(self.word_shape, writer, ensure_ascii=False, indent=4, separators=(", ", ": ")) + + with open(word_pronunciation_file, "w", encoding="utf8") as writer: + json.dump(self.word_pronunciation, writer, ensure_ascii=False, indent=4, separators=(", ", ": ")) + + return ( + vocab_file, + word_shape_file, + word_pronunciation_file, + ) + + +class RoCBertBasicTokenizer: + """ + Constructs a RoCBertBasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). + + Args: + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + never_split (`Iterable`, *optional*): + Collection of tokens which will never be split during tokenization. Only has an effect when + `do_basic_tokenize=True` + tokenize_chinese_chars (`bool`, *optional*, defaults to `True`): + Whether or not to tokenize Chinese characters. + + This should likely be deactivated for Japanese (see this + [issue](https://github.com/huggingface/transformers/issues/328)). + strip_accents (`bool`, *optional*): + Whether or not to strip all accents. If this option is not specified, then it will be determined by the + value for `lowercase` (as in the original BERT). + do_split_on_punc (`bool`, *optional*, defaults to `True`): + In some instances we want to skip the basic punctuation splitting so that later tokenization can capture + the full context of the words, such as contractions. + """ + + def __init__( + self, + do_lower_case=True, + never_split=None, + tokenize_chinese_chars=True, + strip_accents=None, + do_split_on_punc=True, + ): + if never_split is None: + never_split = [] + self.do_lower_case = do_lower_case + self.never_split = set(never_split) + self.tokenize_chinese_chars = tokenize_chinese_chars + self.strip_accents = strip_accents + self.do_split_on_punc = do_split_on_punc + + def tokenize(self, text, never_split=None): + """ + Basic Tokenization of a piece of text. For sub-word tokenization, see WordPieceTokenizer. + + Args: + never_split (`List[str]`, *optional*) + Kept for backward compatibility purposes. Now implemented directly at the base class level (see + [`PreTrainedTokenizer.tokenize`]) List of token not to split. + """ + # union() returns a new set by concatenating the two sets. + never_split = self.never_split.union(set(never_split)) if never_split else self.never_split + text = self._clean_text(text) + + # This was added on November 1st, 2018 for the multilingual and Chinese + # models. This is also applied to the English models now, but it doesn't + # matter since the English models were not trained on any Chinese data + # and generally don't have any Chinese data in them (there are Chinese + # characters in the vocabulary because Wikipedia does have some Chinese + # words in the English Wikipedia.). + if self.tokenize_chinese_chars: + text = self._tokenize_chinese_chars(text) + # prevents treating the same character with different unicode codepoints as different characters + unicode_normalized_text = unicodedata.normalize("NFC", text) + orig_tokens = whitespace_tokenize(unicode_normalized_text) + split_tokens = [] + for token in orig_tokens: + if token not in never_split: + if self.do_lower_case: + token = token.lower() + if self.strip_accents is not False: + token = self._run_strip_accents(token) + elif self.strip_accents: + token = self._run_strip_accents(token) + split_tokens.extend(self._run_split_on_punc(token, never_split)) + + output_tokens = whitespace_tokenize(" ".join(split_tokens)) + return output_tokens + + def _run_strip_accents(self, text): + """Strips accents from a piece of text.""" + text = unicodedata.normalize("NFD", text) + output = [] + for char in text: + cat = unicodedata.category(char) + if cat == "Mn": + continue + output.append(char) + return "".join(output) + + def _run_split_on_punc(self, text, never_split=None): + """Splits punctuation on a piece of text.""" + if not self.do_split_on_punc or (never_split is not None and text in never_split): + return [text] + chars = list(text) + i = 0 + start_new_word = True + output = [] + while i < len(chars): + char = chars[i] + if _is_punctuation(char): + output.append([char]) + start_new_word = True + else: + if start_new_word: + output.append([]) + start_new_word = False + output[-1].append(char) + i += 1 + + return ["".join(x) for x in output] + + def _tokenize_chinese_chars(self, text): + """Adds whitespace around any CJK character.""" + output = [] + for char in text: + cp = ord(char) + if self._is_chinese_char(cp): + output.append(" ") + output.append(char) + output.append(" ") + else: + output.append(char) + return "".join(output) + + def _is_chinese_char(self, cp): + """Checks whether CP is the codepoint of a CJK character.""" + # This defines a "chinese character" as anything in the CJK Unicode block: + # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) + # + # Note that the CJK Unicode block is NOT all Japanese and Korean characters, + # despite its name. The modern Korean Hangul alphabet is a different block, + # as is Japanese Hiragana and Katakana. Those alphabets are used to write + # space-separated words, so they are not treated specially and handled + # like the all of the other languages. + if ( + (cp >= 0x4E00 and cp <= 0x9FFF) + or (cp >= 0x3400 and cp <= 0x4DBF) + or (cp >= 0x20000 and cp <= 0x2A6DF) + or (cp >= 0x2A700 and cp <= 0x2B73F) + or (cp >= 0x2B740 and cp <= 0x2B81F) + or (cp >= 0x2B820 and cp <= 0x2CEAF) + or (cp >= 0xF900 and cp <= 0xFAFF) + or (cp >= 0x2F800 and cp <= 0x2FA1F) + ): + return True + + return False + + def _clean_text(self, text): + """Performs invalid character removal and whitespace cleanup on text.""" + output = [] + for char in text: + cp = ord(char) + if cp == 0 or cp == 0xFFFD or _is_control(char): + continue + if _is_whitespace(char): + output.append(" ") + else: + output.append(char) + return "".join(output) + + +class RoCBertWordpieceTokenizer: + """Runs WordPiece tokenization.""" + + def __init__(self, vocab, unk_token, max_input_chars_per_word=100): + self.vocab = vocab + self.unk_token = unk_token + self.max_input_chars_per_word = max_input_chars_per_word + + def tokenize(self, text): + """ + Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform + tokenization using the given vocabulary. + + For example, `input = "unaffable"` will return as output `["un", "##aff", "##able"]`. + + Args: + text: A single token or whitespace separated tokens. This should have + already been passed through *BasicTokenizer*. + + Returns: + A list of wordpiece tokens. + """ + + output_tokens = [] + for token in whitespace_tokenize(text): + chars = list(token) + if len(chars) > self.max_input_chars_per_word: + output_tokens.append(self.unk_token) + continue + + is_bad = False + start = 0 + sub_tokens = [] + while start < len(chars): + end = len(chars) + cur_substr = None + while start < end: + substr = "".join(chars[start:end]) + if start > 0: + substr = "##" + substr + if substr in self.vocab: + cur_substr = substr + break + end -= 1 + if cur_substr is None: + is_bad = True + break + sub_tokens.append(cur_substr) + start = end + + if is_bad: + output_tokens.append(self.unk_token) + else: + output_tokens.extend(sub_tokens) + return output_tokens + + +__all__ = ["RoCBertTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4d1232523f8ca2c970e91400f2079d4fd44d3511 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_roformer import * + from .modeling_roformer import * + from .tokenization_roformer import * + from .tokenization_roformer_fast import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/configuration_roformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/configuration_roformer.py new file mode 100644 index 0000000000000000000000000000000000000000..2706a19e6b345f7fc313dfcfbf3b468dd15a94e2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/configuration_roformer.py @@ -0,0 +1,73 @@ +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RoFormer model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="junnyu/roformer_chinese_base") +@strict +class RoFormerConfig(PreTrainedConfig): + r""" + rotary_value (`bool`, *optional*, defaults to `False`): + Whether or not apply rotary position embeddings on value layer. + + Example: + + ```python + >>> from transformers import RoFormerModel, RoFormerConfig + + >>> # Initializing a RoFormer junnyu/roformer_chinese_base style configuration + >>> configuration = RoFormerConfig() + + >>> # Initializing a model (with random weights) from the junnyu/roformer_chinese_base style configuration + >>> model = RoFormerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "roformer" + + vocab_size: int = 50000 + embedding_size: int | None = None + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.1 + attention_probs_dropout_prob: float | int = 0.1 + max_position_embeddings: int = 1536 + type_vocab_size: int = 2 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-12 + pad_token_id: int | None = 0 + bos_token_id: int | None = None + eos_token_id: int | list[int] | None = None + rotary_value: bool = False + use_cache: bool = True + is_decoder: bool = False + add_cross_attention: bool = False + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + self.embedding_size = self.hidden_size if self.embedding_size is None else self.embedding_size + super().__post_init__(**kwargs) + + +__all__ = ["RoFormerConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/modeling_roformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/modeling_roformer.py new file mode 100644 index 0000000000000000000000000000000000000000..bf20e90b1c31f397a1b2f3876ed0e9f1e72a5eec --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/modeling_roformer.py @@ -0,0 +1,1307 @@ +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RoFormer model.""" + +import math +from collections.abc import Callable + +import numpy as np +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from ... import initialization as init +from ...activations import ACT2FN, get_activation +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from ...modeling_utils import PreTrainedModel +from ...pytorch_utils import apply_chunking_to_forward +from ...utils import auto_docstring, logging +from .configuration_roformer import RoFormerConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.marian.modeling_marian.MarianSinusoidalPositionalEmbedding with Marian->RoFormer +class RoFormerSinusoidalPositionalEmbedding(nn.Embedding): + """This module produces sinusoidal positional embeddings of any length.""" + + def __init__(self, num_positions: int, embedding_dim: int, padding_idx: int | None = None) -> None: + super().__init__(num_positions, embedding_dim, _freeze=True) + + def create_weight(self): + """ + Identical to the XLM create_sinusoidal_embeddings except features are not interleaved. The cos features are in + the 2nd half of the vector. [dim // 2:] + """ + n_pos, dim = self.weight.shape + position_enc = np.array( + [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)] + ) + out = torch.empty(n_pos, dim, dtype=self.weight.dtype, requires_grad=False) + sentinel = dim // 2 if dim % 2 == 0 else (dim // 2) + 1 + out[:, 0:sentinel] = torch.FloatTensor(np.sin(position_enc[:, 0::2])) + out[:, sentinel:] = torch.FloatTensor(np.cos(position_enc[:, 1::2])) + return out + + @torch.no_grad() + def forward( + self, input_ids_shape: torch.Size, past_key_values_length: int = 0, position_ids: torch.Tensor | None = None + ) -> torch.Tensor: + """`input_ids_shape` is expected to be [bsz x seqlen].""" + if position_ids is None: + bsz, seq_len = input_ids_shape[:2] + position_ids = torch.arange( + past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device + ) + return super().forward(position_ids) + + +class RoFormerEmbeddings(nn.Module): + """Construct the embeddings from word and token_type embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id) + self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size) + + self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=inputs_embeds.device) + + token_type_embeddings = self.token_type_embeddings(token_type_ids) + + embeddings = inputs_embeds + token_type_embeddings + + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class RoFormerSelfAttention(nn.Module): + def __init__(self, config, layer_idx=None): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.is_decoder = config.is_decoder + self.rotary_value = config.rotary_value + self.layer_idx = layer_idx + + def forward( + self, + hidden_states, + attention_mask=None, + sinusoidal_pos=None, + encoder_hidden_states=None, + past_key_values=None, + output_attentions=False, + **kwargs, + ): + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.attention_head_size) + query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_layer from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = encoder_hidden_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_layer = curr_past_key_values.layers[self.layer_idx].keys + value_layer = curr_past_key_values.layers[self.layer_idx].values + else: + kv_shape = (*current_states.shape[:-1], -1, self.attention_head_size) + key_layer = self.key(current_states).view(kv_shape).transpose(1, 2) + value_layer = self.value(current_states).view(kv_shape).transpose(1, 2) + + # Apply RoPE if self attention + if not is_cross_attention and sinusoidal_pos is not None: + if self.rotary_value: + query_layer, key_layer, value_layer = self.apply_rotary_position_embeddings( + sinusoidal_pos, query_layer, key_layer, value_layer + ) + else: + query_layer, key_layer = self.apply_rotary_position_embeddings( + sinusoidal_pos, query_layer, key_layer + ) + + if past_key_values is not None: + # save all key/value_layer to cache to be re-used for fast auto-regressive generation + key_layer, value_layer = curr_past_key_values.update(key_layer, value_layer, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in RoFormerModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + return context_layer, attention_probs + + @staticmethod + def apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer, value_layer=None): + # https://kexue.fm/archives/8265 + # sin [batch_size, num_heads, sequence_length, embed_size_per_head//2] + # cos [batch_size, num_heads, sequence_length, embed_size_per_head//2] + sin, cos = sinusoidal_pos.chunk(2, dim=-1) + # sin [θ0,θ1,θ2......θd/2-1] -> sin_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1] + sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos) + # cos [θ0,θ1,θ2......θd/2-1] -> cos_pos [θ0,θ0,θ1,θ1,θ2,θ2......θd/2-1,θd/2-1] + cos_pos = torch.stack([cos, cos], dim=-1).reshape_as(sinusoidal_pos) + # rotate_half_query_layer [-q1,q0,-q3,q2......,-qd-1,qd-2] + rotate_half_query_layer = torch.stack([-query_layer[..., 1::2], query_layer[..., ::2]], dim=-1).reshape_as( + query_layer + ) + query_layer = query_layer * cos_pos + rotate_half_query_layer * sin_pos + # rotate_half_key_layer [-k1,k0,-k3,k2......,-kd-1,kd-2] + rotate_half_key_layer = torch.stack([-key_layer[..., 1::2], key_layer[..., ::2]], dim=-1).reshape_as(key_layer) + key_layer = key_layer * cos_pos + rotate_half_key_layer * sin_pos + if value_layer is not None: + # rotate_half_value_layer [-v1,v0,-v3,v2......,-vd-1,vd-2] + rotate_half_value_layer = torch.stack([-value_layer[..., 1::2], value_layer[..., ::2]], dim=-1).reshape_as( + value_layer + ) + value_layer = value_layer * cos_pos + rotate_half_value_layer * sin_pos + return query_layer, key_layer, value_layer + return query_layer, key_layer + + +# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->RoFormer +class RoFormerSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class RoFormerAttention(nn.Module): + def __init__(self, config, layer_idx=None): + super().__init__() + self.self = RoFormerSelfAttention(config, layer_idx=layer_idx) + self.output = RoFormerSelfOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + sinusoidal_pos=None, + encoder_hidden_states=None, + past_key_values=None, + output_attentions=False, + **kwargs, + ): + self_outputs = self.self( + hidden_states, + attention_mask=attention_mask, + sinusoidal_pos=sinusoidal_pos, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->RoFormer +class RoFormerIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RoFormer +class RoFormerOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class RoFormerLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_idx=None): + super().__init__() + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = RoFormerAttention(config, layer_idx) + self.is_decoder = config.is_decoder + self.add_cross_attention = config.add_cross_attention + if self.add_cross_attention: + if not self.is_decoder: + raise ValueError(f"{self} should be used as a decoder model if cross attention is added") + self.crossattention = RoFormerAttention(config, layer_idx) + self.intermediate = RoFormerIntermediate(config) + self.output = RoFormerOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + sinusoidal_pos=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + output_attentions=False, + **kwargs, + ): + self_attention_outputs = self.attention( + hidden_states, + attention_mask=attention_mask, + sinusoidal_pos=sinusoidal_pos, + output_attentions=output_attentions, + past_key_values=past_key_values, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + if self.is_decoder and encoder_hidden_states is not None: + if not hasattr(self, "crossattention"): + raise ValueError( + f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention " + "layers by setting `config.add_cross_attention=True`" + ) + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask=encoder_attention_mask, + sinusoidal_pos=sinusoidal_pos, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:] # add cross attentions if we output attention weights + + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + return (layer_output,) + outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class RoFormerEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.embed_positions = RoFormerSinusoidalPositionalEmbedding( + config.max_position_embeddings, config.hidden_size // config.num_attention_heads + ) + self.layer = nn.ModuleList([RoFormerLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + **kwargs, + ): + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + # [sequence_length, embed_size_per_head] -> [batch_size, num_heads, sequence_length, embed_size_per_head] + sinusoidal_pos = self.embed_positions(hidden_states.shape[:-1], past_key_values_length)[None, None, :, :] + + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_outputs = layer_module( + hidden_states, + attention_mask, + sinusoidal_pos, + encoder_hidden_states, + encoder_attention_mask, + past_key_values, + output_attentions, + ) + + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + if self.config.add_cross_attention: + all_cross_attentions = all_cross_attentions + (layer_outputs[2],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + past_key_values, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +# Copied from transformers.models.xlm.modeling_xlm.XLMSequenceSummary with XLM->RoFormer +class RoFormerSequenceSummary(nn.Module): + r""" + Compute a single vector summary of a sequence hidden states. + + Args: + config ([`RoFormerConfig`]): + The config used by the model. Relevant arguments in the config class of the model are (refer to the actual + config class of your model for the default values it uses): + + - **summary_type** (`str`) -- The method to use to make this summary. Accepted values are: + + - `"last"` -- Take the last token hidden state (like XLNet) + - `"first"` -- Take the first token hidden state (like Bert) + - `"mean"` -- Take the mean of all tokens hidden states + - `"cls_index"` -- Supply a Tensor of classification token position (GPT/GPT-2) + - `"attn"` -- Not implemented now, use multi-head attention + + - **summary_use_proj** (`bool`) -- Add a projection after the vector extraction. + - **summary_proj_to_labels** (`bool`) -- If `True`, the projection outputs to `config.num_labels` classes + (otherwise to `config.hidden_size`). + - **summary_activation** (`Optional[str]`) -- Set to `"tanh"` to add a tanh activation to the output, + another string or `None` will add no activation. + - **summary_first_dropout** (`float`) -- Optional dropout probability before the projection and activation. + - **summary_last_dropout** (`float`)-- Optional dropout probability after the projection and activation. + """ + + def __init__(self, config: RoFormerConfig): + super().__init__() + + self.summary_type = getattr(config, "summary_type", "last") + if self.summary_type == "attn": + # We should use a standard multi-head attention module with absolute positional embedding for that. + # Cf. https://github.com/zihangdai/xlnet/blob/master/modeling.py#L253-L276 + # We can probably just use the multi-head attention module of PyTorch >=1.1.0 + raise NotImplementedError + + self.summary = nn.Identity() + if hasattr(config, "summary_use_proj") and config.summary_use_proj: + if hasattr(config, "summary_proj_to_labels") and config.summary_proj_to_labels and config.num_labels > 0: + num_classes = config.num_labels + else: + num_classes = config.hidden_size + self.summary = nn.Linear(config.hidden_size, num_classes) + + activation_string = getattr(config, "summary_activation", None) + self.activation: Callable = get_activation(activation_string) if activation_string else nn.Identity() + + self.first_dropout = nn.Identity() + if hasattr(config, "summary_first_dropout") and config.summary_first_dropout > 0: + self.first_dropout = nn.Dropout(config.summary_first_dropout) + + self.last_dropout = nn.Identity() + if hasattr(config, "summary_last_dropout") and config.summary_last_dropout > 0: + self.last_dropout = nn.Dropout(config.summary_last_dropout) + + def forward( + self, hidden_states: torch.FloatTensor, cls_index: torch.LongTensor | None = None + ) -> torch.FloatTensor: + """ + Compute a single vector summary of a sequence hidden states. + + Args: + hidden_states (`torch.FloatTensor` of shape `[batch_size, seq_len, hidden_size]`): + The hidden states of the last layer. + cls_index (`torch.LongTensor` of shape `[batch_size]` or `[batch_size, ...]` where ... are optional leading dimensions of `hidden_states`, *optional*): + Used if `summary_type == "cls_index"` and takes the last token of the sequence as classification token. + + Returns: + `torch.FloatTensor`: The summary of the sequence hidden states. + """ + if self.summary_type == "last": + output = hidden_states[:, -1] + elif self.summary_type == "first": + output = hidden_states[:, 0] + elif self.summary_type == "mean": + output = hidden_states.mean(dim=1) + elif self.summary_type == "cls_index": + if cls_index is None: + cls_index = torch.full_like( + hidden_states[..., :1, :], + hidden_states.shape[-2] - 1, + dtype=torch.long, + ) + else: + cls_index = cls_index.unsqueeze(-1).unsqueeze(-1) + cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),)) + # shape of cls_index: (bsz, XX, 1, hidden_size) where XX are optional leading dim of hidden_states + output = hidden_states.gather(-2, cls_index).squeeze(-2) # shape (bsz, XX, hidden_size) + elif self.summary_type == "attn": + raise NotImplementedError + + output = self.first_dropout(output) + output = self.summary(output) + output = self.activation(output) + output = self.last_dropout(output) + + return output + + +class RoFormerPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.embedding_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.embedding_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class RoFormerLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = RoFormerPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.embedding_size, config.vocab_size, bias=True) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +# Copied from transformers.models.bert.modeling_bert.BertOnlyMLMHead with Bert->RoFormer +class RoFormerOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = RoFormerLMPredictionHead(config) + + def forward(self, sequence_output: torch.Tensor) -> torch.Tensor: + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +@auto_docstring +class RoFormerPreTrainedModel(PreTrainedModel): + config: RoFormerConfig + base_model_prefix = "roformer" + supports_gradient_checkpointing = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, RoFormerSinusoidalPositionalEmbedding): + init.copy_(module.weight, module.create_weight()) + elif isinstance(module, RoFormerLMPredictionHead): + init.zeros_(module.bias) + + +@auto_docstring( + custom_intro=""" + + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in [Attention is + all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + + To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set + to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and + `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. + """ +) +class RoFormerModel(RoFormerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + self.embeddings = RoFormerEmbeddings(config) + + if config.embedding_size != config.hidden_size: + self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size) + + self.encoder = RoFormerEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> BaseModelOutputWithPastAndCrossAttentions | tuple[torch.Tensor]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if self.config.is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) + input_shape = input_ids.size() + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + batch_size, seq_length = input_shape + device = input_ids.device if input_ids is not None else inputs_embeds.device + + if use_cache and past_key_values is None: + past_key_values = ( + EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + if encoder_hidden_states is not None or self.config.is_encoder_decoder + else DynamicCache(config=self.config) + ) + + past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length() + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + if token_type_ids is None: + token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if self.config.is_decoder and encoder_hidden_states is not None: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + if encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + embedding_output = self.embeddings( + input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds + ) + if hasattr(self, "embeddings_project"): + embedding_output = self.embeddings_project(embedding_output) + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + if not return_dict: + return (sequence_output,) + encoder_outputs[1:] + + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=sequence_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + +@auto_docstring +class RoFormerForMaskedLM(RoFormerPreTrainedModel): + _tied_weights_keys = { + "cls.predictions.decoder.bias": "cls.predictions.bias", + "cls.predictions.decoder.weight": "roformer.embeddings.word_embeddings.weight", + } + + def __init__(self, config): + super().__init__(config) + + if config.is_decoder: + logger.warning( + "If you want to use `RoFormerForMaskedLM` make sure `config.is_decoder=False` for " + "bi-directional self-attention." + ) + + self.roformer = RoFormerModel(config) + self.cls = RoFormerOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + self.cls.predictions.bias = new_embeddings.bias + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> MaskedLMOutput | tuple[torch.Tensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.roformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() # -100 index = padding token + masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (prediction_scores,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return MaskedLMOutput( + loss=masked_lm_loss, + logits=prediction_scores, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + RoFormer Model with a `language modeling` head on top for CLM fine-tuning. + """ +) +class RoFormerForCausalLM(RoFormerPreTrainedModel, GenerationMixin): + _tied_weights_keys = { + "cls.predictions.decoder.bias": "cls.predictions.bias", + "cls.predictions.decoder.weight": "roformer.embeddings.word_embeddings.weight", + } + + def __init__(self, config): + super().__init__(config) + + if not config.is_decoder: + logger.warning("If you want to use `RoFormerForCausalLM` as a standalone, add `is_decoder=True.`") + + self.roformer = RoFormerModel(config) + self.cls = RoFormerOnlyMLMHead(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + self.cls.predictions.bias = new_embeddings.bias + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.FloatTensor | None = None, + past_key_values: Cache | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> CausalLMOutputWithCrossAttentions | tuple[torch.Tensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are + ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, RoFormerForCausalLM, RoFormerConfig + >>> import torch + + >>> tokenizer = AutoTokenizer.from_pretrained("junnyu/roformer_chinese_base") + >>> config = RoFormerConfig.from_pretrained("junnyu/roformer_chinese_base") + >>> config.is_decoder = True + >>> model = RoFormerForCausalLM.from_pretrained("junnyu/roformer_chinese_base", config=config) + + >>> inputs = tokenizer("今天天气非常好。", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> prediction_logits = outputs.logits + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.roformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.cls(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + +class RoFormerClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + self.config = config + + def forward(self, features, **kwargs): + x = features[:, 0, :] # take token (equiv. to [CLS]) + x = self.dropout(x) + x = self.dense(x) + x = ACT2FN[self.config.hidden_act](x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +@auto_docstring( + custom_intro=""" + RoFormer Model transformer with a sequence classification/regression head on top (a linear layer on top of the + pooled output) e.g. for GLUE tasks. + """ +) +class RoFormerForSequenceClassification(RoFormerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.roformer = RoFormerModel(config) + self.classifier = RoFormerClassificationHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> SequenceClassifierOutput | tuple[torch.Tensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.roformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RoFormerForMultipleChoice(RoFormerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.roformer = RoFormerModel(config) + self.sequence_summary = RoFormerSequenceSummary(config) + self.classifier = nn.Linear(config.hidden_size, 1) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> MultipleChoiceModelOutput | tuple[torch.Tensor]: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): + Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, + 1]`: + + - 0 corresponds to a *sentence A* token, + - 1 corresponds to a *sentence B* token. + + [What are token type IDs?](../glossary#token-type-ids) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert *input_ids* indices into associated vectors than the + model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., + num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See + `input_ids` above) + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] + + input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None + attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None + token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None + + inputs_embeds = ( + inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) + if inputs_embeds is not None + else None + ) + + outputs = self.roformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + pooled_output = self.sequence_summary(sequence_output) + logits = self.classifier(pooled_output) + reshaped_logits = logits.view(-1, num_choices) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(reshaped_logits, labels) + + if not return_dict: + output = (reshaped_logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return MultipleChoiceModelOutput( + loss=loss, + logits=reshaped_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RoFormerForTokenClassification(RoFormerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + + self.roformer = RoFormerModel(config) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + self.classifier = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> TokenClassifierOutput | tuple[torch.Tensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.roformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + sequence_output = self.dropout(sequence_output) + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return TokenClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +@auto_docstring +class RoFormerForQuestionAnswering(RoFormerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + config.num_labels = 2 + self.num_labels = config.num_labels + + self.roformer = RoFormerModel(config) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.FloatTensor | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + start_positions: torch.LongTensor | None = None, + end_positions: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> QuestionAnsweringModelOutput | tuple[torch.Tensor]: + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.roformer( + input_ids, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1) + end_logits = end_logits.squeeze(-1) + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[1:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "RoFormerForCausalLM", + "RoFormerForMaskedLM", + "RoFormerForMultipleChoice", + "RoFormerForQuestionAnswering", + "RoFormerForSequenceClassification", + "RoFormerForTokenClassification", + "RoFormerLayer", + "RoFormerModel", + "RoFormerPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/tokenization_roformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/tokenization_roformer.py new file mode 100644 index 0000000000000000000000000000000000000000..fe50570b0499f3502033eb08e7099fb50fe413c1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/tokenization_roformer.py @@ -0,0 +1,169 @@ +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for RoFormer backed by 🤗 Tokenizers.""" + +from tokenizers import Tokenizer, decoders, models, normalizers, pre_tokenizers, processors +from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer + +from ...tokenization_utils_tokenizers import PreTrainedTokenizerFast +from ...utils import logging +from .tokenization_utils import JiebaPreTokenizer + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"} + + +class RoFormerTokenizer(PreTrainedTokenizerFast): + r""" + Construct a RoFormer tokenizer. Based on [Rust Jieba](https://pypi.org/project/rjieba/). + + This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + Example: + + ```python + >>> from transformers import RoFormerTokenizer + + >>> tokenizer = RoFormerTokenizer.from_pretrained("junnyu/roformer_chinese_base") + >>> tokenizer.tokenize("今天天气非常好。") + ['今', '天', '天', '气', '非常', '好', '。'] + ``` + """ + + vocab_files_names = VOCAB_FILES_NAMES + + def __init__( + self, + vocab: dict[str, int] | None = None, + do_lower_case=True, + unk_token="[UNK]", + sep_token="[SEP]", + pad_token="[PAD]", + cls_token="[CLS]", + mask_token="[MASK]", + tokenize_chinese_chars=True, + strip_accents=None, + **kwargs, + ): + tokenizer = Tokenizer(models.WordPiece(vocab, unk_token=str(unk_token))) + tokenizer.normalizer = normalizers.BertNormalizer( + clean_text=True, + handle_chinese_chars=False, + strip_accents=strip_accents, + lowercase=do_lower_case, + ) + tokenizer.pre_tokenizer = pre_tokenizers.PreTokenizer.custom(JiebaPreTokenizer(vocab)) + + tokenizer.decoder = decoders.WordPiece(prefix="##") + self._tokenizer = tokenizer + super().__init__( + do_lower_case=do_lower_case, + unk_token=unk_token, + sep_token=sep_token, + pad_token=pad_token, + cls_token=cls_token, + mask_token=mask_token, + tokenize_chinese_chars=tokenize_chinese_chars, + strip_accents=strip_accents, + **kwargs, + ) + cls_ = str(cls_token) + sep_ = str(sep_token) + self._tokenizer.post_processor = processors.TemplateProcessing( + single=f"{cls_}:0 $A:0 {sep_}:0", + pair=f"{cls_}:0 $A:0 {sep_}:0 $B:1 {sep_}:1", + special_tokens=[ + (cls_, self.cls_token_id), + (sep_, self.sep_token_id), + ], + ) + + def __getstate__(self): + state = self.__dict__.copy() + tokenizer_copy = Tokenizer.from_str(state["_tokenizer"].to_str()) + tokenizer_copy.pre_tokenizer = BertPreTokenizer() + state["_tokenizer"] = tokenizer_copy + return state + + def __setstate__(self, d): + self.__dict__ = d + vocab = self.__dict__["_tokenizer"].get_vocab() + self.__dict__["_tokenizer"].pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer(vocab)) + + def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A RoFormer sequence has the following format: + + - single sequence: `[CLS] X [SEP]` + - pair of sequences: `[CLS] A [SEP] B [SEP]` + + Args: + token_ids_0 (`List[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`List[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + + if token_ids_1 is not None: + output += token_ids_1 + [self.sep_token_id] + + return output + + def create_token_type_ids_from_sequences( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Create token type IDs for RoFormer sequence pairs. + + The first sequence and associated special tokens are mapped to 0, while the second sequence (if provided) and + its trailing separator are mapped to 1. + """ + sep = [self.sep_token_id] + cls = [self.cls_token_id] + + if token_ids_1 is None: + return len(cls + token_ids_0 + sep) * [0] + + return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + files = self._tokenizer.model.save(save_directory, name=filename_prefix) + return tuple(files) + + def save_pretrained( + self, + save_directory, + legacy_format=None, + filename_prefix=None, + push_to_hub=False, + **kwargs, + ): + self.backend_tokenizer.pre_tokenizer = BertPreTokenizer() + result = super().save_pretrained(save_directory, legacy_format, filename_prefix, push_to_hub, **kwargs) + vocab = self.backend_tokenizer.get_vocab() + self.backend_tokenizer.pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer(vocab)) + return result + + +RoFormerTokenizerFast = RoFormerTokenizer + +__all__ = ["RoFormerTokenizer", "RoFormerTokenizerFast"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/tokenization_utils.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/tokenization_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..af1686911e500e4ebae8855a08059c5c179ba424 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/roformer/tokenization_utils.py @@ -0,0 +1,65 @@ +# Copyright 2021 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization utils for RoFormer.""" + +from tokenizers import NormalizedString, PreTokenizedString, normalizers + + +class JiebaPreTokenizer: + def __init__(self, vocab) -> None: + self.vocab = vocab + self.normalizers = normalizers.BertNormalizer( + clean_text=False, + handle_chinese_chars=True, + strip_accents=False, + lowercase=False, + ) + try: + import rjieba + except ImportError: + raise ImportError( + "You need to install rjieba to use RoFormerTokenizer. " + "See https://pypi.org/project/rjieba/ for installation." + ) + self.jieba = rjieba + + def jieba_split(self, i: int, normalized_string: NormalizedString) -> list[NormalizedString]: + splits = [] + + # this code slice normalized_string is too slow (6s) but test_alignment_methods can pass + for token, start, end in self.jieba.tokenize(str(normalized_string), hmm=False): + if token in self.vocab: + splits.append(normalized_string[start:end]) + else: + token_list = self.normalizers.normalize_str(token).split() + for token in token_list: + if token: + end = start + len(token) + splits.append(normalized_string[start:end]) + start = end + + # this code test_alignment_methods can't pass but fast (300ms) + # for token in self.jieba.cut(str(normalized_string), False): + # if token in self.vocab: + # splits.append(NormalizedString(token)) + # else: + # token_list = self.normalizers.normalize_str(token).split() + # for token in token_list: + # if token: + # splits.append(NormalizedString(token)) + + return splits + + def pre_tokenize(self, pretok: PreTokenizedString): + pretok.split(self.jieba_split) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..49c22dc17c4572c25ca00addaa6158941c97a621 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/__init__.py @@ -0,0 +1,33 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_rt_detr import * + from .configuration_rt_detr_resnet import * + from .image_processing_pil_rt_detr import * + from .image_processing_rt_detr import * + from .modeling_rt_detr import * + from .modeling_rt_detr_resnet import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/configuration_rt_detr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/configuration_rt_detr.py new file mode 100644 index 0000000000000000000000000000000000000000..6ed021380cdd9f80de9261b443e5b3154502b05f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/configuration_rt_detr.py @@ -0,0 +1,187 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RT-DETR model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PekingU/rtdetr_r50vd") +@strict +class RTDetrConfig(PreTrainedConfig): + r""" + initializer_bias_prior_prob (`float`, *optional*): + The prior probability used by the bias initializer to initialize biases for `enc_score_head` and `class_embed`. + If `None`, `prior_prob` computed as `prior_prob = 1 / (num_labels + 1)` while initializing model weights. + freeze_backbone_batch_norms (`bool`, *optional*, defaults to `True`): + Whether to freeze the batch normalization layers in the backbone. + encoder_in_channels (`list`, *optional*, defaults to `[512, 1024, 2048]`): + Multi level features input for encoder. + feat_strides (`list[int]`, *optional*, defaults to `[8, 16, 32]`): + Strides used in each feature map. + encode_proj_layers (`list[int]`, *optional*, defaults to `[2]`): + Indexes of the projected layers to be used in the encoder. + positional_encoding_temperature (`int`, *optional*, defaults to 10000): + The temperature parameter used to create the positional encodings. + encoder_activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + eval_size (`tuple[int, int]`, *optional*): + Height and width used to computes the effective height and width of the position embeddings after taking + into account the stride. + normalize_before (`bool`, *optional*, defaults to `False`): + Determine whether to apply layer normalization in the transformer encoder layer before self-attention and + feed-forward modules. + hidden_expansion (`float`, *optional*, defaults to 1.0): + Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. + num_queries (`int`, *optional*, defaults to 300): + Number of object queries. + decoder_in_channels (`list`, *optional*, defaults to `[256, 256, 256]`): + Multi level features dimension for decoder + num_feature_levels (`int`, *optional*, defaults to 3): + The number of input feature levels. + decoder_n_points (`int`, *optional*, defaults to 4): + The number of sampled keys in each feature level for each attention head in the decoder. + decoder_activation_function (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + num_denoising (`int`, *optional*, defaults to 100): + The total number of denoising tasks or queries to be used for contrastive denoising. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + The fraction of denoising labels to which random noise should be added. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale or magnitude of noise to be added to the bounding boxes. + learn_initial_query (`bool`, *optional*, defaults to `False`): + Indicates whether the initial query embeddings for the decoder should be learned during training + anchor_image_size (`tuple[int, int]`, *optional*): + Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. + disable_custom_kernels (`bool`, *optional*, defaults to `True`): + Whether to disable custom kernels. + with_box_refine (`bool`, *optional*, defaults to `True`): + Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes + based on the predictions from the previous layer. + matcher_alpha (`float`, *optional*, defaults to 0.25): + Parameter alpha used by the Hungarian Matcher. + matcher_gamma (`float`, *optional*, defaults to 2.0): + Parameter gamma used by the Hungarian Matcher. + matcher_class_cost (`float`, *optional*, defaults to 2.0): + The relative weight of the class loss used by the Hungarian Matcher. + matcher_bbox_cost (`float`, *optional*, defaults to 5.0): + The relative weight of the bounding box loss used by the Hungarian Matcher. + matcher_giou_cost (`float`, *optional*, defaults to 2.0): + The relative weight of the giou loss of used by the Hungarian Matcher. + use_focal_loss (`bool`, *optional*, defaults to `True`): + Parameter informing if focal focal should be used. + focal_loss_alpha (`float`, *optional*, defaults to 0.75): + Parameter alpha used to compute the focal loss. + focal_loss_gamma (`float`, *optional*, defaults to 2.0): + Parameter gamma used to compute the focal loss. + weight_loss_vfl (`float`, *optional*, defaults to 1.0): + Relative weight of the varifocal loss in the object detection loss. + weight_loss_bbox (`float`, *optional*, defaults to 5.0): + Relative weight of the L1 bounding box loss in the object detection loss. + weight_loss_giou (`float`, *optional*, defaults to 2.0): + Relative weight of the generalized IoU loss in the object detection loss. + + Examples: + + ```python + >>> from transformers import RTDetrConfig, RTDetrModel + + >>> # Initializing a RT-DETR configuration + >>> configuration = RTDetrConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = RTDetrModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "rt_detr" + sub_configs = {"backbone_config": AutoConfig} + layer_types = ["basic", "bottleneck"] + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + initializer_range: float = 0.01 + initializer_bias_prior_prob: float | None = None + layer_norm_eps: float = 1e-5 + batch_norm_eps: float = 1e-5 + backbone_config: dict | PreTrainedConfig | None = None + freeze_backbone_batch_norms: bool = True + encoder_hidden_dim: int = 256 + encoder_in_channels: list[int] | tuple[int, ...] = (512, 1024, 2048) + feat_strides: list[int] | tuple[int, ...] = (8, 16, 32) + encoder_layers: int = 1 + encoder_ffn_dim: int = 1024 + encoder_attention_heads: int = 8 + dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + encode_proj_layers: list[int] | tuple[int, ...] = (2,) + positional_encoding_temperature: int = 10000 + encoder_activation_function: str = "gelu" + activation_function: str = "silu" + eval_size: int | None = None + normalize_before: bool = False + hidden_expansion: float = 1.0 + d_model: int = 256 + num_queries: int = 300 + decoder_in_channels: list[int] | tuple[int, ...] = (256, 256, 256) + decoder_ffn_dim: int = 1024 + num_feature_levels: int = 3 + decoder_n_points: int = 4 + decoder_layers: int = 6 + decoder_attention_heads: int = 8 + decoder_activation_function: str = "relu" + attention_dropout: float | int = 0.0 + num_denoising: int = 100 + label_noise_ratio: float = 0.5 + box_noise_scale: float = 1.0 + learn_initial_query: bool = False + anchor_image_size: int | list[int] | None = None + disable_custom_kernels: bool = True + with_box_refine: bool = True + is_encoder_decoder: bool = True + matcher_alpha: float = 0.25 + matcher_gamma: float = 2.0 + matcher_class_cost: float = 2.0 + matcher_bbox_cost: float = 5.0 + matcher_giou_cost: float = 2.0 + use_focal_loss: bool = True + auxiliary_loss: bool = True + focal_loss_alpha: float = 0.75 + focal_loss_gamma: float = 2.0 + weight_loss_vfl: float = 1.0 + weight_loss_bbox: float = 5.0 + weight_loss_giou: float = 2.0 + eos_coefficient: float = 1e-4 + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="rt_detr_resnet", + default_config_kwargs={"out_indices": [2, 3, 4]}, + **kwargs, + ) + super().__post_init__(**kwargs) + + +__all__ = ["RTDetrConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/configuration_rt_detr_resnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/configuration_rt_detr_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..6bf2fd9b21da83a6db2e9c0185098953c794b7de --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/configuration_rt_detr_resnet.py @@ -0,0 +1,83 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RT-DETR ResNet model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import BackboneConfigMixin +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="microsoft/resnet-50") +@strict +class RTDetrResNetConfig(BackboneConfigMixin, PreTrainedConfig): + r""" + depths (`list[int]`, *optional*, defaults to `[3, 4, 6, 3]`): + Depth (number of layers) for each stage. + layer_type (`str`, *optional*, defaults to `"bottleneck"`): + The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or + `"bottleneck"` (used for larger models like resnet-50 and above). + hidden_act (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` + are supported. + downsample_in_first_stage (`bool`, *optional*, defaults to `False`): + If `True`, the first stage will downsample the inputs using a `stride` of 2. + downsample_in_bottleneck (`bool`, *optional*, defaults to `False`): + If `True`, the first conv 1x1 in ResNetBottleNeckLayer will downsample the inputs using a `stride` of 2. + + Example: + ```python + >>> from transformers import RTDetrResNetConfig, RTDetrResnetBackbone + + >>> # Initializing a ResNet resnet-50 style configuration + >>> configuration = RTDetrResNetConfig() + + >>> # Initializing a model (with random weights) from the resnet-50 style configuration + >>> model = RTDetrResnetBackbone(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "rt_detr_resnet" + layer_types = ["basic", "bottleneck"] + + num_channels: int = 3 + embedding_size: int = 64 + hidden_sizes: list[int] | tuple[int, ...] = (256, 512, 1024, 2048) + depths: list[int] | tuple[int, ...] = (3, 4, 6, 3) + layer_type: str = "bottleneck" + hidden_act: str = "relu" + downsample_in_first_stage: bool = False + downsample_in_bottleneck: bool = False + _out_features: list[str] | None = None + _out_indices: list[int] | None = None + + def __post_init__(self, **kwargs): + self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 1)] + self.set_output_features_output_indices( + out_indices=kwargs.pop("out_indices", None), out_features=kwargs.pop("out_features", None) + ) + self.hidden_sizes = list(self.hidden_sizes) + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.layer_type not in self.layer_types: + raise ValueError(f"layer_type={self.layer_type} is not one of {','.join(self.layer_types)}") + + +__all__ = ["RTDetrResNetConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/image_processing_pil_rt_detr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/image_processing_pil_rt_detr.py new file mode 100644 index 0000000000000000000000000000000000000000..669843e9f949b34e7030d4c86150e63ff20c06cb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/image_processing_pil_rt_detr.py @@ -0,0 +1,581 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/rt_detr/modular_rt_detr.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_rt_detr.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 Baidu Inc and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pathlib +from typing import Any, Optional + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature, SizeDict +from ...image_transforms import ( + PaddingMode, + center_to_corners_format, + corners_to_center_format, + get_size_with_aspect_ratio, + pad, + resize, + safe_squeeze, +) +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + AnnotationFormat, + AnnotationType, + ChannelDimension, + ImageInput, + PILImageResampling, + get_image_size, + get_image_size_for_max_height_width, + get_max_height_width, + validate_annotations, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_torch_available, requires_backends +from ...utils.import_utils import requires + + +if is_torch_available(): + import torch + +SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC) + + +class RTDetrImageProcessorKwargs(ImagesKwargs, total=False): + r""" + format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`): + Data format of the annotations. One of "coco_detection" or "coco_panoptic". + do_convert_annotations (`bool`, *optional*, defaults to `True`): + Controls whether to convert the annotations to the format expected by the RT_DETR model. Converts the + bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`. + Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method. + """ + + format: str | AnnotationFormat + do_convert_annotations: bool + + +def prepare_coco_detection_annotation_pil( + image, + target, + return_segmentation_masks: bool = False, + input_data_format: ChannelDimension | str | None = None, +): + """ + Convert the target in COCO format into the format expected by RTDETR. + """ + image_height, image_width = get_image_size(image, channel_dim=input_data_format) + + image_id = target["image_id"] + image_id = np.asarray([image_id], dtype=np.int64) + + # Get all COCO annotations for the given image. + annotations = target["annotations"] + annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0] + + classes = [obj["category_id"] for obj in annotations] + classes = np.asarray(classes, dtype=np.int64) + + # for conversion to coco api + area = np.asarray([obj["area"] for obj in annotations], dtype=np.float32) + iscrowd = np.asarray([obj.get("iscrowd", 0) for obj in annotations], dtype=np.int64) + + boxes = [obj["bbox"] for obj in annotations] + # guard against no boxes via resizing + boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4) + boxes[:, 2:] += boxes[:, :2] + boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width) + boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height) + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + + new_target = {} + new_target["image_id"] = image_id + new_target["class_labels"] = classes[keep] + new_target["boxes"] = boxes[keep] + new_target["area"] = area[keep] + new_target["iscrowd"] = iscrowd[keep] + new_target["orig_size"] = np.asarray([int(image_height), int(image_width)], dtype=np.int64) + + if annotations and "keypoints" in annotations[0]: + keypoints = [obj["keypoints"] for obj in annotations] + # Converting the filtered keypoints list to a numpy array + keypoints = np.asarray(keypoints, dtype=np.float32) + # Apply the keep mask here to filter the relevant annotations + keypoints = keypoints[keep] + num_keypoints = keypoints.shape[0] + keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints + new_target["keypoints"] = keypoints + + return new_target + + +@requires(backends=("torch",)) +class RTDetrImageProcessorPil(PilBackend): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + format = AnnotationFormat.COCO_DETECTION + do_resize = True + do_rescale = True + do_normalize = False + do_pad = False + size = {"height": 640, "width": 640} + default_to_square = False + model_input_names = ["pixel_values", "pixel_mask"] + valid_kwargs = RTDetrImageProcessorKwargs + do_convert_annotations = True + + def __init__(self, **kwargs: Unpack[RTDetrImageProcessorKwargs]) -> None: + # Backwards compatibility + do_convert_annotations = kwargs.get("do_convert_annotations") + do_normalize = kwargs.get("do_normalize") + if do_convert_annotations is None and getattr(self, "do_convert_annotations", None) is None: + self.do_convert_annotations = do_normalize if do_normalize is not None else self.do_normalize + + super().__init__(**kwargs) + + def prepare_annotation( + self, + image: np.ndarray, + target: dict, + format: AnnotationFormat | None = None, + return_segmentation_masks: bool | None = None, + masks_path: str | pathlib.Path | None = None, + input_data_format: str | ChannelDimension | None = None, + ) -> dict: + """ + Prepare an annotation for feeding into RT_DETR model. + """ + format = format if format is not None else self.format + + if format == AnnotationFormat.COCO_DETECTION: + return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks + target = prepare_coco_detection_annotation_pil( + image, target, return_segmentation_masks, input_data_format=input_data_format + ) + else: + raise ValueError(f"Format {format} is not supported.") + return target + + def resize( + self, + image: np.ndarray, + size: SizeDict, + resample: Optional["PILImageResampling"] = None, + **kwargs, + ) -> np.ndarray: + """ + Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an + int, smaller edge of the image will be matched to this number. + + Args: + image (`np.ndarray`): + Image to resize. + size (`SizeDict`): + Size of the image's `(height, width)` dimensions after resizing. Available options are: + - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`. + Do NOT keep the aspect ratio. + - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting + the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge + less or equal to `longest_edge`. + - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the + aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to + `max_width`. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): + Resampling filter to use if resizing the image. + """ + resample = resample if resample is not None else self.resample + + if size.shortest_edge and size.longest_edge: + # Resize the image so that the shortest edge or the longest edge is of the given size + # while maintaining the aspect ratio of the original image. + new_size = get_size_with_aspect_ratio( + image.shape[-2:], + size.shortest_edge, + size.longest_edge or size.shortest_edge, + ) + elif size.max_height and size.max_width: + new_size = get_image_size_for_max_height_width(image.shape[-2:], size.max_height, size.max_width) + elif size.height and size.width: + new_size = (size.height, size.width) + else: + raise ValueError( + f"Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got {size}." + ) + + image = super().resize( + image, + size=SizeDict(height=new_size[0], width=new_size[1]), + resample=resample, + **kwargs, + ) + return image + + def resize_annotation( + self, + annotation: dict[str, Any], + orig_size: tuple[int, int], + target_size: tuple[int, int], + threshold: float = 0.5, + resample: Optional["PILImageResampling"] = PILImageResampling.NEAREST, + ): + """ + Resizes an annotation to a target size. + + Args: + annotation (`dict[str, Any]`): + The annotation dictionary. + orig_size (`tuple[int, int]`): + The original size of the input image. + target_size (`tuple[int, int]`): + The target size of the image, as returned by the preprocessing `resize` step. + threshold (`float`, *optional*, defaults to 0.5): + The threshold used to binarize the segmentation masks. + resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`): + The resampling filter to use when resizing the masks. + """ + ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(target_size, orig_size)) + ratio_height, ratio_width = ratios + + new_annotation = {} + new_annotation["size"] = target_size + + for key, value in annotation.items(): + if key == "boxes": + boxes = value + scaled_boxes = boxes * np.asarray( + [ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32 + ) + new_annotation["boxes"] = scaled_boxes + elif key == "area": + area = value + scaled_area = area * (ratio_width * ratio_height) + new_annotation["area"] = scaled_area + elif key == "masks": + masks = value[:, None] + masks = np.array([resize(mask, target_size, resample=resample) for mask in masks]) + masks = masks.astype(np.float32) + masks = masks[:, 0] > threshold + new_annotation["masks"] = masks + elif key == "size": + new_annotation["size"] = target_size + else: + new_annotation[key] = value + + return new_annotation + + def normalize_annotation(self, annotation: dict, image_size: tuple[int, int]) -> dict: + image_height, image_width = image_size + norm_annotation = {} + for key, value in annotation.items(): + if key == "boxes": + boxes = value + boxes = corners_to_center_format(boxes) + boxes /= np.asarray([image_width, image_height, image_width, image_height], dtype=np.float32) + norm_annotation[key] = boxes + else: + norm_annotation[key] = value + return norm_annotation + + def _update_annotation_for_padded_image( + self, + annotation: dict, + input_image_size: tuple[int, int], + output_image_size: tuple[int, int], + padding, + update_bboxes, + ) -> dict: + """ + Update the annotation for a padded image. + """ + new_annotation = {} + new_annotation["size"] = output_image_size + ratio_height, ratio_width = (input / output for output, input in zip(output_image_size, input_image_size)) + + for key, value in annotation.items(): + if key == "masks": + masks = value + masks = pad( + masks, + padding, + mode=PaddingMode.CONSTANT, + constant_values=0, + input_data_format=ChannelDimension.FIRST, + ) + masks = safe_squeeze(masks, 1) + new_annotation["masks"] = masks + elif key == "boxes" and update_bboxes: + boxes = value + boxes *= np.asarray( + [ + input_image_size[1] / output_image_size[1], + input_image_size[0] / output_image_size[0], + input_image_size[1] / output_image_size[1], + input_image_size[0] / output_image_size[0], + ] + ) + new_annotation["boxes"] = boxes + elif key == "size": + new_annotation["size"] = output_image_size + else: + new_annotation[key] = value + return new_annotation + + def pad( + self, + image: np.ndarray, + padded_size: tuple[int, int], + annotation: dict[str, Any] | None = None, + update_bboxes: bool = True, + fill: int = 0, + ): + input_height, input_width = get_image_size(image, channel_dim=ChannelDimension.FIRST) + output_height, output_width = padded_size + padding_bottom = output_height - input_height + padding_right = output_width - input_width + if padding_bottom < 0 or padding_right < 0: + raise ValueError( + f"Padding dimensions are negative. Please make sure that the padded size is larger than the " + f"original size. Got padded size: {padded_size}, original size: {(input_height, input_width)}." + ) + if (input_height, input_width) != padded_size: + padding = ((0, padding_bottom), (0, padding_right)) + image = pad( + image, + padding, + mode=PaddingMode.CONSTANT, + constant_values=fill, + data_format=ChannelDimension.FIRST, + input_data_format=ChannelDimension.FIRST, + ) + if annotation is not None: + annotation = self._update_annotation_for_padded_image( + annotation, (input_height, input_width), (output_height, output_width), padding, update_bboxes + ) + + # Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. + pixel_mask = np.zeros(padded_size, dtype=np.int64) + pixel_mask[:input_height, :input_width] = 1 + + return image, pixel_mask, annotation + + @auto_docstring + def preprocess( + self, + images: ImageInput, + annotations: AnnotationType | list[AnnotationType] | None = None, + return_segmentation_masks: bool | None = None, + masks_path: str | pathlib.Path | None = None, + **kwargs: Unpack[RTDetrImageProcessorKwargs], + ) -> BatchFeature: + r""" + annotations (`AnnotationType` or `list[AnnotationType]`, *optional*): + Annotations to transform according to the padding that is applied to the images. + return_segmentation_masks (`bool`, *optional*, defaults to `self.return_segmentation_masks`): + Whether to return segmentation masks. + masks_path (`str` or `pathlib.Path`, *optional*): + Path to the directory containing the segmentation masks. + """ + return super().preprocess(images, annotations, return_segmentation_masks, masks_path, **kwargs) + + def _preprocess( + self, + images: list[np.ndarray], + annotations: AnnotationType | list[AnnotationType] | None, + return_segmentation_masks: bool, + masks_path: str | pathlib.Path | None, + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + do_convert_annotations: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool, + pad_size: SizeDict | None, + format: str | AnnotationFormat | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess an image or a batch of images so that it can be used by the model. + """ + + if annotations is not None and isinstance(annotations, dict): + annotations = [annotations] + + if annotations is not None and len(images) != len(annotations): + raise ValueError( + f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match." + ) + + format = AnnotationFormat(format) + if annotations is not None: + validate_annotations(format, SUPPORTED_ANNOTATION_FORMATS, annotations) + + data = {} + processed_images = [] + processed_annotations = [] + pixel_masks = [] # Initialize pixel_masks here + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image) + if annotations is not None: + annotation = self.prepare_annotation( + image, + annotation, + format, + return_segmentation_masks=return_segmentation_masks, + masks_path=masks_path, + input_data_format=ChannelDimension.FIRST, + ) + + if do_resize: + resized_image = self.resize(image, size=size, resample=resample) + if annotations is not None: + annotation = self.resize_annotation( + annotation, + orig_size=image.shape[-2:], + target_size=resized_image.shape[-2:], + ) + image = resized_image + + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + if do_convert_annotations and annotations is not None: + annotation = self.normalize_annotation(annotation, get_image_size(image, ChannelDimension.FIRST)) + + processed_images.append(image) + processed_annotations.append(annotation) + images = processed_images + annotations = processed_annotations if annotations is not None else None + + if do_pad: + # depends on all resized image shapes so we need another loop + if pad_size is not None: + padded_size = (pad_size.height, pad_size.width) + else: + padded_size = get_max_height_width(images) + + padded_images = [] + padded_annotations = [] + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...} + if padded_size == image.shape[-2:]: + padded_images.append(image) + pixel_masks.append(np.ones(padded_size, dtype=np.int64)) + padded_annotations.append(annotation) + continue + image, pixel_mask, annotation = self.pad( + image, padded_size, annotation=annotation, update_bboxes=do_convert_annotations + ) + padded_images.append(image) + padded_annotations.append(annotation) + pixel_masks.append(pixel_mask) + images = padded_images + annotations = padded_annotations if annotations is not None else None + data.update({"pixel_mask": pixel_masks}) + + data.update({"pixel_values": images}) + encoded_inputs = BatchFeature(data, tensor_type=return_tensors) + if annotations is not None: + encoded_inputs["labels"] = [ + BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations + ] + return encoded_inputs + + @requires(backends=("torch",)) + def post_process_object_detection( + self, + outputs, + threshold: float = 0.5, + target_sizes: TensorType | list[tuple] = None, + use_focal_loss: bool = True, + ): + """ + Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.5): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + use_focal_loss (`bool` defaults to `True`): + Variable informing if the focal loss was used to predict the outputs. If `True`, a sigmoid is applied + to compute the scores of each detection, otherwise, a softmax function is used. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. + """ + requires_backends(self, ["torch"]) + out_logits, out_bbox = outputs.logits, outputs.pred_boxes + # convert from relative cxcywh to absolute xyxy + boxes = center_to_corners_format(out_bbox) + if target_sizes is not None: + if len(out_logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + if isinstance(target_sizes, list): + img_h, img_w = torch.as_tensor(target_sizes).unbind(1) + else: + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device) + boxes = boxes * scale_fct[:, None, :] + + num_top_queries = out_logits.shape[1] + num_classes = out_logits.shape[2] + + if use_focal_loss: + scores = torch.nn.functional.sigmoid(out_logits) + scores, index = torch.topk(scores.flatten(1), num_top_queries, axis=-1) + labels = index % num_classes + index = index // num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + else: + scores = torch.nn.functional.softmax(out_logits)[:, :, :-1] + scores, labels = scores.max(dim=-1) + if scores.shape[1] > num_top_queries: + scores, index = torch.topk(scores, num_top_queries, dim=-1) + labels = torch.gather(labels, dim=1, index=index) + boxes = torch.gather(boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1])) + + results = [] + for score, label, box in zip(scores, labels, boxes): + results.append( + { + "scores": score[score > threshold], + "labels": label[score > threshold], + "boxes": box[score > threshold], + } + ) + + return results + + +__all__ = ["RTDetrImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/image_processing_rt_detr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/image_processing_rt_detr.py new file mode 100644 index 0000000000000000000000000000000000000000..0aadd7ece2e90620927934e5ad896f8af6125276 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/image_processing_rt_detr.py @@ -0,0 +1,555 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/rt_detr/modular_rt_detr.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_rt_detr.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 Baidu Inc and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import pathlib +from typing import Any, Optional + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature, SizeDict +from ...image_transforms import ( + center_to_corners_format, + corners_to_center_format, + get_size_with_aspect_ratio, + safe_squeeze, +) +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + AnnotationFormat, + AnnotationType, + ChannelDimension, + ImageInput, + PILImageResampling, + get_image_size, + get_image_size_for_max_height_width, + get_max_height_width, + validate_annotations, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, requires_backends + + +class RTDetrImageProcessorKwargs(ImagesKwargs, total=False): + r""" + format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`): + Data format of the annotations. One of "coco_detection" or "coco_panoptic". + do_convert_annotations (`bool`, *optional*, defaults to `True`): + Controls whether to convert the annotations to the format expected by the RT_DETR model. Converts the + bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`. + Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method. + """ + + format: str | AnnotationFormat + do_convert_annotations: bool + + +SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC) + + +def prepare_coco_detection_annotation( + image, + target, + return_segmentation_masks: bool = False, + input_data_format: ChannelDimension | str | None = None, +): + """ + Convert the target in COCO format into the format expected by RT-DETR. + """ + image_height, image_width = image.size()[-2:] + + image_id = target["image_id"] + image_id = torch.as_tensor([image_id], dtype=torch.int64, device=image.device) + + # Get all COCO annotations for the given image. + annotations = target["annotations"] + classes = [] + area = [] + boxes = [] + keypoints = [] + for obj in annotations: + if "iscrowd" not in obj or obj["iscrowd"] == 0: + classes.append(obj["category_id"]) + area.append(obj["area"]) + boxes.append(obj["bbox"]) + if "keypoints" in obj: + keypoints.append(obj["keypoints"]) + + classes = torch.as_tensor(classes, dtype=torch.int64, device=image.device) + area = torch.as_tensor(area, dtype=torch.float32, device=image.device) + iscrowd = torch.zeros_like(classes, dtype=torch.int64, device=image.device) + # guard against no boxes via resizing + boxes = torch.as_tensor(boxes, dtype=torch.float32, device=image.device).reshape(-1, 4) + boxes[:, 2:] += boxes[:, :2] + boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width) + boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height) + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + + new_target = { + "image_id": image_id, + "class_labels": classes[keep], + "boxes": boxes[keep], + "area": area[keep], + "iscrowd": iscrowd[keep], + "orig_size": torch.as_tensor([int(image_height), int(image_width)], dtype=torch.int64, device=image.device), + } + + if keypoints: + keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=image.device) + # Apply the keep mask here to filter the relevant annotations + keypoints = keypoints[keep] + num_keypoints = keypoints.shape[0] + keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints + new_target["keypoints"] = keypoints + + return new_target + + +@auto_docstring +class RTDetrImageProcessor(TorchvisionBackend): + valid_kwargs = RTDetrImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + format = AnnotationFormat.COCO_DETECTION + do_resize = True + do_rescale = True + do_normalize = False + do_pad = False + size = {"height": 640, "width": 640} + default_to_square = False + model_input_names = ["pixel_values", "pixel_mask"] + do_convert_annotations = True + + def __init__(self, **kwargs: Unpack[RTDetrImageProcessorKwargs]) -> None: + # Backwards compatibility + do_convert_annotations = kwargs.get("do_convert_annotations") + do_normalize = kwargs.get("do_normalize") + if do_convert_annotations is None and getattr(self, "do_convert_annotations", None) is None: + self.do_convert_annotations = do_normalize if do_normalize is not None else self.do_normalize + + super().__init__(**kwargs) + + def prepare_annotation( + self, + image: torch.Tensor, + target: dict, + format: AnnotationFormat | None = None, + return_segmentation_masks: bool | None = None, + masks_path: str | pathlib.Path | None = None, + input_data_format: str | ChannelDimension | None = None, + ) -> dict: + """ + Prepare an annotation for feeding into RT_DETR model. + """ + format = format if format is not None else self.format + + if format == AnnotationFormat.COCO_DETECTION: + return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks + target = prepare_coco_detection_annotation( + image, target, return_segmentation_masks, input_data_format=input_data_format + ) + else: + raise ValueError(f"Format {format} is not supported.") + return target + + def resize( + self, + image: torch.Tensor, + size: SizeDict, + resample: Optional["PILImageResampling | tvF.InterpolationMode | int"] = None, + **kwargs, + ) -> torch.Tensor: + """ + Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an + int, smaller edge of the image will be matched to this number. + + Args: + image (`torch.Tensor`): + Image to resize. + size (`SizeDict`): + Size of the image's `(height, width)` dimensions after resizing. Available options are: + - `{"height": int, "width": int}`: The image will be resized to the exact size `(height, width)`. + Do NOT keep the aspect ratio. + - `{"shortest_edge": int, "longest_edge": int}`: The image will be resized to a maximum size respecting + the aspect ratio and keeping the shortest edge less or equal to `shortest_edge` and the longest edge + less or equal to `longest_edge`. + - `{"max_height": int, "max_width": int}`: The image will be resized to the maximum size respecting the + aspect ratio and keeping the height less or equal to `max_height` and the width less or equal to + `max_width`. + resample (`PILImageResampling | tvF.InterpolationMode | int`, *optional*, defaults to `PILImageResampling.BILINEAR`): + Resampling filter to use if resizing the image. + """ + if size.shortest_edge and size.longest_edge: + # Resize the image so that the shortest edge or the longest edge is of the given size + # while maintaining the aspect ratio of the original image. + new_size = get_size_with_aspect_ratio(image.shape[-2:], size.shortest_edge, size.longest_edge) + elif size.max_height and size.max_width: + new_size = get_image_size_for_max_height_width(image.shape[-2:], size.max_height, size.max_width) + elif size.height and size.width: + new_size = (size.height, size.width) + else: + raise ValueError( + f"Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got {size}." + ) + + image = super().resize( + image, size=SizeDict(height=new_size[0], width=new_size[1]), resample=resample, **kwargs + ) + return image + + def resize_annotation( + self, + annotation: dict[str, Any], + orig_size: tuple[int, int], + target_size: tuple[int, int], + threshold: float = 0.5, + resample: Optional["PILImageResampling | tvF.InterpolationMode | int"] = PILImageResampling.NEAREST, + ): + """ + Resizes an annotation to a target size. + + Args: + annotation (`dict[str, Any]`): + The annotation dictionary. + orig_size (`tuple[int, int]`): + The original size of the input image. + target_size (`tuple[int, int]`): + The target size of the image, as returned by the preprocessing `resize` step. + threshold (`float`, *optional*, defaults to 0.5): + The threshold used to binarize the segmentation masks. + resample (`PILImageResampling | tvF.InterpolationMode | int`, defaults to `tvF.InterpolationMode.NEAREST_EXACT`): + The resampling filter to use when resizing the masks. + """ + ratio_height, ratio_width = [target / orig for target, orig in zip(target_size, orig_size)] + + new_annotation = {} + new_annotation["size"] = target_size + + for key, value in annotation.items(): + if key == "boxes": + boxes = value + scaled_boxes = boxes * torch.as_tensor( + [ratio_width, ratio_height, ratio_width, ratio_height], dtype=torch.float32, device=boxes.device + ) + new_annotation["boxes"] = scaled_boxes + elif key == "area": + area = value + scaled_area = area * (ratio_width * ratio_height) + new_annotation["area"] = scaled_area + elif key == "masks": + masks = value[:, None] + masks = [ + super(RTDetrImageProcessor, self).resize( + mask, size=SizeDict(height=target_size[0], width=target_size[1]), resample=resample + ) + for mask in masks + ] + masks = torch.stack(masks).to(torch.float32) + masks = masks[:, 0] > threshold + new_annotation["masks"] = masks + elif key == "size": + new_annotation["size"] = target_size + else: + new_annotation[key] = value + + return new_annotation + + def normalize_annotation(self, annotation: dict, image_size: tuple[int, int]) -> dict: + image_height, image_width = image_size + norm_annotation = {} + for key, value in annotation.items(): + if key == "boxes": + boxes = value + boxes = corners_to_center_format(boxes) + boxes /= torch.as_tensor( + [image_width, image_height, image_width, image_height], dtype=torch.float32, device=boxes.device + ) + norm_annotation[key] = boxes + else: + norm_annotation[key] = value + return norm_annotation + + def _update_annotation_for_padded_image( + self, + annotation: dict, + input_image_size: tuple[int, int], + output_image_size: tuple[int, int], + padding, + update_bboxes, + ) -> dict: + """ + Update the annotation for a padded image. + """ + new_annotation = {} + new_annotation["size"] = output_image_size + ratio_height, ratio_width = (input / output for output, input in zip(output_image_size, input_image_size)) + + for key, value in annotation.items(): + if key == "masks": + masks = value + masks = tvF.pad( + masks, + padding, + fill=0, + ) + masks = safe_squeeze(masks, 1) + new_annotation["masks"] = masks + elif key == "boxes" and update_bboxes: + boxes = value + boxes *= torch.as_tensor([ratio_width, ratio_height, ratio_width, ratio_height], device=boxes.device) + new_annotation["boxes"] = boxes + elif key == "size": + new_annotation["size"] = output_image_size + else: + new_annotation[key] = value + return new_annotation + + def pad( + self, + image: torch.Tensor, + padded_size: tuple[int, int], + annotation: dict[str, Any] | None = None, + update_bboxes: bool = True, + fill: int = 0, + ): + original_size = image.size()[-2:] + padding_bottom = padded_size[0] - original_size[0] + padding_right = padded_size[1] - original_size[1] + if padding_bottom < 0 or padding_right < 0: + raise ValueError( + f"Padding dimensions are negative. Please make sure that the padded size is larger than the " + f"original size. Got padded size: {padded_size}, original size: {original_size}." + ) + if original_size != padded_size: + padding = [0, 0, padding_right, padding_bottom] + image = tvF.pad(image, padding, fill=fill) + if annotation is not None: + annotation = self._update_annotation_for_padded_image( + annotation, original_size, padded_size, padding, update_bboxes + ) + + # Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. + pixel_mask = torch.zeros(padded_size, dtype=torch.int64, device=image.device) + pixel_mask[: original_size[0], : original_size[1]] = 1 + + return image, pixel_mask, annotation + + @auto_docstring + def preprocess( + self, + images: ImageInput, + annotations: AnnotationType | list[AnnotationType] | None = None, + return_segmentation_masks: bool | None = None, + masks_path: str | pathlib.Path | None = None, + **kwargs: Unpack[RTDetrImageProcessorKwargs], + ) -> BatchFeature: + r""" + annotations (`AnnotationType` or `list[AnnotationType]`, *optional*): + Annotations to transform according to the padding that is applied to the images. + return_segmentation_masks (`bool`, *optional*, defaults to `self.return_segmentation_masks`): + Whether to return segmentation masks. + masks_path (`str` or `pathlib.Path`, *optional*): + Path to the directory containing the segmentation masks. + """ + return super().preprocess(images, annotations, return_segmentation_masks, masks_path, **kwargs) + + def _preprocess( + self, + images: list["torch.Tensor"], + annotations: AnnotationType | list[AnnotationType] | None, + return_segmentation_masks: bool, + masks_path: str | pathlib.Path | None, + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + do_convert_annotations: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool, + pad_size: SizeDict | None, + format: str | AnnotationFormat | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess an image or a batch of images so that it can be used by the model. + """ + + if annotations is not None and isinstance(annotations, dict): + annotations = [annotations] + + if annotations is not None and len(images) != len(annotations): + raise ValueError( + f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match." + ) + + format = AnnotationFormat(format) + if annotations is not None: + validate_annotations(format, SUPPORTED_ANNOTATION_FORMATS, annotations) + + data = {} + processed_images = [] + processed_annotations = [] + pixel_masks = [] # Initialize pixel_masks here + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image) + if annotations is not None: + annotation = self.prepare_annotation( + image, + annotation, + format, + return_segmentation_masks=return_segmentation_masks, + masks_path=masks_path, + input_data_format=ChannelDimension.FIRST, + ) + + if do_resize: + resized_image = self.resize(image, size=size, resample=resample) + if annotations is not None: + annotation = self.resize_annotation( + annotation, + orig_size=image.size()[-2:], + target_size=resized_image.size()[-2:], + ) + image = resized_image + # Fused rescale and normalize + image = self.rescale_and_normalize(image, do_rescale, rescale_factor, do_normalize, image_mean, image_std) + if do_convert_annotations and annotations is not None: + annotation = self.normalize_annotation(annotation, get_image_size(image, ChannelDimension.FIRST)) + + processed_images.append(image) + processed_annotations.append(annotation) + images = processed_images + annotations = processed_annotations if annotations is not None else None + + if do_pad: + # depends on all resized image shapes so we need another loop + if pad_size is not None: + padded_size = (pad_size.height, pad_size.width) + else: + padded_size = get_max_height_width(images) + + padded_images = [] + padded_annotations = [] + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...} + if padded_size == image.size()[-2:]: + padded_images.append(image) + pixel_masks.append(torch.ones(padded_size, dtype=torch.int64, device=image.device)) + padded_annotations.append(annotation) + continue + image, pixel_mask, annotation = self.pad( + image, padded_size, annotation=annotation, update_bboxes=do_convert_annotations + ) + padded_images.append(image) + padded_annotations.append(annotation) + pixel_masks.append(pixel_mask) + images = padded_images + annotations = padded_annotations if annotations is not None else None + data.update({"pixel_mask": torch.stack(pixel_masks, dim=0)}) + + data.update({"pixel_values": torch.stack(images, dim=0)}) + encoded_inputs = BatchFeature(data, tensor_type=return_tensors) + if annotations is not None: + encoded_inputs["labels"] = [ + BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations + ] + return encoded_inputs + + def post_process_object_detection( + self, + outputs, + threshold: float = 0.5, + target_sizes: TensorType | list[tuple] = None, + use_focal_loss: bool = True, + ): + """ + Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.5): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + use_focal_loss (`bool` defaults to `True`): + Variable informing if the focal loss was used to predict the outputs. If `True`, a sigmoid is applied + to compute the scores of each detection, otherwise, a softmax function is used. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. + """ + requires_backends(self, ["torch"]) + out_logits, out_bbox = outputs.logits, outputs.pred_boxes + # convert from relative cxcywh to absolute xyxy + boxes = center_to_corners_format(out_bbox) + if target_sizes is not None: + if len(out_logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + if isinstance(target_sizes, list): + img_h, img_w = torch.as_tensor(target_sizes).unbind(1) + else: + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device) + boxes = boxes * scale_fct[:, None, :] + + num_top_queries = out_logits.shape[1] + num_classes = out_logits.shape[2] + + if use_focal_loss: + scores = torch.nn.functional.sigmoid(out_logits) + scores, index = torch.topk(scores.flatten(1), num_top_queries, axis=-1) + labels = index % num_classes + index = index // num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + else: + scores = torch.nn.functional.softmax(out_logits)[:, :, :-1] + scores, labels = scores.max(dim=-1) + if scores.shape[1] > num_top_queries: + scores, index = torch.topk(scores, num_top_queries, dim=-1) + labels = torch.gather(labels, dim=1, index=index) + boxes = torch.gather(boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1])) + + results = [] + for score, label, box in zip(scores, labels, boxes): + results.append( + { + "scores": score[score > threshold], + "labels": label[score > threshold], + "boxes": box[score > threshold], + } + ) + + return results + + +__all__ = ["RTDetrImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modeling_rt_detr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modeling_rt_detr.py new file mode 100644 index 0000000000000000000000000000000000000000..c500a532de5eb7ee5da3bc526c4cd2ea4bf90f9d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modeling_rt_detr.py @@ -0,0 +1,1847 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/rt_detr/modular_rt_detr.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_rt_detr.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2024 Baidu Inc and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +import warnings +from collections.abc import Callable +from dataclasses import dataclass + +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2CLS, ACT2FN +from ...backbone_utils import load_backbone +from ...image_transforms import center_to_corners_format, corners_to_center_format +from ...integrations import use_kernel_forward_from_hub +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + torch_compilable_check, + torch_int, +) +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_rt_detr import RTDetrConfig + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the RTDetrDecoder. This class adds two attributes to + BaseModelOutputWithCrossAttentions, namely: + - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) + - a stacked tensor of intermediate reference points. + """ +) +@dataclass +class RTDetrDecoderOutput(ModelOutput): + r""" + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the RT-DETR encoder-decoder model. + """ +) +@dataclass +class RTDetrModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points used for the first decoder layer. + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`): + Logits of predicted bounding boxes coordinates in the encoder stage. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: torch.FloatTensor | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +@auto_docstring( + custom_intro=""" + Output type of [`RTDetrForObjectDetection`]. + """ +) +@dataclass +class RTDetrObjectDetectionOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): + Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a + bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized + scale-invariant IoU loss. + loss_dict (`Dict`, *optional*): + A dictionary containing the individual losses. Useful for logging. + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~RTDetrImageProcessor.post_process_object_detection`] to retrieve the + unnormalized (absolute) bounding boxes. + auxiliary_outputs (`list[Dict]`, *optional*): + Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) + and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and + `pred_boxes`) for each decoder layer. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values + """ + + loss: torch.FloatTensor | None = None + loss_dict: dict | None = None + logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + auxiliary_outputs: list[dict] | None = None + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: tuple[torch.FloatTensor] | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +class RTDetrMLP(nn.Module): + def __init__(self, config: RTDetrConfig, hidden_size: int, intermediate_size: int, activation_function: str): + super().__init__() + self.fc1 = nn.Linear(hidden_size, intermediate_size) + self.fc2 = nn.Linear(intermediate_size, hidden_size) + self.activation_fn = ACT2FN[activation_function] + self.activation_dropout = config.activation_dropout + self.dropout = config.dropout + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + return hidden_states + + +class RTDetrFrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than + torchvision.models.resnet[18,34,50,101] produce nans. + """ + + def __init__(self, n): + super().__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it user-friendly + weight = self.weight.reshape(1, -1, 1, 1) + bias = self.bias.reshape(1, -1, 1, 1) + running_var = self.running_var.reshape(1, -1, 1, 1) + running_mean = self.running_mean.reshape(1, -1, 1, 1) + epsilon = 1e-5 + scale = weight * (running_var + epsilon).rsqrt() + bias = bias - running_mean * scale + return x * scale + bias + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class RTDetrSelfAttention(nn.Module): + """ + Multi-headed self-attention from 'Attention Is All You Need' paper. + + In RT_DETR, position embeddings are added to both queries and keys (but not values) in self-attention. + """ + + def __init__( + self, + config: RTDetrConfig, + hidden_size: int, + num_attention_heads: int, + dropout: float = 0.0, + bias: bool = True, + ): + super().__init__() + self.config = config + self.head_dim = hidden_size // num_attention_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = dropout + self.is_causal = False + + self.k_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.v_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.q_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.o_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Position embeddings are added to both queries and keys (but not values). + """ + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_key_input = hidden_states + position_embeddings if position_embeddings is not None else hidden_states + + query_states = self.q_proj(query_key_input).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(query_key_input).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +def replace_batch_norm(model): + r""" + Recursively replace all `torch.nn.BatchNorm2d` with `RTDetrFrozenBatchNorm2d`. + + Args: + model (torch.nn.Module): + input model + """ + for name, module in model.named_children(): + if isinstance(module, nn.BatchNorm2d): + new_module = RTDetrFrozenBatchNorm2d(module.num_features) + + if module.weight.device != torch.device("meta"): + new_module.weight.copy_(module.weight) + new_module.bias.copy_(module.bias) + new_module.running_mean.copy_(module.running_mean) + new_module.running_var.copy_(module.running_var) + + model._modules[name] = new_module + + if len(list(module.children())) > 0: + replace_batch_norm(module) + + +class RTDetrConvEncoder(nn.Module): + """ + Convolutional backbone using the modeling_rt_detr_resnet.py. + + nn.BatchNorm2d layers are replaced by RTDetrFrozenBatchNorm2d as defined above. + https://github.com/lyuwenyu/RT-DETR/blob/main/rtdetr_pytorch/src/nn/backbone/presnet.py#L142 + """ + + def __init__(self, config): + super().__init__() + + backbone = load_backbone(config) + + if config.freeze_backbone_batch_norms: + # replace batch norm by frozen batch norm + with torch.no_grad(): + replace_batch_norm(backbone) + self.model = backbone + self.intermediate_channel_sizes = self.model.channels + + def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): + # send pixel_values through the model to get list of feature maps + features = self.model(pixel_values).feature_maps + + out = [] + for feature_map in features: + # downsample pixel_mask to match shape of corresponding feature_map + mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] + out.append((feature_map, mask)) + return out + + +class RTDetrConvNormLayer(nn.Module): + def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride, + padding=(kernel_size - 1) // 2 if padding is None else padding, + bias=False, + ) + self.norm = nn.BatchNorm2d(out_channels, config.batch_norm_eps) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_state): + hidden_state = self.conv(hidden_state) + hidden_state = self.norm(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RTDetrEncoderLayer(nn.Module): + def __init__(self, config: RTDetrConfig): + super().__init__() + self.normalize_before = config.normalize_before + self.hidden_size = config.encoder_hidden_dim + + # self-attention + self.self_attn = RTDetrSelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.num_attention_heads, + dropout=config.dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + self.dropout = config.dropout + self.mlp = RTDetrMLP(config, self.hidden_size, config.encoder_ffn_dim, config.encoder_activation_function) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + spatial_position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, hidden_size)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + spatial_position_embeddings (`torch.FloatTensor`, *optional*): + Spatial position embeddings (2D positional encodings of image locations), to be added to both + the queries and keys in self-attention (but not to values). + """ + residual = hidden_states + if self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_embeddings=spatial_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + if self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + residual = hidden_states + + hidden_states = self.mlp(hidden_states) + + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + + if self.training: + if not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +class RTDetrRepVggBlock(nn.Module): + """ + RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again". + """ + + def __init__(self, config: RTDetrConfig): + super().__init__() + + activation = config.activation_function + hidden_channels = int(config.encoder_hidden_dim * config.hidden_expansion) + self.conv1 = RTDetrConvNormLayer(config, hidden_channels, hidden_channels, 3, 1, padding=1) + self.conv2 = RTDetrConvNormLayer(config, hidden_channels, hidden_channels, 1, 1, padding=0) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, x): + y = self.conv1(x) + self.conv2(x) + return self.activation(y) + + +class RTDetrCSPRepLayer(nn.Module): + """ + Cross Stage Partial (CSP) network layer with RepVGG blocks. + """ + + def __init__(self, config: RTDetrConfig): + super().__init__() + + in_channels = config.encoder_hidden_dim * 2 + out_channels = config.encoder_hidden_dim + num_blocks = 3 + activation = config.activation_function + + hidden_channels = int(out_channels * config.hidden_expansion) + self.conv1 = RTDetrConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.conv2 = RTDetrConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.bottlenecks = nn.Sequential(*[RTDetrRepVggBlock(config) for _ in range(num_blocks)]) + if hidden_channels != out_channels: + self.conv3 = RTDetrConvNormLayer(config, hidden_channels, out_channels, 1, 1, activation=activation) + else: + self.conv3 = nn.Identity() + + def forward(self, hidden_state): + hidden_state_1 = self.conv1(hidden_state) + hidden_state_1 = self.bottlenecks(hidden_state_1) + hidden_state_2 = self.conv2(hidden_state) + return self.conv3(hidden_state_1 + hidden_state_2) + + +@use_kernel_forward_from_hub("MultiScaleDeformableAttention") +class MultiScaleDeformableAttention(nn.Module): + def forward( + self, + value: Tensor, + value_spatial_shapes: Tensor, + value_spatial_shapes_list: list[tuple], + level_start_index: Tensor, + sampling_locations: Tensor, + attention_weights: Tensor, + im2col_step: int, + ): + batch_size, _, num_heads, hidden_dim = value.shape + _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape + value_list = value.split([height * width for height, width in value_spatial_shapes_list], dim=1) + sampling_grids = 2 * sampling_locations - 1 + sampling_value_list = [] + for level_id, (height, width) in enumerate(value_spatial_shapes_list): + # batch_size, height*width, num_heads, hidden_dim + # -> batch_size, height*width, num_heads*hidden_dim + # -> batch_size, num_heads*hidden_dim, height*width + # -> batch_size*num_heads, hidden_dim, height, width + value_l_ = ( + value_list[level_id] + .flatten(2) + .transpose(1, 2) + .reshape(batch_size * num_heads, hidden_dim, height, width) + ) + # batch_size, num_queries, num_heads, num_points, 2 + # -> batch_size, num_heads, num_queries, num_points, 2 + # -> batch_size*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[:, :, :, level_id].transpose(1, 2).flatten(0, 1) + # batch_size*num_heads, hidden_dim, num_queries, num_points + sampling_value_l_ = nn.functional.grid_sample( + value_l_, + sampling_grid_l_, + mode="bilinear", + padding_mode="zeros", + align_corners=False, + ) + sampling_value_list.append(sampling_value_l_) + # (batch_size, num_queries, num_heads, num_levels, num_points) + # -> (batch_size, num_heads, num_queries, num_levels, num_points) + # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.transpose(1, 2).reshape( + batch_size * num_heads, 1, num_queries, num_levels * num_points + ) + output = ( + (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights) + .sum(-1) + .view(batch_size, num_heads * hidden_dim, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +class RTDetrMultiscaleDeformableAttention(nn.Module): + """ + Multiscale deformable attention as proposed in Deformable DETR. + """ + + def __init__(self, config: RTDetrConfig, num_heads: int, n_points: int): + super().__init__() + + self.attn = MultiScaleDeformableAttention() + + if config.d_model % num_heads != 0: + raise ValueError( + f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" + ) + dim_per_head = config.d_model // num_heads + # check if dim_per_head is power of 2 + if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): + warnings.warn( + "You'd better set embed_dim (d_model) in RTDetrMultiscaleDeformableAttention to make the" + " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" + " implementation." + ) + + self.im2col_step = 64 + + self.d_model = config.d_model + self.n_levels = config.num_feature_levels + self.n_heads = num_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) + self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) + self.value_proj = nn.Linear(config.d_model, config.d_model) + self.output_proj = nn.Linear(config.d_model, config.d_model) + + self.disable_custom_kernels = config.disable_custom_kernels + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states=None, + encoder_attention_mask=None, + position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # add position embeddings to the hidden states before projecting to queries and keys + if position_embeddings is not None: + hidden_states = hidden_states + position_embeddings + + batch_size, num_queries, _ = hidden_states.shape + batch_size, sequence_length, _ = encoder_hidden_states.shape + total_elements = sum(height * width for height, width in spatial_shapes_list) + torch_compilable_check( + total_elements == sequence_length, + "Make sure to align the spatial shapes with the sequence length of the encoder hidden states", + ) + + value = self.value_proj(encoder_hidden_states) + if attention_mask is not None: + # we invert the attention_mask + value = value.masked_fill(~attention_mask[..., None], float(0)) + value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) + sampling_offsets = self.sampling_offsets(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points, 2 + ) + attention_weights = self.attention_weights(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points + ) + attention_weights = F.softmax(attention_weights, -1).view( + batch_size, num_queries, self.n_heads, self.n_levels, self.n_points + ) + # batch_size, num_queries, n_heads, n_levels, n_points, 2 + num_coordinates = reference_points.shape[-1] + if num_coordinates == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif num_coordinates == 4: + sampling_locations = ( + reference_points[:, :, None, :, None, :2] + + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5 + ) + else: + raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") + + output = self.attn( + value, + spatial_shapes, + spatial_shapes_list, + level_start_index, + sampling_locations, + attention_weights, + self.im2col_step, + ) + + output = self.output_proj(output) + + return output, attention_weights + + +class RTDetrDecoderLayer(nn.Module): + def __init__(self, config: RTDetrConfig): + super().__init__() + self.hidden_size = config.d_model + + # self-attention + self.self_attn = RTDetrSelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + ) + self.dropout = config.dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # cross-attention + self.encoder_attn = RTDetrMultiscaleDeformableAttention( + config, + num_heads=config.decoder_attention_heads, + n_points=config.decoder_n_points, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # feedforward neural networks + self.mlp = RTDetrMLP(config, self.hidden_size, config.decoder_ffn_dim, config.decoder_activation_function) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + object_queries_position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + Input to the layer of shape `(batch, seq_len, hidden_size)`. + object_queries_position_embeddings (`torch.FloatTensor`, *optional*): + Position embeddings for the object query slots. These are added to both queries and keys + in the self-attention layer (not values). + reference_points (`torch.FloatTensor`, *optional*): + Reference points. + spatial_shapes (`torch.LongTensor`, *optional*): + Spatial shapes. + level_start_index (`torch.LongTensor`, *optional*): + Level start index. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, hidden_size)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + """ + residual = hidden_states + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=encoder_attention_mask, + position_embeddings=object_queries_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + + # Cross-Attention + hidden_states, _ = self.encoder_attn( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + position_embeddings=object_queries_position_embeddings, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states + + +class RTDetrSinePositionEmbedding(nn.Module): + """ + 2D sinusoidal position embedding used in RT-DETR hybrid encoder. + """ + + def __init__(self, embed_dim: int = 256, temperature: int = 10000): + super().__init__() + self.embed_dim = embed_dim + self.temperature = temperature + + @compile_compatible_method_lru_cache(maxsize=32) + def forward( + self, + width: int, + height: int, + device: torch.device | str, + dtype: torch.dtype, + ) -> torch.Tensor: + """ + Generate 2D sinusoidal position embeddings. + + Returns: + Position embeddings of shape (1, height*width, embed_dim) + """ + grid_w = torch.arange(torch_int(width), device=device).to(dtype) + grid_h = torch.arange(torch_int(height), device=device).to(dtype) + grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="xy") + if self.embed_dim % 4 != 0: + raise ValueError("Embed dimension must be divisible by 4 for 2D sin-cos position embedding") + pos_dim = self.embed_dim // 4 + omega = torch.arange(pos_dim, device=device).to(dtype) / pos_dim + omega = 1.0 / (self.temperature**omega) + + out_w = grid_w.flatten()[..., None] @ omega[None] + out_h = grid_h.flatten()[..., None] @ omega[None] + + return torch.concat([out_h.sin(), out_h.cos(), out_w.sin(), out_w.cos()], dim=1)[None, :, :] + + +class RTDetrAIFILayer(nn.Module): + """ + AIFI (Attention-based Intra-scale Feature Interaction) layer used in RT-DETR hybrid encoder. + """ + + def __init__(self, config: RTDetrConfig): + super().__init__() + self.config = config + self.encoder_hidden_dim = config.encoder_hidden_dim + self.eval_size = config.eval_size + + self.position_embedding = RTDetrSinePositionEmbedding( + embed_dim=self.encoder_hidden_dim, + temperature=config.positional_encoding_temperature, + ) + self.layers = nn.ModuleList([RTDetrEncoderLayer(config) for _ in range(config.encoder_layers)]) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`): + Feature map to process. + """ + batch_size = hidden_states.shape[0] + height, width = hidden_states.shape[2:] + + hidden_states = hidden_states.flatten(2).permute(0, 2, 1) + + if self.training or self.eval_size is None: + pos_embed = self.position_embedding( + width=width, + height=height, + device=hidden_states.device, + dtype=hidden_states.dtype, + ) + else: + pos_embed = None + + for layer in self.layers: + hidden_states = layer( + hidden_states, + attention_mask=None, + spatial_position_embeddings=pos_embed, + **kwargs, + ) + + hidden_states = ( + hidden_states.permute(0, 2, 1).reshape(batch_size, self.encoder_hidden_dim, height, width).contiguous() + ) + + return hidden_states + + +class RTDetrMLPPredictionHead(nn.Module): + """ + Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, + height and width of a bounding box w.r.t. an image. + + """ + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +@auto_docstring +class RTDetrPreTrainedModel(PreTrainedModel): + config: RTDetrConfig + base_model_prefix = "rt_detr" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = [r"RTDetrHybridEncoder", r"RTDetrDecoderLayer"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, RTDetrForObjectDetection): + if module.model.decoder.class_embed is not None: + for layer in module.model.decoder.class_embed: + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(layer.weight) + init.constant_(layer.bias, bias) + + if module.model.decoder.bbox_embed is not None: + for layer in module.model.decoder.bbox_embed: + init.constant_(layer.layers[-1].weight, 0) + init.constant_(layer.layers[-1].bias, 0) + + elif isinstance(module, RTDetrMultiscaleDeformableAttention): + init.constant_(module.sampling_offsets.weight, 0.0) + default_dtype = torch.get_default_dtype() + thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * ( + 2.0 * math.pi / module.n_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(module.n_heads, 1, 1, 2) + .repeat(1, module.n_levels, module.n_points, 1) + ) + for i in range(module.n_points): + grid_init[:, :, i, :] *= i + 1 + + init.copy_(module.sampling_offsets.bias, grid_init.view(-1)) + init.constant_(module.attention_weights.weight, 0.0) + init.constant_(module.attention_weights.bias, 0.0) + init.xavier_uniform_(module.value_proj.weight) + init.constant_(module.value_proj.bias, 0.0) + init.xavier_uniform_(module.output_proj.weight) + init.constant_(module.output_proj.bias, 0.0) + + elif isinstance(module, RTDetrModel): + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(module.enc_score_head.weight) + init.constant_(module.enc_score_head.bias, bias) + + elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + elif isinstance(module, nn.LayerNorm): + init.ones_(module.weight) + init.zeros_(module.bias) + + if hasattr(module, "weight_embedding") and self.config.learn_initial_query: + init.xavier_uniform_(module.weight_embedding.weight) + if hasattr(module, "denoising_class_embed") and self.config.num_denoising > 0: + init.xavier_uniform_(module.denoising_class_embed.weight) + + +class RTDetrHybridEncoder(RTDetrPreTrainedModel): + """ + Hybrid encoder consisting of AIFI (Attention-based Intra-scale Feature Interaction) layers, + a top-down Feature Pyramid Network (FPN) and a bottom-up Path Aggregation Network (PAN). + More details on the paper: https://huggingface.co/papers/2304.08069 + + Args: + config: RTDetrConfig + """ + + _can_record_outputs = { + "hidden_states": RTDetrAIFILayer, + "attentions": RTDetrSelfAttention, + } + + def __init__(self, config: RTDetrConfig): + super().__init__(config) + self.config = config + self.in_channels = config.encoder_in_channels + self.feat_strides = config.feat_strides + self.encoder_hidden_dim = config.encoder_hidden_dim + self.encode_proj_layers = config.encode_proj_layers + self.positional_encoding_temperature = config.positional_encoding_temperature + self.eval_size = config.eval_size + self.out_channels = [self.encoder_hidden_dim for _ in self.in_channels] + self.out_strides = self.feat_strides + self.num_fpn_stages = len(self.in_channels) - 1 + self.num_pan_stages = len(self.in_channels) - 1 + + # AIFI (Attention-based Intra-scale Feature Interaction) layers + self.aifi = nn.ModuleList([RTDetrAIFILayer(config) for _ in range(len(self.encode_proj_layers))]) + + # top-down FPN + self.lateral_convs = nn.ModuleList() + self.fpn_blocks = nn.ModuleList() + for _ in range(self.num_fpn_stages): + lateral_conv = RTDetrConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=1, + stride=1, + activation=config.activation_function, + ) + fpn_block = RTDetrCSPRepLayer(config) + self.lateral_convs.append(lateral_conv) + self.fpn_blocks.append(fpn_block) + + # bottom-up PAN + self.downsample_convs = nn.ModuleList() + self.pan_blocks = nn.ModuleList() + for _ in range(self.num_pan_stages): + downsample_conv = RTDetrConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=3, + stride=2, + activation=config.activation_function, + ) + pan_block = RTDetrCSPRepLayer(config) + self.downsample_convs.append(downsample_conv) + self.pan_blocks.append(pan_block) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + inputs_embeds=None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + """ + feature_maps = inputs_embeds + + # AIFI: Apply transformer encoder to specified feature levels + if self.config.encoder_layers > 0: + for i, enc_ind in enumerate(self.encode_proj_layers): + feature_maps[enc_ind] = self.aifi[i](feature_maps[enc_ind], **kwargs) + + # top-down FPN + fpn_feature_maps = [feature_maps[-1]] + for idx, (lateral_conv, fpn_block) in enumerate(zip(self.lateral_convs, self.fpn_blocks)): + backbone_feature_map = feature_maps[self.num_fpn_stages - idx - 1] + top_fpn_feature_map = fpn_feature_maps[-1] + # apply lateral block + top_fpn_feature_map = lateral_conv(top_fpn_feature_map) + fpn_feature_maps[-1] = top_fpn_feature_map + # apply fpn block + top_fpn_feature_map = F.interpolate(top_fpn_feature_map, scale_factor=2.0, mode="nearest") + fused_feature_map = torch.concat([top_fpn_feature_map, backbone_feature_map], dim=1) + new_fpn_feature_map = fpn_block(fused_feature_map) + fpn_feature_maps.append(new_fpn_feature_map) + + fpn_feature_maps.reverse() + + # bottom-up PAN + pan_feature_maps = [fpn_feature_maps[0]] + for idx, (downsample_conv, pan_block) in enumerate(zip(self.downsample_convs, self.pan_blocks)): + top_pan_feature_map = pan_feature_maps[-1] + fpn_feature_map = fpn_feature_maps[idx + 1] + downsampled_feature_map = downsample_conv(top_pan_feature_map) + fused_feature_map = torch.concat([downsampled_feature_map, fpn_feature_map], dim=1) + new_pan_feature_map = pan_block(fused_feature_map) + pan_feature_maps.append(new_pan_feature_map) + + return BaseModelOutput(last_hidden_state=pan_feature_maps) + + +def inverse_sigmoid(x, eps=1e-5): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +class RTDetrDecoder(RTDetrPreTrainedModel): + _can_record_outputs = { + "hidden_states": RTDetrDecoderLayer, + "attentions": RTDetrSelfAttention, + "cross_attentions": RTDetrMultiscaleDeformableAttention, + } + + def __init__(self, config: RTDetrConfig): + super().__init__(config) + + self.dropout = config.dropout + self.layers = nn.ModuleList([RTDetrDecoderLayer(config) for _ in range(config.decoder_layers)]) + self.query_pos_head = RTDetrMLPPredictionHead(4, 2 * config.d_model, config.d_model, num_layers=2) + + # hack implementation for iterative bounding box refinement and two-stage Deformable DETR + self.bbox_embed = None + self.class_embed = None + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + The query embeddings that are passed into the decoder. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected + in `[0, 1]`: + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): + Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. + spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): + Spatial shapes of the feature maps. + level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): + Indexes for the start of each feature level. In range `[0, sequence_length]`. + """ + if inputs_embeds is not None: + hidden_states = inputs_embeds + + # decoder layers + intermediate = () + intermediate_reference_points = () + intermediate_logits = () + + reference_points = F.sigmoid(reference_points) + + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L252 + for idx, decoder_layer in enumerate(self.layers): + reference_points_input = reference_points.unsqueeze(2) + object_queries_position_embeddings = self.query_pos_head(reference_points) + + hidden_states = decoder_layer( + hidden_states, + object_queries_position_embeddings=object_queries_position_embeddings, + encoder_hidden_states=encoder_hidden_states, + reference_points=reference_points_input, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + encoder_attention_mask=encoder_attention_mask, + **kwargs, + ) + + # hack implementation for iterative bounding box refinement + if self.bbox_embed is not None: + predicted_corners = self.bbox_embed[idx](hidden_states) + new_reference_points = F.sigmoid(predicted_corners + inverse_sigmoid(reference_points)) + reference_points = new_reference_points.detach() + + intermediate += (hidden_states,) + intermediate_reference_points += ( + (new_reference_points,) if self.bbox_embed is not None else (reference_points,) + ) + + if self.class_embed is not None: + logits = self.class_embed[idx](hidden_states) + intermediate_logits += (logits,) + + # Keep batch_size as first dimension + intermediate = torch.stack(intermediate, dim=1) + intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) + if self.class_embed is not None: + intermediate_logits = torch.stack(intermediate_logits, dim=1) + + return RTDetrDecoderOutput( + last_hidden_state=hidden_states, + intermediate_hidden_states=intermediate, + intermediate_logits=intermediate_logits, + intermediate_reference_points=intermediate_reference_points, + ) + + +def get_contrastive_denoising_training_group( + targets, + num_classes, + num_queries, + class_embed, + num_denoising_queries=100, + label_noise_ratio=0.5, + box_noise_scale=1.0, +): + """ + Creates a contrastive denoising training group using ground-truth samples. It adds noise to labels and boxes. + + Args: + targets (`list[dict]`): + The target objects, each containing 'class_labels' and 'boxes' for objects in an image. + num_classes (`int`): + Total number of classes in the dataset. + num_queries (`int`): + Number of query slots in the transformer. + class_embed (`callable`): + A function or a model layer to embed class labels. + num_denoising_queries (`int`, *optional*, defaults to 100): + Number of denoising queries. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + Ratio of noise applied to labels. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale of noise applied to bounding boxes. + Returns: + `tuple` comprising various elements: + - **input_query_class** (`torch.FloatTensor`) -- + Class queries with applied label noise. + - **input_query_bbox** (`torch.FloatTensor`) -- + Bounding box queries with applied box noise. + - **attn_mask** (`torch.FloatTensor`) -- + Attention mask for separating denoising and reconstruction queries. + - **denoising_meta_values** (`dict`) -- + Metadata including denoising positive indices, number of groups, and split sizes. + """ + + if num_denoising_queries <= 0: + return None, None, None, None + + num_ground_truths = [len(t["class_labels"]) for t in targets] + device = targets[0]["class_labels"].device + + max_gt_num = max(num_ground_truths) + if max_gt_num == 0: + return None, None, None, None + + num_groups_denoising_queries = num_denoising_queries // max_gt_num + num_groups_denoising_queries = 1 if num_groups_denoising_queries == 0 else num_groups_denoising_queries + # pad gt to max_num of a batch + batch_size = len(num_ground_truths) + + input_query_class = torch.full([batch_size, max_gt_num], num_classes, dtype=torch.int32, device=device) + input_query_bbox = torch.zeros([batch_size, max_gt_num, 4], device=device) + pad_gt_mask = torch.zeros([batch_size, max_gt_num], dtype=torch.bool, device=device) + + for i in range(batch_size): + num_gt = num_ground_truths[i] + if num_gt > 0: + input_query_class[i, :num_gt] = targets[i]["class_labels"] + input_query_bbox[i, :num_gt] = targets[i]["boxes"] + pad_gt_mask[i, :num_gt] = 1 + # each group has positive and negative queries. + input_query_class = input_query_class.tile([1, 2 * num_groups_denoising_queries]) + input_query_bbox = input_query_bbox.tile([1, 2 * num_groups_denoising_queries, 1]) + pad_gt_mask = pad_gt_mask.tile([1, 2 * num_groups_denoising_queries]) + # positive and negative mask + negative_gt_mask = torch.zeros([batch_size, max_gt_num * 2, 1], device=device) + negative_gt_mask[:, max_gt_num:] = 1 + negative_gt_mask = negative_gt_mask.tile([1, num_groups_denoising_queries, 1]) + positive_gt_mask = 1 - negative_gt_mask + # contrastive denoising training positive index + positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask + denoise_positive_idx = torch.nonzero(positive_gt_mask)[:, 1] + denoise_positive_idx = torch.split( + denoise_positive_idx, [n * num_groups_denoising_queries for n in num_ground_truths] + ) + # total denoising queries + num_denoising_queries = torch_int(max_gt_num * 2 * num_groups_denoising_queries) + + if label_noise_ratio > 0: + mask = torch.rand_like(input_query_class, dtype=torch.float) < (label_noise_ratio * 0.5) + # randomly put a new one here + new_label = torch.randint_like(mask, 0, num_classes, dtype=input_query_class.dtype) + input_query_class = torch.where(mask & pad_gt_mask, new_label, input_query_class) + + if box_noise_scale > 0: + known_bbox = center_to_corners_format(input_query_bbox) + diff = torch.tile(input_query_bbox[..., 2:] * 0.5, [1, 1, 2]) * box_noise_scale + rand_sign = torch.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0 + rand_part = torch.rand_like(input_query_bbox) + rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (1 - negative_gt_mask) + rand_part *= rand_sign + known_bbox += rand_part * diff + known_bbox.clip_(min=0.0, max=1.0) + input_query_bbox = corners_to_center_format(known_bbox) + input_query_bbox = inverse_sigmoid(input_query_bbox) + + input_query_class = class_embed(input_query_class) + + target_size = num_denoising_queries + num_queries + attn_mask = torch.full([target_size, target_size], 0, dtype=torch.float, device=device) + # match query cannot see the reconstruction + attn_mask[num_denoising_queries:, :num_denoising_queries] = -torch.inf + + # reconstructions cannot see each other + for i in range(num_groups_denoising_queries): + idx_block_start = max_gt_num * 2 * i + idx_block_end = max_gt_num * 2 * (i + 1) + attn_mask[idx_block_start:idx_block_end, :idx_block_start] = -torch.inf + attn_mask[idx_block_start:idx_block_end, idx_block_end:num_denoising_queries] = -torch.inf + + denoising_meta_values = { + "dn_positive_idx": denoise_positive_idx, + "dn_num_group": num_groups_denoising_queries, + "dn_num_split": [num_denoising_queries, num_queries], + } + + return input_query_class, input_query_bbox, attn_mask, denoising_meta_values + + +@auto_docstring( + custom_intro=""" + RT-DETR Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top. + """ +) +class RTDetrModel(RTDetrPreTrainedModel): + def __init__(self, config: RTDetrConfig): + super().__init__(config) + + # Create backbone + self.backbone = RTDetrConvEncoder(config) + intermediate_channel_sizes = self.backbone.intermediate_channel_sizes + + # Create encoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/hybrid_encoder.py#L212 + num_backbone_outs = len(intermediate_channel_sizes) + encoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = intermediate_channel_sizes[i] + encoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.encoder_hidden_dim, kernel_size=1, bias=False), + nn.BatchNorm2d(config.encoder_hidden_dim), + ) + ) + self.encoder_input_proj = nn.ModuleList(encoder_input_proj_list) + + # Create encoder + self.encoder = RTDetrHybridEncoder(config) + + # denoising part + if config.num_denoising > 0: + self.denoising_class_embed = nn.Embedding( + config.num_labels + 1, config.d_model, padding_idx=config.num_labels + ) + + # decoder embedding + if config.learn_initial_query: + self.weight_embedding = nn.Embedding(config.num_queries, config.d_model) + + # encoder head + self.enc_output = nn.Sequential( + nn.Linear(config.d_model, config.d_model), + nn.LayerNorm(config.d_model, eps=config.layer_norm_eps), + ) + self.enc_score_head = nn.Linear(config.d_model, config.num_labels) + self.enc_bbox_head = RTDetrMLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + + # init encoder output anchors and valid_mask + if config.anchor_image_size: + self.anchors, self.valid_mask = self.generate_anchors(dtype=self.dtype) + + # Create decoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L412 + num_backbone_outs = len(config.decoder_in_channels) + decoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = config.decoder_in_channels[i] + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + for _ in range(config.num_feature_levels - num_backbone_outs): + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + in_channels = config.d_model + self.decoder_input_proj = nn.ModuleList(decoder_input_proj_list) + + # decoder + self.decoder = RTDetrDecoder(config) + + self.post_init() + + def freeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(False) + + def unfreeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(True) + + @compile_compatible_method_lru_cache(maxsize=32) + def generate_anchors(self, spatial_shapes=None, grid_size=0.05, device="cpu", dtype=torch.float32): + if spatial_shapes is None: + spatial_shapes = [ + [int(self.config.anchor_image_size[0] / s), int(self.config.anchor_image_size[1] / s)] + for s in self.config.feat_strides + ] + anchors = [] + for level, (height, width) in enumerate(spatial_shapes): + grid_y, grid_x = torch.meshgrid( + torch.arange(end=height, device=device).to(dtype), + torch.arange(end=width, device=device).to(dtype), + indexing="ij", + ) + grid_xy = torch.stack([grid_x, grid_y], -1) + grid_xy = grid_xy.unsqueeze(0) + 0.5 + grid_xy[..., 0] /= width + grid_xy[..., 1] /= height + wh = torch.ones_like(grid_xy) * grid_size * (2.0**level) + anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, height * width, 4)) + # define the valid range for anchor coordinates + eps = 1e-2 + anchors = torch.concat(anchors, 1) + valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True) + anchors = torch.log(anchors / (1 - anchors)) + anchors = torch.where(valid_mask, anchors, torch.tensor(torch.finfo(dtype).max, dtype=dtype, device=device)) + + return anchors, valid_mask + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | RTDetrModelOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, RTDetrModel + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("PekingU/rtdetr_r50vd") + >>> model = RTDetrModel.from_pretrained("PekingU/rtdetr_r50vd") + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 300, 256] + ```""" + if pixel_values is None and inputs_embeds is None: + raise ValueError("You have to specify either pixel_values or inputs_embeds") + + if inputs_embeds is None: + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + if pixel_mask is None: + pixel_mask = torch.ones(((batch_size, height, width)), device=device) + features = self.backbone(pixel_values, pixel_mask) + proj_feats = [self.encoder_input_proj[level](source) for level, (source, mask) in enumerate(features)] + else: + batch_size = inputs_embeds.shape[0] + device = inputs_embeds.device + proj_feats = inputs_embeds + + if encoder_outputs is None: + encoder_outputs = self.encoder( + proj_feats, + **kwargs, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput + elif not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Equivalent to def _get_encoder_input + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L412 + sources = [] + for level, source in enumerate(encoder_outputs.last_hidden_state): + sources.append(self.decoder_input_proj[level](source)) + + # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage + if self.config.num_feature_levels > len(sources): + _len_sources = len(sources) + sources.append(self.decoder_input_proj[_len_sources](encoder_outputs.last_hidden_state)[-1]) + for i in range(_len_sources + 1, self.config.num_feature_levels): + sources.append(self.decoder_input_proj[i](encoder_outputs.last_hidden_state[-1])) + + # Prepare encoder inputs (by flattening) + source_flatten = [] + spatial_shapes_list = [] + spatial_shapes = torch.empty((len(sources), 2), device=device, dtype=torch.long) + for level, source in enumerate(sources): + height, width = source.shape[-2:] + spatial_shapes[level, 0] = height + spatial_shapes[level, 1] = width + spatial_shapes_list.append((height, width)) + source = source.flatten(2).transpose(1, 2) + source_flatten.append(source) + source_flatten = torch.cat(source_flatten, 1) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + + # prepare denoising training + if self.training and self.config.num_denoising > 0 and labels is not None: + ( + denoising_class, + denoising_bbox_unact, + attention_mask, + denoising_meta_values, + ) = get_contrastive_denoising_training_group( + targets=labels, + num_classes=self.config.num_labels, + num_queries=self.config.num_queries, + class_embed=self.denoising_class_embed, + num_denoising_queries=self.config.num_denoising, + label_noise_ratio=self.config.label_noise_ratio, + box_noise_scale=self.config.box_noise_scale, + ) + else: + denoising_class, denoising_bbox_unact, attention_mask, denoising_meta_values = None, None, None, None + + batch_size = len(source_flatten) + device = source_flatten.device + dtype = source_flatten.dtype + + # prepare input for decoder + if self.training or self.config.anchor_image_size is None: + # Pass spatial_shapes as tuple to make it hashable and make sure + # lru_cache is working for generate_anchors() + spatial_shapes_tuple = tuple(spatial_shapes_list) + anchors, valid_mask = self.generate_anchors(spatial_shapes_tuple, device=device, dtype=dtype) + else: + anchors, valid_mask = self.anchors, self.valid_mask + anchors, valid_mask = anchors.to(device, dtype), valid_mask.to(device, dtype) + + # use the valid_mask to selectively retain values in the feature map where the mask is `True` + memory = valid_mask.to(source_flatten.dtype) * source_flatten + + output_memory = self.enc_output(memory) + + enc_outputs_class = self.enc_score_head(output_memory) + enc_outputs_coord_logits = self.enc_bbox_head(output_memory) + anchors + + _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.config.num_queries, dim=1) + + reference_points_unact = enc_outputs_coord_logits.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_coord_logits.shape[-1]) + ) + + enc_topk_bboxes = F.sigmoid(reference_points_unact) + if denoising_bbox_unact is not None: + reference_points_unact = torch.concat([denoising_bbox_unact, reference_points_unact], 1) + + enc_topk_logits = enc_outputs_class.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_class.shape[-1]) + ) + + # extract region features + if self.config.learn_initial_query: + target = self.weight_embedding.tile([batch_size, 1, 1]) + else: + target = output_memory.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, output_memory.shape[-1])) + target = target.detach() + + if denoising_class is not None: + target = torch.concat([denoising_class, target], 1) + + init_reference_points = reference_points_unact.detach() + + # decoder + decoder_outputs = self.decoder( + inputs_embeds=target, + encoder_hidden_states=source_flatten, + encoder_attention_mask=attention_mask, + reference_points=init_reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + return RTDetrModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, + intermediate_logits=decoder_outputs.intermediate_logits, + intermediate_reference_points=decoder_outputs.intermediate_reference_points, + intermediate_predicted_corners=decoder_outputs.intermediate_predicted_corners, + initial_reference_points=decoder_outputs.initial_reference_points, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + init_reference_points=init_reference_points, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + enc_outputs_class=enc_outputs_class, + enc_outputs_coord_logits=enc_outputs_coord_logits, + denoising_meta_values=denoising_meta_values, + ) + + +@auto_docstring( + custom_intro=""" + RT-DETR Model (consisting of a backbone and encoder-decoder) outputting bounding boxes and logits to be further + decoded into scores and classes. + """ +) +class RTDetrForObjectDetection(RTDetrPreTrainedModel): + # When using clones, all layers > 0 will be clones, but layer 0 *is* required + # We can't initialize the model on meta device as some weights are modified during the initialization + _no_split_modules = None + + def __init__(self, config: RTDetrConfig): + super().__init__(config) + self.model = RTDetrModel(config) + num_pred = config.decoder_layers + self.model.decoder.class_embed = nn.ModuleList( + [torch.nn.Linear(config.d_model, config.num_labels) for _ in range(num_pred)] + ) + self.model.decoder.bbox_embed = nn.ModuleList( + [RTDetrMLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) for _ in range(num_pred)] + ) + # if two-stage, the last class_embed and bbox_embed is for region proposal generation + self.post_init() + + def _set_aux_loss(self, outputs_class, outputs_coord): + return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)] + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | RTDetrObjectDetectionOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import RTDetrImageProcessor, RTDetrForObjectDetection + >>> from PIL import Image + >>> import requests + >>> import torch + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd") + >>> model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd") + + >>> # prepare image for the model + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 300, 80] + + >>> boxes = outputs.pred_boxes + >>> list(boxes.shape) + [1, 300, 4] + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> target_sizes = torch.tensor([image.size[::-1]]) + >>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[ + ... 0 + ... ] + + >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): + ... box = [round(i, 2) for i in box.tolist()] + ... print( + ... f"Detected {model.config.id2label[label.item()]} with confidence " + ... f"{round(score.item(), 3)} at location {box}" + ... ) + Detected sofa with confidence 0.97 at location [0.14, 0.38, 640.13, 476.21] + Detected cat with confidence 0.96 at location [343.38, 24.28, 640.14, 371.5] + Detected cat with confidence 0.958 at location [13.23, 54.18, 318.98, 472.22] + Detected remote with confidence 0.951 at location [40.11, 73.44, 175.96, 118.48] + Detected remote with confidence 0.924 at location [333.73, 76.58, 369.97, 186.99] + ```""" + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + labels=labels, + **kwargs, + ) + + denoising_meta_values = outputs.denoising_meta_values if self.training else None + + outputs_class = outputs.intermediate_logits + outputs_coord = outputs.intermediate_reference_points + predicted_corners = outputs.intermediate_predicted_corners + initial_reference_points = outputs.initial_reference_points + + logits = outputs_class[:, -1] + pred_boxes = outputs_coord[:, -1] + + loss, loss_dict, auxiliary_outputs, enc_topk_logits, enc_topk_bboxes = None, None, None, None, None + if labels is not None: + enc_topk_logits = outputs.enc_topk_logits + enc_topk_bboxes = outputs.enc_topk_bboxes + loss, loss_dict, auxiliary_outputs = self.loss_function( + logits, + labels, + self.device, + pred_boxes, + self.config, + outputs_class, + outputs_coord, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + denoising_meta_values=denoising_meta_values, + predicted_corners=predicted_corners, + initial_reference_points=initial_reference_points, + **kwargs, + ) + + return RTDetrObjectDetectionOutput( + loss=loss, + loss_dict=loss_dict, + logits=logits, + pred_boxes=pred_boxes, + auxiliary_outputs=auxiliary_outputs, + last_hidden_state=outputs.last_hidden_state, + intermediate_hidden_states=outputs.intermediate_hidden_states, + intermediate_logits=outputs.intermediate_logits, + intermediate_reference_points=outputs.intermediate_reference_points, + intermediate_predicted_corners=outputs.intermediate_predicted_corners, + initial_reference_points=outputs.initial_reference_points, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + init_reference_points=outputs.init_reference_points, + enc_topk_logits=outputs.enc_topk_logits, + enc_topk_bboxes=outputs.enc_topk_bboxes, + enc_outputs_class=outputs.enc_outputs_class, + enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, + denoising_meta_values=outputs.denoising_meta_values, + ) + + +__all__ = ["RTDetrForObjectDetection", "RTDetrModel", "RTDetrPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modeling_rt_detr_resnet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modeling_rt_detr_resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..e3b0ade23f8f302997b04fc622e760bd7c66fddb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modeling_rt_detr_resnet.py @@ -0,0 +1,412 @@ +# Copyright 2024 Microsoft Research, Inc. and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +PyTorch RTDetr specific ResNet model. The main difference between hugginface ResNet model is that this RTDetrResNet model forces to use shortcut at the first layer in the resnet-18/34 models. +See https://github.com/lyuwenyu/RT-DETR/blob/5b628eaa0a2fc25bdafec7e6148d5296b144af85/rtdetr_pytorch/src/nn/backbone/presnet.py#L126 for details. +""" + +import math + +import torch +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...backbone_utils import BackboneMixin, filter_output_hidden_states +from ...modeling_outputs import BackboneOutput, BaseModelOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, logging +from ...utils.generic import can_return_tuple +from .configuration_rt_detr_resnet import RTDetrResNetConfig + + +logger = logging.get_logger(__name__) + + +# Copied from transformers.models.resnet.modeling_resnet.ResNetConvLayer -> RTDetrResNetConvLayer +class RTDetrResNetConvLayer(nn.Module): + def __init__( + self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: str = "relu" + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, bias=False + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RTDetrResNetEmbeddings(nn.Module): + """ + ResNet Embeddings (stem) composed of a deep aggressive convolution. + """ + + def __init__(self, config: RTDetrResNetConfig): + super().__init__() + self.embedder = nn.Sequential( + *[ + RTDetrResNetConvLayer( + config.num_channels, + config.embedding_size // 2, + kernel_size=3, + stride=2, + activation=config.hidden_act, + ), + RTDetrResNetConvLayer( + config.embedding_size // 2, + config.embedding_size // 2, + kernel_size=3, + stride=1, + activation=config.hidden_act, + ), + RTDetrResNetConvLayer( + config.embedding_size // 2, + config.embedding_size, + kernel_size=3, + stride=1, + activation=config.hidden_act, + ), + ] + ) + self.pooler = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.num_channels = config.num_channels + + def forward(self, pixel_values: Tensor) -> Tensor: + num_channels = pixel_values.shape[1] + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + embedding = self.embedder(pixel_values) + embedding = self.pooler(embedding) + return embedding + + +# Copied from transformers.models.resnet.modeling_resnet.ResNetShortCut -> RTDetrResNetChortCut +class RTDetrResNetShortCut(nn.Module): + """ + ResNet shortcut, used to project the residual features to the correct size. If needed, it is also used to + downsample the input using `stride=2`. + """ + + def __init__(self, in_channels: int, out_channels: int, stride: int = 2): + super().__init__() + self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False) + self.normalization = nn.BatchNorm2d(out_channels) + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + return hidden_state + + +class RTDetrResNetBasicLayer(nn.Module): + """ + A classic ResNet's residual layer composed by two `3x3` convolutions. + See https://github.com/lyuwenyu/RT-DETR/blob/5b628eaa0a2fc25bdafec7e6148d5296b144af85/rtdetr_pytorch/src/nn/backbone/presnet.py#L34. + """ + + def __init__( + self, + config: RTDetrResNetConfig, + in_channels: int, + out_channels: int, + stride: int = 1, + should_apply_shortcut: bool = False, + ): + super().__init__() + if in_channels != out_channels: + self.shortcut = ( + nn.Sequential( + *[nn.AvgPool2d(2, 2, 0, ceil_mode=True), RTDetrResNetShortCut(in_channels, out_channels, stride=1)] + ) + if should_apply_shortcut + else nn.Identity() + ) + else: + self.shortcut = ( + RTDetrResNetShortCut(in_channels, out_channels, stride=stride) + if should_apply_shortcut + else nn.Identity() + ) + self.layer = nn.Sequential( + RTDetrResNetConvLayer(in_channels, out_channels, stride=stride), + RTDetrResNetConvLayer(out_channels, out_channels, activation=None), + ) + self.activation = ACT2FN[config.hidden_act] + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.layer(hidden_state) + residual = self.shortcut(residual) + hidden_state += residual + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RTDetrResNetBottleNeckLayer(nn.Module): + """ + A classic RTDetrResNet's bottleneck layer composed by three `3x3` convolutions. + + The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3` + convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. If + `downsample_in_bottleneck` is true, downsample will be in the first layer instead of the second layer. + """ + + def __init__( + self, + config: RTDetrResNetConfig, + in_channels: int, + out_channels: int, + stride: int = 1, + ): + super().__init__() + reduction = 4 + should_apply_shortcut = in_channels != out_channels or stride != 1 + reduces_channels = out_channels // reduction + if stride == 2: + self.shortcut = nn.Sequential( + *[ + nn.AvgPool2d(2, 2, 0, ceil_mode=True), + RTDetrResNetShortCut(in_channels, out_channels, stride=1) + if should_apply_shortcut + else nn.Identity(), + ] + ) + else: + self.shortcut = ( + RTDetrResNetShortCut(in_channels, out_channels, stride=stride) + if should_apply_shortcut + else nn.Identity() + ) + self.layer = nn.Sequential( + RTDetrResNetConvLayer( + in_channels, reduces_channels, kernel_size=1, stride=stride if config.downsample_in_bottleneck else 1 + ), + RTDetrResNetConvLayer( + reduces_channels, reduces_channels, stride=stride if not config.downsample_in_bottleneck else 1 + ), + RTDetrResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None), + ) + self.activation = ACT2FN[config.hidden_act] + + def forward(self, hidden_state): + residual = hidden_state + hidden_state = self.layer(hidden_state) + residual = self.shortcut(residual) + hidden_state += residual + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RTDetrResNetStage(nn.Module): + """ + A RTDetrResNet stage composed by stacked layers. + """ + + def __init__( + self, + config: RTDetrResNetConfig, + in_channels: int, + out_channels: int, + stride: int = 2, + depth: int = 2, + ): + super().__init__() + + layer = RTDetrResNetBottleNeckLayer if config.layer_type == "bottleneck" else RTDetrResNetBasicLayer + + if config.layer_type == "bottleneck": + first_layer = layer( + config, + in_channels, + out_channels, + stride=stride, + ) + else: + first_layer = layer(config, in_channels, out_channels, stride=stride, should_apply_shortcut=True) + self.layers = nn.Sequential( + first_layer, *[layer(config, out_channels, out_channels) for _ in range(depth - 1)] + ) + + def forward(self, input: Tensor) -> Tensor: + hidden_state = input + for layer in self.layers: + hidden_state = layer(hidden_state) + return hidden_state + + +# Copied from transformers.models.resnet.modeling_resnet.ResNetEncoder with ResNet->RTDetrResNet +class RTDetrResNetEncoder(nn.Module): + def __init__(self, config: RTDetrResNetConfig): + super().__init__() + self.stages = nn.ModuleList([]) + # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input + self.stages.append( + RTDetrResNetStage( + config, + config.embedding_size, + config.hidden_sizes[0], + stride=2 if config.downsample_in_first_stage else 1, + depth=config.depths[0], + ) + ) + in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:]) + for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]): + self.stages.append(RTDetrResNetStage(config, in_channels, out_channels, depth=depth)) + + def forward( + self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True + ) -> BaseModelOutputWithNoAttention: + hidden_states = () if output_hidden_states else None + + for stage_module in self.stages: + if output_hidden_states: + hidden_states = hidden_states + (hidden_state,) + + hidden_state = stage_module(hidden_state) + + if output_hidden_states: + hidden_states = hidden_states + (hidden_state,) + + if not return_dict: + return tuple(v for v in [hidden_state, hidden_states] if v is not None) + + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_state, + hidden_states=hidden_states, + ) + + +@auto_docstring +# Copied from transformers.models.resnet.modeling_resnet.ResNetPreTrainedModel with ResNet->RTDetrResNet +class RTDetrResNetPreTrainedModel(PreTrainedModel): + config: RTDetrResNetConfig + base_model_prefix = "resnet" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = ["RTDetrResNetConvLayer", "RTDetrResNetShortCut"] + + @torch.no_grad() + def _init_weights(self, module): + if isinstance(module, nn.Conv2d): + init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu") + # copied from the `reset_parameters` method of `class Linear(Module)` in `torch`. + elif isinstance(module, nn.Linear): + init.kaiming_uniform_(module.weight, a=math.sqrt(5)) + if module.bias is not None: + fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(module.weight) + bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0 + init.uniform_(module.bias, -bound, bound) + # We need to check it like that as some Detr models replace the BatchNorm2d by their own + elif "BatchNorm" in module.__class__.__name__: + init.ones_(module.weight) + init.zeros_(module.bias) + init.zeros_(module.running_mean) + init.ones_(module.running_var) + if getattr(module, "num_batches_tracked", None) is not None: + init.zeros_(module.num_batches_tracked) + + +@auto_docstring( + custom_intro=""" + ResNet backbone, to be used with frameworks like RTDETR. + """ +) +class RTDetrResNetBackbone(BackboneMixin, RTDetrResNetPreTrainedModel): + has_attentions = False + + def __init__(self, config): + super().__init__(config) + + self.num_features = [config.embedding_size] + config.hidden_sizes + self.embedder = RTDetrResNetEmbeddings(config) + self.encoder = RTDetrResNetEncoder(config) + + # initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @filter_output_hidden_states + @auto_docstring + def forward( + self, + pixel_values: Tensor, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> BackboneOutput: + r""" + Examples: + + ```python + >>> from transformers import RTDetrResNetConfig, RTDetrResNetBackbone + >>> import torch + from ...utils.deprecation import deprecate_kwarg + from ...utils.deprecation import deprecate_kwarg + from ...utils.deprecation import deprecate_kwarg + from ...utils.deprecation import deprecate_kwarg + from ...utils.deprecation import deprecate_kwarg + + >>> config = RTDetrResNetConfig() + >>> model = RTDetrResNetBackbone(config) + + >>> pixel_values = torch.randn(1, 3, 224, 224) + + >>> with torch.no_grad(): + ... outputs = model(pixel_values) + + >>> feature_maps = outputs.feature_maps + >>> list(feature_maps[-1].shape) + [1, 2048, 7, 7] + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + embedding_output = self.embedder(pixel_values) + + outputs = self.encoder(embedding_output, output_hidden_states=True, return_dict=True) + + hidden_states = outputs.hidden_states + + feature_maps = () + for idx, stage in enumerate(self.stage_names): + if stage in self.out_features: + feature_maps += (hidden_states[idx],) + + if not return_dict: + output = (feature_maps,) + if output_hidden_states: + output += (outputs.hidden_states,) + return output + + return BackboneOutput( + feature_maps=feature_maps, + hidden_states=outputs.hidden_states if output_hidden_states else None, + attentions=None, + ) + + +__all__ = [ + "RTDetrResNetBackbone", + "RTDetrResNetPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modular_rt_detr.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modular_rt_detr.py new file mode 100644 index 0000000000000000000000000000000000000000..7bc7663a72cb029c6936dec36335a6c52449762b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr/modular_rt_detr.py @@ -0,0 +1,2139 @@ +# Copyright 2024 Baidu Inc and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +import pathlib +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn + +from ... import initialization as init +from ...activations import ACT2CLS, ACT2FN +from ...backbone_utils import load_backbone +from ...image_processing_backends import PilBackend, TorchvisionBackend +from ...image_processing_utils import BatchFeature, SizeDict +from ...image_transforms import center_to_corners_format, corners_to_center_format +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + AnnotationFormat, + AnnotationType, + ChannelDimension, + PILImageResampling, + get_image_size, + get_max_height_width, + validate_annotations, +) +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...processing_utils import ImagesKwargs, Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import ( + ModelOutput, + TensorType, + TransformersKwargs, + auto_docstring, + logging, + requires_backends, + torch_int, +) +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.import_utils import requires +from ...utils.output_capturing import capture_outputs +from ..conditional_detr.modeling_conditional_detr import inverse_sigmoid +from ..deformable_detr.modeling_deformable_detr import DeformableDetrMultiscaleDeformableAttention +from ..detr.image_processing_detr import DetrImageProcessor +from ..detr.image_processing_pil_detr import DetrImageProcessorPil +from ..detr.modeling_detr import DetrFrozenBatchNorm2d, DetrMLPPredictionHead, DetrSelfAttention, replace_batch_norm +from .configuration_rt_detr import RTDetrConfig + + +logger = logging.get_logger(__name__) + +SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION,) + + +def prepare_coco_detection_annotation( + image, + target, + return_segmentation_masks: bool = False, + input_data_format: ChannelDimension | str | None = None, +): + """ + Convert the target in COCO format into the format expected by RT-DETR. + """ + image_height, image_width = image.size()[-2:] + + image_id = target["image_id"] + image_id = torch.as_tensor([image_id], dtype=torch.int64, device=image.device) + + # Get all COCO annotations for the given image. + annotations = target["annotations"] + classes = [] + area = [] + boxes = [] + keypoints = [] + for obj in annotations: + if "iscrowd" not in obj or obj["iscrowd"] == 0: + classes.append(obj["category_id"]) + area.append(obj["area"]) + boxes.append(obj["bbox"]) + if "keypoints" in obj: + keypoints.append(obj["keypoints"]) + + classes = torch.as_tensor(classes, dtype=torch.int64, device=image.device) + area = torch.as_tensor(area, dtype=torch.float32, device=image.device) + iscrowd = torch.zeros_like(classes, dtype=torch.int64, device=image.device) + # guard against no boxes via resizing + boxes = torch.as_tensor(boxes, dtype=torch.float32, device=image.device).reshape(-1, 4) + boxes[:, 2:] += boxes[:, :2] + boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width) + boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height) + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + + new_target = { + "image_id": image_id, + "class_labels": classes[keep], + "boxes": boxes[keep], + "area": area[keep], + "iscrowd": iscrowd[keep], + "orig_size": torch.as_tensor([int(image_height), int(image_width)], dtype=torch.int64, device=image.device), + } + + if keypoints: + keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=image.device) + # Apply the keep mask here to filter the relevant annotations + keypoints = keypoints[keep] + num_keypoints = keypoints.shape[0] + keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints + new_target["keypoints"] = keypoints + + return new_target + + +def prepare_coco_detection_annotation_pil( + image, + target, + return_segmentation_masks: bool = False, + input_data_format: ChannelDimension | str | None = None, +): + """ + Convert the target in COCO format into the format expected by RTDETR. + """ + image_height, image_width = get_image_size(image, channel_dim=input_data_format) + + image_id = target["image_id"] + image_id = np.asarray([image_id], dtype=np.int64) + + # Get all COCO annotations for the given image. + annotations = target["annotations"] + annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0] + + classes = [obj["category_id"] for obj in annotations] + classes = np.asarray(classes, dtype=np.int64) + + # for conversion to coco api + area = np.asarray([obj["area"] for obj in annotations], dtype=np.float32) + iscrowd = np.asarray([obj.get("iscrowd", 0) for obj in annotations], dtype=np.int64) + + boxes = [obj["bbox"] for obj in annotations] + # guard against no boxes via resizing + boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4) + boxes[:, 2:] += boxes[:, :2] + boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width) + boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height) + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + + new_target = {} + new_target["image_id"] = image_id + new_target["class_labels"] = classes[keep] + new_target["boxes"] = boxes[keep] + new_target["area"] = area[keep] + new_target["iscrowd"] = iscrowd[keep] + new_target["orig_size"] = np.asarray([int(image_height), int(image_width)], dtype=np.int64) + + if annotations and "keypoints" in annotations[0]: + keypoints = [obj["keypoints"] for obj in annotations] + # Converting the filtered keypoints list to a numpy array + keypoints = np.asarray(keypoints, dtype=np.float32) + # Apply the keep mask here to filter the relevant annotations + keypoints = keypoints[keep] + num_keypoints = keypoints.shape[0] + keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints + new_target["keypoints"] = keypoints + + return new_target + + +class RTDetrImageProcessorKwargs(ImagesKwargs, total=False): + r""" + format (`str`, *optional*, defaults to `AnnotationFormat.COCO_DETECTION`): + Data format of the annotations. One of "coco_detection" or "coco_panoptic". + do_convert_annotations (`bool`, *optional*, defaults to `True`): + Controls whether to convert the annotations to the format expected by the RT_DETR model. Converts the + bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`. + Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method. + """ + + format: str | AnnotationFormat + do_convert_annotations: bool + + +class RTDetrImageProcessor(DetrImageProcessor): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + format = AnnotationFormat.COCO_DETECTION + do_convert_annotations = True + do_resize = True + do_rescale = True + do_normalize = False + do_pad = False + size = {"height": 640, "width": 640} + default_to_square = False + model_input_names = ["pixel_values", "pixel_mask"] + valid_kwargs = RTDetrImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[RTDetrImageProcessorKwargs]) -> None: + # Backwards compatibility + do_convert_annotations = kwargs.get("do_convert_annotations") + do_normalize = kwargs.get("do_normalize") + if do_convert_annotations is None and getattr(self, "do_convert_annotations", None) is None: + self.do_convert_annotations = do_normalize if do_normalize is not None else self.do_normalize + + TorchvisionBackend.__init__(self, **kwargs) + + def prepare_annotation( + self, + image: torch.Tensor, + target: dict, + format: AnnotationFormat | None = None, + return_segmentation_masks: bool | None = None, + masks_path: str | pathlib.Path | None = None, + input_data_format: str | ChannelDimension | None = None, + ) -> dict: + format = format if format is not None else self.format + + if format == AnnotationFormat.COCO_DETECTION: + return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks + target = prepare_coco_detection_annotation( + image, target, return_segmentation_masks, input_data_format=input_data_format + ) + else: + raise ValueError(f"Format {format} is not supported.") + return target + + def _preprocess( + self, + images: list["torch.Tensor"], + annotations: AnnotationType | list[AnnotationType] | None, + return_segmentation_masks: bool, + masks_path: str | pathlib.Path | None, + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + do_convert_annotations: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool, + pad_size: SizeDict | None, + format: str | AnnotationFormat | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess an image or a batch of images so that it can be used by the model. + """ + + if annotations is not None and isinstance(annotations, dict): + annotations = [annotations] + + if annotations is not None and len(images) != len(annotations): + raise ValueError( + f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match." + ) + + format = AnnotationFormat(format) + if annotations is not None: + validate_annotations(format, SUPPORTED_ANNOTATION_FORMATS, annotations) + + data = {} + processed_images = [] + processed_annotations = [] + pixel_masks = [] # Initialize pixel_masks here + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image) + if annotations is not None: + annotation = self.prepare_annotation( + image, + annotation, + format, + return_segmentation_masks=return_segmentation_masks, + masks_path=masks_path, + input_data_format=ChannelDimension.FIRST, + ) + + if do_resize: + resized_image = self.resize(image, size=size, resample=resample) + if annotations is not None: + annotation = self.resize_annotation( + annotation, + orig_size=image.size()[-2:], + target_size=resized_image.size()[-2:], + ) + image = resized_image + # Fused rescale and normalize + image = self.rescale_and_normalize(image, do_rescale, rescale_factor, do_normalize, image_mean, image_std) + if do_convert_annotations and annotations is not None: + annotation = self.normalize_annotation(annotation, get_image_size(image, ChannelDimension.FIRST)) + + processed_images.append(image) + processed_annotations.append(annotation) + images = processed_images + annotations = processed_annotations if annotations is not None else None + + if do_pad: + # depends on all resized image shapes so we need another loop + if pad_size is not None: + padded_size = (pad_size.height, pad_size.width) + else: + padded_size = get_max_height_width(images) + + padded_images = [] + padded_annotations = [] + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...} + if padded_size == image.size()[-2:]: + padded_images.append(image) + pixel_masks.append(torch.ones(padded_size, dtype=torch.int64, device=image.device)) + padded_annotations.append(annotation) + continue + image, pixel_mask, annotation = self.pad( + image, padded_size, annotation=annotation, update_bboxes=do_convert_annotations + ) + padded_images.append(image) + padded_annotations.append(annotation) + pixel_masks.append(pixel_mask) + images = padded_images + annotations = padded_annotations if annotations is not None else None + data.update({"pixel_mask": torch.stack(pixel_masks, dim=0)}) + + data.update({"pixel_values": torch.stack(images, dim=0)}) + encoded_inputs = BatchFeature(data, tensor_type=return_tensors) + if annotations is not None: + encoded_inputs["labels"] = [ + BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations + ] + return encoded_inputs + + def post_process_object_detection( + self, + outputs, + threshold: float = 0.5, + target_sizes: TensorType | list[tuple] = None, + use_focal_loss: bool = True, + ): + """ + Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.5): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + use_focal_loss (`bool` defaults to `True`): + Variable informing if the focal loss was used to predict the outputs. If `True`, a sigmoid is applied + to compute the scores of each detection, otherwise, a softmax function is used. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. + """ + requires_backends(self, ["torch"]) + out_logits, out_bbox = outputs.logits, outputs.pred_boxes + # convert from relative cxcywh to absolute xyxy + boxes = center_to_corners_format(out_bbox) + if target_sizes is not None: + if len(out_logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + if isinstance(target_sizes, list): + img_h, img_w = torch.as_tensor(target_sizes).unbind(1) + else: + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device) + boxes = boxes * scale_fct[:, None, :] + + num_top_queries = out_logits.shape[1] + num_classes = out_logits.shape[2] + + if use_focal_loss: + scores = torch.nn.functional.sigmoid(out_logits) + scores, index = torch.topk(scores.flatten(1), num_top_queries, axis=-1) + labels = index % num_classes + index = index // num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + else: + scores = torch.nn.functional.softmax(out_logits)[:, :, :-1] + scores, labels = scores.max(dim=-1) + if scores.shape[1] > num_top_queries: + scores, index = torch.topk(scores, num_top_queries, dim=-1) + labels = torch.gather(labels, dim=1, index=index) + boxes = torch.gather(boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1])) + + results = [] + for score, label, box in zip(scores, labels, boxes): + results.append( + { + "scores": score[score > threshold], + "labels": label[score > threshold], + "boxes": box[score > threshold], + } + ) + + return results + + def post_process_instance_segmentation(self): + raise NotImplementedError("Segmentation post-processing is not implemented for RT-DETR yet.") + + def post_process_semantic_segmentation(self): + raise NotImplementedError("Semantic segmentation post-processing is not implemented for RT-DETR yet.") + + def post_process_panoptic_segmentation(self): + raise NotImplementedError("Panoptic segmentation post-processing is not implemented for RT-DETR yet.") + + +@requires(backends=("torch",)) +class RTDetrImageProcessorPil(DetrImageProcessorPil): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + format = AnnotationFormat.COCO_DETECTION + do_convert_annotations = True + do_resize = True + do_rescale = True + do_normalize = False + do_pad = False + size = {"height": 640, "width": 640} + default_to_square = False + model_input_names = ["pixel_values", "pixel_mask"] + valid_kwargs = RTDetrImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[RTDetrImageProcessorKwargs]) -> None: + # Backwards compatibility + do_convert_annotations = kwargs.get("do_convert_annotations") + do_normalize = kwargs.get("do_normalize") + if do_convert_annotations is None and getattr(self, "do_convert_annotations", None) is None: + self.do_convert_annotations = do_normalize if do_normalize is not None else self.do_normalize + + PilBackend.__init__(self, **kwargs) + + def prepare_annotation( + self, + image: np.ndarray, + target: dict, + format: AnnotationFormat | None = None, + return_segmentation_masks: bool | None = None, + masks_path: str | pathlib.Path | None = None, + input_data_format: str | ChannelDimension | None = None, + ) -> dict: + format = format if format is not None else self.format + + if format == AnnotationFormat.COCO_DETECTION: + return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks + target = prepare_coco_detection_annotation_pil( + image, target, return_segmentation_masks, input_data_format=input_data_format + ) + else: + raise ValueError(f"Format {format} is not supported.") + return target + + def _preprocess( + self, + images: list[np.ndarray], + annotations: AnnotationType | list[AnnotationType] | None, + return_segmentation_masks: bool, + masks_path: str | pathlib.Path | None, + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + do_convert_annotations: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool, + pad_size: SizeDict | None, + format: str | AnnotationFormat | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess an image or a batch of images so that it can be used by the model. + """ + + if annotations is not None and isinstance(annotations, dict): + annotations = [annotations] + + if annotations is not None and len(images) != len(annotations): + raise ValueError( + f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match." + ) + + format = AnnotationFormat(format) + if annotations is not None: + validate_annotations(format, SUPPORTED_ANNOTATION_FORMATS, annotations) + + data = {} + processed_images = [] + processed_annotations = [] + pixel_masks = [] # Initialize pixel_masks here + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image) + if annotations is not None: + annotation = self.prepare_annotation( + image, + annotation, + format, + return_segmentation_masks=return_segmentation_masks, + masks_path=masks_path, + input_data_format=ChannelDimension.FIRST, + ) + + if do_resize: + resized_image = self.resize(image, size=size, resample=resample) + if annotations is not None: + annotation = self.resize_annotation( + annotation, + orig_size=image.shape[-2:], + target_size=resized_image.shape[-2:], + ) + image = resized_image + + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + if do_convert_annotations and annotations is not None: + annotation = self.normalize_annotation(annotation, get_image_size(image, ChannelDimension.FIRST)) + + processed_images.append(image) + processed_annotations.append(annotation) + images = processed_images + annotations = processed_annotations if annotations is not None else None + + if do_pad: + # depends on all resized image shapes so we need another loop + if pad_size is not None: + padded_size = (pad_size.height, pad_size.width) + else: + padded_size = get_max_height_width(images) + + padded_images = [] + padded_annotations = [] + for image, annotation in zip(images, annotations if annotations is not None else [None] * len(images)): + # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...} + if padded_size == image.shape[-2:]: + padded_images.append(image) + pixel_masks.append(np.ones(padded_size, dtype=np.int64)) + padded_annotations.append(annotation) + continue + image, pixel_mask, annotation = self.pad( + image, padded_size, annotation=annotation, update_bboxes=do_convert_annotations + ) + padded_images.append(image) + padded_annotations.append(annotation) + pixel_masks.append(pixel_mask) + images = padded_images + annotations = padded_annotations if annotations is not None else None + data.update({"pixel_mask": pixel_masks}) + + data.update({"pixel_values": images}) + encoded_inputs = BatchFeature(data, tensor_type=return_tensors) + if annotations is not None: + encoded_inputs["labels"] = [ + BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations + ] + return encoded_inputs + + def post_process_object_detection( + self, + outputs, + threshold: float = 0.5, + target_sizes: TensorType | list[tuple] = None, + use_focal_loss: bool = True, + ): + """ + Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.5): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size + `(height, width)` of each image in the batch. If unset, predictions will not be resized. + use_focal_loss (`bool` defaults to `True`): + Variable informing if the focal loss was used to predict the outputs. If `True`, a sigmoid is applied + to compute the scores of each detection, otherwise, a softmax function is used. + + Returns: + `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. + """ + requires_backends(self, ["torch"]) + out_logits, out_bbox = outputs.logits, outputs.pred_boxes + # convert from relative cxcywh to absolute xyxy + boxes = center_to_corners_format(out_bbox) + if target_sizes is not None: + if len(out_logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + if isinstance(target_sizes, list): + img_h, img_w = torch.as_tensor(target_sizes).unbind(1) + else: + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device) + boxes = boxes * scale_fct[:, None, :] + + num_top_queries = out_logits.shape[1] + num_classes = out_logits.shape[2] + + if use_focal_loss: + scores = torch.nn.functional.sigmoid(out_logits) + scores, index = torch.topk(scores.flatten(1), num_top_queries, axis=-1) + labels = index % num_classes + index = index // num_classes + boxes = boxes.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, boxes.shape[-1])) + else: + scores = torch.nn.functional.softmax(out_logits)[:, :, :-1] + scores, labels = scores.max(dim=-1) + if scores.shape[1] > num_top_queries: + scores, index = torch.topk(scores, num_top_queries, dim=-1) + labels = torch.gather(labels, dim=1, index=index) + boxes = torch.gather(boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1])) + + results = [] + for score, label, box in zip(scores, labels, boxes): + results.append( + { + "scores": score[score > threshold], + "labels": label[score > threshold], + "boxes": box[score > threshold], + } + ) + + return results + + def post_process_instance_segmentation(self): + raise NotImplementedError("Segmentation post-processing is not implemented for RT-DETR yet.") + + def post_process_semantic_segmentation(self): + raise NotImplementedError("Semantic segmentation post-processing is not implemented for RT-DETR yet.") + + def post_process_panoptic_segmentation(self): + raise NotImplementedError("Panoptic segmentation post-processing is not implemented for RT-DETR yet.") + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the RTDetrDecoder. This class adds two attributes to + BaseModelOutputWithCrossAttentions, namely: + - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) + - a stacked tensor of intermediate reference points. + """ +) +@dataclass +class RTDetrDecoderOutput(ModelOutput): + r""" + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the RT-DETR encoder-decoder model. + """ +) +@dataclass +class RTDetrModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points used for the first decoder layer. + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`): + Logits of predicted bounding boxes coordinates in the encoder stage. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: torch.FloatTensor | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +@auto_docstring( + custom_intro=""" + Output type of [`RTDetrForObjectDetection`]. + """ +) +@dataclass +class RTDetrObjectDetectionOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): + Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a + bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized + scale-invariant IoU loss. + loss_dict (`Dict`, *optional*): + A dictionary containing the individual losses. Useful for logging. + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~RTDetrImageProcessor.post_process_object_detection`] to retrieve the + unnormalized (absolute) bounding boxes. + auxiliary_outputs (`list[Dict]`, *optional*): + Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) + and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and + `pred_boxes`) for each decoder layer. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values + """ + + loss: torch.FloatTensor | None = None + loss_dict: dict | None = None + logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + auxiliary_outputs: list[dict] | None = None + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: tuple[torch.FloatTensor] | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +class RTDetrMLP(nn.Module): + def __init__(self, config: RTDetrConfig, hidden_size: int, intermediate_size: int, activation_function: str): + super().__init__() + self.fc1 = nn.Linear(hidden_size, intermediate_size) + self.fc2 = nn.Linear(intermediate_size, hidden_size) + self.activation_fn = ACT2FN[activation_function] + self.activation_dropout = config.activation_dropout + self.dropout = config.dropout + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + return hidden_states + + +class RTDetrFrozenBatchNorm2d(DetrFrozenBatchNorm2d): + pass + + +class RTDetrSelfAttention(DetrSelfAttention): + pass + + +def get_contrastive_denoising_training_group( + targets, + num_classes, + num_queries, + class_embed, + num_denoising_queries=100, + label_noise_ratio=0.5, + box_noise_scale=1.0, +): + """ + Creates a contrastive denoising training group using ground-truth samples. It adds noise to labels and boxes. + + Args: + targets (`list[dict]`): + The target objects, each containing 'class_labels' and 'boxes' for objects in an image. + num_classes (`int`): + Total number of classes in the dataset. + num_queries (`int`): + Number of query slots in the transformer. + class_embed (`callable`): + A function or a model layer to embed class labels. + num_denoising_queries (`int`, *optional*, defaults to 100): + Number of denoising queries. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + Ratio of noise applied to labels. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale of noise applied to bounding boxes. + Returns: + `tuple` comprising various elements: + - **input_query_class** (`torch.FloatTensor`) -- + Class queries with applied label noise. + - **input_query_bbox** (`torch.FloatTensor`) -- + Bounding box queries with applied box noise. + - **attn_mask** (`torch.FloatTensor`) -- + Attention mask for separating denoising and reconstruction queries. + - **denoising_meta_values** (`dict`) -- + Metadata including denoising positive indices, number of groups, and split sizes. + """ + + if num_denoising_queries <= 0: + return None, None, None, None + + num_ground_truths = [len(t["class_labels"]) for t in targets] + device = targets[0]["class_labels"].device + + max_gt_num = max(num_ground_truths) + if max_gt_num == 0: + return None, None, None, None + + num_groups_denoising_queries = num_denoising_queries // max_gt_num + num_groups_denoising_queries = 1 if num_groups_denoising_queries == 0 else num_groups_denoising_queries + # pad gt to max_num of a batch + batch_size = len(num_ground_truths) + + input_query_class = torch.full([batch_size, max_gt_num], num_classes, dtype=torch.int32, device=device) + input_query_bbox = torch.zeros([batch_size, max_gt_num, 4], device=device) + pad_gt_mask = torch.zeros([batch_size, max_gt_num], dtype=torch.bool, device=device) + + for i in range(batch_size): + num_gt = num_ground_truths[i] + if num_gt > 0: + input_query_class[i, :num_gt] = targets[i]["class_labels"] + input_query_bbox[i, :num_gt] = targets[i]["boxes"] + pad_gt_mask[i, :num_gt] = 1 + # each group has positive and negative queries. + input_query_class = input_query_class.tile([1, 2 * num_groups_denoising_queries]) + input_query_bbox = input_query_bbox.tile([1, 2 * num_groups_denoising_queries, 1]) + pad_gt_mask = pad_gt_mask.tile([1, 2 * num_groups_denoising_queries]) + # positive and negative mask + negative_gt_mask = torch.zeros([batch_size, max_gt_num * 2, 1], device=device) + negative_gt_mask[:, max_gt_num:] = 1 + negative_gt_mask = negative_gt_mask.tile([1, num_groups_denoising_queries, 1]) + positive_gt_mask = 1 - negative_gt_mask + # contrastive denoising training positive index + positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask + denoise_positive_idx = torch.nonzero(positive_gt_mask)[:, 1] + denoise_positive_idx = torch.split( + denoise_positive_idx, [n * num_groups_denoising_queries for n in num_ground_truths] + ) + # total denoising queries + num_denoising_queries = torch_int(max_gt_num * 2 * num_groups_denoising_queries) + + if label_noise_ratio > 0: + mask = torch.rand_like(input_query_class, dtype=torch.float) < (label_noise_ratio * 0.5) + # randomly put a new one here + new_label = torch.randint_like(mask, 0, num_classes, dtype=input_query_class.dtype) + input_query_class = torch.where(mask & pad_gt_mask, new_label, input_query_class) + + if box_noise_scale > 0: + known_bbox = center_to_corners_format(input_query_bbox) + diff = torch.tile(input_query_bbox[..., 2:] * 0.5, [1, 1, 2]) * box_noise_scale + rand_sign = torch.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0 + rand_part = torch.rand_like(input_query_bbox) + rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (1 - negative_gt_mask) + rand_part *= rand_sign + known_bbox += rand_part * diff + known_bbox.clip_(min=0.0, max=1.0) + input_query_bbox = corners_to_center_format(known_bbox) + input_query_bbox = inverse_sigmoid(input_query_bbox) + + input_query_class = class_embed(input_query_class) + + target_size = num_denoising_queries + num_queries + attn_mask = torch.full([target_size, target_size], 0, dtype=torch.float, device=device) + # match query cannot see the reconstruction + attn_mask[num_denoising_queries:, :num_denoising_queries] = -torch.inf + + # reconstructions cannot see each other + for i in range(num_groups_denoising_queries): + idx_block_start = max_gt_num * 2 * i + idx_block_end = max_gt_num * 2 * (i + 1) + attn_mask[idx_block_start:idx_block_end, :idx_block_start] = -torch.inf + attn_mask[idx_block_start:idx_block_end, idx_block_end:num_denoising_queries] = -torch.inf + + denoising_meta_values = { + "dn_positive_idx": denoise_positive_idx, + "dn_num_group": num_groups_denoising_queries, + "dn_num_split": [num_denoising_queries, num_queries], + } + + return input_query_class, input_query_bbox, attn_mask, denoising_meta_values + + +class RTDetrConvEncoder(nn.Module): + """ + Convolutional backbone using the modeling_rt_detr_resnet.py. + + nn.BatchNorm2d layers are replaced by RTDetrFrozenBatchNorm2d as defined above. + https://github.com/lyuwenyu/RT-DETR/blob/main/rtdetr_pytorch/src/nn/backbone/presnet.py#L142 + """ + + def __init__(self, config): + super().__init__() + + backbone = load_backbone(config) + + if config.freeze_backbone_batch_norms: + # replace batch norm by frozen batch norm + with torch.no_grad(): + replace_batch_norm(backbone) + self.model = backbone + self.intermediate_channel_sizes = self.model.channels + + def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): + # send pixel_values through the model to get list of feature maps + features = self.model(pixel_values).feature_maps + + out = [] + for feature_map in features: + # downsample pixel_mask to match shape of corresponding feature_map + mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] + out.append((feature_map, mask)) + return out + + +class RTDetrConvNormLayer(nn.Module): + def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride, + padding=(kernel_size - 1) // 2 if padding is None else padding, + bias=False, + ) + self.norm = nn.BatchNorm2d(out_channels, config.batch_norm_eps) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_state): + hidden_state = self.conv(hidden_state) + hidden_state = self.norm(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RTDetrEncoderLayer(nn.Module): + def __init__(self, config: RTDetrConfig): + super().__init__() + self.normalize_before = config.normalize_before + self.hidden_size = config.encoder_hidden_dim + + # self-attention + self.self_attn = RTDetrSelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.num_attention_heads, + dropout=config.dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + self.dropout = config.dropout + self.mlp = RTDetrMLP(config, self.hidden_size, config.encoder_ffn_dim, config.encoder_activation_function) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + spatial_position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, hidden_size)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + spatial_position_embeddings (`torch.FloatTensor`, *optional*): + Spatial position embeddings (2D positional encodings of image locations), to be added to both + the queries and keys in self-attention (but not to values). + """ + residual = hidden_states + if self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_embeddings=spatial_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + if self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + residual = hidden_states + + hidden_states = self.mlp(hidden_states) + + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + + if self.training: + if not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +class RTDetrRepVggBlock(nn.Module): + """ + RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again". + """ + + def __init__(self, config: RTDetrConfig): + super().__init__() + + activation = config.activation_function + hidden_channels = int(config.encoder_hidden_dim * config.hidden_expansion) + self.conv1 = RTDetrConvNormLayer(config, hidden_channels, hidden_channels, 3, 1, padding=1) + self.conv2 = RTDetrConvNormLayer(config, hidden_channels, hidden_channels, 1, 1, padding=0) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, x): + y = self.conv1(x) + self.conv2(x) + return self.activation(y) + + +class RTDetrCSPRepLayer(nn.Module): + """ + Cross Stage Partial (CSP) network layer with RepVGG blocks. + """ + + def __init__(self, config: RTDetrConfig): + super().__init__() + + in_channels = config.encoder_hidden_dim * 2 + out_channels = config.encoder_hidden_dim + num_blocks = 3 + activation = config.activation_function + + hidden_channels = int(out_channels * config.hidden_expansion) + self.conv1 = RTDetrConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.conv2 = RTDetrConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.bottlenecks = nn.Sequential(*[RTDetrRepVggBlock(config) for _ in range(num_blocks)]) + if hidden_channels != out_channels: + self.conv3 = RTDetrConvNormLayer(config, hidden_channels, out_channels, 1, 1, activation=activation) + else: + self.conv3 = nn.Identity() + + def forward(self, hidden_state): + hidden_state_1 = self.conv1(hidden_state) + hidden_state_1 = self.bottlenecks(hidden_state_1) + hidden_state_2 = self.conv2(hidden_state) + return self.conv3(hidden_state_1 + hidden_state_2) + + +class RTDetrMultiscaleDeformableAttention(DeformableDetrMultiscaleDeformableAttention): + pass + + +class RTDetrDecoderLayer(nn.Module): + def __init__(self, config: RTDetrConfig): + super().__init__() + self.hidden_size = config.d_model + + # self-attention + self.self_attn = RTDetrSelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + ) + self.dropout = config.dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # cross-attention + self.encoder_attn = RTDetrMultiscaleDeformableAttention( + config, + num_heads=config.decoder_attention_heads, + n_points=config.decoder_n_points, + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # feedforward neural networks + self.mlp = RTDetrMLP(config, self.hidden_size, config.decoder_ffn_dim, config.decoder_activation_function) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + object_queries_position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + Input to the layer of shape `(batch, seq_len, hidden_size)`. + object_queries_position_embeddings (`torch.FloatTensor`, *optional*): + Position embeddings for the object query slots. These are added to both queries and keys + in the self-attention layer (not values). + reference_points (`torch.FloatTensor`, *optional*): + Reference points. + spatial_shapes (`torch.LongTensor`, *optional*): + Spatial shapes. + level_start_index (`torch.LongTensor`, *optional*): + Level start index. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, hidden_size)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + """ + residual = hidden_states + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=encoder_attention_mask, + position_embeddings=object_queries_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + + # Cross-Attention + hidden_states, _ = self.encoder_attn( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + position_embeddings=object_queries_position_embeddings, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states + + +class RTDetrSinePositionEmbedding(nn.Module): + """ + 2D sinusoidal position embedding used in RT-DETR hybrid encoder. + """ + + def __init__(self, embed_dim: int = 256, temperature: int = 10000): + super().__init__() + self.embed_dim = embed_dim + self.temperature = temperature + + @compile_compatible_method_lru_cache(maxsize=32) + def forward( + self, + width: int, + height: int, + device: torch.device | str, + dtype: torch.dtype, + ) -> torch.Tensor: + """ + Generate 2D sinusoidal position embeddings. + + Returns: + Position embeddings of shape (1, height*width, embed_dim) + """ + grid_w = torch.arange(torch_int(width), device=device).to(dtype) + grid_h = torch.arange(torch_int(height), device=device).to(dtype) + grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="xy") + if self.embed_dim % 4 != 0: + raise ValueError("Embed dimension must be divisible by 4 for 2D sin-cos position embedding") + pos_dim = self.embed_dim // 4 + omega = torch.arange(pos_dim, device=device).to(dtype) / pos_dim + omega = 1.0 / (self.temperature**omega) + + out_w = grid_w.flatten()[..., None] @ omega[None] + out_h = grid_h.flatten()[..., None] @ omega[None] + + return torch.concat([out_h.sin(), out_h.cos(), out_w.sin(), out_w.cos()], dim=1)[None, :, :] + + +class RTDetrAIFILayer(nn.Module): + """ + AIFI (Attention-based Intra-scale Feature Interaction) layer used in RT-DETR hybrid encoder. + """ + + def __init__(self, config: RTDetrConfig): + super().__init__() + self.config = config + self.encoder_hidden_dim = config.encoder_hidden_dim + self.eval_size = config.eval_size + + self.position_embedding = RTDetrSinePositionEmbedding( + embed_dim=self.encoder_hidden_dim, + temperature=config.positional_encoding_temperature, + ) + self.layers = nn.ModuleList([RTDetrEncoderLayer(config) for _ in range(config.encoder_layers)]) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`): + Feature map to process. + """ + batch_size = hidden_states.shape[0] + height, width = hidden_states.shape[2:] + + hidden_states = hidden_states.flatten(2).permute(0, 2, 1) + + if self.training or self.eval_size is None: + pos_embed = self.position_embedding( + width=width, + height=height, + device=hidden_states.device, + dtype=hidden_states.dtype, + ) + else: + pos_embed = None + + for layer in self.layers: + hidden_states = layer( + hidden_states, + attention_mask=None, + spatial_position_embeddings=pos_embed, + **kwargs, + ) + + hidden_states = ( + hidden_states.permute(0, 2, 1).reshape(batch_size, self.encoder_hidden_dim, height, width).contiguous() + ) + + return hidden_states + + +class RTDetrMLPPredictionHead(DetrMLPPredictionHead): + pass + + +@auto_docstring +class RTDetrPreTrainedModel(PreTrainedModel): + config: RTDetrConfig + base_model_prefix = "rt_detr" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = [r"RTDetrHybridEncoder", r"RTDetrDecoderLayer"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, RTDetrForObjectDetection): + if module.model.decoder.class_embed is not None: + for layer in module.model.decoder.class_embed: + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(layer.weight) + init.constant_(layer.bias, bias) + + if module.model.decoder.bbox_embed is not None: + for layer in module.model.decoder.bbox_embed: + init.constant_(layer.layers[-1].weight, 0) + init.constant_(layer.layers[-1].bias, 0) + + elif isinstance(module, RTDetrMultiscaleDeformableAttention): + init.constant_(module.sampling_offsets.weight, 0.0) + default_dtype = torch.get_default_dtype() + thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * ( + 2.0 * math.pi / module.n_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(module.n_heads, 1, 1, 2) + .repeat(1, module.n_levels, module.n_points, 1) + ) + for i in range(module.n_points): + grid_init[:, :, i, :] *= i + 1 + + init.copy_(module.sampling_offsets.bias, grid_init.view(-1)) + init.constant_(module.attention_weights.weight, 0.0) + init.constant_(module.attention_weights.bias, 0.0) + init.xavier_uniform_(module.value_proj.weight) + init.constant_(module.value_proj.bias, 0.0) + init.xavier_uniform_(module.output_proj.weight) + init.constant_(module.output_proj.bias, 0.0) + + elif isinstance(module, RTDetrModel): + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(module.enc_score_head.weight) + init.constant_(module.enc_score_head.bias, bias) + + elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + elif isinstance(module, nn.LayerNorm): + init.ones_(module.weight) + init.zeros_(module.bias) + + if hasattr(module, "weight_embedding") and self.config.learn_initial_query: + init.xavier_uniform_(module.weight_embedding.weight) + if hasattr(module, "denoising_class_embed") and self.config.num_denoising > 0: + init.xavier_uniform_(module.denoising_class_embed.weight) + + +class RTDetrHybridEncoder(RTDetrPreTrainedModel): + """ + Hybrid encoder consisting of AIFI (Attention-based Intra-scale Feature Interaction) layers, + a top-down Feature Pyramid Network (FPN) and a bottom-up Path Aggregation Network (PAN). + More details on the paper: https://huggingface.co/papers/2304.08069 + + Args: + config: RTDetrConfig + """ + + _can_record_outputs = { + "hidden_states": RTDetrAIFILayer, + "attentions": RTDetrSelfAttention, + } + + def __init__(self, config: RTDetrConfig): + super().__init__(config) + self.config = config + self.in_channels = config.encoder_in_channels + self.feat_strides = config.feat_strides + self.encoder_hidden_dim = config.encoder_hidden_dim + self.encode_proj_layers = config.encode_proj_layers + self.positional_encoding_temperature = config.positional_encoding_temperature + self.eval_size = config.eval_size + self.out_channels = [self.encoder_hidden_dim for _ in self.in_channels] + self.out_strides = self.feat_strides + self.num_fpn_stages = len(self.in_channels) - 1 + self.num_pan_stages = len(self.in_channels) - 1 + + # AIFI (Attention-based Intra-scale Feature Interaction) layers + self.aifi = nn.ModuleList([RTDetrAIFILayer(config) for _ in range(len(self.encode_proj_layers))]) + + # top-down FPN + self.lateral_convs = nn.ModuleList() + self.fpn_blocks = nn.ModuleList() + for _ in range(self.num_fpn_stages): + lateral_conv = RTDetrConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=1, + stride=1, + activation=config.activation_function, + ) + fpn_block = RTDetrCSPRepLayer(config) + self.lateral_convs.append(lateral_conv) + self.fpn_blocks.append(fpn_block) + + # bottom-up PAN + self.downsample_convs = nn.ModuleList() + self.pan_blocks = nn.ModuleList() + for _ in range(self.num_pan_stages): + downsample_conv = RTDetrConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=3, + stride=2, + activation=config.activation_function, + ) + pan_block = RTDetrCSPRepLayer(config) + self.downsample_convs.append(downsample_conv) + self.pan_blocks.append(pan_block) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + inputs_embeds=None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + """ + feature_maps = inputs_embeds + + # AIFI: Apply transformer encoder to specified feature levels + if self.config.encoder_layers > 0: + for i, enc_ind in enumerate(self.encode_proj_layers): + feature_maps[enc_ind] = self.aifi[i](feature_maps[enc_ind], **kwargs) + + # top-down FPN + fpn_feature_maps = [feature_maps[-1]] + for idx, (lateral_conv, fpn_block) in enumerate(zip(self.lateral_convs, self.fpn_blocks)): + backbone_feature_map = feature_maps[self.num_fpn_stages - idx - 1] + top_fpn_feature_map = fpn_feature_maps[-1] + # apply lateral block + top_fpn_feature_map = lateral_conv(top_fpn_feature_map) + fpn_feature_maps[-1] = top_fpn_feature_map + # apply fpn block + top_fpn_feature_map = F.interpolate(top_fpn_feature_map, scale_factor=2.0, mode="nearest") + fused_feature_map = torch.concat([top_fpn_feature_map, backbone_feature_map], dim=1) + new_fpn_feature_map = fpn_block(fused_feature_map) + fpn_feature_maps.append(new_fpn_feature_map) + + fpn_feature_maps.reverse() + + # bottom-up PAN + pan_feature_maps = [fpn_feature_maps[0]] + for idx, (downsample_conv, pan_block) in enumerate(zip(self.downsample_convs, self.pan_blocks)): + top_pan_feature_map = pan_feature_maps[-1] + fpn_feature_map = fpn_feature_maps[idx + 1] + downsampled_feature_map = downsample_conv(top_pan_feature_map) + fused_feature_map = torch.concat([downsampled_feature_map, fpn_feature_map], dim=1) + new_pan_feature_map = pan_block(fused_feature_map) + pan_feature_maps.append(new_pan_feature_map) + + return BaseModelOutput(last_hidden_state=pan_feature_maps) + + +class RTDetrDecoder(RTDetrPreTrainedModel): + _can_record_outputs = { + "hidden_states": RTDetrDecoderLayer, + "attentions": RTDetrSelfAttention, + "cross_attentions": RTDetrMultiscaleDeformableAttention, + } + + def __init__(self, config: RTDetrConfig): + super().__init__(config) + + self.dropout = config.dropout + self.layers = nn.ModuleList([RTDetrDecoderLayer(config) for _ in range(config.decoder_layers)]) + self.query_pos_head = RTDetrMLPPredictionHead(4, 2 * config.d_model, config.d_model, num_layers=2) + + # hack implementation for iterative bounding box refinement and two-stage Deformable DETR + self.bbox_embed = None + self.class_embed = None + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + The query embeddings that are passed into the decoder. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected + in `[0, 1]`: + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): + Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. + spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): + Spatial shapes of the feature maps. + level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): + Indexes for the start of each feature level. In range `[0, sequence_length]`. + """ + if inputs_embeds is not None: + hidden_states = inputs_embeds + + # decoder layers + intermediate = () + intermediate_reference_points = () + intermediate_logits = () + + reference_points = F.sigmoid(reference_points) + + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L252 + for idx, decoder_layer in enumerate(self.layers): + reference_points_input = reference_points.unsqueeze(2) + object_queries_position_embeddings = self.query_pos_head(reference_points) + + hidden_states = decoder_layer( + hidden_states, + object_queries_position_embeddings=object_queries_position_embeddings, + encoder_hidden_states=encoder_hidden_states, + reference_points=reference_points_input, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + encoder_attention_mask=encoder_attention_mask, + **kwargs, + ) + + # hack implementation for iterative bounding box refinement + if self.bbox_embed is not None: + predicted_corners = self.bbox_embed[idx](hidden_states) + new_reference_points = F.sigmoid(predicted_corners + inverse_sigmoid(reference_points)) + reference_points = new_reference_points.detach() + + intermediate += (hidden_states,) + intermediate_reference_points += ( + (new_reference_points,) if self.bbox_embed is not None else (reference_points,) + ) + + if self.class_embed is not None: + logits = self.class_embed[idx](hidden_states) + intermediate_logits += (logits,) + + # Keep batch_size as first dimension + intermediate = torch.stack(intermediate, dim=1) + intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) + if self.class_embed is not None: + intermediate_logits = torch.stack(intermediate_logits, dim=1) + + return RTDetrDecoderOutput( + last_hidden_state=hidden_states, + intermediate_hidden_states=intermediate, + intermediate_logits=intermediate_logits, + intermediate_reference_points=intermediate_reference_points, + ) + + +@auto_docstring( + custom_intro=""" + RT-DETR Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top. + """ +) +class RTDetrModel(RTDetrPreTrainedModel): + def __init__(self, config: RTDetrConfig): + super().__init__(config) + + # Create backbone + self.backbone = RTDetrConvEncoder(config) + intermediate_channel_sizes = self.backbone.intermediate_channel_sizes + + # Create encoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/hybrid_encoder.py#L212 + num_backbone_outs = len(intermediate_channel_sizes) + encoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = intermediate_channel_sizes[i] + encoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.encoder_hidden_dim, kernel_size=1, bias=False), + nn.BatchNorm2d(config.encoder_hidden_dim), + ) + ) + self.encoder_input_proj = nn.ModuleList(encoder_input_proj_list) + + # Create encoder + self.encoder = RTDetrHybridEncoder(config) + + # denoising part + if config.num_denoising > 0: + self.denoising_class_embed = nn.Embedding( + config.num_labels + 1, config.d_model, padding_idx=config.num_labels + ) + + # decoder embedding + if config.learn_initial_query: + self.weight_embedding = nn.Embedding(config.num_queries, config.d_model) + + # encoder head + self.enc_output = nn.Sequential( + nn.Linear(config.d_model, config.d_model), + nn.LayerNorm(config.d_model, eps=config.layer_norm_eps), + ) + self.enc_score_head = nn.Linear(config.d_model, config.num_labels) + self.enc_bbox_head = RTDetrMLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + + # init encoder output anchors and valid_mask + if config.anchor_image_size: + self.anchors, self.valid_mask = self.generate_anchors(dtype=self.dtype) + + # Create decoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L412 + num_backbone_outs = len(config.decoder_in_channels) + decoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = config.decoder_in_channels[i] + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + for _ in range(config.num_feature_levels - num_backbone_outs): + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + in_channels = config.d_model + self.decoder_input_proj = nn.ModuleList(decoder_input_proj_list) + + # decoder + self.decoder = RTDetrDecoder(config) + + self.post_init() + + def freeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(False) + + def unfreeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(True) + + @compile_compatible_method_lru_cache(maxsize=32) + def generate_anchors(self, spatial_shapes=None, grid_size=0.05, device="cpu", dtype=torch.float32): + if spatial_shapes is None: + spatial_shapes = [ + [int(self.config.anchor_image_size[0] / s), int(self.config.anchor_image_size[1] / s)] + for s in self.config.feat_strides + ] + anchors = [] + for level, (height, width) in enumerate(spatial_shapes): + grid_y, grid_x = torch.meshgrid( + torch.arange(end=height, device=device).to(dtype), + torch.arange(end=width, device=device).to(dtype), + indexing="ij", + ) + grid_xy = torch.stack([grid_x, grid_y], -1) + grid_xy = grid_xy.unsqueeze(0) + 0.5 + grid_xy[..., 0] /= width + grid_xy[..., 1] /= height + wh = torch.ones_like(grid_xy) * grid_size * (2.0**level) + anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, height * width, 4)) + # define the valid range for anchor coordinates + eps = 1e-2 + anchors = torch.concat(anchors, 1) + valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True) + anchors = torch.log(anchors / (1 - anchors)) + anchors = torch.where(valid_mask, anchors, torch.tensor(torch.finfo(dtype).max, dtype=dtype, device=device)) + + return anchors, valid_mask + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | RTDetrModelOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, RTDetrModel + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("PekingU/rtdetr_r50vd") + >>> model = RTDetrModel.from_pretrained("PekingU/rtdetr_r50vd") + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 300, 256] + ```""" + if pixel_values is None and inputs_embeds is None: + raise ValueError("You have to specify either pixel_values or inputs_embeds") + + if inputs_embeds is None: + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + if pixel_mask is None: + pixel_mask = torch.ones(((batch_size, height, width)), device=device) + features = self.backbone(pixel_values, pixel_mask) + proj_feats = [self.encoder_input_proj[level](source) for level, (source, mask) in enumerate(features)] + else: + batch_size = inputs_embeds.shape[0] + device = inputs_embeds.device + proj_feats = inputs_embeds + + if encoder_outputs is None: + encoder_outputs = self.encoder( + proj_feats, + **kwargs, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput + elif not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Equivalent to def _get_encoder_input + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/rtdetr_pytorch/src/zoo/rtdetr/rtdetr_decoder.py#L412 + sources = [] + for level, source in enumerate(encoder_outputs.last_hidden_state): + sources.append(self.decoder_input_proj[level](source)) + + # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage + if self.config.num_feature_levels > len(sources): + _len_sources = len(sources) + sources.append(self.decoder_input_proj[_len_sources](encoder_outputs.last_hidden_state)[-1]) + for i in range(_len_sources + 1, self.config.num_feature_levels): + sources.append(self.decoder_input_proj[i](encoder_outputs.last_hidden_state[-1])) + + # Prepare encoder inputs (by flattening) + source_flatten = [] + spatial_shapes_list = [] + spatial_shapes = torch.empty((len(sources), 2), device=device, dtype=torch.long) + for level, source in enumerate(sources): + height, width = source.shape[-2:] + spatial_shapes[level, 0] = height + spatial_shapes[level, 1] = width + spatial_shapes_list.append((height, width)) + source = source.flatten(2).transpose(1, 2) + source_flatten.append(source) + source_flatten = torch.cat(source_flatten, 1) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + + # prepare denoising training + if self.training and self.config.num_denoising > 0 and labels is not None: + ( + denoising_class, + denoising_bbox_unact, + attention_mask, + denoising_meta_values, + ) = get_contrastive_denoising_training_group( + targets=labels, + num_classes=self.config.num_labels, + num_queries=self.config.num_queries, + class_embed=self.denoising_class_embed, + num_denoising_queries=self.config.num_denoising, + label_noise_ratio=self.config.label_noise_ratio, + box_noise_scale=self.config.box_noise_scale, + ) + else: + denoising_class, denoising_bbox_unact, attention_mask, denoising_meta_values = None, None, None, None + + batch_size = len(source_flatten) + device = source_flatten.device + dtype = source_flatten.dtype + + # prepare input for decoder + if self.training or self.config.anchor_image_size is None: + # Pass spatial_shapes as tuple to make it hashable and make sure + # lru_cache is working for generate_anchors() + spatial_shapes_tuple = tuple(spatial_shapes_list) + anchors, valid_mask = self.generate_anchors(spatial_shapes_tuple, device=device, dtype=dtype) + else: + anchors, valid_mask = self.anchors, self.valid_mask + anchors, valid_mask = anchors.to(device, dtype), valid_mask.to(device, dtype) + + # use the valid_mask to selectively retain values in the feature map where the mask is `True` + memory = valid_mask.to(source_flatten.dtype) * source_flatten + + output_memory = self.enc_output(memory) + + enc_outputs_class = self.enc_score_head(output_memory) + enc_outputs_coord_logits = self.enc_bbox_head(output_memory) + anchors + + _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.config.num_queries, dim=1) + + reference_points_unact = enc_outputs_coord_logits.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_coord_logits.shape[-1]) + ) + + enc_topk_bboxes = F.sigmoid(reference_points_unact) + if denoising_bbox_unact is not None: + reference_points_unact = torch.concat([denoising_bbox_unact, reference_points_unact], 1) + + enc_topk_logits = enc_outputs_class.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_class.shape[-1]) + ) + + # extract region features + if self.config.learn_initial_query: + target = self.weight_embedding.tile([batch_size, 1, 1]) + else: + target = output_memory.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, output_memory.shape[-1])) + target = target.detach() + + if denoising_class is not None: + target = torch.concat([denoising_class, target], 1) + + init_reference_points = reference_points_unact.detach() + + # decoder + decoder_outputs = self.decoder( + inputs_embeds=target, + encoder_hidden_states=source_flatten, + encoder_attention_mask=attention_mask, + reference_points=init_reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + return RTDetrModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, + intermediate_logits=decoder_outputs.intermediate_logits, + intermediate_reference_points=decoder_outputs.intermediate_reference_points, + intermediate_predicted_corners=decoder_outputs.intermediate_predicted_corners, + initial_reference_points=decoder_outputs.initial_reference_points, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + init_reference_points=init_reference_points, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + enc_outputs_class=enc_outputs_class, + enc_outputs_coord_logits=enc_outputs_coord_logits, + denoising_meta_values=denoising_meta_values, + ) + + +@auto_docstring( + custom_intro=""" + RT-DETR Model (consisting of a backbone and encoder-decoder) outputting bounding boxes and logits to be further + decoded into scores and classes. + """ +) +class RTDetrForObjectDetection(RTDetrPreTrainedModel): + # When using clones, all layers > 0 will be clones, but layer 0 *is* required + # We can't initialize the model on meta device as some weights are modified during the initialization + _no_split_modules = None + + def __init__(self, config: RTDetrConfig): + super().__init__(config) + self.model = RTDetrModel(config) + num_pred = config.decoder_layers + self.model.decoder.class_embed = nn.ModuleList( + [torch.nn.Linear(config.d_model, config.num_labels) for _ in range(num_pred)] + ) + self.model.decoder.bbox_embed = nn.ModuleList( + [RTDetrMLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) for _ in range(num_pred)] + ) + # if two-stage, the last class_embed and bbox_embed is for region proposal generation + self.post_init() + + def _set_aux_loss(self, outputs_class, outputs_coord): + return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)] + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | RTDetrObjectDetectionOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import RTDetrImageProcessor, RTDetrForObjectDetection + >>> from PIL import Image + >>> import requests + >>> import torch + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd") + >>> model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd") + + >>> # prepare image for the model + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 300, 80] + + >>> boxes = outputs.pred_boxes + >>> list(boxes.shape) + [1, 300, 4] + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> target_sizes = torch.tensor([image.size[::-1]]) + >>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[ + ... 0 + ... ] + + >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): + ... box = [round(i, 2) for i in box.tolist()] + ... print( + ... f"Detected {model.config.id2label[label.item()]} with confidence " + ... f"{round(score.item(), 3)} at location {box}" + ... ) + Detected sofa with confidence 0.97 at location [0.14, 0.38, 640.13, 476.21] + Detected cat with confidence 0.96 at location [343.38, 24.28, 640.14, 371.5] + Detected cat with confidence 0.958 at location [13.23, 54.18, 318.98, 472.22] + Detected remote with confidence 0.951 at location [40.11, 73.44, 175.96, 118.48] + Detected remote with confidence 0.924 at location [333.73, 76.58, 369.97, 186.99] + ```""" + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + labels=labels, + **kwargs, + ) + + denoising_meta_values = outputs.denoising_meta_values if self.training else None + + outputs_class = outputs.intermediate_logits + outputs_coord = outputs.intermediate_reference_points + predicted_corners = outputs.intermediate_predicted_corners + initial_reference_points = outputs.initial_reference_points + + logits = outputs_class[:, -1] + pred_boxes = outputs_coord[:, -1] + + loss, loss_dict, auxiliary_outputs, enc_topk_logits, enc_topk_bboxes = None, None, None, None, None + if labels is not None: + enc_topk_logits = outputs.enc_topk_logits + enc_topk_bboxes = outputs.enc_topk_bboxes + loss, loss_dict, auxiliary_outputs = self.loss_function( + logits, + labels, + self.device, + pred_boxes, + self.config, + outputs_class, + outputs_coord, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + denoising_meta_values=denoising_meta_values, + predicted_corners=predicted_corners, + initial_reference_points=initial_reference_points, + **kwargs, + ) + + return RTDetrObjectDetectionOutput( + loss=loss, + loss_dict=loss_dict, + logits=logits, + pred_boxes=pred_boxes, + auxiliary_outputs=auxiliary_outputs, + last_hidden_state=outputs.last_hidden_state, + intermediate_hidden_states=outputs.intermediate_hidden_states, + intermediate_logits=outputs.intermediate_logits, + intermediate_reference_points=outputs.intermediate_reference_points, + intermediate_predicted_corners=outputs.intermediate_predicted_corners, + initial_reference_points=outputs.initial_reference_points, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + init_reference_points=outputs.init_reference_points, + enc_topk_logits=outputs.enc_topk_logits, + enc_topk_bboxes=outputs.enc_topk_bboxes, + enc_outputs_class=outputs.enc_outputs_class, + enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, + denoising_meta_values=outputs.denoising_meta_values, + ) + + +__all__ = [ + "RTDetrImageProcessor", + "RTDetrImageProcessorPil", + "RTDetrForObjectDetection", + "RTDetrModel", + "RTDetrPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..efc3772ca6d015271102638c3948958777498fd9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_rt_detr_v2 import * + from .modeling_rt_detr_v2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/configuration_rt_detr_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/configuration_rt_detr_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..e888d88c91a12614494b575b8de08329f79ab173 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/configuration_rt_detr_v2.py @@ -0,0 +1,202 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/rt_detr_v2/modular_rt_detr_v2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_rt_detr_v2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Baidu Inc and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PekingU/rtdetr_r18vd") +@strict +class RTDetrV2Config(PreTrainedConfig): + r""" + initializer_bias_prior_prob (`float`, *optional*): + The prior probability used by the bias initializer to initialize biases for `enc_score_head` and `class_embed`. + If `None`, `prior_prob` computed as `prior_prob = 1 / (num_labels + 1)` while initializing model weights. + freeze_backbone_batch_norms (`bool`, *optional*, defaults to `True`): + Whether to freeze the batch normalization layers in the backbone. + encoder_in_channels (`list`, *optional*, defaults to `[512, 1024, 2048]`): + Multi level features input for encoder. + feat_strides (`list[int]`, *optional*, defaults to `[8, 16, 32]`): + Strides used in each feature map. + encode_proj_layers (`list[int]`, *optional*, defaults to `[2]`): + Indexes of the projected layers to be used in the encoder. + positional_encoding_temperature (`int`, *optional*, defaults to 10000): + The temperature parameter used to create the positional encodings. + encoder_activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + activation_function (`str`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the general layer. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + eval_size (`tuple[int, int]`, *optional*): + Height and width used to compute the effective height and width of the position embeddings after taking + into account the stride. + normalize_before (`bool`, *optional*, defaults to `False`): + Determine whether to apply layer normalization in the transformer encoder layer before self-attention and + feed-forward modules. + hidden_expansion (`float`, *optional*, defaults to 1.0): + Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. + num_queries (`int`, *optional*, defaults to 300): + Number of object queries. + decoder_in_channels (`list`, *optional*, defaults to `[256, 256, 256]`): + Multi level features dimension for decoder + num_feature_levels (`int`, *optional*, defaults to 3): + The number of input feature levels. + decoder_n_points (`int`, *optional*, defaults to 4): + The number of sampled keys in each feature level for each attention head in the decoder. + decoder_activation_function (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + num_denoising (`int`, *optional*, defaults to 100): + The total number of denoising tasks or queries to be used for contrastive denoising. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + The fraction of denoising labels to which random noise should be added. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale or magnitude of noise to be added to the bounding boxes. + learn_initial_query (`bool`, *optional*, defaults to `False`): + Indicates whether the initial query embeddings for the decoder should be learned during training + anchor_image_size (`tuple[int, int]`, *optional*): + Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. + with_box_refine (`bool`, *optional*, defaults to `True`): + Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes + based on the predictions from the previous layer. + matcher_alpha (`float`, *optional*, defaults to 0.25): + Parameter alpha used by the Hungarian Matcher. + matcher_gamma (`float`, *optional*, defaults to 2.0): + Parameter gamma used by the Hungarian Matcher. + matcher_class_cost (`float`, *optional*, defaults to 2.0): + The relative weight of the class loss used by the Hungarian Matcher. + matcher_bbox_cost (`float`, *optional*, defaults to 5.0): + The relative weight of the bounding box loss used by the Hungarian Matcher. + matcher_giou_cost (`float`, *optional*, defaults to 2.0): + The relative weight of the giou loss of used by the Hungarian Matcher. + use_focal_loss (`bool`, *optional*, defaults to `True`): + Parameter informing if focal loss should be used. + focal_loss_alpha (`float`, *optional*, defaults to 0.75): + Parameter alpha used to compute the focal loss. + focal_loss_gamma (`float`, *optional*, defaults to 2.0): + Parameter gamma used to compute the focal loss. + weight_loss_vfl (`float`, *optional*, defaults to 1.0): + Relative weight of the varifocal loss in the object detection loss. + weight_loss_bbox (`float`, *optional*, defaults to 5.0): + Relative weight of the L1 bounding box loss in the object detection loss. + weight_loss_giou (`float`, *optional*, defaults to 2.0): + Relative weight of the generalized IoU loss in the object detection loss. + decoder_n_levels (`int`, *optional*, defaults to 3): + The number of feature levels used by the decoder. + decoder_offset_scale (`float`, *optional*, defaults to 0.5): + Scaling factor applied to the attention offsets in the decoder. + decoder_method (`str`, *optional*, defaults to `"default"`): + The method to use for the decoder: `"default"` or `"discrete"`. + + Examples: + + ```python + >>> from transformers import RTDetrV2Config, RTDetrV2Model + + >>> # Initializing a RT-DETR configuration + >>> configuration = RTDetrV2Config() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = RTDetrV2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "rt_detr_v2" + sub_configs = {"backbone_config": AutoConfig} + layer_types = ["basic", "bottleneck"] + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + initializer_range: float = 0.01 + initializer_bias_prior_prob: float | None = None + layer_norm_eps: float = 1e-5 + batch_norm_eps: float = 1e-5 + backbone_config: dict | PreTrainedConfig | None = None + freeze_backbone_batch_norms: bool = True + encoder_hidden_dim: int = 256 + encoder_in_channels: list[int] | tuple[int, ...] = (512, 1024, 2048) + feat_strides: list[int] | tuple[int, ...] = (8, 16, 32) + encoder_layers: int = 1 + encoder_ffn_dim: int = 1024 + encoder_attention_heads: int = 8 + dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + encode_proj_layers: list[int] | tuple[int, ...] = (2,) + positional_encoding_temperature: int = 10000 + encoder_activation_function: str = "gelu" + activation_function: str = "silu" + eval_size: int | None = None + normalize_before: bool = False + hidden_expansion: float = 1.0 + d_model: int = 256 + num_queries: int = 300 + decoder_in_channels: list[int] | tuple[int, ...] = (256, 256, 256) + decoder_ffn_dim: int = 1024 + num_feature_levels: int = 3 + decoder_n_points: int = 4 + decoder_layers: int = 6 + decoder_attention_heads: int = 8 + decoder_activation_function: str = "relu" + attention_dropout: float | int = 0.0 + num_denoising: int = 100 + label_noise_ratio: float = 0.5 + box_noise_scale: float = 1.0 + learn_initial_query: bool = False + anchor_image_size: int | list[int] | None = None + with_box_refine: bool = True + is_encoder_decoder: bool = True + matcher_alpha: float = 0.25 + matcher_gamma: float = 2.0 + matcher_class_cost: float = 2.0 + matcher_bbox_cost: float = 5.0 + matcher_giou_cost: float = 2.0 + use_focal_loss: bool = True + auxiliary_loss: bool = True + focal_loss_alpha: float = 0.75 + focal_loss_gamma: float = 2.0 + weight_loss_vfl: float = 1.0 + weight_loss_bbox: float = 5.0 + weight_loss_giou: float = 2.0 + eos_coefficient: float = 1e-4 + decoder_n_levels: int = 3 + decoder_offset_scale: float = 0.5 + decoder_method: str = "default" + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="rt_detr_resnet", + default_config_kwargs={"out_indices": [2, 3, 4]}, + **kwargs, + ) + super().__post_init__(**kwargs) + + +__all__ = ["RTDetrV2Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/modeling_rt_detr_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/modeling_rt_detr_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..68ccff24e27c22169648cdcdac419d97d5445007 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/modeling_rt_detr_v2.py @@ -0,0 +1,1873 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/rt_detr_v2/modular_rt_detr_v2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_rt_detr_v2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Baidu Inc and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +import warnings +from collections.abc import Callable +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ... import initialization as init +from ...activations import ACT2CLS, ACT2FN +from ...backbone_utils import load_backbone +from ...image_transforms import center_to_corners_format, corners_to_center_format +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, torch_compilable_check, torch_int +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_rt_detr_v2 import RTDetrV2Config + + +def multi_scale_deformable_attention_v2( + value: Tensor, + value_spatial_shapes: Tensor, + sampling_locations: Tensor, + attention_weights: Tensor, + num_points_list: list[int], + method="default", +) -> Tensor: + batch_size, _, num_heads, hidden_dim = value.shape + _, num_queries, num_heads, num_levels, num_points = sampling_locations.shape + value_list = ( + value.permute(0, 2, 3, 1) + .flatten(0, 1) + .split([height * width for height, width in value_spatial_shapes], dim=-1) + ) + # sampling_offsets [8, 480, 8, 12, 2] + if method == "default": + sampling_grids = 2 * sampling_locations - 1 + elif method == "discrete": + sampling_grids = sampling_locations + sampling_grids = sampling_grids.permute(0, 2, 1, 3, 4).flatten(0, 1) + sampling_grids = sampling_grids.split(num_points_list, dim=-2) + sampling_value_list = [] + for level_id, (height, width) in enumerate(value_spatial_shapes): + # batch_size, height*width, num_heads, hidden_dim + # -> batch_size, height*width, num_heads*hidden_dim + # -> batch_size, num_heads*hidden_dim, height*width + # -> batch_size*num_heads, hidden_dim, height, width + value_l_ = value_list[level_id].reshape(batch_size * num_heads, hidden_dim, height, width) + # batch_size, num_queries, num_heads, num_points, 2 + # -> batch_size, num_heads, num_queries, num_points, 2 + # -> batch_size*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[level_id] + # batch_size*num_heads, hidden_dim, num_queries, num_points + if method == "default": + sampling_value_l_ = nn.functional.grid_sample( + value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False + ) + elif method == "discrete": + sampling_coord = (sampling_grid_l_ * torch.tensor([[width, height]], device=value.device) + 0.5).to( + torch.int64 + ) + + # Separate clamping for x and y coordinates + sampling_coord_x = sampling_coord[..., 0].clamp(0, width - 1) + sampling_coord_y = sampling_coord[..., 1].clamp(0, height - 1) + + # Combine the clamped coordinates + sampling_coord = torch.stack([sampling_coord_x, sampling_coord_y], dim=-1) + sampling_coord = sampling_coord.reshape(batch_size * num_heads, num_queries * num_points_list[level_id], 2) + sampling_idx = ( + torch.arange(sampling_coord.shape[0], device=value.device) + .unsqueeze(-1) + .repeat(1, sampling_coord.shape[1]) + ) + sampling_value_l_ = value_l_[sampling_idx, :, sampling_coord[..., 1], sampling_coord[..., 0]] + sampling_value_l_ = sampling_value_l_.permute(0, 2, 1).reshape( + batch_size * num_heads, hidden_dim, num_queries, num_points_list[level_id] + ) + sampling_value_list.append(sampling_value_l_) + # (batch_size, num_queries, num_heads, num_levels, num_points) + # -> (batch_size, num_heads, num_queries, num_levels, num_points) + # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.permute(0, 2, 1, 3).reshape( + batch_size * num_heads, 1, num_queries, sum(num_points_list) + ) + output = ( + (torch.concat(sampling_value_list, dim=-1) * attention_weights) + .sum(-1) + .view(batch_size, num_heads * hidden_dim, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +# the main change +class RTDetrV2MultiscaleDeformableAttention(nn.Module): + """ + RTDetrV2 version of multiscale deformable attention, extending the base implementation + with improved offset handling and initialization. + """ + + def __init__(self, config: RTDetrV2Config): + super().__init__() + num_heads = config.decoder_attention_heads + n_points = config.decoder_n_points + + if config.d_model % num_heads != 0: + raise ValueError( + f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" + ) + dim_per_head = config.d_model // num_heads + # check if dim_per_head is power of 2 + if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): + warnings.warn( + "You'd better set embed_dim (d_model) in RTDetrV2MultiscaleDeformableAttention to make the" + " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" + " implementation." + ) + + self.im2col_step = 64 + + self.d_model = config.d_model + + # V2-specific attributes + self.n_levels = config.decoder_n_levels + self.n_heads = num_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) + self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) + self.value_proj = nn.Linear(config.d_model, config.d_model) + self.output_proj = nn.Linear(config.d_model, config.d_model) + + self.offset_scale = config.decoder_offset_scale + self.method = config.decoder_method + + # Initialize n_points list and scale + n_points_list = [self.n_points for _ in range(self.n_levels)] + self.n_points_list = n_points_list + n_points_scale = [1 / n for n in n_points_list for _ in range(n)] + self.register_buffer("n_points_scale", torch.tensor(n_points_scale, dtype=torch.float32)) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states=None, + encoder_attention_mask=None, + position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ): + # Process inputs up to sampling locations calculation using parent class logic + if position_embeddings is not None: + hidden_states = hidden_states + position_embeddings + + batch_size, num_queries, _ = hidden_states.shape + batch_size, sequence_length, _ = encoder_hidden_states.shape + torch_compilable_check( + (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == sequence_length, + "Make sure to align the spatial shapes with the sequence length of the encoder hidden states", + ) + + value = self.value_proj(encoder_hidden_states) + if attention_mask is not None: + value = value.masked_fill(~attention_mask[..., None], float(0)) + value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) + + # V2-specific sampling offsets shape + sampling_offsets = self.sampling_offsets(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points, 2 + ) + + attention_weights = self.attention_weights(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points + ) + attention_weights = F.softmax(attention_weights, -1) + + # V2-specific sampling locations calculation + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif reference_points.shape[-1] == 4: + n_points_scale = self.n_points_scale.to(dtype=hidden_states.dtype).unsqueeze(-1) + offset = sampling_offsets * n_points_scale * reference_points[:, :, None, :, 2:] * self.offset_scale + sampling_locations = reference_points[:, :, None, :, :2] + offset + else: + raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") + + # V2-specific attention implementation choice + output = multi_scale_deformable_attention_v2( + value, spatial_shapes_list, sampling_locations, attention_weights, self.n_points_list, self.method + ) + + output = self.output_proj(output) + return output, attention_weights + + +class RTDetrV2MLP(nn.Module): + def __init__(self, config: RTDetrV2Config, hidden_size: int, intermediate_size: int, activation_function: str): + super().__init__() + self.fc1 = nn.Linear(hidden_size, intermediate_size) + self.fc2 = nn.Linear(intermediate_size, hidden_size) + self.activation_fn = ACT2FN[activation_function] + self.activation_dropout = config.activation_dropout + self.dropout = config.dropout + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + return hidden_states + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class RTDetrV2SelfAttention(nn.Module): + """ + Multi-headed self-attention from 'Attention Is All You Need' paper. + + In RT_DETR_V2, position embeddings are added to both queries and keys (but not values) in self-attention. + """ + + def __init__( + self, + config: RTDetrV2Config, + hidden_size: int, + num_attention_heads: int, + dropout: float = 0.0, + bias: bool = True, + ): + super().__init__() + self.config = config + self.head_dim = hidden_size // num_attention_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = dropout + self.is_causal = False + + self.k_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.v_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.q_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + self.o_proj = nn.Linear(hidden_size, hidden_size, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Position embeddings are added to both queries and keys (but not values). + """ + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_key_input = hidden_states + position_embeddings if position_embeddings is not None else hidden_states + + query_states = self.q_proj(query_key_input).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(query_key_input).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class RTDetrV2DecoderLayer(nn.Module): + def __init__(self, config: RTDetrV2Config): + super().__init__() + self.hidden_size = config.d_model + + # self-attention + self.self_attn = RTDetrV2SelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + ) + self.dropout = config.dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # override only the encoder attention module with v2 version + self.encoder_attn = RTDetrV2MultiscaleDeformableAttention(config) + self.encoder_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + # feedforward neural networks + self.mlp = RTDetrV2MLP(config, self.hidden_size, config.decoder_ffn_dim, config.decoder_activation_function) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + object_queries_position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + Input to the layer of shape `(batch, seq_len, hidden_size)`. + object_queries_position_embeddings (`torch.FloatTensor`, *optional*): + Position embeddings for the object query slots. These are added to both queries and keys + in the self-attention layer (not values). + reference_points (`torch.FloatTensor`, *optional*): + Reference points. + spatial_shapes (`torch.LongTensor`, *optional*): + Spatial shapes. + level_start_index (`torch.LongTensor`, *optional*): + Level start index. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, hidden_size)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + """ + residual = hidden_states + + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=encoder_attention_mask, + position_embeddings=object_queries_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + + # Cross-Attention + hidden_states, _ = self.encoder_attn( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + position_embeddings=object_queries_position_embeddings, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states + + +@auto_docstring +class RTDetrV2PreTrainedModel(PreTrainedModel): + config: RTDetrV2Config + base_model_prefix = "rt_detr_v2" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = [r"RTDetrV2HybridEncoder", r"RTDetrV2DecoderLayer"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _supports_flex_attn = True + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, RTDetrV2ForObjectDetection): + if module.model.decoder.class_embed is not None: + for layer in module.model.decoder.class_embed: + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(layer.weight) + init.constant_(layer.bias, bias) + + if module.model.decoder.bbox_embed is not None: + for layer in module.model.decoder.bbox_embed: + init.constant_(layer.layers[-1].weight, 0) + init.constant_(layer.layers[-1].bias, 0) + + elif isinstance(module, RTDetrV2MultiscaleDeformableAttention): + init.constant_(module.sampling_offsets.weight, 0.0) + default_dtype = torch.get_default_dtype() + thetas = torch.arange(module.n_heads, dtype=torch.int64).to(default_dtype) * ( + 2.0 * math.pi / module.n_heads + ) + grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) + grid_init = ( + (grid_init / grid_init.abs().max(-1, keepdim=True)[0]) + .view(module.n_heads, 1, 1, 2) + .repeat(1, module.n_levels, module.n_points, 1) + ) + for i in range(module.n_points): + grid_init[:, :, i, :] *= i + 1 + + init.copy_(module.sampling_offsets.bias, grid_init.view(-1)) + init.constant_(module.attention_weights.weight, 0.0) + init.constant_(module.attention_weights.bias, 0.0) + init.xavier_uniform_(module.value_proj.weight) + init.constant_(module.value_proj.bias, 0.0) + init.xavier_uniform_(module.output_proj.weight) + init.constant_(module.output_proj.bias, 0.0) + + elif isinstance(module, RTDetrV2Model): + prior_prob = self.config.initializer_bias_prior_prob or 1 / (self.config.num_labels + 1) + bias = float(-math.log((1 - prior_prob) / prior_prob)) + init.xavier_uniform_(module.enc_score_head.weight) + init.constant_(module.enc_score_head.bias, bias) + + elif isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + init.zeros_(module.bias) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + + elif isinstance(module, nn.LayerNorm): + init.ones_(module.weight) + init.zeros_(module.bias) + + if hasattr(module, "weight_embedding") and self.config.learn_initial_query: + init.xavier_uniform_(module.weight_embedding.weight) + if hasattr(module, "denoising_class_embed") and self.config.num_denoising > 0: + init.xavier_uniform_(module.denoising_class_embed.weight) + if isinstance(module, RTDetrV2MultiscaleDeformableAttention): + n_points_scale = [1 / n for n in module.n_points_list for _ in range(n)] + init.copy_(module.n_points_scale, torch.tensor(n_points_scale, dtype=torch.float32)) + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the RTDetrV2Decoder. This class adds two attributes to + BaseModelOutputWithCrossAttentions, namely: + - a stacked tensor of intermediate decoder hidden states (i.e. the output of each decoder layer) + - a stacked tensor of intermediate reference points. + """ +) +@dataclass +class RTDetrV2DecoderOutput(ModelOutput): + r""" + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, hidden_size)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + + +def inverse_sigmoid(x, eps=1e-5): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +class RTDetrV2Decoder(RTDetrV2PreTrainedModel): + _can_record_outputs = { + "hidden_states": RTDetrV2DecoderLayer, + "attentions": RTDetrV2SelfAttention, + "cross_attentions": RTDetrV2MultiscaleDeformableAttention, + } + + def __init__(self, config: RTDetrV2Config): + super().__init__(config) + + self.dropout = config.dropout + self.layers = nn.ModuleList([RTDetrV2DecoderLayer(config) for _ in range(config.decoder_layers)]) + self.query_pos_head = RTDetrV2MLPPredictionHead(4, 2 * config.d_model, config.d_model, num_layers=2) + + # hack implementation for iterative bounding box refinement and two-stage Deformable DETR + self.bbox_embed = None + self.class_embed = None + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + The query embeddings that are passed into the decoder. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected + in `[0, 1]`: + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)` is `as_two_stage` else `(batch_size, num_queries, 2)` or , *optional*): + Reference point in range `[0, 1]`, top-left (0,0), bottom-right (1, 1), including padding area. + spatial_shapes (`torch.FloatTensor` of shape `(num_feature_levels, 2)`): + Spatial shapes of the feature maps. + level_start_index (`torch.LongTensor` of shape `(num_feature_levels)`, *optional*): + Indexes for the start of each feature level. In range `[0, sequence_length]`. + """ + if inputs_embeds is not None: + hidden_states = inputs_embeds + + # decoder layers + intermediate = () + intermediate_reference_points = () + intermediate_logits = () + + reference_points = F.sigmoid(reference_points) + + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_pytorch/src/zoo/RTDetrV2/RTDetrV2_decoder.py#L252 + for idx, decoder_layer in enumerate(self.layers): + reference_points_input = reference_points.unsqueeze(2) + object_queries_position_embeddings = self.query_pos_head(reference_points) + + hidden_states = decoder_layer( + hidden_states, + object_queries_position_embeddings=object_queries_position_embeddings, + encoder_hidden_states=encoder_hidden_states, + reference_points=reference_points_input, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + encoder_attention_mask=encoder_attention_mask, + **kwargs, + ) + + # hack implementation for iterative bounding box refinement + if self.bbox_embed is not None: + predicted_corners = self.bbox_embed[idx](hidden_states) + new_reference_points = F.sigmoid(predicted_corners + inverse_sigmoid(reference_points)) + reference_points = new_reference_points.detach() + + intermediate += (hidden_states,) + intermediate_reference_points += ( + (new_reference_points,) if self.bbox_embed is not None else (reference_points,) + ) + + if self.class_embed is not None: + logits = self.class_embed[idx](hidden_states) + intermediate_logits += (logits,) + + # Keep batch_size as first dimension + intermediate = torch.stack(intermediate, dim=1) + intermediate_reference_points = torch.stack(intermediate_reference_points, dim=1) + if self.class_embed is not None: + intermediate_logits = torch.stack(intermediate_logits, dim=1) + + return RTDetrV2DecoderOutput( + last_hidden_state=hidden_states, + intermediate_hidden_states=intermediate, + intermediate_logits=intermediate_logits, + intermediate_reference_points=intermediate_reference_points, + ) + + +@auto_docstring( + custom_intro=""" + Base class for outputs of the RT-DETR encoder-decoder model. + """ +) +@dataclass +class RTDetrV2ModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, sequence_length, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points used for the first decoder layer. + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the encoder stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`): + Logits of predicted bounding boxes coordinates in the encoder stage. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: torch.FloatTensor | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +class RTDetrV2FrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than + torchvision.models.resnet[18,34,50,101] produce nans. + """ + + def __init__(self, n): + super().__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it user-friendly + weight = self.weight.reshape(1, -1, 1, 1) + bias = self.bias.reshape(1, -1, 1, 1) + running_var = self.running_var.reshape(1, -1, 1, 1) + running_mean = self.running_mean.reshape(1, -1, 1, 1) + epsilon = 1e-5 + scale = weight * (running_var + epsilon).rsqrt() + bias = bias - running_mean * scale + return x * scale + bias + + +def replace_batch_norm(model): + r""" + Recursively replace all `torch.nn.BatchNorm2d` with `RTDetrV2FrozenBatchNorm2d`. + + Args: + model (torch.nn.Module): + input model + """ + for name, module in model.named_children(): + if isinstance(module, nn.BatchNorm2d): + new_module = RTDetrV2FrozenBatchNorm2d(module.num_features) + + if module.weight.device != torch.device("meta"): + new_module.weight.copy_(module.weight) + new_module.bias.copy_(module.bias) + new_module.running_mean.copy_(module.running_mean) + new_module.running_var.copy_(module.running_var) + + model._modules[name] = new_module + + if len(list(module.children())) > 0: + replace_batch_norm(module) + + +class RTDetrV2ConvEncoder(nn.Module): + """ + Convolutional backbone using the modeling_rt_detr_v2_resnet.py. + + nn.BatchNorm2d layers are replaced by RTDetrV2FrozenBatchNorm2d as defined above. + https://github.com/lyuwenyu/RT-DETR/blob/main/RTDetrV2_pytorch/src/nn/backbone/presnet.py#L142 + """ + + def __init__(self, config): + super().__init__() + + backbone = load_backbone(config) + + if config.freeze_backbone_batch_norms: + # replace batch norm by frozen batch norm + with torch.no_grad(): + replace_batch_norm(backbone) + self.model = backbone + self.intermediate_channel_sizes = self.model.channels + + def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): + # send pixel_values through the model to get list of feature maps + features = self.model(pixel_values).feature_maps + + out = [] + for feature_map in features: + # downsample pixel_mask to match shape of corresponding feature_map + mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] + out.append((feature_map, mask)) + return out + + +class RTDetrV2ConvNormLayer(nn.Module): + def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + stride, + padding=(kernel_size - 1) // 2 if padding is None else padding, + bias=False, + ) + self.norm = nn.BatchNorm2d(out_channels, config.batch_norm_eps) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_state): + hidden_state = self.conv(hidden_state) + hidden_state = self.norm(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class RTDetrV2EncoderLayer(nn.Module): + def __init__(self, config: RTDetrV2Config): + super().__init__() + self.normalize_before = config.normalize_before + self.hidden_size = config.encoder_hidden_dim + + # self-attention + self.self_attn = RTDetrV2SelfAttention( + config=config, + hidden_size=self.hidden_size, + num_attention_heads=config.num_attention_heads, + dropout=config.dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + self.dropout = config.dropout + self.mlp = RTDetrV2MLP(config, self.hidden_size, config.encoder_ffn_dim, config.encoder_activation_function) + self.final_layer_norm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + spatial_position_embeddings: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, hidden_size)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + spatial_position_embeddings (`torch.FloatTensor`, *optional*): + Spatial position embeddings (2D positional encodings of image locations), to be added to both + the queries and keys in self-attention (but not to values). + """ + residual = hidden_states + if self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_embeddings=spatial_position_embeddings, + **kwargs, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + if self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + residual = hidden_states + + hidden_states = self.mlp(hidden_states) + + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.final_layer_norm(hidden_states) + + if self.training: + if not torch.isfinite(hidden_states).all(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + return hidden_states + + +class RTDetrV2RepVggBlock(nn.Module): + """ + RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again". + """ + + def __init__(self, config: RTDetrV2Config): + super().__init__() + + activation = config.activation_function + hidden_channels = int(config.encoder_hidden_dim * config.hidden_expansion) + self.conv1 = RTDetrV2ConvNormLayer(config, hidden_channels, hidden_channels, 3, 1, padding=1) + self.conv2 = RTDetrV2ConvNormLayer(config, hidden_channels, hidden_channels, 1, 1, padding=0) + self.activation = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, x): + y = self.conv1(x) + self.conv2(x) + return self.activation(y) + + +class RTDetrV2CSPRepLayer(nn.Module): + """ + Cross Stage Partial (CSP) network layer with RepVGG blocks. + """ + + def __init__(self, config: RTDetrV2Config): + super().__init__() + + in_channels = config.encoder_hidden_dim * 2 + out_channels = config.encoder_hidden_dim + num_blocks = 3 + activation = config.activation_function + + hidden_channels = int(out_channels * config.hidden_expansion) + self.conv1 = RTDetrV2ConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.conv2 = RTDetrV2ConvNormLayer(config, in_channels, hidden_channels, 1, 1, activation=activation) + self.bottlenecks = nn.Sequential(*[RTDetrV2RepVggBlock(config) for _ in range(num_blocks)]) + if hidden_channels != out_channels: + self.conv3 = RTDetrV2ConvNormLayer(config, hidden_channels, out_channels, 1, 1, activation=activation) + else: + self.conv3 = nn.Identity() + + def forward(self, hidden_state): + hidden_state_1 = self.conv1(hidden_state) + hidden_state_1 = self.bottlenecks(hidden_state_1) + hidden_state_2 = self.conv2(hidden_state) + return self.conv3(hidden_state_1 + hidden_state_2) + + +class RTDetrV2SinePositionEmbedding(nn.Module): + """ + 2D sinusoidal position embedding used in RT-DETR hybrid encoder. + """ + + def __init__(self, embed_dim: int = 256, temperature: int = 10000): + super().__init__() + self.embed_dim = embed_dim + self.temperature = temperature + + @compile_compatible_method_lru_cache(maxsize=32) + def forward( + self, + width: int, + height: int, + device: torch.device | str, + dtype: torch.dtype, + ) -> torch.Tensor: + """ + Generate 2D sinusoidal position embeddings. + + Returns: + Position embeddings of shape (1, height*width, embed_dim) + """ + grid_w = torch.arange(torch_int(width), device=device).to(dtype) + grid_h = torch.arange(torch_int(height), device=device).to(dtype) + grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="xy") + if self.embed_dim % 4 != 0: + raise ValueError("Embed dimension must be divisible by 4 for 2D sin-cos position embedding") + pos_dim = self.embed_dim // 4 + omega = torch.arange(pos_dim, device=device).to(dtype) / pos_dim + omega = 1.0 / (self.temperature**omega) + + out_w = grid_w.flatten()[..., None] @ omega[None] + out_h = grid_h.flatten()[..., None] @ omega[None] + + return torch.concat([out_h.sin(), out_h.cos(), out_w.sin(), out_w.cos()], dim=1)[None, :, :] + + +class RTDetrV2AIFILayer(nn.Module): + """ + AIFI (Attention-based Intra-scale Feature Interaction) layer used in RT-DETR hybrid encoder. + """ + + def __init__(self, config: RTDetrV2Config): + super().__init__() + self.config = config + self.encoder_hidden_dim = config.encoder_hidden_dim + self.eval_size = config.eval_size + + self.position_embedding = RTDetrV2SinePositionEmbedding( + embed_dim=self.encoder_hidden_dim, + temperature=config.positional_encoding_temperature, + ) + self.layers = nn.ModuleList([RTDetrV2EncoderLayer(config) for _ in range(config.encoder_layers)]) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor` of shape `(batch_size, channels, height, width)`): + Feature map to process. + """ + batch_size = hidden_states.shape[0] + height, width = hidden_states.shape[2:] + + hidden_states = hidden_states.flatten(2).permute(0, 2, 1) + + if self.training or self.eval_size is None: + pos_embed = self.position_embedding( + width=width, + height=height, + device=hidden_states.device, + dtype=hidden_states.dtype, + ) + else: + pos_embed = None + + for layer in self.layers: + hidden_states = layer( + hidden_states, + attention_mask=None, + spatial_position_embeddings=pos_embed, + **kwargs, + ) + + hidden_states = ( + hidden_states.permute(0, 2, 1).reshape(batch_size, self.encoder_hidden_dim, height, width).contiguous() + ) + + return hidden_states + + +class RTDetrV2HybridEncoder(RTDetrV2PreTrainedModel): + """ + Hybrid encoder consisting of AIFI (Attention-based Intra-scale Feature Interaction) layers, + a top-down Feature Pyramid Network (FPN) and a bottom-up Path Aggregation Network (PAN). + More details on the paper: https://huggingface.co/papers/2304.08069 + + Args: + config: RTDetrV2Config + """ + + _can_record_outputs = { + "hidden_states": RTDetrV2AIFILayer, + "attentions": RTDetrV2SelfAttention, + } + + def __init__(self, config: RTDetrV2Config): + super().__init__(config) + self.config = config + self.in_channels = config.encoder_in_channels + self.feat_strides = config.feat_strides + self.encoder_hidden_dim = config.encoder_hidden_dim + self.encode_proj_layers = config.encode_proj_layers + self.positional_encoding_temperature = config.positional_encoding_temperature + self.eval_size = config.eval_size + self.out_channels = [self.encoder_hidden_dim for _ in self.in_channels] + self.out_strides = self.feat_strides + self.num_fpn_stages = len(self.in_channels) - 1 + self.num_pan_stages = len(self.in_channels) - 1 + + # AIFI (Attention-based Intra-scale Feature Interaction) layers + self.aifi = nn.ModuleList([RTDetrV2AIFILayer(config) for _ in range(len(self.encode_proj_layers))]) + + # top-down FPN + self.lateral_convs = nn.ModuleList() + self.fpn_blocks = nn.ModuleList() + for _ in range(self.num_fpn_stages): + lateral_conv = RTDetrV2ConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=1, + stride=1, + activation=config.activation_function, + ) + fpn_block = RTDetrV2CSPRepLayer(config) + self.lateral_convs.append(lateral_conv) + self.fpn_blocks.append(fpn_block) + + # bottom-up PAN + self.downsample_convs = nn.ModuleList() + self.pan_blocks = nn.ModuleList() + for _ in range(self.num_pan_stages): + downsample_conv = RTDetrV2ConvNormLayer( + config, + in_channels=self.encoder_hidden_dim, + out_channels=self.encoder_hidden_dim, + kernel_size=3, + stride=2, + activation=config.activation_function, + ) + pan_block = RTDetrV2CSPRepLayer(config) + self.downsample_convs.append(downsample_conv) + self.pan_blocks.append(pan_block) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + inputs_embeds=None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + """ + feature_maps = inputs_embeds + + # AIFI: Apply transformer encoder to specified feature levels + if self.config.encoder_layers > 0: + for i, enc_ind in enumerate(self.encode_proj_layers): + feature_maps[enc_ind] = self.aifi[i](feature_maps[enc_ind], **kwargs) + + # top-down FPN + fpn_feature_maps = [feature_maps[-1]] + for idx, (lateral_conv, fpn_block) in enumerate(zip(self.lateral_convs, self.fpn_blocks)): + backbone_feature_map = feature_maps[self.num_fpn_stages - idx - 1] + top_fpn_feature_map = fpn_feature_maps[-1] + # apply lateral block + top_fpn_feature_map = lateral_conv(top_fpn_feature_map) + fpn_feature_maps[-1] = top_fpn_feature_map + # apply fpn block + top_fpn_feature_map = F.interpolate(top_fpn_feature_map, scale_factor=2.0, mode="nearest") + fused_feature_map = torch.concat([top_fpn_feature_map, backbone_feature_map], dim=1) + new_fpn_feature_map = fpn_block(fused_feature_map) + fpn_feature_maps.append(new_fpn_feature_map) + + fpn_feature_maps.reverse() + + # bottom-up PAN + pan_feature_maps = [fpn_feature_maps[0]] + for idx, (downsample_conv, pan_block) in enumerate(zip(self.downsample_convs, self.pan_blocks)): + top_pan_feature_map = pan_feature_maps[-1] + fpn_feature_map = fpn_feature_maps[idx + 1] + downsampled_feature_map = downsample_conv(top_pan_feature_map) + fused_feature_map = torch.concat([downsampled_feature_map, fpn_feature_map], dim=1) + new_pan_feature_map = pan_block(fused_feature_map) + pan_feature_maps.append(new_pan_feature_map) + + return BaseModelOutput(last_hidden_state=pan_feature_maps) + + +def get_contrastive_denoising_training_group( + targets, + num_classes, + num_queries, + class_embed, + num_denoising_queries=100, + label_noise_ratio=0.5, + box_noise_scale=1.0, +): + """ + Creates a contrastive denoising training group using ground-truth samples. It adds noise to labels and boxes. + + Args: + targets (`list[dict]`): + The target objects, each containing 'class_labels' and 'boxes' for objects in an image. + num_classes (`int`): + Total number of classes in the dataset. + num_queries (`int`): + Number of query slots in the transformer. + class_embed (`callable`): + A function or a model layer to embed class labels. + num_denoising_queries (`int`, *optional*, defaults to 100): + Number of denoising queries. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + Ratio of noise applied to labels. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale of noise applied to bounding boxes. + Returns: + `tuple` comprising various elements: + - **input_query_class** (`torch.FloatTensor`) -- + Class queries with applied label noise. + - **input_query_bbox** (`torch.FloatTensor`) -- + Bounding box queries with applied box noise. + - **attn_mask** (`torch.FloatTensor`) -- + Attention mask for separating denoising and reconstruction queries. + - **denoising_meta_values** (`dict`) -- + Metadata including denoising positive indices, number of groups, and split sizes. + """ + + if num_denoising_queries <= 0: + return None, None, None, None + + num_ground_truths = [len(t["class_labels"]) for t in targets] + device = targets[0]["class_labels"].device + + max_gt_num = max(num_ground_truths) + if max_gt_num == 0: + return None, None, None, None + + num_groups_denoising_queries = num_denoising_queries // max_gt_num + num_groups_denoising_queries = 1 if num_groups_denoising_queries == 0 else num_groups_denoising_queries + # pad gt to max_num of a batch + batch_size = len(num_ground_truths) + + input_query_class = torch.full([batch_size, max_gt_num], num_classes, dtype=torch.int32, device=device) + input_query_bbox = torch.zeros([batch_size, max_gt_num, 4], device=device) + pad_gt_mask = torch.zeros([batch_size, max_gt_num], dtype=torch.bool, device=device) + + for i in range(batch_size): + num_gt = num_ground_truths[i] + if num_gt > 0: + input_query_class[i, :num_gt] = targets[i]["class_labels"] + input_query_bbox[i, :num_gt] = targets[i]["boxes"] + pad_gt_mask[i, :num_gt] = 1 + # each group has positive and negative queries. + input_query_class = input_query_class.tile([1, 2 * num_groups_denoising_queries]) + input_query_bbox = input_query_bbox.tile([1, 2 * num_groups_denoising_queries, 1]) + pad_gt_mask = pad_gt_mask.tile([1, 2 * num_groups_denoising_queries]) + # positive and negative mask + negative_gt_mask = torch.zeros([batch_size, max_gt_num * 2, 1], device=device) + negative_gt_mask[:, max_gt_num:] = 1 + negative_gt_mask = negative_gt_mask.tile([1, num_groups_denoising_queries, 1]) + positive_gt_mask = 1 - negative_gt_mask + # contrastive denoising training positive index + positive_gt_mask = positive_gt_mask.squeeze(-1) * pad_gt_mask + denoise_positive_idx = torch.nonzero(positive_gt_mask)[:, 1] + denoise_positive_idx = torch.split( + denoise_positive_idx, [n * num_groups_denoising_queries for n in num_ground_truths] + ) + # total denoising queries + num_denoising_queries = torch_int(max_gt_num * 2 * num_groups_denoising_queries) + + if label_noise_ratio > 0: + mask = torch.rand_like(input_query_class, dtype=torch.float) < (label_noise_ratio * 0.5) + # randomly put a new one here + new_label = torch.randint_like(mask, 0, num_classes, dtype=input_query_class.dtype) + input_query_class = torch.where(mask & pad_gt_mask, new_label, input_query_class) + + if box_noise_scale > 0: + known_bbox = center_to_corners_format(input_query_bbox) + diff = torch.tile(input_query_bbox[..., 2:] * 0.5, [1, 1, 2]) * box_noise_scale + rand_sign = torch.randint_like(input_query_bbox, 0, 2) * 2.0 - 1.0 + rand_part = torch.rand_like(input_query_bbox) + rand_part = (rand_part + 1.0) * negative_gt_mask + rand_part * (1 - negative_gt_mask) + rand_part *= rand_sign + known_bbox += rand_part * diff + known_bbox.clip_(min=0.0, max=1.0) + input_query_bbox = corners_to_center_format(known_bbox) + input_query_bbox = inverse_sigmoid(input_query_bbox) + + input_query_class = class_embed(input_query_class) + + target_size = num_denoising_queries + num_queries + attn_mask = torch.full([target_size, target_size], 0, dtype=torch.float, device=device) + # match query cannot see the reconstruction + attn_mask[num_denoising_queries:, :num_denoising_queries] = -torch.inf + + # reconstructions cannot see each other + for i in range(num_groups_denoising_queries): + idx_block_start = max_gt_num * 2 * i + idx_block_end = max_gt_num * 2 * (i + 1) + attn_mask[idx_block_start:idx_block_end, :idx_block_start] = -torch.inf + attn_mask[idx_block_start:idx_block_end, idx_block_end:num_denoising_queries] = -torch.inf + + denoising_meta_values = { + "dn_positive_idx": denoise_positive_idx, + "dn_num_group": num_groups_denoising_queries, + "dn_num_split": [num_denoising_queries, num_queries], + } + + return input_query_class, input_query_bbox, attn_mask, denoising_meta_values + + +@auto_docstring( + custom_intro=""" + RT-DETR Model (consisting of a backbone and encoder-decoder) outputting raw hidden states without any head on top. + """ +) +class RTDetrV2Model(RTDetrV2PreTrainedModel): + def __init__(self, config: RTDetrV2Config): + super().__init__(config) + + # Create backbone + self.backbone = RTDetrV2ConvEncoder(config) + intermediate_channel_sizes = self.backbone.intermediate_channel_sizes + + # Create encoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_pytorch/src/zoo/RTDetrV2/hybrid_encoder.py#L212 + num_backbone_outs = len(intermediate_channel_sizes) + encoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = intermediate_channel_sizes[i] + encoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.encoder_hidden_dim, kernel_size=1, bias=False), + nn.BatchNorm2d(config.encoder_hidden_dim), + ) + ) + self.encoder_input_proj = nn.ModuleList(encoder_input_proj_list) + + # Create encoder + self.encoder = RTDetrV2HybridEncoder(config) + + # denoising part + if config.num_denoising > 0: + self.denoising_class_embed = nn.Embedding( + config.num_labels + 1, config.d_model, padding_idx=config.num_labels + ) + + # decoder embedding + if config.learn_initial_query: + self.weight_embedding = nn.Embedding(config.num_queries, config.d_model) + + # encoder head + self.enc_output = nn.Sequential( + nn.Linear(config.d_model, config.d_model), + nn.LayerNorm(config.d_model, eps=config.layer_norm_eps), + ) + self.enc_score_head = nn.Linear(config.d_model, config.num_labels) + self.enc_bbox_head = RTDetrV2MLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + + # init encoder output anchors and valid_mask + if config.anchor_image_size: + self.anchors, self.valid_mask = self.generate_anchors(dtype=self.dtype) + + # Create decoder input projection layers + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_pytorch/src/zoo/RTDetrV2/RTDetrV2_decoder.py#L412 + num_backbone_outs = len(config.decoder_in_channels) + decoder_input_proj_list = [] + for i in range(num_backbone_outs): + in_channels = config.decoder_in_channels[i] + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + for _ in range(config.num_feature_levels - num_backbone_outs): + decoder_input_proj_list.append( + nn.Sequential( + nn.Conv2d(in_channels, config.d_model, kernel_size=3, stride=2, padding=1, bias=False), + nn.BatchNorm2d(config.d_model, config.batch_norm_eps), + ) + ) + in_channels = config.d_model + self.decoder_input_proj = nn.ModuleList(decoder_input_proj_list) + # decoder + self.decoder = RTDetrV2Decoder(config) + + self.post_init() + + def freeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(False) + + def unfreeze_backbone(self): + for param in self.backbone.parameters(): + param.requires_grad_(True) + + @compile_compatible_method_lru_cache(maxsize=32) + def generate_anchors(self, spatial_shapes=None, grid_size=0.05, device="cpu", dtype=torch.float32): + if spatial_shapes is None: + spatial_shapes = [ + [int(self.config.anchor_image_size[0] / s), int(self.config.anchor_image_size[1] / s)] + for s in self.config.feat_strides + ] + anchors = [] + for level, (height, width) in enumerate(spatial_shapes): + grid_y, grid_x = torch.meshgrid( + torch.arange(end=height, device=device).to(dtype), + torch.arange(end=width, device=device).to(dtype), + indexing="ij", + ) + grid_xy = torch.stack([grid_x, grid_y], -1) + grid_xy = grid_xy.unsqueeze(0) + 0.5 + grid_xy[..., 0] /= width + grid_xy[..., 1] /= height + wh = torch.ones_like(grid_xy) * grid_size * (2.0**level) + anchors.append(torch.concat([grid_xy, wh], -1).reshape(-1, height * width, 4)) + # define the valid range for anchor coordinates + eps = 1e-2 + anchors = torch.concat(anchors, 1) + valid_mask = ((anchors > eps) * (anchors < 1 - eps)).all(-1, keepdim=True) + anchors = torch.log(anchors / (1 - anchors)) + anchors = torch.where(valid_mask, anchors, torch.tensor(torch.finfo(dtype).max, dtype=dtype, device=device)) + + return anchors, valid_mask + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | RTDetrV2ModelOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, RTDetrV2Model + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("PekingU/RTDetrV2_r50vd") + >>> model = RTDetrV2Model.from_pretrained("PekingU/RTDetrV2_r50vd") + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 300, 256] + ```""" + if pixel_values is None and inputs_embeds is None: + raise ValueError("You have to specify either pixel_values or inputs_embeds") + + if inputs_embeds is None: + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + if pixel_mask is None: + pixel_mask = torch.ones(((batch_size, height, width)), device=device) + features = self.backbone(pixel_values, pixel_mask) + proj_feats = [self.encoder_input_proj[level](source) for level, (source, mask) in enumerate(features)] + else: + batch_size = inputs_embeds.shape[0] + device = inputs_embeds.device + proj_feats = inputs_embeds + + if encoder_outputs is None: + encoder_outputs = self.encoder( + proj_feats, + **kwargs, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput + elif not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Equivalent to def _get_encoder_input + # https://github.com/lyuwenyu/RT-DETR/blob/94f5e16708329d2f2716426868ec89aa774af016/RTDetrV2_pytorch/src/zoo/RTDetrV2/RTDetrV2_decoder.py#L412 + sources = [] + for level, source in enumerate(encoder_outputs.last_hidden_state): + sources.append(self.decoder_input_proj[level](source)) + + # Lowest resolution feature maps are obtained via 3x3 stride 2 convolutions on the final stage + if self.config.num_feature_levels > len(sources): + _len_sources = len(sources) + sources.append(self.decoder_input_proj[_len_sources](encoder_outputs.last_hidden_state)[-1]) + for i in range(_len_sources + 1, self.config.num_feature_levels): + sources.append(self.decoder_input_proj[i](encoder_outputs.last_hidden_state[-1])) + + # Prepare encoder inputs (by flattening) + source_flatten = [] + spatial_shapes_list = [] + spatial_shapes = torch.empty((len(sources), 2), device=device, dtype=torch.long) + for level, source in enumerate(sources): + height, width = source.shape[-2:] + spatial_shapes[level, 0] = height + spatial_shapes[level, 1] = width + spatial_shapes_list.append((height, width)) + source = source.flatten(2).transpose(1, 2) + source_flatten.append(source) + source_flatten = torch.cat(source_flatten, 1) + level_start_index = torch.cat((spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])) + + # prepare denoising training + if self.training and self.config.num_denoising > 0 and labels is not None: + ( + denoising_class, + denoising_bbox_unact, + attention_mask, + denoising_meta_values, + ) = get_contrastive_denoising_training_group( + targets=labels, + num_classes=self.config.num_labels, + num_queries=self.config.num_queries, + class_embed=self.denoising_class_embed, + num_denoising_queries=self.config.num_denoising, + label_noise_ratio=self.config.label_noise_ratio, + box_noise_scale=self.config.box_noise_scale, + ) + else: + denoising_class, denoising_bbox_unact, attention_mask, denoising_meta_values = None, None, None, None + + batch_size = len(source_flatten) + device = source_flatten.device + dtype = source_flatten.dtype + + # prepare input for decoder + if self.training or self.config.anchor_image_size is None: + # Pass spatial_shapes as tuple to make it hashable and make sure + # lru_cache is working for generate_anchors() + spatial_shapes_tuple = tuple(spatial_shapes_list) + anchors, valid_mask = self.generate_anchors(spatial_shapes_tuple, device=device, dtype=dtype) + else: + anchors, valid_mask = self.anchors, self.valid_mask + anchors, valid_mask = anchors.to(device, dtype), valid_mask.to(device, dtype) + + # use the valid_mask to selectively retain values in the feature map where the mask is `True` + memory = valid_mask.to(source_flatten.dtype) * source_flatten + + output_memory = self.enc_output(memory) + + enc_outputs_class = self.enc_score_head(output_memory) + enc_outputs_coord_logits = self.enc_bbox_head(output_memory) + anchors + + _, topk_ind = torch.topk(enc_outputs_class.max(-1).values, self.config.num_queries, dim=1) + + reference_points_unact = enc_outputs_coord_logits.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_coord_logits.shape[-1]) + ) + + enc_topk_bboxes = F.sigmoid(reference_points_unact) + if denoising_bbox_unact is not None: + reference_points_unact = torch.concat([denoising_bbox_unact, reference_points_unact], 1) + + enc_topk_logits = enc_outputs_class.gather( + dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, enc_outputs_class.shape[-1]) + ) + + # extract region features + if self.config.learn_initial_query: + target = self.weight_embedding.tile([batch_size, 1, 1]) + else: + target = output_memory.gather(dim=1, index=topk_ind.unsqueeze(-1).repeat(1, 1, output_memory.shape[-1])) + target = target.detach() + + if denoising_class is not None: + target = torch.concat([denoising_class, target], 1) + + init_reference_points = reference_points_unact.detach() + + # decoder + decoder_outputs = self.decoder( + inputs_embeds=target, + encoder_hidden_states=source_flatten, + encoder_attention_mask=attention_mask, + reference_points=init_reference_points, + spatial_shapes=spatial_shapes, + spatial_shapes_list=spatial_shapes_list, + level_start_index=level_start_index, + **kwargs, + ) + + return RTDetrV2ModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, + intermediate_logits=decoder_outputs.intermediate_logits, + intermediate_reference_points=decoder_outputs.intermediate_reference_points, + intermediate_predicted_corners=decoder_outputs.intermediate_predicted_corners, + initial_reference_points=decoder_outputs.initial_reference_points, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + init_reference_points=init_reference_points, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + enc_outputs_class=enc_outputs_class, + enc_outputs_coord_logits=enc_outputs_coord_logits, + denoising_meta_values=denoising_meta_values, + ) + + +class RTDetrV2MLPPredictionHead(nn.Module): + """ + Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, + height and width of a bounding box w.r.t. an image. + + """ + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +@auto_docstring( + custom_intro=""" + Output type of [`RTDetrV2ForObjectDetection`]. + """ +) +@dataclass +class RTDetrV2ObjectDetectionOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): + Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a + bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized + scale-invariant IoU loss. + loss_dict (`Dict`, *optional*): + A dictionary containing the individual losses. Useful for logging. + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~RTDetrV2ImageProcessor.post_process_object_detection`] to retrieve the + unnormalized (absolute) bounding boxes. + auxiliary_outputs (`list[Dict]`, *optional*): + Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) + and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and + `pred_boxes`) for each decoder layer. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`): + Stacked intermediate hidden states (output of each layer of the decoder). + intermediate_logits (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, config.num_labels)`): + Stacked intermediate logits (logits of each layer of the decoder). + intermediate_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate reference points (reference points of each layer of the decoder). + intermediate_predicted_corners (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked intermediate predicted corners (predicted corners of each layer of the decoder). + initial_reference_points (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, 4)`): + Stacked initial reference points (initial reference points of each layer of the decoder). + init_reference_points (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Initial reference points sent through the Transformer decoder. + enc_topk_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_topk_bboxes (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the encoder. + enc_outputs_class (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.num_labels)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Predicted bounding boxes scores where the top `config.two_stage_num_proposals` scoring bounding boxes are + picked as region proposals in the first stage. Output of bounding box binary classification (i.e. + foreground and background). + enc_outputs_coord_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, 4)`, *optional*, returned when `config.with_box_refine=True` and `config.two_stage=True`): + Logits of predicted bounding boxes coordinates in the first stage. + denoising_meta_values (`dict`): + Extra dictionary for the denoising related values + """ + + loss: torch.FloatTensor | None = None + loss_dict: dict | None = None + logits: torch.FloatTensor | None = None + pred_boxes: torch.FloatTensor | None = None + auxiliary_outputs: list[dict] | None = None + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: torch.FloatTensor | None = None + intermediate_logits: torch.FloatTensor | None = None + intermediate_reference_points: torch.FloatTensor | None = None + intermediate_predicted_corners: torch.FloatTensor | None = None + initial_reference_points: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + cross_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + init_reference_points: tuple[torch.FloatTensor] | None = None + enc_topk_logits: torch.FloatTensor | None = None + enc_topk_bboxes: torch.FloatTensor | None = None + enc_outputs_class: torch.FloatTensor | None = None + enc_outputs_coord_logits: torch.FloatTensor | None = None + denoising_meta_values: dict | None = None + + +@auto_docstring( + custom_intro=""" + RT-DETR Model (consisting of a backbone and encoder-decoder) outputting bounding boxes and logits to be further + decoded into scores and classes. + """ +) +class RTDetrV2ForObjectDetection(RTDetrV2PreTrainedModel): + # When using clones, all layers > 0 will be clones, but layer 0 *is* required + # We can't initialize the model on meta device as some weights are modified during the initialization + _no_split_modules = None + _tied_weights_keys = { + r"bbox_embed.(?![0])\d+": r"bbox_embed.0", + r"class_embed.(?![0])\d+": r"^class_embed.0", + "class_embed": "model.decoder.class_embed", + "bbox_embed": "model.decoder.bbox_embed", + } + + def __init__(self, config: RTDetrV2Config): + super().__init__(config) + # RTDETR encoder-decoder model + self.model = RTDetrV2Model(config) + self.class_embed = nn.ModuleList( + [torch.nn.Linear(config.d_model, config.num_labels) for _ in range(config.decoder_layers)] + ) + self.bbox_embed = nn.ModuleList( + [ + RTDetrV2MLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + for _ in range(config.decoder_layers) + ] + ) + self.model.decoder.class_embed = self.class_embed + self.model.decoder.bbox_embed = self.bbox_embed + + # Initialize weights and apply final processing + self.post_init() + + def _set_aux_loss(self, outputs_class, outputs_coord): + return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class, outputs_coord)] + + @auto_docstring + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: torch.LongTensor | None = None, + encoder_outputs: torch.FloatTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: list[dict] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.FloatTensor] | RTDetrV2ObjectDetectionOutput: + r""" + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + labels (`list[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Examples: + + ```python + >>> from transformers import RTDetrV2ImageProcessor, RTDetrV2ForObjectDetection + >>> from PIL import Image + >>> import requests + >>> import torch + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = RTDetrV2ImageProcessor.from_pretrained("PekingU/RTDetrV2_r50vd") + >>> model = RTDetrV2ForObjectDetection.from_pretrained("PekingU/RTDetrV2_r50vd") + + >>> # prepare image for the model + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> logits = outputs.logits + >>> list(logits.shape) + [1, 300, 80] + + >>> boxes = outputs.pred_boxes + >>> list(boxes.shape) + [1, 300, 4] + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> target_sizes = torch.tensor([image.size[::-1]]) + >>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[ + ... 0 + ... ] + + >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): + ... box = [round(i, 2) for i in box.tolist()] + ... print( + ... f"Detected {model.config.id2label[label.item()]} with confidence " + ... f"{round(score.item(), 3)} at location {box}" + ... ) + Detected sofa with confidence 0.97 at location [0.14, 0.38, 640.13, 476.21] + Detected cat with confidence 0.96 at location [343.38, 24.28, 640.14, 371.5] + Detected cat with confidence 0.958 at location [13.23, 54.18, 318.98, 472.22] + Detected remote with confidence 0.951 at location [40.11, 73.44, 175.96, 118.48] + Detected remote with confidence 0.924 at location [333.73, 76.58, 369.97, 186.99] + ```""" + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + labels=labels, + **kwargs, + ) + + denoising_meta_values = outputs.denoising_meta_values if self.training else None + + outputs_class = outputs.intermediate_logits + outputs_coord = outputs.intermediate_reference_points + predicted_corners = outputs.intermediate_predicted_corners + initial_reference_points = outputs.initial_reference_points + + logits = outputs_class[:, -1] + pred_boxes = outputs_coord[:, -1] + + loss, loss_dict, auxiliary_outputs, enc_topk_logits, enc_topk_bboxes = None, None, None, None, None + if labels is not None: + enc_topk_logits = outputs.enc_topk_logits + enc_topk_bboxes = outputs.enc_topk_bboxes + loss, loss_dict, auxiliary_outputs = self.loss_function( + logits, + labels, + self.device, + pred_boxes, + self.config, + outputs_class, + outputs_coord, + enc_topk_logits=enc_topk_logits, + enc_topk_bboxes=enc_topk_bboxes, + denoising_meta_values=denoising_meta_values, + predicted_corners=predicted_corners, + initial_reference_points=initial_reference_points, + **kwargs, + ) + + return RTDetrV2ObjectDetectionOutput( + loss=loss, + loss_dict=loss_dict, + logits=logits, + pred_boxes=pred_boxes, + auxiliary_outputs=auxiliary_outputs, + last_hidden_state=outputs.last_hidden_state, + intermediate_hidden_states=outputs.intermediate_hidden_states, + intermediate_logits=outputs.intermediate_logits, + intermediate_reference_points=outputs.intermediate_reference_points, + intermediate_predicted_corners=outputs.intermediate_predicted_corners, + initial_reference_points=outputs.initial_reference_points, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + init_reference_points=outputs.init_reference_points, + enc_topk_logits=outputs.enc_topk_logits, + enc_topk_bboxes=outputs.enc_topk_bboxes, + enc_outputs_class=outputs.enc_outputs_class, + enc_outputs_coord_logits=outputs.enc_outputs_coord_logits, + denoising_meta_values=outputs.denoising_meta_values, + ) + + +__all__ = ["RTDetrV2Model", "RTDetrV2PreTrainedModel", "RTDetrV2ForObjectDetection"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/modular_rt_detr_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/modular_rt_detr_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..119b69a525eccea041a61b025f0128ab4db662c1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rt_detr_v2/modular_rt_detr_v2.py @@ -0,0 +1,465 @@ +# Copyright 2025 Baidu Inc and The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict +from torch import Tensor + +from ... import initialization as init +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging, torch_compilable_check +from ..auto import AutoConfig +from ..rt_detr.modeling_rt_detr import ( + RTDetrDecoder, + RTDetrDecoderLayer, + RTDetrForObjectDetection, + RTDetrMLPPredictionHead, + RTDetrModel, + RTDetrPreTrainedModel, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PekingU/rtdetr_r18vd") +@strict +class RTDetrV2Config(PreTrainedConfig): + r""" + initializer_bias_prior_prob (`float`, *optional*): + The prior probability used by the bias initializer to initialize biases for `enc_score_head` and `class_embed`. + If `None`, `prior_prob` computed as `prior_prob = 1 / (num_labels + 1)` while initializing model weights. + freeze_backbone_batch_norms (`bool`, *optional*, defaults to `True`): + Whether to freeze the batch normalization layers in the backbone. + encoder_in_channels (`list`, *optional*, defaults to `[512, 1024, 2048]`): + Multi level features input for encoder. + feat_strides (`list[int]`, *optional*, defaults to `[8, 16, 32]`): + Strides used in each feature map. + encode_proj_layers (`list[int]`, *optional*, defaults to `[2]`): + Indexes of the projected layers to be used in the encoder. + positional_encoding_temperature (`int`, *optional*, defaults to 10000): + The temperature parameter used to create the positional encodings. + encoder_activation_function (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + activation_function (`str`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the general layer. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + eval_size (`tuple[int, int]`, *optional*): + Height and width used to compute the effective height and width of the position embeddings after taking + into account the stride. + normalize_before (`bool`, *optional*, defaults to `False`): + Determine whether to apply layer normalization in the transformer encoder layer before self-attention and + feed-forward modules. + hidden_expansion (`float`, *optional*, defaults to 1.0): + Expansion ratio to enlarge the dimension size of RepVGGBlock and CSPRepLayer. + num_queries (`int`, *optional*, defaults to 300): + Number of object queries. + decoder_in_channels (`list`, *optional*, defaults to `[256, 256, 256]`): + Multi level features dimension for decoder + num_feature_levels (`int`, *optional*, defaults to 3): + The number of input feature levels. + decoder_n_points (`int`, *optional*, defaults to 4): + The number of sampled keys in each feature level for each attention head in the decoder. + decoder_activation_function (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the decoder. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + num_denoising (`int`, *optional*, defaults to 100): + The total number of denoising tasks or queries to be used for contrastive denoising. + label_noise_ratio (`float`, *optional*, defaults to 0.5): + The fraction of denoising labels to which random noise should be added. + box_noise_scale (`float`, *optional*, defaults to 1.0): + Scale or magnitude of noise to be added to the bounding boxes. + learn_initial_query (`bool`, *optional*, defaults to `False`): + Indicates whether the initial query embeddings for the decoder should be learned during training + anchor_image_size (`tuple[int, int]`, *optional*): + Height and width of the input image used during evaluation to generate the bounding box anchors. If None, automatic generate anchor is applied. + with_box_refine (`bool`, *optional*, defaults to `True`): + Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes + based on the predictions from the previous layer. + matcher_alpha (`float`, *optional*, defaults to 0.25): + Parameter alpha used by the Hungarian Matcher. + matcher_gamma (`float`, *optional*, defaults to 2.0): + Parameter gamma used by the Hungarian Matcher. + matcher_class_cost (`float`, *optional*, defaults to 2.0): + The relative weight of the class loss used by the Hungarian Matcher. + matcher_bbox_cost (`float`, *optional*, defaults to 5.0): + The relative weight of the bounding box loss used by the Hungarian Matcher. + matcher_giou_cost (`float`, *optional*, defaults to 2.0): + The relative weight of the giou loss of used by the Hungarian Matcher. + use_focal_loss (`bool`, *optional*, defaults to `True`): + Parameter informing if focal loss should be used. + focal_loss_alpha (`float`, *optional*, defaults to 0.75): + Parameter alpha used to compute the focal loss. + focal_loss_gamma (`float`, *optional*, defaults to 2.0): + Parameter gamma used to compute the focal loss. + weight_loss_vfl (`float`, *optional*, defaults to 1.0): + Relative weight of the varifocal loss in the object detection loss. + weight_loss_bbox (`float`, *optional*, defaults to 5.0): + Relative weight of the L1 bounding box loss in the object detection loss. + weight_loss_giou (`float`, *optional*, defaults to 2.0): + Relative weight of the generalized IoU loss in the object detection loss. + decoder_n_levels (`int`, *optional*, defaults to 3): + The number of feature levels used by the decoder. + decoder_offset_scale (`float`, *optional*, defaults to 0.5): + Scaling factor applied to the attention offsets in the decoder. + decoder_method (`str`, *optional*, defaults to `"default"`): + The method to use for the decoder: `"default"` or `"discrete"`. + + Examples: + + ```python + >>> from transformers import RTDetrV2Config, RTDetrV2Model + + >>> # Initializing a RT-DETR configuration + >>> configuration = RTDetrV2Config() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = RTDetrV2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "rt_detr_v2" + sub_configs = {"backbone_config": AutoConfig} + layer_types = ["basic", "bottleneck"] + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + initializer_range: float = 0.01 + initializer_bias_prior_prob: float | None = None + layer_norm_eps: float = 1e-5 + batch_norm_eps: float = 1e-5 + backbone_config: dict | PreTrainedConfig | None = None + freeze_backbone_batch_norms: bool = True + encoder_hidden_dim: int = 256 + encoder_in_channels: list[int] | tuple[int, ...] = (512, 1024, 2048) + feat_strides: list[int] | tuple[int, ...] = (8, 16, 32) + encoder_layers: int = 1 + encoder_ffn_dim: int = 1024 + encoder_attention_heads: int = 8 + dropout: float | int = 0.0 + activation_dropout: float | int = 0.0 + encode_proj_layers: list[int] | tuple[int, ...] = (2,) + positional_encoding_temperature: int = 10000 + encoder_activation_function: str = "gelu" + activation_function: str = "silu" + eval_size: int | None = None + normalize_before: bool = False + hidden_expansion: float = 1.0 + d_model: int = 256 + num_queries: int = 300 + decoder_in_channels: list[int] | tuple[int, ...] = (256, 256, 256) + decoder_ffn_dim: int = 1024 + num_feature_levels: int = 3 + decoder_n_points: int = 4 + decoder_layers: int = 6 + decoder_attention_heads: int = 8 + decoder_activation_function: str = "relu" + attention_dropout: float | int = 0.0 + num_denoising: int = 100 + label_noise_ratio: float = 0.5 + box_noise_scale: float = 1.0 + learn_initial_query: bool = False + anchor_image_size: int | list[int] | None = None + with_box_refine: bool = True + is_encoder_decoder: bool = True + matcher_alpha: float = 0.25 + matcher_gamma: float = 2.0 + matcher_class_cost: float = 2.0 + matcher_bbox_cost: float = 5.0 + matcher_giou_cost: float = 2.0 + use_focal_loss: bool = True + auxiliary_loss: bool = True + focal_loss_alpha: float = 0.75 + focal_loss_gamma: float = 2.0 + weight_loss_vfl: float = 1.0 + weight_loss_bbox: float = 5.0 + weight_loss_giou: float = 2.0 + eos_coefficient: float = 1e-4 + decoder_n_levels: int = 3 + decoder_offset_scale: float = 0.5 + decoder_method: str = "default" + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="rt_detr_resnet", + default_config_kwargs={"out_indices": [2, 3, 4]}, + **kwargs, + ) + super().__post_init__(**kwargs) + + +def multi_scale_deformable_attention_v2( + value: Tensor, + value_spatial_shapes: Tensor, + sampling_locations: Tensor, + attention_weights: Tensor, + num_points_list: list[int], + method="default", +) -> Tensor: + batch_size, _, num_heads, hidden_dim = value.shape + _, num_queries, num_heads, num_levels, num_points = sampling_locations.shape + value_list = ( + value.permute(0, 2, 3, 1) + .flatten(0, 1) + .split([height * width for height, width in value_spatial_shapes], dim=-1) + ) + # sampling_offsets [8, 480, 8, 12, 2] + if method == "default": + sampling_grids = 2 * sampling_locations - 1 + elif method == "discrete": + sampling_grids = sampling_locations + sampling_grids = sampling_grids.permute(0, 2, 1, 3, 4).flatten(0, 1) + sampling_grids = sampling_grids.split(num_points_list, dim=-2) + sampling_value_list = [] + for level_id, (height, width) in enumerate(value_spatial_shapes): + # batch_size, height*width, num_heads, hidden_dim + # -> batch_size, height*width, num_heads*hidden_dim + # -> batch_size, num_heads*hidden_dim, height*width + # -> batch_size*num_heads, hidden_dim, height, width + value_l_ = value_list[level_id].reshape(batch_size * num_heads, hidden_dim, height, width) + # batch_size, num_queries, num_heads, num_points, 2 + # -> batch_size, num_heads, num_queries, num_points, 2 + # -> batch_size*num_heads, num_queries, num_points, 2 + sampling_grid_l_ = sampling_grids[level_id] + # batch_size*num_heads, hidden_dim, num_queries, num_points + if method == "default": + sampling_value_l_ = nn.functional.grid_sample( + value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False + ) + elif method == "discrete": + sampling_coord = (sampling_grid_l_ * torch.tensor([[width, height]], device=value.device) + 0.5).to( + torch.int64 + ) + + # Separate clamping for x and y coordinates + sampling_coord_x = sampling_coord[..., 0].clamp(0, width - 1) + sampling_coord_y = sampling_coord[..., 1].clamp(0, height - 1) + + # Combine the clamped coordinates + sampling_coord = torch.stack([sampling_coord_x, sampling_coord_y], dim=-1) + sampling_coord = sampling_coord.reshape(batch_size * num_heads, num_queries * num_points_list[level_id], 2) + sampling_idx = ( + torch.arange(sampling_coord.shape[0], device=value.device) + .unsqueeze(-1) + .repeat(1, sampling_coord.shape[1]) + ) + sampling_value_l_ = value_l_[sampling_idx, :, sampling_coord[..., 1], sampling_coord[..., 0]] + sampling_value_l_ = sampling_value_l_.permute(0, 2, 1).reshape( + batch_size * num_heads, hidden_dim, num_queries, num_points_list[level_id] + ) + sampling_value_list.append(sampling_value_l_) + # (batch_size, num_queries, num_heads, num_levels, num_points) + # -> (batch_size, num_heads, num_queries, num_levels, num_points) + # -> (batch_size, num_heads, 1, num_queries, num_levels*num_points) + attention_weights = attention_weights.permute(0, 2, 1, 3).reshape( + batch_size * num_heads, 1, num_queries, sum(num_points_list) + ) + output = ( + (torch.concat(sampling_value_list, dim=-1) * attention_weights) + .sum(-1) + .view(batch_size, num_heads * hidden_dim, num_queries) + ) + return output.transpose(1, 2).contiguous() + + +# the main change +class RTDetrV2MultiscaleDeformableAttention(nn.Module): + """ + RTDetrV2 version of multiscale deformable attention, extending the base implementation + with improved offset handling and initialization. + """ + + def __init__(self, config: RTDetrV2Config): + super().__init__() + num_heads = config.decoder_attention_heads + n_points = config.decoder_n_points + + if config.d_model % num_heads != 0: + raise ValueError( + f"embed_dim (d_model) must be divisible by num_heads, but got {config.d_model} and {num_heads}" + ) + dim_per_head = config.d_model // num_heads + # check if dim_per_head is power of 2 + if not ((dim_per_head & (dim_per_head - 1) == 0) and dim_per_head != 0): + warnings.warn( + "You'd better set embed_dim (d_model) in RTDetrV2MultiscaleDeformableAttention to make the" + " dimension of each attention head a power of 2 which is more efficient in the authors' CUDA" + " implementation." + ) + + self.im2col_step = 64 + + self.d_model = config.d_model + + # V2-specific attributes + self.n_levels = config.decoder_n_levels + self.n_heads = num_heads + self.n_points = n_points + + self.sampling_offsets = nn.Linear(config.d_model, num_heads * self.n_levels * n_points * 2) + self.attention_weights = nn.Linear(config.d_model, num_heads * self.n_levels * n_points) + self.value_proj = nn.Linear(config.d_model, config.d_model) + self.output_proj = nn.Linear(config.d_model, config.d_model) + + self.offset_scale = config.decoder_offset_scale + self.method = config.decoder_method + + # Initialize n_points list and scale + n_points_list = [self.n_points for _ in range(self.n_levels)] + self.n_points_list = n_points_list + n_points_scale = [1 / n for n in n_points_list for _ in range(n)] + self.register_buffer("n_points_scale", torch.tensor(n_points_scale, dtype=torch.float32)) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states=None, + encoder_attention_mask=None, + position_embeddings: torch.Tensor | None = None, + reference_points=None, + spatial_shapes=None, + spatial_shapes_list=None, + level_start_index=None, + **kwargs: Unpack[TransformersKwargs], + ): + # Process inputs up to sampling locations calculation using parent class logic + if position_embeddings is not None: + hidden_states = hidden_states + position_embeddings + + batch_size, num_queries, _ = hidden_states.shape + batch_size, sequence_length, _ = encoder_hidden_states.shape + torch_compilable_check( + (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == sequence_length, + "Make sure to align the spatial shapes with the sequence length of the encoder hidden states", + ) + + value = self.value_proj(encoder_hidden_states) + if attention_mask is not None: + value = value.masked_fill(~attention_mask[..., None], float(0)) + value = value.view(batch_size, sequence_length, self.n_heads, self.d_model // self.n_heads) + + # V2-specific sampling offsets shape + sampling_offsets = self.sampling_offsets(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points, 2 + ) + + attention_weights = self.attention_weights(hidden_states).view( + batch_size, num_queries, self.n_heads, self.n_levels * self.n_points + ) + attention_weights = F.softmax(attention_weights, -1) + + # V2-specific sampling locations calculation + if reference_points.shape[-1] == 2: + offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) + sampling_locations = ( + reference_points[:, :, None, :, None, :] + + sampling_offsets / offset_normalizer[None, None, None, :, None, :] + ) + elif reference_points.shape[-1] == 4: + n_points_scale = self.n_points_scale.to(dtype=hidden_states.dtype).unsqueeze(-1) + offset = sampling_offsets * n_points_scale * reference_points[:, :, None, :, 2:] * self.offset_scale + sampling_locations = reference_points[:, :, None, :, :2] + offset + else: + raise ValueError(f"Last dim of reference_points must be 2 or 4, but got {reference_points.shape[-1]}") + + # V2-specific attention implementation choice + output = multi_scale_deformable_attention_v2( + value, spatial_shapes_list, sampling_locations, attention_weights, self.n_points_list, self.method + ) + + output = self.output_proj(output) + return output, attention_weights + + +class RTDetrV2DecoderLayer(RTDetrDecoderLayer): + def __init__(self, config: RTDetrV2Config): + # initialize parent class + super().__init__(config) + # override only the encoder attention module with v2 version + self.encoder_attn = RTDetrV2MultiscaleDeformableAttention(config) + + +class RTDetrV2PreTrainedModel(RTDetrPreTrainedModel): + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, RTDetrV2MultiscaleDeformableAttention): + n_points_scale = [1 / n for n in module.n_points_list for _ in range(n)] + init.copy_(module.n_points_scale, torch.tensor(n_points_scale, dtype=torch.float32)) + + +class RTDetrV2Decoder(RTDetrDecoder): + def __init__(self, config: RTDetrV2Config): + super().__init__(config) + self.layers = nn.ModuleList([RTDetrV2DecoderLayer(config) for _ in range(config.decoder_layers)]) + + +class RTDetrV2Model(RTDetrModel): + def __init__(self, config: RTDetrV2Config): + super().__init__(config) + # decoder + self.decoder = RTDetrV2Decoder(config) + + +class RTDetrV2MLPPredictionHead(RTDetrMLPPredictionHead): + pass + + +class RTDetrV2ForObjectDetection(RTDetrForObjectDetection, RTDetrV2PreTrainedModel): + _tied_weights_keys = { + r"bbox_embed.(?![0])\d+": r"bbox_embed.0", + r"class_embed.(?![0])\d+": r"^class_embed.0", + "class_embed": "model.decoder.class_embed", + "bbox_embed": "model.decoder.bbox_embed", + } + + def __init__(self, config: RTDetrV2Config): + RTDetrV2PreTrainedModel.__init__(self, config) + # RTDETR encoder-decoder model + self.model = RTDetrV2Model(config) + self.class_embed = nn.ModuleList( + [torch.nn.Linear(config.d_model, config.num_labels) for _ in range(config.decoder_layers)] + ) + self.bbox_embed = nn.ModuleList( + [ + RTDetrV2MLPPredictionHead(config.d_model, config.d_model, 4, num_layers=3) + for _ in range(config.decoder_layers) + ] + ) + self.model.decoder.class_embed = self.class_embed + self.model.decoder.bbox_embed = self.bbox_embed + + # Initialize weights and apply final processing + self.post_init() + + +__all__ = [ + "RTDetrV2Config", + "RTDetrV2Model", + "RTDetrV2PreTrainedModel", + "RTDetrV2ForObjectDetection", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..cbfdb86827a0f5551fc99ad84edf2c2684444e96 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_rwkv import * + from .modeling_rwkv import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/configuration_rwkv.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/configuration_rwkv.py new file mode 100644 index 0000000000000000000000000000000000000000..09f1d05a3a083a792758761c3c8b3763b1f7a597 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/configuration_rwkv.py @@ -0,0 +1,76 @@ +# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""RWKV configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="RWKV/rwkv-4-169m-pile") +@strict +class RwkvConfig(PreTrainedConfig): + r""" + context_length (`int`, *optional*, defaults to 1024): + The maximum sequence length that this model can be used with in a single forward (using it in RNN mode + lets use any sequence length). + attention_hidden_size (`int`, *optional*): + Dimensionality of the attention hidden states. Will default to `hidden_size` if unset. + rescale_every (`int`, *optional*, defaults to 6): + At inference, the hidden states (and weights of the corresponding output layers) are divided by 2 every + `rescale_every` layer. If set to 0 or a negative number, no rescale is done. + + Example: + + ```python + >>> from transformers import RwkvConfig, RwkvModel + + >>> # Initializing a Rwkv configuration + >>> configuration = RwkvConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = RwkvModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "rwkv" + attribute_map = {"max_position_embeddings": "context_length"} + + vocab_size: int = 50277 + context_length: int = 1024 + hidden_size: int = 4096 + num_hidden_layers: int = 32 + attention_hidden_size: int | None = None + intermediate_size: int | None = None + layer_norm_epsilon: float = 1e-5 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 0 + rescale_every: int = 6 + tie_word_embeddings: bool = False + use_cache: bool = True + + def __post_init__(self, **kwargs): + self.attention_hidden_size = ( + self.attention_hidden_size if self.attention_hidden_size is not None else self.hidden_size + ) + self.intermediate_size = self.intermediate_size if self.intermediate_size is not None else 4 * self.hidden_size + + super().__post_init__(**kwargs) + + +__all__ = ["RwkvConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/modeling_rwkv.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/modeling_rwkv.py new file mode 100644 index 0000000000000000000000000000000000000000..9e30001251465e92dbf2734c252563fa36b89f0a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/rwkv/modeling_rwkv.py @@ -0,0 +1,758 @@ +# Copyright 2023 Bo Peng and HuggingFace Inc. team. +# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch RWKV model.""" + +import math +from dataclasses import dataclass + +import torch +from torch import nn + +from ... import initialization as init +from ...generation import GenerationMixin +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_utils import PreTrainedModel +from ...utils import ( + ModelOutput, + auto_docstring, + is_bitsandbytes_available, + is_kernels_available, + is_ninja_available, + is_torch_cuda_available, + logging, +) +from .configuration_rwkv import RwkvConfig + + +logger = logging.get_logger(__name__) + + +rwkv_cuda_kernel = None + + +def load_wkv_cuda_kernel(context_length): + global rwkv_cuda_kernel + if not is_kernels_available(): + raise ImportError("kernels is not installed, please install it with `pip install kernels`") + + from ...integrations.hub_kernels import get_kernel + + rwkv_cuda_kernel = get_kernel("kernels-community/rwkv") + rwkv_cuda_kernel.max_seq_length = context_length + + +class RwkvLinearAttention(torch.autograd.Function): + @staticmethod + def forward(ctx, time_decay, time_first, key, value, state=None, return_state=False): + batch_size, seq_len, hidden_size = key.size() + if seq_len > rwkv_cuda_kernel.max_seq_length: + raise ValueError( + f"Cannot process a batch with {seq_len} tokens at the same time, use a maximum of " + f"{rwkv_cuda_kernel.max_seq_length} with this model." + ) + if batch_size * hidden_size % min(hidden_size, 32) != 0: + raise ValueError( + f"The product of batch size ({batch_size}) and hidden size ({hidden_size}) needs to be a round " + f"multiple of {min(hidden_size, 32)}." + ) + + ctx.input_dtype = key.dtype + + if ( + time_decay.device.type != "cuda" + or time_first.device.type != "cuda" + or key.device.type != "cuda" + or value.device.type != "cuda" + ): + raise ValueError("Calling the CUDA kernel for wkv attention requires all tensors to be on CUDA devices.") + + time_decay = -torch.exp(time_decay.float().contiguous()) + if key.dtype == torch.float16: + time_first = time_first.float() + key = key.float() + value = value.float() + time_first = time_first.contiguous() + key = key.contiguous() + value = value.contiguous() + # The CUDA kernel will fill this tensor. + output = torch.empty_like(key, memory_format=torch.contiguous_format) + if return_state or state is not None: + if state is None: + state = torch.zeros( + batch_size, + hidden_size, + 3, + dtype=torch.float32, + device=key.device, + memory_format=torch.contiguous_format, + ) + state[:, :, 2] -= 1e38 + else: + state = torch.cat([s.unsqueeze(2) for s in state], dim=2).contiguous() + if key.dtype == torch.bfloat16: + forward_func = rwkv_cuda_kernel.forward_with_state_bf16 + else: + forward_func = rwkv_cuda_kernel.forward_with_state + forward_func(time_decay, time_first, key, value, output, state) + else: + forward_func = rwkv_cuda_kernel.forward_bf16 if key.dtype == torch.bfloat16 else rwkv_cuda_kernel.forward + forward_func(time_decay, time_first, key, value, output) + + ctx.save_for_backward(time_decay, time_first, key, value, output) + + if state is not None: + state = [s.squeeze(2) for s in torch.chunk(state, 3, dim=2)] + + return output.to(ctx.input_dtype), state + + @staticmethod + # g stands for grad + def backward(ctx, g_output, g_state=None): + input_dtype = ctx.input_dtype + + time_decay, time_first, key, value, output = ctx.saved_tensors + # The CUDA kernel will fill those tensors. + g_time_decay = torch.empty_like( + time_decay, + memory_format=torch.contiguous_format, + dtype=torch.bfloat16 if input_dtype == torch.bfloat16 else torch.float32, + ) + g_time_first = torch.empty_like(time_first, memory_format=torch.contiguous_format) + g_key = torch.empty_like(key, memory_format=torch.contiguous_format) + g_value = torch.empty_like(value, memory_format=torch.contiguous_format) + + if input_dtype == torch.float16: + g_output = g_output.float() + backward_func = rwkv_cuda_kernel.backward_bf16 if input_dtype == torch.bfloat16 else rwkv_cuda_kernel.backward + backward_func( + time_decay, + time_first, + key, + value, + output, + g_output.contiguous(), + g_time_decay, + g_time_first, + g_key, + g_value, + ) + + return ( + g_time_decay.to(input_dtype), + g_time_first.to(input_dtype), + g_key.to(input_dtype), + g_value.to(input_dtype), + None, + None, + ) + + +def rwkv_linear_attention_cpu(time_decay, time_first, key, value, state=None, return_state=False): + # For CPU fallback. Will be slower and probably take more memory than the custom CUDA kernel if not executed + # within a torch.no_grad. + _, seq_length, _ = key.size() + output = torch.zeros_like(key) + + if state is None: + num_state = torch.zeros_like(key[:, 0], dtype=torch.float32) + den_state = torch.zeros_like(key[:, 0], dtype=torch.float32) + max_state = torch.zeros_like(key[:, 0], dtype=torch.float32) - 1e38 + else: + num_state, den_state, max_state = state + # For numerical stability + # real_numerator_state = num_state * torch.exp(max_state) + # real_denominator_state = den_state * torch.exp(max_state) + + time_decay = -torch.exp(time_decay) + + for current_index in range(seq_length): + current_key = key[:, current_index].float() + current_value = value[:, current_index] + + # wkv computation at time t + max_for_output = torch.maximum(max_state, current_key + time_first) + e1 = torch.exp(max_state - max_for_output) + e2 = torch.exp(current_key + time_first - max_for_output) + numerator = e1 * num_state + e2 * current_value + denominator = e1 * den_state + e2 + output[:, current_index] = (numerator / denominator).to(output.dtype) + + # Update state for next iteration + max_for_state = torch.maximum(max_state + time_decay, current_key) + e1 = torch.exp(max_state + time_decay - max_for_state) + e2 = torch.exp(current_key - max_for_state) + num_state = e1 * num_state + e2 * current_value + den_state = e1 * den_state + e2 + max_state = max_for_state + + if return_state or state is not None: + state = [num_state, den_state, max_state] + + return output, state + + +def rwkv_linear_attention(time_decay, time_first, key, value, state=None, return_state=False): + no_cuda = any(t.device.type != "cuda" for t in [time_decay, time_first, key, value]) + # Launching the CUDA kernel for just one token will actually be slower (there is no for loop in the CPU version + # in this case). + one_token = key.size(1) == 1 + if rwkv_cuda_kernel is None or no_cuda or one_token: + return rwkv_linear_attention_cpu(time_decay, time_first, key, value, state=state, return_state=return_state) + else: + return RwkvLinearAttention.apply(time_decay, time_first, key, value, state, return_state) + + +class RwkvSelfAttention(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.config = config + kernel_loaded = rwkv_cuda_kernel is not None and rwkv_cuda_kernel.max_seq_length == config.context_length + if is_ninja_available() and is_torch_cuda_available() and not kernel_loaded: + try: + load_wkv_cuda_kernel(config.context_length) + except Exception: + logger.info("Could not load the custom CUDA kernel for RWKV attention.") + self.layer_id = layer_id + hidden_size = config.hidden_size + attention_hidden_size = ( + config.attention_hidden_size if config.attention_hidden_size is not None else hidden_size + ) + self.attention_hidden_size = attention_hidden_size + + self.time_decay = nn.Parameter(torch.empty(attention_hidden_size)) + self.time_first = nn.Parameter(torch.empty(attention_hidden_size)) + + self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size)) + self.time_mix_value = nn.Parameter(torch.empty(1, 1, hidden_size)) + self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size)) + + self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) + self.key = nn.Linear(hidden_size, attention_hidden_size, bias=False) + self.value = nn.Linear(hidden_size, attention_hidden_size, bias=False) + self.receptance = nn.Linear(hidden_size, attention_hidden_size, bias=False) + self.output = nn.Linear(attention_hidden_size, hidden_size, bias=False) + + # TODO: maybe jit, otherwise move inside forward + def extract_key_value(self, hidden, state=None): + # Mix hidden with the previous timestep to produce key, value, receptance + if hidden.size(1) == 1 and state is not None: + shifted = state[1][:, :, self.layer_id] + else: + shifted = self.time_shift(hidden) + if state is not None: + shifted[:, 0] = state[1][:, :, self.layer_id] + key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key) + value = hidden * self.time_mix_value + shifted * (1 - self.time_mix_value) + receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance) + + key = self.key(key) + value = self.value(value) + receptance = torch.sigmoid(self.receptance(receptance)) + if state is not None: + state[1][:, :, self.layer_id] = hidden[:, -1] + return receptance, key, value, state + + def forward(self, hidden, state=None, use_cache=False): + receptance, key, value, state = self.extract_key_value(hidden, state=state) + layer_state = tuple(s[:, :, self.layer_id] for s in state[2:]) if state is not None else None + rwkv, layer_state = rwkv_linear_attention( + self.time_decay, + self.time_first, + key, + value, + state=layer_state, + return_state=use_cache, + ) + + if layer_state is not None: + state[2][:, :, self.layer_id] = layer_state[0] + state[3][:, :, self.layer_id] = layer_state[1] + state[4][:, :, self.layer_id] = layer_state[2] + + return self.output(receptance * rwkv), state + + +class RwkvFeedForward(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.config = config + self.layer_id = layer_id + hidden_size = config.hidden_size + intermediate_size = ( + config.intermediate_size if config.intermediate_size is not None else 4 * config.hidden_size + ) + + self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) + self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size)) + self.time_mix_receptance = nn.Parameter(torch.empty(1, 1, hidden_size)) + + self.key = nn.Linear(hidden_size, intermediate_size, bias=False) + self.receptance = nn.Linear(hidden_size, hidden_size, bias=False) + self.value = nn.Linear(intermediate_size, hidden_size, bias=False) + + def forward(self, hidden, state=None): + if hidden.size(1) == 1 and state is not None: + shifted = state[0][:, :, self.layer_id] + else: + shifted = self.time_shift(hidden) + if state is not None: + shifted[:, 0] = state[0][:, :, self.layer_id] + key = hidden * self.time_mix_key + shifted * (1 - self.time_mix_key) + receptance = hidden * self.time_mix_receptance + shifted * (1 - self.time_mix_receptance) + + key = torch.square(torch.relu(self.key(key))) + value = self.value(key) + receptance = torch.sigmoid(self.receptance(receptance)) + + if state is not None: + state[0][:, :, self.layer_id] = hidden[:, -1] + + return receptance * value, state + + +class RwkvBlock(GradientCheckpointingLayer): + def __init__(self, config, layer_id): + super().__init__() + self.config = config + self.layer_id = layer_id + + if layer_id == 0: + self.pre_ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon) + + self.ln1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon) + self.ln2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon) + + self.attention = RwkvSelfAttention(config, layer_id) + self.feed_forward = RwkvFeedForward(config, layer_id) + + def forward(self, hidden, state=None, use_cache=False, output_attentions=False): + if self.layer_id == 0: + hidden = self.pre_ln(hidden) + + attention, state = self.attention(self.ln1(hidden), state=state, use_cache=use_cache) + hidden = hidden + attention + + feed_forward, state = self.feed_forward(self.ln2(hidden), state=state) + hidden = hidden + feed_forward + + outputs = (hidden, state) + if output_attentions: + outputs += (attention,) + else: + outputs += (None,) + + return outputs + + +@auto_docstring +class RwkvPreTrainedModel(PreTrainedModel): + config: RwkvConfig + base_model_prefix = "rwkv" + _no_split_modules = ["RwkvBlock"] + _keep_in_fp32_modules = ["time_decay", "time_first"] + supports_gradient_checkpointing = True + _is_stateful = True + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + """Initialize the weights.""" + if isinstance(module, RwkvSelfAttention): + layer_id = module.layer_id + num_hidden_layers = module.config.num_hidden_layers + hidden_size = module.config.hidden_size + attention_hidden_size = module.attention_hidden_size + + ratio_0_to_1 = layer_id / (num_hidden_layers - 1) # 0 to 1 + ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0 + + time_weight = torch.tensor( + [i / hidden_size for i in range(hidden_size)], + dtype=module.time_mix_key.dtype, + device=module.time_mix_key.device, + ) + time_weight = time_weight[None, None, :] + + decay_speed = [ + -5 + 8 * (h / (attention_hidden_size - 1)) ** (0.7 + 1.3 * ratio_0_to_1) + for h in range(attention_hidden_size) + ] + decay_speed = torch.tensor(decay_speed, dtype=module.time_decay.dtype, device=module.time_decay.device) + zigzag = ( + torch.tensor( + [(i + 1) % 3 - 1 for i in range(attention_hidden_size)], + dtype=module.time_first.dtype, + device=module.time_first.device, + ) + * 0.5 + ) + + init.copy_(module.time_decay, decay_speed) + init.copy_(module.time_first, torch.ones_like(module.time_first * math.log(0.3) + zigzag)) + + init.copy_(module.time_mix_key, torch.pow(time_weight, ratio_1_to_almost0)) + init.copy_(module.time_mix_value, torch.pow(time_weight, ratio_1_to_almost0) + 0.3 * ratio_0_to_1) + init.copy_(module.time_mix_receptance, torch.pow(time_weight, 0.5 * ratio_1_to_almost0)) + elif isinstance(module, RwkvFeedForward): + layer_id = module.layer_id + num_hidden_layers = module.config.num_hidden_layers + hidden_size = module.config.hidden_size + + ratio_1_to_almost0 = 1.0 - (layer_id / num_hidden_layers) # 1 to ~0 + + time_weight = torch.tensor( + [i / hidden_size for i in range(hidden_size)], + dtype=module.time_mix_key.dtype, + device=module.time_mix_key.device, + ) + time_weight = time_weight[None, None, :] + + init.copy_(module.time_mix_key, torch.pow(time_weight, ratio_1_to_almost0)) + init.copy_(module.time_mix_receptance, torch.pow(time_weight, ratio_1_to_almost0)) + elif isinstance(module, nn.Linear): + shape = module.weight.shape + gain = 1.0 + scale = 1.0 # extra scale for gain + if module.bias is not None: + init.zeros_(module.bias) + if shape[0] > shape[1]: + gain = math.sqrt(shape[0] / shape[1]) + if shape[0] == self.config.vocab_size and shape[1] == self.config.hidden_size: # final projection? + scale = 0.5 + + gain *= scale + init.orthogonal_(module.weight, gain=gain) + elif isinstance(module, nn.Embedding): + shape = module.weight.shape + gain = 1e-4 * math.sqrt(max(shape[0], shape[1])) + init.orthogonal_(module.weight, gain=gain) + elif isinstance(module, nn.LayerNorm): + init.ones_(module.weight) + init.zeros_(module.bias) + + +@auto_docstring( + custom_intro=""" + Class for the RWKV model outputs. + """ +) +@dataclass +class RwkvOutput(ModelOutput): + r""" + state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`): + The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to + avoid providing the old `input_ids`. + """ + + last_hidden_state: torch.FloatTensor | None = None + state: list[torch.FloatTensor] | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for causal language model (or autoregressive) outputs. + """ +) +@dataclass +class RwkvCausalLMOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`): + The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to + avoid providing the old `input_ids`. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + state: list[torch.FloatTensor] | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@auto_docstring +class RwkvModel(RwkvPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size) + self.blocks = nn.ModuleList([RwkvBlock(config, layer_id=idx) for idx in range(config.num_hidden_layers)]) + self.ln_out = nn.LayerNorm(config.hidden_size) + + self.layers_are_rescaled = False + + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, new_embeddings): + self.embeddings = new_embeddings + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + state: list[torch.FloatTensor] | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | RwkvOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): + `input_ids_length` = `sequence_length` if `past_key_values` is `None` else + `past_key_values.get_seq_length()` (`sequence_length` of input past key value states). Indices of input + sequence tokens in the vocabulary. + + If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as + `input_ids`. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + state (tuple of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`, *optional*): + If passed along, the model uses the previous state in all the blocks (which will give the output for the + `input_ids` provided as if the model add `state_input_ids + input_ids` as context). + use_cache (`bool`, *optional*): + If set to `True`, the last state is returned and can be used to quickly generate the next logits. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if attention_mask is not None: + logger.warning_once("`attention_mask` was passed, but it is unused in this model.") + + if self.training == self.layers_are_rescaled: + self._rescale_layers() + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is None and inputs_embeds is None: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embeddings(input_ids) + + if use_cache and state is None: + shape = (inputs_embeds.size(0), self.config.hidden_size, self.config.num_hidden_layers) + state = [ + torch.zeros( + *shape, dtype=inputs_embeds.dtype if i <= 1 else torch.float32, device=inputs_embeds.device + ) + for i in range(5) + ] + state[4] -= 1e30 + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + hidden_states = inputs_embeds + + all_self_attentions = () if output_attentions else None + all_hidden_states = () if output_hidden_states else None + for idx, block in enumerate(self.blocks): + hidden_states, state, attentions = block( + hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions + ) + + if ( + self.layers_are_rescaled + and self.config.rescale_every > 0 + and (idx + 1) % self.config.rescale_every == 0 + ): + hidden_states = hidden_states / 2 + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if output_attentions: + all_self_attentions = all_self_attentions + (attentions,) + + hidden_states = self.ln_out(hidden_states) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(x for x in [hidden_states, state, all_hidden_states, all_self_attentions] if x is not None) + + return RwkvOutput( + last_hidden_state=hidden_states, + state=state, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + def _rescale_layers(self): + # Layers should be rescaled for inference only. + if self.layers_are_rescaled == (not self.training): + return + if self.config.rescale_every > 0: + with torch.no_grad(): + for block_id, block in enumerate(self.blocks): + if self.training: + block.attention.output.weight.mul_(2 ** int(block_id // self.config.rescale_every)) + block.feed_forward.value.weight.mul_(2 ** int(block_id // self.config.rescale_every)) + else: + # Deal with quantization statistics + if hasattr(block.attention.output.weight, "SCB"): + block.attention.output.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every)) + block.feed_forward.value.weight.SCB.div_(2 ** int(block_id // self.config.rescale_every)) + elif hasattr(block.attention.output.weight, "quant_state"): + self._bnb_4bit_dequantize_and_rescale(block.attention.output, block_id) + self._bnb_4bit_dequantize_and_rescale(block.feed_forward.value, block_id) + else: + block.attention.output.weight.div_(2 ** int(block_id // self.config.rescale_every)) + block.feed_forward.value.weight.div_(2 ** int(block_id // self.config.rescale_every)) + + self.layers_are_rescaled = not self.training + + def _bnb_4bit_dequantize_and_rescale(self, target_layer, block_id): + r""" + Perform the dequantization and rescaling of the weights of a given layer. After that operation the layer will + be quantized again. + """ + if not is_bitsandbytes_available(): + raise ImportError("Please install bitsandbytes to use this method.") + import bitsandbytes as bnb + + dequant_weights = bnb.functional.dequantize_4bit(target_layer.weight.data, target_layer.weight.quant_state) + + dequant_weights.div_(2 ** int(block_id // self.config.rescale_every)) + + # re-quantize the model: + # we need to put it first on CPU then back to the device + # this will create an overhead :/ + # We set requires_grad=False as we cannot compute gradients on top of 4bit parameters anyway and to avoid + # bugs with bnb + quant_weight = bnb.nn.Params4bit(dequant_weights.to("cpu"), requires_grad=False).to(dequant_weights.device) + setattr(target_layer, "weight", quant_weight) + + +@auto_docstring( + custom_intro=""" + The RWKV Model transformer with a language modeling head on top (linear layer with weights tied to the input + embeddings). + """ +) +class RwkvForCausalLM(RwkvPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"head.weight": "rwkv.embeddings.weight"} + + def __init__(self, config): + super().__init__(config) + self.rwkv = RwkvModel(config) + self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.head + + def set_output_embeddings(self, new_embeddings): + self.head = new_embeddings + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + state: list[torch.FloatTensor] | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs, + ) -> tuple | RwkvCausalLMOutput: + r""" + input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`): + `input_ids_length` = `sequence_length` if `past_key_values` is `None` else + `past_key_values.get_seq_length()` (`sequence_length` of input past key value states). Indices of input + sequence tokens in the vocabulary. + + If `past_key_values` is used, only `input_ids` that do not have their past calculated should be passed as + `input_ids`. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + state (tuple of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`, *optional*): + If passed along, the model uses the previous state in all the blocks (which will give the output for the + `input_ids` provided as if the model add `state_input_ids + input_ids` as context). + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + use_cache (`bool`, *optional*): + If set to `True`, the last state is returned and can be used to quickly generate the next logits. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + rwkv_outputs = self.rwkv( + input_ids, + inputs_embeds=inputs_embeds, + state=state, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = rwkv_outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + if not return_dict: + output = (logits,) + rwkv_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return RwkvCausalLMOutput( + loss=loss, + logits=logits, + state=rwkv_outputs.state, + hidden_states=rwkv_outputs.hidden_states, + attentions=rwkv_outputs.attentions, + ) + + +__all__ = ["RwkvForCausalLM", "RwkvModel", "RwkvPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..219f7367a41cb8496aece3bbd08e01ec09a535dd --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam import * + from .image_processing_pil_sam import * + from .image_processing_sam import * + from .modeling_sam import * + from .processing_sam import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/configuration_sam.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/configuration_sam.py new file mode 100644 index 0000000000000000000000000000000000000000..0e337992f6532cf50eb9fda5f4cf76be94e0ae2f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/configuration_sam.py @@ -0,0 +1,212 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAM model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="facebook/sam-vit-huge") +@strict +class SamPromptEncoderConfig(PreTrainedConfig): + r""" + mask_input_channels (`int`, *optional*, defaults to 16): + The number of channels to be fed to the `MaskDecoder` module. + num_point_embeddings (`int`, *optional*, defaults to 4): + The number of point embeddings to be used. + """ + + base_config_key = "prompt_encoder_config" + + hidden_size: int = 256 + image_size: int | list[int] | tuple[int, int] = 1024 + patch_size: int | list[int] | tuple[int, int] = 16 + mask_input_channels: int = 16 + num_point_embeddings: int = 4 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + + def __post_init__(self, **kwargs): + self.image_embedding_size = self.image_size // self.patch_size + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="facebook/sam-vit-huge") +@strict +class SamMaskDecoderConfig(PreTrainedConfig): + r""" + mlp_dim (`int`, *optional*, defaults to 2048): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + attention_downsample_rate (`int`, *optional*, defaults to 2): + The downsampling rate of the attention layer. + num_multimask_outputs (`int`, *optional*, defaults to 3): + The number of outputs from the `SamMaskDecoder` module. In the Segment Anything paper, this is set to 3. + iou_head_depth (`int`, *optional*, defaults to 3): + The number of layers in the IoU head module. + iou_head_hidden_dim (`int`, *optional*, defaults to 256): + The dimensionality of the hidden states in the IoU head module. + """ + + base_config_key = "mask_decoder_config" + + hidden_size: int = 256 + hidden_act: str = "relu" + mlp_dim: int = 2048 + num_hidden_layers: int = 2 + num_attention_heads: int = 8 + attention_downsample_rate: int = 2 + num_multimask_outputs: int = 3 + iou_head_depth: int = 3 + iou_head_hidden_dim: int = 256 + layer_norm_eps: float = 1e-6 + + +@auto_docstring(checkpoint="facebook/sam-vit-huge") +@strict +class SamVisionConfig(PreTrainedConfig): + r""" + output_channels (`int`, *optional*, defaults to 256): + Dimensionality of the output channels in the Patch Encoder. + use_rel_pos (`bool`, *optional*, defaults to `True`): + Whether to use relative position embedding. + window_size (`int`, *optional*, defaults to 14): + Window size for relative position. + global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`): + The indexes of the global attention layers. + num_pos_feats (`int`, *optional*, defaults to 128): + The dimensionality of the position embedding. + mlp_dim (`int`, *optional*): + The dimensionality of the MLP layer in the Transformer encoder. If `None`, defaults to `mlp_ratio * + hidden_size`. + + Example: + + ```python + >>> from transformers import ( + ... SamVisionConfig, + ... SamVisionModel, + ... ) + + >>> # Initializing a SamVisionConfig with `"facebook/sam-vit-huge"` style configuration + >>> configuration = SamVisionConfig() + + >>> # Initializing a SamVisionModel (with random weights) from the `"facebook/sam-vit-huge"` style configuration + >>> model = SamVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + base_config_key = "vision_config" + model_type = "sam_vision_model" + + hidden_size: int = 768 + output_channels: int = 256 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 1024 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-06 + attention_dropout: float | int = 0.0 + initializer_range: float = 1e-10 + qkv_bias: bool = True + mlp_ratio: float = 4.0 + use_abs_pos: bool = True + use_rel_pos: bool = True + window_size: int = 14 + global_attn_indexes: list[int] | tuple[int, ...] = (2, 5, 8, 11) + num_pos_feats: int = 128 + mlp_dim: int | None = None + + def __post_init__(self, **kwargs): + self.mlp_dim = int(self.hidden_size * self.mlp_ratio) if self.mlp_dim is None else self.mlp_dim + self.scale = self.hidden_size // 2 + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="facebook/sam-vit-huge") +@strict +class SamConfig(PreTrainedConfig): + r""" + prompt_encoder_config (Union[`dict`, `SamPromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`SamPromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `SamMaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`SamMaskDecoderConfig`]. + + Example: + + ```python + >>> from transformers import ( + ... SamVisionConfig, + ... SamPromptEncoderConfig, + ... SamMaskDecoderConfig, + ... SamModel, + ... ) + + >>> # Initializing a SamConfig with `"facebook/sam-vit-huge"` style configuration + >>> configuration = SamConfig() + + >>> # Initializing a SamModel (with random weights) from the `"facebook/sam-vit-huge"` style configuration + >>> model = SamModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a SamConfig from a SamVisionConfig, SamPromptEncoderConfig, and SamMaskDecoderConfig + + >>> # Initializing SAM vision, SAM Q-Former and language model configurations + >>> vision_config = SamVisionConfig() + >>> prompt_encoder_config = SamPromptEncoderConfig() + >>> mask_decoder_config = SamMaskDecoderConfig() + + >>> config = SamConfig(vision_config, prompt_encoder_config, mask_decoder_config) + ```""" + + model_type = "sam" + sub_configs = { + "prompt_encoder_config": SamPromptEncoderConfig, + "mask_decoder_config": SamMaskDecoderConfig, + "vision_config": SamVisionConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + prompt_encoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = SamVisionConfig(**self.vision_config) + elif self.vision_config is None: + self.vision_config = SamVisionConfig() + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = SamPromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = SamPromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = SamMaskDecoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = SamMaskDecoderConfig() + + super().__post_init__(**kwargs) + + +__all__ = ["SamConfig", "SamMaskDecoderConfig", "SamPromptEncoderConfig", "SamVisionConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/image_processing_pil_sam.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/image_processing_pil_sam.py new file mode 100644 index 0000000000000000000000000000000000000000..c127e7ef0c325d81386691f8bfa423fb525a6099 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/image_processing_pil_sam.py @@ -0,0 +1,788 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SAM.""" + +import math +from collections.abc import Iterable +from copy import deepcopy +from itertools import product +from typing import TYPE_CHECKING, Any, Optional + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, + get_image_size, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_torch_available, is_vision_available +from ...utils.import_utils import requires + + +if is_vision_available(): + import PIL + +if is_torch_available(): + import torch + +if TYPE_CHECKING: + import torch + + +def get_resize_output_image_size( + input_image: np.ndarray, + longest_edge: int, + input_data_format: str | ChannelDimension | None = None, +) -> tuple[int, int]: + height, width = get_image_size(input_image, input_data_format) + if height >= width: + ratio = width * 1.0 / height + new_height = longest_edge + new_width = int(new_height * ratio) + else: + ratio = height * 1.0 / width + new_width = longest_edge + new_height = int(new_width * ratio) + return (new_height, new_width) + + +# Adapted from transformers.models.sam.image_processing_sam.SamImageProcessorKwargs +class SamImageProcessorKwargs(ImagesKwargs, total=False): + r""" + mask_size (`dict[str, int]`, *optional*): + The size `{"longest_edge": int}` to resize the segmentation maps to. + mask_pad_size (`dict[str, int]`, *optional*): + The size `{"height": int, "width": int}` to pad the segmentation maps to. Must be larger than any segmentation + map size provided for preprocessing. + """ + + mask_size: dict[str, int] + mask_pad_size: dict[str, int] + + +@auto_docstring +@requires(backends=("torch",)) +class SamImageProcessorPil(PilBackend): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"longest_edge": 1024} + mask_size = {"longest_edge": 256} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + do_pad = True + pad_size = {"height": 1024, "width": 1024} + mask_pad_size = {"height": 256, "width": 256} + valid_kwargs = SamImageProcessorKwargs + + def __init__(self, **kwargs: Unpack[SamImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + segmentation_maps: ImageInput | None = None, + **kwargs: Unpack[SamImageProcessorKwargs], + ) -> BatchFeature: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to preprocess. + """ + return super().preprocess(images, segmentation_maps, **kwargs) + + def _standardize_kwargs( + self, + mask_size: int | Iterable[int] | dict[str, int] | SizeDict | None = None, + mask_pad_size: int | Iterable[int] | dict[str, int] | SizeDict | None = None, + **kwargs, + ) -> dict: + """ + Update kwargs that need further processing before being validated + Can be overridden by subclasses to customize the processing of kwargs. + """ + kwargs = super()._standardize_kwargs(**kwargs) + if mask_size is not None and not isinstance(mask_size, SizeDict): + mask_size = SizeDict(**get_size_dict(mask_size, param_name="mask_size")) + if mask_pad_size is not None and not isinstance(mask_pad_size, SizeDict): + mask_pad_size = SizeDict(**get_size_dict(mask_pad_size, param_name="mask_pad_size")) + + kwargs["mask_size"] = mask_size + kwargs["mask_pad_size"] = mask_pad_size + + return kwargs + + def _get_preprocess_shape(self, old_shape: tuple[int, int], longest_edge: int): + """ + Compute the output size given input size and target long side length. + """ + oldh, oldw = old_shape + scale = longest_edge * 1.0 / max(oldh, oldw) + newh, neww = oldh * scale, oldw * scale + newh = int(newh + 0.5) + neww = int(neww + 0.5) + return (newh, neww) + + def resize( + self, + image: np.ndarray, + size: SizeDict, + resample: PILImageResampling | None = None, + **kwargs, + ) -> np.ndarray: + """ + Resize an image to `(size["height"], size["width"])`. + + Args: + image (`np.ndarray`): + Image to resize. + size (`SizeDict`): + Dictionary in the format `{"longest_edge": int}` specifying the size of the output image. The longest + edge of the image will be resized to the specified size, while the other edge will be resized to + maintain the aspect ratio. + resample (`PILImageResampling | int | None`, *optional*): + Resampling filter to use when resizing the image. + + Returns: + `np.ndarray`: The resized image. + """ + if not size.longest_edge: + raise ValueError(f"The `size` dictionary must contain the key `longest_edge`. Got {size.keys()}") + input_size = get_image_size(image, channel_dim=ChannelDimension.FIRST) + output_height, output_width = self._get_preprocess_shape(input_size, size.longest_edge) + return super().resize( + image, size=SizeDict(height=output_height, width=output_width), resample=resample, **kwargs + ) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + return_tensors: str | TensorType | None, + device: str | None = None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess image-like inputs. + """ + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format + ) + original_sizes = [image.shape[-2:] for image in images] + images_kwargs = kwargs.copy() + pixel_values, reshaped_input_sizes = self._preprocess(images, **images_kwargs) + data = { + "pixel_values": pixel_values, + "original_sizes": original_sizes, + "reshaped_input_sizes": reshaped_input_sizes, + } + + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + "resample": PILImageResampling.NEAREST, + "size": segmentation_maps_kwargs.pop("mask_size"), + "pad_size": segmentation_maps_kwargs.pop("mask_pad_size"), + } + ) + processed_segmentation_maps, _ = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + data["labels"] = [m.squeeze(0).astype(np.int64) for m in processed_segmentation_maps] + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: PILImageResampling | None, + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + **kwargs, + ) -> tuple[list[np.ndarray], list[tuple[int, int]]]: + """ + Preprocess images using PIL backend. + """ + processed_images = [] + reshaped_input_sizes = [] + for image in images: + if do_resize: + image = self.resize(image=image, size=size, resample=resample) + reshaped_input_sizes.append(image.shape[-2:]) + + if do_center_crop: + image = self.center_crop(image, crop_size) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size) + + return processed_images, reshaped_input_sizes + + def generate_crop_boxes( + self, + image: "np.ndarray | PIL.Image.Image | torch.Tensor", + target_size, + crop_n_layers: int = 0, + overlap_ratio: float = 512 / 1500, + points_per_crop: int | None = 32, + crop_n_points_downscale_factor: list[int] | None = 1, + device: Optional["torch.device"] = None, + ): + """ + Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + + Args: + image (`np.ndarray | PIL.Image.Image | torch.Tensor`): + Input original image + target_size (`int`): + Target size of the resized image + crop_n_layers (`int`, *optional*, defaults to 0): + If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where + each layer has 2**i_layer number of image crops. + overlap_ratio (`float`, *optional*, defaults to 512/1500): + Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of + the image length. Later layers with more crops scale down this overlap. + points_per_crop (`int`, *optional*, defaults to 32): + Number of points to sample from each crop. + crop_n_points_downscale_factor (`list[int]`, *optional*, defaults to 1): + The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n. + device (`torch.device`, *optional*, defaults to None): + Device to use for the computation. If None, cpu will be used. + """ + image = self.process_image(image) + crop_boxes, points_per_crop, cropped_images, input_labels = _generate_crop_boxes( + image, + target_size, + crop_n_layers, + overlap_ratio, + points_per_crop, + crop_n_points_downscale_factor, + ) + if device is None: + device = torch.device("cpu") + crop_boxes = torch.tensor(crop_boxes, device=device) + points_per_crop = torch.tensor(points_per_crop, device=device) + # cropped_images stays as np.ndarray + input_labels = torch.tensor(input_labels, device=device) + + return crop_boxes, points_per_crop, cropped_images, input_labels + + def filter_masks( + self, + masks, + iou_scores, + original_size, + cropped_box_image, + pred_iou_thresh=0.88, + stability_score_thresh=0.95, + mask_threshold=0, + stability_score_offset=1, + ): + """ + Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being + that the iou scores needs to be greater than `pred_iou_thresh`. The second criterion is that the stability + score needs to be greater than `stability_score_thresh`. The method also converts the predicted masks to + bounding boxes and pad the predicted masks if necessary. + + Args: + masks (`torch.Tensor`): + Input masks. + iou_scores (`torch.Tensor`): + List of IoU scores. + original_size (`tuple[int,int]`): + Size of the original image. + cropped_box_image (`np.ndarray`): + The cropped image. + pred_iou_thresh (`float`, *optional*, defaults to 0.88): + The threshold for the iou scores. + stability_score_thresh (`float`, *optional*, defaults to 0.95): + The threshold for the stability score. + mask_threshold (`float`, *optional*, defaults to 0): + The threshold for the predicted masks. + stability_score_offset (`float`, *optional*, defaults to 1): + The offset for the stability score used in the `_compute_stability_score` method. + """ + if not is_torch_available(): + raise ImportError("PyTorch is required for filter_masks") + original_height, original_width = original_size + iou_scores = iou_scores.flatten(0, 1) + masks = masks.flatten(0, 1) + + if masks.shape[0] != iou_scores.shape[0]: + raise ValueError("masks and iou_scores must have the same batch size.") + + if masks.device != iou_scores.device: + iou_scores = iou_scores.to(masks.device) + + batch_size = masks.shape[0] + + keep_mask = torch.ones(batch_size, dtype=torch.bool, device=masks.device) + + if pred_iou_thresh > 0.0: + keep_mask = keep_mask & (iou_scores > pred_iou_thresh) + + # compute stability score + if stability_score_thresh > 0.0: + stability_scores = _compute_stability_score(masks, mask_threshold, stability_score_offset) + keep_mask = keep_mask & (stability_scores > stability_score_thresh) + + scores = iou_scores[keep_mask] + masks = masks[keep_mask] + + # binarize masks + masks = masks > mask_threshold + converted_boxes = _batched_mask_to_box(masks) + + keep_mask = ~_is_box_near_crop_edge( + converted_boxes, cropped_box_image, [0, 0, original_width, original_height] + ) + + scores = scores[keep_mask] + masks = masks[keep_mask] + converted_boxes = converted_boxes[keep_mask] + + masks = _pad_masks(masks, cropped_box_image, original_height, original_width) + # conversion to rle is necessary to run non-maximum suppression + masks = _mask_to_rle(masks) + + return masks, scores, converted_boxes + + def post_process_masks( + self, + masks, + original_sizes, + reshaped_input_sizes, + mask_threshold=0.0, + binarize=True, + pad_size=None, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[List[torch.Tensor], List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + reshaped_input_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The size of each image as it is fed to the model, in (height, width) format. Used to remove padding. + mask_threshold (`float`, *optional*, defaults to 0.0): + The threshold to use for binarizing the masks. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + pad_size (`int`, *optional*, defaults to `self.pad_size`): + The target size the images were padded to before being passed to the model. If None, the target size is + assumed to be the processor's `pad_size`. + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + if not is_torch_available(): + raise ImportError("PyTorch is required for post_process_masks") + import torch + import torch.nn.functional as F + + pad_size = self.pad_size if pad_size is None else pad_size + target_image_size = (pad_size["height"], pad_size["width"]) + if isinstance(original_sizes, (torch.Tensor, np.ndarray)): + original_sizes = original_sizes.tolist() + if isinstance(reshaped_input_sizes, (torch.Tensor, np.ndarray)): + reshaped_input_sizes = reshaped_input_sizes.tolist() + + output_masks = [] + for i, original_size in enumerate(original_sizes): + if isinstance(masks[i], np.ndarray): + masks[i] = torch.from_numpy(masks[i]) + elif not isinstance(masks[i], torch.Tensor): + raise TypeError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`") + interpolated_mask = F.interpolate(masks[i], target_image_size, mode="bilinear", align_corners=False) + interpolated_mask = interpolated_mask[..., : reshaped_input_sizes[i][0], : reshaped_input_sizes[i][1]] + interpolated_mask = F.interpolate(interpolated_mask, original_size, mode="bilinear", align_corners=False) + if binarize: + interpolated_mask = interpolated_mask > mask_threshold + output_masks.append(interpolated_mask) + + return output_masks + + def post_process_for_mask_generation(self, all_masks, all_scores, all_boxes, crops_nms_thresh): + """ + Post processes mask that are generated by calling the Non Maximum Suppression algorithm on the predicted masks. + + Args: + all_masks (`torch.Tensor`): + List of all predicted segmentation masks + all_scores (`torch.Tensor`): + List of all predicted iou scores + all_boxes (`torch.Tensor`): + List of all bounding boxes of the predicted masks + crops_nms_thresh (`float`): + Threshold for NMS (Non Maximum Suppression) algorithm. + """ + return _post_process_for_mask_generation(all_masks, all_scores, all_boxes, crops_nms_thresh) + + +def _compute_stability_score(masks: "torch.Tensor", mask_threshold: float, stability_score_offset: int): + # One mask is always contained inside the other. + # Save memory by preventing unnecessary cast to torch.int64 + intersections = ( + (masks > (mask_threshold + stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32) + ) + unions = (masks > (mask_threshold - stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32) + stability_scores = intersections / unions + return stability_scores + + +def _batched_mask_to_box(masks: "torch.Tensor"): + """ + Computes the bounding boxes around the given input masks. The bounding boxes are in the XYXY format which + corresponds the following required indices: + - LEFT: left hand side of the bounding box + - TOP: top of the bounding box + - RIGHT: right of the bounding box + - BOTTOM: bottom of the bounding box + + Return [0,0,0,0] for an empty mask. For input shape channel_1 x channel_2 x ... x height x width, the output shape + is channel_1 x channel_2 x ... x 4. + + Args: + - masks (`torch.Tensor` of shape `(batch, nb_mask, height, width)`) + """ + # torch.max below raises an error on empty inputs, just skip in this case + + if torch.numel(masks) == 0: + return torch.zeros(*masks.shape[:-2], 4, device=masks.device) + + # Normalize shape to Cxheightxwidth + shape = masks.shape + height, width = shape[-2:] + + # Get top and bottom edges + in_height, _ = torch.max(masks, dim=-1) + in_height_coords = in_height * torch.arange(height, device=in_height.device)[None, :] + bottom_edges, _ = torch.max(in_height_coords, dim=-1) + in_height_coords = in_height_coords + height * (~in_height) + top_edges, _ = torch.min(in_height_coords, dim=-1) + + # Get left and right edges + in_width, _ = torch.max(masks, dim=-2) + in_width_coords = in_width * torch.arange(width, device=in_width.device)[None, :] + right_edges, _ = torch.max(in_width_coords, dim=-1) + in_width_coords = in_width_coords + width * (~in_width) + left_edges, _ = torch.min(in_width_coords, dim=-1) + + # If the mask is empty the right edge will be to the left of the left edge. + # Replace these boxes with [0, 0, 0, 0] + empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) + out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) + out = out * (~empty_filter).unsqueeze(-1) + + # Return to original shape + out = out.reshape(*shape[:-2], 4) + return out + + +def _is_box_near_crop_edge(boxes, crop_box, orig_box, atol=20.0): + """Filter masks at the edge of a crop, but not at the edge of the original image.""" + crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) + orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) + + left, top, _, _ = crop_box + offset = torch.tensor([[left, top, left, top]], device=boxes.device) + # Check if boxes has a channel dimension + if len(boxes.shape) == 3: + offset = offset.unsqueeze(1) + boxes = (boxes + offset).float() + + near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) + near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) + near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) + return torch.any(near_crop_edge, dim=1) + + +def _pad_masks(masks, crop_box: list[int], orig_height: int, orig_width: int): + left, top, right, bottom = crop_box + if left == 0 and top == 0 and right == orig_width and bottom == orig_height: + return masks + # Coordinate transform masks + pad_x, pad_y = orig_width - (right - left), orig_height - (bottom - top) + pad = (left, pad_x - left, top, pad_y - top) + return torch.nn.functional.pad(masks, pad, value=0) + + +def _generate_crop_boxes( + image, + target_size: int, + crop_n_layers: int = 0, + overlap_ratio: float = 512 / 1500, + points_per_crop: int | None = 32, + crop_n_points_downscale_factor: list[int] | None = 1, +) -> tuple[list[list[int]], list[int]]: + """ + Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + + Args: + image (`np.ndarray`): + Image to generate crops for. + target_size (`int`): + Size of the smallest crop. + crop_n_layers (`int`, *optional*): + If `crops_n_layers>0`, mask prediction will be run again on crops of the image. Sets the number of layers + to run, where each layer has 2**i_layer number of image crops. + overlap_ratio (`int`, *optional*): + Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the + image length. Later layers with more crops scale down this overlap. + points_per_crop (`int`, *optional*): + Number of points to sample per crop. + crop_n_points_downscale_factor (`int`, *optional*): + The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n. + """ + + if isinstance(image, list): + raise ValueError("Only one image is allowed for crop generation.") + original_size = image.shape[-2:] + + points_grid = [] + for i in range(crop_n_layers + 1): + n_points = int(points_per_crop / (crop_n_points_downscale_factor**i)) + points_grid.append(_build_point_grid(n_points)) + + crop_boxes, layer_idxs = _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size) + + cropped_images, point_grid_per_crop = _generate_crop_images( + crop_boxes, image, points_grid, layer_idxs, target_size, original_size + ) + crop_boxes = np.array(crop_boxes) + crop_boxes = crop_boxes.astype(np.float32) + points_per_crop = np.array([point_grid_per_crop]) + points_per_crop = np.transpose(points_per_crop, axes=(0, 2, 1, 3)) + + input_labels = np.ones_like(points_per_crop[:, :, :, 0], dtype=np.int64) + + return crop_boxes, points_per_crop, cropped_images, input_labels + + +def _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size): + """ + Generates 2 ** (layers idx + 1) crops for each crop_n_layers. Crops are in the XYWH format : The XYWH format + consists of the following required indices: + - X: X coordinate of the top left of the bounding box + - Y: Y coordinate of the top left of the bounding box + - W: width of the bounding box + - H: height of the bounding box + """ + crop_boxes, layer_idxs = [], [] + im_height, im_width = original_size + short_side = min(im_height, im_width) + + # Original image + crop_boxes.append([0, 0, im_width, im_height]) + layer_idxs.append(0) + for i_layer in range(crop_n_layers): + n_crops_per_side = 2 ** (i_layer + 1) + overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) + + crop_width = int(math.ceil((overlap * (n_crops_per_side - 1) + im_width) / n_crops_per_side)) + crop_height = int(math.ceil((overlap * (n_crops_per_side - 1) + im_height) / n_crops_per_side)) + + crop_box_x0 = [int((crop_width - overlap) * i) for i in range(n_crops_per_side)] + crop_box_y0 = [int((crop_height - overlap) * i) for i in range(n_crops_per_side)] + + for left, top in product(crop_box_x0, crop_box_y0): + box = [left, top, min(left + crop_width, im_width), min(top + crop_height, im_height)] + crop_boxes.append(box) + layer_idxs.append(i_layer + 1) + + return crop_boxes, layer_idxs + + +def _build_point_grid(n_per_side: int) -> np.ndarray: + """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" + offset = 1 / (2 * n_per_side) + points_one_side = np.linspace(offset, 1 - offset, n_per_side) + points_x = np.tile(points_one_side[None, :], (n_per_side, 1)) + points_y = np.tile(points_one_side[:, None], (1, n_per_side)) + points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2) + return points + + +def _generate_crop_images( + crop_boxes, image, points_grid, layer_idxs, target_size, original_size, input_data_format=None +): + """ + Takes as an input bounding boxes that are used to crop the image. Based in the crops, the corresponding points are + also passed. + """ + cropped_images = [] + total_points_per_crop = [] + for i, crop_box in enumerate(crop_boxes): + left, top, right, bottom = crop_box + cropped_im = image[:, top:bottom, left:right] + + cropped_images.append(cropped_im) + + cropped_im_size = cropped_im.shape[-2:] + points_scale = np.array(cropped_im_size)[None, ::-1] + + points = points_grid[layer_idxs[i]] * points_scale + normalized_points = _normalize_coordinates(target_size, points, original_size) + total_points_per_crop.append(normalized_points) + + return cropped_images, total_points_per_crop + + +def _normalize_coordinates( + target_size: int, coords: np.ndarray, original_size: tuple[int, int], is_bounding_box=False +) -> np.ndarray: + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (height, width) + format. + """ + old_height, old_width = original_size + + scale = target_size * 1.0 / max(old_height, old_width) + new_height, new_width = old_height * scale, old_width * scale + new_width = int(new_width + 0.5) + new_height = int(new_height + 0.5) + + coords = deepcopy(coords).astype(float) + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + + coords[..., 0] = coords[..., 0] * (new_width / old_width) + coords[..., 1] = coords[..., 1] * (new_height / old_height) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + +def _rle_to_mask(rle: dict[str, Any]) -> np.ndarray: + """Compute a binary mask from an uncompressed RLE.""" + height, width = rle["size"] + mask = np.empty(height * width, dtype=bool) + idx = 0 + parity = False + for count in rle["counts"]: + mask[idx : idx + count] = parity + idx += count + parity = not parity + mask = mask.reshape(width, height) + return mask.transpose() # Reshape to original shape + + +def _post_process_for_mask_generation(rle_masks, iou_scores, mask_boxes, amg_crops_nms_thresh=0.7): + """ + Perform NMS (Non Maximum Suppression) on the outputs. + + Args: + rle_masks (`torch.Tensor`): + binary masks in the RLE format + iou_scores (`torch.Tensor` of shape (nb_masks, 1)): + iou_scores predicted by the model + mask_boxes (`torch.Tensor`): + The bounding boxes corresponding to segmentation masks + amg_crops_nms_thresh (`float`, *optional*, defaults to 0.7): + NMS threshold. + """ + if not is_torch_available(): + raise ImportError("PyTorch is required for post_process_for_mask_generation") + + import torch + from torchvision.ops.boxes import batched_nms + + keep_by_nms = batched_nms( + boxes=mask_boxes.float(), + scores=iou_scores, + idxs=torch.zeros(mask_boxes.shape[0]), + iou_threshold=amg_crops_nms_thresh, + ) + + iou_scores = iou_scores[keep_by_nms] + rle_masks = [rle_masks[i] for i in keep_by_nms] + mask_boxes = mask_boxes[keep_by_nms] + masks = [_rle_to_mask(rle) for rle in rle_masks] + + return masks, iou_scores, rle_masks, mask_boxes + + +def _mask_to_rle(input_mask: "torch.Tensor"): + """ + Encodes masks the run-length encoding (RLE), in the format expected by pycoco tools. + """ + # Put in fortran order and flatten height and width + batch_size, height, width = input_mask.shape + input_mask = input_mask.permute(0, 2, 1).flatten(1) + + # Compute change indices + diff = input_mask[:, 1:] ^ input_mask[:, :-1] + change_indices = diff.nonzero() + + # Encode run length + out = [] + for i in range(batch_size): + cur_idxs = change_indices[change_indices[:, 0] == i, 1] + 1 + if len(cur_idxs) == 0: + # No changes => either all 0 or all 1 + # If the entire mask is 0, RLE is [height*width] or if the entire mask is 1, RLE is [0, height*width]. + if input_mask[i, 0] == 0: + out.append({"size": [height, width], "counts": [height * width]}) + else: + out.append({"size": [height, width], "counts": [0, height * width]}) + continue + btw_idxs = cur_idxs[1:] - cur_idxs[:-1] + counts = [] if input_mask[i, 0] == 0 else [0] + counts += [cur_idxs[0].item()] + btw_idxs.tolist() + [height * width - cur_idxs[-1].item()] + out.append({"size": [height, width], "counts": counts}) + return out + + +__all__ = ["SamImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/image_processing_sam.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/image_processing_sam.py new file mode 100644 index 0000000000000000000000000000000000000000..a6b30fca76c0049a78bffc416a29f198f589b7c6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/image_processing_sam.py @@ -0,0 +1,761 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SAM.""" + +import math +from collections.abc import Iterable +from copy import deepcopy +from itertools import product +from typing import Any, Optional, Union + +import numpy as np +import torch +from torch.nn import functional as F +from torchvision.ops.boxes import batched_nms +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_vision_available + + +if is_vision_available(): + import PIL + + +class SamImageProcessorKwargs(ImagesKwargs, total=False): + r""" + mask_size (`dict[str, int]`, *optional*): + The size `{"longest_edge": int}` to resize the segmentation maps to. + mask_pad_size (`dict[str, int]`, *optional*): + The size `{"height": int, "width": int}` to pad the segmentation maps to. Must be larger than any segmentation + map size provided for preprocessing. + """ + + mask_size: dict[str, int] + mask_pad_size: dict[str, int] + + +@auto_docstring +class SamImageProcessor(TorchvisionBackend): + valid_kwargs = SamImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"longest_edge": 1024} + mask_size = {"longest_edge": 256} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + do_pad = True + pad_size = {"height": 1024, "width": 1024} + mask_pad_size = {"height": 256, "width": 256} + + def __init__(self, **kwargs: Unpack[SamImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + segmentation_maps: ImageInput | None = None, + **kwargs: Unpack[SamImageProcessorKwargs], + ) -> BatchFeature: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to preprocess. + """ + return super().preprocess(images, segmentation_maps, **kwargs) + + def _standardize_kwargs( + self, + mask_size: int | Iterable[int] | dict[str, int] | SizeDict | None = None, + mask_pad_size: int | Iterable[int] | dict[str, int] | SizeDict | None = None, + **kwargs, + ) -> dict: + """ + Update kwargs that need further processing before being validated + Can be overridden by subclasses to customize the processing of kwargs. + """ + kwargs = super()._standardize_kwargs(**kwargs) + if mask_size is not None and not isinstance(mask_size, SizeDict): + mask_size = SizeDict(**get_size_dict(mask_size, param_name="mask_size")) + if mask_pad_size is not None and not isinstance(mask_pad_size, SizeDict): + mask_pad_size = SizeDict(**get_size_dict(mask_pad_size, param_name="mask_pad_size")) + + kwargs["mask_size"] = mask_size + kwargs["mask_pad_size"] = mask_pad_size + + return kwargs + + def _get_preprocess_shape(self, old_shape: tuple[int, int], longest_edge: int): + """ + Compute the output size given input size and target long side length. + """ + oldh, oldw = old_shape + scale = longest_edge * 1.0 / max(oldh, oldw) + newh, neww = oldh * scale, oldw * scale + newh = int(newh + 0.5) + neww = int(neww + 0.5) + return (newh, neww) + + def resize( + self, + image: "torch.Tensor", + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + **kwargs, + ) -> "torch.Tensor": + """ + Resize an image to `(size["height"], size["width"])`. + + Args: + image (`torch.Tensor`): + Image to resize. + size (`SizeDict`): + Dictionary in the format `{"longest_edge": int}` specifying the size of the output image. The longest + edge of the image will be resized to the specified size, while the other edge will be resized to + maintain the aspect ratio. + resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): + Resampling filter to use when resizing the image. + + Returns: + `torch.Tensor`: The resized image. + """ + if not size.longest_edge: + raise ValueError(f"The `size` dictionary must contain the key `longest_edge`. Got {size.keys()}") + input_size = image.shape[-2:] + output_height, output_width = self._get_preprocess_shape(input_size, size.longest_edge) + return super().resize( + image, size=SizeDict(height=output_height, width=output_width), resample=resample, **kwargs + ) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + return_tensors: str | TensorType | None, + device: Union[str, "torch.device"] | None = None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess image-like inputs. + """ + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device + ) + original_sizes = [image.shape[-2:] for image in images] + images_kwargs = kwargs.copy() + pixel_values, reshaped_input_sizes = self._preprocess(images, **images_kwargs) + data = { + "pixel_values": pixel_values, + "original_sizes": original_sizes, + "reshaped_input_sizes": reshaped_input_sizes, + } + + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + "resample": PILImageResampling.NEAREST, + "size": segmentation_maps_kwargs.pop("mask_size"), + "pad_size": segmentation_maps_kwargs.pop("mask_pad_size"), + } + ) + processed_segmentation_maps, _ = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + data["labels"] = [m.squeeze(0).to(torch.int64) for m in processed_segmentation_maps] + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + **kwargs, + ) -> tuple[list["torch.Tensor"], list[tuple[int, int]]]: + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(image=stacked_images, size=size, resample=resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + reshaped_input_sizes = [image.shape[-2:] for image in resized_images] + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return processed_images, reshaped_input_sizes + + def generate_crop_boxes( + self, + image: "np.ndarray | PIL.Image.Image | torch.Tensor", + target_size, + crop_n_layers: int = 0, + overlap_ratio: float = 512 / 1500, + points_per_crop: int | None = 32, + crop_n_points_downscale_factor: list[int] | None = 1, + device: Optional["torch.device"] = None, + ): + """ + Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + + Args: + image (`torch.Tensor`): + Input original image + target_size (`int`): + Target size of the resized image + crop_n_layers (`int`, *optional*, defaults to 0): + If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where + each layer has 2**i_layer number of image crops. + overlap_ratio (`float`, *optional*, defaults to 512/1500): + Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of + the image length. Later layers with more crops scale down this overlap. + points_per_crop (`int`, *optional*, defaults to 32): + Number of points to sample from each crop. + crop_n_points_downscale_factor (`list[int]`, *optional*, defaults to 1): + The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n. + device (`torch.device`, *optional*, defaults to None): + Device to use for the computation. If None, cpu will be used. + """ + image = self.process_image(image) + crop_boxes, points_per_crop, cropped_images, input_labels = _generate_crop_boxes( + image, + target_size, + crop_n_layers, + overlap_ratio, + points_per_crop, + crop_n_points_downscale_factor, + ) + if device is None: + device = torch.device("cpu") + crop_boxes = crop_boxes.to(device) + points_per_crop = points_per_crop.to(device) + # cropped_images stays as torch.Tensor + input_labels = input_labels.to(device) + + return crop_boxes, points_per_crop, cropped_images, input_labels + + def filter_masks( + self, + masks, + iou_scores, + original_size, + cropped_box_image, + pred_iou_thresh=0.88, + stability_score_thresh=0.95, + mask_threshold=0, + stability_score_offset=1, + ): + """ + Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being + that the iou scores needs to be greater than `pred_iou_thresh`. The second criterion is that the stability + score needs to be greater than `stability_score_thresh`. The method also converts the predicted masks to + bounding boxes and pad the predicted masks if necessary. + + Args: + masks (`torch.Tensor`): + Input masks. + iou_scores (`torch.Tensor`): + List of IoU scores. + original_size (`tuple[int,int]`): + Size of the original image. + cropped_box_image (`torch.Tensor`): + The cropped image. + pred_iou_thresh (`float`, *optional*, defaults to 0.88): + The threshold for the iou scores. + stability_score_thresh (`float`, *optional*, defaults to 0.95): + The threshold for the stability score. + mask_threshold (`float`, *optional*, defaults to 0): + The threshold for the predicted masks. + stability_score_offset (`float`, *optional*, defaults to 1): + The offset for the stability score used in the `_compute_stability_score` method. + """ + original_height, original_width = original_size + iou_scores = iou_scores.flatten(0, 1) + masks = masks.flatten(0, 1) + + if masks.shape[0] != iou_scores.shape[0]: + raise ValueError("masks and iou_scores must have the same batch size.") + + if masks.device != iou_scores.device: + iou_scores = iou_scores.to(masks.device) + + batch_size = masks.shape[0] + + keep_mask = torch.ones(batch_size, dtype=torch.bool, device=masks.device) + + if pred_iou_thresh > 0.0: + keep_mask = keep_mask & (iou_scores > pred_iou_thresh) + + # compute stability score + if stability_score_thresh > 0.0: + stability_scores = _compute_stability_score(masks, mask_threshold, stability_score_offset) + keep_mask = keep_mask & (stability_scores > stability_score_thresh) + + scores = iou_scores[keep_mask] + masks = masks[keep_mask] + + # binarize masks + masks = masks > mask_threshold + converted_boxes = _batched_mask_to_box(masks) + + keep_mask = ~_is_box_near_crop_edge( + converted_boxes, cropped_box_image, [0, 0, original_width, original_height] + ) + + scores = scores[keep_mask] + masks = masks[keep_mask] + converted_boxes = converted_boxes[keep_mask] + + masks = _pad_masks(masks, cropped_box_image, original_height, original_width) + # conversion to rle is necessary to run non-maximum suppression + masks = _mask_to_rle(masks) + + return masks, scores, converted_boxes + + def post_process_masks( + self, + masks, + original_sizes, + reshaped_input_sizes, + mask_threshold=0.0, + binarize=True, + pad_size=None, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[List[torch.Tensor], List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + reshaped_input_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The size of each image as it is fed to the model, in (height, width) format. Used to remove padding. + mask_threshold (`float`, *optional*, defaults to 0.0): + The threshold to use for binarizing the masks. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + pad_size (`int`, *optional*, defaults to `self.pad_size`): + The target size the images were padded to before being passed to the model. If None, the target size is + assumed to be the processor's `pad_size`. + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + pad_size = self.pad_size if pad_size is None else pad_size + target_image_size = (pad_size["height"], pad_size["width"]) + if isinstance(original_sizes, (torch.Tensor, np.ndarray)): + original_sizes = original_sizes.tolist() + if isinstance(reshaped_input_sizes, (torch.Tensor, np.ndarray)): + reshaped_input_sizes = reshaped_input_sizes.tolist() + + output_masks = [] + for i, original_size in enumerate(original_sizes): + if isinstance(masks[i], np.ndarray): + masks[i] = torch.from_numpy(masks[i]) + elif not isinstance(masks[i], torch.Tensor): + raise TypeError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`") + interpolated_mask = F.interpolate(masks[i], target_image_size, mode="bilinear", align_corners=False) + interpolated_mask = interpolated_mask[..., : reshaped_input_sizes[i][0], : reshaped_input_sizes[i][1]] + interpolated_mask = F.interpolate(interpolated_mask, original_size, mode="bilinear", align_corners=False) + if binarize: + interpolated_mask = interpolated_mask > mask_threshold + output_masks.append(interpolated_mask) + + return output_masks + + def post_process_for_mask_generation(self, all_masks, all_scores, all_boxes, crops_nms_thresh): + """ + Post processes mask that are generated by calling the Non Maximum Suppression algorithm on the predicted masks. + + Args: + all_masks (`torch.Tensor`): + List of all predicted segmentation masks + all_scores (`torch.Tensor`): + List of all predicted iou scores + all_boxes (`torch.Tensor`): + List of all bounding boxes of the predicted masks + crops_nms_thresh (`float`): + Threshold for NMS (Non Maximum Suppression) algorithm. + """ + return _post_process_for_mask_generation(all_masks, all_scores, all_boxes, crops_nms_thresh) + + +def _compute_stability_score(masks: "torch.Tensor", mask_threshold: float, stability_score_offset: int): + # One mask is always contained inside the other. + # Save memory by preventing unnecessary cast to torch.int64 + intersections = ( + (masks > (mask_threshold + stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32) + ) + unions = (masks > (mask_threshold - stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32) + stability_scores = intersections / unions + return stability_scores + + +def _batched_mask_to_box(masks: "torch.Tensor"): + """ + Computes the bounding boxes around the given input masks. The bounding boxes are in the XYXY format which + corresponds the following required indices: + - LEFT: left hand side of the bounding box + - TOP: top of the bounding box + - RIGHT: right of the bounding box + - BOTTOM: bottom of the bounding box + + Return [0,0,0,0] for an empty mask. For input shape channel_1 x channel_2 x ... x height x width, the output shape + is channel_1 x channel_2 x ... x 4. + + Args: + - masks (`torch.Tensor` of shape `(batch, nb_mask, height, width)`) + """ + # torch.max below raises an error on empty inputs, just skip in this case + + if torch.numel(masks) == 0: + return torch.zeros(*masks.shape[:-2], 4, device=masks.device) + + # Normalize shape to Cxheightxwidth + shape = masks.shape + height, width = shape[-2:] + + # Get top and bottom edges + in_height, _ = torch.max(masks, dim=-1) + in_height_coords = in_height * torch.arange(height, device=in_height.device)[None, :] + bottom_edges, _ = torch.max(in_height_coords, dim=-1) + in_height_coords = in_height_coords + height * (~in_height) + top_edges, _ = torch.min(in_height_coords, dim=-1) + + # Get left and right edges + in_width, _ = torch.max(masks, dim=-2) + in_width_coords = in_width * torch.arange(width, device=in_width.device)[None, :] + right_edges, _ = torch.max(in_width_coords, dim=-1) + in_width_coords = in_width_coords + width * (~in_width) + left_edges, _ = torch.min(in_width_coords, dim=-1) + + # If the mask is empty the right edge will be to the left of the left edge. + # Replace these boxes with [0, 0, 0, 0] + empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) + out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) + out = out * (~empty_filter).unsqueeze(-1) + + # Return to original shape + out = out.reshape(*shape[:-2], 4) + return out + + +def _is_box_near_crop_edge(boxes, crop_box, orig_box, atol=20.0): + """Filter masks at the edge of a crop, but not at the edge of the original image.""" + crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) + orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) + + left, top, _, _ = crop_box + offset = torch.tensor([[left, top, left, top]], device=boxes.device) + # Check if boxes has a channel dimension + if len(boxes.shape) == 3: + offset = offset.unsqueeze(1) + boxes = (boxes + offset).float() + + near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) + near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) + near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) + return torch.any(near_crop_edge, dim=1) + + +def _pad_masks(masks, crop_box: list[int], orig_height: int, orig_width: int): + left, top, right, bottom = crop_box + if left == 0 and top == 0 and right == orig_width and bottom == orig_height: + return masks + # Coordinate transform masks + pad_x, pad_y = orig_width - (right - left), orig_height - (bottom - top) + pad = (left, pad_x - left, top, pad_y - top) + return torch.nn.functional.pad(masks, pad, value=0) + + +def _generate_crop_boxes( + image, + target_size: int, + crop_n_layers: int = 0, + overlap_ratio: float = 512 / 1500, + points_per_crop: int | None = 32, + crop_n_points_downscale_factor: list[int] | None = 1, +) -> tuple[list[list[int]], list[int]]: + """ + Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + + Args: + image (`torch.Tensor`): + Image to generate crops for. + target_size (`int`): + Size of the smallest crop. + crop_n_layers (`int`, *optional*): + If `crops_n_layers>0`, mask prediction will be run again on crops of the image. Sets the number of layers + to run, where each layer has 2**i_layer number of image crops. + overlap_ratio (`int`, *optional*): + Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the + image length. Later layers with more crops scale down this overlap. + points_per_crop (`int`, *optional*): + Number of points to sample per crop. + crop_n_points_downscale_factor (`int`, *optional*): + The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n. + """ + + if isinstance(image, list): + raise ValueError("Only one image is allowed for crop generation.") + original_size = image.shape[-2:] + + points_grid = [] + for i in range(crop_n_layers + 1): + n_points = int(points_per_crop / (crop_n_points_downscale_factor**i)) + points_grid.append(_build_point_grid(n_points)) + + crop_boxes, layer_idxs = _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size) + + cropped_images, point_grid_per_crop = _generate_crop_images( + crop_boxes, image, points_grid, layer_idxs, target_size, original_size + ) + crop_boxes = torch.tensor(crop_boxes) + crop_boxes = crop_boxes.float() + points_per_crop = torch.stack(point_grid_per_crop) + points_per_crop = points_per_crop.unsqueeze(0).permute(0, 2, 1, 3) + cropped_images = torch.stack(cropped_images) + + input_labels = torch.ones_like(points_per_crop[:, :, :, 0], dtype=torch.int64) + + return crop_boxes, points_per_crop, cropped_images, input_labels + + +def _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size): + """ + Generates 2 ** (layers idx + 1) crops for each crop_n_layers. Crops are in the XYWH format : The XYWH format + consists of the following required indices: + - X: X coordinate of the top left of the bounding box + - Y: Y coordinate of the top left of the bounding box + - W: width of the bounding box + - H: height of the bounding box + """ + crop_boxes, layer_idxs = [], [] + im_height, im_width = original_size + short_side = min(im_height, im_width) + + # Original image + crop_boxes.append([0, 0, im_width, im_height]) + layer_idxs.append(0) + for i_layer in range(crop_n_layers): + n_crops_per_side = 2 ** (i_layer + 1) + overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) + + crop_width = int(math.ceil((overlap * (n_crops_per_side - 1) + im_width) / n_crops_per_side)) + crop_height = int(math.ceil((overlap * (n_crops_per_side - 1) + im_height) / n_crops_per_side)) + + crop_box_x0 = [int((crop_width - overlap) * i) for i in range(n_crops_per_side)] + crop_box_y0 = [int((crop_height - overlap) * i) for i in range(n_crops_per_side)] + + for left, top in product(crop_box_x0, crop_box_y0): + box = [left, top, min(left + crop_width, im_width), min(top + crop_height, im_height)] + crop_boxes.append(box) + layer_idxs.append(i_layer + 1) + + return crop_boxes, layer_idxs + + +def _build_point_grid(n_per_side: int) -> torch.Tensor: + """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" + offset = 1 / (2 * n_per_side) + points_one_side = torch.linspace(offset, 1 - offset, n_per_side) + points_x = torch.tile(points_one_side[None, :], (n_per_side, 1)) + points_y = torch.tile(points_one_side[:, None], (1, n_per_side)) + points = torch.stack([points_x, points_y], dim=-1).reshape(-1, 2) + return points + + +def _generate_crop_images( + crop_boxes, image, points_grid, layer_idxs, target_size, original_size, input_data_format=None +): + """ + Takes as an input bounding boxes that are used to crop the image. Based in the crops, the corresponding points are + also passed. + """ + cropped_images = [] + total_points_per_crop = [] + for i, crop_box in enumerate(crop_boxes): + left, top, right, bottom = crop_box + cropped_im = image[:, top:bottom, left:right] + + cropped_images.append(cropped_im) + + cropped_im_size = cropped_im.shape[-2:] + points_scale = torch.tensor(cropped_im_size).flip(dims=(0,)).unsqueeze(0) + + points = points_grid[layer_idxs[i]] * points_scale + normalized_points = _normalize_coordinates(target_size, points, original_size) + total_points_per_crop.append(normalized_points) + + return cropped_images, total_points_per_crop + + +def _normalize_coordinates( + target_size: int, coords: torch.Tensor, original_size: tuple[int, int], is_bounding_box=False +) -> torch.Tensor: + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (height, width) + format. + """ + old_height, old_width = original_size + + scale = target_size * 1.0 / max(old_height, old_width) + new_height, new_width = old_height * scale, old_width * scale + new_width = int(new_width + 0.5) + new_height = int(new_height + 0.5) + + coords = deepcopy(coords).float() + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + + coords[..., 0] = coords[..., 0] * (new_width / old_width) + coords[..., 1] = coords[..., 1] * (new_height / old_height) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + +def _rle_to_mask(rle: dict[str, Any]) -> torch.Tensor: + """Compute a binary mask from an uncompressed RLE.""" + height, width = rle["size"] + mask = torch.empty(height * width, dtype=bool) + idx = 0 + parity = False + for count in rle["counts"]: + mask[idx : idx + count] = parity + idx += count + parity = not parity + mask = mask.reshape(width, height) + return mask.transpose(0, 1) # Reshape to original shape + + +def _post_process_for_mask_generation(rle_masks, iou_scores, mask_boxes, amg_crops_nms_thresh=0.7): + """ + Perform NMS (Non Maximum Suppression) on the outputs. + + Args: + rle_masks (`torch.Tensor`): + binary masks in the RLE format + iou_scores (`torch.Tensor` of shape (nb_masks, 1)): + iou_scores predicted by the model + mask_boxes (`torch.Tensor`): + The bounding boxes corresponding to segmentation masks + amg_crops_nms_thresh (`float`, *optional*, defaults to 0.7): + NMS threshold. + """ + keep_by_nms = batched_nms( + boxes=mask_boxes.float(), + scores=iou_scores, + idxs=torch.zeros(mask_boxes.shape[0]), + iou_threshold=amg_crops_nms_thresh, + ) + + iou_scores = iou_scores[keep_by_nms] + rle_masks = [rle_masks[i] for i in keep_by_nms] + mask_boxes = mask_boxes[keep_by_nms] + masks = [_rle_to_mask(rle) for rle in rle_masks] + + return masks, iou_scores, rle_masks, mask_boxes + + +def _mask_to_rle(input_mask: "torch.Tensor"): + """ + Encodes masks the run-length encoding (RLE), in the format expected by pycoco tools. + """ + # Put in fortran order and flatten height and width + batch_size, height, width = input_mask.shape + input_mask = input_mask.permute(0, 2, 1).flatten(1) + + # Compute change indices + diff = input_mask[:, 1:] ^ input_mask[:, :-1] + change_indices = diff.nonzero() + + # Encode run length + out = [] + for i in range(batch_size): + cur_idxs = change_indices[change_indices[:, 0] == i, 1] + 1 + if len(cur_idxs) == 0: + # No changes => either all 0 or all 1 + # If the entire mask is 0, RLE is [height*width] or if the entire mask is 1, RLE is [0, height*width]. + if input_mask[i, 0] == 0: + out.append({"size": [height, width], "counts": [height * width]}) + else: + out.append({"size": [height, width], "counts": [0, height * width]}) + continue + btw_idxs = cur_idxs[1:] - cur_idxs[:-1] + counts = [] if input_mask[i, 0] == 0 else [0] + counts += [cur_idxs[0].item()] + btw_idxs.tolist() + [height * width - cur_idxs[-1].item()] + out.append({"size": [height, width], "counts": counts}) + return out + + +__all__ = ["SamImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/modeling_sam.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/modeling_sam.py new file mode 100644 index 0000000000000000000000000000000000000000..d42358034c5342efee3b1083ee426102c414f220 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/modeling_sam.py @@ -0,0 +1,1361 @@ +# Copyright 2023 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SAM model.""" + +import collections +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, auto_docstring, logging +from ...utils.generic import TransformersKwargs, merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_sam import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring( + custom_intro=""" + Base class for sam vision model's outputs that also contains image embeddings obtained by applying the projection + layer to the pooler_output. + """ +) +@dataclass +class SamVisionEncoderOutput(ModelOutput): + r""" + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + """ + + image_embeds: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@auto_docstring( + custom_intro=""" + Base class for Segment-Anything model's output + """ +) +@dataclass +class SamImageSegmentationOutput(ModelOutput): + r""" + iou_scores (`torch.FloatTensor` of shape `(batch_size, num_masks)`): + The iou scores of the predicted masks. + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_masks, height, width)`): + The predicted low resolutions masks. Needs to be post-processed by the processor + vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the vision model at the output of each layer plus the optional initial embedding outputs. + vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + iou_scores: torch.FloatTensor | None = None + pred_masks: torch.FloatTensor | None = None + vision_hidden_states: tuple[torch.FloatTensor, ...] | None = None + vision_attentions: tuple[torch.FloatTensor, ...] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None + + +class SamPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values): + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).permute(0, 2, 3, 1) + return embeddings + + +class SamMLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim) + self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size) + self.act = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.lin1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.lin2(hidden_states) + return hidden_states + + +# Copied from transformers.models.convnext.modeling_convnext.ConvNextLayerNorm with ConvNext->Sam +class SamLayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class SamAttention(nn.Module): + """ + SAM's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and + values. + """ + + def __init__(self, config, downsample_rate=None): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + + downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate + + self.internal_dim = config.hidden_size // downsample_rate + self.num_attention_heads = config.num_attention_heads + if self.internal_dim % config.num_attention_heads != 0: + raise ValueError("num_attention_heads must divide hidden_size.") + self.scaling = (self.internal_dim // config.num_attention_heads) ** -0.5 + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.v_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.out_proj = nn.Linear(self.internal_dim, self.hidden_size) + + self.is_causal = False + + def _separate_heads(self, hidden_states: Tensor, num_attention_heads: int) -> Tensor: + batch, point_batch_size, n_tokens, channel = hidden_states.shape + c_per_head = channel // num_attention_heads + hidden_states = hidden_states.reshape(batch * point_batch_size, n_tokens, num_attention_heads, c_per_head) + return hidden_states.transpose(1, 2) + + def _recombine_heads(self, hidden_states: Tensor, point_batch_size: int) -> Tensor: + batch, n_tokens, n_heads, c_per_head = hidden_states.shape + return hidden_states.reshape(batch // point_batch_size, point_batch_size, n_tokens, n_heads * c_per_head) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + attention_similarity: Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Tensor: + # Input projections + query = self.q_proj(query) + key = self.k_proj(key) + value = self.v_proj(value) + + point_batch_size = query.shape[1] + # Separate into heads + query = self._separate_heads(query, self.num_attention_heads) + key = self._separate_heads(key, self.num_attention_heads) + value = self._separate_heads(value, self.num_attention_heads) + + # SamAttention + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_similarity, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = self._recombine_heads(attn_output, point_batch_size) + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class SamTwoWayAttentionBlock(nn.Module): + def __init__(self, config, attention_downsample_rate: int = 2, skip_first_layer_pe: bool = False): + """ + A transformer block with four layers: + (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on + sparse inputs (4) cross attention of dense inputs -> sparse inputs + + Arguments: + config (`SamMaskDecoderConfig`): + The configuration file used to instantiate the block + attention_downsample_rate (*optionalk*, int, defaults to 2): + The downsample ratio of the block used to reduce the inner dim of the attention. + skip_first_layer_pe (*optional*, bool, defaults to `False`): + Whether or not to skip the addition of the query_point_embedding on the first layer. + """ + super().__init__() + + self.hidden_size = config.hidden_size + self.layer_norm_eps = config.layer_norm_eps + + self.self_attn = SamAttention(config, downsample_rate=1) + self.layer_norm1 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps) + + self.cross_attn_token_to_image = SamAttention(config, downsample_rate=attention_downsample_rate) + self.layer_norm2 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps) + + self.mlp = SamMLPBlock(config) + self.layer_norm3 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps) + + self.layer_norm4 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps) + self.cross_attn_image_to_token = SamAttention(config, downsample_rate=attention_downsample_rate) + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, + queries: Tensor, + keys: Tensor, + query_point_embedding: Tensor, + key_point_embedding: Tensor, + attention_similarity: Tensor, + **kwargs: Unpack[TransformersKwargs], + ): + # Self attention block + if self.skip_first_layer_pe: + queries, _ = self.self_attn(query=queries, key=queries, value=queries) + else: + query = queries + query_point_embedding + attn_out, _ = self.self_attn(query=query, key=query, value=queries) + queries = queries + attn_out + queries = self.layer_norm1(queries) + + # Cross attention block, tokens attending to image embedding + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_token_to_image( + query=query, key=key, value=keys, attention_similarity=attention_similarity + ) + queries = queries + attn_out + + queries = self.layer_norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.layer_norm3(queries) + + # Cross attention block, image embedding attending to tokens + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_image_to_token(query=key, key=query, value=queries) + keys = keys + attn_out + + keys = self.layer_norm4(keys) + return queries, keys, attn_out + + +class SamTwoWayTransformer(nn.Module): + def __init__(self, config: SamMaskDecoderConfig): + super().__init__() + self.config = config + + self.num_hidden_layers = config.num_hidden_layers + self.layers = nn.ModuleList() + + for i in range(self.num_hidden_layers): + self.layers.append(SamTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0))) + + self.final_attn_token_to_image = SamAttention(config) + self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size) + + def forward( + self, + point_embeddings: Tensor, + image_embeddings: Tensor, + image_positional_embeddings: Tensor, + attention_similarity: Tensor, + target_embedding=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + if image_embeddings is None: + raise ValueError("You have to specify an image_embedding") + + image_embeddings = image_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + image_positional_embeddings = image_positional_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + + # Prepare queries + queries = point_embeddings + keys = image_embeddings + + # Apply transformer blocks and final layernorm + for layer in self.layers: + if target_embedding is not None: + queries += target_embedding + + queries, keys, _ = layer( + queries=queries, + keys=keys, + query_point_embedding=point_embeddings, + key_point_embedding=image_positional_embeddings, + attention_similarity=attention_similarity, + **kwargs, + ) + # Apply the final attention layer from the points to the image + query = queries + point_embeddings + key = keys + image_positional_embeddings + + attn_out, _ = self.final_attn_token_to_image(query=query, key=key, value=keys) + + queries = queries + attn_out + queries = self.layer_norm_final_attn(queries) + return queries, keys + + +class SamFeedForward(nn.Module): + def __init__( + self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int, sigmoid_output: bool = False + ): + super().__init__() + self.num_layers = num_layers + self.activation = nn.ReLU() + self.proj_in = nn.Linear(input_dim, hidden_dim) + self.proj_out = nn.Linear(hidden_dim, output_dim) + self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)]) + self.sigmoid_output = sigmoid_output + + def forward(self, hidden_states): + hidden_states = self.proj_in(hidden_states) + hidden_states = self.activation(hidden_states) + for layer in self.layers: + hidden_states = self.activation(layer(hidden_states)) + + hidden_states = self.proj_out(hidden_states) + if self.sigmoid_output: + hidden_states = F.sigmoid(hidden_states) + return hidden_states + + +class SamMaskDecoder(nn.Module): + def __init__(self, config: SamMaskDecoderConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + + self.num_multimask_outputs = config.num_multimask_outputs + self.num_mask_tokens = config.num_multimask_outputs + 1 + + self.iou_token = nn.Embedding(1, self.hidden_size) + self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size) + + self.transformer = SamTwoWayTransformer(config) + + # should we create a new class for this? + self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + self.upscale_layer_norm = SamLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.activation = nn.GELU() + + mlps_list = [] + for _ in range(self.num_mask_tokens): + mlps_list += [SamFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)] + self.output_hypernetworks_mlps = nn.ModuleList(mlps_list) + + self.iou_prediction_head = SamFeedForward( + self.hidden_size, config.iou_head_hidden_dim, self.num_mask_tokens, config.iou_head_depth + ) + + def forward( + self, + image_embeddings: torch.Tensor, + image_positional_embeddings: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + attention_similarity: torch.Tensor | None = None, + target_embedding: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Args: + image_embeddings (`torch.Tensor`): + the embeddings from the image encoder + image_positional_embedding (`torch.Tensor`): + positional encoding with the shape of image_embeddings + sparse_prompt_embeddings (`torch.Tensor`): + The embeddings of the points and boxes + dense_prompt_embeddings (`torch.Tensor`): + the embeddings of the mask inputs + multimask_output (bool): + Whether to return multiple masks or a single mask. + """ + batch_size, num_channels, height, width = image_embeddings.shape + point_batch_size = sparse_prompt_embeddings.shape[1] if sparse_prompt_embeddings is not None else 1 + # Concatenate output tokens + output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight], dim=0) + output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) + + if sparse_prompt_embeddings is not None: + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2) + else: + tokens = output_tokens + point_embeddings = tokens.to(self.iou_token.weight.dtype) + + # Expand per-image data in batch direction to be per-point + image_embeddings = image_embeddings + dense_prompt_embeddings + image_embeddings = image_embeddings.repeat_interleave(point_batch_size, 0) + image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) + + # Run the transformer, image_positional_embedding are consumed + point_embedding, image_embeddings = self.transformer( + point_embeddings=point_embeddings, + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + ) + iou_token_out = point_embedding[:, :, 0, :] + mask_tokens_out = point_embedding[:, :, 1 : (1 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + image_embeddings = image_embeddings.transpose(2, 3).reshape( + batch_size * point_batch_size, num_channels, height, width + ) + + upscaled_embedding = self.upscale_conv1(image_embeddings) + upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) + upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding)) + + hyper_in_list = [] + for i in range(self.num_mask_tokens): + current_mlp = self.output_hypernetworks_mlps[i] + hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])] + hyper_in = torch.stack(hyper_in_list, dim=2) + + _, num_channels, height, width = upscaled_embedding.shape + upscaled_embedding = upscaled_embedding.reshape(batch_size, point_batch_size, num_channels, height * width) + masks = (hyper_in @ upscaled_embedding).reshape(batch_size, point_batch_size, -1, height, width) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + + # Select the correct mask or masks for output + if multimask_output: + mask_slice = slice(1, None) + else: + mask_slice = slice(0, 1) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + return masks, iou_pred + + +class SamPositionalEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.scale = config.scale + self.positional_embedding = nn.Parameter(self.scale * torch.randn((2, config.num_pos_feats))) + + def forward(self, input_coords, input_shape=None): + """Positionally encode points that are normalized to [0,1].""" + coordinates = input_coords.clone() + + if input_shape is not None: + coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1] + coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0] + + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coordinates = 2 * coordinates - 1 + coordinates = coordinates.to(self.positional_embedding.dtype) + coordinates = coordinates @ self.positional_embedding + coordinates = 2 * np.pi * coordinates + # outputs d_1 x ... x d_n x channel shape + return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1) + + +class SamMaskEmbedding(nn.Module): + def __init__(self, config: SamPromptEncoderConfig): + super().__init__() + self.mask_input_channels = config.mask_input_channels // 4 + self.activation = ACT2FN[config.hidden_act] + self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2) + self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2) + self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1) + self.layer_norm1 = SamLayerNorm( + self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first" + ) + self.layer_norm2 = SamLayerNorm( + self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first" + ) + + def forward(self, masks): + hidden_states = self.conv1(masks) + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.activation(hidden_states) + dense_embeddings = self.conv3(hidden_states) + return dense_embeddings + + +class SamPromptEncoder(nn.Module): + def __init__(self, config: SamConfig): + super().__init__() + self.shared_embedding = SamPositionalEmbedding(config.vision_config) + config = config.prompt_encoder_config + self.mask_embed = SamMaskEmbedding(config) + self.no_mask_embed = nn.Embedding(1, config.hidden_size) + + self.image_embedding_size = (config.image_embedding_size, config.image_embedding_size) + self.input_image_size = config.image_size + + self.point_embed = nn.ModuleList( + [nn.Embedding(1, config.hidden_size) for i in range(config.num_point_embeddings)] + ) + self.hidden_size = config.hidden_size + self.not_a_point_embed = nn.Embedding(1, config.hidden_size) + + def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + target_point_shape = (points.shape[0], points.shape[1], 1, points.shape[-1]) + target_labels_shape = (points.shape[0], points.shape[1], 1) + padding_point = torch.zeros(target_point_shape, device=points.device) + padding_label = -torch.ones(target_labels_shape, device=labels.device) + points = torch.cat([points, padding_point], dim=2) + labels = torch.cat([labels, padding_label], dim=2) + input_shape = (self.input_image_size, self.input_image_size) + point_embedding = self.shared_embedding(points, input_shape) + + # torch.where and expanding the labels tensor is required by the ONNX export + point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding) + + # This is required for the ONNX export. The dtype, device need to be explicitly + # specified as otherwise torch.onnx.export interprets as double + point_embedding = torch.where(labels[..., None] != -10, point_embedding, torch.zeros_like(point_embedding)) + + point_embedding = torch.where( + (labels == 0)[:, :, :, None], + point_embedding + self.point_embed[0].weight[None, None, :, :], + point_embedding, + ) + + point_embedding = torch.where( + (labels == 1)[:, :, :, None], + point_embedding + self.point_embed[1].weight[None, None, :, :], + point_embedding, + ) + + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + batch_size, nb_boxes = boxes.shape[:2] + coords = boxes.reshape(batch_size, nb_boxes, 2, 2) + input_shape = (self.input_image_size, self.input_image_size) + corner_embedding = self.shared_embedding(coords, input_shape) + corner_embedding[:, :, 0, :] += self.point_embed[2].weight + corner_embedding[:, :, 1, :] += self.point_embed[3].weight + return corner_embedding + + def forward( + self, + input_points: tuple[torch.Tensor, torch.Tensor] | None, + input_labels: torch.Tensor | None, + input_boxes: torch.Tensor | None, + input_masks: torch.Tensor | None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense embeddings. + + Args: + points (`torch.Tensor`, *optional*): + point coordinates and labels to embed. + boxes (`torch.Tensor`, *optional*): + boxes to embed + masks (`torch.Tensor`, *optional*): + masks to embed + """ + sparse_embeddings = None + batch_size = 1 + if input_points is not None: + batch_size = input_points.shape[0] + if input_labels is None: + raise ValueError("If points are provided, labels must also be provided.") + point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None)) + sparse_embeddings = point_embeddings + if input_boxes is not None: + batch_size = input_boxes.shape[0] + box_embeddings = self._embed_boxes(input_boxes) + if sparse_embeddings is None: + sparse_embeddings = box_embeddings + else: + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2) + if input_masks is not None: + dense_embeddings = self.mask_embed(input_masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings + + +class SamVisionAttention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__(self, config, window_size): + super().__init__() + input_size = ( + (config.image_size // config.patch_size, config.image_size // config.patch_size) + if window_size == 0 + else (window_size, window_size) + ) + + self.num_attention_heads = config.num_attention_heads + head_dim = config.hidden_size // config.num_attention_heads + self.scale = head_dim**-0.5 + self.dropout = config.attention_dropout + + self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias) + self.proj = nn.Linear(config.hidden_size, config.hidden_size) + + self.use_rel_pos = config.use_rel_pos + if self.use_rel_pos: + if input_size is None: + raise ValueError("Input size must be provided if using relative positional encoding.") + + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + + Args: + q_size (int): + size of the query. + k_size (int): + size of key k. + rel_pos (`torch.Tensor`): + relative position embeddings (L, channel). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + def get_decomposed_rel_pos( + self, + query: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: tuple[int, int], + k_size: tuple[int, int], + ) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py + + Args: + query (`torch.Tensor`): + query q in the attention layer with shape (batch_size, query_height * query_width, channel). + rel_pos_h (`torch.Tensor`): + relative position embeddings (Lh, channel) for height axis. + rel_pos_w (`torch.Tensor`): + relative position embeddings (Lw, channel) for width axis. + q_size (tuple): + spatial sequence size of query q with (query_height, query_width). + k_size (tuple): + spatial sequence size of key k with (key_height, key_width). + + Returns: + decomposed_rel_pos (`torch.Tensor`): + decomposed relative position embeddings. + """ + query_height, query_width = q_size + key_height, key_width = k_size + relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h) + relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w) + + batch_size, _, dim = query.shape + reshaped_query = query.reshape(batch_size, query_height, query_width, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height) + rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width) + + decomposed_rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + + return decomposed_rel_pos + + def forward(self, hidden_states: torch.Tensor, output_attentions=None) -> tuple[torch.Tensor, torch.Tensor]: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, batch_size, nHead, height * width, channel) + qkv = ( + self.qkv(hidden_states) + .reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + .permute(2, 0, 3, 1, 4) + ) + # q, k, v with shape (batch_size * nHead, height * width, channel) + query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0) + + attn_weights = (query * self.scale) @ key.transpose(-2, -1) + + if self.use_rel_pos: + decomposed_rel_pos = self.get_decomposed_rel_pos( + query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width) + ) + decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights) + attn_weights = attn_weights + decomposed_rel_pos + + attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype) + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1) + attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + return attn_output, attn_weights + + +class SamVisionSdpaAttention(SamVisionAttention): + """ + Multi-head Attention block with relative position embeddings. + Using SDPA instead of the default attention. + """ + + def __init__(self, config, window_size): + super().__init__(config, window_size) + + def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor: + if output_attentions: + logger.warning_once( + f"{self.__class__.__name__} does not support `output_attentions=True`. The returned attention weights will " + "be `None`. If you want to get attention weights, please set `attn_implementation='eager'` when loading the model." + ) + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, B, nHead, H * W, C) + qkv = ( + self.qkv(hidden_states) + .reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + .permute(2, 0, 3, 1, 4) + ) + # q, k, v with shape (B * nHead, H * W, C) + query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0) + + attn_bias = None + if self.use_rel_pos: + decomposed_rel_pos = self.get_decomposed_rel_pos( + query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width) + ) + decomposed_rel_pos = decomposed_rel_pos.reshape( + batch_size, self.num_attention_heads, height * width, height * width + ) + attn_bias = decomposed_rel_pos + + query = query.view(batch_size, self.num_attention_heads, height * width, -1) + key = key.view(batch_size, self.num_attention_heads, height * width, -1) + value = value.view(batch_size, self.num_attention_heads, height * width, -1) + + attn_output = torch.nn.functional.scaled_dot_product_attention(query, key, value, attn_mask=attn_bias) + + attn_output = ( + attn_output.view(batch_size, self.num_attention_heads, height, width, -1) + .permute(0, 2, 3, 1, 4) + .reshape(batch_size, height, width, -1) + ) + + attn_output = self.proj(attn_output) + return attn_output, None + + +SAM_VISION_ATTENTION_CLASSES = { + "eager": SamVisionAttention, + "sdpa": SamVisionSdpaAttention, +} + + +class SamVisionLayer(GradientCheckpointingLayer): + def __init__(self, config, window_size): + super().__init__() + self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attn = SAM_VISION_ATTENTION_CLASSES[config._attn_implementation](config, window_size) + self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = SamMLPBlock(config) + self.window_size = window_size + + def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> tuple[torch.Tensor, tuple[int, int]]: + """ + Args: + Partition into non-overlapping windows with padding if needed. + hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window + size. + + Returns: + windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel]. + (pad_height, pad_width): padded height and width before partition + """ + batch_size, height, width, channel = hidden_states.shape + + pad_h = (window_size - height % window_size) % window_size + pad_w = (window_size - width % window_size) % window_size + hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h)) + pad_height, pad_width = height + pad_h, width + pad_w + + hidden_states = hidden_states.reshape( + batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel + ) + windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel) + return windows, (pad_height, pad_width) + + def window_unpartition( + self, windows: torch.Tensor, window_size: int, padding_shape: tuple[int, int], original_shape: tuple[int, int] + ) -> torch.Tensor: + """ + Args: + Window unpartition into original sequences and removing padding. + hidden_states (tensor): + input tokens with [batch_size * num_windows, window_size, window_size, channel]. + window_size (int): + window size. + padding_shape (Tuple): + padded height and width (pad_height, pad_width). + original_shape (Tuple): original height and width (height, width) before padding. + + Returns: + hidden_states: unpartitioned sequences with [batch_size, height, width, channel]. + """ + pad_height, pad_width = padding_shape + height, width = original_shape + batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size) + hidden_states = windows.reshape( + batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1 + ) + hidden_states = ( + hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1) + ) + + hidden_states = hidden_states[:, :height, :width, :].contiguous() + return hidden_states + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.FloatTensor]: + residual = hidden_states + hidden_states = self.layer_norm1(hidden_states) + # Window partition + if self.window_size > 0: + height, width = hidden_states.shape[1], hidden_states.shape[2] + hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size) + + hidden_states, attn_weights = self.attn( + hidden_states=hidden_states, + ) + # Reverse window partition + if self.window_size > 0: + hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width)) + + hidden_states = residual + hidden_states + layernorm_output = self.layer_norm2(hidden_states) + hidden_states = hidden_states + self.mlp(layernorm_output) + return hidden_states + + +class SamVisionNeck(nn.Module): + def __init__(self, config: SamVisionConfig): + super().__init__() + self.config = config + + self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False) + self.layer_norm1 = SamLayerNorm(config.output_channels, data_format="channels_first") + self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False) + self.layer_norm2 = SamLayerNorm(config.output_channels, data_format="channels_first") + + def forward(self, hidden_states): + hidden_states = hidden_states.permute(0, 3, 1, 2) + hidden_states = self.conv1(hidden_states) + hidden_states = self.layer_norm1(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + return hidden_states + + +@auto_docstring +class SamPreTrainedModel(PreTrainedModel): + config: SamConfig + base_model_prefix = "sam" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = ["SamVisionAttention"] + supports_gradient_checkpointing = True + _supports_sdpa = True + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + super()._init_weights(module) + if isinstance(module, SamVisionAttention): + if module.use_rel_pos: + init.zeros_(module.rel_pos_h) + init.zeros_(module.rel_pos_w) + elif isinstance(module, SamVisionEncoder): + if self.config.use_abs_pos: + init.zeros_(module.pos_embed) + elif isinstance(module, SamPositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + + +class SamVisionEncoder(SamPreTrainedModel): + _can_record_outputs = {"hidden_states": SamVisionLayer, "attentions": SamVisionAttention} + + def __init__(self, config: SamVisionConfig): + super().__init__(config) + self.config = config + self.image_size = config.image_size + self.patch_embed = SamPatchEmbeddings(config) + + self.pos_embed = None + if config.use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros( + 1, + config.image_size // config.patch_size, + config.image_size // config.patch_size, + config.hidden_size, + ) + ) + + self.layers = nn.ModuleList() + for i in range(config.num_hidden_layers): + layer = SamVisionLayer( + config, + window_size=config.window_size if i not in config.global_attn_indexes else 0, + ) + self.layers.append(layer) + + self.neck = SamVisionNeck(config) + + self.gradient_checkpointing = False + self.post_init() + + def get_input_embeddings(self): + return self.patch_embed + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, pixel_values: torch.FloatTensor | None = None, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | SamVisionEncoderOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.patch_embed(pixel_values) + if self.pos_embed is not None: + hidden_states = hidden_states + self.pos_embed + for layer_module in self.layers: + hidden_states = layer_module(hidden_states) + hidden_states = self.neck(hidden_states) + return SamVisionEncoderOutput( + last_hidden_state=hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + The vision model from Sam without any head or projection on top. + """ +) +class SamVisionModel(SamPreTrainedModel): + config: SamVisionConfig + main_input_name = "pixel_values" + + def __init__(self, config: SamVisionConfig): + super().__init__(config) + self.vision_encoder = SamVisionEncoder(config) + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_encoder.patch_embed + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | SamVisionEncoderOutput: + return self.vision_encoder(pixel_values, **kwargs) + + +@auto_docstring( + custom_intro=""" + Segment Anything Model (SAM) for generating segmentation masks, given an input image and + input points and labels, boxes, or masks. + """ +) +class SamModel(SamPreTrainedModel): + input_modalities = ("image", "text") + _can_record_outputs = {"mask_decoder_attentions": OutputRecorder(SamTwoWayAttentionBlock, index=2)} + _tied_weights_keys = { + "prompt_encoder.shared_embedding.positional_embedding": "shared_image_embedding.positional_embedding" + } + + def __init__(self, config: SamConfig): + super().__init__(config) + self.shared_image_embedding = SamPositionalEmbedding(config.vision_config) + + self.vision_encoder = SamVisionEncoder(config.vision_config) + self.prompt_encoder = SamPromptEncoder(config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + self.mask_decoder = SamMaskDecoder(config.mask_decoder_config) + self.post_init() + + def get_input_embeddings(self): + return self.vision_encoder.get_input_embeddings() + + def get_image_wide_positional_embeddings(self): + size = self.config.prompt_encoder_config.image_embedding_size + target_device = self.shared_image_embedding.positional_embedding.device + target_dtype = self.shared_image_embedding.positional_embedding.dtype + grid = torch.ones((size, size), device=target_device, dtype=target_dtype) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / size + x_embed = x_embed / size + + positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1)) + return positional_embedding.permute(2, 0, 1).unsqueeze(0) # channel x height x width + + @torch.no_grad() + def get_image_embeddings(self, pixel_values, **kwargs: Unpack[TransformersKwargs]): + r""" + Returns the image embeddings by passing the pixel values through the vision encoder. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Input pixel values + """ + vision_output = self.vision_encoder( + pixel_values, + **kwargs, + ) + image_embeddings = vision_output[0] + return image_embeddings + + @torch.no_grad() + def get_prompt_embeddings( + self, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + ): + r""" + Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder. + + Args: + input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`): + Optional input points for the prompt encoder. The padding of the point is automatically done by the + processor. `point_batch_size` refers to the number of masks that we want the model to predict per + point. The model will output `point_batch_size` times 3 masks in total. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points_per_image)`): + Optional input labels for the prompt encoder. The padding of the labels is automatically done by the + processor, or can be fed by the user. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes_per_image, 4)`): + Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the + processor. users can also pass manually the input boxes. + input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`): + Optional input masks for the prompt encoder. + """ + prompt_output = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + return prompt_output + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> SamImageSegmentationOutput: + r""" + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoModel, AutoProcessor + + >>> model = AutoModel.from_pretrained("facebook/sam-vit-base") + >>> processor = AutoProcessor.from_pretrained("facebook/sam-vit-base") + + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" + >>> with httpx.stream("GET", url) as response: + ... raw_image = Image.open(BytesIO(response.read())).convert("RGB") + >>> input_points = [[[400, 650]]] # 2D location of a window on the car + >>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt") + + >>> # Get segmentation mask + >>> outputs = model(**inputs) + + >>> # Postprocess masks + >>> masks = processor.post_process_masks( + ... outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"] + ... ) + ``` + """ + if pixel_values is None and image_embeddings is None: + raise ValueError("Either pixel_values or image_embeddings must be provided.") + + if pixel_values is not None and image_embeddings is not None: + raise ValueError("Only one of pixel_values and image_embeddings can be provided.") + + if input_points is not None and len(input_points.shape) != 4: + raise ValueError( + "The input_points must be a 4D tensor. Of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`.", + f" got {input_points.shape}.", + ) + if input_boxes is not None and len(input_boxes.shape) != 3: + raise ValueError( + "The input_points must be a 3D tensor. Of shape `batch_size`, `nb_boxes`, `4`.", + f" got {input_boxes.shape}.", + ) + if input_points is not None and input_boxes is not None: + point_batch_size = input_points.shape[1] + box_batch_size = input_boxes.shape[1] + if point_batch_size != box_batch_size: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {point_batch_size} and {box_batch_size}." + ) + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings.shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + vision_attentions = None + vision_hidden_states = None + + if pixel_values is not None: + vision_outputs: SamVisionEncoderOutput = self.vision_encoder(pixel_values, **kwargs) + image_embeddings = vision_outputs.last_hidden_state + vision_hidden_states = vision_outputs.hidden_states + vision_attentions = vision_outputs.attentions + + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + if input_points is not None and image_embeddings.shape[0] != input_points.shape[0]: + raise ValueError( + "The batch size of the image embeddings and the input points must be the same. ", + f"Got {image_embeddings.shape[0]} and {input_points.shape[0]} respectively.", + " if you want to pass multiple points for the same image, make sure that you passed ", + " input_points of shape (batch_size, point_batch_size, num_points_per_image, 3) and ", + " input_labels of shape (batch_size, point_batch_size, num_points_per_image)", + ) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + + low_res_masks, iou_predictions = self.mask_decoder( + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + ) + + return SamImageSegmentationOutput( + iou_scores=iou_predictions, + pred_masks=low_res_masks, + vision_hidden_states=vision_hidden_states, + vision_attentions=vision_attentions, + ) + + +__all__ = ["SamVisionModel", "SamModel", "SamPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/processing_sam.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/processing_sam.py new file mode 100644 index 0000000000000000000000000000000000000000..017d30d7eef9e7a667b8563ca40c896610215c9b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam/processing_sam.py @@ -0,0 +1,292 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for SAM. +""" + +from copy import deepcopy +from typing import Union + +import numpy as np + +from ...image_utils import ImageInput +from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin +from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput +from ...utils import auto_docstring, is_torch_available + + +if is_torch_available(): + import torch + +NestedList = list[Union[float | int | None, "NestedList"]] + + +class SamImagesKwargs(ImagesKwargs, total=False): + """ + segmentation_maps (`ImageInput`, *optional*): + Ground truth segmentation maps to process alongside the input images. These maps are used for training + or evaluation purposes and are resized and normalized to match the processed image dimensions. + input_points (`NestedList`, *optional*): + Input points for prompt-based segmentation. Should be a nested list with structure + `[image_level, object_level, point_level, [x, y]]` where each point is specified as `[x, y]` coordinates + in the original image space. Points are normalized to the target image size before being passed to the model. + input_labels (`NestedList`, *optional*): + Labels for the input points, indicating whether each point is a foreground (1) or background (0) point. + Should be a nested list with structure `[image_level, object_level, point_level]`. Must have the same + structure as `input_points` (excluding the coordinate dimension). + input_boxes (`NestedList`, *optional*): + Bounding boxes for prompt-based segmentation. Should be a nested list with structure + `[image_level, box_level, [x1, y1, x2, y2]]` where each box is specified as `[x1, y1, x2, y2]` coordinates + in the original image space. Boxes are normalized to the target image size before being passed to the model. + point_pad_value (`int`, *optional*, defaults to `-10`): + The value used for padding input points when batching sequences of different lengths. This value marks + padded positions and is preserved during coordinate normalization to distinguish real points from padding. + mask_size (`dict[str, int]`, *optional*): + Dictionary specifying the target mask size with keys `"height"` and `"width"`. This determines the + resolution of the output segmentation masks generated by the model. + mask_pad_size (`dict[str, int]`, *optional*): + Dictionary specifying the padding size for masks with keys `"height"` and `"width"`. This is used when + batching masks of different sizes to ensure consistent dimensions. + """ + + segmentation_maps: ImageInput | None + input_points: "NestedList | torch.Tensor | None" + input_labels: "NestedList | int | torch.Tensor | None" + input_boxes: "NestedList | torch.Tensor | None" + point_pad_value: int | None + mask_size: dict[str, int] + mask_pad_size: dict[str, int] + + +class SamProcessorKwargs(ProcessingKwargs, total=False): + images_kwargs: SamImagesKwargs + _defaults = { + "images_kwargs": { + "point_pad_value": -10, + } + } + + +@auto_docstring +class SamProcessor(ProcessorMixin): + def __init__(self, image_processor): + super().__init__(image_processor) + self.target_size = self.image_processor.size["longest_edge"] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + **kwargs, + ) -> BatchEncoding: + output_kwargs = self._merge_kwargs( + SamProcessorKwargs, + tokenizer_init_kwargs={}, + **kwargs, + ) + input_points = output_kwargs["images_kwargs"].pop("input_points", None) + input_labels = output_kwargs["images_kwargs"].pop("input_labels", None) + input_boxes = output_kwargs["images_kwargs"].pop("input_boxes", None) + point_pad_value = output_kwargs["images_kwargs"].pop("point_pad_value", None) + + encoding_image_processor = self.image_processor( + images, + **output_kwargs["images_kwargs"], + ) + + # pop arguments that are not used in the forward but used nevertheless + original_sizes = encoding_image_processor["original_sizes"] + + if hasattr(original_sizes, "numpy"): + original_sizes = original_sizes.numpy() + + input_points, input_labels, input_boxes = self._check_and_preprocess_points( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + ) + + encoding_image_processor = self._normalize_and_convert( + encoding_image_processor, + original_sizes, + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + return_tensors=output_kwargs["images_kwargs"].get("return_tensors"), + point_pad_value=point_pad_value, + ) + + return encoding_image_processor + + def _normalize_and_convert( + self, + encoding_image_processor, + original_sizes, + input_points=None, + input_labels=None, + input_boxes=None, + return_tensors="pt", + point_pad_value=-10, + ): + if input_points is not None: + if len(original_sizes) != len(input_points): + input_points = [ + self._normalize_coordinates(self.target_size, point, original_sizes[0]) for point in input_points + ] + else: + input_points = [ + self._normalize_coordinates(self.target_size, point, original_size) + for point, original_size in zip(input_points, original_sizes) + ] + # check that all arrays have the same shape + if not all(point.shape == input_points[0].shape for point in input_points): + if input_labels is not None: + input_points, input_labels = self._pad_points_and_labels( + input_points, input_labels, point_pad_value + ) + + input_points = np.array(input_points) + + if input_labels is not None: + input_labels = np.array(input_labels) + + if input_boxes is not None: + if len(original_sizes) != len(input_boxes): + input_boxes = [ + self._normalize_coordinates(self.target_size, box, original_sizes[0], is_bounding_box=True) + for box in input_boxes + ] + else: + input_boxes = [ + self._normalize_coordinates(self.target_size, box, original_size, is_bounding_box=True) + for box, original_size in zip(input_boxes, original_sizes) + ] + input_boxes = np.array(input_boxes) + + if input_boxes is not None: + if return_tensors == "pt": + input_boxes = torch.from_numpy(input_boxes) + # boxes batch size of 1 by default + input_boxes = input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes + encoding_image_processor.update({"input_boxes": input_boxes}) + if input_points is not None: + if return_tensors == "pt": + input_points = torch.from_numpy(input_points) + # point batch size of 1 by default + input_points = input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points + encoding_image_processor.update({"input_points": input_points}) + if input_labels is not None: + if return_tensors == "pt": + input_labels = torch.from_numpy(input_labels) + # point batch size of 1 by default + input_labels = input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels + encoding_image_processor.update({"input_labels": input_labels}) + + return encoding_image_processor + + def _pad_points_and_labels(self, input_points, input_labels, point_pad_value): + r""" + The method pads the 2D points and labels to the maximum number of points in the batch. + """ + expected_nb_points = max(point.shape[0] for point in input_points) + processed_input_points = [] + for i, point in enumerate(input_points): + if point.shape[0] != expected_nb_points: + point = np.concatenate( + [point, np.zeros((expected_nb_points - point.shape[0], 2)) + point_pad_value], axis=0 + ) + input_labels[i] = np.append(input_labels[i], [point_pad_value]) + processed_input_points.append(point) + input_points = processed_input_points + return input_points, input_labels + + def _normalize_coordinates( + self, target_size: int, coords: np.ndarray, original_size, is_bounding_box=False + ) -> np.ndarray: + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format. + """ + old_h, old_w = original_size + new_h, new_w = self.image_processor._get_preprocess_shape(original_size, longest_edge=target_size) + coords = deepcopy(coords).astype(float) + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + def _check_and_preprocess_points( + self, + input_points=None, + input_labels=None, + input_boxes=None, + ): + r""" + Check and preprocesses the 2D points, labels and bounding boxes. It checks if the input is valid and if they + are, it converts the coordinates of the points and bounding boxes. If a user passes directly a `torch.Tensor`, + it is converted to a `numpy.ndarray` and then to a `list`. + """ + if input_points is not None: + if hasattr(input_points, "numpy"): + input_points = input_points.numpy().tolist() + + if not isinstance(input_points, list) or not isinstance(input_points[0], list): + raise ValueError("Input points must be a list of list of floating points.") + input_points = [np.array(input_point) for input_point in input_points] + else: + input_points = None + + if input_labels is not None: + if hasattr(input_labels, "numpy"): + input_labels = input_labels.numpy().tolist() + + if not isinstance(input_labels, list) or not isinstance(input_labels[0], list): + raise ValueError("Input labels must be a list of list integers.") + input_labels = [np.array(label) for label in input_labels] + else: + input_labels = None + + if input_boxes is not None: + if hasattr(input_boxes, "numpy"): + input_boxes = input_boxes.numpy().tolist() + + if ( + not isinstance(input_boxes, list) + or not isinstance(input_boxes[0], list) + or not isinstance(input_boxes[0][0], list) + ): + raise ValueError("Input boxes must be a list of list of list of floating points.") + input_boxes = [np.array(box).astype(np.float32) for box in input_boxes] + else: + input_boxes = None + + return input_points, input_labels, input_boxes + + @property + def model_input_names(self): + image_processor_input_names = self.image_processor.model_input_names + return list(image_processor_input_names + ["original_sizes", "reshaped_input_sizes"]) + + def post_process_masks(self, *args, **kwargs): + return self.image_processor.post_process_masks(*args, **kwargs) + + +__all__ = ["SamProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4a91a3a1d7959e971a6c3e7b37fba043b40a8346 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam2 import * + from .image_processing_sam2 import * + from .modeling_sam2 import * + from .processing_sam2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/configuration_sam2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/configuration_sam2.py new file mode 100644 index 0000000000000000000000000000000000000000..852c7b818687144a9fc4bf61ef70f8c83f5687e5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/configuration_sam2.py @@ -0,0 +1,298 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAM2 model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="facebook/sam2.1-hiera-tiny") +@strict +class Sam2HieraDetConfig(PreTrainedConfig): + r""" + patch_kernel_size (`list[int]`, *optional*, defaults to `[7, 7]`): + The kernel size of the patch. + patch_stride (`list[int]`, *optional*, defaults to `[4, 4]`): + The stride of the patch. + patch_padding (`list[int]`, *optional*, defaults to `[3, 3]`): + The padding of the patch. + query_stride (`list[int]`, *optional*, defaults to `[2, 2]`): + The downsample stride between stages. + window_positional_embedding_background_size (`list[int]`, *optional*, defaults to `[7, 7]`): + The window size per stage when not using global attention. + num_query_pool_stages (`int`, *optional*, defaults to 3): + The number of query pool stages. + blocks_per_stage (`list[int]`, *optional*, defaults to `[1, 2, 7, 2]`): + The number of blocks per stage. + embed_dim_per_stage (`list[int]`, *optional*, defaults to `[96, 192, 384, 768]`): + The embedding dimension per stage. + num_attention_heads_per_stage (`list[int]`, *optional*, defaults to `[1, 2, 4, 8]`): + The number of attention heads per stage. + window_size_per_stage (`list[int]`, *optional*, defaults to `[8, 4, 14, 7]`): + The window size per stage. + global_attention_blocks (`list[int]`, *optional*, defaults to `[5, 7, 9]`): + The blocks where global attention is used. + """ + + base_config_key = "backbone_config" + model_type = "sam2_hiera_det_model" + + hidden_size: int = 96 + num_attention_heads: int = 1 + num_channels: int = 3 + image_size: int | list[int] | None = None + patch_kernel_size: int | list[int] | None = None + patch_stride: int | list[int] | None = None + patch_padding: int | list[int] | None = None + query_stride: int | list[int] | None = None + window_positional_embedding_background_size: list[int] | None = None + num_query_pool_stages: int = 3 + blocks_per_stage: list[int] | None = None + embed_dim_per_stage: list[int] | None = None + num_attention_heads_per_stage: list[int] | None = None + window_size_per_stage: list[int] | None = None + global_attention_blocks: list[int] | None = None + mlp_ratio: float = 4.0 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + self.image_size = self.image_size if self.image_size is not None else [1024, 1024] + self.patch_kernel_size = self.patch_kernel_size if self.patch_kernel_size is not None else [7, 7] + self.patch_stride = self.patch_stride if self.patch_stride is not None else [4, 4] + self.patch_padding = self.patch_padding if self.patch_padding is not None else [3, 3] + self.query_stride = self.query_stride if self.query_stride is not None else [2, 2] + self.window_positional_embedding_background_size = ( + self.window_positional_embedding_background_size + if self.window_positional_embedding_background_size is not None + else [7, 7] + ) + self.blocks_per_stage = self.blocks_per_stage if self.blocks_per_stage is not None else [1, 2, 7, 2] + self.embed_dim_per_stage = ( + self.embed_dim_per_stage if self.embed_dim_per_stage is not None else [96, 192, 384, 768] + ) + self.num_attention_heads_per_stage = ( + self.num_attention_heads_per_stage if self.num_attention_heads_per_stage is not None else [1, 2, 4, 8] + ) + self.window_size_per_stage = ( + self.window_size_per_stage if self.window_size_per_stage is not None else [8, 4, 14, 7] + ) + self.global_attention_blocks = ( + self.global_attention_blocks if self.global_attention_blocks is not None else [5, 7, 9] + ) + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="facebook/sam2.1-hiera-tiny") +@strict +class Sam2VisionConfig(PreTrainedConfig): + r""" + backbone_channel_list (`List[int]`, *optional*, defaults to `[768, 384, 192, 96]`): + The list of channel dimensions for the backbone. + backbone_feature_sizes (`List[List[int]]`, *optional*, defaults to `[[256, 256], [128, 128], [64, 64]]`): + The spatial sizes of the feature maps from the backbone. + fpn_hidden_size (`int`, *optional*, defaults to 256): + The hidden dimension of the FPN. + fpn_kernel_size (`int`, *optional*, defaults to 1): + The kernel size for the convolutions in the neck. + fpn_stride (`int`, *optional*, defaults to 1): + The stride for the convolutions in the neck. + fpn_padding (`int`, *optional*, defaults to 0): + The padding for the convolutions in the neck. + fpn_top_down_levels (`List[int]`, *optional*, defaults to `[2, 3]`): + The levels for the top-down FPN connections. + num_feature_levels (`int`, *optional*, defaults to 3): + The number of feature levels from the FPN to use. + """ + + base_config_key = "vision_config" + model_type = "sam2_vision_model" + sub_configs = { + "backbone_config": AutoConfig, + } + + backbone_config: dict | PreTrainedConfig | None = None + backbone_channel_list: list[int] | None = None + backbone_feature_sizes: list | None = None + fpn_hidden_size: int = 256 + fpn_kernel_size: int = 1 + fpn_stride: int = 1 + fpn_padding: int = 0 + fpn_top_down_levels: list[int] | None = None + num_feature_levels: int = 3 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + self.backbone_channel_list = ( + [768, 384, 192, 96] if self.backbone_channel_list is None else self.backbone_channel_list + ) + self.backbone_feature_sizes = ( + [[256, 256], [128, 128], [64, 64]] if self.backbone_feature_sizes is None else self.backbone_feature_sizes + ) + self.fpn_top_down_levels = [2, 3] if self.fpn_top_down_levels is None else self.fpn_top_down_levels + + if isinstance(self.backbone_config, dict): + self.backbone_config["model_type"] = self.backbone_config.get("model_type", "sam2_hiera_det_model") + self.backbone_config = CONFIG_MAPPING[self.backbone_config["model_type"]](**self.backbone_config) + elif self.backbone_config is None: + self.backbone_config = Sam2HieraDetConfig() + + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="facebook/sam2.1-hiera-tiny") +@strict +class Sam2PromptEncoderConfig(PreTrainedConfig): + r""" + mask_input_channels (`int`, *optional*, defaults to 16): + The number of channels to be fed to the `MaskDecoder` module. + num_point_embeddings (`int`, *optional*, defaults to 4): + The number of point embeddings to be used. + scale (`float`, *optional*, defaults to 1): + The scale factor for the prompt encoder. + """ + + base_config_key = "prompt_encoder_config" + + hidden_size: int = 256 + image_size: int | list[int] | tuple[int, int] = 1024 + patch_size: int | list[int] | tuple[int, int] = 16 + mask_input_channels: int = 16 + num_point_embeddings: int = 4 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + scale: int = 1 + + +@auto_docstring(checkpoint="facebook/sam2.1-hiera-tiny") +@strict +class Sam2MaskDecoderConfig(PreTrainedConfig): + r""" + mlp_dim (`int`, *optional*, defaults to 2048): + The dimension of the MLP in the two-way transformer. + attention_downsample_rate (`int`, *optional*, defaults to 2): + The downsample rate for the attention layers. + num_multimask_outputs (`int`, *optional*, defaults to 3): + The number of multimask outputs. + iou_head_depth (`int`, *optional*, defaults to 3): + The depth of the IoU head. + iou_head_hidden_dim (`int`, *optional*, defaults to 256): + The hidden dimension of the IoU head. + dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`): + Whether to use dynamic multimask via stability. + dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05): + The stability delta for the dynamic multimask. + dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98): + The stability threshold for the dynamic multimask. + """ + + base_config_key = "mask_decoder_config" + + hidden_size: int = 256 + hidden_act: str = "gelu" + mlp_dim: int = 2048 + num_hidden_layers: int = 2 + num_attention_heads: int = 8 + attention_downsample_rate: int = 2 + num_multimask_outputs: int = 3 + iou_head_depth: int = 3 + iou_head_hidden_dim: int = 256 + dynamic_multimask_via_stability: bool = True + dynamic_multimask_stability_delta: float = 0.05 + dynamic_multimask_stability_thresh: float = 0.98 + + +@auto_docstring(checkpoint="facebook/sam2.1-hiera-tiny") +@strict +class Sam2Config(PreTrainedConfig): + r""" + prompt_encoder_config (Union[`dict`, `Sam2PromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam2PromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `Sam2MaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam2MaskDecoderConfig`]. + + Example: + + ```python + >>> from transformers import ( + ... Sam2VisionConfig, + ... Sam2PromptEncoderConfig, + ... Sam2MaskDecoderConfig, + ... Sam2Model, + ... ) + + >>> # Initializing a Sam2Config with `"facebook/sam2.1_hiera_tiny"` style configuration + >>> configuration = Sam2Config() + + >>> # Initializing a Sam2Model (with random weights) from the `"facebook/sam2.1_hiera_tiny"` style configuration + >>> model = Sam2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Sam2Config from a Sam2VisionConfig, Sam2PromptEncoderConfig, and Sam2MaskDecoderConfig + + >>> # Initializing SAM2 vision encoder, memory attention, and memory encoder configurations + >>> vision_config = Sam2VisionConfig() + >>> prompt_encoder_config = Sam2PromptEncoderConfig() + >>> mask_decoder_config = Sam2MaskDecoderConfig() + + >>> config = Sam2Config(vision_config, prompt_encoder_config, mask_decoder_config) + ```""" + + model_type = "sam2" + sub_configs = { + "vision_config": AutoConfig, + "prompt_encoder_config": Sam2PromptEncoderConfig, + "mask_decoder_config": Sam2MaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + prompt_encoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam2_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam2_vision_model"]() + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = Sam2PromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = Sam2PromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam2MaskDecoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = Sam2MaskDecoderConfig() + + super().__post_init__(**kwargs) + + +__all__ = [ + "Sam2Config", + "Sam2HieraDetConfig", + "Sam2VisionConfig", + "Sam2PromptEncoderConfig", + "Sam2MaskDecoderConfig", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/image_processing_sam2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/image_processing_sam2.py new file mode 100644 index 0000000000000000000000000000000000000000..e5eb4d2d747d727dbad65a126cfc7f4cc25f07c9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/image_processing_sam2.py @@ -0,0 +1,688 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam2/modular_sam2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import math +from copy import deepcopy +from itertools import product +from typing import Any, Optional, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torchvision.ops.boxes import batched_nms + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_vision_available + + +if is_vision_available(): + import PIL + + +class Sam2ImageProcessorKwargs(ImagesKwargs, total=False): + r""" + mask_size (`dict[str, int]`, *optional*): + The size `{"height": int, "width": int}` to resize the segmentation maps to. + """ + + mask_size: dict[str, int] + + +def _compute_stability_score(masks: "torch.Tensor", mask_threshold: float, stability_score_offset: int): + # One mask is always contained inside the other. + # Save memory by preventing unnecessary cast to torch.int64 + intersections = ( + (masks > (mask_threshold + stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32) + ) + unions = (masks > (mask_threshold - stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32) + stability_scores = intersections / unions + return stability_scores + + +def _batched_mask_to_box(masks: "torch.Tensor"): + """ + Computes the bounding boxes around the given input masks. The bounding boxes are in the XYXY format which + corresponds the following required indices: + - LEFT: left hand side of the bounding box + - TOP: top of the bounding box + - RIGHT: right of the bounding box + - BOTTOM: bottom of the bounding box + + Return [0,0,0,0] for an empty mask. For input shape channel_1 x channel_2 x ... x height x width, the output shape + is channel_1 x channel_2 x ... x 4. + + Args: + - masks (`torch.Tensor` of shape `(batch, nb_mask, height, width)`) + """ + # torch.max below raises an error on empty inputs, just skip in this case + + if torch.numel(masks) == 0: + return torch.zeros(*masks.shape[:-2], 4, device=masks.device) + + # Normalize shape to Cxheightxwidth + shape = masks.shape + height, width = shape[-2:] + + # Get top and bottom edges + in_height, _ = torch.max(masks, dim=-1) + in_height_coords = in_height * torch.arange(height, device=in_height.device)[None, :] + bottom_edges, _ = torch.max(in_height_coords, dim=-1) + in_height_coords = in_height_coords + height * (~in_height) + top_edges, _ = torch.min(in_height_coords, dim=-1) + + # Get left and right edges + in_width, _ = torch.max(masks, dim=-2) + in_width_coords = in_width * torch.arange(width, device=in_width.device)[None, :] + right_edges, _ = torch.max(in_width_coords, dim=-1) + in_width_coords = in_width_coords + width * (~in_width) + left_edges, _ = torch.min(in_width_coords, dim=-1) + + # If the mask is empty the right edge will be to the left of the left edge. + # Replace these boxes with [0, 0, 0, 0] + empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) + out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) + out = out * (~empty_filter).unsqueeze(-1) + + # Return to original shape + out = out.reshape(*shape[:-2], 4) + return out + + +def _is_box_near_crop_edge(boxes, crop_box, orig_box, atol=20.0): + """Filter masks at the edge of a crop, but not at the edge of the original image.""" + crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) + orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) + + left, top, _, _ = crop_box + offset = torch.tensor([[left, top, left, top]], device=boxes.device) + # Check if boxes has a channel dimension + if len(boxes.shape) == 3: + offset = offset.unsqueeze(1) + boxes = (boxes + offset).float() + + near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) + near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) + near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) + return torch.any(near_crop_edge, dim=1) + + +def _pad_masks(masks, crop_box: list[int], orig_height: int, orig_width: int): + left, top, right, bottom = crop_box + if left == 0 and top == 0 and right == orig_width and bottom == orig_height: + return masks + # Coordinate transform masks + pad_x, pad_y = orig_width - (right - left), orig_height - (bottom - top) + pad = (left, pad_x - left, top, pad_y - top) + return torch.nn.functional.pad(masks, pad, value=0) + + +def _generate_crop_boxes( + image, + target_size: int, + crop_n_layers: int = 0, + overlap_ratio: float = 512 / 1500, + points_per_crop: int | None = 32, + crop_n_points_downscale_factor: list[int] | None = 1, +) -> tuple[list[list[int]], list[int]]: + """ + Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + + Args: + image (`torch.Tensor`): + Image to generate crops for. + target_size (`int`): + Size of the smallest crop. + crop_n_layers (`int`, *optional*): + If `crops_n_layers>0`, mask prediction will be run again on crops of the image. Sets the number of layers + to run, where each layer has 2**i_layer number of image crops. + overlap_ratio (`int`, *optional*): + Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the + image length. Later layers with more crops scale down this overlap. + points_per_crop (`int`, *optional*): + Number of points to sam2ple per crop. + crop_n_points_downscale_factor (`int`, *optional*): + The number of points-per-side sam2pled in layer n is scaled down by crop_n_points_downscale_factor**n. + """ + + if isinstance(image, list): + raise ValueError("Only one image is allowed for crop generation.") + original_size = image.shape[-2:] + + points_grid = [] + for i in range(crop_n_layers + 1): + n_points = int(points_per_crop / (crop_n_points_downscale_factor**i)) + points_grid.append(_build_point_grid(n_points)) + + crop_boxes, layer_idxs = _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size) + + cropped_images, point_grid_per_crop = _generate_crop_images( + crop_boxes, image, points_grid, layer_idxs, target_size, original_size + ) + crop_boxes = torch.tensor(crop_boxes) + crop_boxes = crop_boxes.float() + points_per_crop = torch.stack(point_grid_per_crop) + points_per_crop = points_per_crop.unsqueeze(0).permute(0, 2, 1, 3) + cropped_images = torch.stack(cropped_images) + + input_labels = torch.ones_like(points_per_crop[:, :, :, 0], dtype=torch.int64) + + return crop_boxes, points_per_crop, cropped_images, input_labels + + +def _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size): + """ + Generates 2 ** (layers idx + 1) crops for each crop_n_layers. Crops are in the XYWH format : The XYWH format + consists of the following required indices: + - X: X coordinate of the top left of the bounding box + - Y: Y coordinate of the top left of the bounding box + - W: width of the bounding box + - H: height of the bounding box + """ + crop_boxes, layer_idxs = [], [] + im_height, im_width = original_size + short_side = min(im_height, im_width) + + # Original image + crop_boxes.append([0, 0, im_width, im_height]) + layer_idxs.append(0) + for i_layer in range(crop_n_layers): + n_crops_per_side = 2 ** (i_layer + 1) + overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) + + crop_width = int(math.ceil((overlap * (n_crops_per_side - 1) + im_width) / n_crops_per_side)) + crop_height = int(math.ceil((overlap * (n_crops_per_side - 1) + im_height) / n_crops_per_side)) + + crop_box_x0 = [int((crop_width - overlap) * i) for i in range(n_crops_per_side)] + crop_box_y0 = [int((crop_height - overlap) * i) for i in range(n_crops_per_side)] + + for left, top in product(crop_box_x0, crop_box_y0): + box = [left, top, min(left + crop_width, im_width), min(top + crop_height, im_height)] + crop_boxes.append(box) + layer_idxs.append(i_layer + 1) + + return crop_boxes, layer_idxs + + +def _build_point_grid(n_per_side: int) -> torch.Tensor: + """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" + offset = 1 / (2 * n_per_side) + points_one_side = torch.linspace(offset, 1 - offset, n_per_side) + points_x = torch.tile(points_one_side[None, :], (n_per_side, 1)) + points_y = torch.tile(points_one_side[:, None], (1, n_per_side)) + points = torch.stack([points_x, points_y], dim=-1).reshape(-1, 2) + return points + + +def _generate_crop_images( + crop_boxes, image, points_grid, layer_idxs, target_size, original_size, input_data_format=None +): + """ + Takes as an input bounding boxes that are used to crop the image. Based in the crops, the corresponding points are + also passed. + """ + cropped_images = [] + total_points_per_crop = [] + for i, crop_box in enumerate(crop_boxes): + left, top, right, bottom = crop_box + cropped_im = image[:, top:bottom, left:right] + + cropped_images.append(cropped_im) + + cropped_im_size = cropped_im.shape[-2:] + points_scale = torch.tensor(cropped_im_size).flip(dims=(0,)).unsqueeze(0) + + points = points_grid[layer_idxs[i]] * points_scale + normalized_points = _normalize_coordinates(target_size, points, original_size) + total_points_per_crop.append(normalized_points) + + return cropped_images, total_points_per_crop + + +def _normalize_coordinates( + target_size: int, coords: torch.Tensor, original_size: tuple[int, int], is_bounding_box=False +) -> torch.Tensor: + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (height, width) + format. + """ + old_height, old_width = original_size + + scale = target_size * 1.0 / max(old_height, old_width) + new_height, new_width = old_height * scale, old_width * scale + new_width = int(new_width + 0.5) + new_height = int(new_height + 0.5) + + coords = deepcopy(coords).float() + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + + coords[..., 0] = coords[..., 0] * (new_width / old_width) + coords[..., 1] = coords[..., 1] * (new_height / old_height) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + +def _rle_to_mask(rle: dict[str, Any]) -> torch.Tensor: + """Compute a binary mask from an uncompressed RLE.""" + height, width = rle["size"] + mask = torch.empty(height * width, dtype=bool) + idx = 0 + parity = False + for count in rle["counts"]: + mask[idx : idx + count] = parity + idx += count + parity = not parity + mask = mask.reshape(width, height) + return mask.transpose(0, 1) # Reshape to original shape + + +def _post_process_for_mask_generation(rle_masks, iou_scores, mask_boxes, amg_crops_nms_thresh=0.7): + """ + Perform NMS (Non Maximum Suppression) on the outputs. + + Args: + rle_masks (`torch.Tensor`): + binary masks in the RLE format + iou_scores (`torch.Tensor` of shape (nb_masks, 1)): + iou_scores predicted by the model + mask_boxes (`torch.Tensor`): + The bounding boxes corresponding to segmentation masks + amg_crops_nms_thresh (`float`, *optional*, defaults to 0.7): + NMS threshold. + """ + keep_by_nms = batched_nms( + boxes=mask_boxes.float(), + scores=iou_scores, + idxs=torch.zeros(mask_boxes.shape[0]), + iou_threshold=amg_crops_nms_thresh, + ) + + iou_scores = iou_scores[keep_by_nms] + rle_masks = [rle_masks[i] for i in keep_by_nms] + mask_boxes = mask_boxes[keep_by_nms] + masks = [_rle_to_mask(rle) for rle in rle_masks] + + return masks, iou_scores, rle_masks, mask_boxes + + +def _mask_to_rle(input_mask: "torch.Tensor"): + """ + Encodes masks the run-length encoding (RLE), in the format expected by pycoco tools. + """ + # Put in fortran order and flatten height and width + batch_size, height, width = input_mask.shape + input_mask = input_mask.permute(0, 2, 1).flatten(1) + + # Compute change indices + diff = input_mask[:, 1:] ^ input_mask[:, :-1] + change_indices = diff.nonzero() + + # Encode run length + out = [] + for i in range(batch_size): + cur_idxs = change_indices[change_indices[:, 0] == i, 1] + 1 + if len(cur_idxs) == 0: + # No changes => either all 0 or all 1 + # If the entire mask is 0, RLE is [height*width] or if the entire mask is 1, RLE is [0, height*width]. + if input_mask[i, 0] == 0: + out.append({"size": [height, width], "counts": [height * width]}) + else: + out.append({"size": [height, width], "counts": [0, height * width]}) + continue + btw_idxs = cur_idxs[1:] - cur_idxs[:-1] + counts = [] if input_mask[i, 0] == 0 else [0] + counts += [cur_idxs[0].item()] + btw_idxs.tolist() + [height * width - cur_idxs[-1].item()] + out.append({"size": [height, width], "counts": counts}) + return out + + +@auto_docstring +class Sam2ImageProcessor(TorchvisionBackend): + valid_kwargs = Sam2ImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 1024, "width": 1024} + mask_size = {"height": 256, "width": 256} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + + # disable SAM padding logic + do_pad = None + pad_size = None + mask_pad_size = None + + def __init__(self, **kwargs: Unpack[Sam2ImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + segmentation_maps: ImageInput | None = None, + **kwargs: Unpack[Sam2ImageProcessorKwargs], + ) -> BatchFeature: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to preprocess. + """ + return super().preprocess(images, segmentation_maps, **kwargs) + + def _standardize_kwargs( + self, + mask_size: SizeDict | None = None, + **kwargs, + ) -> dict: + """ + Update kwargs that need further processing before being validated. + """ + if mask_size is not None and not isinstance(mask_size, SizeDict): + mask_size = SizeDict(**get_size_dict(mask_size, param_name="mask_size")) + kwargs["mask_size"] = mask_size + return super()._standardize_kwargs(**kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + device: Union[str, "torch.device"] | None = None, + **kwargs: Unpack[Sam2ImageProcessorKwargs], + ) -> BatchFeature: + """ + Preprocess image-like inputs. + """ + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device + ) + original_sizes = [image.shape[-2:] for image in images] + images_kwargs = kwargs.copy() + pixel_values = self._preprocess(images, **images_kwargs) + data = { + "pixel_values": pixel_values, + "original_sizes": original_sizes, + } + + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + "resample": PILImageResampling.NEAREST, + "size": segmentation_maps_kwargs.pop("mask_size"), + } + ) + processed_segmentation_maps = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + data["labels"] = processed_segmentation_maps.squeeze(1).to(torch.int64) + + return BatchFeature(data=data, tensor_type=kwargs["return_tensors"]) + + def _preprocess( + self, + images: list["torch.Tensor"], + return_tensors: str | TensorType | None, + **kwargs, + ) -> "torch.Tensor": + return super()._preprocess(images, return_tensors=return_tensors, **kwargs).pixel_values + + def generate_crop_boxes( + self, + image: "np.ndarray | PIL.Image.Image | torch.Tensor", + target_size, + crop_n_layers: int = 0, + overlap_ratio: float = 512 / 1500, + points_per_crop: int | None = 32, + crop_n_points_downscale_factor: list[int] | None = 1, + device: Optional["torch.device"] = None, + ): + """ + Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + + Args: + image (`torch.Tensor`): + Input original image + target_size (`int`): + Target size of the resized image + crop_n_layers (`int`, *optional*, defaults to 0): + If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where + each layer has 2**i_layer number of image crops. + overlap_ratio (`float`, *optional*, defaults to 512/1500): + Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of + the image length. Later layers with more crops scale down this overlap. + points_per_crop (`int`, *optional*, defaults to 32): + Number of points to sam2ple from each crop. + crop_n_points_downscale_factor (`list[int]`, *optional*, defaults to 1): + The number of points-per-side sam2pled in layer n is scaled down by crop_n_points_downscale_factor**n. + device (`torch.device`, *optional*, defaults to None): + Device to use for the computation. If None, cpu will be used. + """ + image = self.process_image(image) + crop_boxes, points_per_crop, cropped_images, input_labels = _generate_crop_boxes( + image, + target_size, + crop_n_layers, + overlap_ratio, + points_per_crop, + crop_n_points_downscale_factor, + ) + if device is None: + device = torch.device("cpu") + crop_boxes = crop_boxes.to(device) + points_per_crop = points_per_crop.to(device) + # cropped_images stays as torch.Tensor + input_labels = input_labels.to(device) + + return crop_boxes, points_per_crop, cropped_images, input_labels + + def filter_masks( + self, + masks, + iou_scores, + original_size, + cropped_box_image, + pred_iou_thresh=0.88, + stability_score_thresh=0.95, + mask_threshold=0, + stability_score_offset=1, + ): + """ + Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being + that the iou scores needs to be greater than `pred_iou_thresh`. The second criterion is that the stability + score needs to be greater than `stability_score_thresh`. The method also converts the predicted masks to + bounding boxes and pad the predicted masks if necessary. + + Args: + masks (`torch.Tensor`): + Input masks. + iou_scores (`torch.Tensor`): + List of IoU scores. + original_size (`tuple[int,int]`): + Size of the original image. + cropped_box_image (`torch.Tensor`): + The cropped image. + pred_iou_thresh (`float`, *optional*, defaults to 0.88): + The threshold for the iou scores. + stability_score_thresh (`float`, *optional*, defaults to 0.95): + The threshold for the stability score. + mask_threshold (`float`, *optional*, defaults to 0): + The threshold for the predicted masks. + stability_score_offset (`float`, *optional*, defaults to 1): + The offset for the stability score used in the `_compute_stability_score` method. + """ + original_height, original_width = original_size + iou_scores = iou_scores.flatten(0, 1) + masks = masks.flatten(0, 1) + + if masks.shape[0] != iou_scores.shape[0]: + raise ValueError("masks and iou_scores must have the sam2e batch size.") + + if masks.device != iou_scores.device: + iou_scores = iou_scores.to(masks.device) + + batch_size = masks.shape[0] + + keep_mask = torch.ones(batch_size, dtype=torch.bool, device=masks.device) + + if pred_iou_thresh > 0.0: + keep_mask = keep_mask & (iou_scores > pred_iou_thresh) + + # compute stability score + if stability_score_thresh > 0.0: + stability_scores = _compute_stability_score(masks, mask_threshold, stability_score_offset) + keep_mask = keep_mask & (stability_scores > stability_score_thresh) + + scores = iou_scores[keep_mask] + masks = masks[keep_mask] + + # binarize masks + masks = masks > mask_threshold + converted_boxes = _batched_mask_to_box(masks) + + keep_mask = ~_is_box_near_crop_edge( + converted_boxes, cropped_box_image, [0, 0, original_width, original_height] + ) + + scores = scores[keep_mask] + masks = masks[keep_mask] + converted_boxes = converted_boxes[keep_mask] + + masks = _pad_masks(masks, cropped_box_image, original_height, original_width) + # conversion to rle is necessary to run non-maximum suppression + masks = _mask_to_rle(masks) + + return masks, scores, converted_boxes + + def post_process_masks( + self, + masks, + original_sizes, + mask_threshold=0.0, + binarize=True, + max_hole_area=0.0, + max_sprinkle_area=0.0, + apply_non_overlapping_constraints=False, + **kwargs, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[torch.Tensor, List[torch.Tensor], np.ndarray, List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + mask_threshold (`float`, *optional*, defaults to 0.0): + Threshold for binarization and post-processing operations. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + max_hole_area (`float`, *optional*, defaults to 0.0): + The maximum area of a hole to fill. + max_sprinkle_area (`float`, *optional*, defaults to 0.0): + The maximum area of a sprinkle to fill. + apply_non_overlapping_constraints (`bool`, *optional*, defaults to `False`): + Whether to apply non-overlapping constraints to the masks. + + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + if isinstance(original_sizes, (torch.Tensor, np.ndarray)): + original_sizes = original_sizes.tolist() + # TODO: add connected components kernel for postprocessing + output_masks = [] + for i, original_size in enumerate(original_sizes): + if isinstance(masks[i], np.ndarray): + masks[i] = torch.from_numpy(masks[i]) + elif not isinstance(masks[i], torch.Tensor): + raise TypeError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`") + interpolated_mask = F.interpolate(masks[i], original_size, mode="bilinear", align_corners=False) + if apply_non_overlapping_constraints: + interpolated_mask = self._apply_non_overlapping_constraints(interpolated_mask) + if binarize: + interpolated_mask = interpolated_mask > mask_threshold + output_masks.append(interpolated_mask) + + return output_masks + + def post_process_for_mask_generation(self, all_masks, all_scores, all_boxes, crops_nms_thresh): + """ + Post processes mask that are generated by calling the Non Maximum Suppression algorithm on the predicted masks. + + Args: + all_masks (`torch.Tensor`): + List of all predicted segmentation masks + all_scores (`torch.Tensor`): + List of all predicted iou scores + all_boxes (`torch.Tensor`): + List of all bounding boxes of the predicted masks + crops_nms_thresh (`float`): + Threshold for NMS (Non Maximum Suppression) algorithm. + """ + return _post_process_for_mask_generation(all_masks, all_scores, all_boxes, crops_nms_thresh) + + def _apply_non_overlapping_constraints(self, pred_masks: torch.Tensor) -> torch.Tensor: + """ + Apply non-overlapping constraints to the object scores in pred_masks. Here we + keep only the highest scoring object at each spatial location in pred_masks. + """ + batch_size = pred_masks.size(0) + if batch_size == 1: + return pred_masks + + device = pred_masks.device + # "max_obj_inds": object index of the object with the highest score at each location + max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) + # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` + batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] + keep = max_obj_inds == batch_obj_inds + # suppress overlapping regions' scores below -10.0 so that the foreground regions + # don't overlap (here sigmoid(-10.0)=4.5398e-05) + pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) + return pred_masks + + +__all__ = ["Sam2ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/modeling_sam2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/modeling_sam2.py new file mode 100644 index 0000000000000000000000000000000000000000..d4ccb9b20c0dc0983bb94c89bf8273f7dccc0eb0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/modeling_sam2.py @@ -0,0 +1,1608 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam2/modular_sam2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import ( + ModelOutput, + auto_docstring, + can_return_tuple, + logging, +) +from ...utils.generic import TransformersKwargs, is_flash_attention_requested, merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from ..auto import AutoModel +from .configuration_sam2 import ( + Sam2Config, + Sam2HieraDetConfig, + Sam2MaskDecoderConfig, + Sam2PromptEncoderConfig, + Sam2VisionConfig, +) + + +logger = logging.get_logger(__name__) + + +@dataclass +@auto_docstring(custom_intro="Base class for the vision encoder's outputs.") +class Sam2VisionEncoderOutput(BaseModelOutputWithPooling): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the + model at the output of each stage. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + fpn_hidden_states (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Feature maps from the Feature Pyramid Network neck. + fpn_position_encoding (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Positional encodings corresponding to the `fpn_hidden_states`. + """ + + fpn_hidden_states: torch.FloatTensor | None = None + fpn_position_encoding: torch.FloatTensor | None = None + + +@auto_docstring(custom_intro="Base class for the Sam2 model's output.") +@dataclass +class Sam2ImageSegmentationOutput(ModelOutput): + r""" + iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`): + The Intersection over Union (IoU) scores of the predicted masks. + pred_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, height, width)`): + The predicted low-resolution masks. This is an alias for `low_res_masks`. These masks need to be post-processed + by the processor to be brought to the original image size. + object_score_logits (`torch.FloatTensor` of shape `(batch_size, point_batch_size, 1)`): + Logits for the object score, indicating if an object is present. + image_embeddings (`tuple(torch.FloatTensor)`): + The features from the FPN, which are used by the mask decoder. This is a tuple of `torch.FloatTensor` where each + tensor has shape `(batch_size, channels, height, width)`. + vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. + Hidden-states of the vision model at the output of each stage. + vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the vision model. + mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the mask decoder. + """ + + iou_scores: torch.FloatTensor | None = None + pred_masks: torch.FloatTensor | None = None + object_score_logits: torch.FloatTensor | None = None + image_embeddings: tuple[torch.FloatTensor, ...] = None + vision_hidden_states: tuple[torch.FloatTensor, ...] | None = None + vision_attentions: tuple[torch.FloatTensor, ...] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None + + +class Sam2PatchEmbeddings(nn.Module): + r""" + Turns pixel values into patch embeddings for transformer consumption. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`Sam2ImageProcessor.__call__`] for details. + + Returns: + embeddings (`torch.FloatTensor`): + Patch embeddings depend on image_size, patch_kernel_size, patch_stride and patch_padding + """ + + def __init__(self, config: Sam2HieraDetConfig): + super().__init__() + num_channels = config.num_channels + hidden_size = config.hidden_size + + self.projection = nn.Conv2d( + num_channels, + hidden_size, + kernel_size=config.patch_kernel_size, + stride=config.patch_stride, + padding=config.patch_padding, + ) + + def forward(self, pixel_values): + _, num_channels, height, width = pixel_values.shape + embeddings = self.projection(pixel_values.to(self.projection.weight.dtype)).permute(0, 2, 3, 1) + return embeddings + + +# copied and adapted from original implementation, also practically equal to DetrSinePositionEmbedding +class Sam2SinePositionEmbedding(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one used by the Attention is all you + need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: float | None = None + ): + super().__init__() + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + self.scale = 2 * math.pi if scale is None else scale + + @compile_compatible_method_lru_cache(maxsize=1) + def forward( + self, + shape: torch.Size, + device: torch.device | str, + dtype: torch.dtype, + mask: Tensor | None = None, + ) -> Tensor: + if mask is None: + mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool) + not_mask = (~mask).to(dtype) + y_embed = not_mask.cumsum(1) + x_embed = not_mask.cumsum(2) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype) + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +class Sam2VisionNeck(nn.Module): + def __init__(self, config: Sam2VisionConfig): + super().__init__() + self.config = config + + self.position_encoding = Sam2SinePositionEmbedding(num_pos_feats=config.fpn_hidden_size // 2, normalize=True) + self.convs = nn.ModuleList() + for in_channels in config.backbone_channel_list: + self.convs.append( + nn.Conv2d( + in_channels=in_channels, + out_channels=config.fpn_hidden_size, + kernel_size=config.fpn_kernel_size, + stride=config.fpn_stride, + padding=config.fpn_padding, + ), + ) + self.fpn_top_down_levels = config.fpn_top_down_levels + + def forward(self, hidden_states: torch.Tensor) -> tuple[tuple[torch.Tensor, ...], tuple[torch.Tensor, ...]]: + fpn_hidden_states = () + fpn_position_encoding = () + + # forward in top-down order (from low to high resolution) + n = len(self.convs) - 1 + for i in range(n, -1, -1): + lateral_features = hidden_states[i].permute(0, 3, 1, 2) + lateral_features = self.convs[n - i](lateral_features.to(self.convs[i].weight.dtype)) + if i not in self.fpn_top_down_levels or i == n: + prev_features = lateral_features + else: + top_down_features = F.interpolate( + prev_features.to(dtype=torch.float32), + scale_factor=2.0, + mode="nearest", + align_corners=None, + antialias=False, + ).to(lateral_features.dtype) + prev_features = lateral_features + top_down_features + + prev_position_encoding = self.position_encoding( + prev_features.shape, prev_features.device, prev_features.dtype + ).to(prev_features.dtype) + + fpn_hidden_states += (prev_features,) + fpn_position_encoding += (prev_position_encoding,) + + return fpn_hidden_states, fpn_position_encoding + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +def do_pool(x: torch.Tensor, query_stride: int | None = None) -> torch.Tensor: + if query_stride is None: + return x + # (B, H, W, C) -> (B, C, H, W) + x = x.permute(0, 3, 1, 2) + x = nn.functional.max_pool2d(x, kernel_size=query_stride, stride=query_stride, ceil_mode=False) + # (B, C, H', W') -> (B, H', W', C) + x = x.permute(0, 2, 3, 1) + return x + + +class Sam2MultiScaleAttention(nn.Module): + def __init__( + self, + config: Sam2HieraDetConfig, + dim: int, + dim_out: int, + num_attention_heads: int, + query_stride: tuple[int, int] | None = None, + ): + super().__init__() + + self.config = config + + self.dim = dim + self.dim_out = dim_out + self.query_stride = query_stride + + self.num_attention_heads = num_attention_heads + head_dim = dim_out // num_attention_heads + self.scale = head_dim**-0.5 + self.qkv = nn.Linear(dim, dim_out * 3) + self.proj = nn.Linear(dim_out, dim_out) + + self.is_causal = False + + def forward(self, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (B, H * W, 3, nHead, C) + qkv = self.qkv(hidden_states).reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + # q, k, v with shape (B, H * W, nheads, C) + query, key, value = torch.unbind(qkv, 2) + + attn_weights = (query * self.scale) @ key.transpose(-2, -1) + attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype) + + # Q pooling (for downsample at stage changes) + if self.query_stride: + query = do_pool(query.reshape(batch_size, height, width, -1), self.query_stride) + height, width = query.shape[1:3] # downsampled shape + query = query.reshape(batch_size, height * width, self.num_attention_heads, -1) + + # transpose query, key, value to (B, nHead, H * W, C) + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = value.transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + attn_output, _ = attention_interface( + self, + query, + key, + value, + attention_mask=None, + is_causal=self.is_causal, + scaling=self.scale, + **kwargs, + ) + attn_output = attn_output.reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + + return attn_output + + +class Sam2FeedForward(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + activation: str = "relu", + sigmoid_output: bool = False, + ): + super().__init__() + self.num_layers = num_layers + self.activation = ACT2FN[activation] + self.proj_in = nn.Linear(input_dim, hidden_dim) + self.proj_out = nn.Linear(hidden_dim, output_dim) + self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)]) + self.sigmoid_output = sigmoid_output + + def forward(self, hidden_states): + hidden_states = self.proj_in(hidden_states) + hidden_states = self.activation(hidden_states) + for layer in self.layers: + hidden_states = self.activation(layer(hidden_states)) + + hidden_states = self.proj_out(hidden_states) + if self.sigmoid_output: + hidden_states = F.sigmoid(hidden_states) + return hidden_states + + +def window_partition(hidden_state, window_size): + """ + Partition into non-overlapping windows with padding if needed. + + Args: + hidden_state (`torch.Tensor`): + Input tokens with [batch_size, height, width, num_channels]. + window_size (`int`): + Window size. + + Returns: + `tuple(torch.FloatTensor)` comprising various elements: + - windows: windows after partition with [batch_size * num_windows, window_size, window_size, num_channels]. + - (padded_height, padded_width): padded height and width before partition + """ + batch_size, height, width, num_channels = hidden_state.shape + + pad_height = (window_size - height % window_size) % window_size + pad_width = (window_size - width % window_size) % window_size + + # Noop in case pad_width == 0 and pad_height == 0. + hidden_state = nn.functional.pad(hidden_state, (0, 0, 0, pad_width, 0, pad_height)) + + padded_height, padded_width = height + pad_height, width + pad_width + + hidden_state = hidden_state.view( + batch_size, padded_height // window_size, window_size, padded_width // window_size, window_size, num_channels + ) + windows = hidden_state.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels) + return windows, (padded_height, padded_width) + + +def window_unpartition(windows, window_size, pad_height_width, height_width): + """ + Window unpartition into original sequences and removing padding. + + Args: + windows (`torch.Tensor`): + Input tokens with [batch_size * num_windows, window_size, window_size, num_channels]. + window_size (`int`): + Window size. + pad_height_width (`tuple[int]`): + Padded height and width (padded_height, padded_width). + height_width (`tuple[int]`): + Original height and width before padding. + + Returns: + hidden_state: unpartitioned sequences with [batch_size, height, width, num_channels]. + """ + padded_height, padded_width = pad_height_width + height, width = height_width + batch_size = windows.shape[0] // (padded_height * padded_width // window_size // window_size) + hidden_state = windows.view( + batch_size, padded_height // window_size, padded_width // window_size, window_size, window_size, -1 + ) + hidden_state = hidden_state.permute(0, 1, 3, 2, 4, 5).contiguous() + hidden_state = hidden_state.view(batch_size, padded_height, padded_width, -1) + + # We always have height <= padded_height and width <= padded_width + hidden_state = hidden_state[:, :height, :width, :].contiguous() + return hidden_state + + +class Sam2MultiScaleBlock(GradientCheckpointingLayer): + def __init__( + self, + config: Sam2HieraDetConfig, + stage_idx: int, + block_idx: int, + total_block_idx: int, + ): + super().__init__() + + # take embed dim from previous stage if first block of stage + self.dim = ( + config.embed_dim_per_stage[stage_idx - 1] + if stage_idx > 0 and block_idx == 0 + else config.embed_dim_per_stage[stage_idx] + ) + self.dim_out = config.embed_dim_per_stage[stage_idx] + self.layer_norm1 = nn.LayerNorm(self.dim, eps=config.layer_norm_eps) + # take window size from previous stage if first block of stage + self.window_size = ( + config.window_size_per_stage[stage_idx - 1] + if stage_idx > 0 and block_idx == 0 + else config.window_size_per_stage[stage_idx] + ) + self.window_size = 0 if total_block_idx in config.global_attention_blocks else self.window_size + # use query stride for first block of stage if stage is a query pool stage + self.query_stride = ( + config.query_stride if 0 < stage_idx <= config.num_query_pool_stages and block_idx == 0 else None + ) + + self.attn = Sam2MultiScaleAttention( + config, + self.dim, + self.dim_out, + num_attention_heads=config.num_attention_heads_per_stage[stage_idx], + query_stride=self.query_stride, + ) + self.layer_norm2 = nn.LayerNorm(self.dim_out, eps=config.layer_norm_eps) + self.mlp = Sam2FeedForward( + self.dim_out, + int(self.dim_out * config.mlp_ratio), + self.dim_out, + num_layers=2, + activation=config.hidden_act, + ) + if self.dim != self.dim_out: + self.proj = nn.Linear(self.dim, self.dim_out) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states # batch_size, height, width, channel + + hidden_states = self.layer_norm1(hidden_states) + + # Skip connection + if self.dim != self.dim_out: + residual = do_pool(self.proj(hidden_states), self.query_stride) + + # Window partition + window_size = self.window_size + if self.window_size > 0: + H, W = hidden_states.shape[1], hidden_states.shape[2] + hidden_states, pad_hw = window_partition(hidden_states, window_size) + + # Window Attention + Q Pooling (if stage change) + attn_output = self.attn( + hidden_states=hidden_states, + **kwargs, + ) + hidden_states = attn_output + if self.query_stride: + # Shapes have changed due to Q pooling + window_size = self.window_size // self.query_stride[0] + H, W = residual.shape[1:3] + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + pad_hw = (H + pad_h, W + pad_w) + + # Reverse window partition + if self.window_size > 0: + hidden_states = window_unpartition(hidden_states, window_size, pad_hw, (H, W)) + + hidden_states = residual + hidden_states + layernorm_output = self.layer_norm2(hidden_states) + hidden_states = hidden_states + self.mlp(layernorm_output) + + return hidden_states + + +@auto_docstring( + custom_intro=""" + Hiera model's outputs that also contains a pooling of the last hidden states. + """ +) +@dataclass +class Sam2HieraDetModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`): + hidden-states at the output of the last layer of the model. + intermediate_hidden_states (`tuple[torch.FloatTensor]` of shape `(batch_size, height, width, hidden_size)`): + Sequence of hidden-states at the output of the intermediate layers of the model. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: tuple[torch.FloatTensor, ...] | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@auto_docstring +class Sam2PreTrainedModel(PreTrainedModel): + config_class = Sam2Config + base_model_prefix = "sam2" + main_input_name = "pixel_values" + input_modalities = ("image",) + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _keys_to_ignore_on_load_unexpected = [ + r"^memory_.*", + r"^mask_downsample.*", + r"^object_pointer_proj.*", + r"^temporal_positional_encoding_projection_layer.*", + "no_memory_positional_encoding", + "no_object_pointer", + "occlusion_spatial_embedding_parameter", + ] + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Sam2HieraDetModel): + if module.pos_embed is not None: + init.zeros_(module.pos_embed) + if module.pos_embed_window is not None: + init.zeros_(module.pos_embed_window) + elif isinstance(module, Sam2PositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + elif isinstance(module, Sam2Model): + if module.no_memory_embedding is not None: + init.zeros_(module.no_memory_embedding) + + +class Sam2HieraDetModel(Sam2PreTrainedModel): + config_class = Sam2HieraDetConfig + main_input_name = "pixel_values" + _can_record_outputs = { + "hidden_states": Sam2MultiScaleBlock, + "attentions": Sam2MultiScaleAttention, + } + + def __init__(self, config: Sam2HieraDetConfig): + super().__init__(config) + + self.patch_embed = Sam2PatchEmbeddings(config) + # Windowed positional embedding (https://huggingface.co/papers/2311.05613) + self.pos_embed = nn.Parameter( + torch.zeros(1, config.hidden_size, *config.window_positional_embedding_background_size) + ) + self.pos_embed_window = nn.Parameter( + torch.zeros(1, config.hidden_size, config.window_size_per_stage[0], config.window_size_per_stage[0]) + ) + self.stage_ends = (np.cumsum(config.blocks_per_stage) - 1).tolist() + self.blocks = nn.ModuleList() + total_block_idx = 0 + for stage_idx, blocks_per_stage in enumerate(config.blocks_per_stage): + for block_idx in range(blocks_per_stage): + block = Sam2MultiScaleBlock( + config=config, stage_idx=stage_idx, block_idx=block_idx, total_block_idx=total_block_idx + ) + self.blocks.append(block) + total_block_idx += 1 + + self.post_init() + + def get_input_embeddings(self): + return self.patch_embed + + def _get_pos_embed(self, hw: tuple[int, int]) -> torch.Tensor: + h, w = hw + window_embed = self.pos_embed_window + pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode="bicubic") + pos_embed = pos_embed + window_embed.tile([x // y for x, y in zip(pos_embed.shape, window_embed.shape)]) + pos_embed = pos_embed.permute(0, 2, 3, 1) + return pos_embed + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam2HieraDetModelOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.patch_embed(pixel_values) + hidden_states = hidden_states + self._get_pos_embed(hidden_states.shape[1:3]) + + intermediate_hidden_states = () + for i, block_module in enumerate(self.blocks): + hidden_states = block_module(hidden_states, **kwargs) + + if i in self.stage_ends: + intermediate_hidden_states = intermediate_hidden_states + (hidden_states,) + + return Sam2HieraDetModelOutput( + last_hidden_state=hidden_states, + intermediate_hidden_states=intermediate_hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + The vision model from Sam without any head or projection on top. + """ +) +class Sam2VisionModel(Sam2PreTrainedModel): + config_class = Sam2VisionConfig + main_input_name = "pixel_values" + _can_record_outputs = { + "hidden_states": Sam2MultiScaleBlock, + "attentions": Sam2MultiScaleAttention, + } + + def __init__(self, config: Sam2VisionConfig): + super().__init__(config) + self.config = config + + self.backbone = AutoModel.from_config(config.backbone_config) + + self.neck = Sam2VisionNeck(config) + self.num_feature_levels = config.num_feature_levels + + self.post_init() + + def get_input_embeddings(self): + return self.backbone.get_input_embeddings() + + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam2VisionEncoderOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Forward through backbone + backbone_output = self.backbone(pixel_values, **kwargs) + hidden_states = backbone_output.last_hidden_state + intermediate_hidden_states = backbone_output.intermediate_hidden_states + + fpn_hidden_states, fpn_position_encoding = self.neck(intermediate_hidden_states) + # Select last `num_feature_levels` feature levels from FPN and reverse order to get features from high to low resolution + fpn_hidden_states = fpn_hidden_states[-self.num_feature_levels :][::-1] + fpn_position_encoding = fpn_position_encoding[-self.num_feature_levels :][::-1] + + return Sam2VisionEncoderOutput( + last_hidden_state=hidden_states, + fpn_hidden_states=fpn_hidden_states, + fpn_position_encoding=fpn_position_encoding, + hidden_states=backbone_output.hidden_states, + attentions=backbone_output.attentions, + ) + + +class Sam2PositionalEmbedding(nn.Module): + def __init__(self, config: Sam2PromptEncoderConfig): + super().__init__() + self.scale = config.scale + positional_embedding = self.scale * torch.randn((2, config.hidden_size // 2)) + self.register_buffer("positional_embedding", positional_embedding) + + def forward(self, input_coords, input_shape=None): + """Positionally encode points that are normalized to [0,1].""" + coordinates = input_coords.clone() + + if input_shape is not None: + coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1] + coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0] + coordinates.to(torch.float32) + + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coordinates = 2 * coordinates - 1 + coordinates = coordinates.to(self.positional_embedding.dtype) + coordinates = coordinates @ self.positional_embedding + coordinates = 2 * np.pi * coordinates + # outputs d_1 x ... x d_n x channel shape + return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1) + + +class Sam2MaskEmbedding(nn.Module): + def __init__(self, config: Sam2PromptEncoderConfig): + super().__init__() + self.mask_input_channels = config.mask_input_channels // 4 + self.activation = ACT2FN[config.hidden_act] + self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2) + self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2) + self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1) + self.layer_norm1 = Sam2LayerNorm( + self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first" + ) + self.layer_norm2 = Sam2LayerNorm( + self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first" + ) + + def forward(self, masks): + hidden_states = self.conv1(masks) + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.activation(hidden_states) + dense_embeddings = self.conv3(hidden_states) + return dense_embeddings + + +class Sam2PromptEncoder(nn.Module): + def __init__(self, config: Sam2PromptEncoderConfig): + super().__init__() + self.shared_embedding = Sam2PositionalEmbedding(config) + self.mask_embed = Sam2MaskEmbedding(config) + self.no_mask_embed = nn.Embedding(1, config.hidden_size) + + self.image_embedding_size = (config.image_size // config.patch_size, config.image_size // config.patch_size) + self.mask_input_size = (4 * config.image_size // config.patch_size, 4 * config.image_size // config.patch_size) + self.input_image_size = config.image_size + + self.point_embed = nn.Embedding(config.num_point_embeddings, config.hidden_size) + self.hidden_size = config.hidden_size + self.not_a_point_embed = nn.Embedding(1, config.hidden_size) + + def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + points = torch.nn.functional.pad(points, (0, 0, 0, 1), mode="constant", value=0) + labels = torch.nn.functional.pad(labels, (0, 1), mode="constant", value=-1) + input_shape = (self.input_image_size, self.input_image_size) + point_embedding = self.shared_embedding(points, input_shape) + + # torch.where and expanding the labels tensor is required by the ONNX export + point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding) + + # This is required for the ONNX export. The dtype, device need to be explicitly + # specified as otherwise torch.onnx.export interprets as double + point_embedding = torch.where( + labels[..., None] != -10, + point_embedding, + torch.zeros_like(point_embedding), + ) + + # Add point embeddings for labels >= 0 + point_embedding = point_embedding + self.point_embed(labels.clamp(min=0)) * (labels >= 0).unsqueeze(-1) + + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.view(*boxes.shape[:2], 2, 2) + # add padding point for consistency with the original implementation + coords = torch.nn.functional.pad(coords, (0, 0, 0, 1), mode="constant", value=0) + corner_embedding = self.shared_embedding(coords, (self.input_image_size, self.input_image_size)) + corner_embedding[:, :, 0, :] += self.point_embed.weight[2] + corner_embedding[:, :, 1, :] += self.point_embed.weight[3] + corner_embedding[:, :, 2, :] = self.not_a_point_embed.weight.expand_as(corner_embedding[:, :, 2, :]) + return corner_embedding + + def forward( + self, + input_points: tuple[torch.Tensor, torch.Tensor] | None, + input_labels: torch.Tensor | None, + input_boxes: torch.Tensor | None, + input_masks: torch.Tensor | None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense embeddings. + + Args: + points (`torch.Tensor`, *optional*): + point coordinates and labels to embed. + boxes (`torch.Tensor`, *optional*): + boxes to embed + masks (`torch.Tensor`, *optional*): + masks to embed + """ + sparse_embeddings = None + batch_size = 1 + if input_points is not None: + batch_size = input_points.shape[0] + if input_labels is None: + raise ValueError("If points are provided, labels must also be provided.") + point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None)) + sparse_embeddings = point_embeddings + if input_boxes is not None: + batch_size = input_boxes.shape[0] + box_embeddings = self._embed_boxes(input_boxes) + if sparse_embeddings is None: + sparse_embeddings = box_embeddings + else: + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2) + if input_masks is not None: + dense_embeddings = self.mask_embed(input_masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings + + +class Sam2Attention(nn.Module): + """ + SAM2's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and + values. + """ + + def __init__(self, config, downsample_rate=None): + super().__init__() + downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate + self.config = config + self.hidden_size = config.hidden_size + self.internal_dim = config.hidden_size // downsample_rate + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.internal_dim // config.num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.v_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.o_proj = nn.Linear(self.internal_dim, self.hidden_size) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_similarity: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # Input projections + batch_size, point_batch_size = query.shape[:2] + new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim) + + query = self.q_proj(query).view(*new_shape).transpose(1, 2) + key = self.k_proj(key).view(*new_shape).transpose(1, 2) + value = self.v_proj(value).view(*new_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config) and attention_similarity is not None: + # Target guided masks are represented as float masks and are incompatible with Flash Attention + # Fallback to SDPA for this call only so the rest of the model can still benefit from FA + attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"] + logger.warning_once( + "Falling back to SDPA for target-guided attention because " + "Flash Attention does not support additive bias masks." + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_similarity, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = attn_output.reshape( + batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +class Sam2TwoWayAttentionBlock(GradientCheckpointingLayer): + def __init__(self, config: Sam2MaskDecoderConfig, skip_first_layer_pe: bool = False): + """ + A transformer block with four layers: + (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on + sparse inputs (4) cross attention of dense inputs -> sparse inputs + + Arguments: + config (`Sam2MaskDecoderConfig`): + The configuration file used to instantiate the block + attention_downsample_rate (*optionalk*, int, defaults to 2): + The downsample ratio of the block used to reduce the inner dim of the attention. + skip_first_layer_pe (*optional*, bool, defaults to `False`): + Whether or not to skip the addition of the query_point_embedding on the first layer. + """ + super().__init__() + self.self_attn = Sam2Attention(config, downsample_rate=1) + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + + self.cross_attn_token_to_image = Sam2Attention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam2FeedForward( + config.hidden_size, config.mlp_dim, config.hidden_size, num_layers=config.num_hidden_layers + ) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + self.layer_norm4 = nn.LayerNorm(config.hidden_size) + self.cross_attn_image_to_token = Sam2Attention(config) + + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, + queries: Tensor, + keys: Tensor, + query_point_embedding: Tensor, + key_point_embedding: Tensor, + attention_similarity: Tensor, + **kwargs: Unpack[TransformersKwargs], + ): + # Self attention block + if self.skip_first_layer_pe: + queries, _ = self.self_attn(query=queries, key=queries, value=queries) + else: + query = queries + query_point_embedding + attn_out, _ = self.self_attn(query=query, key=query, value=queries) + queries = queries + attn_out + queries = self.layer_norm1(queries) + + # Cross attention block, tokens attending to image embedding + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_token_to_image( + query=query, key=key, value=keys, attention_similarity=attention_similarity + ) + queries = queries + attn_out + + queries = self.layer_norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.layer_norm3(queries) + + # Cross attention block, image embedding attending to tokens + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_image_to_token(query=key, key=query, value=queries) + keys = keys + attn_out + + keys = self.layer_norm4(keys) + return queries, keys, attn_out + + +class Sam2TwoWayTransformer(nn.Module): + def __init__(self, config: Sam2MaskDecoderConfig): + super().__init__() + self.config = config + + self.num_hidden_layers = config.num_hidden_layers + self.layers = nn.ModuleList() + + for i in range(self.num_hidden_layers): + self.layers.append(Sam2TwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0))) + + self.final_attn_token_to_image = Sam2Attention(config) + self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size) + + def forward( + self, + point_embeddings: Tensor, + image_embeddings: Tensor, + image_positional_embeddings: Tensor, + attention_similarity: Tensor, + target_embedding=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + if image_embeddings is None: + raise ValueError("You have to specify an image_embedding") + + image_embeddings = image_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + image_positional_embeddings = image_positional_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + + # Prepare queries + queries = point_embeddings + keys = image_embeddings + + # Apply transformer blocks and final layernorm + for layer in self.layers: + if target_embedding is not None: + queries += target_embedding + + queries, keys, _ = layer( + queries=queries, + keys=keys, + query_point_embedding=point_embeddings, + key_point_embedding=image_positional_embeddings, + attention_similarity=attention_similarity, + **kwargs, + ) + # Apply the final attention layer from the points to the image + query = queries + point_embeddings + key = keys + image_positional_embeddings + + attn_out, _ = self.final_attn_token_to_image(query=query, key=key, value=keys) + + queries = queries + attn_out + queries = self.layer_norm_final_attn(queries) + return queries, keys + + +class Sam2LayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +class Sam2MaskDecoder(nn.Module): + def __init__(self, config: Sam2MaskDecoderConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + + self.num_multimask_outputs = config.num_multimask_outputs + self.num_mask_tokens = config.num_multimask_outputs + 1 + + self.iou_token = nn.Embedding(1, self.hidden_size) + self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size) + + self.transformer = Sam2TwoWayTransformer(config) + + # should we create a new class for this? + self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + self.upscale_layer_norm = Sam2LayerNorm(self.hidden_size // 4, data_format="channels_first") + self.activation = nn.GELU() + + mlps_list = [] + for _ in range(self.num_mask_tokens): + mlps_list += [Sam2FeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)] + self.output_hypernetworks_mlps = nn.ModuleList(mlps_list) + self.iou_prediction_head = Sam2FeedForward( + self.hidden_size, + config.iou_head_hidden_dim, + self.num_mask_tokens, + config.iou_head_depth, + sigmoid_output=True, + ) + + self.conv_s0 = nn.Conv2d(config.hidden_size, config.hidden_size // 8, kernel_size=1, stride=1) + self.conv_s1 = nn.Conv2d(config.hidden_size, config.hidden_size // 4, kernel_size=1, stride=1) + + self.obj_score_token = nn.Embedding(1, self.hidden_size) + self.pred_obj_score_head = Sam2FeedForward(self.hidden_size, self.hidden_size, 1, 3) + + self.dynamic_multimask_via_stability = config.dynamic_multimask_via_stability + self.dynamic_multimask_stability_delta = config.dynamic_multimask_stability_delta + self.dynamic_multimask_stability_thresh = config.dynamic_multimask_stability_thresh + + def forward( + self, + image_embeddings: torch.Tensor, + image_positional_embeddings: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + high_resolution_features: list[torch.Tensor], + attention_similarity: torch.Tensor | None = None, + target_embedding: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Args: + image_embeddings (`torch.Tensor`): + The embeddings from the image encoder. + image_positional_embeddings (`torch.Tensor`): + Positional encoding with the shape of image_embeddings. + sparse_prompt_embeddings (`torch.Tensor`): + The embeddings of the points and boxes. + dense_prompt_embeddings (`torch.Tensor`): + The embeddings of the mask inputs. + multimask_output (`bool`): + Whether to return multiple masks or a single mask. + high_resolution_features (`list[torch.Tensor]`, *optional*): + The high-resolution features from the vision encoder. + attention_similarity (`torch.Tensor`, *optional*): + The attention similarity tensor. + target_embedding (`torch.Tensor`, *optional*): + The target embedding. + """ + batch_size, num_channels, height, width = image_embeddings.shape + point_batch_size = sparse_prompt_embeddings.shape[1] + # Concatenate output tokens + output_tokens = torch.cat( + [ + self.obj_score_token.weight, + self.iou_token.weight, + self.mask_tokens.weight, + ], + dim=0, + ) + output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) + + if sparse_prompt_embeddings.shape[0] != 0: + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2) + else: + tokens = output_tokens + point_embeddings = tokens.to(self.iou_token.weight.dtype) + + # Expand per-image data in batch direction to be per-mask + image_embeddings = image_embeddings + dense_prompt_embeddings + image_embeddings = image_embeddings.repeat_interleave(point_batch_size, dim=0) + image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) + # Run the transformer + point_embeddings, image_embeddings = self.transformer( + point_embeddings=point_embeddings, + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + iou_token_out = point_embeddings[:, :, 1, :] + mask_tokens_out = point_embeddings[:, :, 2 : (2 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + image_embeddings = image_embeddings.transpose(2, 3).view( + batch_size * point_batch_size, num_channels, height, width + ) + + feat_s0, feat_s1 = high_resolution_features + feat_s0 = feat_s0.repeat_interleave(point_batch_size, dim=0) + feat_s1 = feat_s1.repeat_interleave(point_batch_size, dim=0) + upscaled_embedding = self.upscale_conv1(image_embeddings) + feat_s1 + upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) + upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding) + feat_s0) + + hyper_in_list: list[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + current_mlp = self.output_hypernetworks_mlps[i] + hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])] + hyper_in = torch.stack(hyper_in_list, dim=2) + + _, num_channels, height, width = upscaled_embedding.shape + upscaled_embedding = upscaled_embedding.view(batch_size, point_batch_size, num_channels, height * width) + masks = (hyper_in @ upscaled_embedding).view(batch_size, point_batch_size, -1, height, width) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + object_score_logits = self.pred_obj_score_head(point_embeddings[:, :, 0, :]) + + # Select the correct mask or masks for output + if multimask_output: + mask_slice = slice(1, None) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + elif self.dynamic_multimask_via_stability and not self.training: + mask_slice = slice(0, 1) + masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred) + else: + mask_slice = slice(0, 1) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + + sam_tokens_out = mask_tokens_out[:, :, mask_slice] # [b, 3, c] shape + + return masks, iou_pred, sam_tokens_out, object_score_logits + + def _get_stability_scores(self, mask_logits): + """ + Compute stability scores of the mask logits based on the IoU between upper and + lower thresholds. + """ + mask_logits = mask_logits.flatten(-2) + stability_delta = self.dynamic_multimask_stability_delta + area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() + area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() + stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) + return stability_scores + + def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): + """ + When outputting a single mask, if the stability score from the current single-mask + output (based on output token 0) falls below a threshold, we instead select from + multi-mask outputs (based on output token 1~3) the mask with the highest predicted + IoU score. This is intended to ensure a valid mask for both clicking and tracking. + """ + # The best mask from multimask output tokens (1~3) + multimask_logits = all_mask_logits[:, :, 1:, :, :] + multimask_iou_scores = all_iou_scores[:, :, 1:] + best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1) # [B, P] + best_scores_inds_expanded = best_scores_inds.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) + best_scores_inds_expanded = best_scores_inds_expanded.expand( + -1, -1, 1, multimask_logits.size(-2), multimask_logits.size(-1) + ) + best_multimask_logits = torch.gather(multimask_logits, 2, best_scores_inds_expanded) # [B, P, 1, H, W] + best_multimask_iou_scores = torch.gather(multimask_iou_scores, 2, best_scores_inds.unsqueeze(-1)) # [B, P, 1] + + # The mask from singlemask output token 0 and its stability score + singlemask_logits = all_mask_logits[:, :, 0:1, :, :] + singlemask_iou_scores = all_iou_scores[:, :, 0:1] + stability_scores = self._get_stability_scores(singlemask_logits) + is_stable = stability_scores >= self.dynamic_multimask_stability_thresh + + # Dynamically fall back to best multimask output upon low stability scores. + mask_logits_out = torch.where( + is_stable[..., None, None].expand_as(singlemask_logits), + singlemask_logits, + best_multimask_logits, + ) + iou_scores_out = torch.where( + is_stable.expand_as(singlemask_iou_scores), + singlemask_iou_scores, + best_multimask_iou_scores, + ) + return mask_logits_out, iou_scores_out + + +@auto_docstring( + custom_intro=""" + Segment Anything Model 2 (SAM 2) for generating segmentation masks, given an input image and + input points and labels, boxes, or masks. + """ +) +class Sam2Model(Sam2PreTrainedModel): + input_modalities = ("image", "text") + _can_record_outputs = {"mask_decoder_attentions": OutputRecorder(Sam2TwoWayAttentionBlock, index=2)} + _tied_weights_keys = {} + + def __init__(self, config: Sam2Config): + super().__init__(config) + self.shared_image_embedding = Sam2PositionalEmbedding(config.prompt_encoder_config) + self.vision_encoder = AutoModel.from_config(config.vision_config) + self.prompt_encoder = Sam2PromptEncoder(config.prompt_encoder_config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + self.mask_decoder = Sam2MaskDecoder(config.mask_decoder_config) + + self.num_feature_levels = config.vision_config.num_feature_levels + self.backbone_feature_sizes = config.vision_config.backbone_feature_sizes + # a single token to indicate no memory embedding from previous frames + self.hidden_dim = config.vision_config.fpn_hidden_size + self.no_memory_embedding = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + + self.post_init() + + def get_input_embeddings(self): + return self.vision_encoder.get_input_embeddings() + + def get_image_wide_positional_embeddings(self) -> torch.Tensor: + size = self.prompt_encoder.image_embedding_size + target_device = self.shared_image_embedding.positional_embedding.device + target_dtype = self.shared_image_embedding.positional_embedding.dtype + grid = torch.ones(size, device=target_device, dtype=target_dtype) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / size[0] + x_embed = x_embed / size[1] + + positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1)) + return positional_embedding.permute(2, 0, 1).unsqueeze(0) # channel x height x width + + @torch.no_grad() + def get_image_embeddings( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> list[torch.Tensor]: + r""" + Returns the image embeddings by passing the pixel values through the vision encoder. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Input pixel values + """ + batch_size = pixel_values.shape[0] + image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + return image_embeddings + + @torch.no_grad() + def get_prompt_embeddings( + self, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + ): + r""" + Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder. + + Args: + input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`): + Optional input points for the prompt encoder. The padding of the point is automatically done by the + processor. `point_batch_size` refers to the number of masks that we want the model to predict per + point. The model will output `point_batch_size` times 3 masks in total. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points_per_image)`): + Optional input labels for the prompt encoder. The padding of the labels is automatically done by the + processor, or can be fed by the user. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes_per_image, 4)`): + Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the + processor. users can also pass manually the input boxes. + input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`): + Optional input masks for the prompt encoder. + """ + prompt_output = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + return prompt_output + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam2ImageSegmentationOutput: + r""" + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decoder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoModel, AutoProcessor + + >>> model = AutoModel.from_pretrained("danelcsb/sam2.1_hiera_tiny") + >>> processor = AutoProcessor.from_pretrained("danelcsb/sam2.1_hiera_tiny") + + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" + >>> with httpx.stream("GET", url) as response: + ... raw_image = Image.open(BytesIO(response.read())).convert("RGB") + >>> input_points = [[[400, 650]]] # 2D location of a window on the car + >>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt") + + >>> # Get segmentation mask + >>> outputs = model(**inputs) + + >>> # Postprocess masks + >>> masks = processor.post_process_masks( + ... outputs.pred_masks, inputs["original_sizes"] + ... ) + ``` + """ + if not ((pixel_values is None) ^ (image_embeddings is None)): + raise ValueError("Exactly one of pixel_values or image_embeddings must be provided.") + if input_points is not None and input_boxes is not None: + if input_points.shape[1] != input_boxes.shape[1]: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {input_points.shape[1]} and {input_boxes.shape[1]}." + ) + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings[-1].shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + vision_attentions = None + vision_hidden_states = None + + if pixel_values is not None: + image_outputs: Sam2VisionEncoderOutput = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + vision_hidden_states = image_outputs.hidden_states + vision_attentions = image_outputs.attentions + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + if input_points is None and input_boxes is None: + # If no points are provide, pad with an empty point (with label -1) + input_points = torch.zeros( + batch_size, 1, 1, 2, dtype=image_embeddings[-1].dtype, device=image_embeddings[-1].device + ) + input_labels = -torch.ones(batch_size, 1, 1, dtype=torch.int32, device=image_embeddings[-1].device) + + if input_masks is not None: + # If mask_inputs is provided, downsize it into low-res mask input if needed + # and feed it as a dense mask prompt into the SAM mask encoder + if input_masks.shape[-2:] != self.prompt_encoder.mask_input_size: + input_masks = F.interpolate( + input_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(input_masks.dtype) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + low_res_multimasks, iou_scores, _, object_score_logits = self.mask_decoder( + image_embeddings=image_embeddings[-1], + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + high_resolution_features=image_embeddings[:-1], + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + + return Sam2ImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_multimasks, + object_score_logits=object_score_logits, + image_embeddings=image_embeddings, + vision_hidden_states=vision_hidden_states, + vision_attentions=vision_attentions, + ) + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam2VisionEncoderOutput: + r""" + pixel_values (`torch.FloatTensor`): + Input pixel values of shape `(batch_size, num_channels, height, width)`. + """ + vision_outputs: Sam2VisionEncoderOutput = self.vision_encoder(pixel_values, return_dict=True, **kwargs) + + feature_maps = vision_outputs.fpn_hidden_states + feature_maps_position_embeddings = vision_outputs.fpn_position_encoding + + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + feature_maps = list(feature_maps) + feature_maps[0] = self.mask_decoder.conv_s0(feature_maps[0]) + feature_maps[1] = self.mask_decoder.conv_s1(feature_maps[1]) + + # flatten NxCxHxW to HWxNxC + feature_maps = [feature_map.flatten(2).permute(2, 0, 1) for feature_map in feature_maps] + feature_maps_position_embeddings = [ + feature_map_position_embedding.flatten(2).permute(2, 0, 1) + for feature_map_position_embedding in feature_maps_position_embeddings + ] + vision_outputs.fpn_hidden_states = feature_maps + vision_outputs.fpn_position_encoding = feature_maps_position_embeddings + + return vision_outputs + + +__all__ = ["Sam2Model", "Sam2VisionModel", "Sam2PreTrainedModel", "Sam2HieraDetModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/modular_sam2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/modular_sam2.py new file mode 100644 index 0000000000000000000000000000000000000000..4a67444a004622133d4dcbb843eafd34e1a5f03c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/modular_sam2.py @@ -0,0 +1,1438 @@ +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SAM 2 model.""" + +from collections.abc import Callable +from dataclasses import dataclass +from typing import Union + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...activations import ACT2FN +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ModelOutput, TensorType, auto_docstring, can_return_tuple, logging +from ...utils.generic import ( + TransformersKwargs, + is_flash_attention_requested, + merge_with_config_defaults, +) +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from ..maskformer.modeling_maskformer import MaskFormerSinePositionEmbedding +from ..sam.image_processing_sam import SamImageProcessor +from ..sam.modeling_sam import ( + SamLayerNorm, + SamMaskDecoder, + SamMaskEmbedding, + SamModel, + SamPromptEncoder, + SamTwoWayAttentionBlock, + SamTwoWayTransformer, + eager_attention_forward, +) +from ..vitdet.modeling_vitdet import window_partition, window_unpartition +from .configuration_sam2 import ( + Sam2Config, + Sam2HieraDetConfig, + Sam2MaskDecoderConfig, + Sam2PromptEncoderConfig, + Sam2VisionConfig, +) + + +logger = logging.get_logger(__name__) + + +class Sam2ImageProcessorKwargs(ImagesKwargs, total=False): + r""" + mask_size (`dict[str, int]`, *optional*): + The size `{"height": int, "width": int}` to resize the segmentation maps to. + """ + + mask_size: dict[str, int] + + +@auto_docstring +class Sam2ImageProcessor(SamImageProcessor): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 1024, "width": 1024} + mask_size = {"height": 256, "width": 256} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + + valid_kwargs = Sam2ImageProcessorKwargs + + # disable SAM padding logic + do_pad = None + pad_size = None + mask_pad_size = None + + def __init__(self, **kwargs: Unpack[Sam2ImageProcessorKwargs]): + TorchvisionBackend.__init__(self, **kwargs) + + def _preprocess( + self, + images: list["torch.Tensor"], + return_tensors: str | TensorType | None, + **kwargs, + ) -> "torch.Tensor": + return TorchvisionBackend._preprocess(self, images, return_tensors=return_tensors, **kwargs).pixel_values + + @auto_docstring + def preprocess( + self, + images: ImageInput, + segmentation_maps: ImageInput | None = None, + **kwargs: Unpack[Sam2ImageProcessorKwargs], + ) -> BatchFeature: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to preprocess. + """ + return super().preprocess(images, segmentation_maps, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + device: Union[str, "torch.device"] | None = None, + **kwargs: Unpack[Sam2ImageProcessorKwargs], + ) -> BatchFeature: + """ + Preprocess image-like inputs. + """ + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device + ) + original_sizes = [image.shape[-2:] for image in images] + images_kwargs = kwargs.copy() + pixel_values = self._preprocess(images, **images_kwargs) + data = { + "pixel_values": pixel_values, + "original_sizes": original_sizes, + } + + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + "resample": PILImageResampling.NEAREST, + "size": segmentation_maps_kwargs.pop("mask_size"), + } + ) + processed_segmentation_maps = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + data["labels"] = processed_segmentation_maps.squeeze(1).to(torch.int64) + + return BatchFeature(data=data, tensor_type=kwargs["return_tensors"]) + + def _standardize_kwargs( + self, + mask_size: SizeDict | None = None, + **kwargs, + ) -> dict: + """ + Update kwargs that need further processing before being validated. + """ + if mask_size is not None and not isinstance(mask_size, SizeDict): + mask_size = SizeDict(**get_size_dict(mask_size, param_name="mask_size")) + kwargs["mask_size"] = mask_size + return TorchvisionBackend._standardize_kwargs(self, **kwargs) + + def _apply_non_overlapping_constraints(self, pred_masks: torch.Tensor) -> torch.Tensor: + """ + Apply non-overlapping constraints to the object scores in pred_masks. Here we + keep only the highest scoring object at each spatial location in pred_masks. + """ + batch_size = pred_masks.size(0) + if batch_size == 1: + return pred_masks + + device = pred_masks.device + # "max_obj_inds": object index of the object with the highest score at each location + max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) + # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` + batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] + keep = max_obj_inds == batch_obj_inds + # suppress overlapping regions' scores below -10.0 so that the foreground regions + # don't overlap (here sigmoid(-10.0)=4.5398e-05) + pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) + return pred_masks + + def post_process_masks( + self, + masks, + original_sizes, + mask_threshold=0.0, + binarize=True, + max_hole_area=0.0, + max_sprinkle_area=0.0, + apply_non_overlapping_constraints=False, + **kwargs, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[torch.Tensor, List[torch.Tensor], np.ndarray, List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + mask_threshold (`float`, *optional*, defaults to 0.0): + Threshold for binarization and post-processing operations. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + max_hole_area (`float`, *optional*, defaults to 0.0): + The maximum area of a hole to fill. + max_sprinkle_area (`float`, *optional*, defaults to 0.0): + The maximum area of a sprinkle to fill. + apply_non_overlapping_constraints (`bool`, *optional*, defaults to `False`): + Whether to apply non-overlapping constraints to the masks. + + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + if isinstance(original_sizes, (torch.Tensor, np.ndarray)): + original_sizes = original_sizes.tolist() + # TODO: add connected components kernel for postprocessing + output_masks = [] + for i, original_size in enumerate(original_sizes): + if isinstance(masks[i], np.ndarray): + masks[i] = torch.from_numpy(masks[i]) + elif not isinstance(masks[i], torch.Tensor): + raise TypeError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`") + interpolated_mask = F.interpolate(masks[i], original_size, mode="bilinear", align_corners=False) + if apply_non_overlapping_constraints: + interpolated_mask = self._apply_non_overlapping_constraints(interpolated_mask) + if binarize: + interpolated_mask = interpolated_mask > mask_threshold + output_masks.append(interpolated_mask) + + return output_masks + + def _get_preprocess_shape(self): + raise NotImplementedError("No _get_preprocess_shape for SAM 2.") + + def resize(self): + raise NotImplementedError("No need to override resize for SAM 2.") + + +@dataclass +@auto_docstring(custom_intro="Base class for the vision encoder's outputs.") +class Sam2VisionEncoderOutput(BaseModelOutputWithPooling): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the + model at the output of each stage. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + fpn_hidden_states (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Feature maps from the Feature Pyramid Network neck. + fpn_position_encoding (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Positional encodings corresponding to the `fpn_hidden_states`. + """ + + fpn_hidden_states: torch.FloatTensor | None = None + fpn_position_encoding: torch.FloatTensor | None = None + + +@auto_docstring(custom_intro="Base class for the Sam2 model's output.") +@dataclass +class Sam2ImageSegmentationOutput(ModelOutput): + r""" + iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`): + The Intersection over Union (IoU) scores of the predicted masks. + pred_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, height, width)`): + The predicted low-resolution masks. This is an alias for `low_res_masks`. These masks need to be post-processed + by the processor to be brought to the original image size. + object_score_logits (`torch.FloatTensor` of shape `(batch_size, point_batch_size, 1)`): + Logits for the object score, indicating if an object is present. + image_embeddings (`tuple(torch.FloatTensor)`): + The features from the FPN, which are used by the mask decoder. This is a tuple of `torch.FloatTensor` where each + tensor has shape `(batch_size, channels, height, width)`. + vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. + Hidden-states of the vision model at the output of each stage. + vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the vision model. + mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the mask decoder. + """ + + iou_scores: torch.FloatTensor | None = None + pred_masks: torch.FloatTensor | None = None + object_score_logits: torch.FloatTensor | None = None + image_embeddings: tuple[torch.FloatTensor, ...] = None + vision_hidden_states: tuple[torch.FloatTensor, ...] | None = None + vision_attentions: tuple[torch.FloatTensor, ...] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None + + +class Sam2PatchEmbeddings(nn.Module): + r""" + Turns pixel values into patch embeddings for transformer consumption. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Pixel values can be obtained using + [`AutoImageProcessor`]. See [`Sam2ImageProcessor.__call__`] for details. + + Returns: + embeddings (`torch.FloatTensor`): + Patch embeddings depend on image_size, patch_kernel_size, patch_stride and patch_padding + """ + + def __init__(self, config: Sam2HieraDetConfig): + super().__init__() + num_channels = config.num_channels + hidden_size = config.hidden_size + + self.projection = nn.Conv2d( + num_channels, + hidden_size, + kernel_size=config.patch_kernel_size, + stride=config.patch_stride, + padding=config.patch_padding, + ) + + def forward(self, pixel_values): + _, num_channels, height, width = pixel_values.shape + embeddings = self.projection(pixel_values.to(self.projection.weight.dtype)).permute(0, 2, 3, 1) + return embeddings + + +class Sam2SinePositionEmbedding(MaskFormerSinePositionEmbedding): + pass + + +class Sam2VisionNeck(nn.Module): + def __init__(self, config: Sam2VisionConfig): + super().__init__() + self.config = config + + self.position_encoding = Sam2SinePositionEmbedding(num_pos_feats=config.fpn_hidden_size // 2, normalize=True) + self.convs = nn.ModuleList() + for in_channels in config.backbone_channel_list: + self.convs.append( + nn.Conv2d( + in_channels=in_channels, + out_channels=config.fpn_hidden_size, + kernel_size=config.fpn_kernel_size, + stride=config.fpn_stride, + padding=config.fpn_padding, + ), + ) + self.fpn_top_down_levels = config.fpn_top_down_levels + + def forward(self, hidden_states: torch.Tensor) -> tuple[tuple[torch.Tensor, ...], tuple[torch.Tensor, ...]]: + fpn_hidden_states = () + fpn_position_encoding = () + + # forward in top-down order (from low to high resolution) + n = len(self.convs) - 1 + for i in range(n, -1, -1): + lateral_features = hidden_states[i].permute(0, 3, 1, 2) + lateral_features = self.convs[n - i](lateral_features.to(self.convs[i].weight.dtype)) + if i not in self.fpn_top_down_levels or i == n: + prev_features = lateral_features + else: + top_down_features = F.interpolate( + prev_features.to(dtype=torch.float32), + scale_factor=2.0, + mode="nearest", + align_corners=None, + antialias=False, + ).to(lateral_features.dtype) + prev_features = lateral_features + top_down_features + + prev_position_encoding = self.position_encoding( + prev_features.shape, prev_features.device, prev_features.dtype + ).to(prev_features.dtype) + + fpn_hidden_states += (prev_features,) + fpn_position_encoding += (prev_position_encoding,) + + return fpn_hidden_states, fpn_position_encoding + + +def do_pool(x: torch.Tensor, query_stride: int | None = None) -> torch.Tensor: + if query_stride is None: + return x + # (B, H, W, C) -> (B, C, H, W) + x = x.permute(0, 3, 1, 2) + x = nn.functional.max_pool2d(x, kernel_size=query_stride, stride=query_stride, ceil_mode=False) + # (B, C, H', W') -> (B, H', W', C) + x = x.permute(0, 2, 3, 1) + return x + + +class Sam2MultiScaleAttention(nn.Module): + def __init__( + self, + config: Sam2HieraDetConfig, + dim: int, + dim_out: int, + num_attention_heads: int, + query_stride: tuple[int, int] | None = None, + ): + super().__init__() + + self.config = config + + self.dim = dim + self.dim_out = dim_out + self.query_stride = query_stride + + self.num_attention_heads = num_attention_heads + head_dim = dim_out // num_attention_heads + self.scale = head_dim**-0.5 + self.qkv = nn.Linear(dim, dim_out * 3) + self.proj = nn.Linear(dim_out, dim_out) + + self.is_causal = False + + def forward(self, hidden_states: torch.Tensor, **kwargs) -> torch.Tensor: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (B, H * W, 3, nHead, C) + qkv = self.qkv(hidden_states).reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + # q, k, v with shape (B, H * W, nheads, C) + query, key, value = torch.unbind(qkv, 2) + + attn_weights = (query * self.scale) @ key.transpose(-2, -1) + attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype) + + # Q pooling (for downsample at stage changes) + if self.query_stride: + query = do_pool(query.reshape(batch_size, height, width, -1), self.query_stride) + height, width = query.shape[1:3] # downsampled shape + query = query.reshape(batch_size, height * width, self.num_attention_heads, -1) + + # transpose query, key, value to (B, nHead, H * W, C) + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = value.transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + attn_output, _ = attention_interface( + self, + query, + key, + value, + attention_mask=None, + is_causal=self.is_causal, + scaling=self.scale, + **kwargs, + ) + attn_output = attn_output.reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + + return attn_output + + +class Sam2FeedForward(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + activation: str = "relu", + sigmoid_output: bool = False, + ): + super().__init__() + self.num_layers = num_layers + self.activation = ACT2FN[activation] + self.proj_in = nn.Linear(input_dim, hidden_dim) + self.proj_out = nn.Linear(hidden_dim, output_dim) + self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)]) + self.sigmoid_output = sigmoid_output + + def forward(self, hidden_states): + hidden_states = self.proj_in(hidden_states) + hidden_states = self.activation(hidden_states) + for layer in self.layers: + hidden_states = self.activation(layer(hidden_states)) + + hidden_states = self.proj_out(hidden_states) + if self.sigmoid_output: + hidden_states = F.sigmoid(hidden_states) + return hidden_states + + +class Sam2MultiScaleBlock(GradientCheckpointingLayer): + def __init__( + self, + config: Sam2HieraDetConfig, + stage_idx: int, + block_idx: int, + total_block_idx: int, + ): + super().__init__() + + # take embed dim from previous stage if first block of stage + self.dim = ( + config.embed_dim_per_stage[stage_idx - 1] + if stage_idx > 0 and block_idx == 0 + else config.embed_dim_per_stage[stage_idx] + ) + self.dim_out = config.embed_dim_per_stage[stage_idx] + self.layer_norm1 = nn.LayerNorm(self.dim, eps=config.layer_norm_eps) + # take window size from previous stage if first block of stage + self.window_size = ( + config.window_size_per_stage[stage_idx - 1] + if stage_idx > 0 and block_idx == 0 + else config.window_size_per_stage[stage_idx] + ) + self.window_size = 0 if total_block_idx in config.global_attention_blocks else self.window_size + # use query stride for first block of stage if stage is a query pool stage + self.query_stride = ( + config.query_stride if 0 < stage_idx <= config.num_query_pool_stages and block_idx == 0 else None + ) + + self.attn = Sam2MultiScaleAttention( + config, + self.dim, + self.dim_out, + num_attention_heads=config.num_attention_heads_per_stage[stage_idx], + query_stride=self.query_stride, + ) + self.layer_norm2 = nn.LayerNorm(self.dim_out, eps=config.layer_norm_eps) + self.mlp = Sam2FeedForward( + self.dim_out, + int(self.dim_out * config.mlp_ratio), + self.dim_out, + num_layers=2, + activation=config.hidden_act, + ) + if self.dim != self.dim_out: + self.proj = nn.Linear(self.dim, self.dim_out) + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states # batch_size, height, width, channel + + hidden_states = self.layer_norm1(hidden_states) + + # Skip connection + if self.dim != self.dim_out: + residual = do_pool(self.proj(hidden_states), self.query_stride) + + # Window partition + window_size = self.window_size + if self.window_size > 0: + H, W = hidden_states.shape[1], hidden_states.shape[2] + hidden_states, pad_hw = window_partition(hidden_states, window_size) + + # Window Attention + Q Pooling (if stage change) + attn_output = self.attn( + hidden_states=hidden_states, + **kwargs, + ) + hidden_states = attn_output + if self.query_stride: + # Shapes have changed due to Q pooling + window_size = self.window_size // self.query_stride[0] + H, W = residual.shape[1:3] + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + pad_hw = (H + pad_h, W + pad_w) + + # Reverse window partition + if self.window_size > 0: + hidden_states = window_unpartition(hidden_states, window_size, pad_hw, (H, W)) + + hidden_states = residual + hidden_states + layernorm_output = self.layer_norm2(hidden_states) + hidden_states = hidden_states + self.mlp(layernorm_output) + + return hidden_states + + +@auto_docstring( + custom_intro=""" + Hiera model's outputs that also contains a pooling of the last hidden states. + """ +) +@dataclass +class Sam2HieraDetModelOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`): + hidden-states at the output of the last layer of the model. + intermediate_hidden_states (`tuple[torch.FloatTensor]` of shape `(batch_size, height, width, hidden_size)`): + Sequence of hidden-states at the output of the intermediate layers of the model. + """ + + last_hidden_state: torch.FloatTensor | None = None + intermediate_hidden_states: tuple[torch.FloatTensor, ...] | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@auto_docstring +class Sam2PreTrainedModel(PreTrainedModel): + config_class = Sam2Config + base_model_prefix = "sam2" + main_input_name = "pixel_values" + input_modalities = ("image",) + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _keys_to_ignore_on_load_unexpected = [ + r"^memory_.*", + r"^mask_downsample.*", + r"^object_pointer_proj.*", + r"^temporal_positional_encoding_projection_layer.*", + "no_memory_positional_encoding", + "no_object_pointer", + "occlusion_spatial_embedding_parameter", + ] + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Sam2HieraDetModel): + if module.pos_embed is not None: + init.zeros_(module.pos_embed) + if module.pos_embed_window is not None: + init.zeros_(module.pos_embed_window) + elif isinstance(module, Sam2PositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + elif isinstance(module, Sam2Model): + if module.no_memory_embedding is not None: + init.zeros_(module.no_memory_embedding) + + +class Sam2HieraDetModel(Sam2PreTrainedModel): + config_class = Sam2HieraDetConfig + main_input_name = "pixel_values" + _can_record_outputs = { + "hidden_states": Sam2MultiScaleBlock, + "attentions": Sam2MultiScaleAttention, + } + + def __init__(self, config: Sam2HieraDetConfig): + super().__init__(config) + + self.patch_embed = Sam2PatchEmbeddings(config) + # Windowed positional embedding (https://huggingface.co/papers/2311.05613) + self.pos_embed = nn.Parameter( + torch.zeros(1, config.hidden_size, *config.window_positional_embedding_background_size) + ) + self.pos_embed_window = nn.Parameter( + torch.zeros(1, config.hidden_size, config.window_size_per_stage[0], config.window_size_per_stage[0]) + ) + self.stage_ends = (np.cumsum(config.blocks_per_stage) - 1).tolist() + self.blocks = nn.ModuleList() + total_block_idx = 0 + for stage_idx, blocks_per_stage in enumerate(config.blocks_per_stage): + for block_idx in range(blocks_per_stage): + block = Sam2MultiScaleBlock( + config=config, stage_idx=stage_idx, block_idx=block_idx, total_block_idx=total_block_idx + ) + self.blocks.append(block) + total_block_idx += 1 + + self.post_init() + + def get_input_embeddings(self): + return self.patch_embed + + def _get_pos_embed(self, hw: tuple[int, int]) -> torch.Tensor: + h, w = hw + window_embed = self.pos_embed_window + pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode="bicubic") + pos_embed = pos_embed + window_embed.tile([x // y for x, y in zip(pos_embed.shape, window_embed.shape)]) + pos_embed = pos_embed.permute(0, 2, 3, 1) + return pos_embed + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam2HieraDetModelOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.patch_embed(pixel_values) + hidden_states = hidden_states + self._get_pos_embed(hidden_states.shape[1:3]) + + intermediate_hidden_states = () + for i, block_module in enumerate(self.blocks): + hidden_states = block_module(hidden_states, **kwargs) + + if i in self.stage_ends: + intermediate_hidden_states = intermediate_hidden_states + (hidden_states,) + + return Sam2HieraDetModelOutput( + last_hidden_state=hidden_states, + intermediate_hidden_states=intermediate_hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + The vision model from Sam without any head or projection on top. + """ +) +class Sam2VisionModel(Sam2PreTrainedModel): + config_class = Sam2VisionConfig + main_input_name = "pixel_values" + _can_record_outputs = { + "hidden_states": Sam2MultiScaleBlock, + "attentions": Sam2MultiScaleAttention, + } + + def __init__(self, config: Sam2VisionConfig): + super().__init__(config) + self.config = config + + self.backbone = AutoModel.from_config(config.backbone_config) + + self.neck = Sam2VisionNeck(config) + self.num_feature_levels = config.num_feature_levels + + self.post_init() + + def get_input_embeddings(self): + return self.backbone.get_input_embeddings() + + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam2VisionEncoderOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + # Forward through backbone + backbone_output = self.backbone(pixel_values, **kwargs) + hidden_states = backbone_output.last_hidden_state + intermediate_hidden_states = backbone_output.intermediate_hidden_states + + fpn_hidden_states, fpn_position_encoding = self.neck(intermediate_hidden_states) + # Select last `num_feature_levels` feature levels from FPN and reverse order to get features from high to low resolution + fpn_hidden_states = fpn_hidden_states[-self.num_feature_levels :][::-1] + fpn_position_encoding = fpn_position_encoding[-self.num_feature_levels :][::-1] + + return Sam2VisionEncoderOutput( + last_hidden_state=hidden_states, + fpn_hidden_states=fpn_hidden_states, + fpn_position_encoding=fpn_position_encoding, + hidden_states=backbone_output.hidden_states, + attentions=backbone_output.attentions, + ) + + +class Sam2PositionalEmbedding(nn.Module): + def __init__(self, config: Sam2PromptEncoderConfig): + super().__init__() + self.scale = config.scale + positional_embedding = self.scale * torch.randn((2, config.hidden_size // 2)) + self.register_buffer("positional_embedding", positional_embedding) + + def forward(self, input_coords, input_shape=None): + """Positionally encode points that are normalized to [0,1].""" + coordinates = input_coords.clone() + + if input_shape is not None: + coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1] + coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0] + coordinates.to(torch.float32) + + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coordinates = 2 * coordinates - 1 + coordinates = coordinates.to(self.positional_embedding.dtype) + coordinates = coordinates @ self.positional_embedding + coordinates = 2 * np.pi * coordinates + # outputs d_1 x ... x d_n x channel shape + return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1) + + +class Sam2MaskEmbedding(SamMaskEmbedding): + pass + + +class Sam2PromptEncoder(SamPromptEncoder): + def __init__(self, config: Sam2PromptEncoderConfig): + nn.Module.__init__(self) + self.shared_embedding = Sam2PositionalEmbedding(config) + self.mask_embed = Sam2MaskEmbedding(config) + self.no_mask_embed = nn.Embedding(1, config.hidden_size) + + self.image_embedding_size = (config.image_size // config.patch_size, config.image_size // config.patch_size) + self.mask_input_size = (4 * config.image_size // config.patch_size, 4 * config.image_size // config.patch_size) + self.input_image_size = config.image_size + + self.point_embed = nn.Embedding(config.num_point_embeddings, config.hidden_size) + self.hidden_size = config.hidden_size + self.not_a_point_embed = nn.Embedding(1, config.hidden_size) + + def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + points = torch.nn.functional.pad(points, (0, 0, 0, 1), mode="constant", value=0) + labels = torch.nn.functional.pad(labels, (0, 1), mode="constant", value=-1) + input_shape = (self.input_image_size, self.input_image_size) + point_embedding = self.shared_embedding(points, input_shape) + + # torch.where and expanding the labels tensor is required by the ONNX export + point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding) + + # This is required for the ONNX export. The dtype, device need to be explicitly + # specified as otherwise torch.onnx.export interprets as double + point_embedding = torch.where( + labels[..., None] != -10, + point_embedding, + torch.zeros_like(point_embedding), + ) + + # Add point embeddings for labels >= 0 + point_embedding = point_embedding + self.point_embed(labels.clamp(min=0)) * (labels >= 0).unsqueeze(-1) + + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.view(*boxes.shape[:2], 2, 2) + # add padding point for consistency with the original implementation + coords = torch.nn.functional.pad(coords, (0, 0, 0, 1), mode="constant", value=0) + corner_embedding = self.shared_embedding(coords, (self.input_image_size, self.input_image_size)) + corner_embedding[:, :, 0, :] += self.point_embed.weight[2] + corner_embedding[:, :, 1, :] += self.point_embed.weight[3] + corner_embedding[:, :, 2, :] = self.not_a_point_embed.weight.expand_as(corner_embedding[:, :, 2, :]) + return corner_embedding + + +class Sam2Attention(nn.Module): + """ + SAM2's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and + values. + """ + + def __init__(self, config, downsample_rate=None): + super().__init__() + downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate + self.config = config + self.hidden_size = config.hidden_size + self.internal_dim = config.hidden_size // downsample_rate + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.internal_dim // config.num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.v_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.o_proj = nn.Linear(self.internal_dim, self.hidden_size) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_similarity: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # Input projections + batch_size, point_batch_size = query.shape[:2] + new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim) + + query = self.q_proj(query).view(*new_shape).transpose(1, 2) + key = self.k_proj(key).view(*new_shape).transpose(1, 2) + value = self.v_proj(value).view(*new_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config) and attention_similarity is not None: + # Target guided masks are represented as float masks and are incompatible with Flash Attention + # Fallback to SDPA for this call only so the rest of the model can still benefit from FA + attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"] + logger.warning_once( + "Falling back to SDPA for target-guided attention because " + "Flash Attention does not support additive bias masks." + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_similarity, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = attn_output.reshape( + batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +class Sam2TwoWayAttentionBlock(SamTwoWayAttentionBlock, GradientCheckpointingLayer): + def __init__(self, config: Sam2MaskDecoderConfig, skip_first_layer_pe: bool = False): + nn.Module.__init__(self) + self.self_attn = Sam2Attention(config, downsample_rate=1) + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + + self.cross_attn_token_to_image = Sam2Attention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam2FeedForward( + config.hidden_size, config.mlp_dim, config.hidden_size, num_layers=config.num_hidden_layers + ) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + self.layer_norm4 = nn.LayerNorm(config.hidden_size) + self.cross_attn_image_to_token = Sam2Attention(config) + + self.skip_first_layer_pe = skip_first_layer_pe + + +class Sam2TwoWayTransformer(SamTwoWayTransformer): + pass + + +class Sam2LayerNorm(SamLayerNorm): + pass + + +class Sam2MaskDecoder(SamMaskDecoder): + def __init__(self, config: Sam2MaskDecoderConfig): + super().__init__(config) + del self.iou_prediction_head + self.iou_prediction_head = Sam2FeedForward( + self.hidden_size, + config.iou_head_hidden_dim, + self.num_mask_tokens, + config.iou_head_depth, + sigmoid_output=True, + ) + + self.conv_s0 = nn.Conv2d(config.hidden_size, config.hidden_size // 8, kernel_size=1, stride=1) + self.conv_s1 = nn.Conv2d(config.hidden_size, config.hidden_size // 4, kernel_size=1, stride=1) + + self.obj_score_token = nn.Embedding(1, self.hidden_size) + self.pred_obj_score_head = Sam2FeedForward(self.hidden_size, self.hidden_size, 1, 3) + + self.dynamic_multimask_via_stability = config.dynamic_multimask_via_stability + self.dynamic_multimask_stability_delta = config.dynamic_multimask_stability_delta + self.dynamic_multimask_stability_thresh = config.dynamic_multimask_stability_thresh + + def _get_stability_scores(self, mask_logits): + """ + Compute stability scores of the mask logits based on the IoU between upper and + lower thresholds. + """ + mask_logits = mask_logits.flatten(-2) + stability_delta = self.dynamic_multimask_stability_delta + area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() + area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() + stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) + return stability_scores + + def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): + """ + When outputting a single mask, if the stability score from the current single-mask + output (based on output token 0) falls below a threshold, we instead select from + multi-mask outputs (based on output token 1~3) the mask with the highest predicted + IoU score. This is intended to ensure a valid mask for both clicking and tracking. + """ + # The best mask from multimask output tokens (1~3) + multimask_logits = all_mask_logits[:, :, 1:, :, :] + multimask_iou_scores = all_iou_scores[:, :, 1:] + best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1) # [B, P] + best_scores_inds_expanded = best_scores_inds.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) + best_scores_inds_expanded = best_scores_inds_expanded.expand( + -1, -1, 1, multimask_logits.size(-2), multimask_logits.size(-1) + ) + best_multimask_logits = torch.gather(multimask_logits, 2, best_scores_inds_expanded) # [B, P, 1, H, W] + best_multimask_iou_scores = torch.gather(multimask_iou_scores, 2, best_scores_inds.unsqueeze(-1)) # [B, P, 1] + + # The mask from singlemask output token 0 and its stability score + singlemask_logits = all_mask_logits[:, :, 0:1, :, :] + singlemask_iou_scores = all_iou_scores[:, :, 0:1] + stability_scores = self._get_stability_scores(singlemask_logits) + is_stable = stability_scores >= self.dynamic_multimask_stability_thresh + + # Dynamically fall back to best multimask output upon low stability scores. + mask_logits_out = torch.where( + is_stable[..., None, None].expand_as(singlemask_logits), + singlemask_logits, + best_multimask_logits, + ) + iou_scores_out = torch.where( + is_stable.expand_as(singlemask_iou_scores), + singlemask_iou_scores, + best_multimask_iou_scores, + ) + return mask_logits_out, iou_scores_out + + def forward( + self, + image_embeddings: torch.Tensor, + image_positional_embeddings: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + high_resolution_features: list[torch.Tensor], + attention_similarity: torch.Tensor | None = None, + target_embedding: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Args: + image_embeddings (`torch.Tensor`): + The embeddings from the image encoder. + image_positional_embeddings (`torch.Tensor`): + Positional encoding with the shape of image_embeddings. + sparse_prompt_embeddings (`torch.Tensor`): + The embeddings of the points and boxes. + dense_prompt_embeddings (`torch.Tensor`): + The embeddings of the mask inputs. + multimask_output (`bool`): + Whether to return multiple masks or a single mask. + high_resolution_features (`list[torch.Tensor]`, *optional*): + The high-resolution features from the vision encoder. + attention_similarity (`torch.Tensor`, *optional*): + The attention similarity tensor. + target_embedding (`torch.Tensor`, *optional*): + The target embedding. + """ + batch_size, num_channels, height, width = image_embeddings.shape + point_batch_size = sparse_prompt_embeddings.shape[1] + # Concatenate output tokens + output_tokens = torch.cat( + [ + self.obj_score_token.weight, + self.iou_token.weight, + self.mask_tokens.weight, + ], + dim=0, + ) + output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) + + if sparse_prompt_embeddings.shape[0] != 0: + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2) + else: + tokens = output_tokens + point_embeddings = tokens.to(self.iou_token.weight.dtype) + + # Expand per-image data in batch direction to be per-mask + image_embeddings = image_embeddings + dense_prompt_embeddings + image_embeddings = image_embeddings.repeat_interleave(point_batch_size, dim=0) + image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) + # Run the transformer + point_embeddings, image_embeddings = self.transformer( + point_embeddings=point_embeddings, + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + iou_token_out = point_embeddings[:, :, 1, :] + mask_tokens_out = point_embeddings[:, :, 2 : (2 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + image_embeddings = image_embeddings.transpose(2, 3).view( + batch_size * point_batch_size, num_channels, height, width + ) + + feat_s0, feat_s1 = high_resolution_features + feat_s0 = feat_s0.repeat_interleave(point_batch_size, dim=0) + feat_s1 = feat_s1.repeat_interleave(point_batch_size, dim=0) + upscaled_embedding = self.upscale_conv1(image_embeddings) + feat_s1 + upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) + upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding) + feat_s0) + + hyper_in_list: list[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + current_mlp = self.output_hypernetworks_mlps[i] + hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])] + hyper_in = torch.stack(hyper_in_list, dim=2) + + _, num_channels, height, width = upscaled_embedding.shape + upscaled_embedding = upscaled_embedding.view(batch_size, point_batch_size, num_channels, height * width) + masks = (hyper_in @ upscaled_embedding).view(batch_size, point_batch_size, -1, height, width) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + object_score_logits = self.pred_obj_score_head(point_embeddings[:, :, 0, :]) + + # Select the correct mask or masks for output + if multimask_output: + mask_slice = slice(1, None) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + elif self.dynamic_multimask_via_stability and not self.training: + mask_slice = slice(0, 1) + masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred) + else: + mask_slice = slice(0, 1) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + + sam_tokens_out = mask_tokens_out[:, :, mask_slice] # [b, 3, c] shape + + return masks, iou_pred, sam_tokens_out, object_score_logits + + +@auto_docstring( + custom_intro=""" + Segment Anything Model 2 (SAM 2) for generating segmentation masks, given an input image and + input points and labels, boxes, or masks. + """ +) +class Sam2Model(SamModel): + _tied_weights_keys = {} + + def __init__(self, config: Sam2Config): + PreTrainedModel.__init__(self, config) + self.shared_image_embedding = Sam2PositionalEmbedding(config.prompt_encoder_config) + self.vision_encoder = AutoModel.from_config(config.vision_config) + self.prompt_encoder = Sam2PromptEncoder(config.prompt_encoder_config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + self.mask_decoder = Sam2MaskDecoder(config.mask_decoder_config) + + self.num_feature_levels = config.vision_config.num_feature_levels + self.backbone_feature_sizes = config.vision_config.backbone_feature_sizes + # a single token to indicate no memory embedding from previous frames + self.hidden_dim = config.vision_config.fpn_hidden_size + self.no_memory_embedding = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + + self.post_init() + + def get_image_wide_positional_embeddings(self) -> torch.Tensor: + size = self.prompt_encoder.image_embedding_size + target_device = self.shared_image_embedding.positional_embedding.device + target_dtype = self.shared_image_embedding.positional_embedding.dtype + grid = torch.ones(size, device=target_device, dtype=target_dtype) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / size[0] + x_embed = x_embed / size[1] + + positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1)) + return positional_embedding.permute(2, 0, 1).unsqueeze(0) # channel x height x width + + @torch.no_grad() + def get_image_embeddings( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> list[torch.Tensor]: + r""" + Returns the image embeddings by passing the pixel values through the vision encoder. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Input pixel values + """ + batch_size = pixel_values.shape[0] + image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + return image_embeddings + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam2VisionEncoderOutput: + r""" + pixel_values (`torch.FloatTensor`): + Input pixel values of shape `(batch_size, num_channels, height, width)`. + """ + vision_outputs: Sam2VisionEncoderOutput = self.vision_encoder(pixel_values, return_dict=True, **kwargs) + + feature_maps = vision_outputs.fpn_hidden_states + feature_maps_position_embeddings = vision_outputs.fpn_position_encoding + + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + feature_maps = list(feature_maps) + feature_maps[0] = self.mask_decoder.conv_s0(feature_maps[0]) + feature_maps[1] = self.mask_decoder.conv_s1(feature_maps[1]) + + # flatten NxCxHxW to HWxNxC + feature_maps = [feature_map.flatten(2).permute(2, 0, 1) for feature_map in feature_maps] + feature_maps_position_embeddings = [ + feature_map_position_embedding.flatten(2).permute(2, 0, 1) + for feature_map_position_embedding in feature_maps_position_embeddings + ] + vision_outputs.fpn_hidden_states = feature_maps + vision_outputs.fpn_position_encoding = feature_maps_position_embeddings + + return vision_outputs + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam2ImageSegmentationOutput: + r""" + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decoder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoModel, AutoProcessor + + >>> model = AutoModel.from_pretrained("danelcsb/sam2.1_hiera_tiny") + >>> processor = AutoProcessor.from_pretrained("danelcsb/sam2.1_hiera_tiny") + + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" + >>> with httpx.stream("GET", url) as response: + ... raw_image = Image.open(BytesIO(response.read())).convert("RGB") + >>> input_points = [[[400, 650]]] # 2D location of a window on the car + >>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt") + + >>> # Get segmentation mask + >>> outputs = model(**inputs) + + >>> # Postprocess masks + >>> masks = processor.post_process_masks( + ... outputs.pred_masks, inputs["original_sizes"] + ... ) + ``` + """ + if not ((pixel_values is None) ^ (image_embeddings is None)): + raise ValueError("Exactly one of pixel_values or image_embeddings must be provided.") + if input_points is not None and input_boxes is not None: + if input_points.shape[1] != input_boxes.shape[1]: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {input_points.shape[1]} and {input_boxes.shape[1]}." + ) + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings[-1].shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + vision_attentions = None + vision_hidden_states = None + + if pixel_values is not None: + image_outputs: Sam2VisionEncoderOutput = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + vision_hidden_states = image_outputs.hidden_states + vision_attentions = image_outputs.attentions + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + if input_points is None and input_boxes is None: + # If no points are provide, pad with an empty point (with label -1) + input_points = torch.zeros( + batch_size, 1, 1, 2, dtype=image_embeddings[-1].dtype, device=image_embeddings[-1].device + ) + input_labels = -torch.ones(batch_size, 1, 1, dtype=torch.int32, device=image_embeddings[-1].device) + + if input_masks is not None: + # If mask_inputs is provided, downsize it into low-res mask input if needed + # and feed it as a dense mask prompt into the SAM mask encoder + if input_masks.shape[-2:] != self.prompt_encoder.mask_input_size: + input_masks = F.interpolate( + input_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(input_masks.dtype) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + low_res_multimasks, iou_scores, _, object_score_logits = self.mask_decoder( + image_embeddings=image_embeddings[-1], + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + high_resolution_features=image_embeddings[:-1], + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + + return Sam2ImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_multimasks, + object_score_logits=object_score_logits, + image_embeddings=image_embeddings, + vision_hidden_states=vision_hidden_states, + vision_attentions=vision_attentions, + ) + + +__all__ = [ + "Sam2Model", + "Sam2VisionModel", + "Sam2PreTrainedModel", + "Sam2ImageProcessor", + "Sam2HieraDetModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/processing_sam2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/processing_sam2.py new file mode 100644 index 0000000000000000000000000000000000000000..2d91799b96ac83733f3e9c493c45cb24bd501f71 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2/processing_sam2.py @@ -0,0 +1,511 @@ +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for SAM2. +""" + +from copy import deepcopy + +import numpy as np + +from ...image_utils import ImageInput +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding +from ...utils import TensorType, auto_docstring, is_torch_available, logging +from ...utils.import_utils import requires + + +logger = logging.get_logger(__name__) + +if is_torch_available(): + import torch + + +@requires(backends=("torch",)) +@auto_docstring +class Sam2Processor(ProcessorMixin): + def __init__(self, image_processor, target_size: int | None = None, point_pad_value: int = -10, **kwargs): + r""" + target_size (`int`, *optional*): + The target size (in pixels) for normalizing input points and bounding boxes. If not provided, defaults + to the image processor's size configuration. All input coordinates (points and boxes) are normalized + to this size before being passed to the model. This ensures consistent coordinate representation + regardless of the original image dimensions. + point_pad_value (`int`, *optional*, defaults to -10): + The value used for padding input points when batching sequences of different lengths. This value is + used to mark padded positions and is preserved during coordinate normalization. + """ + super().__init__(image_processor, **kwargs) + self.point_pad_value = point_pad_value + self.target_size = target_size if target_size is not None else self.image_processor.size["height"] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + segmentation_maps: ImageInput | None = None, + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + original_sizes: list[list[float]] | torch.Tensor | None = None, + return_tensors: str | TensorType | None = None, + **kwargs, + ) -> BatchEncoding: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to process. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + original_sizes (`list[list[float]]`, `torch.Tensor`, *optional*): + The original sizes of the images. + + Returns: + A [`BatchEncoding`] with the following fields: + - `pixel_values` (`torch.Tensor`): The processed image(s). + - `original_sizes` (`list[list[float]]`): The original sizes of the images. + - `labels` (`torch.Tensor`): The processed segmentation maps (if provided). + - `input_points` (`torch.Tensor`): The processed points. + - `input_labels` (`torch.Tensor`): The processed labels. + - `input_boxes` (`torch.Tensor`): The processed bounding boxes. + """ + if images is not None: + encoding_image_processor = self.image_processor( + images, + segmentation_maps=segmentation_maps, + return_tensors=return_tensors, + **kwargs, + ) + elif original_sizes is not None: + if isinstance(original_sizes, torch.Tensor): + original_sizes = original_sizes.cpu().tolist() + encoding_image_processor = BatchEncoding({"original_sizes": original_sizes}, tensor_type=return_tensors) + else: + raise ValueError("Either images or original_sizes must be provided") + + # pop arguments that are not used in the forward but used nevertheless + original_sizes = encoding_image_processor["original_sizes"] + # Check original_sizes is of length 1 or len(images) + if images is not None and len(original_sizes) != 1 and len(original_sizes) != len(images): + raise ValueError( + "original_sizes must be of length 1 or len(images). If you are passing a single image, you must pass a single original_size." + ) + + # Process input points, labels, and boxes if provided + if input_points is not None or input_labels is not None or input_boxes is not None: + # Validate and convert inputs to standardized format + processed_points = self._validate_single_input( + input_points, + expected_depth=4, + input_name="points", + expected_format="[image level, object level, point level, point coordinates]", + expected_coord_size=2, + ) + processed_labels = self._validate_single_input( + input_labels, + expected_depth=3, + input_name="labels", + expected_format="[image level, object level, point level]", + ) + processed_boxes = self._validate_single_input( + input_boxes, + expected_depth=3, + input_name="boxes", + expected_format="[image level, box level, box coordinates]", + expected_coord_size=4, + ) + + # Get padding requirements for all inputs + if processed_points is not None: + points_max_dims = self._get_nested_dimensions(processed_points)[:3] + if processed_labels is not None: + labels_max_dims = self._get_nested_dimensions(processed_labels)[:3] + if processed_boxes is not None: + boxes_max_dims = self._get_nested_dimensions(processed_boxes)[:2] + + # Ensure points and labels have consistent dimensions + if processed_points is not None and processed_labels is not None: + if points_max_dims != labels_max_dims: + raise ValueError( + "Input points and labels have inconsistent dimensions. Please ensure they have the same dimensions." + ) + + # Check that boxes don't need padding (model limitation) + if processed_boxes is not None and len(processed_boxes) >= 2: + if any(len(img_boxes) < boxes_max_dims[1] for img_boxes in processed_boxes): + raise ValueError( + "Input boxes have inconsistent dimensions that would require padding, " + "but boxes cannot be padded due to model limitations. " + "Please ensure all images have the same number of boxes." + ) + + # Pad and normalize all inputs to final tensor format + if processed_points is not None: + padded_points = self._pad_nested_list(processed_points, points_max_dims + [2]) + final_points = torch.tensor(padded_points, dtype=torch.float32) + self._normalize_tensor_coordinates(final_points, original_sizes, preserve_padding=True) + encoding_image_processor.update({"input_points": final_points}) + + if processed_labels is not None: + padded_labels = self._pad_nested_list(processed_labels, labels_max_dims) + final_labels = torch.tensor(padded_labels, dtype=torch.int64) + encoding_image_processor.update({"input_labels": final_labels}) + + if processed_boxes is not None: + final_boxes = torch.tensor(processed_boxes, dtype=torch.float32) + self._normalize_tensor_coordinates(final_boxes, original_sizes, is_bounding_box=True) + encoding_image_processor.update({"input_boxes": final_boxes}) + + return encoding_image_processor + + def _normalize_coordinates( + self, target_size: int, coords: "torch.Tensor", original_size, is_bounding_box=False + ) -> "torch.Tensor": + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format. + + Args: + target_size (`int`): + The target size of the image. + coords (`torch.Tensor`): + The coordinates to be normalized. + original_size (`tuple`): + The original size of the image. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether the coordinates are bounding boxes. + """ + old_h, old_w = original_size + new_h, new_w = target_size, target_size + coords = deepcopy(coords).float() + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + def _convert_to_nested_list(self, data, expected_depth, current_depth=0): + """ + Recursively convert various input formats (tensors, numpy arrays, lists) to nested lists. + + Args: + data: Input data in any format + expected_depth: Expected nesting depth + current_depth: Current depth in recursion + + Returns: + Nested list representation of the data + """ + if data is None: + return None + + # Convert tensor/numpy to list if we're at a leaf level or if it's a multi-dimensional array + if isinstance(data, torch.Tensor): # PyTorch tensor + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small tensor + return data.numpy().tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, np.ndarray): # NumPy array + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small array + return data.tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, list): + if current_depth == expected_depth: + # We've reached the expected depth, return as is + return data + else: + # Continue recursion + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, (int, float)): + return data + else: + raise TypeError(f"Unsupported data type: {type(data)}") + + def _get_nested_dimensions(self, nested_list, max_dims=None): + """ + Get the maximum dimensions at each level of nesting. + + Args: + nested_list (`list`): + Nested list structure. + max_dims (`list`, *optional*): + Current maximum dimensions (for recursion). + + Returns: + `list`: A list of maximum dimensions for each nesting level. + """ + if max_dims is None: + max_dims = [] + + if not isinstance(nested_list, list): + return max_dims + + if len(max_dims) == 0: + max_dims.append(len(nested_list)) + else: + max_dims[0] = max(max_dims[0], len(nested_list)) + + if len(nested_list) > 0: + for item in nested_list: + if isinstance(item, list): + sub_dims = self._get_nested_dimensions(item) + # Merge sub_dims into max_dims + for i, dim in enumerate(sub_dims): + if i + 1 >= len(max_dims): + max_dims.append(dim) + else: + max_dims[i + 1] = max(max_dims[i + 1], dim) + + return max_dims + + def _pad_nested_list(self, nested_list, target_dims, current_level=0, pad_value=None): + """ + Recursively pad a nested list to match target dimensions. + + Args: + nested_list (`list`): + Nested list to pad. + target_dims (`list`): + Target dimensions for each level. + current_level (`int`, *optional*, defaults to 0): + Current nesting level. + pad_value (`int`, *optional*): + Value to use for padding. + + Returns: + `list`: The padded nested list. + """ + if pad_value is None: + pad_value = self.point_pad_value + + if current_level >= len(target_dims): + return nested_list + + # Ensure we have a list + if not isinstance(nested_list, list): + nested_list = [nested_list] + + # Pad current level + current_size = len(nested_list) + target_size = target_dims[current_level] + + # Pad with appropriate values + if current_level == len(target_dims) - 1: + # At the coordinate level, pad with pad_value + nested_list.extend([pad_value] * (target_size - current_size)) + else: + # At higher levels, pad with nested structures + if current_size > 0: + # Create appropriately sized template + if current_level < len(target_dims) - 2: + # For non-coordinate levels, create empty nested structure + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + else: + # For coordinate level, create list of pad_values + template = [pad_value] * target_dims[current_level + 1] + + nested_list.extend([deepcopy(template) for _ in range(target_size - current_size)]) + else: + # Create from scratch + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + nested_list.extend([deepcopy(template) for _ in range(target_size)]) + + # Recursively pad sublists + if current_level < len(target_dims) - 1: + for i in range(len(nested_list)): + if isinstance(nested_list[i], list): + nested_list[i] = self._pad_nested_list(nested_list[i], target_dims, current_level + 1, pad_value) + + return nested_list + + def _create_empty_nested_structure(self, dims, pad_value): + """ + Create an empty nested structure with given dimensions filled with pad_value. + + Args: + dims (`list`): + The dimensions of the nested structure. + pad_value (`int`): + The value to fill the structure with. + """ + if len(dims) == 1: + return [pad_value] * dims[0] + else: + return [self._create_empty_nested_structure(dims[1:], pad_value) for _ in range(dims[0])] + + def _get_nesting_level(self, input_list): + """ + Get the nesting level of a list structure. + + Args: + input_list (`list`): + The list to get the nesting level of. + """ + if isinstance(input_list, list): + if len(input_list) == 0: + return 1 + return 1 + self._get_nesting_level(input_list[0]) + elif isinstance(input_list, (np.ndarray, torch.Tensor)): + # For arrays/tensors, the nesting level is the number of dimensions + return len(input_list.shape) + return 0 + + def _validate_single_input( + self, + data: torch.Tensor | np.ndarray | list, + expected_depth: int, + input_name: str, + expected_format: str, + expected_coord_size: int | None = None, + ) -> list: + """ + Validate a single input by ensuring proper nesting and raising an error if the input is not valid. + + Args: + data (`torch.Tensor`, `np.ndarray`, or `list`): + Input data to process. + expected_depth (`int`): + Expected nesting depth. + input_name (`str`): + Name of the input for error messages. + expected_format (`str`): + The expected format of the input. + expected_coord_size (`int`, *optional*): + Expected coordinate size (2 for points, 4 for boxes, None for labels). + . + """ + if data is None: + return None + + # Handle tensors and numpy arrays first + if isinstance(data, (torch.Tensor, np.ndarray)): + # For tensors/arrays, we can directly check the number of dimensions + if data.ndim != expected_depth: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_depth} dimensions. The expected nesting format is {expected_format}. Got {data.ndim} dimensions." + ) + elif expected_coord_size is not None: + if data.shape[-1] != expected_coord_size: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_coord_size} as the last dimension, got {data.shape[-1]}." + ) + return self._convert_to_nested_list(data, expected_depth) + + # Handle nested lists + if isinstance(data, list): + current_depth = self._get_nesting_level(data) + if current_depth != expected_depth: + raise ValueError( + f"Input {input_name} must be a nested list with {expected_depth} levels. The expected nesting format is {expected_format}. Got {current_depth} levels." + ) + return self._convert_to_nested_list(data, expected_depth) + + def _normalize_tensor_coordinates(self, tensor, original_sizes, is_bounding_box=False, preserve_padding=False): + """ + Helper method to normalize coordinates in a tensor across multiple images. + + Args: + tensor (`torch.Tensor`): + Input tensor with coordinates. + original_sizes (`list`): + Original image sizes. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether coordinates are bounding boxes. + preserve_padding (`bool`, *optional*, defaults to `False`): + Whether to preserve padding values (for points). + """ + if preserve_padding: + # For points: avoid normalizing pad values + mask = tensor != self.point_pad_value + coord_mask = mask.all(dim=-1, keepdim=True) + + for img_idx in range(len(original_sizes)): + if img_idx < tensor.shape[0]: + original_size = original_sizes[img_idx] if img_idx < len(original_sizes) else original_sizes[0] + normalized_coords = self._normalize_coordinates( + self.target_size, tensor[img_idx], original_size, is_bounding_box=is_bounding_box + ) + + if preserve_padding: + # Only update non-padded values + img_mask = coord_mask[img_idx] + tensor[img_idx] = torch.where( + img_mask.expand_as(tensor[img_idx]), normalized_coords, tensor[img_idx] + ) + else: + tensor[img_idx] = normalized_coords + + def post_process_masks( + self, + masks, + original_sizes, + mask_threshold=0.0, + binarize=True, + max_hole_area=0.0, + max_sprinkle_area=0.0, + apply_non_overlapping_constraints=False, + **kwargs, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[List[torch.Tensor], List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + mask_threshold (`float`, *optional*, defaults to 0.0): + Threshold for binarization and post-processing operations. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + max_hole_area (`float`, *optional*, defaults to 0.0): + The maximum area of a hole to fill. + max_sprinkle_area (`float`, *optional*, defaults to 0.0): + The maximum area of a sprinkle to fill. + apply_non_overlapping_constraints (`bool`, *optional*, defaults to `False`): + Whether to apply non-overlapping constraints to the masks. + + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + return self.image_processor.post_process_masks( + masks, + original_sizes, + mask_threshold, + binarize, + max_hole_area, + max_sprinkle_area, + apply_non_overlapping_constraints, + **kwargs, + ) + + @property + def model_input_names(self): + image_processor_input_names = self.image_processor.model_input_names + return list(image_processor_input_names + ["original_sizes"]) + + +__all__ = ["Sam2Processor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..565e8bcaf4d0d327ff1dbf21343008b24e0e844b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam2_video import * + from .modeling_sam2_video import * + from .processing_sam2_video import * + from .video_processing_sam2_video import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/configuration_sam2_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/configuration_sam2_video.py new file mode 100644 index 0000000000000000000000000000000000000000..9c8fe47c617bacadd407de09e50c662b01024463 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/configuration_sam2_video.py @@ -0,0 +1,274 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam2_video/modular_sam2_video.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam2_video.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="facebook/sam2_video.1-hiera-tiny") +@strict +class Sam2VideoPromptEncoderConfig(PreTrainedConfig): + r""" + mask_input_channels (`int`, *optional*, defaults to 16): + The number of channels to be fed to the `MaskDecoder` module. + num_point_embeddings (`int`, *optional*, defaults to 4): + The number of point embeddings to be used. + scale (`float`, *optional*, defaults to 1): + The scale factor for the prompt encoder. + """ + + base_config_key = "prompt_encoder_config" + + hidden_size: int = 256 + image_size: int | list[int] | tuple[int, int] = 1024 + patch_size: int | list[int] | tuple[int, int] = 16 + mask_input_channels: int = 16 + num_point_embeddings: int = 4 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + scale: int = 1 + + +@auto_docstring(checkpoint="facebook/sam2_video.1-hiera-tiny") +@strict +class Sam2VideoMaskDecoderConfig(PreTrainedConfig): + r""" + mlp_dim (`int`, *optional*, defaults to 2048): + The dimension of the MLP in the two-way transformer. + attention_downsample_rate (`int`, *optional*, defaults to 2): + The downsample rate for the attention layers. + num_multimask_outputs (`int`, *optional*, defaults to 3): + The number of multimask outputs. + iou_head_depth (`int`, *optional*, defaults to 3): + The depth of the IoU head. + iou_head_hidden_dim (`int`, *optional*, defaults to 256): + The hidden dimension of the IoU head. + dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`): + Whether to use dynamic multimask via stability. + dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05): + The stability delta for the dynamic multimask. + dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98): + The stability threshold for the dynamic multimask. + """ + + base_config_key = "mask_decoder_config" + + hidden_size: int = 256 + hidden_act: str = "gelu" + mlp_dim: int = 2048 + num_hidden_layers: int = 2 + num_attention_heads: int = 8 + attention_downsample_rate: int = 2 + num_multimask_outputs: int = 3 + iou_head_depth: int = 3 + iou_head_hidden_dim: int = 256 + dynamic_multimask_via_stability: bool = True + dynamic_multimask_stability_delta: float = 0.05 + dynamic_multimask_stability_thresh: float = 0.98 + + +@auto_docstring(checkpoint="facebook/sam2.1-hiera-tiny") +@strict +class Sam2VideoConfig(PreTrainedConfig): + r""" + prompt_encoder_config (Union[`dict`, `Sam2PromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam2PromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `Sam2MaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam2MaskDecoderConfig`]. + initializer_range (`float`, *optional*, defaults to 0.02): + Standard deviation for parameter initialization. + num_maskmem (`int`, *optional*, defaults to 7): + The number of memory slots for the mask memory. + sigmoid_scale_for_mem_enc (`float`, *optional*, defaults to 20.0): + Scale factor for the sigmoid function in the memory encoder. + sigmoid_bias_for_mem_enc (`float`, *optional*, defaults to -10.0): + Bias for the sigmoid function in the memory encoder. + enable_occlusion_spatial_embedding (`bool`, *optional*, defaults to `True`): + Whether to enable spatial embedding for occlusions. + multimask_output_in_sam (`bool`, *optional*, defaults to `True`): + Whether to output multiple masks from the SAM head. + multimask_min_pt_num (`int`, *optional*, defaults to 0): + The minimum number of points to trigger multimask output. + multimask_max_pt_num (`int`, *optional*, defaults to 1): + The maximum number of points to trigger multimask output. + multimask_output_for_tracking (`bool`, *optional*, defaults to `True`): + Whether to use multimask output for tracking. + max_object_pointers_in_encoder (`int`, *optional*, defaults to 16): + The maximum number of object pointers in the encoder. + max_cond_frame_num (`int`, *optional*, defaults to -1): + Maximum number of conditioning frames to use in memory attention. Set to -1 to use all conditioning frames. + enable_temporal_pos_encoding_for_object_pointers (`bool`, *optional*, defaults to `True`): + Whether to enable temporal positional encoding for object pointers. + memory_attention_hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the memory attention hidden states. + memory_attention_num_layers (`int`, *optional*, defaults to 4): + The number of layers in the memory attention module. + memory_attention_num_attention_heads (`int`, *optional*, defaults to 1): + Number of attention heads for each attention layer in the memory attention. + memory_attention_downsample_rate (`int`, *optional*, defaults to 1): + The downsample rate for the attention layers. + memory_attention_feed_forward_hidden_size (`int`, *optional*, defaults to 2048): + The dimension of the feedforward network in the memory attention module. + memory_attention_feed_forward_hidden_act (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function in the feedforward network in the memory attention module. + memory_attention_dropout (`float`, *optional*, defaults to 0.1): + The dropout rate for the memory attention module. + memory_attention_rope_theta (`float`, *optional*, defaults to 10000): + The Rope theta parameter. + memory_attention_rope_feat_sizes (`list[int]`, *optional*, defaults to `[64, 64]`): + The feature sizes for the Rope positional encoding. + memory_attention_rope_dropout (`float`, *optional*, defaults to 0.1): + The dropout rate for the Rope positional encoding. + memory_encoder_hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the memory encoder hidden states. + memory_encoder_output_channels (`int`, *optional*, defaults to 64): + The number of output channels for the memory encoder. + mask_downsampler_embed_dim (`int`, *optional*, defaults to 256): + The dimension of the mask downsampler embedding. + mask_downsampler_kernel_size (`int`, *optional*, defaults to 3): + The kernel size for the mask downsampler. + mask_downsampler_stride (`int`, *optional*, defaults to 2): + The stride for the mask downsampler. + mask_downsampler_padding (`int`, *optional*, defaults to 1): + The padding for the mask downsampler. + mask_downsampler_total_stride (`int`, *optional*, defaults to 16): + The total stride for the mask downsampler. + mask_downsampler_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function in the mask downsampler. + memory_fuser_num_layers (`int`, *optional*, defaults to 2): + The number of layers in the memory fuser. + memory_fuser_embed_dim (`int`, *optional*, defaults to 256): + The dimension of the embedding layer in the memory fuser. + memory_fuser_intermediate_dim (`int`, *optional*, defaults to 1024): + The dimension of the intermediate layer in the memory fuser. + memory_fuser_kernel_size (`int`, *optional*, defaults to 7): + The kernel size for the memory fuser. + memory_fuser_padding (`int`, *optional*, defaults to 3): + The padding for the memory fuser. + memory_fuser_layer_scale_init_value (`float`, *optional*, defaults to 1e-06): + The initial value for the layer scale in the memory fuser. + memory_fuser_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function in the memory fuser.. + + Example: + + ```python + >>> from transformers import ( + ... Sam2VisionConfig, + ... Sam2PromptEncoderConfig, + ... Sam2MaskDecoderConfig, + ... Sam2Model, + ... ) + + >>> # Initializing a Sam2Config with `"facebook/sam2.1_hiera_tiny"` style configuration + >>> configuration = Sam2config() + + >>> # Initializing a Sam2Model (with random weights) from the `"facebook/sam2.1_hiera_tiny"` style configuration + >>> model = Sam2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Sam2Config from a Sam2VisionConfig, Sam2PromptEncoderConfig, and Sam2MaskDecoderConfig + + >>> # Initializing SAM2 vision encoder, memory attention, and memory encoder configurations + >>> vision_config = Sam2VisionConfig() + >>> prompt_encoder_config = Sam2PromptEncoderConfig() + >>> mask_decoder_config = Sam2MaskDecoderConfig() + + >>> config = Sam2Config(vision_config, prompt_encoder_config, mask_decoder_config) + ```""" + + model_type = "sam2_video" + sub_configs = { + "vision_config": AutoConfig, + "prompt_encoder_config": Sam2VideoPromptEncoderConfig, + "mask_decoder_config": Sam2VideoMaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + prompt_encoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + num_maskmem: int = 7 + image_size: int | list[int] | tuple[int, int] = 1024 + sigmoid_scale_for_mem_enc: float = 20.0 + sigmoid_bias_for_mem_enc: float = -10.0 + enable_occlusion_spatial_embedding: bool = True + multimask_output_in_sam: bool = True + multimask_min_pt_num: int = 0 + multimask_max_pt_num: int = 1 + multimask_output_for_tracking: bool = True + max_object_pointers_in_encoder: int = 16 + max_cond_frame_num: int = -1 + enable_temporal_pos_encoding_for_object_pointers: bool = True + memory_attention_hidden_size: int = 256 + memory_attention_num_layers: int = 4 + memory_attention_num_attention_heads: int = 1 + memory_attention_downsample_rate: int = 1 + memory_attention_feed_forward_hidden_size: int = 2048 + memory_attention_feed_forward_hidden_act: str = "relu" + memory_attention_dropout: float | int = 0.1 + memory_attention_rope_theta: int = 10000 + memory_attention_rope_feat_sizes: list[int] | None = None + memory_attention_rope_dropout: float | int = 0.1 + memory_encoder_hidden_size: int = 256 + memory_encoder_output_channels: int = 64 + mask_downsampler_embed_dim: int = 256 + mask_downsampler_kernel_size: int = 3 + mask_downsampler_stride: int = 2 + mask_downsampler_padding: int = 1 + mask_downsampler_total_stride: int = 16 + mask_downsampler_hidden_act: str = "gelu" + memory_fuser_num_layers: int = 2 + memory_fuser_embed_dim: int = 256 + memory_fuser_intermediate_dim: int = 1024 + memory_fuser_kernel_size: int = 7 + memory_fuser_padding: int = 3 + memory_fuser_layer_scale_init_value: float = 1e-6 + memory_fuser_hidden_act: str = "gelu" + + def __post_init__(self, **kwargs): + self.memory_attention_rope_feat_sizes = ( + [64, 64] if self.memory_attention_rope_feat_sizes is None else self.memory_attention_rope_feat_sizes + ) + + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam2_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam2_vision_model"]() + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = Sam2VideoPromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = Sam2VideoPromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam2VideoPromptEncoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = Sam2VideoMaskDecoderConfig() + + super().__post_init__(**kwargs) + + +__all__ = ["Sam2VideoMaskDecoderConfig", "Sam2VideoPromptEncoderConfig", "Sam2VideoConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/modeling_sam2_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/modeling_sam2_video.py new file mode 100644 index 0000000000000000000000000000000000000000..57288c6802dd4f2b2ab03a03a953bc9bb04322d9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/modeling_sam2_video.py @@ -0,0 +1,2795 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam2_video/modular_sam2_video.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam2_video.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections import OrderedDict +from collections.abc import Callable, Iterator +from dataclasses import dataclass +from typing import Any + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from tqdm import tqdm + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging +from ...utils.generic import TransformersKwargs, is_flash_attention_requested +from ...utils.output_capturing import OutputRecorder +from ..auto import AutoModel +from .configuration_sam2_video import Sam2VideoConfig, Sam2VideoMaskDecoderConfig, Sam2VideoPromptEncoderConfig + + +logger = logging.get_logger(__name__) + + +class Sam2VideoInferenceCache: + """Cache for vision features and model constants.""" + + def __init__( + self, + inference_device: torch.device | str = "cpu", + inference_state_device: torch.device | str = "cpu", + max_vision_features_cache_size: int = 1, + ): + self.inference_device = inference_device + self.inference_state_device = inference_state_device + self.max_vision_features_cache_size = max_vision_features_cache_size + + self._vision_features = {} + + def cache_vision_features(self, frame_idx: int, features: dict): + """Cache vision features with automatic device management.""" + cached = {} + if len(self._vision_features) >= self.max_vision_features_cache_size: + # remove the oldest frame + self._vision_features.pop(min(self._vision_features.keys())) + + for key, value in features.items(): + if isinstance(value, torch.Tensor): + cached[key] = value.to(self.inference_state_device, non_blocking=True) + elif isinstance(value, (list, tuple)) and value and isinstance(value[0], torch.Tensor): + cached[key] = [v.to(self.inference_state_device, non_blocking=True) for v in value] + else: + cached[key] = value + self._vision_features[frame_idx] = cached + + def get_vision_features(self, frame_idx: int) -> dict | None: + """Get cached vision features, automatically moved to inference device.""" + if frame_idx not in self._vision_features: + return None + + cached = self._vision_features[frame_idx] + moved = {} + for key, value in cached.items(): + if isinstance(value, torch.Tensor): + moved[key] = value.to(self.inference_device, non_blocking=True) + elif isinstance(value, (list, tuple)) and value and isinstance(value[0], torch.Tensor): + moved[key] = [v.to(self.inference_device, non_blocking=True) for v in value] + else: + moved[key] = value + return moved + + def clear_all(self): + """Clear all cached data.""" + self._vision_features.clear() + + +class Sam2VideoInferenceSession: + r""" + Manages video inference session parameters, state and cache. + + Args: + video (`torch.FloatTensor`, *optional*): + The video to process. No need to provide when streaming. + video_height (`int`, *optional*): + The height of the video. + video_width (`int`, *optional*): + The width of the video. + inference_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to use for inference. + inference_state_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to store the inference state on. + video_storage_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to store the video on. + dtype (`torch.dtype`, *optional*, defaults to `"float32"`): + The dtype to use for the video. + max_vision_features_cache_size (`int`, *optional*, defaults to 1): + The maximum number of vision features to cache. + """ + + def __init__( + self, + video: torch.FloatTensor | None = None, + video_height: int | None = None, + video_width: int | None = None, + inference_device: torch.device | str = "cpu", + inference_state_device: torch.device | str = "cpu", + video_storage_device: torch.device | str = "cpu", + dtype: torch.dtype | str = "float32", + max_vision_features_cache_size: int = 1, + ): + # store as a dictionary to avoid double memory allocation with torch.cat when adding new frames + self.processed_frames = ( + dict(enumerate(video.to(video_storage_device, dtype=dtype))) if video is not None else None + ) + self.video_height = video_height + self.video_width = video_width + + self.inference_device = inference_device + self.inference_state_device = inference_state_device + self.video_storage_device = video_storage_device + self.dtype = dtype + self.max_vision_features_cache_size = max_vision_features_cache_size + + # Cache for computed features + self.cache = Sam2VideoInferenceCache( + inference_device=self.inference_device, + inference_state_device=self.inference_state_device, + max_vision_features_cache_size=self.max_vision_features_cache_size, + ) + + # Persistent object tracking state + self._obj_id_to_idx = OrderedDict() + self._obj_idx_to_id = OrderedDict() + self.obj_ids = [] + + # Persistent user inputs + self.point_inputs_per_obj = {} + self.mask_inputs_per_obj = {} + + # Persistent model outputs/history + self.output_dict_per_obj = {} + self.frames_tracked_per_obj = {} + + # Session state flags + self.obj_with_new_inputs = [] + + @property + def num_frames(self) -> int | None: + return len(self.processed_frames) if self.processed_frames is not None else None + + # Object management + def obj_id_to_idx(self, obj_id: int) -> int: + """Map object ID to index, creating new entry if needed.""" + obj_idx = self._obj_id_to_idx.get(obj_id, None) + if obj_idx is not None: + return obj_idx + + obj_idx = len(self._obj_id_to_idx) + self._obj_id_to_idx[obj_id] = obj_idx + self._obj_idx_to_id[obj_idx] = obj_id + self.obj_ids = list(self._obj_id_to_idx) + + self.point_inputs_per_obj[obj_idx] = {} + self.mask_inputs_per_obj[obj_idx] = {} + self.output_dict_per_obj[obj_idx] = { + "cond_frame_outputs": {}, + "non_cond_frame_outputs": {}, + } + self.frames_tracked_per_obj[obj_idx] = {} + + return obj_idx + + # Video Inference specific functions + def obj_idx_to_id(self, obj_idx: int) -> int: + """Map model-side object index to client-side object id.""" + return self._obj_idx_to_id[obj_idx] + + def get_obj_num(self) -> int: + """Get the total number of unique object ids received so far in this session.""" + return len(self._obj_idx_to_id) + + # Input management with device handling + def add_point_inputs(self, obj_idx: int, frame_idx: int, inputs: dict): + """Add point inputs with automatic device placement.""" + device_inputs = {} + for key, value in inputs.items(): + if isinstance(value, torch.Tensor): + device_inputs[key] = value.to(self.inference_device, non_blocking=False) + else: + device_inputs[key] = value + self.point_inputs_per_obj[obj_idx][frame_idx] = device_inputs + + def remove_point_inputs(self, obj_idx: int, frame_idx: int): + """Remove point inputs.""" + self.point_inputs_per_obj[obj_idx].pop(frame_idx, None) + + def add_mask_inputs(self, obj_idx: int, frame_idx: int, inputs: torch.Tensor): + """Add mask inputs with automatic device placement.""" + self.mask_inputs_per_obj[obj_idx][frame_idx] = inputs.to( + self.inference_device, dtype=self.dtype, non_blocking=True + ) + + def remove_mask_inputs(self, obj_idx: int, frame_idx: int): + """Remove mask inputs.""" + self.mask_inputs_per_obj[obj_idx].pop(frame_idx, None) + + # Output management with smart device placement + def store_output( + self, + obj_idx: int, + frame_idx: int, + output_key: str | None = None, + output_value: torch.Tensor | dict | None = None, + is_conditioning_frame: bool = True, + ): + """ + Store output with smart device management. + If output_key is None, the output is stored as a dictionary. + + Args: + obj_idx (int): The index of the object. + frame_idx (int): The index of the frame. + output_key (Optional[str]): The key of the output. If None, the output is stored as a dictionary. + output_value (Optional[Union[torch.Tensor, dict]]): The value of the output. + is_conditioning_frame (bool): Whether the output is for a conditioning frame. + """ + storage_key = "cond_frame_outputs" if is_conditioning_frame else "non_cond_frame_outputs" + + if output_key is None and isinstance(output_value, dict): + self.output_dict_per_obj[obj_idx][storage_key][frame_idx] = {} + for key, value in output_value.items(): + self.store_output(obj_idx, frame_idx, key, value, is_conditioning_frame) + return + + # Device placement: small tensors stay on inference device, large ones go to inference state device + if output_key in ["object_pointer", "object_score_logits"]: # Small tensors + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value + elif isinstance(output_value, torch.Tensor): # Large tensors like masks, features + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value.to( + self.inference_state_device, non_blocking=True + ) + else: + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value + + def get_output( + self, + obj_idx: int, + frame_idx: int, + output_key: str, + is_conditioning_frame: bool = True, + ): + """ + Get output with smart device management. + + Args: + obj_idx (int): The index of the object. + frame_idx (int): The index of the frame. + output_key (str): The key of the output. + is_conditioning_frame (bool): Whether the output is for a conditioning frame. + """ + storage_key = "cond_frame_outputs" if is_conditioning_frame else "non_cond_frame_outputs" + out = self.output_dict_per_obj[obj_idx][storage_key].get(frame_idx, None) + # move to inference device if needed + if out is None: + return None + value = out[output_key] + if isinstance(value, torch.Tensor): + value = value.to(self.inference_device, non_blocking=True) + return value + + # Video frame management + def add_new_frame(self, pixel_values: torch.Tensor, frame_idx: int | None = None) -> int: + """Add new frame with automatic device placement.""" + pixel_values = pixel_values.to(self.video_storage_device, dtype=self.dtype, non_blocking=True) + if pixel_values.dim() == 4: + pixel_values = pixel_values.squeeze(0) + + if frame_idx is None: + frame_idx = len(self.processed_frames) if self.processed_frames is not None else 0 + + if self.processed_frames is None: + self.processed_frames = {frame_idx: pixel_values} + else: + self.processed_frames[frame_idx] = pixel_values + + return frame_idx + + def get_frame(self, frame_idx: int) -> torch.Tensor: + """Get frame from video.""" + return self.processed_frames[frame_idx].to(self.inference_device, non_blocking=True) + + def reset_tracking_data(self): + """Reset tracking data but keep cache.""" + self._obj_id_to_idx.clear() + self._obj_idx_to_id.clear() + self.obj_ids.clear() + self.point_inputs_per_obj.clear() + self.mask_inputs_per_obj.clear() + self.output_dict_per_obj.clear() + self.frames_tracked_per_obj.clear() + self.obj_with_new_inputs = [] + # Note: cache and video data are preserved + + def reset_inference_session(self): + """Reset tracking data and cache.""" + self._obj_id_to_idx.clear() + self._obj_idx_to_id.clear() + self.obj_ids.clear() + self.point_inputs_per_obj.clear() + self.mask_inputs_per_obj.clear() + self.output_dict_per_obj.clear() + self.frames_tracked_per_obj.clear() + self.obj_with_new_inputs = [] + self.cache.clear_all() + + +class Sam2VideoLayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +# copied and adapted from original implementation, also practically equal to DetrSinePositionEmbedding +class Sam2VideoPositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one used by the Attention is all you + need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: float | None = None + ): + super().__init__() + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + self.scale = 2 * math.pi if scale is None else scale + + @compile_compatible_method_lru_cache(maxsize=1) + def forward( + self, + shape: torch.Size, + device: torch.device | str, + dtype: torch.dtype, + mask: Tensor | None = None, + ) -> Tensor: + if mask is None: + mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool) + not_mask = (~mask).to(dtype) + y_embed = not_mask.cumsum(1) + x_embed = not_mask.cumsum(2) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype) + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Sam2VideoAttention(nn.Module): + """ + SAM2_VIDEO's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and + values. + """ + + def __init__(self, config, downsample_rate=None): + super().__init__() + downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate + self.config = config + self.hidden_size = config.hidden_size + self.internal_dim = config.hidden_size // downsample_rate + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.internal_dim // config.num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.v_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.o_proj = nn.Linear(self.internal_dim, self.hidden_size) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_similarity: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # Input projections + batch_size, point_batch_size = query.shape[:2] + new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim) + + query = self.q_proj(query).view(*new_shape).transpose(1, 2) + key = self.k_proj(key).view(*new_shape).transpose(1, 2) + value = self.v_proj(value).view(*new_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config) and attention_similarity is not None: + # Target guided masks are represented as float masks and are incompatible with Flash Attention + # Fallback to SDPA for this call only so the rest of the model can still benefit from FA + attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"] + logger.warning_once( + "Falling back to SDPA for target-guided attention because " + "Flash Attention does not support additive bias masks." + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_similarity, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = attn_output.reshape( + batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +class Sam2VideoTwoWayAttentionBlock(GradientCheckpointingLayer): + def __init__(self, config: Sam2VideoMaskDecoderConfig, skip_first_layer_pe: bool = False): + """ + A transformer block with four layers: + (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on + sparse inputs (4) cross attention of dense inputs -> sparse inputs + + Arguments: + config (`Sam2VideoMaskDecoderConfig`): + The configuration file used to instantiate the block + attention_downsample_rate (*optionalk*, int, defaults to 2): + The downsample ratio of the block used to reduce the inner dim of the attention. + skip_first_layer_pe (*optional*, bool, defaults to `False`): + Whether or not to skip the addition of the query_point_embedding on the first layer. + """ + super().__init__() + self.self_attn = Sam2VideoAttention(config, downsample_rate=1) + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + + self.cross_attn_token_to_image = Sam2VideoAttention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam2VideoFeedForward( + config.hidden_size, config.mlp_dim, config.hidden_size, num_layers=config.num_hidden_layers + ) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + self.layer_norm4 = nn.LayerNorm(config.hidden_size) + self.cross_attn_image_to_token = Sam2VideoAttention(config) + + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, + queries: Tensor, + keys: Tensor, + query_point_embedding: Tensor, + key_point_embedding: Tensor, + attention_similarity: Tensor, + **kwargs: Unpack[TransformersKwargs], + ): + # Self attention block + if self.skip_first_layer_pe: + queries, _ = self.self_attn(query=queries, key=queries, value=queries) + else: + query = queries + query_point_embedding + attn_out, _ = self.self_attn(query=query, key=query, value=queries) + queries = queries + attn_out + queries = self.layer_norm1(queries) + + # Cross attention block, tokens attending to image embedding + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_token_to_image( + query=query, key=key, value=keys, attention_similarity=attention_similarity + ) + queries = queries + attn_out + + queries = self.layer_norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.layer_norm3(queries) + + # Cross attention block, image embedding attending to tokens + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_image_to_token(query=key, key=query, value=queries) + keys = keys + attn_out + + keys = self.layer_norm4(keys) + return queries, keys, attn_out + + +class Sam2VideoFeedForward(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + activation: str = "relu", + sigmoid_output: bool = False, + ): + super().__init__() + self.num_layers = num_layers + self.activation = ACT2FN[activation] + self.proj_in = nn.Linear(input_dim, hidden_dim) + self.proj_out = nn.Linear(hidden_dim, output_dim) + self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)]) + self.sigmoid_output = sigmoid_output + + def forward(self, hidden_states): + hidden_states = self.proj_in(hidden_states) + hidden_states = self.activation(hidden_states) + for layer in self.layers: + hidden_states = self.activation(layer(hidden_states)) + + hidden_states = self.proj_out(hidden_states) + if self.sigmoid_output: + hidden_states = F.sigmoid(hidden_states) + return hidden_states + + +@auto_docstring(custom_intro="Base class for the Sam2Video model's output.") +@dataclass +class Sam2VideoImageSegmentationOutput(ModelOutput): + r""" + iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`): + The Intersection over Union (IoU) scores of the predicted masks. + pred_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, height, width)`): + The predicted low-resolution masks. This is an alias for `low_res_masks`. These masks need to be post-processed + by the processor to be brought to the original image size. + object_score_logits (`torch.FloatTensor` of shape `(batch_size, point_batch_size, 1)`): + Logits for the object score, indicating if an object is present. + image_embeddings (`tuple(torch.FloatTensor)`): + The features from the FPN, which are used by the mask decoder. This is a tuple of `torch.FloatTensor` where each + tensor has shape `(batch_size, channels, height, width)`. + vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. + Hidden-states of the vision model at the output of each stage. + vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the vision model. + mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the mask decoder. + high_res_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, image_size, image_size)`, *optional*): + The predicted masks, upscaled to the original image size. Only used for Sam2VideoModel. + object_pointer (`torch.FloatTensor` of shape `(batch_size, point_batch_size, hidden_size)`, *optional*): + A tensor representing the object pointer, used for tracking in videos. Only used for Sam2VideoModel. + """ + + iou_scores: torch.FloatTensor | None = None + pred_masks: torch.FloatTensor | None = None + object_score_logits: torch.FloatTensor | None = None + image_embeddings: tuple[torch.FloatTensor, ...] = None + vision_hidden_states: tuple[torch.FloatTensor, ...] | None = None + vision_attentions: tuple[torch.FloatTensor, ...] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None + + high_res_masks: torch.FloatTensor | None = None + object_pointer: torch.FloatTensor | None = None + + +@auto_docstring(custom_intro="Base class for the Sam2 model's output.") +@dataclass +class Sam2VideoSegmentationOutput(ModelOutput): + r""" + object_ids (`list[int]`, *optional*): + List of object IDs being tracked in the current frame. + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_masks, height, width)`): + The predicted masks stored at the model's resolution. + object_score_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): + Logits for the object scores, indicating if objects are present. + frame_idx (`int`): + The frame index of the video. + """ + + object_ids: list[int] | None = None + pred_masks: torch.FloatTensor | None = None + object_score_logits: torch.FloatTensor | None = None + frame_idx: int | None = None + + +@auto_docstring +class Sam2VideoPreTrainedModel(PreTrainedModel): + config_class = Sam2VideoConfig + base_model_prefix = "sam2_video" + main_input_name = "pixel_values" + input_modalities = "video" + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Sam2VideoModel): + if module.no_memory_positional_encoding is not None: + init.zeros_(module.no_memory_positional_encoding) + if module.memory_temporal_positional_encoding is not None: + init.zeros_(module.memory_temporal_positional_encoding) + if module.no_object_pointer is not None: + init.zeros_(module.no_object_pointer) + if module.occlusion_spatial_embedding_parameter is not None: + init.zeros_(module.occlusion_spatial_embedding_parameter) + if isinstance(module, Sam2VideoMemoryFuserCXBlock): + if module.scale is not None: + init.zeros_(module.scale) + elif isinstance(module, Sam2VideoVisionRotaryEmbedding): + inv_freq = module.create_inv_freq() + init.copy_(module.rope_embeddings_cos, inv_freq.cos()) + init.copy_(module.rope_embeddings_sin, inv_freq.sin()) + elif isinstance(module, Sam2VideoPositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + + +class Sam2VideoVisionRotaryEmbedding(nn.Module): + """ + Vision Rotary Position Embedding for SAM2, following transformers library standards. + Supports 2D (axial) rotary embeddings for spatial dimensions. + """ + + def __init__(self, config: Sam2VideoConfig): + super().__init__() + self.dim = config.memory_attention_hidden_size // ( + config.memory_attention_downsample_rate * config.memory_attention_num_attention_heads + ) + # Ensure even dimension for proper axial splitting + if self.dim % 4 != 0: + raise ValueError("Dimension must be divisible by 4 for axial RoPE") + self.end_x, self.end_y = config.memory_attention_rope_feat_sizes + self.memory_attention_rope_theta = config.memory_attention_rope_theta + + # directly register the cos and sin embeddings as we have a fixed feature shape + inv_freq = self.create_inv_freq() + self.register_buffer("rope_embeddings_cos", inv_freq.cos(), persistent=False) + self.register_buffer("rope_embeddings_sin", inv_freq.sin(), persistent=False) + + @torch.no_grad() + def forward(self) -> tuple[torch.Tensor, torch.Tensor]: + # As the feature map size is fixed, we can just return the pre-computed embeddings. + return self.rope_embeddings_cos, self.rope_embeddings_sin + + def create_inv_freq(self): + freqs = 1.0 / ( + self.memory_attention_rope_theta ** (torch.arange(0, self.dim, 4)[: (self.dim // 4)].float() / self.dim) + ) + # Generate 2D position indices for axial rotary embedding + flattened_indices = torch.arange(self.end_x * self.end_y, dtype=torch.long) + x_positions = flattened_indices % self.end_x + y_positions = torch.div(flattened_indices, self.end_x, rounding_mode="floor") + freqs_x = torch.outer(x_positions, freqs).float() + freqs_y = torch.outer(y_positions, freqs).float() + inv_freq = torch.cat([freqs_x, freqs_y], dim=-1) + inv_freq = inv_freq.repeat_interleave(2, dim=-1) + return inv_freq + + +def rotate_pairwise(x): + """ + pairwise rotation of the hidden dims of the input. Differerent from Llama Half-Tensor Rotation. + + This is an optimized version of the following more explicit implementation: + ```python + x_rotated = torch.zeros_like(x, dtype=x.dtype, device=x.device) + x_rotated[..., ::2] = -x[..., 1::2] + x_rotated[..., 1::2] = x[..., ::2] + return x_rotated + ``` + """ + x = x.view(*x.shape[:-1], -1, 2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return x.flatten(start_dim=-2) + + +# TODO: This leads to ~1e-07 max diff and ~1e-09 avg diff for q_embed and k_embed from the original implementation, most likely due to the use of complex tensors in the original implementation. +def apply_rotary_pos_emb_2d( + q: torch.Tensor, + k: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + num_k_exclude_rope: int = 0, + repeat_freqs_k: bool = False, +) -> tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary position embedding to query and key tensors for vision models. + Follows the standard transformers library pattern. + + Args: + q: Query tensor of shape (..., seq_len, head_dim) + k: Key tensor of shape (..., seq_len, head_dim) + cos: Cosine position embedding of shape (seq_len, head_dim) + sin: Sine position embedding of shape (seq_len, head_dim) + repeat_freqs_k: Whether to repeat frequencies for keys (for cross-attention) + + Returns: + Rotated (q, k) tensors + """ + k_rot, k_pass = k[..., : k.shape[-2] - num_k_exclude_rope, :], k[..., k.shape[-2] - num_k_exclude_rope :, :] + q_embed = q.float() # force upscale to float32 as in the original implementation + q_embed = (q_embed * cos) + (rotate_pairwise(q_embed) * sin) + if k_rot.shape[-2] == 0: + # Handle case where keys might be empty due to dropout + return q_embed.type_as(q), torch.cat([k_rot, k_pass], dim=-2) + + # Handle key tensor - may need to repeat frequencies if different sequence length + if repeat_freqs_k and k_rot.shape[-2] != q.shape[-2]: + # Repeat cos/sin to match key sequence length + repeat_factor = k_rot.shape[-2] // q.shape[-2] + cos_k = cos.repeat(1, 1, repeat_factor, 1) + sin_k = sin.repeat(1, 1, repeat_factor, 1) + else: + cos_k = cos + sin_k = sin + + # Apply rotary embedding to keys + k_embed = k_rot.float() # force upscale to float32 as in the original implementation + k_embed = (k_embed * cos_k) + (rotate_pairwise(k_embed) * sin_k) + # Concatenate back to full shape + k_embed = torch.cat([k_embed.type_as(k), k_pass], dim=-2) + return q_embed.type_as(q), k_embed + + +class Sam2VideoRoPEAttention(nn.Module): + """Attention with rotary position encoding.""" + + def __init__( + self, + config: Sam2VideoConfig, + kv_in_dim: int | None = None, + rope_k_repeat=False, + ): + super().__init__() + self.config = config + self.hidden_size = config.memory_attention_hidden_size + self.internal_dim = self.hidden_size // config.memory_attention_downsample_rate + self.num_attention_heads = config.memory_attention_num_attention_heads + self.head_dim = self.internal_dim // config.memory_attention_num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.kv_in_dim = kv_in_dim if kv_in_dim is not None else self.hidden_size + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.o_proj = nn.Linear(self.internal_dim, self.hidden_size) + + self.rope_k_repeat = rope_k_repeat + self.dropout_p = config.memory_attention_rope_dropout + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + num_k_exclude_rope: int = 0, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> Tensor: + # Input projections + batch_size, point_batch_size = query.shape[:2] + new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim) + + query = self.q_proj(query).view(*new_shape).transpose(1, 2) + key = self.k_proj(key).view(*new_shape).transpose(1, 2) + value = self.v_proj(value).view(*new_shape).transpose(1, 2) + + cos, sin = position_embeddings + # Apply rotary position encoding, excluding some keys if specified + query, key = apply_rotary_pos_emb_2d( + query, key, cos, sin, repeat_freqs_k=self.rope_k_repeat, num_k_exclude_rope=num_k_exclude_rope + ) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=None, + dropout=0.0 if not self.training else self.dropout_p, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + attn_output = attn_output.reshape( + batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Sam2VideoMemoryAttentionLayer(nn.Module): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + hidden_size = config.memory_attention_hidden_size + self.self_attn = Sam2VideoRoPEAttention(config) + self.cross_attn_image = Sam2VideoRoPEAttention(config, kv_in_dim=64, rope_k_repeat=True) + + # Implementation of Feedforward model + self.linear1 = nn.Linear(hidden_size, config.memory_attention_feed_forward_hidden_size) + self.dropout = nn.Dropout(config.memory_attention_dropout) + self.linear2 = nn.Linear(config.memory_attention_feed_forward_hidden_size, hidden_size) + + self.layer_norm1 = nn.LayerNorm(hidden_size) + self.layer_norm2 = nn.LayerNorm(hidden_size) + self.layer_norm3 = nn.LayerNorm(hidden_size) + self.dropout1 = nn.Dropout(config.memory_attention_dropout) + self.dropout2 = nn.Dropout(config.memory_attention_dropout) + self.dropout3 = nn.Dropout(config.memory_attention_dropout) + + self.activation = ACT2FN[config.memory_attention_feed_forward_hidden_act] + + def forward( + self, + queries: Tensor, + keys: Tensor, + key_point_embedding: Tensor, + rope_position_embeddings: tuple[Tensor, Tensor], + num_k_exclude_rope: int = 0, + ) -> torch.Tensor: + # Self-Attention + query = self.layer_norm1(queries) + query, _ = self.self_attn(query=query, key=query, value=query, position_embeddings=rope_position_embeddings) + queries = queries + self.dropout1(query) + + # Cross-Attention + query = self.layer_norm2(queries) + query, _ = self.cross_attn_image( + query=query, + key=keys + key_point_embedding, + value=keys, + position_embeddings=rope_position_embeddings, + num_k_exclude_rope=num_k_exclude_rope, + ) + queries = queries + self.dropout2(query) + # MLP + query = self.layer_norm3(queries) + query = self.linear2(self.dropout(self.activation(self.linear1(query)))) + queries = queries + self.dropout3(query) + return queries + + +class Sam2VideoMemoryAttention(nn.Module): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + self.layers = nn.ModuleList( + [Sam2VideoMemoryAttentionLayer(config) for _ in range(config.memory_attention_num_layers)] + ) + self.layer_norm = nn.LayerNorm(config.memory_attention_hidden_size) + self.rotary_emb = Sam2VideoVisionRotaryEmbedding(config=config) + + def forward( + self, + current_vision_features: torch.Tensor, + memory: torch.Tensor, + current_vision_position_embeddings: Tensor | None = None, + memory_posision_embeddings: Tensor | None = None, + num_object_pointer_tokens: int = 0, + ): + """ + Args: + current_vision_features (`torch.FloatTensor`): + The current vision features used for self-attention. + memory (`torch.FloatTensor`): + The memory features used for cross-attention. + current_vision_position_embeddings (`torch.FloatTensor`, *optional*): + The position embeddings for the current vision features. + memory_posision_embeddings (`torch.FloatTensor`, *optional*): + The position embeddings for the memory features. + num_object_pointer_tokens (`int`, *optional*, defaults to 0): + The number of object pointer tokens. + """ + output = current_vision_features + if current_vision_position_embeddings is not None: + output = output + 0.1 * current_vision_position_embeddings + + # Convert to batch first + output = output.transpose(0, 1) + memory = memory.transpose(0, 1).unsqueeze(1) + memory_posision_embeddings = memory_posision_embeddings.transpose(0, 1).unsqueeze(1) + rope_position_embeddings = self.rotary_emb() + for layer in self.layers: + output = layer( + queries=output.unsqueeze(1) if output.ndim == 3 else output, + keys=memory, + key_point_embedding=memory_posision_embeddings, + rope_position_embeddings=rope_position_embeddings, + num_k_exclude_rope=num_object_pointer_tokens, + ) + + normed_output = self.layer_norm(output) + + # Convert back to seq first + normed_output = normed_output.transpose(0, 1) + + return normed_output + + +# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt) +class Sam2VideoMemoryFuserCXBlock(GradientCheckpointingLayer): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + self.depthwise_conv = nn.Conv2d( + config.memory_fuser_embed_dim, + config.memory_fuser_embed_dim, + kernel_size=config.memory_fuser_kernel_size, + padding=config.memory_fuser_padding, + groups=config.memory_fuser_embed_dim, + ) # depthwise conv + self.layer_norm = Sam2VideoLayerNorm(config.memory_fuser_embed_dim, eps=1e-6, data_format="channels_first") + self.activation = ACT2FN[config.memory_fuser_hidden_act] + self.pointwise_conv1 = nn.Linear( + config.memory_fuser_embed_dim, config.memory_fuser_intermediate_dim + ) # pointwise/1x1 convs, implemented with linear layers + self.pointwise_conv2 = nn.Linear(config.memory_fuser_intermediate_dim, config.memory_fuser_embed_dim) + self.scale = nn.Parameter( + config.memory_fuser_layer_scale_init_value * torch.ones(config.memory_fuser_embed_dim), + requires_grad=True, + ) + + def forward(self, hidden_states): + input = hidden_states + hidden_states = self.depthwise_conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + hidden_states = self.pointwise_conv1(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.pointwise_conv2(hidden_states) + hidden_states = self.scale * hidden_states + hidden_states = hidden_states.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + hidden_states = input + hidden_states + return hidden_states + + +class Sam2VideoMemoryFuser(nn.Module): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + self.layers = nn.ModuleList( + [Sam2VideoMemoryFuserCXBlock(config) for _ in range(config.memory_fuser_num_layers)] + ) + + def forward(self, hidden_states): + # normally hidden_states: (N, C, H, W) + for layer in self.layers: + hidden_states = layer(hidden_states) + return hidden_states + + +class Sam2VideoMaskDownSamplerLayer(nn.Module): + def __init__(self, config: Sam2VideoConfig, in_channels: int, out_channels: int): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=config.mask_downsampler_kernel_size, + stride=config.mask_downsampler_stride, + padding=config.mask_downsampler_padding, + ) + self.layer_norm = Sam2VideoLayerNorm(out_channels, eps=1e-6, data_format="channels_first") + self.activation = ACT2FN[config.mask_downsampler_hidden_act] + + def forward(self, x): + return self.activation(self.layer_norm(self.conv(x))) + + +class Sam2VideoMaskDownSampler(nn.Module): + """ + Progressively downsample a mask by total_stride, each time by stride. + Note that LayerNorm is applied per *token*, like in ViT. + + With each downsample (by a factor stride**2), channel capacity increases by the same factor. + In the end, we linearly project to embed_dim channels. + """ + + def __init__(self, config: Sam2VideoConfig): + super().__init__() + + num_layers = int(math.log2(config.mask_downsampler_total_stride) // math.log2(config.mask_downsampler_stride)) + + self.layers = nn.ModuleList() + self.activation = ACT2FN[config.mask_downsampler_hidden_act] + mask_in_chans, mask_out_chans = 1, 1 + for _ in range(num_layers): + mask_out_chans = mask_in_chans * (config.mask_downsampler_stride**2) + self.layers.append(Sam2VideoMaskDownSamplerLayer(config, mask_in_chans, mask_out_chans)) + mask_in_chans = mask_out_chans + + self.final_conv = nn.Conv2d(mask_out_chans, config.mask_downsampler_embed_dim, kernel_size=1) + + def forward(self, x): + for layer in self.layers: + x = layer(x) + x = self.final_conv(x) + return x + + +class Sam2VideoMemoryEncoder(nn.Module): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + + hidden_size = config.memory_encoder_hidden_size + output_channels = config.memory_encoder_output_channels + self.mask_downsampler = Sam2VideoMaskDownSampler(config) + self.feature_projection = nn.Conv2d(hidden_size, hidden_size, kernel_size=1) + self.memory_fuser = Sam2VideoMemoryFuser(config) + self.position_encoding = Sam2VideoPositionEmbeddingSine(num_pos_feats=output_channels // 2, normalize=True) + self.projection = nn.Conv2d(hidden_size, output_channels, kernel_size=1) + + def forward( + self, + vision_features: torch.Tensor, + masks: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor]: + ## Process masks + masks = self.mask_downsampler(masks) + ## Fuse pixel_features and downsampled masks + + vision_features = self.feature_projection(vision_features) + vision_features = vision_features + masks + vision_features = self.memory_fuser(vision_features) + vision_features = self.projection(vision_features) + + vision_pos_enc = self.position_encoding(vision_features.shape, vision_features.device, vision_features.dtype) + + return vision_features, vision_pos_enc + + +class Sam2VideoPositionalEmbedding(nn.Module): + def __init__(self, config: Sam2VideoPromptEncoderConfig): + super().__init__() + self.scale = config.scale + positional_embedding = self.scale * torch.randn((2, config.hidden_size // 2)) + self.register_buffer("positional_embedding", positional_embedding) + + def forward(self, input_coords, input_shape=None): + """Positionally encode points that are normalized to [0,1].""" + coordinates = input_coords.clone() + + if input_shape is not None: + coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1] + coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0] + coordinates.to(torch.float32) + + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coordinates = 2 * coordinates - 1 + coordinates = coordinates.to(self.positional_embedding.dtype) + coordinates = coordinates @ self.positional_embedding + coordinates = 2 * np.pi * coordinates + # outputs d_1 x ... x d_n x channel shape + return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1) + + +@dataclass +@auto_docstring(custom_intro="Base class for the vision encoder's outputs.") +class Sam2VideoVisionEncoderOutput(BaseModelOutputWithPooling): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the + model at the output of each stage. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + fpn_hidden_states (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Feature maps from the Feature Pyramid Network neck. + fpn_position_encoding (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Positional encodings corresponding to the `fpn_hidden_states`. + """ + + fpn_hidden_states: torch.FloatTensor | None = None + fpn_position_encoding: torch.FloatTensor | None = None + + +class Sam2VideoMaskEmbedding(nn.Module): + def __init__(self, config: Sam2VideoPromptEncoderConfig): + super().__init__() + self.mask_input_channels = config.mask_input_channels // 4 + self.activation = ACT2FN[config.hidden_act] + self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2) + self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2) + self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1) + self.layer_norm1 = Sam2VideoLayerNorm( + self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first" + ) + self.layer_norm2 = Sam2VideoLayerNorm( + self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first" + ) + + def forward(self, masks): + hidden_states = self.conv1(masks) + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.activation(hidden_states) + dense_embeddings = self.conv3(hidden_states) + return dense_embeddings + + +class Sam2VideoPromptEncoder(nn.Module): + def __init__(self, config: Sam2VideoPromptEncoderConfig): + super().__init__() + self.shared_embedding = Sam2VideoPositionalEmbedding(config) + self.mask_embed = Sam2VideoMaskEmbedding(config) + self.no_mask_embed = nn.Embedding(1, config.hidden_size) + + self.image_embedding_size = (config.image_size // config.patch_size, config.image_size // config.patch_size) + self.mask_input_size = (4 * config.image_size // config.patch_size, 4 * config.image_size // config.patch_size) + self.input_image_size = config.image_size + + self.point_embed = nn.Embedding(config.num_point_embeddings, config.hidden_size) + self.hidden_size = config.hidden_size + self.not_a_point_embed = nn.Embedding(1, config.hidden_size) + + def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + points = torch.nn.functional.pad(points, (0, 0, 0, 1), mode="constant", value=0) + labels = torch.nn.functional.pad(labels, (0, 1), mode="constant", value=-1) + input_shape = (self.input_image_size, self.input_image_size) + point_embedding = self.shared_embedding(points, input_shape) + + # torch.where and expanding the labels tensor is required by the ONNX export + point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding) + + # This is required for the ONNX export. The dtype, device need to be explicitly + # specified as otherwise torch.onnx.export interprets as double + point_embedding = torch.where( + labels[..., None] != -10, + point_embedding, + torch.zeros_like(point_embedding), + ) + + # Add point embeddings for labels >= 0 + point_embedding = point_embedding + self.point_embed(labels.clamp(min=0)) * (labels >= 0).unsqueeze(-1) + + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.view(*boxes.shape[:2], 2, 2) + # add padding point for consistency with the original implementation + coords = torch.nn.functional.pad(coords, (0, 0, 0, 1), mode="constant", value=0) + corner_embedding = self.shared_embedding(coords, (self.input_image_size, self.input_image_size)) + corner_embedding[:, :, 0, :] += self.point_embed.weight[2] + corner_embedding[:, :, 1, :] += self.point_embed.weight[3] + corner_embedding[:, :, 2, :] = self.not_a_point_embed.weight.expand_as(corner_embedding[:, :, 2, :]) + return corner_embedding + + def forward( + self, + input_points: tuple[torch.Tensor, torch.Tensor] | None, + input_labels: torch.Tensor | None, + input_boxes: torch.Tensor | None, + input_masks: torch.Tensor | None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense embeddings. + + Args: + points (`torch.Tensor`, *optional*): + point coordinates and labels to embed. + boxes (`torch.Tensor`, *optional*): + boxes to embed + masks (`torch.Tensor`, *optional*): + masks to embed + """ + sparse_embeddings = None + batch_size = 1 + if input_points is not None: + batch_size = input_points.shape[0] + if input_labels is None: + raise ValueError("If points are provided, labels must also be provided.") + point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None)) + sparse_embeddings = point_embeddings + if input_boxes is not None: + batch_size = input_boxes.shape[0] + box_embeddings = self._embed_boxes(input_boxes) + if sparse_embeddings is None: + sparse_embeddings = box_embeddings + else: + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2) + if input_masks is not None: + dense_embeddings = self.mask_embed(input_masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings + + +class Sam2VideoTwoWayTransformer(nn.Module): + def __init__(self, config: Sam2VideoMaskDecoderConfig): + super().__init__() + self.config = config + + self.num_hidden_layers = config.num_hidden_layers + self.layers = nn.ModuleList() + + for i in range(self.num_hidden_layers): + self.layers.append(Sam2VideoTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0))) + + self.final_attn_token_to_image = Sam2VideoAttention(config) + self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size) + + def forward( + self, + point_embeddings: Tensor, + image_embeddings: Tensor, + image_positional_embeddings: Tensor, + attention_similarity: Tensor, + target_embedding=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + if image_embeddings is None: + raise ValueError("You have to specify an image_embedding") + + image_embeddings = image_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + image_positional_embeddings = image_positional_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + + # Prepare queries + queries = point_embeddings + keys = image_embeddings + + # Apply transformer blocks and final layernorm + for layer in self.layers: + if target_embedding is not None: + queries += target_embedding + + queries, keys, _ = layer( + queries=queries, + keys=keys, + query_point_embedding=point_embeddings, + key_point_embedding=image_positional_embeddings, + attention_similarity=attention_similarity, + **kwargs, + ) + # Apply the final attention layer from the points to the image + query = queries + point_embeddings + key = keys + image_positional_embeddings + + attn_out, _ = self.final_attn_token_to_image(query=query, key=key, value=keys) + + queries = queries + attn_out + queries = self.layer_norm_final_attn(queries) + return queries, keys + + +class Sam2VideoMaskDecoder(nn.Module): + def __init__(self, config: Sam2VideoMaskDecoderConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + + self.num_multimask_outputs = config.num_multimask_outputs + self.num_mask_tokens = config.num_multimask_outputs + 1 + + self.iou_token = nn.Embedding(1, self.hidden_size) + self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size) + + self.transformer = Sam2VideoTwoWayTransformer(config) + + # should we create a new class for this? + self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + self.upscale_layer_norm = Sam2VideoLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.activation = nn.GELU() + + mlps_list = [] + for _ in range(self.num_mask_tokens): + mlps_list += [Sam2VideoFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)] + self.output_hypernetworks_mlps = nn.ModuleList(mlps_list) + self.iou_prediction_head = Sam2VideoFeedForward( + self.hidden_size, + config.iou_head_hidden_dim, + self.num_mask_tokens, + config.iou_head_depth, + sigmoid_output=True, + ) + + self.conv_s0 = nn.Conv2d(config.hidden_size, config.hidden_size // 8, kernel_size=1, stride=1) + self.conv_s1 = nn.Conv2d(config.hidden_size, config.hidden_size // 4, kernel_size=1, stride=1) + + self.obj_score_token = nn.Embedding(1, self.hidden_size) + self.pred_obj_score_head = Sam2VideoFeedForward(self.hidden_size, self.hidden_size, 1, 3) + + self.dynamic_multimask_via_stability = config.dynamic_multimask_via_stability + self.dynamic_multimask_stability_delta = config.dynamic_multimask_stability_delta + self.dynamic_multimask_stability_thresh = config.dynamic_multimask_stability_thresh + + def forward( + self, + image_embeddings: torch.Tensor, + image_positional_embeddings: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + high_resolution_features: list[torch.Tensor], + attention_similarity: torch.Tensor | None = None, + target_embedding: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Args: + image_embeddings (`torch.Tensor`): + The embeddings from the image encoder. + image_positional_embeddings (`torch.Tensor`): + Positional encoding with the shape of image_embeddings. + sparse_prompt_embeddings (`torch.Tensor`): + The embeddings of the points and boxes. + dense_prompt_embeddings (`torch.Tensor`): + The embeddings of the mask inputs. + multimask_output (`bool`): + Whether to return multiple masks or a single mask. + high_resolution_features (`list[torch.Tensor]`, *optional*): + The high-resolution features from the vision encoder. + attention_similarity (`torch.Tensor`, *optional*): + The attention similarity tensor. + target_embedding (`torch.Tensor`, *optional*): + The target embedding. + """ + batch_size, num_channels, height, width = image_embeddings.shape + point_batch_size = sparse_prompt_embeddings.shape[1] + # Concatenate output tokens + output_tokens = torch.cat( + [ + self.obj_score_token.weight, + self.iou_token.weight, + self.mask_tokens.weight, + ], + dim=0, + ) + output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) + + if sparse_prompt_embeddings.shape[0] != 0: + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2) + else: + tokens = output_tokens + point_embeddings = tokens.to(self.iou_token.weight.dtype) + + # Expand per-image data in batch direction to be per-mask + image_embeddings = image_embeddings + dense_prompt_embeddings + image_embeddings = image_embeddings.repeat_interleave(point_batch_size, dim=0) + image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) + # Run the transformer + point_embeddings, image_embeddings = self.transformer( + point_embeddings=point_embeddings, + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + iou_token_out = point_embeddings[:, :, 1, :] + mask_tokens_out = point_embeddings[:, :, 2 : (2 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + image_embeddings = image_embeddings.transpose(2, 3).view( + batch_size * point_batch_size, num_channels, height, width + ) + + feat_s0, feat_s1 = high_resolution_features + feat_s0 = feat_s0.repeat_interleave(point_batch_size, dim=0) + feat_s1 = feat_s1.repeat_interleave(point_batch_size, dim=0) + upscaled_embedding = self.upscale_conv1(image_embeddings) + feat_s1 + upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) + upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding) + feat_s0) + + hyper_in_list: list[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + current_mlp = self.output_hypernetworks_mlps[i] + hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])] + hyper_in = torch.stack(hyper_in_list, dim=2) + + _, num_channels, height, width = upscaled_embedding.shape + upscaled_embedding = upscaled_embedding.view(batch_size, point_batch_size, num_channels, height * width) + masks = (hyper_in @ upscaled_embedding).view(batch_size, point_batch_size, -1, height, width) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + object_score_logits = self.pred_obj_score_head(point_embeddings[:, :, 0, :]) + + # Select the correct mask or masks for output + if multimask_output: + mask_slice = slice(1, None) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + elif self.dynamic_multimask_via_stability and not self.training: + mask_slice = slice(0, 1) + masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred) + else: + mask_slice = slice(0, 1) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + + sam_tokens_out = mask_tokens_out[:, :, mask_slice] # [b, 3, c] shape + + return masks, iou_pred, sam_tokens_out, object_score_logits + + def _get_stability_scores(self, mask_logits): + """ + Compute stability scores of the mask logits based on the IoU between upper and + lower thresholds. + """ + mask_logits = mask_logits.flatten(-2) + stability_delta = self.dynamic_multimask_stability_delta + area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() + area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() + stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) + return stability_scores + + def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): + """ + When outputting a single mask, if the stability score from the current single-mask + output (based on output token 0) falls below a threshold, we instead select from + multi-mask outputs (based on output token 1~3) the mask with the highest predicted + IoU score. This is intended to ensure a valid mask for both clicking and tracking. + """ + # The best mask from multimask output tokens (1~3) + multimask_logits = all_mask_logits[:, :, 1:, :, :] + multimask_iou_scores = all_iou_scores[:, :, 1:] + best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1) # [B, P] + best_scores_inds_expanded = best_scores_inds.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) + best_scores_inds_expanded = best_scores_inds_expanded.expand( + -1, -1, 1, multimask_logits.size(-2), multimask_logits.size(-1) + ) + best_multimask_logits = torch.gather(multimask_logits, 2, best_scores_inds_expanded) # [B, P, 1, H, W] + best_multimask_iou_scores = torch.gather(multimask_iou_scores, 2, best_scores_inds.unsqueeze(-1)) # [B, P, 1] + + # The mask from singlemask output token 0 and its stability score + singlemask_logits = all_mask_logits[:, :, 0:1, :, :] + singlemask_iou_scores = all_iou_scores[:, :, 0:1] + stability_scores = self._get_stability_scores(singlemask_logits) + is_stable = stability_scores >= self.dynamic_multimask_stability_thresh + + # Dynamically fall back to best multimask output upon low stability scores. + mask_logits_out = torch.where( + is_stable[..., None, None].expand_as(singlemask_logits), + singlemask_logits, + best_multimask_logits, + ) + iou_scores_out = torch.where( + is_stable.expand_as(singlemask_iou_scores), + singlemask_iou_scores, + best_multimask_iou_scores, + ) + return mask_logits_out, iou_scores_out + + +# a large negative value as a placeholder score for missing objects +NO_OBJ_SCORE = -1024.0 + + +def get_1d_sine_pe(pos_inds, dim, temperature=10000): + """ + Get 1D sine positional embedding as in the original Transformer paper. + """ + pe_dim = dim // 2 + dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device) + dim_t = temperature ** (2 * (dim_t // 2) / pe_dim) + + pos_embed = pos_inds.unsqueeze(-1) / dim_t + pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1) + return pos_embed + + +@auto_docstring +class Sam2VideoModel(Sam2VideoPreTrainedModel): + input_modalities = ("video", "text") + _can_record_outputs = {"mask_decoder_attentions": OutputRecorder(Sam2VideoTwoWayAttentionBlock, index=2)} + _tied_weights_keys = {} + _keys_to_ignore_on_load_unexpected = [] + + def __init__(self, config: Sam2VideoConfig): + super().__init__(config) + self.shared_image_embedding = Sam2VideoPositionalEmbedding(config.prompt_encoder_config) + self.vision_encoder = AutoModel.from_config(config.vision_config) + self.prompt_encoder = Sam2VideoPromptEncoder(config.prompt_encoder_config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + self.mask_decoder = Sam2VideoMaskDecoder(config.mask_decoder_config) + + self.num_feature_levels = config.vision_config.num_feature_levels + self.backbone_feature_sizes = config.vision_config.backbone_feature_sizes + # a single token to indicate no memory embedding from previous frames + self.hidden_dim = config.vision_config.fpn_hidden_size + self.no_memory_embedding = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + self.config = config + # For video sequence inference + self.image_size = config.image_size + self.memory_attention = Sam2VideoMemoryAttention(config) + self.memory_encoder = Sam2VideoMemoryEncoder(config) + self.no_memory_positional_encoding = torch.nn.Parameter( + torch.zeros(1, 1, config.vision_config.fpn_hidden_size) + ) + self.mem_dim = config.memory_encoder_output_channels + self.num_maskmem = config.num_maskmem # Number of memories accessible + # Temporal encoding of the memories + self.memory_temporal_positional_encoding = torch.nn.Parameter( + torch.zeros(self.num_maskmem, 1, 1, self.mem_dim) + ) + + self.no_object_pointer = torch.nn.Parameter(torch.zeros(1, self.hidden_dim)) + # A conv layer to downsample the mask prompt to stride 4 (the same stride as + # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale, + # so that it can be fed into the SAM mask decoder to generate a pointer. + self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4) + # a feedforward layer on SAM output tokens to turn them into object pointers + self.object_pointer_proj = Sam2VideoFeedForward(self.hidden_dim, self.hidden_dim, self.hidden_dim, 3) + + if self.config.enable_temporal_pos_encoding_for_object_pointers: + # a linear projection on temporal positional encoding in object pointers to + # avoid potential interference with spatial positional encoding + self.temporal_positional_encoding_projection_layer = torch.nn.Linear(self.hidden_dim, self.mem_dim) + else: + self.temporal_positional_encoding_projection_layer = torch.nn.Identity() + + self.occlusion_spatial_embedding_parameter = None # compatibility with Sam2 + if config.enable_occlusion_spatial_embedding: + self.occlusion_spatial_embedding_parameter = torch.nn.Parameter(torch.zeros(1, self.mem_dim)) + + self.post_init() + + def get_input_embeddings(self): + return self.vision_encoder.get_input_embeddings() + + def get_image_wide_positional_embeddings(self) -> torch.Tensor: + size = self.prompt_encoder.image_embedding_size + target_device = self.shared_image_embedding.positional_embedding.device + target_dtype = self.shared_image_embedding.positional_embedding.dtype + grid = torch.ones(size, device=target_device, dtype=target_dtype) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / size[0] + x_embed = x_embed / size[1] + + positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1)) + return positional_embedding.permute(2, 0, 1).unsqueeze(0) # channel x height x width + + @torch.no_grad() + def get_image_embeddings( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> list[torch.Tensor]: + r""" + Returns the image embeddings by passing the pixel values through the vision encoder. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Input pixel values + """ + batch_size = pixel_values.shape[0] + image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + return image_embeddings + + @torch.no_grad() + def get_prompt_embeddings( + self, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + r""" + Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder. + + Args: + input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`): + Optional input points for the prompt encoder. The padding of the point is automatically done by the + processor. `point_batch_size` refers to the number of masks that we want the model to predict per + point. The model will output `point_batch_size` times 3 masks in total. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points_per_image)`): + Optional input labels for the prompt encoder. The padding of the labels is automatically done by the + processor, or can be fed by the user. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes_per_image, 4)`): + Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the + processor. users can also pass manually the input boxes. + input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`): + Optional input masks for the prompt encoder. + """ + prompt_output = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + return prompt_output + + @torch.inference_mode() + @auto_docstring(custom_intro="Propagate the objects through a streamed video frame.") + def forward( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int | None = None, + frame: torch.Tensor | None = None, + reverse: bool = False, + run_mem_encoder: bool = True, + **kwargs, + ) -> Sam2VideoSegmentationOutput: + r""" + inference_session (`Sam2VideoInferenceSession`): + The video inference session object. + frame_idx (`int`, *optional*): + The index of the frame on which to run inference. No need to provide when inferring + on a new streamed frame. + frame (`torch.Tensor`, *optional*): + The frame to process. Provide when streaming. + reverse (`bool`, *optional*, defaults to `False`): + Whether to propagate in reverse. + run_mem_encoder (`bool`, *optional*, defaults to `True`): + Whether to run the memory encoder on predicted masks. The memory encoder is batched across all objects for efficiency. + """ + if frame is not None: + frame_idx = inference_session.add_new_frame(frame, frame_idx) + + if frame is not None and inference_session.get_obj_num() == 0: + raise ValueError("No objects are provided for tracking; please add inputs first.") + + num_objects = inference_session.get_obj_num() + pred_masks_per_obj = [None] * num_objects + object_score_logits_per_obj = [None] * num_objects + + # Collect data for batched memory encoding + objects_needing_memory_encoding = [] + high_res_masks_for_memory = [] + object_score_logits_for_memory = [] + is_mask_from_pts_per_obj = [] + + # Note: We avoid batched inference here because per-object inputs (clicks/masks) + # can differ across objects. + for obj_idx in range(num_objects): + obj_id = inference_session.obj_idx_to_id(obj_idx) + has_new_inputs = obj_id in inference_session.obj_with_new_inputs + has_cond_output = frame_idx in inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + # If this object has no new inputs and this frame already has a + # conditioning output, reuse the cached masks instead of recomputing. + if (not has_new_inputs) and has_cond_output: + pred_masks = inference_session.get_output(obj_idx, frame_idx, "pred_masks", is_conditioning_frame=True) + object_score_logits = inference_session.get_output( + obj_idx, frame_idx, "object_score_logits", is_conditioning_frame=True + ) + is_init_cond_frame = True + else: + # Defaults when there are no new inputs + is_init_cond_frame = False + point_inputs = None + mask_inputs = None + + if has_new_inputs: + is_init_cond_frame = frame_idx not in inference_session.frames_tracked_per_obj[obj_idx] + if is_init_cond_frame: + reverse = False + point_inputs = inference_session.point_inputs_per_obj[obj_idx].get(frame_idx, None) + mask_inputs = inference_session.mask_inputs_per_obj[obj_idx].get(frame_idx, None) + if point_inputs is not None or mask_inputs is not None: + inference_session.obj_with_new_inputs.remove(obj_id) + + current_out = self._run_single_frame_inference( + inference_session=inference_session, + obj_idx=obj_idx, + frame_idx=frame_idx, + batch_size=1, # run on the slice of a single object + is_init_cond_frame=is_init_cond_frame, + point_inputs=point_inputs, + mask_inputs=mask_inputs, + reverse=reverse, + streaming=frame is not None, + ) + inference_session.store_output( + obj_idx, frame_idx, output_value=current_out, is_conditioning_frame=is_init_cond_frame + ) + pred_masks = current_out["pred_masks"] + object_score_logits = current_out["object_score_logits"] + + # Collect data for batched memory encoding + if run_mem_encoder and self.num_maskmem > 0: + objects_needing_memory_encoding.append(obj_idx) + high_res_masks_for_memory.append(current_out["high_res_masks"]) + object_score_logits_for_memory.append(object_score_logits) + is_mask_from_pts_per_obj.append(point_inputs is not None or mask_inputs is not None) + + pred_masks_per_obj[obj_idx] = pred_masks + object_score_logits_per_obj[obj_idx] = object_score_logits.squeeze(-1) + if not is_init_cond_frame: + # only for tracked frames, not for initial conditioning frames + inference_session.frames_tracked_per_obj[obj_idx][frame_idx] = {"reverse": reverse} + + # Batch encode memories for all objects at once + self._batch_encode_memories( + inference_session=inference_session, + frame_idx=frame_idx, + objects_needing_memory_encoding=objects_needing_memory_encoding, + high_res_masks_for_memory=high_res_masks_for_memory, + object_score_logits_for_memory=object_score_logits_for_memory, + is_mask_from_pts_per_obj=is_mask_from_pts_per_obj, + ) + + # Resize the output mask to the original video resolution (we directly use + # the mask scores on GPU for output to avoid any CPU conversion in between) + if len(pred_masks_per_obj) > 1: + all_pred_masks = torch.cat(pred_masks_per_obj, dim=0) + all_object_score_logits = torch.cat(object_score_logits_per_obj, dim=0) + else: + all_pred_masks = pred_masks_per_obj[0] + all_object_score_logits = object_score_logits_per_obj[0] + + return Sam2VideoSegmentationOutput( + object_ids=inference_session.obj_ids.copy(), + pred_masks=all_pred_masks, + object_score_logits=all_object_score_logits, + frame_idx=frame_idx, + ) + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam2VideoVisionEncoderOutput: + r""" + pixel_values (`torch.FloatTensor`): + Input pixel values of shape `(batch_size, num_channels, height, width)`. + """ + vision_outputs: Sam2VideoVisionEncoderOutput = self.vision_encoder(pixel_values, return_dict=True, **kwargs) + + feature_maps = vision_outputs.fpn_hidden_states + feature_maps_position_embeddings = vision_outputs.fpn_position_encoding + + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + feature_maps = list(feature_maps) + feature_maps[0] = self.mask_decoder.conv_s0(feature_maps[0]) + feature_maps[1] = self.mask_decoder.conv_s1(feature_maps[1]) + + # flatten NxCxHxW to HWxNxC + feature_maps = [feature_map.flatten(2).permute(2, 0, 1) for feature_map in feature_maps] + feature_maps_position_embeddings = [ + feature_map_position_embedding.flatten(2).permute(2, 0, 1) + for feature_map_position_embedding in feature_maps_position_embeddings + ] + vision_outputs.fpn_hidden_states = feature_maps + vision_outputs.fpn_position_encoding = feature_maps_position_embeddings + + return vision_outputs + + def _prepare_vision_features( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + batch_size: int, + ) -> tuple[torch.Tensor, list[torch.Tensor]]: + """Prepare vision features for a frame.""" + + # Check if features are cached + if cached_features := inference_session.cache.get_vision_features(frame_idx): + vision_feats = cached_features["vision_feats"] + vision_pos_embeds = cached_features["vision_pos_embeds"] + else: + # Compute features using image encoder + image_batch = inference_session.get_frame(frame_idx).unsqueeze(0) # Add batch dimension + image_outputs = self.get_image_features(image_batch, return_dict=True) + vision_feats = image_outputs.fpn_hidden_states + vision_pos_embeds = image_outputs.fpn_position_encoding + # Cache features + inference_session.cache.cache_vision_features( + frame_idx, {"vision_feats": vision_feats, "vision_pos_embeds": vision_pos_embeds} + ) + + # Expand to batch size if needed + if batch_size > 1: + vision_feats = vision_feats.expand(batch_size, -1, -1, -1) + vision_pos_embeds = [pe.expand(batch_size, -1, -1, -1) for pe in vision_pos_embeds] + + return vision_feats, vision_pos_embeds + + def _single_frame_forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam2VideoImageSegmentationOutput: + """ + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decoder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + """ + if not ((pixel_values is None) ^ (image_embeddings is None)): + raise ValueError("Exactly one of pixel_values or image_embeddings must be provided.") + if input_points is not None and input_boxes is not None: + if input_points.shape[1] != input_boxes.shape[1]: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {input_points.shape[1]} and {input_boxes.shape[1]}." + ) + elif input_points is not None: + num_objects = input_points.shape[1] + elif input_boxes is not None: + num_objects = input_boxes.shape[1] + elif input_masks is not None: + num_objects = input_masks.shape[1] + else: + num_objects = 1 + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings[-1].shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + vision_attentions = None + vision_hidden_states = None + + if pixel_values is not None: + image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + vision_hidden_states = image_outputs.hidden_states + vision_attentions = image_outputs.attentions + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + if input_points is None and input_boxes is None: + # If no points are provide, pad with an empty point (with label -1) + input_points = torch.zeros( + batch_size, 1, 1, 2, dtype=image_embeddings[-1].dtype, device=image_embeddings[-1].device + ) + input_labels = -torch.ones(batch_size, 1, 1, dtype=torch.int32, device=image_embeddings[-1].device) + + if input_masks is not None: + # If mask_inputs is provided, downsize it into low-res mask input if needed + # and feed it as a dense mask prompt into the SAM mask encoder + if input_masks.shape[-2:] != self.prompt_encoder.mask_input_size: + input_masks = F.interpolate( + input_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(input_masks.dtype) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + low_res_multimasks, iou_scores, sam_output_tokens, object_score_logits = self.mask_decoder( + image_embeddings=image_embeddings[-1], + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + high_resolution_features=image_embeddings[:-1], + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + + is_obj_appearing = object_score_logits > 0 + # Mask used for spatial memories is always a *hard* choice between obj and no obj, + # consistent with the actual mask prediction + low_res_multimasks = torch.where( + is_obj_appearing[:, None, None], + low_res_multimasks, + NO_OBJ_SCORE, + ) + + # convert masks from possibly bfloat16 (or float16) to float32 + # (older PyTorch versions before 2.1 don't support `interpolate` on bf16) + high_res_multimasks = ( + F.interpolate( + low_res_multimasks.squeeze(1).float(), + size=(self.image_size, self.image_size), + mode="bilinear", + align_corners=False, + ) + .unsqueeze(1) + .to(low_res_multimasks.dtype) + ) + sam_output_token = sam_output_tokens[:, :, 0] + if multimask_output: + # take the best mask prediction (with the highest IoU estimation) + best_iou_inds = torch.argmax(iou_scores, dim=-1) + batch_inds = torch.arange(batch_size, device=high_res_multimasks.device) + object_batch_inds = torch.arange(num_objects, device=high_res_multimasks.device) + low_res_masks = low_res_multimasks[batch_inds, object_batch_inds, best_iou_inds] + high_res_masks = high_res_multimasks[batch_inds, object_batch_inds, best_iou_inds] + if sam_output_tokens.size(2) > 1: + sam_output_token = sam_output_tokens[batch_inds, object_batch_inds, best_iou_inds] + else: + low_res_masks, high_res_masks = low_res_multimasks[:, :, 0], high_res_multimasks[:, :, 0] + + # Extract object pointer from the SAM output token (with occlusion handling) + object_pointer = self.object_pointer_proj(sam_output_token) + lambda_is_obj_appearing = is_obj_appearing.to(object_pointer.dtype) + + object_pointer = lambda_is_obj_appearing * object_pointer + object_pointer = object_pointer + (1 - lambda_is_obj_appearing) * self.no_object_pointer + + return Sam2VideoImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_masks, + high_res_masks=high_res_masks, + object_pointer=object_pointer, + object_score_logits=object_score_logits, + image_embeddings=image_embeddings, + vision_hidden_states=vision_hidden_states, + vision_attentions=vision_attentions, + ) + + def _use_mask_as_output( + self, + backbone_features: torch.Tensor, + high_res_features: list[torch.Tensor], + mask_inputs: torch.Tensor, + ) -> Sam2VideoImageSegmentationOutput: + """ + Directly turn binary `mask_inputs` into a output mask logits without using SAM. + (same input and output shapes as in forward above). + """ + # Use -10/+20 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid). + out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05 + mask_inputs_float = mask_inputs.to(backbone_features[0].dtype) + + # Ensure mask is at self.image_size resolution for consistency + if mask_inputs_float.shape[-2:] != (self.image_size, self.image_size): + mask_inputs_float = F.interpolate( + mask_inputs_float.float(), + size=(self.image_size, self.image_size), + align_corners=False, + mode="bilinear", + antialias=True, + ).to(mask_inputs.dtype) + + high_res_masks = mask_inputs_float * out_scale + out_bias + low_res_masks = F.interpolate( + high_res_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(backbone_features[0].dtype) + # a dummy IoU prediction of all 1's under mask input + iou_scores = mask_inputs.new_ones(mask_inputs.size(0), 1).to(backbone_features[0].dtype) + # produce an object pointer using the SAM decoder from the mask input + object_pointer = self._single_frame_forward( + input_masks=self.mask_downsample(mask_inputs_float.to(backbone_features[0].dtype)), + image_embeddings=high_res_features + [backbone_features], + ).object_pointer + # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem; + # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying + # on the object_scores from the SAM decoder. + is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1) + is_obj_appearing = is_obj_appearing[..., None] + lambda_is_obj_appearing = is_obj_appearing.to(backbone_features[0].dtype) + object_score_logits = out_scale * lambda_is_obj_appearing + out_bias + object_pointer = lambda_is_obj_appearing * object_pointer + object_pointer = object_pointer + (1 - lambda_is_obj_appearing) * self.no_object_pointer + return Sam2VideoImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_masks, + high_res_masks=high_res_masks, + object_pointer=object_pointer, + object_score_logits=object_score_logits.unsqueeze(-1), + image_embeddings=high_res_features + [backbone_features], + ) + + def _select_closest_cond_frames(self, frame_idx, cond_frame_outputs, max_cond_frame_num): + """ + Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs` + that are temporally closest to the current frame at `frame_idx`. Here, we take + - a) the closest conditioning frame before `frame_idx` (if any); + - b) the closest conditioning frame after `frame_idx` (if any); + - c) any other temporally closest conditioning frames until reaching a total + of `max_cond_frame_num` conditioning frames. + + Outputs: + - selected_outputs: selected items (keys & values) from `cond_frame_outputs`. + - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`. + """ + if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num: + selected_outputs = cond_frame_outputs + unselected_outputs = {} + else: + selected_outputs = {} + # the closest conditioning frame before `frame_idx` (if any) + idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None) + if idx_before is not None: + selected_outputs[idx_before] = cond_frame_outputs[idx_before] + + # the closest conditioning frame after `frame_idx` (if any) + idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None) + if idx_after is not None: + selected_outputs[idx_after] = cond_frame_outputs[idx_after] + + # add other temporally closest conditioning frames until reaching a total + # of `max_cond_frame_num` conditioning frames. + num_remain = max_cond_frame_num - len(selected_outputs) + inds_remain = sorted( + (t for t in cond_frame_outputs if t not in selected_outputs), + key=lambda x: abs(x - frame_idx), + )[:num_remain] + selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain) + unselected_outputs = {t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs} + + return selected_outputs, unselected_outputs + + def _gather_memory_frame_outputs( + self, + inference_session: Sam2VideoInferenceSession, + obj_idx: int, + frame_idx: int, + track_in_reverse_time: bool = False, + ) -> list[tuple[int, dict]]: + """ + Get memory frames from conditioning and non-conditioning outputs. + + Returns: + List of (relative_temporal_offset, output_data) tuples. + """ + temporal_positions_and_previous_outputs = [] + + # Add conditioning frame outputs (limited by max_cond_frame_num) + conditioning_outputs = inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + if not conditioning_outputs: + raise ValueError( + "maskmem_features in conditioning outputs cannot be empty when not is_initial_conditioning_frame" + ) + conditioning_outputs, unselected_conditioning_outputs = self._select_closest_cond_frames( + frame_idx, conditioning_outputs, max_cond_frame_num=self.config.max_cond_frame_num + ) + + # Store (temporal_position, output_data) tuples + temporal_positions_and_previous_outputs = [(0, out) for out in conditioning_outputs.values()] + + # Add non-conditioning memory frames (up to self.num_maskmem - 1) + # These are typically frames tracked by the model without direct user input. + # Frames are selected with a stride, prioritizing the most recent ones. Here we only support stride = 1 for simplicity. + for relative_temporal_offset in range(self.num_maskmem - 1, 0, -1): + # relative_temporal_offset: how many frames before (or after if reversing) the current frame + if not track_in_reverse_time: + previous_frame_idx = frame_idx - relative_temporal_offset + else: + previous_frame_idx = frame_idx + relative_temporal_offset + + # check if the output is already stored without using get_output to avoid unnecessary memory transfers between CPU and GPU + output_data = inference_session.output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].get( + previous_frame_idx, unselected_conditioning_outputs.get(previous_frame_idx, None) + ) + + temporal_positions_and_previous_outputs.append((relative_temporal_offset, output_data)) + + return temporal_positions_and_previous_outputs + + def _build_memory_attention_inputs( + self, + temporal_positions_and_previous_outputs: list[tuple[int, dict]], + device: torch.device, + ) -> tuple[list[torch.Tensor], list[torch.Tensor]]: + """ + Concatenate memory features and positional embeddings from previous frames. + + Returns: + Tuple of (memories_to_concatenate, memory_positional_embeddings_to_concatenate). + """ + memories_to_concatenate = [] + memory_positional_embeddings_to_concatenate = [] + + for relative_temporal_offset, prev_output_data in temporal_positions_and_previous_outputs: + if prev_output_data is None: + continue # Skip if no output data for this temporal position (e.g., padding frames) + + # Load memory features (potentially from CPU to GPU) + # Features are flattened: (Batch, Channels, H, W) -> (H*W, Batch, Channels) + memory_features = prev_output_data["maskmem_features"].to(device, non_blocking=True) + memories_to_concatenate.append(memory_features) + + # Spatial positional encoding (potentially from CPU to GPU) + spatial_memory_pos_embed = prev_output_data["maskmem_pos_enc"].to(device, non_blocking=True) + + # Add temporal positional encoding + # self.memory_temporal_positional_encoding shape: (NumMaskMem, 1, 1, MemDim) + combined_memory_pos_embed = ( + spatial_memory_pos_embed + self.memory_temporal_positional_encoding[relative_temporal_offset - 1] + ) + memory_positional_embeddings_to_concatenate.append(combined_memory_pos_embed) + + return memories_to_concatenate, memory_positional_embeddings_to_concatenate + + def _get_object_pointers( + self, + inference_session: Sam2VideoInferenceSession, + obj_idx: int, + frame_idx: int, + num_total_frames: int, + device: torch.device, + track_in_reverse_time: bool = False, + streaming: bool = False, + ) -> tuple[list[int], list[torch.Tensor], int]: + """ + Get object pointers and their positional embeddings from past frames. + + Returns: + Tuple of (temporal_offsets, pointer_tokens, max_object_pointers_to_use). + """ + temporal_position_sign_multiplier = -1 if track_in_reverse_time else 1 + + # Determine max object pointers to use + if streaming: + max_object_pointers_to_use = self.config.max_object_pointers_in_encoder + else: + max_object_pointers_to_use = min(num_total_frames, self.config.max_object_pointers_in_encoder) + + temporal_offsets: list[int] = [] + pointer_tokens: list[torch.Tensor] = [] + + # Add object pointers from selected conditioning frames + # Optionally, only include pointers from past frames during evaluation + conditioning_outputs = inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + eligible_conditioning_outputs = conditioning_outputs + if not self.training: + eligible_conditioning_outputs = { + temporal_idx: out + for temporal_idx, out in conditioning_outputs.items() + if (temporal_idx >= frame_idx if track_in_reverse_time else temporal_idx <= frame_idx) + } + + for temporal_idx, out_data in eligible_conditioning_outputs.items(): + temporal_difference = (frame_idx - temporal_idx) * temporal_position_sign_multiplier + temporal_offsets.append(temporal_difference) + pointer_tokens.append(out_data["object_pointer"].to(device)) + + # Add object pointers from non-conditioning frames (up to max_object_pointers_to_use - 1) + for t_diff_offset in range(1, max_object_pointers_to_use): + ref_frame_idx = frame_idx + t_diff_offset if track_in_reverse_time else frame_idx - t_diff_offset + if ref_frame_idx < 0 or ( + not streaming and num_total_frames is not None and ref_frame_idx >= num_total_frames + ): + break # Stop if frame index is out of bounds + + # check if the output is already stored without using get_output to avoid unnecessary memory transfers between CPU and GPU + out_data = inference_session.output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].get( + ref_frame_idx, None + ) + if out_data is not None: + temporal_offsets.append(t_diff_offset) + pointer_tokens.append(out_data["object_pointer"].to(device)) + + return temporal_offsets, pointer_tokens, max_object_pointers_to_use + + def _process_object_pointers( + self, + temporal_offsets: list[int], + pointer_tokens: list[torch.Tensor], + max_object_pointers_to_use: int, + batch_size: int, + num_channels: int, + device: torch.device, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Process object pointers and compute their positional embeddings. + + Returns: + Tuple of (object_pointers, object_pointers_pos_embed). + """ + if not pointer_tokens: + return None, None + + # Stack object pointers: List of (Batch, Channels) -> (SeqLen_ptr, Batch, Channels) + object_pointers = torch.stack(pointer_tokens, dim=0) + + if self.config.enable_temporal_pos_encoding_for_object_pointers: + max_temporal_diff = float(max_object_pointers_to_use - 1) + # Determine dimensionality for temporal positional encoding of pointers + pointer_tpos_dim = num_channels + + # Normalize temporal differences before sine PE calculation + normalized_temporal_diffs = ( + torch.tensor(temporal_offsets, device=device, dtype=torch.float32) / max_temporal_diff + ) + sine_pe = get_1d_sine_pe(normalized_temporal_diffs, dim=pointer_tpos_dim).to(object_pointers.dtype) + projected_sine_pe = self.temporal_positional_encoding_projection_layer(sine_pe) + object_pointers_pos_embed = projected_sine_pe.unsqueeze(1).expand(-1, batch_size, self.mem_dim) + else: + object_pointers_pos_embed = object_pointers.new_zeros( + len(temporal_offsets), batch_size, self.mem_dim, dtype=object_pointers.dtype + ) + + if self.mem_dim < num_channels: + # If memory dimension is smaller, reshape/split pointers and repeat positional encoding + num_splits = num_channels // self.mem_dim + object_pointers = object_pointers.reshape(-1, batch_size, num_splits, self.mem_dim) + object_pointers = object_pointers.permute(0, 2, 1, 3).flatten( + 0, 1 + ) # (SeqLen_ptr*num_splits, Batch, MemDim) + object_pointers_pos_embed = object_pointers_pos_embed.repeat_interleave(num_splits, dim=0) + + return object_pointers, object_pointers_pos_embed + + def _prepare_memory_conditioned_features( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_idx: int, + is_initial_conditioning_frame: bool, + current_vision_features: list[torch.Tensor], + current_vision_positional_embeddings: list[torch.Tensor], + num_total_frames: int, + track_in_reverse_time: bool = False, + streaming: bool = False, + ) -> torch.Tensor: + """ + Fuse current frame's visual features with memory from previous frames for enhanced object tracking. + + This method conditions the current frame's visual features on temporal memory from previous frames, + enabling consistent object tracking across video sequences. For initial conditioning frames, it uses + no-memory embeddings. For subsequent frames, it retrieves and integrates memory features from both + conditioning frames (user interactions) and non-conditioning frames (tracked results) via cross-attention. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The video inference session object. + frame_idx (`int`): + Index of the current frame being processed. + obj_idx (`int`): + Index of the object being processed. + is_initial_conditioning_frame (`bool`): + Whether this is an initial conditioning frame with user inputs (True) or a subsequent + tracking frame (False). + current_vision_features (`torch.Tensor`): + Highest-level vision features of shape `(seq_len, batch_size, channels)`. + current_vision_positional_embeddings (`torch.Tensor`): + Positional embedding tensors corresponding to the highest-level vision features. + num_total_frames (`int`): + Total number of frames in the video sequence. + track_in_reverse_time (`bool`, *optional*, defaults to `False`): + Whether tracking is performed in reverse temporal order. + streaming (`bool`, *optional*, defaults to `False`): + Whether this is streaming inference mode. + + Returns: + `torch.Tensor`: Memory-conditioned feature tensor of shape `(batch_size, channels, height, width)` + suitable for input to the SAM decoder. + """ + # Get dimensions from the highest-level (lowest-resolution) feature map + batch_size = current_vision_features.size(1) + num_channels = self.hidden_dim + height, width = self.backbone_feature_sizes[-1] + device = current_vision_features.device + + # If memory is disabled (e.g., for single image SAM), return current features directly. + if self.num_maskmem == 0: + # Permute (SeqLen, Batch, Channels) -> (Batch, Channels, SeqLen) then view as (Batch, Channels, Height, Width) + # Assuming SeqLen = Height * Width for the last feature map + current_feature_map = current_vision_features.permute(1, 2, 0).view( + batch_size, num_channels, height, width + ) + return current_feature_map + + # Step 1: Handle initial conditioning frames + if is_initial_conditioning_frame: + # For initial conditioning frames, no prior memory is used directly in this block. + # If configured, directly add a learnable "no memory" embedding. + # current_vision_features has shape (SeqLen, Batch, Channels) + conditioned_feature_map_flat = current_vision_features + self.no_memory_embedding + # Reshape to (Batch, Channels, Height, Width) + conditioned_feature_map = conditioned_feature_map_flat.permute(1, 2, 0).view( + batch_size, num_channels, height, width + ) + return conditioned_feature_map + + # Step 2: Get memory frames and concatenate their features + temporal_positions_and_previous_outputs = self._gather_memory_frame_outputs( + inference_session, obj_idx, frame_idx, track_in_reverse_time + ) + + memories_to_concatenate, memory_positional_embeddings_to_concatenate = self._build_memory_attention_inputs( + temporal_positions_and_previous_outputs, device + ) + + # Step 3: Get and process object pointers + temporal_offsets, pointer_tokens, max_object_pointers_to_use = self._get_object_pointers( + inference_session, obj_idx, frame_idx, num_total_frames, device, track_in_reverse_time, streaming + ) + + num_object_pointer_tokens = 0 + if pointer_tokens: + object_pointers, object_pointers_pos_embed = self._process_object_pointers( + temporal_offsets, pointer_tokens, max_object_pointers_to_use, batch_size, num_channels, device + ) + + if object_pointers is not None: + memories_to_concatenate.append(object_pointers) + memory_positional_embeddings_to_concatenate.append(object_pointers_pos_embed) + num_object_pointer_tokens = object_pointers.shape[0] + + # Step 4: Concatenate all retrieved memories and their positional embeddings + combined_memory = torch.cat(memories_to_concatenate, dim=0).to(dtype=inference_session.dtype) + combined_memory_positional_embeddings = torch.cat(memory_positional_embeddings_to_concatenate, dim=0) + + # Step 5: Forward through the memory attention mechanism + conditioned_feature_map_flat = self.memory_attention( + current_vision_features=current_vision_features, + current_vision_position_embeddings=current_vision_positional_embeddings, + memory=combined_memory, + memory_posision_embeddings=combined_memory_positional_embeddings, # Corrected typo from API + num_object_pointer_tokens=num_object_pointer_tokens, + ) + + # Reshape from (Batch, H*W, Channels) to (Batch, Channels, Height, Width) + conditioned_feature_map = ( + conditioned_feature_map_flat.squeeze(1).permute(0, 2, 1).view(batch_size, num_channels, height, width) + ) + return conditioned_feature_map + + def _use_multimask(self, is_init_cond_frame: bool, point_inputs: dict | None) -> bool: + """Whether to use multimask output in the SAM head.""" + num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(2) + multimask_output = ( + self.config.multimask_output_in_sam + and (is_init_cond_frame or self.config.multimask_output_for_tracking) + and (self.config.multimask_min_pt_num <= num_pts <= self.config.multimask_max_pt_num) + ) + return multimask_output + + def _run_single_frame_inference( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_idx: int, + batch_size: int, + is_init_cond_frame: bool, + point_inputs: torch.Tensor | None, + mask_inputs: torch.Tensor | None, + reverse: bool, + prev_sam_mask_logits: torch.Tensor | None = None, + streaming: bool = False, + ) -> dict[str, Any]: + """ + Perform a single tracking step for video object segmentation. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The video inference session object. + frame_idx (`int`): + Index of the current frame. + obj_idx (`int`): + Index of the current object. + batch_size (`int`): + Batch size of the current frame. + is_init_cond_frame (`bool`): + Whether this is an initial conditioning frame with user inputs. + point_inputs (`dict`, *optional*): + Point prompt inputs for the current frame. + mask_inputs (`torch.Tensor`, *optional*): + Mask prompt inputs for the current frame. + reverse (`bool`, *optional*, defaults to `False`): + Whether to track in reverse time order. + prev_sam_mask_logits (`torch.Tensor`, *optional*): + Previously predicted SAM mask logits that can be fed with new clicks. + streaming (`bool`, *optional*, defaults to `False`): + Whether this is streaming inference. + + Returns: + `dict`: Dictionary containing the tracking results for the current frame, including: + - pred_masks: Predicted low-resolution masks. + - object_pointer: Object pointer for memory. + - high_res_masks: High-resolution masks for batched memory encoding. + - object_score_logits: Object score logits (inference only). + """ + # Retrieve correct image features + current_vision_feats, current_vision_pos_embeds = self._prepare_vision_features( + inference_session, frame_idx, batch_size + ) + # point and mask should not appear as input simultaneously on the same frame + if point_inputs is not None and mask_inputs is not None: + raise ValueError( + "point_inputs and mask_inputs should not appear as input simultaneously on the same frame" + ) + # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW + if len(current_vision_feats) > 1: + high_res_features = [ + x.permute(1, 2, 0).view(x.size(1), x.size(2), *s) + for x, s in zip(current_vision_feats[:-1], self.backbone_feature_sizes[:-1]) + ] + else: + high_res_features = None + if mask_inputs is not None: + # We directly output the mask input (see it as a GT mask) without using a SAM prompt encoder + mask decoder. + pix_feat = current_vision_feats[-1].permute(1, 2, 0) + pix_feat = pix_feat.view(-1, self.hidden_dim, *self.backbone_feature_sizes[-1]) + sam_outputs = self._use_mask_as_output(pix_feat, high_res_features, mask_inputs) + else: + # fused the visual feature with previous memory features in the memory bank + pix_feat = self._prepare_memory_conditioned_features( + inference_session=inference_session, + frame_idx=frame_idx, + obj_idx=obj_idx, + is_initial_conditioning_frame=is_init_cond_frame, + current_vision_features=current_vision_feats[-1], + current_vision_positional_embeddings=current_vision_pos_embeds[-1], + num_total_frames=inference_session.num_frames, + track_in_reverse_time=reverse, + streaming=streaming, + ) + # apply SAM-style segmentation head + # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder, + # e.g. in demo where such logits come from earlier interaction instead of correction sampling + # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead) + if prev_sam_mask_logits is not None: + mask_inputs = prev_sam_mask_logits + multimask_output = self._use_multimask(is_init_cond_frame, point_inputs) + sam_outputs = self._single_frame_forward( + pixel_values=None, # Vision features already computed + input_points=point_inputs["point_coords"] if point_inputs is not None else None, + input_labels=point_inputs["point_labels"] if point_inputs is not None else None, + input_masks=mask_inputs, + image_embeddings=high_res_features + [pix_feat], + multimask_output=multimask_output, + ) + + # Memory encoding is now handled in batch by the caller (forward method) + current_out = { + "pred_masks": sam_outputs.pred_masks, + "object_pointer": sam_outputs.object_pointer, + "high_res_masks": sam_outputs.high_res_masks, # Needed for batched memory encoding + } + if not self.training: + current_out["object_score_logits"] = sam_outputs.object_score_logits + + return current_out + + def _encode_new_memory( + self, + current_vision_feats: torch.Tensor, + pred_masks_high_res: torch.Tensor, + object_score_logits: torch.Tensor, + is_mask_from_pts: bool, + ) -> tuple[torch.Tensor, list[torch.Tensor]]: + """Encode the current image and its prediction into a memory feature.""" + batch_size = current_vision_feats.size(1) # batch size on this frame + channels = self.hidden_dim + height, width = self.backbone_feature_sizes[-1] # top-level (lowest-resolution) feature size + + mask_input_size_h, mask_input_size_w = self.prompt_encoder.mask_input_size + mask_mem_size_h = mask_input_size_h * 4 + mask_mem_size_w = mask_input_size_w * 4 + if pred_masks_high_res.shape[2:] != (mask_mem_size_h, mask_mem_size_w): + # downsample the predicted high-res masks into the mask encoder input size + pred_masks_high_res = F.interpolate( + pred_masks_high_res.float(), + size=(mask_mem_size_h, mask_mem_size_w), + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(pred_masks_high_res.dtype) + + # top-level feature, (HW)BC => BCHW + pix_feat = current_vision_feats.permute(1, 2, 0).view(batch_size, channels, height, width) + if is_mask_from_pts and not self.training: + # binarize the mask logits + mask_for_mem = (pred_masks_high_res > 0).to(pred_masks_high_res.dtype) + else: + # apply sigmoid on the raw mask logits to turn them into range (0, 1) + mask_for_mem = torch.sigmoid(pred_masks_high_res) + # apply scale and bias terms to the sigmoid probabilities + mask_for_mem = mask_for_mem * self.config.sigmoid_scale_for_mem_enc + mask_for_mem = mask_for_mem + self.config.sigmoid_bias_for_mem_enc + + maskmem_features, maskmem_pos_enc = self.memory_encoder( + pix_feat, + mask_for_mem, + ) + # add a no-object embedding to the spatial memory to indicate that the frame + # is predicted to be occluded (i.e. no object is appearing in the frame) + if self.occlusion_spatial_embedding_parameter is not None: + is_obj_appearing = (object_score_logits > 0).float() + maskmem_features += (1 - is_obj_appearing[..., None]) * self.occlusion_spatial_embedding_parameter[ + ..., None, None + ].expand(*maskmem_features.shape) + + # convert to bfloat16 to save memory, and for consistency with the original implementation + maskmem_features = maskmem_features.to(torch.bfloat16).flatten(2).permute(2, 0, 1) + maskmem_pos_enc = maskmem_pos_enc.to(pred_masks_high_res.dtype).flatten(2).permute(2, 0, 1) + + return maskmem_features, maskmem_pos_enc + + def _batch_encode_memories( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + objects_needing_memory_encoding: list[int], + high_res_masks_for_memory: list[torch.Tensor], + object_score_logits_for_memory: list[torch.Tensor], + is_mask_from_pts_per_obj: list[bool], + ): + """ + Batch encode memories for multiple objects at once. + + Args: + inference_session: The video inference session object + frame_idx: Index of the current frame + objects_needing_memory_encoding: List of object indices that need memory encoding + high_res_masks_for_memory: List of high-resolution masks for each object + object_score_logits_for_memory: List of object score logits for each object + is_mask_from_pts_per_obj: List of booleans indicating if mask is from points for each object + """ + if not objects_needing_memory_encoding: + return + + # Get vision features once for all objects + current_vision_feats, _ = self._prepare_vision_features(inference_session, frame_idx, batch_size=1) + + # Stack all high-res masks and object scores + high_res_masks_batched = torch.cat(high_res_masks_for_memory, dim=0) + object_score_logits_batched = torch.cat(object_score_logits_for_memory, dim=0) + + # Expand vision features to match batch size + expanded_vision_feats = current_vision_feats[-1].expand(-1, len(objects_needing_memory_encoding), -1) + + # Encode all memories in one batch call + maskmem_features_batched, maskmem_pos_enc_batched = self._encode_new_memory( + current_vision_feats=expanded_vision_feats, + pred_masks_high_res=high_res_masks_batched, + object_score_logits=object_score_logits_batched, + is_mask_from_pts=any(is_mask_from_pts_per_obj), + ) + + # Split and store encoded memories per object + for i, obj_idx in enumerate(objects_needing_memory_encoding): + # Extract per-object memory from batched result + maskmem_features = maskmem_features_batched[:, i : i + 1] + maskmem_pos_enc = maskmem_pos_enc_batched[:, i : i + 1] + + # Update the stored output with memory features + output_dict = inference_session.output_dict_per_obj[obj_idx] + # Determine if this was a conditioning frame + storage_key = ( + "cond_frame_outputs" if frame_idx in output_dict["cond_frame_outputs"] else "non_cond_frame_outputs" + ) + if frame_idx in output_dict[storage_key]: + output_dict[storage_key][frame_idx]["maskmem_features"] = maskmem_features + output_dict[storage_key][frame_idx]["maskmem_pos_enc"] = maskmem_pos_enc + + @torch.inference_mode() + @auto_docstring( + custom_intro=""" + Propagate the objects through the video frames. Used when initializing an inference session with a whole video. + Yields Sam2VideoSegmentationOutput for each frame. + """ + ) + def propagate_in_video_iterator( + self, + inference_session: Sam2VideoInferenceSession, + start_frame_idx: int | None = None, + max_frame_num_to_track: int | None = None, + reverse: bool = False, + show_progress_bar: bool = False, + ) -> Iterator[Sam2VideoSegmentationOutput]: + r""" + inference_session (`Sam2VideoInferenceSession`): + The video inference session object. + start_frame_idx (`int`, *optional*): + The starting frame index for propagation. + Need to be provided if `forward` hasn't been called on new inputs yet. + If not provided, the starting frame index will be the earliest frame with input points. + max_frame_num_to_track (`int`, *optional*): + The maximum number of frames to track. + reverse (`bool`, *optional*, defaults to `False`): + Whether to propagate in reverse. + show_progress_bar (`bool`, *optional*, defaults to `False`): + Whether to show a progress bar during propagation. + """ + num_frames = inference_session.num_frames + + # set start index, end index, and processing order + if start_frame_idx is None: + # default: start from the earliest frame with input points + frames_with_inputs = [ + frame_idx + for obj_output_dict in inference_session.output_dict_per_obj.values() + for frame_idx in obj_output_dict["cond_frame_outputs"] + ] + if not frames_with_inputs: + raise ValueError( + "Cannot determine the starting frame index; please specify it manually, or run inference on a frame with inputs first." + ) + start_frame_idx = min(frames_with_inputs) + if max_frame_num_to_track is None: + # default: track all the frames in the video + max_frame_num_to_track = num_frames + if reverse: + end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0) + if start_frame_idx > 0: + processing_order = range(start_frame_idx, end_frame_idx - 1, -1) + else: + processing_order = [] # skip reverse tracking if starting from frame 0 + else: + end_frame_idx = min(start_frame_idx + max_frame_num_to_track, num_frames - 1) + processing_order = range(start_frame_idx, end_frame_idx + 1) + + for frame_idx in tqdm(processing_order, desc="propagate in video", disable=not show_progress_bar): + sam2_video_output = self(inference_session, frame_idx=frame_idx, reverse=reverse) + yield sam2_video_output + + +__all__ = ["Sam2VideoModel", "Sam2VideoInferenceSession", "Sam2VideoPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/modular_sam2_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/modular_sam2_video.py new file mode 100644 index 0000000000000000000000000000000000000000..df61bfb604a1887481dd6221f30173e79792d102 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/modular_sam2_video.py @@ -0,0 +1,2521 @@ +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SAM 2 model.""" + +import math +from collections import OrderedDict +from collections.abc import Callable, Iterator +from dataclasses import dataclass +from typing import Any, Union + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict +from torch import Tensor +from tqdm import tqdm + +from ... import initialization as init +from ...activations import ACT2FN +from ...configuration_utils import PreTrainedConfig +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import ProcessorMixin, Unpack +from ...utils import ( + ModelOutput, + auto_docstring, + logging, +) +from ...utils.generic import TransformersKwargs +from ...utils.output_capturing import OutputRecorder +from ...video_utils import VideoInput +from ..auto import CONFIG_MAPPING, AutoConfig +from ..sam2.configuration_sam2 import ( + Sam2MaskDecoderConfig, + Sam2PromptEncoderConfig, +) +from ..sam2.modeling_sam2 import ( + Sam2FeedForward, + Sam2ImageSegmentationOutput, + Sam2LayerNorm, + Sam2Model, + Sam2PositionalEmbedding, + Sam2SinePositionEmbedding, + Sam2TwoWayAttentionBlock, + eager_attention_forward, +) +from ..sam2.processing_sam2 import Sam2Processor + + +logger = logging.get_logger(__name__) + + +class Sam2VideoPromptEncoderConfig(Sam2PromptEncoderConfig): + pass + + +class Sam2VideoMaskDecoderConfig(Sam2MaskDecoderConfig): + pass + + +@auto_docstring(checkpoint="facebook/sam2.1-hiera-tiny") +@strict +class Sam2VideoConfig(PreTrainedConfig): + r""" + prompt_encoder_config (Union[`dict`, `Sam2PromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam2PromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `Sam2MaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam2MaskDecoderConfig`]. + initializer_range (`float`, *optional*, defaults to 0.02): + Standard deviation for parameter initialization. + num_maskmem (`int`, *optional*, defaults to 7): + The number of memory slots for the mask memory. + sigmoid_scale_for_mem_enc (`float`, *optional*, defaults to 20.0): + Scale factor for the sigmoid function in the memory encoder. + sigmoid_bias_for_mem_enc (`float`, *optional*, defaults to -10.0): + Bias for the sigmoid function in the memory encoder. + enable_occlusion_spatial_embedding (`bool`, *optional*, defaults to `True`): + Whether to enable spatial embedding for occlusions. + multimask_output_in_sam (`bool`, *optional*, defaults to `True`): + Whether to output multiple masks from the SAM head. + multimask_min_pt_num (`int`, *optional*, defaults to 0): + The minimum number of points to trigger multimask output. + multimask_max_pt_num (`int`, *optional*, defaults to 1): + The maximum number of points to trigger multimask output. + multimask_output_for_tracking (`bool`, *optional*, defaults to `True`): + Whether to use multimask output for tracking. + max_object_pointers_in_encoder (`int`, *optional*, defaults to 16): + The maximum number of object pointers in the encoder. + max_cond_frame_num (`int`, *optional*, defaults to -1): + Maximum number of conditioning frames to use in memory attention. Set to -1 to use all conditioning frames. + enable_temporal_pos_encoding_for_object_pointers (`bool`, *optional*, defaults to `True`): + Whether to enable temporal positional encoding for object pointers. + memory_attention_hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the memory attention hidden states. + memory_attention_num_layers (`int`, *optional*, defaults to 4): + The number of layers in the memory attention module. + memory_attention_num_attention_heads (`int`, *optional*, defaults to 1): + Number of attention heads for each attention layer in the memory attention. + memory_attention_downsample_rate (`int`, *optional*, defaults to 1): + The downsample rate for the attention layers. + memory_attention_feed_forward_hidden_size (`int`, *optional*, defaults to 2048): + The dimension of the feedforward network in the memory attention module. + memory_attention_feed_forward_hidden_act (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function in the feedforward network in the memory attention module. + memory_attention_dropout (`float`, *optional*, defaults to 0.1): + The dropout rate for the memory attention module. + memory_attention_rope_theta (`float`, *optional*, defaults to 10000): + The Rope theta parameter. + memory_attention_rope_feat_sizes (`list[int]`, *optional*, defaults to `[64, 64]`): + The feature sizes for the Rope positional encoding. + memory_attention_rope_dropout (`float`, *optional*, defaults to 0.1): + The dropout rate for the Rope positional encoding. + memory_encoder_hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the memory encoder hidden states. + memory_encoder_output_channels (`int`, *optional*, defaults to 64): + The number of output channels for the memory encoder. + mask_downsampler_embed_dim (`int`, *optional*, defaults to 256): + The dimension of the mask downsampler embedding. + mask_downsampler_kernel_size (`int`, *optional*, defaults to 3): + The kernel size for the mask downsampler. + mask_downsampler_stride (`int`, *optional*, defaults to 2): + The stride for the mask downsampler. + mask_downsampler_padding (`int`, *optional*, defaults to 1): + The padding for the mask downsampler. + mask_downsampler_total_stride (`int`, *optional*, defaults to 16): + The total stride for the mask downsampler. + mask_downsampler_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function in the mask downsampler. + memory_fuser_num_layers (`int`, *optional*, defaults to 2): + The number of layers in the memory fuser. + memory_fuser_embed_dim (`int`, *optional*, defaults to 256): + The dimension of the embedding layer in the memory fuser. + memory_fuser_intermediate_dim (`int`, *optional*, defaults to 1024): + The dimension of the intermediate layer in the memory fuser. + memory_fuser_kernel_size (`int`, *optional*, defaults to 7): + The kernel size for the memory fuser. + memory_fuser_padding (`int`, *optional*, defaults to 3): + The padding for the memory fuser. + memory_fuser_layer_scale_init_value (`float`, *optional*, defaults to 1e-06): + The initial value for the layer scale in the memory fuser. + memory_fuser_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function in the memory fuser.. + + Example: + + ```python + >>> from transformers import ( + ... Sam2VisionConfig, + ... Sam2PromptEncoderConfig, + ... Sam2MaskDecoderConfig, + ... Sam2Model, + ... ) + + >>> # Initializing a Sam2Config with `"facebook/sam2.1_hiera_tiny"` style configuration + >>> configuration = Sam2config() + + >>> # Initializing a Sam2Model (with random weights) from the `"facebook/sam2.1_hiera_tiny"` style configuration + >>> model = Sam2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Sam2Config from a Sam2VisionConfig, Sam2PromptEncoderConfig, and Sam2MaskDecoderConfig + + >>> # Initializing SAM2 vision encoder, memory attention, and memory encoder configurations + >>> vision_config = Sam2VisionConfig() + >>> prompt_encoder_config = Sam2PromptEncoderConfig() + >>> mask_decoder_config = Sam2MaskDecoderConfig() + + >>> config = Sam2Config(vision_config, prompt_encoder_config, mask_decoder_config) + ```""" + + model_type = "sam2_video" + sub_configs = { + "vision_config": AutoConfig, + "prompt_encoder_config": Sam2VideoPromptEncoderConfig, + "mask_decoder_config": Sam2VideoMaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + prompt_encoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + num_maskmem: int = 7 + image_size: int | list[int] | tuple[int, int] = 1024 + sigmoid_scale_for_mem_enc: float = 20.0 + sigmoid_bias_for_mem_enc: float = -10.0 + enable_occlusion_spatial_embedding: bool = True + multimask_output_in_sam: bool = True + multimask_min_pt_num: int = 0 + multimask_max_pt_num: int = 1 + multimask_output_for_tracking: bool = True + max_object_pointers_in_encoder: int = 16 + max_cond_frame_num: int = -1 + enable_temporal_pos_encoding_for_object_pointers: bool = True + memory_attention_hidden_size: int = 256 + memory_attention_num_layers: int = 4 + memory_attention_num_attention_heads: int = 1 + memory_attention_downsample_rate: int = 1 + memory_attention_feed_forward_hidden_size: int = 2048 + memory_attention_feed_forward_hidden_act: str = "relu" + memory_attention_dropout: float | int = 0.1 + memory_attention_rope_theta: int = 10000 + memory_attention_rope_feat_sizes: list[int] | None = None + memory_attention_rope_dropout: float | int = 0.1 + memory_encoder_hidden_size: int = 256 + memory_encoder_output_channels: int = 64 + mask_downsampler_embed_dim: int = 256 + mask_downsampler_kernel_size: int = 3 + mask_downsampler_stride: int = 2 + mask_downsampler_padding: int = 1 + mask_downsampler_total_stride: int = 16 + mask_downsampler_hidden_act: str = "gelu" + memory_fuser_num_layers: int = 2 + memory_fuser_embed_dim: int = 256 + memory_fuser_intermediate_dim: int = 1024 + memory_fuser_kernel_size: int = 7 + memory_fuser_padding: int = 3 + memory_fuser_layer_scale_init_value: float = 1e-6 + memory_fuser_hidden_act: str = "gelu" + + def __post_init__(self, **kwargs): + self.memory_attention_rope_feat_sizes = ( + [64, 64] if self.memory_attention_rope_feat_sizes is None else self.memory_attention_rope_feat_sizes + ) + + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam2_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam2_vision_model"]() + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = Sam2VideoPromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = Sam2VideoPromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam2VideoPromptEncoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = Sam2VideoMaskDecoderConfig() + + super().__post_init__(**kwargs) + + +class Sam2VideoInferenceCache: + """Cache for vision features and model constants.""" + + def __init__( + self, + inference_device: torch.device | str = "cpu", + inference_state_device: torch.device | str = "cpu", + max_vision_features_cache_size: int = 1, + ): + self.inference_device = inference_device + self.inference_state_device = inference_state_device + self.max_vision_features_cache_size = max_vision_features_cache_size + + self._vision_features = {} + + def cache_vision_features(self, frame_idx: int, features: dict): + """Cache vision features with automatic device management.""" + cached = {} + if len(self._vision_features) >= self.max_vision_features_cache_size: + # remove the oldest frame + self._vision_features.pop(min(self._vision_features.keys())) + + for key, value in features.items(): + if isinstance(value, torch.Tensor): + cached[key] = value.to(self.inference_state_device, non_blocking=True) + elif isinstance(value, (list, tuple)) and value and isinstance(value[0], torch.Tensor): + cached[key] = [v.to(self.inference_state_device, non_blocking=True) for v in value] + else: + cached[key] = value + self._vision_features[frame_idx] = cached + + def get_vision_features(self, frame_idx: int) -> dict | None: + """Get cached vision features, automatically moved to inference device.""" + if frame_idx not in self._vision_features: + return None + + cached = self._vision_features[frame_idx] + moved = {} + for key, value in cached.items(): + if isinstance(value, torch.Tensor): + moved[key] = value.to(self.inference_device, non_blocking=True) + elif isinstance(value, (list, tuple)) and value and isinstance(value[0], torch.Tensor): + moved[key] = [v.to(self.inference_device, non_blocking=True) for v in value] + else: + moved[key] = value + return moved + + def clear_all(self): + """Clear all cached data.""" + self._vision_features.clear() + + +class Sam2VideoInferenceSession: + r""" + Manages video inference session parameters, state and cache. + + Args: + video (`torch.FloatTensor`, *optional*): + The video to process. No need to provide when streaming. + video_height (`int`, *optional*): + The height of the video. + video_width (`int`, *optional*): + The width of the video. + inference_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to use for inference. + inference_state_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to store the inference state on. + video_storage_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to store the video on. + dtype (`torch.dtype`, *optional*, defaults to `"float32"`): + The dtype to use for the video. + max_vision_features_cache_size (`int`, *optional*, defaults to 1): + The maximum number of vision features to cache. + """ + + def __init__( + self, + video: torch.FloatTensor | None = None, + video_height: int | None = None, + video_width: int | None = None, + inference_device: torch.device | str = "cpu", + inference_state_device: torch.device | str = "cpu", + video_storage_device: torch.device | str = "cpu", + dtype: torch.dtype | str = "float32", + max_vision_features_cache_size: int = 1, + ): + # store as a dictionary to avoid double memory allocation with torch.cat when adding new frames + self.processed_frames = ( + dict(enumerate(video.to(video_storage_device, dtype=dtype))) if video is not None else None + ) + self.video_height = video_height + self.video_width = video_width + + self.inference_device = inference_device + self.inference_state_device = inference_state_device + self.video_storage_device = video_storage_device + self.dtype = dtype + self.max_vision_features_cache_size = max_vision_features_cache_size + + # Cache for computed features + self.cache = Sam2VideoInferenceCache( + inference_device=self.inference_device, + inference_state_device=self.inference_state_device, + max_vision_features_cache_size=self.max_vision_features_cache_size, + ) + + # Persistent object tracking state + self._obj_id_to_idx = OrderedDict() + self._obj_idx_to_id = OrderedDict() + self.obj_ids = [] + + # Persistent user inputs + self.point_inputs_per_obj = {} + self.mask_inputs_per_obj = {} + + # Persistent model outputs/history + self.output_dict_per_obj = {} + self.frames_tracked_per_obj = {} + + # Session state flags + self.obj_with_new_inputs = [] + + @property + def num_frames(self) -> int | None: + return len(self.processed_frames) if self.processed_frames is not None else None + + # Object management + def obj_id_to_idx(self, obj_id: int) -> int: + """Map object ID to index, creating new entry if needed.""" + obj_idx = self._obj_id_to_idx.get(obj_id, None) + if obj_idx is not None: + return obj_idx + + obj_idx = len(self._obj_id_to_idx) + self._obj_id_to_idx[obj_id] = obj_idx + self._obj_idx_to_id[obj_idx] = obj_id + self.obj_ids = list(self._obj_id_to_idx) + + self.point_inputs_per_obj[obj_idx] = {} + self.mask_inputs_per_obj[obj_idx] = {} + self.output_dict_per_obj[obj_idx] = { + "cond_frame_outputs": {}, + "non_cond_frame_outputs": {}, + } + self.frames_tracked_per_obj[obj_idx] = {} + + return obj_idx + + # Video Inference specific functions + def obj_idx_to_id(self, obj_idx: int) -> int: + """Map model-side object index to client-side object id.""" + return self._obj_idx_to_id[obj_idx] + + def get_obj_num(self) -> int: + """Get the total number of unique object ids received so far in this session.""" + return len(self._obj_idx_to_id) + + # Input management with device handling + def add_point_inputs(self, obj_idx: int, frame_idx: int, inputs: dict): + """Add point inputs with automatic device placement.""" + device_inputs = {} + for key, value in inputs.items(): + if isinstance(value, torch.Tensor): + device_inputs[key] = value.to(self.inference_device, non_blocking=False) + else: + device_inputs[key] = value + self.point_inputs_per_obj[obj_idx][frame_idx] = device_inputs + + def remove_point_inputs(self, obj_idx: int, frame_idx: int): + """Remove point inputs.""" + self.point_inputs_per_obj[obj_idx].pop(frame_idx, None) + + def add_mask_inputs(self, obj_idx: int, frame_idx: int, inputs: torch.Tensor): + """Add mask inputs with automatic device placement.""" + self.mask_inputs_per_obj[obj_idx][frame_idx] = inputs.to( + self.inference_device, dtype=self.dtype, non_blocking=True + ) + + def remove_mask_inputs(self, obj_idx: int, frame_idx: int): + """Remove mask inputs.""" + self.mask_inputs_per_obj[obj_idx].pop(frame_idx, None) + + # Output management with smart device placement + def store_output( + self, + obj_idx: int, + frame_idx: int, + output_key: str | None = None, + output_value: torch.Tensor | dict | None = None, + is_conditioning_frame: bool = True, + ): + """ + Store output with smart device management. + If output_key is None, the output is stored as a dictionary. + + Args: + obj_idx (int): The index of the object. + frame_idx (int): The index of the frame. + output_key (Optional[str]): The key of the output. If None, the output is stored as a dictionary. + output_value (Optional[Union[torch.Tensor, dict]]): The value of the output. + is_conditioning_frame (bool): Whether the output is for a conditioning frame. + """ + storage_key = "cond_frame_outputs" if is_conditioning_frame else "non_cond_frame_outputs" + + if output_key is None and isinstance(output_value, dict): + self.output_dict_per_obj[obj_idx][storage_key][frame_idx] = {} + for key, value in output_value.items(): + self.store_output(obj_idx, frame_idx, key, value, is_conditioning_frame) + return + + # Device placement: small tensors stay on inference device, large ones go to inference state device + if output_key in ["object_pointer", "object_score_logits"]: # Small tensors + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value + elif isinstance(output_value, torch.Tensor): # Large tensors like masks, features + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value.to( + self.inference_state_device, non_blocking=True + ) + else: + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value + + def get_output( + self, + obj_idx: int, + frame_idx: int, + output_key: str, + is_conditioning_frame: bool = True, + ): + """ + Get output with smart device management. + + Args: + obj_idx (int): The index of the object. + frame_idx (int): The index of the frame. + output_key (str): The key of the output. + is_conditioning_frame (bool): Whether the output is for a conditioning frame. + """ + storage_key = "cond_frame_outputs" if is_conditioning_frame else "non_cond_frame_outputs" + out = self.output_dict_per_obj[obj_idx][storage_key].get(frame_idx, None) + # move to inference device if needed + if out is None: + return None + value = out[output_key] + if isinstance(value, torch.Tensor): + value = value.to(self.inference_device, non_blocking=True) + return value + + # Video frame management + def add_new_frame(self, pixel_values: torch.Tensor, frame_idx: int | None = None) -> int: + """Add new frame with automatic device placement.""" + pixel_values = pixel_values.to(self.video_storage_device, dtype=self.dtype, non_blocking=True) + if pixel_values.dim() == 4: + pixel_values = pixel_values.squeeze(0) + + if frame_idx is None: + frame_idx = len(self.processed_frames) if self.processed_frames is not None else 0 + + if self.processed_frames is None: + self.processed_frames = {frame_idx: pixel_values} + else: + self.processed_frames[frame_idx] = pixel_values + + return frame_idx + + def get_frame(self, frame_idx: int) -> torch.Tensor: + """Get frame from video.""" + return self.processed_frames[frame_idx].to(self.inference_device, non_blocking=True) + + def reset_tracking_data(self): + """Reset tracking data but keep cache.""" + self._obj_id_to_idx.clear() + self._obj_idx_to_id.clear() + self.obj_ids.clear() + self.point_inputs_per_obj.clear() + self.mask_inputs_per_obj.clear() + self.output_dict_per_obj.clear() + self.frames_tracked_per_obj.clear() + self.obj_with_new_inputs = [] + # Note: cache and video data are preserved + + def reset_inference_session(self): + """Reset tracking data and cache.""" + self._obj_id_to_idx.clear() + self._obj_idx_to_id.clear() + self.obj_ids.clear() + self.point_inputs_per_obj.clear() + self.mask_inputs_per_obj.clear() + self.output_dict_per_obj.clear() + self.frames_tracked_per_obj.clear() + self.obj_with_new_inputs = [] + self.cache.clear_all() + + +class Sam2VideoProcessor(Sam2Processor): + def __init__( + self, image_processor, video_processor, target_size: int | None = None, point_pad_value: int = -10, **kwargs + ): + ProcessorMixin.__init__(self, image_processor, video_processor, **kwargs) + self.point_pad_value = point_pad_value + self.target_size = target_size if target_size is not None else self.image_processor.size["height"] + + def init_video_session( + self, + video: VideoInput | None = None, + inference_device: Union[str, "torch.device"] = "cpu", + inference_state_device: Union[str, "torch.device"] | None = None, + processing_device: Union[str, "torch.device"] | None = None, + video_storage_device: Union[str, "torch.device"] | None = None, + max_vision_features_cache_size: int = 1, + dtype: torch.dtype = torch.float32, + ): + """ + Initializes a video session for inference. + If a video is provided (async inference), the video will be processed and stored on the `video_storage_device`. + + Args: + video (`VideoInput`, *optional*): + The video to process. No need to provide when streaming. + inference_device (`str` or `torch.device`, *optional*, defaults to "cpu"): + The device to use for inference. + inference_state_device (`str` or `torch.device`, *optional*): + The device to store the inference state on. + processing_device (`str` or `torch.device`, *optional*): + The device to use for video processing. + video_storage_device (`str` or `torch.device`, *optional*): + The device to store the processed video frames on. + max_vision_features_cache_size (`int`, *optional*, defaults to 1): + The maximum number of vision features to cache. + dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): + The torch dtype to use for the whole session. + """ + video_storage_device = video_storage_device if video_storage_device is not None else inference_device + inference_state_device = inference_state_device if inference_state_device is not None else inference_device + processing_device = processing_device if processing_device is not None else inference_device + pixel_values_video = None + video_height = None + video_width = None + if video is not None: + processed_video = self.video_processor(videos=video, device=processing_device, return_tensors="pt") + pixel_values_video = processed_video.pixel_values_videos[0] + video_height = processed_video.original_sizes[0][0] + video_width = processed_video.original_sizes[0][1] + inference_session = Sam2VideoInferenceSession( + video=pixel_values_video, + video_height=video_height, + video_width=video_width, + inference_device=inference_device, + video_storage_device=video_storage_device, + inference_state_device=inference_state_device, + dtype=dtype, + max_vision_features_cache_size=max_vision_features_cache_size, + ) + return inference_session + + def add_inputs_to_inference_session( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_ids: list[int] | int, + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + input_masks: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor] | None = None, + original_size: tuple[int, int] | None = None, + clear_old_inputs: bool = True, + ) -> Sam2VideoInferenceSession: + """ + Process new points, boxes, or masks for a video frame and add them to the inference session. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]` or `int`): + The object ID(s) to associate with the points or box. + These can be any integers and can be reused later on to specify an object. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + input_masks (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, or `list[torch.Tensor]`, *optional*): + The mask(s) to add to the frame. + original_size (`tuple[int, int]`, *optional*): + The original size of the video. Provide when streaming. + clear_old_inputs (`bool`, *optional*, defaults to `True`): + Whether to clear old inputs for the object. + """ + + if isinstance(obj_ids, int): + obj_ids = [obj_ids] + + # Validate inputs + if (input_points is not None) != (input_labels is not None): + raise ValueError("points and labels must be provided together") + if input_points is None and input_boxes is None and input_masks is None: + raise ValueError("at least one of points, boxes, or masks must be provided as input") + if input_masks is not None and (input_points is not None or input_boxes is not None): + raise ValueError("masks cannot be provided together with points or boxes") + + if input_masks is not None: + return self.process_new_mask_for_video_frame(inference_session, frame_idx, obj_ids, input_masks) + else: + return self.process_new_points_or_boxes_for_video_frame( + inference_session, + frame_idx, + obj_ids, + input_points, + input_labels, + input_boxes, + original_size, + clear_old_inputs, + ) + + def process_new_points_or_boxes_for_video_frame( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_ids: list[int], + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + original_size: tuple[int, int] | None = None, + clear_old_inputs: bool = True, + ) -> Sam2VideoInferenceSession: + """ + Process new points or boxes for a video frame and add them to the inference session. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]`): + The object ID(s) to associate with the points or box. + These can be any integers and can be reused later on to specify an object. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + original_size (`tuple[int, int]`, *optional*): + The original size of the video. Provide when streaming. + clear_old_inputs (`bool`, *optional*, defaults to `True`): + Whether to clear old inputs for the object. + """ + if original_size is not None: + inference_session.video_height = original_size[0] + inference_session.video_width = original_size[1] + elif inference_session.video_height is None or inference_session.video_width is None: + raise ValueError("original_size must be provided when adding points or boxes on a first streamed frame") + + original_sizes = [[inference_session.video_height, inference_session.video_width]] + + encoded_inputs = self( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + original_sizes=original_sizes, + return_tensors="pt", + ) + input_points = encoded_inputs.get("input_points", None) + input_labels = encoded_inputs.get("input_labels", None) + input_boxes = encoded_inputs.get("input_boxes", None) + + if input_points is not None: + if input_points.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of points ({input_points.shape[1]})" + ) + else: + input_points = torch.zeros(1, len(obj_ids), 0, 2, dtype=torch.float32) + if input_labels is not None: + if input_labels.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of labels ({input_labels.shape[1]})" + ) + else: + input_labels = torch.zeros(1, len(obj_ids), 0, dtype=torch.int32) + if input_boxes is not None: + if input_boxes.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of boxes ({input_boxes.shape[1]})" + ) + + if input_boxes is not None: + if not clear_old_inputs: + raise ValueError( + "cannot add box without clearing old points, since " + "box prompt must be provided before any point prompt " + "(please use clear_old_points=True instead)" + ) + box_coords = input_boxes.reshape(1, -1, 2, 2) + box_labels = torch.tensor([2, 3], dtype=torch.int32).repeat(1, box_coords.shape[1], 1) + input_points = torch.cat([box_coords, input_points], dim=2) + input_labels = torch.cat([box_labels, input_labels], dim=2) + + for obj_id, idx in zip(obj_ids, range(len(obj_ids))): + obj_idx = inference_session.obj_id_to_idx(obj_id) + input_points_for_obj = input_points[:, idx, :, :].unsqueeze(1) + input_labels_for_obj = input_labels[:, idx, :].unsqueeze(1) + # Handle existing points + if not clear_old_inputs: + existing_points = inference_session.point_inputs_per_obj[obj_idx].get(frame_idx, None) + if existing_points is not None: + # Concatenate with existing points + input_points_for_obj = torch.cat( + [existing_points["point_coords"].to(input_points_for_obj.device), input_points_for_obj], dim=2 + ) + input_labels_for_obj = torch.cat( + [existing_points["point_labels"].to(input_labels_for_obj.device), input_labels_for_obj], dim=2 + ) + point_inputs = { + "point_coords": input_points_for_obj, + "point_labels": input_labels_for_obj, + } + + inference_session.add_point_inputs(obj_idx, frame_idx, point_inputs) + inference_session.remove_mask_inputs(obj_idx, frame_idx) # Clear any mask inputs + + inference_session.obj_with_new_inputs = obj_ids + + def process_new_mask_for_video_frame( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_ids: list[int], + input_masks: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor], + ): + """ + Add new mask to a frame and add them to the inference session. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]`): + The object ID(s) to associate with the mask. + These can be any integers and can be reused later on to specify an object. + input_masks (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, or `list[torch.Tensor]`): + The mask(s) to add to the frame. + """ + if not isinstance(input_masks, list): + input_masks = [input_masks] + if len(input_masks) != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of masks ({len(input_masks)})" + ) + + for obj_id, mask in zip(obj_ids, input_masks): + obj_idx = inference_session.obj_id_to_idx(obj_id) + + device = inference_session.inference_device + + # Process mask + if not isinstance(mask, torch.Tensor): + mask = torch.tensor(mask, dtype=torch.bool) + nb_dim = mask.dim() + if nb_dim > 4 or nb_dim < 2: + raise ValueError(f"Mask has an unsupported number of dimensions: {nb_dim}") + for i in range(4 - nb_dim): + mask = mask.unsqueeze(0) + + mask_H, mask_W = mask.shape[-2:] + mask_inputs_orig = mask.to(device) + mask_inputs_orig = mask_inputs_orig.float().to(device) + + # Resize mask if needed + if mask_H != self.target_size or mask_W != self.target_size: + mask_inputs = torch.nn.functional.interpolate( + mask_inputs_orig, + size=(self.target_size, self.target_size), + align_corners=False, + mode="bilinear", + antialias=True, + ) + mask_inputs = (mask_inputs >= 0.5).float() + else: + mask_inputs = mask_inputs_orig + + inference_session.add_mask_inputs(obj_idx, frame_idx, mask_inputs) + inference_session.remove_point_inputs(obj_idx, frame_idx) # Clear any point inputs + + inference_session.obj_with_new_inputs = obj_ids + + +class Sam2VideoLayerNorm(Sam2LayerNorm): + pass + + +class Sam2VideoPositionEmbeddingSine(Sam2SinePositionEmbedding): + pass + + +class Sam2VideoTwoWayAttentionBlock(Sam2TwoWayAttentionBlock): + pass + + +class Sam2VideoFeedForward(Sam2FeedForward): + pass + + +class Sam2VideoImageSegmentationOutput(Sam2ImageSegmentationOutput): + r""" + iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`): + The Intersection over Union (IoU) scores of the predicted masks. + pred_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, height, width)`): + The predicted low-resolution masks. This is an alias for `low_res_masks`. These masks need to be post-processed + by the processor to be brought to the original image size. + object_score_logits (`torch.FloatTensor` of shape `(batch_size, point_batch_size, 1)`): + Logits for the object score, indicating if an object is present. + image_embeddings (`tuple(torch.FloatTensor)`): + The features from the FPN, which are used by the mask decoder. This is a tuple of `torch.FloatTensor` where each + tensor has shape `(batch_size, channels, height, width)`. + vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. + Hidden-states of the vision model at the output of each stage. + vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the vision model. + mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the mask decoder. + high_res_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, image_size, image_size)`, *optional*): + The predicted masks, upscaled to the original image size. Only used for Sam2VideoModel. + object_pointer (`torch.FloatTensor` of shape `(batch_size, point_batch_size, hidden_size)`, *optional*): + A tensor representing the object pointer, used for tracking in videos. Only used for Sam2VideoModel. + """ + + high_res_masks: torch.FloatTensor | None = None + object_pointer: torch.FloatTensor | None = None + + +@auto_docstring(custom_intro="Base class for the Sam2 model's output.") +@dataclass +class Sam2VideoSegmentationOutput(ModelOutput): + r""" + object_ids (`list[int]`, *optional*): + List of object IDs being tracked in the current frame. + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_masks, height, width)`): + The predicted masks stored at the model's resolution. + object_score_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): + Logits for the object scores, indicating if objects are present. + frame_idx (`int`): + The frame index of the video. + """ + + object_ids: list[int] | None = None + pred_masks: torch.FloatTensor | None = None + object_score_logits: torch.FloatTensor | None = None + frame_idx: int | None = None + + +@auto_docstring +class Sam2VideoPreTrainedModel(PreTrainedModel): + config_class = Sam2VideoConfig + base_model_prefix = "sam2_video" + main_input_name = "pixel_values" + input_modalities = "video" + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Sam2VideoModel): + if module.no_memory_positional_encoding is not None: + init.zeros_(module.no_memory_positional_encoding) + if module.memory_temporal_positional_encoding is not None: + init.zeros_(module.memory_temporal_positional_encoding) + if module.no_object_pointer is not None: + init.zeros_(module.no_object_pointer) + if module.occlusion_spatial_embedding_parameter is not None: + init.zeros_(module.occlusion_spatial_embedding_parameter) + if isinstance(module, Sam2VideoMemoryFuserCXBlock): + if module.scale is not None: + init.zeros_(module.scale) + elif isinstance(module, Sam2VideoVisionRotaryEmbedding): + inv_freq = module.create_inv_freq() + init.copy_(module.rope_embeddings_cos, inv_freq.cos()) + init.copy_(module.rope_embeddings_sin, inv_freq.sin()) + elif isinstance(module, Sam2VideoPositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + + +class Sam2VideoVisionRotaryEmbedding(nn.Module): + """ + Vision Rotary Position Embedding for SAM2, following transformers library standards. + Supports 2D (axial) rotary embeddings for spatial dimensions. + """ + + def __init__(self, config: Sam2VideoConfig): + super().__init__() + self.dim = config.memory_attention_hidden_size // ( + config.memory_attention_downsample_rate * config.memory_attention_num_attention_heads + ) + # Ensure even dimension for proper axial splitting + if self.dim % 4 != 0: + raise ValueError("Dimension must be divisible by 4 for axial RoPE") + self.end_x, self.end_y = config.memory_attention_rope_feat_sizes + self.memory_attention_rope_theta = config.memory_attention_rope_theta + + # directly register the cos and sin embeddings as we have a fixed feature shape + inv_freq = self.create_inv_freq() + self.register_buffer("rope_embeddings_cos", inv_freq.cos(), persistent=False) + self.register_buffer("rope_embeddings_sin", inv_freq.sin(), persistent=False) + + @torch.no_grad() + def forward(self) -> tuple[torch.Tensor, torch.Tensor]: + # As the feature map size is fixed, we can just return the pre-computed embeddings. + return self.rope_embeddings_cos, self.rope_embeddings_sin + + def create_inv_freq(self): + freqs = 1.0 / ( + self.memory_attention_rope_theta ** (torch.arange(0, self.dim, 4)[: (self.dim // 4)].float() / self.dim) + ) + # Generate 2D position indices for axial rotary embedding + flattened_indices = torch.arange(self.end_x * self.end_y, dtype=torch.long) + x_positions = flattened_indices % self.end_x + y_positions = torch.div(flattened_indices, self.end_x, rounding_mode="floor") + freqs_x = torch.outer(x_positions, freqs).float() + freqs_y = torch.outer(y_positions, freqs).float() + inv_freq = torch.cat([freqs_x, freqs_y], dim=-1) + inv_freq = inv_freq.repeat_interleave(2, dim=-1) + return inv_freq + + +def rotate_pairwise(x): + """ + pairwise rotation of the hidden dims of the input. Differerent from Llama Half-Tensor Rotation. + + This is an optimized version of the following more explicit implementation: + ```python + x_rotated = torch.zeros_like(x, dtype=x.dtype, device=x.device) + x_rotated[..., ::2] = -x[..., 1::2] + x_rotated[..., 1::2] = x[..., ::2] + return x_rotated + ``` + """ + x = x.view(*x.shape[:-1], -1, 2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return x.flatten(start_dim=-2) + + +# TODO: This leads to ~1e-07 max diff and ~1e-09 avg diff for q_embed and k_embed from the original implementation, most likely due to the use of complex tensors in the original implementation. +def apply_rotary_pos_emb_2d( + q: torch.Tensor, + k: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + num_k_exclude_rope: int = 0, + repeat_freqs_k: bool = False, +) -> tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary position embedding to query and key tensors for vision models. + Follows the standard transformers library pattern. + + Args: + q: Query tensor of shape (..., seq_len, head_dim) + k: Key tensor of shape (..., seq_len, head_dim) + cos: Cosine position embedding of shape (seq_len, head_dim) + sin: Sine position embedding of shape (seq_len, head_dim) + repeat_freqs_k: Whether to repeat frequencies for keys (for cross-attention) + + Returns: + Rotated (q, k) tensors + """ + k_rot, k_pass = k[..., : k.shape[-2] - num_k_exclude_rope, :], k[..., k.shape[-2] - num_k_exclude_rope :, :] + q_embed = q.float() # force upscale to float32 as in the original implementation + q_embed = (q_embed * cos) + (rotate_pairwise(q_embed) * sin) + if k_rot.shape[-2] == 0: + # Handle case where keys might be empty due to dropout + return q_embed.type_as(q), torch.cat([k_rot, k_pass], dim=-2) + + # Handle key tensor - may need to repeat frequencies if different sequence length + if repeat_freqs_k and k_rot.shape[-2] != q.shape[-2]: + # Repeat cos/sin to match key sequence length + repeat_factor = k_rot.shape[-2] // q.shape[-2] + cos_k = cos.repeat(1, 1, repeat_factor, 1) + sin_k = sin.repeat(1, 1, repeat_factor, 1) + else: + cos_k = cos + sin_k = sin + + # Apply rotary embedding to keys + k_embed = k_rot.float() # force upscale to float32 as in the original implementation + k_embed = (k_embed * cos_k) + (rotate_pairwise(k_embed) * sin_k) + # Concatenate back to full shape + k_embed = torch.cat([k_embed.type_as(k), k_pass], dim=-2) + return q_embed.type_as(q), k_embed + + +class Sam2VideoRoPEAttention(nn.Module): + """Attention with rotary position encoding.""" + + def __init__( + self, + config: Sam2VideoConfig, + kv_in_dim: int | None = None, + rope_k_repeat=False, + ): + super().__init__() + self.config = config + self.hidden_size = config.memory_attention_hidden_size + self.internal_dim = self.hidden_size // config.memory_attention_downsample_rate + self.num_attention_heads = config.memory_attention_num_attention_heads + self.head_dim = self.internal_dim // config.memory_attention_num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.kv_in_dim = kv_in_dim if kv_in_dim is not None else self.hidden_size + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.o_proj = nn.Linear(self.internal_dim, self.hidden_size) + + self.rope_k_repeat = rope_k_repeat + self.dropout_p = config.memory_attention_rope_dropout + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + num_k_exclude_rope: int = 0, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> Tensor: + # Input projections + batch_size, point_batch_size = query.shape[:2] + new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim) + + query = self.q_proj(query).view(*new_shape).transpose(1, 2) + key = self.k_proj(key).view(*new_shape).transpose(1, 2) + value = self.v_proj(value).view(*new_shape).transpose(1, 2) + + cos, sin = position_embeddings + # Apply rotary position encoding, excluding some keys if specified + query, key = apply_rotary_pos_emb_2d( + query, key, cos, sin, repeat_freqs_k=self.rope_k_repeat, num_k_exclude_rope=num_k_exclude_rope + ) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=None, + dropout=0.0 if not self.training else self.dropout_p, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + attn_output = attn_output.reshape( + batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Sam2VideoMemoryAttentionLayer(nn.Module): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + hidden_size = config.memory_attention_hidden_size + self.self_attn = Sam2VideoRoPEAttention(config) + self.cross_attn_image = Sam2VideoRoPEAttention(config, kv_in_dim=64, rope_k_repeat=True) + + # Implementation of Feedforward model + self.linear1 = nn.Linear(hidden_size, config.memory_attention_feed_forward_hidden_size) + self.dropout = nn.Dropout(config.memory_attention_dropout) + self.linear2 = nn.Linear(config.memory_attention_feed_forward_hidden_size, hidden_size) + + self.layer_norm1 = nn.LayerNorm(hidden_size) + self.layer_norm2 = nn.LayerNorm(hidden_size) + self.layer_norm3 = nn.LayerNorm(hidden_size) + self.dropout1 = nn.Dropout(config.memory_attention_dropout) + self.dropout2 = nn.Dropout(config.memory_attention_dropout) + self.dropout3 = nn.Dropout(config.memory_attention_dropout) + + self.activation = ACT2FN[config.memory_attention_feed_forward_hidden_act] + + def forward( + self, + queries: Tensor, + keys: Tensor, + key_point_embedding: Tensor, + rope_position_embeddings: tuple[Tensor, Tensor], + num_k_exclude_rope: int = 0, + ) -> torch.Tensor: + # Self-Attention + query = self.layer_norm1(queries) + query, _ = self.self_attn(query=query, key=query, value=query, position_embeddings=rope_position_embeddings) + queries = queries + self.dropout1(query) + + # Cross-Attention + query = self.layer_norm2(queries) + query, _ = self.cross_attn_image( + query=query, + key=keys + key_point_embedding, + value=keys, + position_embeddings=rope_position_embeddings, + num_k_exclude_rope=num_k_exclude_rope, + ) + queries = queries + self.dropout2(query) + # MLP + query = self.layer_norm3(queries) + query = self.linear2(self.dropout(self.activation(self.linear1(query)))) + queries = queries + self.dropout3(query) + return queries + + +class Sam2VideoMemoryAttention(nn.Module): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + self.layers = nn.ModuleList( + [Sam2VideoMemoryAttentionLayer(config) for _ in range(config.memory_attention_num_layers)] + ) + self.layer_norm = nn.LayerNorm(config.memory_attention_hidden_size) + self.rotary_emb = Sam2VideoVisionRotaryEmbedding(config=config) + + def forward( + self, + current_vision_features: torch.Tensor, + memory: torch.Tensor, + current_vision_position_embeddings: Tensor | None = None, + memory_posision_embeddings: Tensor | None = None, + num_object_pointer_tokens: int = 0, + ): + """ + Args: + current_vision_features (`torch.FloatTensor`): + The current vision features used for self-attention. + memory (`torch.FloatTensor`): + The memory features used for cross-attention. + current_vision_position_embeddings (`torch.FloatTensor`, *optional*): + The position embeddings for the current vision features. + memory_posision_embeddings (`torch.FloatTensor`, *optional*): + The position embeddings for the memory features. + num_object_pointer_tokens (`int`, *optional*, defaults to 0): + The number of object pointer tokens. + """ + output = current_vision_features + if current_vision_position_embeddings is not None: + output = output + 0.1 * current_vision_position_embeddings + + # Convert to batch first + output = output.transpose(0, 1) + memory = memory.transpose(0, 1).unsqueeze(1) + memory_posision_embeddings = memory_posision_embeddings.transpose(0, 1).unsqueeze(1) + rope_position_embeddings = self.rotary_emb() + for layer in self.layers: + output = layer( + queries=output.unsqueeze(1) if output.ndim == 3 else output, + keys=memory, + key_point_embedding=memory_posision_embeddings, + rope_position_embeddings=rope_position_embeddings, + num_k_exclude_rope=num_object_pointer_tokens, + ) + + normed_output = self.layer_norm(output) + + # Convert back to seq first + normed_output = normed_output.transpose(0, 1) + + return normed_output + + +# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt) +class Sam2VideoMemoryFuserCXBlock(GradientCheckpointingLayer): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + self.depthwise_conv = nn.Conv2d( + config.memory_fuser_embed_dim, + config.memory_fuser_embed_dim, + kernel_size=config.memory_fuser_kernel_size, + padding=config.memory_fuser_padding, + groups=config.memory_fuser_embed_dim, + ) # depthwise conv + self.layer_norm = Sam2VideoLayerNorm(config.memory_fuser_embed_dim, eps=1e-6, data_format="channels_first") + self.activation = ACT2FN[config.memory_fuser_hidden_act] + self.pointwise_conv1 = nn.Linear( + config.memory_fuser_embed_dim, config.memory_fuser_intermediate_dim + ) # pointwise/1x1 convs, implemented with linear layers + self.pointwise_conv2 = nn.Linear(config.memory_fuser_intermediate_dim, config.memory_fuser_embed_dim) + self.scale = nn.Parameter( + config.memory_fuser_layer_scale_init_value * torch.ones(config.memory_fuser_embed_dim), + requires_grad=True, + ) + + def forward(self, hidden_states): + input = hidden_states + hidden_states = self.depthwise_conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + hidden_states = self.pointwise_conv1(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.pointwise_conv2(hidden_states) + hidden_states = self.scale * hidden_states + hidden_states = hidden_states.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + hidden_states = input + hidden_states + return hidden_states + + +class Sam2VideoMemoryFuser(nn.Module): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + self.layers = nn.ModuleList( + [Sam2VideoMemoryFuserCXBlock(config) for _ in range(config.memory_fuser_num_layers)] + ) + + def forward(self, hidden_states): + # normally hidden_states: (N, C, H, W) + for layer in self.layers: + hidden_states = layer(hidden_states) + return hidden_states + + +class Sam2VideoMaskDownSamplerLayer(nn.Module): + def __init__(self, config: Sam2VideoConfig, in_channels: int, out_channels: int): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=config.mask_downsampler_kernel_size, + stride=config.mask_downsampler_stride, + padding=config.mask_downsampler_padding, + ) + self.layer_norm = Sam2VideoLayerNorm(out_channels, eps=1e-6, data_format="channels_first") + self.activation = ACT2FN[config.mask_downsampler_hidden_act] + + def forward(self, x): + return self.activation(self.layer_norm(self.conv(x))) + + +class Sam2VideoMaskDownSampler(nn.Module): + """ + Progressively downsample a mask by total_stride, each time by stride. + Note that LayerNorm is applied per *token*, like in ViT. + + With each downsample (by a factor stride**2), channel capacity increases by the same factor. + In the end, we linearly project to embed_dim channels. + """ + + def __init__(self, config: Sam2VideoConfig): + super().__init__() + + num_layers = int(math.log2(config.mask_downsampler_total_stride) // math.log2(config.mask_downsampler_stride)) + + self.layers = nn.ModuleList() + self.activation = ACT2FN[config.mask_downsampler_hidden_act] + mask_in_chans, mask_out_chans = 1, 1 + for _ in range(num_layers): + mask_out_chans = mask_in_chans * (config.mask_downsampler_stride**2) + self.layers.append(Sam2VideoMaskDownSamplerLayer(config, mask_in_chans, mask_out_chans)) + mask_in_chans = mask_out_chans + + self.final_conv = nn.Conv2d(mask_out_chans, config.mask_downsampler_embed_dim, kernel_size=1) + + def forward(self, x): + for layer in self.layers: + x = layer(x) + x = self.final_conv(x) + return x + + +class Sam2VideoMemoryEncoder(nn.Module): + def __init__(self, config: Sam2VideoConfig): + super().__init__() + + hidden_size = config.memory_encoder_hidden_size + output_channels = config.memory_encoder_output_channels + self.mask_downsampler = Sam2VideoMaskDownSampler(config) + self.feature_projection = nn.Conv2d(hidden_size, hidden_size, kernel_size=1) + self.memory_fuser = Sam2VideoMemoryFuser(config) + self.position_encoding = Sam2VideoPositionEmbeddingSine(num_pos_feats=output_channels // 2, normalize=True) + self.projection = nn.Conv2d(hidden_size, output_channels, kernel_size=1) + + def forward( + self, + vision_features: torch.Tensor, + masks: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor]: + ## Process masks + masks = self.mask_downsampler(masks) + ## Fuse pixel_features and downsampled masks + + vision_features = self.feature_projection(vision_features) + vision_features = vision_features + masks + vision_features = self.memory_fuser(vision_features) + vision_features = self.projection(vision_features) + + vision_pos_enc = self.position_encoding(vision_features.shape, vision_features.device, vision_features.dtype) + + return vision_features, vision_pos_enc + + +class Sam2VideoPositionalEmbedding(Sam2PositionalEmbedding): + pass + + +# a large negative value as a placeholder score for missing objects +NO_OBJ_SCORE = -1024.0 + + +def get_1d_sine_pe(pos_inds, dim, temperature=10000): + """ + Get 1D sine positional embedding as in the original Transformer paper. + """ + pe_dim = dim // 2 + dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device) + dim_t = temperature ** (2 * (dim_t // 2) / pe_dim) + + pos_embed = pos_inds.unsqueeze(-1) / dim_t + pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1) + return pos_embed + + +@auto_docstring +class Sam2VideoModel(Sam2Model): + input_modalities = ("video", "text") + _keys_to_ignore_on_load_unexpected = [] + _can_record_outputs = {"mask_decoder_attentions": OutputRecorder(Sam2VideoTwoWayAttentionBlock, index=2)} + + def __init__(self, config: Sam2VideoConfig): + super().__init__(config) + self.config = config + # For video sequence inference + self.image_size = config.image_size + self.memory_attention = Sam2VideoMemoryAttention(config) + self.memory_encoder = Sam2VideoMemoryEncoder(config) + self.no_memory_positional_encoding = torch.nn.Parameter( + torch.zeros(1, 1, config.vision_config.fpn_hidden_size) + ) + self.mem_dim = config.memory_encoder_output_channels + self.num_maskmem = config.num_maskmem # Number of memories accessible + # Temporal encoding of the memories + self.memory_temporal_positional_encoding = torch.nn.Parameter( + torch.zeros(self.num_maskmem, 1, 1, self.mem_dim) + ) + + self.no_object_pointer = torch.nn.Parameter(torch.zeros(1, self.hidden_dim)) + # A conv layer to downsample the mask prompt to stride 4 (the same stride as + # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale, + # so that it can be fed into the SAM mask decoder to generate a pointer. + self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4) + # a feedforward layer on SAM output tokens to turn them into object pointers + self.object_pointer_proj = Sam2VideoFeedForward(self.hidden_dim, self.hidden_dim, self.hidden_dim, 3) + + if self.config.enable_temporal_pos_encoding_for_object_pointers: + # a linear projection on temporal positional encoding in object pointers to + # avoid potential interference with spatial positional encoding + self.temporal_positional_encoding_projection_layer = torch.nn.Linear(self.hidden_dim, self.mem_dim) + else: + self.temporal_positional_encoding_projection_layer = torch.nn.Identity() + + self.occlusion_spatial_embedding_parameter = None # compatibility with Sam2 + if config.enable_occlusion_spatial_embedding: + self.occlusion_spatial_embedding_parameter = torch.nn.Parameter(torch.zeros(1, self.mem_dim)) + + self.post_init() + + @torch.no_grad() + def get_prompt_embeddings( + self, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + r""" + Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder. + + Args: + input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`): + Optional input points for the prompt encoder. The padding of the point is automatically done by the + processor. `point_batch_size` refers to the number of masks that we want the model to predict per + point. The model will output `point_batch_size` times 3 masks in total. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points_per_image)`): + Optional input labels for the prompt encoder. The padding of the labels is automatically done by the + processor, or can be fed by the user. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes_per_image, 4)`): + Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the + processor. users can also pass manually the input boxes. + input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`): + Optional input masks for the prompt encoder. + """ + prompt_output = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + return prompt_output + + def _prepare_vision_features( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + batch_size: int, + ) -> tuple[torch.Tensor, list[torch.Tensor]]: + """Prepare vision features for a frame.""" + + # Check if features are cached + if cached_features := inference_session.cache.get_vision_features(frame_idx): + vision_feats = cached_features["vision_feats"] + vision_pos_embeds = cached_features["vision_pos_embeds"] + else: + # Compute features using image encoder + image_batch = inference_session.get_frame(frame_idx).unsqueeze(0) # Add batch dimension + image_outputs = self.get_image_features(image_batch, return_dict=True) + vision_feats = image_outputs.fpn_hidden_states + vision_pos_embeds = image_outputs.fpn_position_encoding + # Cache features + inference_session.cache.cache_vision_features( + frame_idx, {"vision_feats": vision_feats, "vision_pos_embeds": vision_pos_embeds} + ) + + # Expand to batch size if needed + if batch_size > 1: + vision_feats = vision_feats.expand(batch_size, -1, -1, -1) + vision_pos_embeds = [pe.expand(batch_size, -1, -1, -1) for pe in vision_pos_embeds] + + return vision_feats, vision_pos_embeds + + def _single_frame_forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam2VideoImageSegmentationOutput: + """ + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decoder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + """ + if not ((pixel_values is None) ^ (image_embeddings is None)): + raise ValueError("Exactly one of pixel_values or image_embeddings must be provided.") + if input_points is not None and input_boxes is not None: + if input_points.shape[1] != input_boxes.shape[1]: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {input_points.shape[1]} and {input_boxes.shape[1]}." + ) + elif input_points is not None: + num_objects = input_points.shape[1] + elif input_boxes is not None: + num_objects = input_boxes.shape[1] + elif input_masks is not None: + num_objects = input_masks.shape[1] + else: + num_objects = 1 + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings[-1].shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + vision_attentions = None + vision_hidden_states = None + + if pixel_values is not None: + image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + vision_hidden_states = image_outputs.hidden_states + vision_attentions = image_outputs.attentions + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + if input_points is None and input_boxes is None: + # If no points are provide, pad with an empty point (with label -1) + input_points = torch.zeros( + batch_size, 1, 1, 2, dtype=image_embeddings[-1].dtype, device=image_embeddings[-1].device + ) + input_labels = -torch.ones(batch_size, 1, 1, dtype=torch.int32, device=image_embeddings[-1].device) + + if input_masks is not None: + # If mask_inputs is provided, downsize it into low-res mask input if needed + # and feed it as a dense mask prompt into the SAM mask encoder + if input_masks.shape[-2:] != self.prompt_encoder.mask_input_size: + input_masks = F.interpolate( + input_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(input_masks.dtype) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + low_res_multimasks, iou_scores, sam_output_tokens, object_score_logits = self.mask_decoder( + image_embeddings=image_embeddings[-1], + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + high_resolution_features=image_embeddings[:-1], + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + + is_obj_appearing = object_score_logits > 0 + # Mask used for spatial memories is always a *hard* choice between obj and no obj, + # consistent with the actual mask prediction + low_res_multimasks = torch.where( + is_obj_appearing[:, None, None], + low_res_multimasks, + NO_OBJ_SCORE, + ) + + # convert masks from possibly bfloat16 (or float16) to float32 + # (older PyTorch versions before 2.1 don't support `interpolate` on bf16) + high_res_multimasks = ( + F.interpolate( + low_res_multimasks.squeeze(1).float(), + size=(self.image_size, self.image_size), + mode="bilinear", + align_corners=False, + ) + .unsqueeze(1) + .to(low_res_multimasks.dtype) + ) + sam_output_token = sam_output_tokens[:, :, 0] + if multimask_output: + # take the best mask prediction (with the highest IoU estimation) + best_iou_inds = torch.argmax(iou_scores, dim=-1) + batch_inds = torch.arange(batch_size, device=high_res_multimasks.device) + object_batch_inds = torch.arange(num_objects, device=high_res_multimasks.device) + low_res_masks = low_res_multimasks[batch_inds, object_batch_inds, best_iou_inds] + high_res_masks = high_res_multimasks[batch_inds, object_batch_inds, best_iou_inds] + if sam_output_tokens.size(2) > 1: + sam_output_token = sam_output_tokens[batch_inds, object_batch_inds, best_iou_inds] + else: + low_res_masks, high_res_masks = low_res_multimasks[:, :, 0], high_res_multimasks[:, :, 0] + + # Extract object pointer from the SAM output token (with occlusion handling) + object_pointer = self.object_pointer_proj(sam_output_token) + lambda_is_obj_appearing = is_obj_appearing.to(object_pointer.dtype) + + object_pointer = lambda_is_obj_appearing * object_pointer + object_pointer = object_pointer + (1 - lambda_is_obj_appearing) * self.no_object_pointer + + return Sam2VideoImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_masks, + high_res_masks=high_res_masks, + object_pointer=object_pointer, + object_score_logits=object_score_logits, + image_embeddings=image_embeddings, + vision_hidden_states=vision_hidden_states, + vision_attentions=vision_attentions, + ) + + def _use_mask_as_output( + self, + backbone_features: torch.Tensor, + high_res_features: list[torch.Tensor], + mask_inputs: torch.Tensor, + ) -> Sam2VideoImageSegmentationOutput: + """ + Directly turn binary `mask_inputs` into a output mask logits without using SAM. + (same input and output shapes as in forward above). + """ + # Use -10/+20 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid). + out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05 + mask_inputs_float = mask_inputs.to(backbone_features[0].dtype) + + # Ensure mask is at self.image_size resolution for consistency + if mask_inputs_float.shape[-2:] != (self.image_size, self.image_size): + mask_inputs_float = F.interpolate( + mask_inputs_float.float(), + size=(self.image_size, self.image_size), + align_corners=False, + mode="bilinear", + antialias=True, + ).to(mask_inputs.dtype) + + high_res_masks = mask_inputs_float * out_scale + out_bias + low_res_masks = F.interpolate( + high_res_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(backbone_features[0].dtype) + # a dummy IoU prediction of all 1's under mask input + iou_scores = mask_inputs.new_ones(mask_inputs.size(0), 1).to(backbone_features[0].dtype) + # produce an object pointer using the SAM decoder from the mask input + object_pointer = self._single_frame_forward( + input_masks=self.mask_downsample(mask_inputs_float.to(backbone_features[0].dtype)), + image_embeddings=high_res_features + [backbone_features], + ).object_pointer + # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem; + # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying + # on the object_scores from the SAM decoder. + is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1) + is_obj_appearing = is_obj_appearing[..., None] + lambda_is_obj_appearing = is_obj_appearing.to(backbone_features[0].dtype) + object_score_logits = out_scale * lambda_is_obj_appearing + out_bias + object_pointer = lambda_is_obj_appearing * object_pointer + object_pointer = object_pointer + (1 - lambda_is_obj_appearing) * self.no_object_pointer + return Sam2VideoImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_masks, + high_res_masks=high_res_masks, + object_pointer=object_pointer, + object_score_logits=object_score_logits.unsqueeze(-1), + image_embeddings=high_res_features + [backbone_features], + ) + + def _select_closest_cond_frames(self, frame_idx, cond_frame_outputs, max_cond_frame_num): + """ + Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs` + that are temporally closest to the current frame at `frame_idx`. Here, we take + - a) the closest conditioning frame before `frame_idx` (if any); + - b) the closest conditioning frame after `frame_idx` (if any); + - c) any other temporally closest conditioning frames until reaching a total + of `max_cond_frame_num` conditioning frames. + + Outputs: + - selected_outputs: selected items (keys & values) from `cond_frame_outputs`. + - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`. + """ + if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num: + selected_outputs = cond_frame_outputs + unselected_outputs = {} + else: + selected_outputs = {} + # the closest conditioning frame before `frame_idx` (if any) + idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None) + if idx_before is not None: + selected_outputs[idx_before] = cond_frame_outputs[idx_before] + + # the closest conditioning frame after `frame_idx` (if any) + idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None) + if idx_after is not None: + selected_outputs[idx_after] = cond_frame_outputs[idx_after] + + # add other temporally closest conditioning frames until reaching a total + # of `max_cond_frame_num` conditioning frames. + num_remain = max_cond_frame_num - len(selected_outputs) + inds_remain = sorted( + (t for t in cond_frame_outputs if t not in selected_outputs), + key=lambda x: abs(x - frame_idx), + )[:num_remain] + selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain) + unselected_outputs = {t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs} + + return selected_outputs, unselected_outputs + + def _gather_memory_frame_outputs( + self, + inference_session: Sam2VideoInferenceSession, + obj_idx: int, + frame_idx: int, + track_in_reverse_time: bool = False, + ) -> list[tuple[int, dict]]: + """ + Get memory frames from conditioning and non-conditioning outputs. + + Returns: + List of (relative_temporal_offset, output_data) tuples. + """ + temporal_positions_and_previous_outputs = [] + + # Add conditioning frame outputs (limited by max_cond_frame_num) + conditioning_outputs = inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + if not conditioning_outputs: + raise ValueError( + "maskmem_features in conditioning outputs cannot be empty when not is_initial_conditioning_frame" + ) + conditioning_outputs, unselected_conditioning_outputs = self._select_closest_cond_frames( + frame_idx, conditioning_outputs, max_cond_frame_num=self.config.max_cond_frame_num + ) + + # Store (temporal_position, output_data) tuples + temporal_positions_and_previous_outputs = [(0, out) for out in conditioning_outputs.values()] + + # Add non-conditioning memory frames (up to self.num_maskmem - 1) + # These are typically frames tracked by the model without direct user input. + # Frames are selected with a stride, prioritizing the most recent ones. Here we only support stride = 1 for simplicity. + for relative_temporal_offset in range(self.num_maskmem - 1, 0, -1): + # relative_temporal_offset: how many frames before (or after if reversing) the current frame + if not track_in_reverse_time: + previous_frame_idx = frame_idx - relative_temporal_offset + else: + previous_frame_idx = frame_idx + relative_temporal_offset + + # check if the output is already stored without using get_output to avoid unnecessary memory transfers between CPU and GPU + output_data = inference_session.output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].get( + previous_frame_idx, unselected_conditioning_outputs.get(previous_frame_idx, None) + ) + + temporal_positions_and_previous_outputs.append((relative_temporal_offset, output_data)) + + return temporal_positions_and_previous_outputs + + def _build_memory_attention_inputs( + self, + temporal_positions_and_previous_outputs: list[tuple[int, dict]], + device: torch.device, + ) -> tuple[list[torch.Tensor], list[torch.Tensor]]: + """ + Concatenate memory features and positional embeddings from previous frames. + + Returns: + Tuple of (memories_to_concatenate, memory_positional_embeddings_to_concatenate). + """ + memories_to_concatenate = [] + memory_positional_embeddings_to_concatenate = [] + + for relative_temporal_offset, prev_output_data in temporal_positions_and_previous_outputs: + if prev_output_data is None: + continue # Skip if no output data for this temporal position (e.g., padding frames) + + # Load memory features (potentially from CPU to GPU) + # Features are flattened: (Batch, Channels, H, W) -> (H*W, Batch, Channels) + memory_features = prev_output_data["maskmem_features"].to(device, non_blocking=True) + memories_to_concatenate.append(memory_features) + + # Spatial positional encoding (potentially from CPU to GPU) + spatial_memory_pos_embed = prev_output_data["maskmem_pos_enc"].to(device, non_blocking=True) + + # Add temporal positional encoding + # self.memory_temporal_positional_encoding shape: (NumMaskMem, 1, 1, MemDim) + combined_memory_pos_embed = ( + spatial_memory_pos_embed + self.memory_temporal_positional_encoding[relative_temporal_offset - 1] + ) + memory_positional_embeddings_to_concatenate.append(combined_memory_pos_embed) + + return memories_to_concatenate, memory_positional_embeddings_to_concatenate + + def _get_object_pointers( + self, + inference_session: Sam2VideoInferenceSession, + obj_idx: int, + frame_idx: int, + num_total_frames: int, + device: torch.device, + track_in_reverse_time: bool = False, + streaming: bool = False, + ) -> tuple[list[int], list[torch.Tensor], int]: + """ + Get object pointers and their positional embeddings from past frames. + + Returns: + Tuple of (temporal_offsets, pointer_tokens, max_object_pointers_to_use). + """ + temporal_position_sign_multiplier = -1 if track_in_reverse_time else 1 + + # Determine max object pointers to use + if streaming: + max_object_pointers_to_use = self.config.max_object_pointers_in_encoder + else: + max_object_pointers_to_use = min(num_total_frames, self.config.max_object_pointers_in_encoder) + + temporal_offsets: list[int] = [] + pointer_tokens: list[torch.Tensor] = [] + + # Add object pointers from selected conditioning frames + # Optionally, only include pointers from past frames during evaluation + conditioning_outputs = inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + eligible_conditioning_outputs = conditioning_outputs + if not self.training: + eligible_conditioning_outputs = { + temporal_idx: out + for temporal_idx, out in conditioning_outputs.items() + if (temporal_idx >= frame_idx if track_in_reverse_time else temporal_idx <= frame_idx) + } + + for temporal_idx, out_data in eligible_conditioning_outputs.items(): + temporal_difference = (frame_idx - temporal_idx) * temporal_position_sign_multiplier + temporal_offsets.append(temporal_difference) + pointer_tokens.append(out_data["object_pointer"].to(device)) + + # Add object pointers from non-conditioning frames (up to max_object_pointers_to_use - 1) + for t_diff_offset in range(1, max_object_pointers_to_use): + ref_frame_idx = frame_idx + t_diff_offset if track_in_reverse_time else frame_idx - t_diff_offset + if ref_frame_idx < 0 or ( + not streaming and num_total_frames is not None and ref_frame_idx >= num_total_frames + ): + break # Stop if frame index is out of bounds + + # check if the output is already stored without using get_output to avoid unnecessary memory transfers between CPU and GPU + out_data = inference_session.output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].get( + ref_frame_idx, None + ) + if out_data is not None: + temporal_offsets.append(t_diff_offset) + pointer_tokens.append(out_data["object_pointer"].to(device)) + + return temporal_offsets, pointer_tokens, max_object_pointers_to_use + + def _process_object_pointers( + self, + temporal_offsets: list[int], + pointer_tokens: list[torch.Tensor], + max_object_pointers_to_use: int, + batch_size: int, + num_channels: int, + device: torch.device, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Process object pointers and compute their positional embeddings. + + Returns: + Tuple of (object_pointers, object_pointers_pos_embed). + """ + if not pointer_tokens: + return None, None + + # Stack object pointers: List of (Batch, Channels) -> (SeqLen_ptr, Batch, Channels) + object_pointers = torch.stack(pointer_tokens, dim=0) + + if self.config.enable_temporal_pos_encoding_for_object_pointers: + max_temporal_diff = float(max_object_pointers_to_use - 1) + # Determine dimensionality for temporal positional encoding of pointers + pointer_tpos_dim = num_channels + + # Normalize temporal differences before sine PE calculation + normalized_temporal_diffs = ( + torch.tensor(temporal_offsets, device=device, dtype=torch.float32) / max_temporal_diff + ) + sine_pe = get_1d_sine_pe(normalized_temporal_diffs, dim=pointer_tpos_dim).to(object_pointers.dtype) + projected_sine_pe = self.temporal_positional_encoding_projection_layer(sine_pe) + object_pointers_pos_embed = projected_sine_pe.unsqueeze(1).expand(-1, batch_size, self.mem_dim) + else: + object_pointers_pos_embed = object_pointers.new_zeros( + len(temporal_offsets), batch_size, self.mem_dim, dtype=object_pointers.dtype + ) + + if self.mem_dim < num_channels: + # If memory dimension is smaller, reshape/split pointers and repeat positional encoding + num_splits = num_channels // self.mem_dim + object_pointers = object_pointers.reshape(-1, batch_size, num_splits, self.mem_dim) + object_pointers = object_pointers.permute(0, 2, 1, 3).flatten( + 0, 1 + ) # (SeqLen_ptr*num_splits, Batch, MemDim) + object_pointers_pos_embed = object_pointers_pos_embed.repeat_interleave(num_splits, dim=0) + + return object_pointers, object_pointers_pos_embed + + def _prepare_memory_conditioned_features( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_idx: int, + is_initial_conditioning_frame: bool, + current_vision_features: list[torch.Tensor], + current_vision_positional_embeddings: list[torch.Tensor], + num_total_frames: int, + track_in_reverse_time: bool = False, + streaming: bool = False, + ) -> torch.Tensor: + """ + Fuse current frame's visual features with memory from previous frames for enhanced object tracking. + + This method conditions the current frame's visual features on temporal memory from previous frames, + enabling consistent object tracking across video sequences. For initial conditioning frames, it uses + no-memory embeddings. For subsequent frames, it retrieves and integrates memory features from both + conditioning frames (user interactions) and non-conditioning frames (tracked results) via cross-attention. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The video inference session object. + frame_idx (`int`): + Index of the current frame being processed. + obj_idx (`int`): + Index of the object being processed. + is_initial_conditioning_frame (`bool`): + Whether this is an initial conditioning frame with user inputs (True) or a subsequent + tracking frame (False). + current_vision_features (`torch.Tensor`): + Highest-level vision features of shape `(seq_len, batch_size, channels)`. + current_vision_positional_embeddings (`torch.Tensor`): + Positional embedding tensors corresponding to the highest-level vision features. + num_total_frames (`int`): + Total number of frames in the video sequence. + track_in_reverse_time (`bool`, *optional*, defaults to `False`): + Whether tracking is performed in reverse temporal order. + streaming (`bool`, *optional*, defaults to `False`): + Whether this is streaming inference mode. + + Returns: + `torch.Tensor`: Memory-conditioned feature tensor of shape `(batch_size, channels, height, width)` + suitable for input to the SAM decoder. + """ + # Get dimensions from the highest-level (lowest-resolution) feature map + batch_size = current_vision_features.size(1) + num_channels = self.hidden_dim + height, width = self.backbone_feature_sizes[-1] + device = current_vision_features.device + + # If memory is disabled (e.g., for single image SAM), return current features directly. + if self.num_maskmem == 0: + # Permute (SeqLen, Batch, Channels) -> (Batch, Channels, SeqLen) then view as (Batch, Channels, Height, Width) + # Assuming SeqLen = Height * Width for the last feature map + current_feature_map = current_vision_features.permute(1, 2, 0).view( + batch_size, num_channels, height, width + ) + return current_feature_map + + # Step 1: Handle initial conditioning frames + if is_initial_conditioning_frame: + # For initial conditioning frames, no prior memory is used directly in this block. + # If configured, directly add a learnable "no memory" embedding. + # current_vision_features has shape (SeqLen, Batch, Channels) + conditioned_feature_map_flat = current_vision_features + self.no_memory_embedding + # Reshape to (Batch, Channels, Height, Width) + conditioned_feature_map = conditioned_feature_map_flat.permute(1, 2, 0).view( + batch_size, num_channels, height, width + ) + return conditioned_feature_map + + # Step 2: Get memory frames and concatenate their features + temporal_positions_and_previous_outputs = self._gather_memory_frame_outputs( + inference_session, obj_idx, frame_idx, track_in_reverse_time + ) + + memories_to_concatenate, memory_positional_embeddings_to_concatenate = self._build_memory_attention_inputs( + temporal_positions_and_previous_outputs, device + ) + + # Step 3: Get and process object pointers + temporal_offsets, pointer_tokens, max_object_pointers_to_use = self._get_object_pointers( + inference_session, obj_idx, frame_idx, num_total_frames, device, track_in_reverse_time, streaming + ) + + num_object_pointer_tokens = 0 + if pointer_tokens: + object_pointers, object_pointers_pos_embed = self._process_object_pointers( + temporal_offsets, pointer_tokens, max_object_pointers_to_use, batch_size, num_channels, device + ) + + if object_pointers is not None: + memories_to_concatenate.append(object_pointers) + memory_positional_embeddings_to_concatenate.append(object_pointers_pos_embed) + num_object_pointer_tokens = object_pointers.shape[0] + + # Step 4: Concatenate all retrieved memories and their positional embeddings + combined_memory = torch.cat(memories_to_concatenate, dim=0).to(dtype=inference_session.dtype) + combined_memory_positional_embeddings = torch.cat(memory_positional_embeddings_to_concatenate, dim=0) + + # Step 5: Forward through the memory attention mechanism + conditioned_feature_map_flat = self.memory_attention( + current_vision_features=current_vision_features, + current_vision_position_embeddings=current_vision_positional_embeddings, + memory=combined_memory, + memory_posision_embeddings=combined_memory_positional_embeddings, # Corrected typo from API + num_object_pointer_tokens=num_object_pointer_tokens, + ) + + # Reshape from (Batch, H*W, Channels) to (Batch, Channels, Height, Width) + conditioned_feature_map = ( + conditioned_feature_map_flat.squeeze(1).permute(0, 2, 1).view(batch_size, num_channels, height, width) + ) + return conditioned_feature_map + + def _use_multimask(self, is_init_cond_frame: bool, point_inputs: dict | None) -> bool: + """Whether to use multimask output in the SAM head.""" + num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(2) + multimask_output = ( + self.config.multimask_output_in_sam + and (is_init_cond_frame or self.config.multimask_output_for_tracking) + and (self.config.multimask_min_pt_num <= num_pts <= self.config.multimask_max_pt_num) + ) + return multimask_output + + def _run_single_frame_inference( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_idx: int, + batch_size: int, + is_init_cond_frame: bool, + point_inputs: torch.Tensor | None, + mask_inputs: torch.Tensor | None, + reverse: bool, + prev_sam_mask_logits: torch.Tensor | None = None, + streaming: bool = False, + ) -> dict[str, Any]: + """ + Perform a single tracking step for video object segmentation. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The video inference session object. + frame_idx (`int`): + Index of the current frame. + obj_idx (`int`): + Index of the current object. + batch_size (`int`): + Batch size of the current frame. + is_init_cond_frame (`bool`): + Whether this is an initial conditioning frame with user inputs. + point_inputs (`dict`, *optional*): + Point prompt inputs for the current frame. + mask_inputs (`torch.Tensor`, *optional*): + Mask prompt inputs for the current frame. + reverse (`bool`, *optional*, defaults to `False`): + Whether to track in reverse time order. + prev_sam_mask_logits (`torch.Tensor`, *optional*): + Previously predicted SAM mask logits that can be fed with new clicks. + streaming (`bool`, *optional*, defaults to `False`): + Whether this is streaming inference. + + Returns: + `dict`: Dictionary containing the tracking results for the current frame, including: + - pred_masks: Predicted low-resolution masks. + - object_pointer: Object pointer for memory. + - high_res_masks: High-resolution masks for batched memory encoding. + - object_score_logits: Object score logits (inference only). + """ + # Retrieve correct image features + current_vision_feats, current_vision_pos_embeds = self._prepare_vision_features( + inference_session, frame_idx, batch_size + ) + # point and mask should not appear as input simultaneously on the same frame + if point_inputs is not None and mask_inputs is not None: + raise ValueError( + "point_inputs and mask_inputs should not appear as input simultaneously on the same frame" + ) + # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW + if len(current_vision_feats) > 1: + high_res_features = [ + x.permute(1, 2, 0).view(x.size(1), x.size(2), *s) + for x, s in zip(current_vision_feats[:-1], self.backbone_feature_sizes[:-1]) + ] + else: + high_res_features = None + if mask_inputs is not None: + # We directly output the mask input (see it as a GT mask) without using a SAM prompt encoder + mask decoder. + pix_feat = current_vision_feats[-1].permute(1, 2, 0) + pix_feat = pix_feat.view(-1, self.hidden_dim, *self.backbone_feature_sizes[-1]) + sam_outputs = self._use_mask_as_output(pix_feat, high_res_features, mask_inputs) + else: + # fused the visual feature with previous memory features in the memory bank + pix_feat = self._prepare_memory_conditioned_features( + inference_session=inference_session, + frame_idx=frame_idx, + obj_idx=obj_idx, + is_initial_conditioning_frame=is_init_cond_frame, + current_vision_features=current_vision_feats[-1], + current_vision_positional_embeddings=current_vision_pos_embeds[-1], + num_total_frames=inference_session.num_frames, + track_in_reverse_time=reverse, + streaming=streaming, + ) + # apply SAM-style segmentation head + # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder, + # e.g. in demo where such logits come from earlier interaction instead of correction sampling + # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead) + if prev_sam_mask_logits is not None: + mask_inputs = prev_sam_mask_logits + multimask_output = self._use_multimask(is_init_cond_frame, point_inputs) + sam_outputs = self._single_frame_forward( + pixel_values=None, # Vision features already computed + input_points=point_inputs["point_coords"] if point_inputs is not None else None, + input_labels=point_inputs["point_labels"] if point_inputs is not None else None, + input_masks=mask_inputs, + image_embeddings=high_res_features + [pix_feat], + multimask_output=multimask_output, + ) + + # Memory encoding is now handled in batch by the caller (forward method) + current_out = { + "pred_masks": sam_outputs.pred_masks, + "object_pointer": sam_outputs.object_pointer, + "high_res_masks": sam_outputs.high_res_masks, # Needed for batched memory encoding + } + if not self.training: + current_out["object_score_logits"] = sam_outputs.object_score_logits + + return current_out + + def _encode_new_memory( + self, + current_vision_feats: torch.Tensor, + pred_masks_high_res: torch.Tensor, + object_score_logits: torch.Tensor, + is_mask_from_pts: bool, + ) -> tuple[torch.Tensor, list[torch.Tensor]]: + """Encode the current image and its prediction into a memory feature.""" + batch_size = current_vision_feats.size(1) # batch size on this frame + channels = self.hidden_dim + height, width = self.backbone_feature_sizes[-1] # top-level (lowest-resolution) feature size + + mask_input_size_h, mask_input_size_w = self.prompt_encoder.mask_input_size + mask_mem_size_h = mask_input_size_h * 4 + mask_mem_size_w = mask_input_size_w * 4 + if pred_masks_high_res.shape[2:] != (mask_mem_size_h, mask_mem_size_w): + # downsample the predicted high-res masks into the mask encoder input size + pred_masks_high_res = F.interpolate( + pred_masks_high_res.float(), + size=(mask_mem_size_h, mask_mem_size_w), + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(pred_masks_high_res.dtype) + + # top-level feature, (HW)BC => BCHW + pix_feat = current_vision_feats.permute(1, 2, 0).view(batch_size, channels, height, width) + if is_mask_from_pts and not self.training: + # binarize the mask logits + mask_for_mem = (pred_masks_high_res > 0).to(pred_masks_high_res.dtype) + else: + # apply sigmoid on the raw mask logits to turn them into range (0, 1) + mask_for_mem = torch.sigmoid(pred_masks_high_res) + # apply scale and bias terms to the sigmoid probabilities + mask_for_mem = mask_for_mem * self.config.sigmoid_scale_for_mem_enc + mask_for_mem = mask_for_mem + self.config.sigmoid_bias_for_mem_enc + + maskmem_features, maskmem_pos_enc = self.memory_encoder( + pix_feat, + mask_for_mem, + ) + # add a no-object embedding to the spatial memory to indicate that the frame + # is predicted to be occluded (i.e. no object is appearing in the frame) + if self.occlusion_spatial_embedding_parameter is not None: + is_obj_appearing = (object_score_logits > 0).float() + maskmem_features += (1 - is_obj_appearing[..., None]) * self.occlusion_spatial_embedding_parameter[ + ..., None, None + ].expand(*maskmem_features.shape) + + # convert to bfloat16 to save memory, and for consistency with the original implementation + maskmem_features = maskmem_features.to(torch.bfloat16).flatten(2).permute(2, 0, 1) + maskmem_pos_enc = maskmem_pos_enc.to(pred_masks_high_res.dtype).flatten(2).permute(2, 0, 1) + + return maskmem_features, maskmem_pos_enc + + @torch.inference_mode() + @auto_docstring(custom_intro="Propagate the objects through a streamed video frame.") + def forward( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int | None = None, + frame: torch.Tensor | None = None, + reverse: bool = False, + run_mem_encoder: bool = True, + **kwargs, + ) -> Sam2VideoSegmentationOutput: + r""" + inference_session (`Sam2VideoInferenceSession`): + The video inference session object. + frame_idx (`int`, *optional*): + The index of the frame on which to run inference. No need to provide when inferring + on a new streamed frame. + frame (`torch.Tensor`, *optional*): + The frame to process. Provide when streaming. + reverse (`bool`, *optional*, defaults to `False`): + Whether to propagate in reverse. + run_mem_encoder (`bool`, *optional*, defaults to `True`): + Whether to run the memory encoder on predicted masks. The memory encoder is batched across all objects for efficiency. + """ + if frame is not None: + frame_idx = inference_session.add_new_frame(frame, frame_idx) + + if frame is not None and inference_session.get_obj_num() == 0: + raise ValueError("No objects are provided for tracking; please add inputs first.") + + num_objects = inference_session.get_obj_num() + pred_masks_per_obj = [None] * num_objects + object_score_logits_per_obj = [None] * num_objects + + # Collect data for batched memory encoding + objects_needing_memory_encoding = [] + high_res_masks_for_memory = [] + object_score_logits_for_memory = [] + is_mask_from_pts_per_obj = [] + + # Note: We avoid batched inference here because per-object inputs (clicks/masks) + # can differ across objects. + for obj_idx in range(num_objects): + obj_id = inference_session.obj_idx_to_id(obj_idx) + has_new_inputs = obj_id in inference_session.obj_with_new_inputs + has_cond_output = frame_idx in inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + # If this object has no new inputs and this frame already has a + # conditioning output, reuse the cached masks instead of recomputing. + if (not has_new_inputs) and has_cond_output: + pred_masks = inference_session.get_output(obj_idx, frame_idx, "pred_masks", is_conditioning_frame=True) + object_score_logits = inference_session.get_output( + obj_idx, frame_idx, "object_score_logits", is_conditioning_frame=True + ) + is_init_cond_frame = True + else: + # Defaults when there are no new inputs + is_init_cond_frame = False + point_inputs = None + mask_inputs = None + + if has_new_inputs: + is_init_cond_frame = frame_idx not in inference_session.frames_tracked_per_obj[obj_idx] + if is_init_cond_frame: + reverse = False + point_inputs = inference_session.point_inputs_per_obj[obj_idx].get(frame_idx, None) + mask_inputs = inference_session.mask_inputs_per_obj[obj_idx].get(frame_idx, None) + if point_inputs is not None or mask_inputs is not None: + inference_session.obj_with_new_inputs.remove(obj_id) + + current_out = self._run_single_frame_inference( + inference_session=inference_session, + obj_idx=obj_idx, + frame_idx=frame_idx, + batch_size=1, # run on the slice of a single object + is_init_cond_frame=is_init_cond_frame, + point_inputs=point_inputs, + mask_inputs=mask_inputs, + reverse=reverse, + streaming=frame is not None, + ) + inference_session.store_output( + obj_idx, frame_idx, output_value=current_out, is_conditioning_frame=is_init_cond_frame + ) + pred_masks = current_out["pred_masks"] + object_score_logits = current_out["object_score_logits"] + + # Collect data for batched memory encoding + if run_mem_encoder and self.num_maskmem > 0: + objects_needing_memory_encoding.append(obj_idx) + high_res_masks_for_memory.append(current_out["high_res_masks"]) + object_score_logits_for_memory.append(object_score_logits) + is_mask_from_pts_per_obj.append(point_inputs is not None or mask_inputs is not None) + + pred_masks_per_obj[obj_idx] = pred_masks + object_score_logits_per_obj[obj_idx] = object_score_logits.squeeze(-1) + if not is_init_cond_frame: + # only for tracked frames, not for initial conditioning frames + inference_session.frames_tracked_per_obj[obj_idx][frame_idx] = {"reverse": reverse} + + # Batch encode memories for all objects at once + self._batch_encode_memories( + inference_session=inference_session, + frame_idx=frame_idx, + objects_needing_memory_encoding=objects_needing_memory_encoding, + high_res_masks_for_memory=high_res_masks_for_memory, + object_score_logits_for_memory=object_score_logits_for_memory, + is_mask_from_pts_per_obj=is_mask_from_pts_per_obj, + ) + + # Resize the output mask to the original video resolution (we directly use + # the mask scores on GPU for output to avoid any CPU conversion in between) + if len(pred_masks_per_obj) > 1: + all_pred_masks = torch.cat(pred_masks_per_obj, dim=0) + all_object_score_logits = torch.cat(object_score_logits_per_obj, dim=0) + else: + all_pred_masks = pred_masks_per_obj[0] + all_object_score_logits = object_score_logits_per_obj[0] + + return Sam2VideoSegmentationOutput( + object_ids=inference_session.obj_ids.copy(), + pred_masks=all_pred_masks, + object_score_logits=all_object_score_logits, + frame_idx=frame_idx, + ) + + def _batch_encode_memories( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + objects_needing_memory_encoding: list[int], + high_res_masks_for_memory: list[torch.Tensor], + object_score_logits_for_memory: list[torch.Tensor], + is_mask_from_pts_per_obj: list[bool], + ): + """ + Batch encode memories for multiple objects at once. + + Args: + inference_session: The video inference session object + frame_idx: Index of the current frame + objects_needing_memory_encoding: List of object indices that need memory encoding + high_res_masks_for_memory: List of high-resolution masks for each object + object_score_logits_for_memory: List of object score logits for each object + is_mask_from_pts_per_obj: List of booleans indicating if mask is from points for each object + """ + if not objects_needing_memory_encoding: + return + + # Get vision features once for all objects + current_vision_feats, _ = self._prepare_vision_features(inference_session, frame_idx, batch_size=1) + + # Stack all high-res masks and object scores + high_res_masks_batched = torch.cat(high_res_masks_for_memory, dim=0) + object_score_logits_batched = torch.cat(object_score_logits_for_memory, dim=0) + + # Expand vision features to match batch size + expanded_vision_feats = current_vision_feats[-1].expand(-1, len(objects_needing_memory_encoding), -1) + + # Encode all memories in one batch call + maskmem_features_batched, maskmem_pos_enc_batched = self._encode_new_memory( + current_vision_feats=expanded_vision_feats, + pred_masks_high_res=high_res_masks_batched, + object_score_logits=object_score_logits_batched, + is_mask_from_pts=any(is_mask_from_pts_per_obj), + ) + + # Split and store encoded memories per object + for i, obj_idx in enumerate(objects_needing_memory_encoding): + # Extract per-object memory from batched result + maskmem_features = maskmem_features_batched[:, i : i + 1] + maskmem_pos_enc = maskmem_pos_enc_batched[:, i : i + 1] + + # Update the stored output with memory features + output_dict = inference_session.output_dict_per_obj[obj_idx] + # Determine if this was a conditioning frame + storage_key = ( + "cond_frame_outputs" if frame_idx in output_dict["cond_frame_outputs"] else "non_cond_frame_outputs" + ) + if frame_idx in output_dict[storage_key]: + output_dict[storage_key][frame_idx]["maskmem_features"] = maskmem_features + output_dict[storage_key][frame_idx]["maskmem_pos_enc"] = maskmem_pos_enc + + @torch.inference_mode() + @auto_docstring( + custom_intro=""" + Propagate the objects through the video frames. Used when initializing an inference session with a whole video. + Yields Sam2VideoSegmentationOutput for each frame. + """ + ) + def propagate_in_video_iterator( + self, + inference_session: Sam2VideoInferenceSession, + start_frame_idx: int | None = None, + max_frame_num_to_track: int | None = None, + reverse: bool = False, + show_progress_bar: bool = False, + ) -> Iterator[Sam2VideoSegmentationOutput]: + r""" + inference_session (`Sam2VideoInferenceSession`): + The video inference session object. + start_frame_idx (`int`, *optional*): + The starting frame index for propagation. + Need to be provided if `forward` hasn't been called on new inputs yet. + If not provided, the starting frame index will be the earliest frame with input points. + max_frame_num_to_track (`int`, *optional*): + The maximum number of frames to track. + reverse (`bool`, *optional*, defaults to `False`): + Whether to propagate in reverse. + show_progress_bar (`bool`, *optional*, defaults to `False`): + Whether to show a progress bar during propagation. + """ + num_frames = inference_session.num_frames + + # set start index, end index, and processing order + if start_frame_idx is None: + # default: start from the earliest frame with input points + frames_with_inputs = [ + frame_idx + for obj_output_dict in inference_session.output_dict_per_obj.values() + for frame_idx in obj_output_dict["cond_frame_outputs"] + ] + if not frames_with_inputs: + raise ValueError( + "Cannot determine the starting frame index; please specify it manually, or run inference on a frame with inputs first." + ) + start_frame_idx = min(frames_with_inputs) + if max_frame_num_to_track is None: + # default: track all the frames in the video + max_frame_num_to_track = num_frames + if reverse: + end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0) + if start_frame_idx > 0: + processing_order = range(start_frame_idx, end_frame_idx - 1, -1) + else: + processing_order = [] # skip reverse tracking if starting from frame 0 + else: + end_frame_idx = min(start_frame_idx + max_frame_num_to_track, num_frames - 1) + processing_order = range(start_frame_idx, end_frame_idx + 1) + + for frame_idx in tqdm(processing_order, desc="propagate in video", disable=not show_progress_bar): + sam2_video_output = self(inference_session, frame_idx=frame_idx, reverse=reverse) + yield sam2_video_output + + +__all__ = [ + "Sam2VideoModel", + "Sam2VideoInferenceSession", + "Sam2VideoPreTrainedModel", + "Sam2VideoMaskDecoderConfig", + "Sam2VideoPromptEncoderConfig", + "Sam2VideoProcessor", + "Sam2VideoConfig", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/processing_sam2_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/processing_sam2_video.py new file mode 100644 index 0000000000000000000000000000000000000000..416c5c543fe2325e6058dd166c07e1c2b8eb72a7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/processing_sam2_video.py @@ -0,0 +1,801 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam2_video/modular_sam2_video.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam2_video.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from copy import deepcopy +from typing import Union + +import numpy as np +import torch + +from ...image_utils import ImageInput +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires +from ...video_utils import VideoInput +from .modeling_sam2_video import Sam2VideoInferenceSession + + +@requires(backends=("torch",)) +@auto_docstring +class Sam2VideoProcessor(ProcessorMixin): + def __init__( + self, image_processor, video_processor, target_size: int | None = None, point_pad_value: int = -10, **kwargs + ): + r""" + target_size (`int`, *optional*): + The target size (in pixels) for normalizing input points and bounding boxes. If not provided, defaults + to the image processor's size configuration. All input coordinates (points and boxes) are normalized + to this size before being passed to the model. This ensures consistent coordinate representation + regardless of the original image dimensions. + point_pad_value (`int`, *optional*, defaults to -10): + The value used for padding input points when batching sequences of different lengths. This value is + used to mark padded positions and is preserved during coordinate normalization. + """ + super().__init__(image_processor, video_processor, **kwargs) + self.point_pad_value = point_pad_value + self.target_size = target_size if target_size is not None else self.image_processor.size["height"] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + segmentation_maps: ImageInput | None = None, + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + original_sizes: list[list[float]] | torch.Tensor | None = None, + return_tensors: str | TensorType | None = None, + **kwargs, + ) -> BatchEncoding: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to process. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + original_sizes (`list[list[float]]`, `torch.Tensor`, *optional*): + The original sizes of the images. + + Returns: + A [`BatchEncoding`] with the following fields: + - `pixel_values` (`torch.Tensor`): The processed image(s). + - `original_sizes` (`list[list[float]]`): The original sizes of the images. + - `labels` (`torch.Tensor`): The processed segmentation maps (if provided). + - `input_points` (`torch.Tensor`): The processed points. + - `input_labels` (`torch.Tensor`): The processed labels. + - `input_boxes` (`torch.Tensor`): The processed bounding boxes. + """ + if images is not None: + encoding_image_processor = self.image_processor( + images, + segmentation_maps=segmentation_maps, + return_tensors=return_tensors, + **kwargs, + ) + elif original_sizes is not None: + if isinstance(original_sizes, torch.Tensor): + original_sizes = original_sizes.cpu().tolist() + encoding_image_processor = BatchEncoding({"original_sizes": original_sizes}, tensor_type=return_tensors) + else: + raise ValueError("Either images or original_sizes must be provided") + + # pop arguments that are not used in the forward but used nevertheless + original_sizes = encoding_image_processor["original_sizes"] + # Check original_sizes is of length 1 or len(images) + if images is not None and len(original_sizes) != 1 and len(original_sizes) != len(images): + raise ValueError( + "original_sizes must be of length 1 or len(images). If you are passing a single image, you must pass a single original_size." + ) + + # Process input points, labels, and boxes if provided + if input_points is not None or input_labels is not None or input_boxes is not None: + # Validate and convert inputs to standardized format + processed_points = self._validate_single_input( + input_points, + expected_depth=4, + input_name="points", + expected_format="[image level, object level, point level, point coordinates]", + expected_coord_size=2, + ) + processed_labels = self._validate_single_input( + input_labels, + expected_depth=3, + input_name="labels", + expected_format="[image level, object level, point level]", + ) + processed_boxes = self._validate_single_input( + input_boxes, + expected_depth=3, + input_name="boxes", + expected_format="[image level, box level, box coordinates]", + expected_coord_size=4, + ) + + # Get padding requirements for all inputs + if processed_points is not None: + points_max_dims = self._get_nested_dimensions(processed_points)[:3] + if processed_labels is not None: + labels_max_dims = self._get_nested_dimensions(processed_labels)[:3] + if processed_boxes is not None: + boxes_max_dims = self._get_nested_dimensions(processed_boxes)[:2] + + # Ensure points and labels have consistent dimensions + if processed_points is not None and processed_labels is not None: + if points_max_dims != labels_max_dims: + raise ValueError( + "Input points and labels have inconsistent dimensions. Please ensure they have the same dimensions." + ) + + # Check that boxes don't need padding (model limitation) + if processed_boxes is not None and len(processed_boxes) >= 2: + if any(len(img_boxes) < boxes_max_dims[1] for img_boxes in processed_boxes): + raise ValueError( + "Input boxes have inconsistent dimensions that would require padding, " + "but boxes cannot be padded due to model limitations. " + "Please ensure all images have the same number of boxes." + ) + + # Pad and normalize all inputs to final tensor format + if processed_points is not None: + padded_points = self._pad_nested_list(processed_points, points_max_dims + [2]) + final_points = torch.tensor(padded_points, dtype=torch.float32) + self._normalize_tensor_coordinates(final_points, original_sizes, preserve_padding=True) + encoding_image_processor.update({"input_points": final_points}) + + if processed_labels is not None: + padded_labels = self._pad_nested_list(processed_labels, labels_max_dims) + final_labels = torch.tensor(padded_labels, dtype=torch.int64) + encoding_image_processor.update({"input_labels": final_labels}) + + if processed_boxes is not None: + final_boxes = torch.tensor(processed_boxes, dtype=torch.float32) + self._normalize_tensor_coordinates(final_boxes, original_sizes, is_bounding_box=True) + encoding_image_processor.update({"input_boxes": final_boxes}) + + return encoding_image_processor + + def _normalize_coordinates( + self, target_size: int, coords: "torch.Tensor", original_size, is_bounding_box=False + ) -> "torch.Tensor": + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format. + + Args: + target_size (`int`): + The target size of the image. + coords (`torch.Tensor`): + The coordinates to be normalized. + original_size (`tuple`): + The original size of the image. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether the coordinates are bounding boxes. + """ + old_h, old_w = original_size + new_h, new_w = target_size, target_size + coords = deepcopy(coords).float() + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + def _convert_to_nested_list(self, data, expected_depth, current_depth=0): + """ + Recursively convert various input formats (tensors, numpy arrays, lists) to nested lists. + + Args: + data: Input data in any format + expected_depth: Expected nesting depth + current_depth: Current depth in recursion + + Returns: + Nested list representation of the data + """ + if data is None: + return None + + # Convert tensor/numpy to list if we're at a leaf level or if it's a multi-dimensional array + if isinstance(data, torch.Tensor): # PyTorch tensor + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small tensor + return data.numpy().tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, np.ndarray): # NumPy array + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small array + return data.tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, list): + if current_depth == expected_depth: + # We've reached the expected depth, return as is + return data + else: + # Continue recursion + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, (int, float)): + return data + else: + raise TypeError(f"Unsupported data type: {type(data)}") + + def _get_nested_dimensions(self, nested_list, max_dims=None): + """ + Get the maximum dimensions at each level of nesting. + + Args: + nested_list (`list`): + Nested list structure. + max_dims (`list`, *optional*): + Current maximum dimensions (for recursion). + + Returns: + `list`: A list of maximum dimensions for each nesting level. + """ + if max_dims is None: + max_dims = [] + + if not isinstance(nested_list, list): + return max_dims + + if len(max_dims) == 0: + max_dims.append(len(nested_list)) + else: + max_dims[0] = max(max_dims[0], len(nested_list)) + + if len(nested_list) > 0: + for item in nested_list: + if isinstance(item, list): + sub_dims = self._get_nested_dimensions(item) + # Merge sub_dims into max_dims + for i, dim in enumerate(sub_dims): + if i + 1 >= len(max_dims): + max_dims.append(dim) + else: + max_dims[i + 1] = max(max_dims[i + 1], dim) + + return max_dims + + def _pad_nested_list(self, nested_list, target_dims, current_level=0, pad_value=None): + """ + Recursively pad a nested list to match target dimensions. + + Args: + nested_list (`list`): + Nested list to pad. + target_dims (`list`): + Target dimensions for each level. + current_level (`int`, *optional*, defaults to 0): + Current nesting level. + pad_value (`int`, *optional*): + Value to use for padding. + + Returns: + `list`: The padded nested list. + """ + if pad_value is None: + pad_value = self.point_pad_value + + if current_level >= len(target_dims): + return nested_list + + # Ensure we have a list + if not isinstance(nested_list, list): + nested_list = [nested_list] + + # Pad current level + current_size = len(nested_list) + target_size = target_dims[current_level] + + # Pad with appropriate values + if current_level == len(target_dims) - 1: + # At the coordinate level, pad with pad_value + nested_list.extend([pad_value] * (target_size - current_size)) + else: + # At higher levels, pad with nested structures + if current_size > 0: + # Create appropriately sized template + if current_level < len(target_dims) - 2: + # For non-coordinate levels, create empty nested structure + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + else: + # For coordinate level, create list of pad_values + template = [pad_value] * target_dims[current_level + 1] + + nested_list.extend([deepcopy(template) for _ in range(target_size - current_size)]) + else: + # Create from scratch + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + nested_list.extend([deepcopy(template) for _ in range(target_size)]) + + # Recursively pad sublists + if current_level < len(target_dims) - 1: + for i in range(len(nested_list)): + if isinstance(nested_list[i], list): + nested_list[i] = self._pad_nested_list(nested_list[i], target_dims, current_level + 1, pad_value) + + return nested_list + + def _create_empty_nested_structure(self, dims, pad_value): + """ + Create an empty nested structure with given dimensions filled with pad_value. + + Args: + dims (`list`): + The dimensions of the nested structure. + pad_value (`int`): + The value to fill the structure with. + """ + if len(dims) == 1: + return [pad_value] * dims[0] + else: + return [self._create_empty_nested_structure(dims[1:], pad_value) for _ in range(dims[0])] + + def _get_nesting_level(self, input_list): + """ + Get the nesting level of a list structure. + + Args: + input_list (`list`): + The list to get the nesting level of. + """ + if isinstance(input_list, list): + if len(input_list) == 0: + return 1 + return 1 + self._get_nesting_level(input_list[0]) + elif isinstance(input_list, (np.ndarray, torch.Tensor)): + # For arrays/tensors, the nesting level is the number of dimensions + return len(input_list.shape) + return 0 + + def _validate_single_input( + self, + data: torch.Tensor | np.ndarray | list, + expected_depth: int, + input_name: str, + expected_format: str, + expected_coord_size: int | None = None, + ) -> list: + """ + Validate a single input by ensuring proper nesting and raising an error if the input is not valid. + + Args: + data (`torch.Tensor`, `np.ndarray`, or `list`): + Input data to process. + expected_depth (`int`): + Expected nesting depth. + input_name (`str`): + Name of the input for error messages. + expected_format (`str`): + The expected format of the input. + expected_coord_size (`int`, *optional*): + Expected coordinate size (2 for points, 4 for boxes, None for labels). + . + """ + if data is None: + return None + + # Handle tensors and numpy arrays first + if isinstance(data, (torch.Tensor, np.ndarray)): + # For tensors/arrays, we can directly check the number of dimensions + if data.ndim != expected_depth: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_depth} dimensions. The expected nesting format is {expected_format}. Got {data.ndim} dimensions." + ) + elif expected_coord_size is not None: + if data.shape[-1] != expected_coord_size: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_coord_size} as the last dimension, got {data.shape[-1]}." + ) + return self._convert_to_nested_list(data, expected_depth) + + # Handle nested lists + if isinstance(data, list): + current_depth = self._get_nesting_level(data) + if current_depth != expected_depth: + raise ValueError( + f"Input {input_name} must be a nested list with {expected_depth} levels. The expected nesting format is {expected_format}. Got {current_depth} levels." + ) + return self._convert_to_nested_list(data, expected_depth) + + def _normalize_tensor_coordinates(self, tensor, original_sizes, is_bounding_box=False, preserve_padding=False): + """ + Helper method to normalize coordinates in a tensor across multiple images. + + Args: + tensor (`torch.Tensor`): + Input tensor with coordinates. + original_sizes (`list`): + Original image sizes. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether coordinates are bounding boxes. + preserve_padding (`bool`, *optional*, defaults to `False`): + Whether to preserve padding values (for points). + """ + if preserve_padding: + # For points: avoid normalizing pad values + mask = tensor != self.point_pad_value + coord_mask = mask.all(dim=-1, keepdim=True) + + for img_idx in range(len(original_sizes)): + if img_idx < tensor.shape[0]: + original_size = original_sizes[img_idx] if img_idx < len(original_sizes) else original_sizes[0] + normalized_coords = self._normalize_coordinates( + self.target_size, tensor[img_idx], original_size, is_bounding_box=is_bounding_box + ) + + if preserve_padding: + # Only update non-padded values + img_mask = coord_mask[img_idx] + tensor[img_idx] = torch.where( + img_mask.expand_as(tensor[img_idx]), normalized_coords, tensor[img_idx] + ) + else: + tensor[img_idx] = normalized_coords + + def post_process_masks( + self, + masks, + original_sizes, + mask_threshold=0.0, + binarize=True, + max_hole_area=0.0, + max_sprinkle_area=0.0, + apply_non_overlapping_constraints=False, + **kwargs, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[List[torch.Tensor], List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + mask_threshold (`float`, *optional*, defaults to 0.0): + Threshold for binarization and post-processing operations. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + max_hole_area (`float`, *optional*, defaults to 0.0): + The maximum area of a hole to fill. + max_sprinkle_area (`float`, *optional*, defaults to 0.0): + The maximum area of a sprinkle to fill. + apply_non_overlapping_constraints (`bool`, *optional*, defaults to `False`): + Whether to apply non-overlapping constraints to the masks. + + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + return self.image_processor.post_process_masks( + masks, + original_sizes, + mask_threshold, + binarize, + max_hole_area, + max_sprinkle_area, + apply_non_overlapping_constraints, + **kwargs, + ) + + @property + def model_input_names(self): + image_processor_input_names = self.image_processor.model_input_names + return list(image_processor_input_names + ["original_sizes"]) + + def init_video_session( + self, + video: VideoInput | None = None, + inference_device: Union[str, "torch.device"] = "cpu", + inference_state_device: Union[str, "torch.device"] | None = None, + processing_device: Union[str, "torch.device"] | None = None, + video_storage_device: Union[str, "torch.device"] | None = None, + max_vision_features_cache_size: int = 1, + dtype: torch.dtype = torch.float32, + ): + """ + Initializes a video session for inference. + If a video is provided (async inference), the video will be processed and stored on the `video_storage_device`. + + Args: + video (`VideoInput`, *optional*): + The video to process. No need to provide when streaming. + inference_device (`str` or `torch.device`, *optional*, defaults to "cpu"): + The device to use for inference. + inference_state_device (`str` or `torch.device`, *optional*): + The device to store the inference state on. + processing_device (`str` or `torch.device`, *optional*): + The device to use for video processing. + video_storage_device (`str` or `torch.device`, *optional*): + The device to store the processed video frames on. + max_vision_features_cache_size (`int`, *optional*, defaults to 1): + The maximum number of vision features to cache. + dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): + The torch dtype to use for the whole session. + """ + video_storage_device = video_storage_device if video_storage_device is not None else inference_device + inference_state_device = inference_state_device if inference_state_device is not None else inference_device + processing_device = processing_device if processing_device is not None else inference_device + pixel_values_video = None + video_height = None + video_width = None + if video is not None: + processed_video = self.video_processor(videos=video, device=processing_device, return_tensors="pt") + pixel_values_video = processed_video.pixel_values_videos[0] + video_height = processed_video.original_sizes[0][0] + video_width = processed_video.original_sizes[0][1] + inference_session = Sam2VideoInferenceSession( + video=pixel_values_video, + video_height=video_height, + video_width=video_width, + inference_device=inference_device, + video_storage_device=video_storage_device, + inference_state_device=inference_state_device, + dtype=dtype, + max_vision_features_cache_size=max_vision_features_cache_size, + ) + return inference_session + + def add_inputs_to_inference_session( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_ids: list[int] | int, + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + input_masks: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor] | None = None, + original_size: tuple[int, int] | None = None, + clear_old_inputs: bool = True, + ) -> Sam2VideoInferenceSession: + """ + Process new points, boxes, or masks for a video frame and add them to the inference session. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]` or `int`): + The object ID(s) to associate with the points or box. + These can be any integers and can be reused later on to specify an object. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + input_masks (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, or `list[torch.Tensor]`, *optional*): + The mask(s) to add to the frame. + original_size (`tuple[int, int]`, *optional*): + The original size of the video. Provide when streaming. + clear_old_inputs (`bool`, *optional*, defaults to `True`): + Whether to clear old inputs for the object. + """ + + if isinstance(obj_ids, int): + obj_ids = [obj_ids] + + # Validate inputs + if (input_points is not None) != (input_labels is not None): + raise ValueError("points and labels must be provided together") + if input_points is None and input_boxes is None and input_masks is None: + raise ValueError("at least one of points, boxes, or masks must be provided as input") + if input_masks is not None and (input_points is not None or input_boxes is not None): + raise ValueError("masks cannot be provided together with points or boxes") + + if input_masks is not None: + return self.process_new_mask_for_video_frame(inference_session, frame_idx, obj_ids, input_masks) + else: + return self.process_new_points_or_boxes_for_video_frame( + inference_session, + frame_idx, + obj_ids, + input_points, + input_labels, + input_boxes, + original_size, + clear_old_inputs, + ) + + def process_new_points_or_boxes_for_video_frame( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_ids: list[int], + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + original_size: tuple[int, int] | None = None, + clear_old_inputs: bool = True, + ) -> Sam2VideoInferenceSession: + """ + Process new points or boxes for a video frame and add them to the inference session. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]`): + The object ID(s) to associate with the points or box. + These can be any integers and can be reused later on to specify an object. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + original_size (`tuple[int, int]`, *optional*): + The original size of the video. Provide when streaming. + clear_old_inputs (`bool`, *optional*, defaults to `True`): + Whether to clear old inputs for the object. + """ + if original_size is not None: + inference_session.video_height = original_size[0] + inference_session.video_width = original_size[1] + elif inference_session.video_height is None or inference_session.video_width is None: + raise ValueError("original_size must be provided when adding points or boxes on a first streamed frame") + + original_sizes = [[inference_session.video_height, inference_session.video_width]] + + encoded_inputs = self( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + original_sizes=original_sizes, + return_tensors="pt", + ) + input_points = encoded_inputs.get("input_points", None) + input_labels = encoded_inputs.get("input_labels", None) + input_boxes = encoded_inputs.get("input_boxes", None) + + if input_points is not None: + if input_points.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of points ({input_points.shape[1]})" + ) + else: + input_points = torch.zeros(1, len(obj_ids), 0, 2, dtype=torch.float32) + if input_labels is not None: + if input_labels.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of labels ({input_labels.shape[1]})" + ) + else: + input_labels = torch.zeros(1, len(obj_ids), 0, dtype=torch.int32) + if input_boxes is not None: + if input_boxes.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of boxes ({input_boxes.shape[1]})" + ) + + if input_boxes is not None: + if not clear_old_inputs: + raise ValueError( + "cannot add box without clearing old points, since " + "box prompt must be provided before any point prompt " + "(please use clear_old_points=True instead)" + ) + box_coords = input_boxes.reshape(1, -1, 2, 2) + box_labels = torch.tensor([2, 3], dtype=torch.int32).repeat(1, box_coords.shape[1], 1) + input_points = torch.cat([box_coords, input_points], dim=2) + input_labels = torch.cat([box_labels, input_labels], dim=2) + + for obj_id, idx in zip(obj_ids, range(len(obj_ids))): + obj_idx = inference_session.obj_id_to_idx(obj_id) + input_points_for_obj = input_points[:, idx, :, :].unsqueeze(1) + input_labels_for_obj = input_labels[:, idx, :].unsqueeze(1) + # Handle existing points + if not clear_old_inputs: + existing_points = inference_session.point_inputs_per_obj[obj_idx].get(frame_idx, None) + if existing_points is not None: + # Concatenate with existing points + input_points_for_obj = torch.cat( + [existing_points["point_coords"].to(input_points_for_obj.device), input_points_for_obj], dim=2 + ) + input_labels_for_obj = torch.cat( + [existing_points["point_labels"].to(input_labels_for_obj.device), input_labels_for_obj], dim=2 + ) + point_inputs = { + "point_coords": input_points_for_obj, + "point_labels": input_labels_for_obj, + } + + inference_session.add_point_inputs(obj_idx, frame_idx, point_inputs) + inference_session.remove_mask_inputs(obj_idx, frame_idx) # Clear any mask inputs + + inference_session.obj_with_new_inputs = obj_ids + + def process_new_mask_for_video_frame( + self, + inference_session: Sam2VideoInferenceSession, + frame_idx: int, + obj_ids: list[int], + input_masks: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor], + ): + """ + Add new mask to a frame and add them to the inference session. + + Args: + inference_session (`Sam2VideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]`): + The object ID(s) to associate with the mask. + These can be any integers and can be reused later on to specify an object. + input_masks (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, or `list[torch.Tensor]`): + The mask(s) to add to the frame. + """ + if not isinstance(input_masks, list): + input_masks = [input_masks] + if len(input_masks) != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of masks ({len(input_masks)})" + ) + + for obj_id, mask in zip(obj_ids, input_masks): + obj_idx = inference_session.obj_id_to_idx(obj_id) + + device = inference_session.inference_device + + # Process mask + if not isinstance(mask, torch.Tensor): + mask = torch.tensor(mask, dtype=torch.bool) + nb_dim = mask.dim() + if nb_dim > 4 or nb_dim < 2: + raise ValueError(f"Mask has an unsupported number of dimensions: {nb_dim}") + for i in range(4 - nb_dim): + mask = mask.unsqueeze(0) + + mask_H, mask_W = mask.shape[-2:] + mask_inputs_orig = mask.to(device) + mask_inputs_orig = mask_inputs_orig.float().to(device) + + # Resize mask if needed + if mask_H != self.target_size or mask_W != self.target_size: + mask_inputs = torch.nn.functional.interpolate( + mask_inputs_orig, + size=(self.target_size, self.target_size), + align_corners=False, + mode="bilinear", + antialias=True, + ) + mask_inputs = (mask_inputs >= 0.5).float() + else: + mask_inputs = mask_inputs_orig + + inference_session.add_mask_inputs(obj_idx, frame_idx, mask_inputs) + inference_session.remove_point_inputs(obj_idx, frame_idx) # Clear any point inputs + + inference_session.obj_with_new_inputs = obj_ids + + +__all__ = ["Sam2VideoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/video_processing_sam2_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/video_processing_sam2_video.py new file mode 100644 index 0000000000000000000000000000000000000000..a1dad3a0f4df1dcba48bc8caf1d128a6f0a603f2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam2_video/video_processing_sam2_video.py @@ -0,0 +1,104 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Fast Image processor class for SAM2.""" + +import numpy as np +import torch +import torch.nn.functional as F + +from ...image_processing_utils import BatchFeature +from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling, SizeDict +from ...utils import TensorType +from ...video_processing_utils import BaseVideoProcessor + + +class Sam2VideoVideoProcessor(BaseVideoProcessor): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 1024, "width": 1024} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + model_input_names = ["pixel_values"] + + def _preprocess( + self, + videos: list["torch.Tensor"], + size: SizeDict, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + original_sizes = [video.shape[-2:] for video in videos] + reshaped_input_sizes = [(size.height, size.width) for _ in range(len(videos))] + batch_feature = super()._preprocess(videos, size=size, return_tensors=return_tensors, **kwargs) + batch_feature = BatchFeature( + data={ + "original_sizes": original_sizes, + "reshaped_input_sizes": reshaped_input_sizes, + **batch_feature.data, + }, + tensor_type=return_tensors, + ) + return batch_feature + + def post_process_masks( + self, masks, original_sizes, reshaped_input_sizes, mask_threshold=0.0, binarize=True, pad_size=None + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[List[torch.Tensor], List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + reshaped_input_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The size of each image as it is fed to the model, in (height, width) format. Used to remove padding. + mask_threshold (`float`, *optional*, defaults to 0.0): + The threshold to use for binarizing the masks. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + pad_size (`int`, *optional*, defaults to `self.pad_size`): + The target size the images were padded to before being passed to the model. If None, the target size is + assumed to be the processor's `pad_size`. + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + pad_size = self.size if pad_size is None else pad_size + target_image_size = (pad_size["height"], pad_size["width"]) + if isinstance(original_sizes, (torch.Tensor, np.ndarray)): + original_sizes = original_sizes.tolist() + if isinstance(reshaped_input_sizes, (torch.Tensor, np.ndarray)): + reshaped_input_sizes = reshaped_input_sizes.tolist() + output_masks = [] + for i, original_size in enumerate(original_sizes): + if isinstance(masks[i], np.ndarray): + masks[i] = torch.from_numpy(masks[i]) + elif not isinstance(masks[i], torch.Tensor): + raise TypeError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`") + interpolated_mask = F.interpolate(masks[i], target_image_size, mode="bilinear", align_corners=False) + interpolated_mask = interpolated_mask[..., : reshaped_input_sizes[i][0], : reshaped_input_sizes[i][1]] + interpolated_mask = F.interpolate(interpolated_mask, original_size, mode="bilinear", align_corners=False) + if binarize: + interpolated_mask = interpolated_mask > mask_threshold + output_masks.append(interpolated_mask) + + return output_masks + + +__all__ = ["Sam2VideoVideoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..91a812e800d0eed7f28dde05aa1750866da119b5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam3 import * + from .image_processing_sam3 import * + from .modeling_sam3 import * + from .processing_sam3 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/configuration_sam3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/configuration_sam3.py new file mode 100644 index 0000000000000000000000000000000000000000..9eb7cf459c905523b2affc3749bad13f48bd04e4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/configuration_sam3.py @@ -0,0 +1,311 @@ +# Copyright 2025 Meta AI and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAM3 model configuration""" + +from huggingface_hub.dataclasses import strict + +from transformers import CLIPTextConfig + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3ViTConfig(PreTrainedConfig): + r""" + rope_theta (`float`, *optional*, defaults to 10000.0): + Base frequency for RoPE. + window_size (`int`, *optional*, defaults to 24): + Window size for windowed attention. + global_attn_indexes (`list[int]`, *optional*, defaults to `[7, 15, 23, 31]`): + Indexes of layers with global attention. + pretrain_image_size (`int`, *optional*, defaults to 336): + Pretrained model image size for position embedding initialization. + hidden_dropout (`float`, *optional*, defaults to 0.0): + Dropout probability for hidden states. + """ + + base_config_key = "backbone_config" + model_type = "sam3_vit_model" + + hidden_size: int = 1024 + intermediate_size: int = 4736 + num_hidden_layers: int = 32 + num_attention_heads: int = 16 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 1008 + patch_size: int | list[int] | tuple[int, int] = 14 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + attention_dropout: float | int = 0.0 + rope_theta: float = 10000.0 + window_size: int = 24 + global_attn_indexes: list[int] | None = None + layer_scale_init_value: float | None = None + pretrain_image_size: int | list[int] | tuple[int, int] = 336 + hidden_dropout: float | int = 0.0 + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + super().__post_init__(**kwargs) + if self.global_attn_indexes is None: + self.global_attn_indexes = [7, 15, 23, 31] + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3VisionConfig(PreTrainedConfig): + r""" + fpn_hidden_size (`int`, *optional*, defaults to 256): + The hidden dimension of the FPN. + backbone_feature_sizes (`List[List[int]]`, *optional*, defaults to `[[288, 288], [144, 144], [72, 72]]`): + The spatial sizes (height, width) of the feature maps from the backbone at different scales. + scale_factors (`list[float]`, *optional*, defaults to `[4.0, 2.0, 1.0, 0.5]`): + Scale factors for FPN multi-scale features. List of scaling factors for each FPN level. + """ + + base_config_key = "vision_config" + model_type = "sam3_vision_model" + sub_configs = { + "backbone_config": AutoConfig, + } + + backbone_config: dict | PreTrainedConfig | None = None + fpn_hidden_size: int = 256 + backbone_feature_sizes: list | None = None + scale_factors: list[float] | None = None + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + self.scale_factors = [4.0, 2.0, 1.0, 0.5] if self.scale_factors is None else self.scale_factors + if self.backbone_feature_sizes is None: + self.backbone_feature_sizes = [[288, 288], [144, 144], [72, 72]] + + if isinstance(self.backbone_config, dict): + self.backbone_config["model_type"] = self.backbone_config.get("model_type", "sam3_vit_model") + self.backbone_config = CONFIG_MAPPING[self.backbone_config["model_type"]](**self.backbone_config) + elif self.backbone_config is None: + self.backbone_config = CONFIG_MAPPING["sam3_vit_model"]() + + super().__post_init__(**kwargs) + + @property + def image_size(self): + """Image size for the vision encoder.""" + return self.backbone_config.image_size + + @image_size.setter + def image_size(self, value): + """Set the image size and propagate to backbone.""" + self.backbone_config.image_size = value + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3GeometryEncoderConfig(PreTrainedConfig): + r""" + roi_size (`int`, *optional*, defaults to 7): + ROI size for box pooling operations. + """ + + model_type = "sam3_geometry_encoder" + + hidden_size: int = 256 + num_layers: int = 3 + num_attention_heads: int = 8 + intermediate_size: int = 2048 + dropout: float | int = 0.1 + hidden_act: str = "relu" + hidden_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + roi_size: int = 7 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3DETREncoderConfig(PreTrainedConfig): + r""" + hidden_dropout (`float`, *optional*, defaults to 0.0): + Dropout probability for hidden states. + """ + + model_type = "sam3_detr_encoder" + + hidden_size: int = 256 + num_layers: int = 6 + num_attention_heads: int = 8 + intermediate_size: int = 2048 + dropout: float | int = 0.1 + hidden_act: str = "relu" + hidden_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3DETRDecoderConfig(PreTrainedConfig): + r""" + num_queries (`int`, *optional*, defaults to 200): + Number of object queries. + """ + + model_type = "sam3_detr_decoder" + + hidden_size: int = 256 + num_layers: int = 6 + num_queries: int = 200 + num_attention_heads: int = 8 + intermediate_size: int = 2048 + dropout: float | int = 0.1 + hidden_act: str = "relu" + hidden_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3MaskDecoderConfig(PreTrainedConfig): + r""" + num_upsampling_stages (`int`, *optional*, defaults to 3): + Number of upsampling stages in the pixel decoder (FPN). + """ + + model_type = "sam3_mask_decoder" + + hidden_size: int = 256 + num_upsampling_stages: int = 3 + layer_norm_eps: float = 1e-6 + dropout: float | int = 0.0 + num_attention_heads: int = 8 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3Config(PreTrainedConfig): + r""" + geometry_encoder_config (`dict` or `Sam3GeometryEncoderConfig`, *optional*): + Configuration for the geometry encoder. + detr_encoder_config (`dict` or `Sam3DETREncoderConfig`, *optional*): + Configuration for the DETR encoder. + detr_decoder_config (`dict` or `Sam3DETRDecoderConfig`, *optional*): + Configuration for the DETR decoder. + mask_decoder_config (`dict` or `Sam3MaskDecoderConfig`, *optional*): + Configuration for the mask decoder. + + Example: + ```python + >>> from transformers import Sam3Config, Sam3Model + + >>> # Initializing a SAM3 configuration + >>> configuration = Sam3Config() + + >>> # Initializing a model from the configuration + >>> model = Sam3Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "sam3" + is_composition = True + sub_configs = { + "vision_config": Sam3VisionConfig, + "text_config": CLIPTextConfig, + "geometry_encoder_config": Sam3GeometryEncoderConfig, + "detr_encoder_config": Sam3DETREncoderConfig, + "detr_decoder_config": Sam3DETRDecoderConfig, + "mask_decoder_config": Sam3MaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + geometry_encoder_config: dict | PreTrainedConfig | None = None + detr_encoder_config: dict | PreTrainedConfig | None = None + detr_decoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + if self.vision_config is None: + self.vision_config = Sam3VisionConfig() + if isinstance(self.vision_config, dict): + self.vision_config = Sam3VisionConfig(**self.vision_config) + + if self.text_config is None: + self.text_config = CLIPTextConfig( + **{ + "vocab_size": 49408, + "hidden_size": 1024, + "intermediate_size": 4096, # hidden_size * mlp_ratio (1024 * 4) + "projection_dim": 512, # CLIP's internal projection dimension + "num_hidden_layers": 24, + "num_attention_heads": 16, + "max_position_embeddings": 32, + "hidden_act": "gelu", + } + ) + if isinstance(self.text_config, dict): + self.text_config = CLIPTextConfig(**self.text_config) + + if self.geometry_encoder_config is None: + self.geometry_encoder_config = Sam3GeometryEncoderConfig() + if isinstance(self.geometry_encoder_config, dict): + self.geometry_encoder_config = Sam3GeometryEncoderConfig(**self.geometry_encoder_config) + + if self.detr_encoder_config is None: + self.detr_encoder_config = Sam3DETREncoderConfig() + if isinstance(self.detr_encoder_config, dict): + self.detr_encoder_config = Sam3DETREncoderConfig(**self.detr_encoder_config) + + if self.detr_decoder_config is None: + self.detr_decoder_config = Sam3DETRDecoderConfig() + if isinstance(self.detr_decoder_config, dict): + self.detr_decoder_config = Sam3DETRDecoderConfig(**self.detr_decoder_config) + + if self.mask_decoder_config is None: + self.mask_decoder_config = Sam3MaskDecoderConfig() + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam3MaskDecoderConfig(**self.mask_decoder_config) + + super().__post_init__(**kwargs) + + @property + def image_size(self): + """Image size for the SAM3 model.""" + return self.vision_config.image_size + + @image_size.setter + def image_size(self, value): + """Set the image size and propagate to vision config.""" + self.vision_config.image_size = value + + +__all__ = [ + "Sam3Config", + "Sam3ViTConfig", + "Sam3VisionConfig", + "Sam3GeometryEncoderConfig", + "Sam3DETREncoderConfig", + "Sam3DETRDecoderConfig", + "Sam3MaskDecoderConfig", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/image_processing_sam3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/image_processing_sam3.py new file mode 100644 index 0000000000000000000000000000000000000000..f4b8d5f03e0867ff84d0b1f243cd9fdb946d7d6b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/image_processing_sam3.py @@ -0,0 +1,910 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3/modular_sam3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from copy import deepcopy +from itertools import product +from typing import Any, Optional, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torchvision.ops.boxes import batched_nms + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature, get_size_dict +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_vision_available + + +if is_vision_available(): + import PIL + + +class Sam3ImageProcessorKwargs(ImagesKwargs, total=False): + r""" + mask_size (`dict[str, int]`, *optional*): + The size `{"height": int, "width": int}` to resize the segmentation maps to. + """ + + mask_size: dict[str, int] + + +def _compute_stability_score(masks: "torch.Tensor", mask_threshold: float, stability_score_offset: int): + # One mask is always contained inside the other. + # Save memory by preventing unnecessary cast to torch.int64 + intersections = ( + (masks > (mask_threshold + stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32) + ) + unions = (masks > (mask_threshold - stability_score_offset)).sum(-1, dtype=torch.int16).sum(-1, dtype=torch.int32) + stability_scores = intersections / unions + return stability_scores + + +def _batched_mask_to_box(masks: "torch.Tensor"): + """ + Computes the bounding boxes around the given input masks. The bounding boxes are in the XYXY format which + corresponds the following required indices: + - LEFT: left hand side of the bounding box + - TOP: top of the bounding box + - RIGHT: right of the bounding box + - BOTTOM: bottom of the bounding box + + Return [0,0,0,0] for an empty mask. For input shape channel_1 x channel_2 x ... x height x width, the output shape + is channel_1 x channel_2 x ... x 4. + + Args: + - masks (`torch.Tensor` of shape `(batch, nb_mask, height, width)`) + """ + # torch.max below raises an error on empty inputs, just skip in this case + + if torch.numel(masks) == 0: + return torch.zeros(*masks.shape[:-2], 4, device=masks.device) + + # Normalize shape to Cxheightxwidth + shape = masks.shape + height, width = shape[-2:] + + # Get top and bottom edges + in_height, _ = torch.max(masks, dim=-1) + in_height_coords = in_height * torch.arange(height, device=in_height.device)[None, :] + bottom_edges, _ = torch.max(in_height_coords, dim=-1) + in_height_coords = in_height_coords + height * (~in_height) + top_edges, _ = torch.min(in_height_coords, dim=-1) + + # Get left and right edges + in_width, _ = torch.max(masks, dim=-2) + in_width_coords = in_width * torch.arange(width, device=in_width.device)[None, :] + right_edges, _ = torch.max(in_width_coords, dim=-1) + in_width_coords = in_width_coords + width * (~in_width) + left_edges, _ = torch.min(in_width_coords, dim=-1) + + # If the mask is empty the right edge will be to the left of the left edge. + # Replace these boxes with [0, 0, 0, 0] + empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) + out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) + out = out * (~empty_filter).unsqueeze(-1) + + # Return to original shape + out = out.reshape(*shape[:-2], 4) + return out + + +def _is_box_near_crop_edge(boxes, crop_box, orig_box, atol=20.0): + """Filter masks at the edge of a crop, but not at the edge of the original image.""" + crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) + orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) + + left, top, _, _ = crop_box + offset = torch.tensor([[left, top, left, top]], device=boxes.device) + # Check if boxes has a channel dimension + if len(boxes.shape) == 3: + offset = offset.unsqueeze(1) + boxes = (boxes + offset).float() + + near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) + near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) + near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) + return torch.any(near_crop_edge, dim=1) + + +def _pad_masks(masks, crop_box: list[int], orig_height: int, orig_width: int): + left, top, right, bottom = crop_box + if left == 0 and top == 0 and right == orig_width and bottom == orig_height: + return masks + # Coordinate transform masks + pad_x, pad_y = orig_width - (right - left), orig_height - (bottom - top) + pad = (left, pad_x - left, top, pad_y - top) + return torch.nn.functional.pad(masks, pad, value=0) + + +def _generate_crop_boxes( + image, + target_size: int, + crop_n_layers: int = 0, + overlap_ratio: float = 512 / 1500, + points_per_crop: int | None = 32, + crop_n_points_downscale_factor: list[int] | None = 1, +) -> tuple[list[list[int]], list[int]]: + """ + Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + + Args: + image (`torch.Tensor`): + Image to generate crops for. + target_size (`int`): + Size of the smallest crop. + crop_n_layers (`int`, *optional*): + If `crops_n_layers>0`, mask prediction will be run again on crops of the image. Sets the number of layers + to run, where each layer has 2**i_layer number of image crops. + overlap_ratio (`int`, *optional*): + Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the + image length. Later layers with more crops scale down this overlap. + points_per_crop (`int`, *optional*): + Number of points to sam3ple per crop. + crop_n_points_downscale_factor (`int`, *optional*): + The number of points-per-side sam3pled in layer n is scaled down by crop_n_points_downscale_factor**n. + """ + + if isinstance(image, list): + raise ValueError("Only one image is allowed for crop generation.") + original_size = image.shape[-2:] + + points_grid = [] + for i in range(crop_n_layers + 1): + n_points = int(points_per_crop / (crop_n_points_downscale_factor**i)) + points_grid.append(_build_point_grid(n_points)) + + crop_boxes, layer_idxs = _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size) + + cropped_images, point_grid_per_crop = _generate_crop_images( + crop_boxes, image, points_grid, layer_idxs, target_size, original_size + ) + crop_boxes = torch.tensor(crop_boxes) + crop_boxes = crop_boxes.float() + points_per_crop = torch.stack(point_grid_per_crop) + points_per_crop = points_per_crop.unsqueeze(0).permute(0, 2, 1, 3) + cropped_images = torch.stack(cropped_images) + + input_labels = torch.ones_like(points_per_crop[:, :, :, 0], dtype=torch.int64) + + return crop_boxes, points_per_crop, cropped_images, input_labels + + +def _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size): + """ + Generates 2 ** (layers idx + 1) crops for each crop_n_layers. Crops are in the XYWH format : The XYWH format + consists of the following required indices: + - X: X coordinate of the top left of the bounding box + - Y: Y coordinate of the top left of the bounding box + - W: width of the bounding box + - H: height of the bounding box + """ + crop_boxes, layer_idxs = [], [] + im_height, im_width = original_size + short_side = min(im_height, im_width) + + # Original image + crop_boxes.append([0, 0, im_width, im_height]) + layer_idxs.append(0) + for i_layer in range(crop_n_layers): + n_crops_per_side = 2 ** (i_layer + 1) + overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) + + crop_width = int(math.ceil((overlap * (n_crops_per_side - 1) + im_width) / n_crops_per_side)) + crop_height = int(math.ceil((overlap * (n_crops_per_side - 1) + im_height) / n_crops_per_side)) + + crop_box_x0 = [int((crop_width - overlap) * i) for i in range(n_crops_per_side)] + crop_box_y0 = [int((crop_height - overlap) * i) for i in range(n_crops_per_side)] + + for left, top in product(crop_box_x0, crop_box_y0): + box = [left, top, min(left + crop_width, im_width), min(top + crop_height, im_height)] + crop_boxes.append(box) + layer_idxs.append(i_layer + 1) + + return crop_boxes, layer_idxs + + +def _build_point_grid(n_per_side: int) -> torch.Tensor: + """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" + offset = 1 / (2 * n_per_side) + points_one_side = torch.linspace(offset, 1 - offset, n_per_side) + points_x = torch.tile(points_one_side[None, :], (n_per_side, 1)) + points_y = torch.tile(points_one_side[:, None], (1, n_per_side)) + points = torch.stack([points_x, points_y], dim=-1).reshape(-1, 2) + return points + + +def _generate_crop_images( + crop_boxes, image, points_grid, layer_idxs, target_size, original_size, input_data_format=None +): + """ + Takes as an input bounding boxes that are used to crop the image. Based in the crops, the corresponding points are + also passed. + """ + cropped_images = [] + total_points_per_crop = [] + for i, crop_box in enumerate(crop_boxes): + left, top, right, bottom = crop_box + cropped_im = image[:, top:bottom, left:right] + + cropped_images.append(cropped_im) + + cropped_im_size = cropped_im.shape[-2:] + points_scale = torch.tensor(cropped_im_size).flip(dims=(0,)).unsqueeze(0) + + points = points_grid[layer_idxs[i]] * points_scale + normalized_points = _normalize_coordinates(target_size, points, original_size) + total_points_per_crop.append(normalized_points) + + return cropped_images, total_points_per_crop + + +def _normalize_coordinates( + target_size: int, coords: torch.Tensor, original_size: tuple[int, int], is_bounding_box=False +) -> torch.Tensor: + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (height, width) + format. + """ + old_height, old_width = original_size + + scale = target_size * 1.0 / max(old_height, old_width) + new_height, new_width = old_height * scale, old_width * scale + new_width = int(new_width + 0.5) + new_height = int(new_height + 0.5) + + coords = deepcopy(coords).float() + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + + coords[..., 0] = coords[..., 0] * (new_width / old_width) + coords[..., 1] = coords[..., 1] * (new_height / old_height) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + +def _rle_to_mask(rle: dict[str, Any]) -> torch.Tensor: + """Compute a binary mask from an uncompressed RLE.""" + height, width = rle["size"] + mask = torch.empty(height * width, dtype=bool) + idx = 0 + parity = False + for count in rle["counts"]: + mask[idx : idx + count] = parity + idx += count + parity = not parity + mask = mask.reshape(width, height) + return mask.transpose(0, 1) # Reshape to original shape + + +def _post_process_for_mask_generation(rle_masks, iou_scores, mask_boxes, amg_crops_nms_thresh=0.7): + """ + Perform NMS (Non Maximum Suppression) on the outputs. + + Args: + rle_masks (`torch.Tensor`): + binary masks in the RLE format + iou_scores (`torch.Tensor` of shape (nb_masks, 1)): + iou_scores predicted by the model + mask_boxes (`torch.Tensor`): + The bounding boxes corresponding to segmentation masks + amg_crops_nms_thresh (`float`, *optional*, defaults to 0.7): + NMS threshold. + """ + keep_by_nms = batched_nms( + boxes=mask_boxes.float(), + scores=iou_scores, + idxs=torch.zeros(mask_boxes.shape[0]), + iou_threshold=amg_crops_nms_thresh, + ) + + iou_scores = iou_scores[keep_by_nms] + rle_masks = [rle_masks[i] for i in keep_by_nms] + mask_boxes = mask_boxes[keep_by_nms] + masks = [_rle_to_mask(rle) for rle in rle_masks] + + return masks, iou_scores, rle_masks, mask_boxes + + +def _mask_to_rle(input_mask: "torch.Tensor"): + """ + Encodes masks the run-length encoding (RLE), in the format expected by pycoco tools. + """ + # Put in fortran order and flatten height and width + batch_size, height, width = input_mask.shape + input_mask = input_mask.permute(0, 2, 1).flatten(1) + + # Compute change indices + diff = input_mask[:, 1:] ^ input_mask[:, :-1] + change_indices = diff.nonzero() + + # Encode run length + out = [] + for i in range(batch_size): + cur_idxs = change_indices[change_indices[:, 0] == i, 1] + 1 + if len(cur_idxs) == 0: + # No changes => either all 0 or all 1 + # If the entire mask is 0, RLE is [height*width] or if the entire mask is 1, RLE is [0, height*width]. + if input_mask[i, 0] == 0: + out.append({"size": [height, width], "counts": [height * width]}) + else: + out.append({"size": [height, width], "counts": [0, height * width]}) + continue + btw_idxs = cur_idxs[1:] - cur_idxs[:-1] + counts = [] if input_mask[i, 0] == 0 else [0] + counts += [cur_idxs[0].item()] + btw_idxs.tolist() + [height * width - cur_idxs[-1].item()] + out.append({"size": [height, width], "counts": counts}) + return out + + +def _scale_boxes(boxes, target_sizes): + """ + Scale batch of bounding boxes to the target sizes. + + Args: + boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): + Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. + target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): + Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. + + Returns: + `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. + """ + + if isinstance(target_sizes, (list, tuple)): + image_height = torch.tensor([i[0] for i in target_sizes]) + image_width = torch.tensor([i[1] for i in target_sizes]) + elif isinstance(target_sizes, torch.Tensor): + image_height, image_width = target_sizes.unbind(1) + else: + raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor") + + scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1) + scale_factor = scale_factor.unsqueeze(1).to(boxes.device) + boxes = boxes * scale_factor + return boxes + + +@auto_docstring +class Sam3ImageProcessor(TorchvisionBackend): + valid_kwargs = Sam3ImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 1008, "width": 1008} + mask_size = {"height": 288, "width": 288} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + + # disable SAM padding logic + do_pad = None + pad_size = None + mask_pad_size = None + + def __init__(self, **kwargs: Unpack[Sam3ImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + segmentation_maps: ImageInput | None = None, + **kwargs: Unpack[Sam3ImageProcessorKwargs], + ) -> BatchFeature: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to preprocess. + """ + return super().preprocess(images, segmentation_maps, **kwargs) + + def _standardize_kwargs( + self, + mask_size: SizeDict | None = None, + **kwargs, + ) -> dict: + """ + Update kwargs that need further processing before being validated. + """ + if mask_size is not None and not isinstance(mask_size, SizeDict): + mask_size = SizeDict(**get_size_dict(mask_size, param_name="mask_size")) + kwargs["mask_size"] = mask_size + return super()._standardize_kwargs(**kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + device: Union[str, "torch.device"] | None = None, + **kwargs: Unpack[Sam3ImageProcessorKwargs], + ) -> BatchFeature: + """ + Preprocess image-like inputs. + """ + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device + ) + original_sizes = [image.shape[-2:] for image in images] + images_kwargs = kwargs.copy() + pixel_values = self._preprocess(images, **images_kwargs) + data = { + "pixel_values": pixel_values, + "original_sizes": original_sizes, + } + + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + "resample": PILImageResampling.NEAREST, + "size": segmentation_maps_kwargs.pop("mask_size"), + } + ) + processed_segmentation_maps = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + data["labels"] = processed_segmentation_maps.squeeze(1).to(torch.int64) + + return BatchFeature(data=data, tensor_type=kwargs["return_tensors"]) + + def _preprocess( + self, + images: list["torch.Tensor"], + return_tensors: str | TensorType | None, + **kwargs, + ) -> "torch.Tensor": + return super()._preprocess(images, return_tensors=return_tensors, **kwargs).pixel_values + + def generate_crop_boxes( + self, + image: "np.ndarray | PIL.Image.Image | torch.Tensor", + target_size, + crop_n_layers: int = 0, + overlap_ratio: float = 512 / 1500, + points_per_crop: int | None = 32, + crop_n_points_downscale_factor: list[int] | None = 1, + device: Optional["torch.device"] = None, + ): + """ + Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer. + + Args: + image (`torch.Tensor`): + Input original image + target_size (`int`): + Target size of the resized image + crop_n_layers (`int`, *optional*, defaults to 0): + If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where + each layer has 2**i_layer number of image crops. + overlap_ratio (`float`, *optional*, defaults to 512/1500): + Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of + the image length. Later layers with more crops scale down this overlap. + points_per_crop (`int`, *optional*, defaults to 32): + Number of points to sam3ple from each crop. + crop_n_points_downscale_factor (`list[int]`, *optional*, defaults to 1): + The number of points-per-side sam3pled in layer n is scaled down by crop_n_points_downscale_factor**n. + device (`torch.device`, *optional*, defaults to None): + Device to use for the computation. If None, cpu will be used. + """ + image = self.process_image(image) + crop_boxes, points_per_crop, cropped_images, input_labels = _generate_crop_boxes( + image, + target_size, + crop_n_layers, + overlap_ratio, + points_per_crop, + crop_n_points_downscale_factor, + ) + if device is None: + device = torch.device("cpu") + crop_boxes = crop_boxes.to(device) + points_per_crop = points_per_crop.to(device) + # cropped_images stays as torch.Tensor + input_labels = input_labels.to(device) + + return crop_boxes, points_per_crop, cropped_images, input_labels + + def filter_masks( + self, + masks, + iou_scores, + original_size, + cropped_box_image, + pred_iou_thresh=0.88, + stability_score_thresh=0.95, + mask_threshold=0, + stability_score_offset=1, + ): + """ + Filters the predicted masks by selecting only the ones that meets several criteria. The first criterion being + that the iou scores needs to be greater than `pred_iou_thresh`. The second criterion is that the stability + score needs to be greater than `stability_score_thresh`. The method also converts the predicted masks to + bounding boxes and pad the predicted masks if necessary. + + Args: + masks (`torch.Tensor`): + Input masks. + iou_scores (`torch.Tensor`): + List of IoU scores. + original_size (`tuple[int,int]`): + Size of the original image. + cropped_box_image (`torch.Tensor`): + The cropped image. + pred_iou_thresh (`float`, *optional*, defaults to 0.88): + The threshold for the iou scores. + stability_score_thresh (`float`, *optional*, defaults to 0.95): + The threshold for the stability score. + mask_threshold (`float`, *optional*, defaults to 0): + The threshold for the predicted masks. + stability_score_offset (`float`, *optional*, defaults to 1): + The offset for the stability score used in the `_compute_stability_score` method. + """ + original_height, original_width = original_size + iou_scores = iou_scores.flatten(0, 1) + masks = masks.flatten(0, 1) + + if masks.shape[0] != iou_scores.shape[0]: + raise ValueError("masks and iou_scores must have the sam3e batch size.") + + if masks.device != iou_scores.device: + iou_scores = iou_scores.to(masks.device) + + batch_size = masks.shape[0] + + keep_mask = torch.ones(batch_size, dtype=torch.bool, device=masks.device) + + if pred_iou_thresh > 0.0: + keep_mask = keep_mask & (iou_scores > pred_iou_thresh) + + # compute stability score + if stability_score_thresh > 0.0: + stability_scores = _compute_stability_score(masks, mask_threshold, stability_score_offset) + keep_mask = keep_mask & (stability_scores > stability_score_thresh) + + scores = iou_scores[keep_mask] + masks = masks[keep_mask] + + # binarize masks + masks = masks > mask_threshold + converted_boxes = _batched_mask_to_box(masks) + + keep_mask = ~_is_box_near_crop_edge( + converted_boxes, cropped_box_image, [0, 0, original_width, original_height] + ) + + scores = scores[keep_mask] + masks = masks[keep_mask] + converted_boxes = converted_boxes[keep_mask] + + masks = _pad_masks(masks, cropped_box_image, original_height, original_width) + # conversion to rle is necessary to run non-maximum suppression + masks = _mask_to_rle(masks) + + return masks, scores, converted_boxes + + def post_process_masks( + self, + masks, + original_sizes, + mask_threshold=0.0, + binarize=True, + max_hole_area=0.0, + max_sprinkle_area=0.0, + apply_non_overlapping_constraints=False, + **kwargs, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[torch.Tensor, List[torch.Tensor], np.ndarray, List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + mask_threshold (`float`, *optional*, defaults to 0.0): + Threshold for binarization and post-processing operations. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + max_hole_area (`float`, *optional*, defaults to 0.0): + The maximum area of a hole to fill. + max_sprinkle_area (`float`, *optional*, defaults to 0.0): + The maximum area of a sprinkle to fill. + apply_non_overlapping_constraints (`bool`, *optional*, defaults to `False`): + Whether to apply non-overlapping constraints to the masks. + + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + if isinstance(original_sizes, (torch.Tensor, np.ndarray)): + original_sizes = original_sizes.tolist() + # TODO: add connected components kernel for postprocessing + output_masks = [] + for i, original_size in enumerate(original_sizes): + if isinstance(masks[i], np.ndarray): + masks[i] = torch.from_numpy(masks[i]) + elif not isinstance(masks[i], torch.Tensor): + raise TypeError("Input masks should be a list of `torch.tensors` or a list of `np.ndarray`") + interpolated_mask = F.interpolate(masks[i], original_size, mode="bilinear", align_corners=False) + if apply_non_overlapping_constraints: + interpolated_mask = self._apply_non_overlapping_constraints(interpolated_mask) + if binarize: + interpolated_mask = interpolated_mask > mask_threshold + output_masks.append(interpolated_mask) + + return output_masks + + def post_process_for_mask_generation(self, all_masks, all_scores, all_boxes, crops_nms_thresh): + """ + Post processes mask that are generated by calling the Non Maximum Suppression algorithm on the predicted masks. + + Args: + all_masks (`torch.Tensor`): + List of all predicted segmentation masks + all_scores (`torch.Tensor`): + List of all predicted iou scores + all_boxes (`torch.Tensor`): + List of all bounding boxes of the predicted masks + crops_nms_thresh (`float`): + Threshold for NMS (Non Maximum Suppression) algorithm. + """ + return _post_process_for_mask_generation(all_masks, all_scores, all_boxes, crops_nms_thresh) + + def _apply_non_overlapping_constraints(self, pred_masks: torch.Tensor) -> torch.Tensor: + """ + Apply non-overlapping constraints to the object scores in pred_masks. Here we + keep only the highest scoring object at each spatial location in pred_masks. + """ + batch_size = pred_masks.size(0) + if batch_size == 1: + return pred_masks + + device = pred_masks.device + # "max_obj_inds": object index of the object with the highest score at each location + max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) + # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` + batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] + keep = max_obj_inds == batch_obj_inds + # suppress overlapping regions' scores below -10.0 so that the foreground regions + # don't overlap (here sigmoid(-10.0)=4.5398e-05) + pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) + return pred_masks + + def post_process_semantic_segmentation( + self, outputs, target_sizes: list[tuple] | None = None, threshold: float = 0.5 + ): + """ + Converts the output of [`Sam3Model`] into semantic segmentation maps. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing semantic_seg. + target_sizes (`list[tuple]` of length `batch_size`, *optional*): + List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, + predictions will not be resized. + threshold (`float`, *optional*, defaults to 0.5): + Threshold for binarizing the semantic segmentation masks. + + Returns: + semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic + segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is + specified). Each entry is a binary mask (0 or 1). + """ + # Get semantic segmentation output + # semantic_seg has shape (batch_size, 1, height, width) + semantic_logits = outputs.semantic_seg + + if semantic_logits is None: + raise ValueError( + "Semantic segmentation output is not available in the model outputs. " + "Make sure the model was run with semantic segmentation enabled." + ) + + # Apply sigmoid to convert logits to probabilities + semantic_probs = semantic_logits.sigmoid() + + # Resize and binarize semantic segmentation maps + if target_sizes is not None: + if len(semantic_logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + + semantic_segmentation = [] + + for idx in range(len(semantic_logits)): + resized_probs = torch.nn.functional.interpolate( + semantic_probs[idx].unsqueeze(dim=0), + size=target_sizes[idx], + mode="bilinear", + align_corners=False, + ) + # Binarize: values > threshold become 1, otherwise 0 + semantic_map = (resized_probs[0, 0] > threshold).to(torch.long) + semantic_segmentation.append(semantic_map) + else: + # Binarize without resizing + semantic_segmentation = (semantic_probs[:, 0] > threshold).to(torch.long) + semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] + + return semantic_segmentation + + def post_process_object_detection(self, outputs, threshold: float = 0.3, target_sizes: list[tuple] | None = None): + """ + Converts the raw output of [`Sam3Model`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing pred_boxes, pred_logits, and optionally presence_logits. + threshold (`float`, *optional*, defaults to 0.3): + Score threshold to keep object detection predictions. + target_sizes (`list[tuple[int, int]]`, *optional*): + List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the + batch. If unset, predictions will not be resized. + + Returns: + `list[dict]`: A list of dictionaries, each dictionary containing the following keys: + - **scores** (`torch.Tensor`): The confidence scores for each predicted box on the image. + - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, + bottom_right_y) format. + """ + pred_logits = outputs.pred_logits # (batch_size, num_queries) + pred_boxes = outputs.pred_boxes # (batch_size, num_queries, 4) in xyxy format + presence_logits = outputs.presence_logits # (batch_size, 1) or None + + batch_size = pred_logits.shape[0] + + if target_sizes is not None and len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as images") + + # Compute scores: combine pred_logits with presence_logits if available + batch_scores = pred_logits.sigmoid() + if presence_logits is not None: + presence_scores = presence_logits.sigmoid() # (batch_size, 1) + batch_scores = batch_scores * presence_scores # Broadcast multiplication + + # Boxes are already in xyxy format from the model + batch_boxes = pred_boxes + + # Convert from relative [0, 1] to absolute [0, height/width] coordinates + if target_sizes is not None: + batch_boxes = _scale_boxes(batch_boxes, target_sizes) + + results = [] + for scores, boxes in zip(batch_scores, batch_boxes): + keep = scores > threshold + scores = scores[keep] + boxes = boxes[keep] + results.append({"scores": scores, "boxes": boxes}) + + return results + + def post_process_instance_segmentation( + self, + outputs, + threshold: float = 0.3, + mask_threshold: float = 0.5, + target_sizes: list[tuple] | None = None, + ): + """ + Converts the raw output of [`Sam3Model`] into instance segmentation predictions with bounding boxes and masks. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing pred_boxes, pred_logits, pred_masks, and optionally + presence_logits. + threshold (`float`, *optional*, defaults to 0.3): + Score threshold to keep instance predictions. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold for binarizing the predicted masks. + target_sizes (`list[tuple[int, int]]`, *optional*): + List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the + batch. If unset, predictions will not be resized. + + Returns: + `list[dict]`: A list of dictionaries, each dictionary containing the following keys: + - **scores** (`torch.Tensor`): The confidence scores for each predicted instance on the image. + - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, + bottom_right_y) format. + - **masks** (`torch.Tensor`): Binary segmentation masks for each instance, shape (num_instances, + height, width). + """ + pred_logits = outputs.pred_logits # (batch_size, num_queries) + pred_boxes = outputs.pred_boxes # (batch_size, num_queries, 4) in xyxy format + pred_masks = outputs.pred_masks # (batch_size, num_queries, height, width) + presence_logits = outputs.presence_logits # (batch_size, 1) or None + + batch_size = pred_logits.shape[0] + + if target_sizes is not None and len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as images") + + # Compute scores: combine pred_logits with presence_logits if available + batch_scores = pred_logits.sigmoid() + if presence_logits is not None: + presence_scores = presence_logits.sigmoid() # (batch_size, 1) + batch_scores = batch_scores * presence_scores # Broadcast multiplication + + # Apply sigmoid to mask logits + batch_masks = pred_masks.sigmoid() + + # Boxes are already in xyxy format from the model + batch_boxes = pred_boxes + + # Scale boxes to target sizes if provided + if target_sizes is not None: + batch_boxes = _scale_boxes(batch_boxes, target_sizes) + + results = [] + for idx, (scores, boxes, masks) in enumerate(zip(batch_scores, batch_boxes, batch_masks)): + # Filter by score threshold + keep = scores > threshold + scores = scores[keep] + boxes = boxes[keep] + masks = masks[keep] # (num_keep, height, width) + + # Resize masks to target size if provided + if target_sizes is not None: + target_size = target_sizes[idx] + if len(masks) > 0: + masks = torch.nn.functional.interpolate( + masks.unsqueeze(0), # (1, num_keep, height, width) + size=target_size, + mode="bilinear", + align_corners=False, + ).squeeze(0) # (num_keep, target_height, target_width) + + # Binarize masks + masks = (masks > mask_threshold).to(torch.long) + + results.append({"scores": scores, "boxes": boxes, "masks": masks}) + + return results + + +__all__ = ["Sam3ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/modeling_sam3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/modeling_sam3.py new file mode 100644 index 0000000000000000000000000000000000000000..2e5333cc65e10bac5f49211e86e831fdbb518962 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/modeling_sam3.py @@ -0,0 +1,2429 @@ +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable, Iterable +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ...utils import is_torchvision_available + + +if is_torchvision_available(): + import torchvision + +from transformers import CLIPTextModelWithProjection + +from ... import initialization as init +from ...activations import ACT2FN +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPooling, + ModelOutput, +) +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import auto_docstring, can_return_tuple, logging +from ...utils.generic import ( + TransformersKwargs, + is_flash_attention_requested, + merge_with_config_defaults, +) +from ...utils.import_utils import requires +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from .configuration_sam3 import ( + Sam3Config, + Sam3DETRDecoderConfig, + Sam3DETREncoderConfig, + Sam3GeometryEncoderConfig, + Sam3MaskDecoderConfig, + Sam3VisionConfig, + Sam3ViTConfig, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring +@dataclass +class Sam3VisionEncoderOutput(BaseModelOutputWithPooling): + r""" + fpn_hidden_states (`tuple[torch.FloatTensor]`): + Tuple of multi-level FPN feature maps. + fpn_position_encoding (`tuple[torch.FloatTensor]`): + Tuple of position encodings for each FPN level. + """ + + fpn_hidden_states: tuple[torch.FloatTensor, ...] = None + fpn_position_encoding: tuple[torch.FloatTensor, ...] = None + + +@dataclass +@auto_docstring +class Sam3GeometryEncoderOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_prompts, hidden_size)`): + Encoded geometry prompt features (boxes). + attention_mask (`torch.BoolTensor` of shape `(batch_size, num_prompts)`, *optional*): + Attention mask for geometry prompts where True indicates valid positions and False indicates padding. + """ + + last_hidden_state: torch.FloatTensor = None + attention_mask: torch.BoolTensor | None = None + + +@dataclass +@auto_docstring +class Sam3DETREncoderOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Encoded vision features (flattened from multi-level features). + pos_embeds_flattened (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Flattened position embeddings for the vision features. + text_features (`torch.FloatTensor` of shape `(batch_size, text_seq_len, hidden_size)`, *optional*): + Text features (may be pooled after encoder processing). + spatial_shapes (`torch.LongTensor` of shape `(num_levels, 2)`, *optional*): + Spatial shapes (height, width) for each feature pyramid level. + hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all encoder layers. + attentions (`tuple[torch.FloatTensor]`, *optional*): + Tuple of attention weights from all encoder layers. + """ + + last_hidden_state: torch.FloatTensor = None + pos_embeds_flattened: torch.FloatTensor | None = None + text_features: torch.FloatTensor | None = None + spatial_shapes: torch.LongTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@dataclass +@auto_docstring +class Sam3DETRDecoderOutput(ModelOutput): + r""" + intermediate_hidden_states (`torch.FloatTensor` of shape `(num_layers, batch_size, num_queries, hidden_size)`): + Decoder hidden states from all layers. + reference_boxes (`torch.FloatTensor` of shape `(num_layers, batch_size, num_queries, 4)`): + Predicted reference boxes from all decoder layers in (cx, cy, w, h) format. + presence_logits (`torch.FloatTensor` of shape `(num_layers, batch_size, 1)`): + Presence logits from all decoder layers indicating object presence confidence. + hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all decoder layers. + attentions (`tuple[torch.FloatTensor]`, *optional*): + Tuple of attention weights from all decoder layers (self-attention and cross-attention). + """ + + intermediate_hidden_states: torch.FloatTensor = None + reference_boxes: torch.FloatTensor = None + presence_logits: torch.FloatTensor = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@dataclass +@auto_docstring +class Sam3MaskDecoderOutput(ModelOutput): + r""" + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`): + Predicted segmentation masks for each query. + semantic_seg (`torch.FloatTensor` of shape `(batch_size, 1, height, width)`, *optional*): + Semantic segmentation output. + attentions (`tuple[torch.FloatTensor]`, *optional*): + Tuple of attention weights from mask decoder cross-attention layers. + """ + + pred_masks: torch.FloatTensor = None + semantic_seg: torch.FloatTensor | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@dataclass +@auto_docstring +class Sam3ImageSegmentationOutput(ModelOutput): + r""" + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`): + Predicted segmentation masks for each query. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Predicted bounding boxes in (x1, y1, x2, y2) format. + pred_logits (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*): + Classification confidence scores for each query, computed via dot product between + decoder query features and text features. + presence_logits (`torch.FloatTensor` of shape `(batch_size, 1)`, *optional*): + Presence logits from the DETR decoder presence token (last layer only). These indicate whether objects + are present in the scene. Can be used to compute final scores by multiplying with pred_logits: + `final_scores = pred_logits.sigmoid() * presence_logits.sigmoid()`. + semantic_seg (`torch.FloatTensor` of shape `(batch_size, 1, height, width)`, *optional*): + Semantic segmentation output. + decoder_hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all DETR decoder layers. Each tensor has shape `(batch_size, num_queries, hidden_size)`. + decoder_reference_boxes (`torch.FloatTensor` of shape `(num_layers, batch_size, num_queries, 4)`, *optional*): + Reference boxes from all DETR decoder layers. + encoder_hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all DETR encoder layers. + vision_hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all vision encoder (ViT) layers. + vision_attentions (`tuple[torch.FloatTensor]`, *optional*): + Attention weights from vision encoder (ViT) layers. + detr_encoder_attentions (`tuple[torch.FloatTensor]`, *optional*): + Attention weights from DETR encoder layers. + detr_decoder_attentions (`tuple[torch.FloatTensor]`, *optional*): + Attention weights from DETR decoder layers (self-attention and cross-attention). + mask_decoder_attentions (`tuple[torch.FloatTensor]`, *optional*): + Attention weights from mask decoder layers. + """ + + pred_masks: torch.FloatTensor = None + pred_boxes: torch.FloatTensor = None + pred_logits: torch.FloatTensor | None = None + presence_logits: torch.FloatTensor | None = None + semantic_seg: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_reference_boxes: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + vision_hidden_states: tuple[torch.FloatTensor] | None = None + vision_attentions: tuple[torch.FloatTensor] | None = None + detr_encoder_attentions: tuple[torch.FloatTensor] | None = None + detr_decoder_attentions: tuple[torch.FloatTensor] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor] | None = None + + +def inverse_sigmoid(x: torch.Tensor, eps: float = 1e-3) -> torch.Tensor: + """The inverse function for sigmoid activation function.""" + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +def concat_padded_sequences(seq1, mask1, seq2, mask2, return_index: bool = False): + """ + Concatenates two right-padded sequences, such that the resulting sequence + is contiguous and also right-padded. + + Tensors are batch-first, masks are batch-first with True=valid, False=padding. + + Args: + seq1: A tensor of shape (batch_size, seq1_length, hidden_size). + mask1: A tensor of shape (batch_size, seq1_length) with True=valid, False=padding. + seq2: A tensor of shape (batch_size, seq2_length, hidden_size). + mask2: A tensor of shape (batch_size, seq2_length) with True=valid, False=padding. + return_index: If True, also returns the index of the ids of the element of seq2 + in the concatenated sequence. This can be used to retrieve the elements of seq2. + + Returns: + A tuple (concatenated_sequence, concatenated_mask) if return_index is False, + otherwise (concatenated_sequence, concatenated_mask, index). + The concatenated_mask uses True=valid, False=padding convention. + """ + batch_size, seq1_length, hidden_size = seq1.shape + batch_size2, seq2_length, hidden_size2 = seq2.shape + + assert batch_size == batch_size2 == mask1.size(0) == mask2.size(0) + assert hidden_size == hidden_size2 + assert seq1_length == mask1.size(1) + assert seq2_length == mask2.size(1) + + actual_seq1_lengths = mask1.sum(dim=-1) + actual_seq2_lengths = mask2.sum(dim=-1) + + final_lengths = actual_seq1_lengths + actual_seq2_lengths + max_length = seq1_length + seq2_length + + concatenated_mask = ( + torch.arange(max_length, device=seq2.device)[None].repeat(batch_size, 1) < final_lengths[:, None] + ) + + concatenated_sequence = torch.zeros((batch_size, max_length, hidden_size), device=seq2.device, dtype=seq2.dtype) + concatenated_sequence[:, :seq1_length, :] = seq1 + + # Shift seq2 elements to start at the end of valid seq1 + index = torch.arange(seq2_length, device=seq2.device)[None].repeat(batch_size, 1) + index = index + actual_seq1_lengths[:, None] + + # Scatter seq2 into the right positions + concatenated_sequence = concatenated_sequence.scatter(1, index[:, :, None].expand(-1, -1, hidden_size), seq2) + + if return_index: + return concatenated_sequence, concatenated_mask, index + + return concatenated_sequence, concatenated_mask + + +def box_cxcywh_to_xyxy(x): + """Convert boxes from (cx, cy, w, h) format to (x1, y1, x2, y2) format.""" + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +class Sam3MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Sam3Attention(nn.Module): + """ + Multi-head attention. + Handles standard [batch_size, seq_len, hidden_size] tensors. + """ + + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.hidden_size // config.num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.q_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.k_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.v_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.o_proj = nn.Linear(self.hidden_size, self.hidden_size) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Args: + query: [batch_size, query_len, hidden_size] + key: [batch_size, key_len, hidden_size] + value: [batch_size, value_len, hidden_size] + attention_mask: [batch_size, num_heads, query_len, key_len] or broadcastable + + Returns: + Tuple of (output, attention_weights) + output: [batch_size, query_len, hidden_size] + attention_weights: [batch_size, num_heads, query_len, key_len] + """ + batch_size = query.shape[0] + query_len = query.shape[1] + key_len = key.shape[1] + + query = self.q_proj(query).view(batch_size, query_len, self.num_attention_heads, self.head_dim).transpose(1, 2) + key = self.k_proj(key).view(batch_size, key_len, self.num_attention_heads, self.head_dim).transpose(1, 2) + value = self.v_proj(value).view(batch_size, key_len, self.num_attention_heads, self.head_dim).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if ( + is_flash_attention_requested(self.config) + and attention_mask is not None + and attention_mask.dtype != torch.bool + ): + # Relative position bias tensors are represented as float masks and are incompatible with Flash Attention + # Fallback to SDPA for this call only so the rest of the model can still benefit from FA + attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"] + logger.warning_once( + "Sam3Attention: falling back to SDPA for relative-position cross-attention because " + "Flash Attention does not support additive bias masks." + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_mask, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = attn_output.reshape(batch_size, query_len, self.num_attention_heads * self.head_dim).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +class Sam3ViTRotaryEmbedding(nn.Module): + """ + Vision Rotary Position Embedding for SAM3, following transformers library standards. + Supports 2D (axial) rotary embeddings for spatial dimensions. + """ + + def __init__(self, config: Sam3ViTConfig, end_x: int, end_y: int, scale: float = 1.0): + super().__init__() + dim = config.hidden_size // config.num_attention_heads + # Ensure even dimension for proper axial splitting + if dim % 4 != 0: + raise ValueError("Dimension must be divisible by 4 for axial RoPE") + self.end_x, self.end_y = end_x, end_y + self.dim = dim + self.rope_theta = config.rope_theta + self.scale = scale + freqs = 1.0 / (config.rope_theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) + + flattened_indices = torch.arange(end_x * end_y, dtype=torch.long) + x_positions = (flattened_indices % end_x) * scale + y_positions = torch.div(flattened_indices, end_x, rounding_mode="floor") * scale + freqs_x = torch.outer(x_positions, freqs).float() + freqs_y = torch.outer(y_positions, freqs).float() + inv_freq = torch.cat([freqs_x, freqs_y], dim=-1) + inv_freq = inv_freq.repeat_interleave(2, dim=-1) + # directly register the cos and sin embeddings as we have a fixed feature shape + self.register_buffer("rope_embeddings_cos", inv_freq.cos(), persistent=False) + self.register_buffer("rope_embeddings_sin", inv_freq.sin(), persistent=False) + + @torch.no_grad() + def forward(self) -> tuple[torch.Tensor, torch.Tensor]: + # As the feature map size is fixed for each stage, we can just return the pre-computed embeddings. + return self.rope_embeddings_cos, self.rope_embeddings_sin + + +def rotate_pairwise(x): + """ + pairwise rotation of the hidden dims of the input. Differerent from Llama Half-Tensor Rotation. + + This is an optimized version of the following more explicit implementation: + ```python + x_rotated = torch.zeros_like(x, dtype=x.dtype, device=x.device) + x_rotated[..., ::2] = -x[..., 1::2] + x_rotated[..., 1::2] = x[..., ::2] + return x_rotated + ``` + """ + x = x.view(*x.shape[:-1], -1, 2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return x.flatten(start_dim=-2) + + +def apply_rotary_pos_emb_2d( + q: torch.Tensor, + k: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary position embedding to query and key tensors for self-attention. + + Args: + q: Query tensor of shape (batch_size, num_windows, seq_len, num_heads, head_dim) + k: Key tensor of shape (batch_size, num_windows, seq_len, num_heads, head_dim) + cos: Cosine position embedding of shape (seq_len, head_dim) + sin: Sine position embedding of shape (seq_len, head_dim) + + Returns: + Rotated (q, k) tensors + """ + q_embed = q.float() + q_embed = (q_embed * cos) + (rotate_pairwise(q_embed) * sin) + + k_embed = k.float() + k_embed = (k_embed * cos) + (rotate_pairwise(k_embed) * sin) + + return q_embed.type_as(q), k_embed.type_as(k) + + +class Sam3ViTRoPEAttention(nn.Module): + """Self-attention with rotary position encoding.""" + + def __init__(self, config: Sam3ViTConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.hidden_size // config.num_attention_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = False + + self.q_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.k_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.v_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.o_proj = nn.Linear(self.hidden_size, self.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + **kwargs: Unpack[TransformersKwargs], + ) -> Tensor: + batch_size, height, width, _ = hidden_states.shape + seq_len = height * width + new_shape = (batch_size, seq_len, self.num_attention_heads, self.head_dim) + query = self.q_proj(hidden_states).view(*new_shape).transpose(1, 2) + key = self.k_proj(hidden_states).view(*new_shape).transpose(1, 2) + value = self.v_proj(hidden_states).view(*new_shape).transpose(1, 2) + cos, sin = position_embeddings + query, key = apply_rotary_pos_emb_2d(query, key, cos=cos, sin=sin) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=None, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + attn_output = attn_output.reshape(batch_size, height, width, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Sam3ViTPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config: Sam3ViTConfig): + super().__init__() + image_size, patch_size = config.pretrain_image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + + image_size = image_size if isinstance(image_size, Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size, bias=False) + + def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: + embeddings = self.projection(pixel_values.to(self.projection.weight.dtype)).flatten(2).transpose(1, 2) + return embeddings + + +class Sam3ViTEmbeddings(nn.Module): + """ + Construct the patch embeddings and position embeddings for SAM3 ViT. + + Position embeddings are tiled (not interpolated) when resizing to match different input sizes. + """ + + def __init__(self, config: Sam3ViTConfig): + super().__init__() + + self.patch_embeddings = Sam3ViTPatchEmbeddings(config) + num_patches = self.patch_embeddings.num_patches + self.position_embeddings = nn.Parameter( + torch.randn(1, num_patches, config.hidden_size) + ) # !Remove cls token in convert weights! + + self.dropout = nn.Dropout(config.hidden_dropout) + self.patch_size = config.patch_size + + def _tile_position_embeddings( + self, + position_embeddings: torch.Tensor, + height: int, + width: int, + ) -> torch.Tensor: + """ + Tile position embeddings to match target spatial dimensions. + Args: + position_embeddings: Shape [1, num_pretrain_patches, hidden_size] + height: Target height in patches + width: Target width in patches + + Returns: + Shape [1, height * width, hidden_size] + """ + pretrain_size = int(position_embeddings.shape[1] ** 0.5) + + # Skip tiling if sizes match (but always tile during tracing for consistent graph) + if not torch.jit.is_tracing() and pretrain_size == height and pretrain_size == width: + return position_embeddings.reshape(1, height * width, -1) + + # Tile position embeddings to match target spatial dimensions + hidden_size = position_embeddings.shape[-1] + pos_embed = position_embeddings.reshape(1, pretrain_size, pretrain_size, hidden_size).permute(0, 3, 1, 2) + repeat_h = height // pretrain_size + 1 + repeat_w = width // pretrain_size + 1 + pos_embed = pos_embed.tile([1, 1, repeat_h, repeat_w])[:, :, :height, :width] + return pos_embed.permute(0, 2, 3, 1).reshape(1, height * width, hidden_size) + + def forward( + self, + pixel_values: torch.Tensor, + interpolate_pos_encoding: bool = False, + ) -> torch.Tensor: + height, width = pixel_values.shape[-2:] + embeddings = self.patch_embeddings(pixel_values) + + # Calculate spatial dimensions in patches + height_patches = height // self.patch_size + width_patches = width // self.patch_size + + position_embeddings = self._tile_position_embeddings( + self.position_embeddings, + height_patches, + width_patches, + ) + embeddings = embeddings + position_embeddings + embeddings = self.dropout(embeddings) + + return embeddings + + +def window_partition(hidden_state, window_size): + """ + Partition into non-overlapping windows with padding if needed. + + Args: + hidden_state (`torch.Tensor`): + Input tokens with [batch_size, height, width, num_channels]. + window_size (`int`): + Window size. + + Returns: + `tuple(torch.FloatTensor)` comprising various elements: + - windows: windows after partition with [batch_size * num_windows, window_size, window_size, num_channels]. + - (padded_height, padded_width): padded height and width before partition + """ + batch_size, height, width, num_channels = hidden_state.shape + pad_height = (window_size - height % window_size) % window_size + pad_width = (window_size - width % window_size) % window_size + + # Noop in case pad_width == 0 and pad_height == 0. + hidden_state = nn.functional.pad(hidden_state, (0, 0, 0, pad_width, 0, pad_height)) + + padded_height, padded_width = height + pad_height, width + pad_width + + hidden_state = hidden_state.view( + batch_size, padded_height // window_size, window_size, padded_width // window_size, window_size, num_channels + ) + windows = hidden_state.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, num_channels) + return windows, (padded_height, padded_width) + + +def window_unpartition(windows, window_size, pad_height_width, height_width): + """ + Window unpartition into original sequences and removing padding. + + Args: + windows (`torch.Tensor`): + Input tokens with [batch_size * num_windows, window_size, window_size, num_channels]. + window_size (`int`): + Window size. + pad_height_width (`tuple[int]`): + Padded height and width (padded_height, padded_width). + height_width (`tuple[int]`): + Original height and width before padding. + + Returns: + hidden_state: unpartitioned sequences with [batch_size, height, width, num_channels]. + """ + padded_height, padded_width = pad_height_width + height, width = height_width + batch_size = windows.shape[0] // (padded_height * padded_width // window_size // window_size) + hidden_state = windows.view( + batch_size, padded_height // window_size, padded_width // window_size, window_size, window_size, -1 + ) + hidden_state = hidden_state.permute(0, 1, 3, 2, 4, 5).contiguous() + hidden_state = hidden_state.view(batch_size, padded_height, padded_width, -1) + + # We always have height <= padded_height and width <= padded_width + hidden_state = hidden_state[:, :height, :width, :].contiguous() + return hidden_state + + +class Sam3ViTLayerScale(nn.Module): + def __init__(self, config) -> None: + super().__init__() + self.lambda1 = nn.Parameter(config.layer_scale_init_value * torch.ones(config.hidden_size)) + + def forward(self, hidden_state: torch.Tensor) -> torch.Tensor: + return hidden_state * self.lambda1 + + +class Sam3ViTLayer(GradientCheckpointingLayer): + """Vision Transformer layer with rotary position embeddings and optional windowed attention.""" + + def __init__(self, config: Sam3ViTConfig, window_size: int = 0) -> None: + super().__init__() + + hidden_size = config.hidden_size + image_size = config.image_size + image_size = image_size if isinstance(image_size, (list, tuple)) else (image_size, image_size) + + patch_size = config.patch_size + patch_size = patch_size if isinstance(patch_size, (list, tuple)) else (patch_size, patch_size) + + input_size = (image_size[0] // patch_size[0], image_size[1] // patch_size[1]) + self.layer_norm1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + rotary_input_size = input_size if window_size == 0 else (window_size, window_size) + rotary_scale = config.window_size / rotary_input_size[0] + self.rotary_emb = Sam3ViTRotaryEmbedding( + config, end_x=rotary_input_size[0], end_y=rotary_input_size[1], scale=rotary_scale + ) + self.attention = Sam3ViTRoPEAttention(config) + self.layer_norm2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps) + self.mlp = Sam3MLP(config) + self.dropout = nn.Dropout(config.hidden_dropout) + + self.window_size = window_size + + def forward( + self, + hidden_states: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + + if self.window_size > 0: + height, width = hidden_states.shape[1], hidden_states.shape[2] + # Partition into non-overlapping windows for efficient attention + hidden_states, pad_height_width = window_partition(hidden_states, self.window_size) + + position_embeddings = self.rotary_emb() + hidden_states, _ = self.attention(hidden_states, position_embeddings, **kwargs) + + if self.window_size > 0: + # Reverse window partition to restore original spatial layout + hidden_states = window_unpartition(hidden_states, self.window_size, pad_height_width, (height, width)) + + hidden_states = residual + hidden_states + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.dropout(hidden_states) + + return hidden_states + + +@auto_docstring +@requires(backends=("torch", "torchvision")) +class Sam3PreTrainedModel(PreTrainedModel): + config_class = Sam3Config + base_model_prefix = "sam3" + main_input_name = "pixel_values" + input_modalities = ["image", "text"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_attention_backend = True + + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Sam3ViTEmbeddings): + init.normal_(module.position_embeddings, mean=0.0, std=self.config.initializer_range) + elif isinstance(module, Sam3ViTRotaryEmbedding): + end_x, end_y = module.end_x, module.end_y + dim = module.dim + freqs = 1.0 / (module.rope_theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) + flattened_indices = torch.arange(end_x * end_y, dtype=torch.long) + x_positions = (flattened_indices % end_x) * module.scale + y_positions = torch.div(flattened_indices, end_x, rounding_mode="floor") * module.scale + freqs_x = torch.outer(x_positions, freqs).float() + freqs_y = torch.outer(y_positions, freqs).float() + inv_freq = torch.cat([freqs_x, freqs_y], dim=-1) + inv_freq = inv_freq.repeat_interleave(2, dim=-1) + init.copy_(module.rope_embeddings_cos, inv_freq.cos()) + init.copy_(module.rope_embeddings_sin, inv_freq.sin()) + + +@auto_docstring +class Sam3ViTModel(Sam3PreTrainedModel): + config: Sam3ViTConfig + _can_record_outputs = { + "hidden_states": Sam3ViTLayer, + "attentions": Sam3ViTRoPEAttention, + } + + def __init__(self, config: Sam3ViTConfig): + super().__init__(config) + self.config = config + self.embeddings = Sam3ViTEmbeddings(config) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layers = nn.ModuleList( + [ + Sam3ViTLayer(config, window_size=config.window_size if i not in config.global_attn_indexes else 0) + for i in range(config.num_hidden_layers) + ] + ) + self.post_init() + + def get_input_embeddings(self) -> Sam3ViTPatchEmbeddings: + return self.embeddings.patch_embeddings + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + hidden_states = self.embeddings(pixel_values) # [batch_size, seq_len, hidden_size] + + batch_size = hidden_states.shape[0] + height = pixel_values.shape[-2] // self.config.patch_size + width = pixel_values.shape[-1] // self.config.patch_size + hidden_size = hidden_states.shape[-1] + + # Reshape to spatial format for windowed attention: [batch_size, height, width, hidden_size] + hidden_states = hidden_states.view(batch_size, height, width, hidden_size) + + hidden_states = self.layer_norm(hidden_states) + for layer in self.layers: + hidden_states = layer(hidden_states, **kwargs) + + # Reshape back to sequence format: [batch_size, height*width, hidden_size] + hidden_states = hidden_states.view(batch_size, height * width, hidden_size) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +class Sam3SinePositionEmbedding(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one used by the Attention is all you + need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: float | None = None + ): + super().__init__() + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + self.scale = 2 * math.pi if scale is None else scale + + def encode_1d_positions(self, x: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """ + Encode 1D coordinate pairs using sine/cosine positional embeddings. + + Args: + x: 1D tensor of x coordinates (flattened) + y: 1D tensor of y coordinates (flattened) + + Returns: + Tuple of (pos_x, pos_y) positional embeddings + """ + x_embed = x * self.scale + y_embed = y * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=x.device).to(x.dtype) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, None] / dim_t + pos_y = y_embed[:, None] / dim_t + pos_x = torch.stack((pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2).flatten(1) + pos_y = torch.stack((pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2).flatten(1) + return pos_x, pos_y + + def encode_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """ + Encode 4D box coordinates (x, y, w, h) for decoder conditioning using sine/cosine embeddings. + + Args: + boxes: Box coordinates [batch_size, num_queries, 4] in (x, y, w, h) format + + Returns: + Position embeddings [batch_size, num_queries, num_pos_feats*4] + """ + assert boxes.size(-1) == 4, f"Expected 4D box coordinates (x, y, w, h), got shape {boxes.shape}" + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=boxes.device).to(boxes.dtype) + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) + + x_embed = boxes[:, :, 0] * self.scale + y_embed = boxes[:, :, 1] * self.scale + w_embed = boxes[:, :, 2] * self.scale + h_embed = boxes[:, :, 3] * self.scale + + pos_x = x_embed[:, :, None] / dim_t + pos_y = y_embed[:, :, None] / dim_t + pos_w = w_embed[:, :, None] / dim_t + pos_h = h_embed[:, :, None] / dim_t + + pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) + pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2) + pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2) + pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2) + + pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2) + + return pos + + @compile_compatible_method_lru_cache(maxsize=4) + def forward( + self, + shape: torch.Size, + device: torch.device | str, + dtype: torch.dtype, + mask: Tensor | None = None, + ) -> Tensor: + if mask is None: + mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool) + not_mask = (~mask).to(dtype) + y_embed = not_mask.cumsum(1) + x_embed = not_mask.cumsum(2) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype) + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +class Sam3FPNLayer(nn.Module): + def __init__(self, in_channels: int, fpn_dim: int, scale_factor: float): + super().__init__() + self.scale_factor = scale_factor + + # Build the upsampling/downsampling layers based on scale factor + self.scale_layers = nn.ModuleList() + + if scale_factor == 4.0: + self.scale_layers.append(nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)) + self.scale_layers.append(nn.GELU()) + self.scale_layers.append(nn.ConvTranspose2d(in_channels // 2, in_channels // 4, kernel_size=2, stride=2)) + intermediate_channels = in_channels // 4 + elif scale_factor == 2.0: + self.scale_layers.append(nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)) + intermediate_channels = in_channels // 2 + elif scale_factor == 1.0: + intermediate_channels = in_channels + elif scale_factor == 0.5: + self.scale_layers.append(nn.MaxPool2d(kernel_size=2, stride=2)) + intermediate_channels = in_channels + else: + raise NotImplementedError(f"scale_factor={scale_factor} is not supported yet.") + + self.proj1 = nn.Conv2d(in_channels=intermediate_channels, out_channels=fpn_dim, kernel_size=1) + self.proj2 = nn.Conv2d(in_channels=fpn_dim, out_channels=fpn_dim, kernel_size=3, padding=1) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = hidden_states.to(self.proj1.weight.dtype) + for layer in self.scale_layers: + hidden_states = layer(hidden_states) + + hidden_states = self.proj1(hidden_states) + hidden_states = self.proj2(hidden_states) + + return hidden_states + + +class Sam3VisionNeck(nn.Module): + def __init__(self, config: Sam3VisionConfig): + super().__init__() + self.config = config + + self.position_encoding = Sam3SinePositionEmbedding(num_pos_feats=config.fpn_hidden_size // 2, normalize=True) + + # Create one FPN layer per scale factor + self.fpn_layers = nn.ModuleList( + [ + Sam3FPNLayer( + in_channels=config.backbone_config.hidden_size, fpn_dim=config.fpn_hidden_size, scale_factor=scale + ) + for scale in config.scale_factors + ] + ) + + def forward(self, hidden_states: torch.Tensor) -> tuple[tuple[torch.Tensor, ...], tuple[torch.Tensor, ...]]: + fpn_hidden_states = () + fpn_position_encoding = () + + for fpn_layer in self.fpn_layers: + fpn_output = fpn_layer(hidden_states) + fpn_hidden_states += (fpn_output,) + # Generate position encoding for this FPN level + pos_enc = self.position_encoding(fpn_output.shape, fpn_output.device, fpn_output.dtype) + fpn_position_encoding += (pos_enc,) + + return fpn_hidden_states, fpn_position_encoding + + +@auto_docstring( + custom_intro=""" + The vision model from Sam without any head or projection on top. + """ +) +class Sam3VisionModel(Sam3PreTrainedModel): + config_class = Sam3VisionConfig + main_input_name = "pixel_values" + + def __init__(self, config: Sam3VisionConfig): + super().__init__(config) + self.config = config + self.backbone = AutoModel.from_config(config.backbone_config) + self.neck = Sam3VisionNeck(config) + + self.post_init() + + def get_input_embeddings(self): + return self.backbone.get_input_embeddings() + + @can_return_tuple + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3VisionEncoderOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + backbone_output = self.backbone(pixel_values, **kwargs) + hidden_states = backbone_output.last_hidden_state # [batch_size, seq_len, hidden_size] + + # Reshape for FPN neck: [batch_size, seq_len, hidden_size] -> [batch_size, hidden_size, height, width] + batch_size = hidden_states.shape[0] + height = pixel_values.shape[-2] // self.config.backbone_config.patch_size + width = pixel_values.shape[-1] // self.config.backbone_config.patch_size + hidden_states_spatial = hidden_states.view(batch_size, height, width, -1).permute(0, 3, 1, 2) + fpn_hidden_states, fpn_position_encoding = self.neck(hidden_states_spatial) + + return Sam3VisionEncoderOutput( + last_hidden_state=hidden_states, + fpn_hidden_states=fpn_hidden_states, + fpn_position_encoding=fpn_position_encoding, + hidden_states=backbone_output.hidden_states, + attentions=backbone_output.attentions, + ) + + +class Sam3GeometryEncoderLayer(nn.Module): + def __init__(self, config: Sam3GeometryEncoderConfig): + super().__init__() + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + self.self_attn = Sam3Attention(config) + self.dropout = nn.Dropout(config.dropout) + + self.cross_attn = Sam3Attention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam3MLP(config) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + def forward( + self, + prompt_feats: Tensor, + vision_feats: Tensor, + vision_pos_encoding: Tensor, + prompt_mask: Tensor, + **kwargs: Unpack[TransformersKwargs], + ): + residual = prompt_feats + hidden_states = self.layer_norm1(prompt_feats) + hidden_states, _ = self.self_attn( + query=hidden_states, key=hidden_states, value=hidden_states, attention_mask=prompt_mask, **kwargs + ) + hidden_states = self.dropout(hidden_states) + residual + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + key = vision_feats + vision_pos_encoding + hidden_states, _ = self.cross_attn(query=hidden_states, key=key, value=vision_feats, **kwargs) + hidden_states = self.dropout(hidden_states) + residual + residual = hidden_states + hidden_states = self.layer_norm3(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = self.dropout(hidden_states) + residual + + return hidden_states + + +class Sam3GeometryEncoder(nn.Module): + """ + Encoder for geometric prompts (boxes). + + Boxes are encoded using three approaches: + - Direct projection: linear projection from coordinate space to hidden_size + - Pooling: pool features from the backbone at the specified location (ROI align for boxes) + - Position encoding: use position encoding of the box center + + These encodings are combined additively and further processed with transformer layers. + """ + + def __init__(self, config: Sam3GeometryEncoderConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.roi_size = config.roi_size + + self.position_encoding = Sam3SinePositionEmbedding(num_pos_feats=config.hidden_size // 2, normalize=True) + self.label_embed = nn.Embedding(2, self.hidden_size) + self.cls_embed = nn.Embedding(1, self.hidden_size) + + # Box encoding layers + self.boxes_direct_project = nn.Linear(4, self.hidden_size) + self.boxes_pool_project = nn.Conv2d(self.hidden_size, self.hidden_size, self.roi_size) + self.boxes_pos_enc_project = nn.Linear(self.hidden_size + 2, self.hidden_size) + + # Image feature normalization + self.vision_layer_norm = nn.LayerNorm(self.hidden_size) + + # Prompt projection and normalization + self.final_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.prompt_layer_norm = nn.LayerNorm(self.hidden_size) + + # Transformer layers + self.layers = nn.ModuleList([Sam3GeometryEncoderLayer(config) for _ in range(config.num_layers)]) + self.output_layer_norm = nn.LayerNorm(self.hidden_size) + + def _encode_box_coordinates( + self, center_x: torch.Tensor, center_y: torch.Tensor, width: torch.Tensor, height: torch.Tensor + ) -> torch.Tensor: + """ + Encode box coordinates by combining position-encoded centers with raw width/height. + + Args: + center_x: 1D tensor of box center x coordinates + center_y: 1D tensor of box center y coordinates + width: 1D tensor of box widths + height: 1D tensor of box heights + + Returns: + Encoded box coordinates [N, embedding_dim] + """ + pos_x, pos_y = self.position_encoding.encode_1d_positions(center_x, center_y) + pos = torch.cat((pos_y, pos_x, height[:, None], width[:, None]), dim=1) + return pos + + def _encode_boxes(self, boxes, boxes_mask, boxes_labels, vision_features): + """Encode box prompts. Mask convention: True=valid, False=padding.""" + batch_size, num_boxes = boxes.shape[:2] + height, width = vision_features.shape[-2:] + boxes_embed = self.boxes_direct_project(boxes) + + # Pool features using ROI align + # Convert boxes from CxCyWH to xyxy format and denormalize + boxes_xyxy = box_cxcywh_to_xyxy(boxes) + scale = torch.tensor([width, height, width, height], dtype=boxes_xyxy.dtype, device=boxes_xyxy.device) + scale = scale.view(1, 1, 4) + boxes_xyxy = boxes_xyxy * scale + # ROI align expects list of boxes per batch element, + # convert from bfloat16 to float16 as roi_align only supports float16 and float32 + dtype = torch.float16 if vision_features.dtype == torch.bfloat16 else vision_features.dtype + sampled_features = torchvision.ops.roi_align( + vision_features.to(dtype), boxes_xyxy.to(dtype).unbind(0), self.roi_size + ).to(vision_features.dtype) + + pooled_projection = self.boxes_pool_project(sampled_features) + pooled_projection = pooled_projection.view(batch_size, num_boxes, self.hidden_size) + boxes_embed = boxes_embed + pooled_projection + + # Add position encoding + center_x, center_y, box_width, box_height = boxes.unbind(-1) + pos_enc = self._encode_box_coordinates( + center_x.flatten(), center_y.flatten(), box_width.flatten(), box_height.flatten() + ) + pos_enc = pos_enc.view(batch_size, num_boxes, pos_enc.shape[-1]) + pos_projection = self.boxes_pos_enc_project(pos_enc) + boxes_embed = boxes_embed + pos_projection + + # Add label embeddings (positive/negative) + label_embed = self.label_embed(boxes_labels.long()) + return label_embed + boxes_embed, boxes_mask + + def forward( + self, + box_embeddings: torch.Tensor, + box_mask: torch.Tensor, + box_labels: torch.Tensor, + img_feats: tuple[torch.Tensor, ...], + img_pos_embeds: tuple[torch.Tensor, ...] | None = None, + ): + """ + Forward pass for encoding geometric prompts. + + Args: + box_embeddings: Box coordinates in CxCyWH format [batch_size, num_boxes, 4] + box_mask: Attention mask for boxes [batch_size, num_boxes] + box_labels: Labels for boxes (positive/negative) [batch_size, num_boxes] + img_feats: Image features from vision encoder + img_pos_embeds: Optional position embeddings for image features + + Returns: + Sam3GeometryEncoderOutput containing encoded geometry features and attention mask. + """ + batch_size = box_embeddings.shape[0] + + # Prepare vision features for cross-attention: flatten spatial dimensions + vision_feats = img_feats[-1] # [B, C, H, W] + vision_pos_embeds = img_pos_embeds[-1] if img_pos_embeds is not None else torch.zeros_like(vision_feats) + vision_feats_flat = vision_feats.flatten(2).transpose(1, 2) # [B, H*W, C] + vision_pos_embeds_flat = vision_pos_embeds.flatten(2).transpose(1, 2) # [B, H*W, C] + + # Normalize image features for pooling operations + img_feats_last = img_feats[-1] # [B, C, H, W] + img_feats_last = img_feats_last.permute(0, 2, 3, 1) # [B, H, W, C] + normalized_img_feats = self.vision_layer_norm(img_feats_last) + normalized_img_feats = normalized_img_feats.permute(0, 3, 1, 2) # [B, C, H, W] + + prompt_embeds, prompt_mask = self._encode_boxes(box_embeddings, box_mask, box_labels, normalized_img_feats) + + # Add CLS token (always valid) + cls_embed = self.cls_embed.weight.view(1, self.hidden_size).unsqueeze(0).expand(batch_size, -1, -1) + cls_mask = torch.ones(batch_size, 1, dtype=prompt_mask.dtype, device=prompt_mask.device) + prompt_embeds, prompt_mask = concat_padded_sequences(prompt_embeds, prompt_mask, cls_embed, cls_mask) + + prompt_embeds = self.prompt_layer_norm(self.final_proj(prompt_embeds)) + + # Create bidirectional attention mask for transformer layers + prompt_attention_mask = None + if prompt_mask is not None: + prompt_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=prompt_embeds, + attention_mask=prompt_mask, + ) + + # Apply transformer layers with cross-attention to vision features + for layer in self.layers: + prompt_embeds = layer( + prompt_feats=prompt_embeds, + vision_feats=vision_feats_flat, + vision_pos_encoding=vision_pos_embeds_flat, + prompt_mask=prompt_attention_mask, + ) + + # Final output normalization + prompt_embeds = self.output_layer_norm(prompt_embeds) + + return Sam3GeometryEncoderOutput( + last_hidden_state=prompt_embeds, + attention_mask=prompt_mask, + ) + + +class Sam3DetrEncoderLayer(nn.Module): + """DETR encoder layer with self-attention and cross-attention.""" + + def __init__(self, config: Sam3DETREncoderConfig): + super().__init__() + self.config = config + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + self.self_attn = Sam3Attention(config) + self.dropout = nn.Dropout(config.dropout) + + self.cross_attn = Sam3Attention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam3MLP(config) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + def forward( + self, + vision_feats: Tensor, + prompt_feats: Tensor, + vision_pos_encoding: Tensor, + prompt_cross_attn_mask: Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ): + """ + Forward pass for DETR encoder layer. + + Args: + vision_feats: Vision features [batch_size, vision_len, hidden_size] (main hidden states) + prompt_feats: Text prompt features [batch_size, text_len, hidden_size] + vision_pos_encoding: Position encoding for vision [batch_size, vision_len, hidden_size] + prompt_cross_attn_mask: Cross-attention mask for prompt features + + Returns: + Updated vision features [batch_size, vision_len, hidden_size] + """ + # Self-attention on vision features with position encoding + residual = vision_feats + hidden_states = self.layer_norm1(vision_feats) + hidden_states_with_pos = hidden_states + vision_pos_encoding + hidden_states, _ = self.self_attn( + query=hidden_states_with_pos, + key=hidden_states_with_pos, + value=hidden_states, + **kwargs, + ) + hidden_states = self.dropout(hidden_states) + residual + + # Cross-attention: vision queries attend to text/prompt features + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + + hidden_states, _ = self.cross_attn( + query=hidden_states, + key=prompt_feats, + value=prompt_feats, + attention_mask=prompt_cross_attn_mask, + **kwargs, + ) + hidden_states = self.dropout(hidden_states) + residual + + # MLP + residual = hidden_states + hidden_states = self.layer_norm3(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = self.dropout(hidden_states) + residual + + return hidden_states + + +class Sam3DetrEncoder(Sam3PreTrainedModel): + """ + DETR-style encoder that processes multi-level vision features with text fusion. + + This encoder processes vision features from multiple levels (e.g., FPN features at different + resolutions) and fuses them with text prompts through a stack of transformer encoder layers. + """ + + _can_record_outputs = { + "hidden_states": Sam3DetrEncoderLayer, + "attentions": Sam3Attention, + } + + def __init__(self, config: Sam3DETREncoderConfig): + super().__init__(config) + self.config = config + self.hidden_size = config.hidden_size + + self.layers = nn.ModuleList([Sam3DetrEncoderLayer(config) for _ in range(config.num_layers)]) + + self.post_init() + + def _prepare_multilevel_features( + self, + vision_features: list[torch.Tensor], + vision_pos_embeds: list[torch.Tensor], + ): + """ + Prepare multi-level vision features by flattening spatial dimensions and adding level embeddings. + + Args: + vision_features: List of vision features at different levels [batch_size, channels, height, width] + vision_pos_embeds: List of position embeddings for each level [batch_size, channels, height, width] + + Returns: + Tuple containing flattened features, position embeddings, and spatial metadata + """ + features_flattened = [] + pos_embeds_flattened = [] + spatial_shapes = [] + + for features, pos_embed in zip(vision_features, vision_pos_embeds): + height, width = features.shape[-2:] + spatial_shapes.append((height, width)) + + # Flatten spatial dimensions: [batch_size, channels, height, width] -> [batch_size, height*width, channels] + features = features.flatten(2).transpose(1, 2) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + + features_flattened.append(features) + pos_embeds_flattened.append(pos_embed) + + # Concatenate all levels into single sequence + features_flattened = torch.cat(features_flattened, dim=1) + pos_embeds_flattened = torch.cat(pos_embeds_flattened, dim=1) + + spatial_shapes = torch.tensor(spatial_shapes, dtype=torch.long, device=features_flattened.device) + + return ( + features_flattened, + pos_embeds_flattened, + spatial_shapes, + ) + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + vision_features: list[torch.Tensor], + text_features: torch.Tensor, + vision_pos_embeds: list[torch.Tensor] | None = None, + text_mask: torch.Tensor | None = None, + spatial_sizes: list[tuple[int, int]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3DETREncoderOutput: + """ + Forward pass for the DETR encoder. + + Args: + vision_features: List of vision features at different levels + text_features: Text prompt features [batch_size, seq_len, hidden_size] + vision_pos_embeds: Optional list of position embeddings for each level + text_mask: Optional text padding mask [batch_size, seq_len] + spatial_sizes: Optional list of (height, width) tuples for reshaping + + Returns: + Sam3DETREncoderOutput containing encoded features and metadata. + """ + batch_size = vision_features[0].shape[0] if vision_features[0].dim() == 4 else vision_features[0].shape[1] + + # TODO: See if we can remove that reshaping and just use the features as is. + if spatial_sizes is not None: + for i, (height, width) in enumerate(spatial_sizes): + # Reshape from [height*width, batch_size, channels] to [batch_size, channels, height, width] + vision_features[i] = vision_features[i].reshape(height, width, batch_size, -1).permute(2, 3, 0, 1) + vision_pos_embeds[i] = vision_pos_embeds[i].reshape(height, width, batch_size, -1).permute(2, 3, 0, 1) + + # Flatten multi-level features for encoder processing + ( + features_flattened, + pos_embeds_flattened, + spatial_shapes, + ) = self._prepare_multilevel_features(vision_features, vision_pos_embeds) + + prompt_cross_attn_mask = None + if text_mask is not None: + prompt_cross_attn_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=features_flattened, + attention_mask=text_mask, + encoder_hidden_states=text_features, + ) + + hidden_states = features_flattened + for layer in self.layers: + hidden_states = layer( + hidden_states, + prompt_feats=text_features, + vision_pos_encoding=pos_embeds_flattened, + prompt_cross_attn_mask=prompt_cross_attn_mask, + **kwargs, + ) + return Sam3DETREncoderOutput( + last_hidden_state=hidden_states, + pos_embeds_flattened=pos_embeds_flattened, + text_features=text_features, + spatial_shapes=spatial_shapes, + ) + + +class Sam3DecoderMLP(nn.Module): + """Simple 2 or 3-layer MLP for decoder components.""" + + def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 2): + super().__init__() + if num_layers == 2: + self.layer1 = nn.Linear(input_dim, hidden_dim) + self.layer2 = nn.Linear(hidden_dim, output_dim) + self.layer3 = None + elif num_layers == 3: + self.layer1 = nn.Linear(input_dim, hidden_dim) + self.layer2 = nn.Linear(hidden_dim, hidden_dim) + self.layer3 = nn.Linear(hidden_dim, output_dim) + else: + raise ValueError(f"Only 2 or 3 layers supported, got {num_layers}") + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = F.relu(self.layer1(x)) + if self.layer3 is not None: + x = F.relu(self.layer2(x)) + x = self.layer3(x) + else: + x = self.layer2(x) + return x + + +class Sam3DetrDecoderLayer(nn.Module): + """DETR decoder layer with self-attention, text cross-attention, and vision cross-attention.""" + + def __init__(self, config: Sam3DETRDecoderConfig): + super().__init__() + self.config = config + self.self_attn = Sam3Attention(config) + self.self_attn_dropout = nn.Dropout(config.dropout) + self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size) + + self.text_cross_attn = Sam3Attention(config) + self.text_cross_attn_dropout = nn.Dropout(config.dropout) + self.text_cross_attn_layer_norm = nn.LayerNorm(config.hidden_size) + + self.vision_cross_attn = Sam3Attention(config) + self.vision_cross_attn_dropout = nn.Dropout(config.dropout) + self.vision_cross_attn_layer_norm = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam3MLP(config) + self.mlp_layer_norm = nn.LayerNorm(config.hidden_size) + self.mlp_dropout = nn.Dropout(config.dropout) + + def forward( + self, + hidden_states: torch.Tensor, + query_pos: torch.Tensor, + text_features: torch.Tensor, + vision_features: torch.Tensor, + vision_pos_encoding: torch.Tensor, + text_cross_attn_mask: torch.Tensor | None = None, + vision_cross_attn_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Forward pass for decoder layer. + + Args: + hidden_states: Query features [batch_size, num_queries + 1, hidden_size] (includes presence token at position 0) + query_pos: Query position embeddings [batch_size, num_queries, hidden_size] + text_features: Text features [batch_size, seq_len, hidden_size] + vision_features: Vision features [batch_size, height*width, hidden_size] + vision_pos_encoding: Vision position encoding [batch_size, height*width, hidden_size] + text_cross_attn_mask: Text cross-attention mask + vision_cross_attn_mask: Vision cross-attention mask, already expanded for presence token + + Returns: + Updated hidden states (including presence token at position 0) + """ + # Prepend zeros to query_pos for presence token + query_pos = F.pad(query_pos, (0, 0, 1, 0), mode="constant", value=0) + + # Self-attention with query position encoding + residual = hidden_states + query_with_pos = hidden_states + query_pos + attn_output, _ = self.self_attn( + query=query_with_pos, + key=query_with_pos, + value=hidden_states, + attention_mask=None, + **kwargs, + ) + hidden_states = residual + self.self_attn_dropout(attn_output) + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Text cross-attention: queries attend to text features + residual = hidden_states + query_with_pos = hidden_states + query_pos + + attn_output, _ = self.text_cross_attn( + query=query_with_pos, + key=text_features, + value=text_features, + attention_mask=text_cross_attn_mask, + **kwargs, + ) + hidden_states = residual + self.text_cross_attn_dropout(attn_output) + hidden_states = self.text_cross_attn_layer_norm(hidden_states) + + # Vision cross-attention: queries attend to vision features (with RPB) + residual = hidden_states + query_with_pos = hidden_states + query_pos + key_with_pos = vision_features + vision_pos_encoding + attn_output, _ = self.vision_cross_attn( + query=query_with_pos, + key=key_with_pos, + value=vision_features, + attention_mask=vision_cross_attn_mask, + **kwargs, + ) + hidden_states = residual + self.vision_cross_attn_dropout(attn_output) + hidden_states = self.vision_cross_attn_layer_norm(hidden_states) + + # MLP + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.mlp_dropout(hidden_states) + hidden_states = self.mlp_layer_norm(hidden_states) + + return hidden_states + + +class Sam3DetrDecoder(Sam3PreTrainedModel): + """ + DETR-style decoder with box refinement and presence token. + + Simplified version that assumes: + - Box refinement is always enabled + - Intermediate outputs are always returned + - BoxRPB (relative position bias) with log-scale encoding + - Presence token is used + """ + + _can_record_outputs = { + "hidden_states": Sam3DetrDecoderLayer, + "attentions": Sam3Attention, + } + + def __init__( + self, + config: Sam3DETRDecoderConfig, + ): + super().__init__(config) + self.config = config + self.hidden_size = config.hidden_size + + self.layers = nn.ModuleList([Sam3DetrDecoderLayer(config) for _ in range(config.num_layers)]) + + self.output_layer_norm = nn.LayerNorm(config.hidden_size) + + self.box_head = Sam3DecoderMLP(config.hidden_size, config.hidden_size, 4, 3) + + self.query_embed = nn.Embedding(config.num_queries, config.hidden_size) + self.reference_points = nn.Embedding(config.num_queries, 4) + + self.presence_token = nn.Embedding(1, config.hidden_size) + self.presence_head = Sam3DecoderMLP(config.hidden_size, config.hidden_size, 1, 3) + self.presence_layer_norm = nn.LayerNorm(config.hidden_size) + self.clamp_presence_logit_max_val = 10.0 + + self.ref_point_head = Sam3DecoderMLP(2 * config.hidden_size, config.hidden_size, config.hidden_size, 2) + + self.box_rpb_embed_x = Sam3DecoderMLP(2, config.hidden_size, config.num_attention_heads, 2) + self.box_rpb_embed_y = Sam3DecoderMLP(2, config.hidden_size, config.num_attention_heads, 2) + + self.position_encoding = Sam3SinePositionEmbedding(num_pos_feats=config.hidden_size // 2, normalize=False) + + self.post_init() + + @compile_compatible_method_lru_cache(maxsize=1) + def _get_coords( + self, height: torch.Tensor, width: torch.Tensor, dtype: torch.dtype, device: torch.device + ) -> tuple[torch.Tensor, torch.Tensor]: + """Generate normalized coordinate grids.""" + coords_h = torch.arange(0, height, device=device, dtype=dtype) / height + coords_w = torch.arange(0, width, device=device, dtype=dtype) / width + return coords_h, coords_w + + def _get_rpb_matrix( + self, reference_boxes: torch.Tensor, spatial_shape: tuple[torch.Tensor, torch.Tensor] + ) -> torch.Tensor: + """ + Compute box relative position bias (RPB) matrix using log-scale encoding. + RPB helps the decoder attend to relevant spatial locations based on predicted box positions. + + Args: + reference_boxes: Reference boxes [batch_size, num_queries, 4] in sigmoid space + spatial_shape: (height, width) of the vision features as tensors + + Returns: + RPB matrix [batch_size, num_heads, num_queries, height*width] + """ + height, width = spatial_shape + boxes_xyxy = box_cxcywh_to_xyxy(reference_boxes) + batch_size, num_queries, _ = boxes_xyxy.shape + + # Generate coordinate grids + coords_h, coords_w = self._get_coords( + height, width, dtype=reference_boxes.dtype, device=reference_boxes.device + ) + + # Compute deltas between coordinates and box boundaries + deltas_y = coords_h.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 1:4:2] + deltas_y = deltas_y.view(batch_size, num_queries, -1, 2) + deltas_x = coords_w.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 0:3:2] + deltas_x = deltas_x.view(batch_size, num_queries, -1, 2) + + # Apply log-scale encoding + deltas_x_log = deltas_x * 8 + deltas_x_log = torch.sign(deltas_x_log) * torch.log2(torch.abs(deltas_x_log) + 1.0) / math.log2(8) + deltas_y_log = deltas_y * 8 + deltas_y_log = torch.sign(deltas_y_log) * torch.log2(torch.abs(deltas_y_log) + 1.0) / math.log2(8) + + # Embed deltas + deltas_x = self.box_rpb_embed_x(deltas_x_log) # [batch_size, num_queries, width, num_heads] + deltas_y = self.box_rpb_embed_y(deltas_y_log) # [batch_size, num_queries, height, num_heads] + + # Combine into 2D bias matrix + rpb_matrix = deltas_y.unsqueeze(3) + deltas_x.unsqueeze( + 2 + ) # [batch_size, num_queries, height, width, num_heads] + rpb_matrix = rpb_matrix.flatten(2, 3) # [batch_size, num_queries, height*width, num_heads] + rpb_matrix = rpb_matrix.permute(0, 3, 1, 2).contiguous() # [batch_size, num_heads, num_queries, height*width] + return rpb_matrix + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + vision_features: torch.Tensor, + text_features: torch.Tensor, + vision_pos_encoding: torch.Tensor, + text_mask: torch.Tensor | None = None, + spatial_shapes: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3DETRDecoderOutput: + """ + Forward pass for the DETR decoder. + + Args: + vision_features: Vision features [batch_size, height*width, hidden_size] + text_features: Text features [batch_size, seq_len, hidden_size] + vision_pos_encoding: Vision position encoding [batch_size, height*width, hidden_size] + text_mask: Text padding mask [batch_size, seq_len] where True=valid, False=padding + spatial_shapes: Spatial shapes [num_levels, 2] + + Returns: + Sam3DETRDecoderOutput containing decoder outputs from all layers. + """ + batch_size = vision_features.shape[0] + + query_embeds = self.query_embed.weight.unsqueeze(0).expand(batch_size, -1, -1) + reference_boxes = self.reference_points.weight.unsqueeze(0).expand(batch_size, -1, -1) + reference_boxes = reference_boxes.sigmoid() + presence_token = self.presence_token.weight.unsqueeze(0).expand(batch_size, -1, -1) + + # Concatenate presence token with query embeddings + hidden_states = torch.cat([presence_token, query_embeds], dim=1) + + text_cross_attn_mask = None + if text_mask is not None: + text_cross_attn_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=text_mask, + encoder_hidden_states=text_features, + ) + + intermediate_outputs = [] + intermediate_boxes = [reference_boxes] + intermediate_presence_logits = [] + + for layer in self.layers: + # Generate sine embeddings for conditional queries + reference_points_input = reference_boxes.unsqueeze(2) + query_sine_embed = self.position_encoding.encode_boxes(reference_points_input[:, :, 0, :]) + query_pos = self.ref_point_head(query_sine_embed) + + # Compute box relative position bias (RPB) attention mask + vision_cross_attn_mask = None + if spatial_shapes is not None and spatial_shapes.shape[0] == 1: + spatial_shape = (spatial_shapes[0, 0], spatial_shapes[0, 1]) + rpb_matrix = self._get_rpb_matrix(reference_boxes, spatial_shape) + # Prepend zeros row for presence token (it attends to all vision tokens equally) + vision_cross_attn_mask = F.pad(rpb_matrix, (0, 0, 1, 0), mode="constant", value=0) + + hidden_states = layer( + hidden_states, + query_pos=query_pos, + text_features=text_features, + vision_features=vision_features, + vision_pos_encoding=vision_pos_encoding, + text_cross_attn_mask=text_cross_attn_mask, + vision_cross_attn_mask=vision_cross_attn_mask, + **kwargs, + ) + + # Extract query hidden states (without presence token) for box refinement + query_hidden_states = hidden_states[:, 1:] + + # Box refinement: predict delta and update reference boxes + reference_boxes_before_sigmoid = inverse_sigmoid(reference_boxes) + delta_boxes = self.box_head(self.output_layer_norm(query_hidden_states)) + new_reference_boxes = (delta_boxes + reference_boxes_before_sigmoid).sigmoid() + reference_boxes = new_reference_boxes.detach() + + intermediate_outputs.append(self.output_layer_norm(query_hidden_states)) + intermediate_boxes.append(new_reference_boxes) + + # Process presence token + presence_hidden = hidden_states[:, :1] + presence_logits = self.presence_head(self.presence_layer_norm(presence_hidden)).squeeze(-1) + presence_logits = presence_logits.clamp( + min=-self.clamp_presence_logit_max_val, max=self.clamp_presence_logit_max_val + ) + intermediate_presence_logits.append(presence_logits) + + # Stack outputs from all layers + intermediate_outputs = torch.stack(intermediate_outputs) + intermediate_boxes = torch.stack(intermediate_boxes[:-1]) + intermediate_presence_logits = torch.stack(intermediate_presence_logits) + + return Sam3DETRDecoderOutput( + intermediate_hidden_states=intermediate_outputs, + reference_boxes=intermediate_boxes, + presence_logits=intermediate_presence_logits, + ) + + +class Sam3DotProductScoring(nn.Module): + """ + Computes classification scores by computing dot product between projected decoder queries and pooled text features. + This is used to determine confidence/presence scores for each query. + """ + + def __init__(self, config: Sam3Config): + super().__init__() + self.config = config + hidden_size = config.detr_decoder_config.hidden_size + projection_dim = config.detr_decoder_config.hidden_size + + self.text_mlp = Sam3DecoderMLP( + input_dim=hidden_size, + hidden_dim=config.detr_decoder_config.intermediate_size, + output_dim=hidden_size, + num_layers=2, + ) + self.text_mlp_dropout = nn.Dropout(config.detr_decoder_config.dropout) + self.text_mlp_out_norm = nn.LayerNorm(hidden_size) + + # Projections for text and query features + self.text_proj = nn.Linear(hidden_size, projection_dim) + self.query_proj = nn.Linear(hidden_size, projection_dim) + + # Scale factor for dot product + self.scale = float(1.0 / np.sqrt(projection_dim)) + + # Clamping to avoid numerical issues + self.clamp_logits = True + self.clamp_max_val = 12.0 + + def _pool_text_features(self, text_features: torch.Tensor, text_mask: torch.Tensor | None) -> torch.Tensor: + """ + Mean pool text features, accounting for padding. + + Args: + text_features: [batch_size, seq_len, hidden_size] + text_mask: [batch_size, seq_len] where True indicates valid tokens, False indicates padding + + Returns: + pooled_text: [batch_size, hidden_size] + """ + if text_mask is None: + # No padding, simple mean + return text_features.mean(dim=1) + + is_valid = text_mask.to(text_features.dtype).unsqueeze(-1) # [batch_size, seq_len, 1] + + # Count valid tokens per batch + num_valid = is_valid.sum(dim=1).clamp(min=1.0) # [batch_size, 1] + + # Mean pool only over valid tokens + pooled_text = (text_features * is_valid).sum(dim=1) / num_valid # [batch_size, hidden_size] + + return pooled_text + + def forward( + self, + decoder_hidden_states: torch.Tensor, + text_features: torch.Tensor, + text_mask: torch.Tensor | None = None, + ) -> torch.Tensor: + """ + Compute classification scores via dot product. + + Args: + decoder_hidden_states: [num_layers, batch_size, num_queries, hidden_size] + text_features: [batch_size, seq_len, hidden_size] + text_mask: [batch_size, seq_len] where True=valid, False=padding + + Returns: + scores: [num_layers, batch_size, num_queries, 1] + """ + orig_text_features = text_features + text_features = self.text_mlp(text_features) + text_features = self.text_mlp_dropout(text_features) + text_features = text_features + orig_text_features + text_features = self.text_mlp_out_norm(text_features) + + pooled_text = self._pool_text_features(text_features, text_mask) + + proj_text = self.text_proj(pooled_text) + proj_queries = self.query_proj(decoder_hidden_states) + + proj_text = proj_text.unsqueeze(-1) + scores = torch.matmul(proj_queries, proj_text.unsqueeze(0)) + scores = scores * self.scale + if self.clamp_logits: + scores = scores.clamp(min=-self.clamp_max_val, max=self.clamp_max_val) + + return scores + + +class Sam3MaskEmbedder(nn.Module): + """ + MLP that embeds object queries for mask prediction. + Similar to MaskFormer's mask embedder. + """ + + def __init__(self, config: Sam3MaskDecoderConfig): + super().__init__() + self.config = config + hidden_size = config.hidden_size + + self.layers = nn.ModuleList( + [ + nn.Linear(hidden_size, hidden_size), + nn.Linear(hidden_size, hidden_size), + nn.Linear(hidden_size, hidden_size), + ] + ) + self.activation = nn.ReLU() + + def forward(self, queries: torch.Tensor) -> torch.Tensor: + """ + Args: + queries: Query embeddings [batch_size, num_queries, hidden_size] + + Returns: + Mask embeddings [batch_size, num_queries, hidden_size] + """ + hidden_states = queries + for i, layer in enumerate(self.layers): + hidden_states = layer(hidden_states) + if i < len(self.layers) - 1: + hidden_states = self.activation(hidden_states) + return hidden_states + + +class Sam3PixelDecoder(nn.Module): + """ + Feature Pyramid Network (FPN) decoder that generates pixel-level features. + Inspired by MaskFormer's pixel decoder. + """ + + def __init__(self, config: Sam3MaskDecoderConfig): + super().__init__() + self.config = config + hidden_size = config.hidden_size + num_upsampling_stages = config.num_upsampling_stages + + # Create conv layers and norms for FPN + self.conv_layers = nn.ModuleList( + [ + nn.Conv2d(hidden_size, hidden_size, kernel_size=3, stride=1, padding=1) + for _ in range(num_upsampling_stages) + ] + ) + self.norms = nn.ModuleList([nn.GroupNorm(8, hidden_size) for _ in range(num_upsampling_stages)]) + + self.out_channels = hidden_size + + def forward(self, backbone_features: list[torch.Tensor]) -> torch.Tensor: + """ + Args: + backbone_features: List of backbone features [batch_size, hidden_size, H_i, W_i] + from low to high resolution (assumes already projected to hidden_size) + + Returns: + Pixel embeddings [batch_size, hidden_size, H, W] at the finest resolution + """ + # Start from the coarsest feature (last in list) + prev_fpn = backbone_features[-1] + # Iterate through features from coarse to fine (excluding the last which we started with) + for layer_idx, backbone_feat in enumerate(reversed(backbone_features[:-1])): + # Upsample previous FPN output to match current backbone feature size + prev_fpn = F.interpolate(prev_fpn, size=backbone_feat.shape[-2:], mode="nearest") + + # Add skip connection + prev_fpn = prev_fpn + backbone_feat + + # Apply conv and norm + prev_fpn = self.conv_layers[layer_idx](prev_fpn) + prev_fpn = self.norms[layer_idx](prev_fpn) + prev_fpn = F.relu(prev_fpn) + + return prev_fpn + + +class Sam3MaskDecoder(Sam3PreTrainedModel): + """ + Mask decoder that combines object queries with pixel-level features to predict instance masks. + Also produces a semantic segmentation output and supports cross-attention to prompts. + """ + + _can_record_outputs = { + "attentions": Sam3Attention, + } + + def __init__(self, config: Sam3MaskDecoderConfig): + super().__init__(config) + self.config = config + hidden_size = config.hidden_size + + # Pixel decoder (FPN) + self.pixel_decoder = Sam3PixelDecoder(config) + + # Mask embedder (MLP to transform queries) + self.mask_embedder = Sam3MaskEmbedder(config) + + # Projection from pixel decoder output to mask embedding space + self.instance_projection = nn.Conv2d(self.pixel_decoder.out_channels, hidden_size, kernel_size=1) + + # Semantic segmentation head (always present in UniversalSegmentationHead) + self.semantic_projection = nn.Conv2d(self.pixel_decoder.out_channels, 1, kernel_size=1) + + self.prompt_cross_attn = Sam3Attention(config) + self.prompt_cross_attn_norm = nn.LayerNorm(hidden_size) + self.prompt_cross_attn_dropout = nn.Dropout(config.dropout) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + decoder_queries: torch.Tensor, + backbone_features: list[torch.Tensor], + encoder_hidden_states: torch.Tensor, + prompt_features: torch.Tensor | None = None, + prompt_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3MaskDecoderOutput: + """ + Args: + decoder_queries: Decoder output queries [batch_size, num_queries, hidden_size] + backbone_features: List of backbone features to process through FPN + encoder_hidden_states: Encoder outputs [batch_size, seq_len, hidden_size] + prompt_features: Prompt features (text + geometry) for cross-attention [batch_size, prompt_len, hidden_size] + prompt_mask: Padding mask [batch_size, prompt_len] where True=valid, False=padding + + Returns: + Sam3MaskDecoderOutput containing predicted masks and semantic segmentation. + """ + if prompt_features is not None: + # Cross-attention: encoder features attend to prompt features + residual = encoder_hidden_states + normed_hidden_states = self.prompt_cross_attn_norm(encoder_hidden_states) + + cross_attn_mask = None + if prompt_mask is not None: + cross_attn_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=normed_hidden_states, + encoder_hidden_states=prompt_features, + attention_mask=prompt_mask, + ) + + attn_output, _ = self.prompt_cross_attn( + query=normed_hidden_states, + key=prompt_features, + value=prompt_features, + attention_mask=cross_attn_mask, + **kwargs, + ) + encoder_hidden_states = residual + self.prompt_cross_attn_dropout(attn_output) + + # Process backbone features through FPN to get pixel embeddings + pixel_embed = self._embed_pixels( + backbone_features=backbone_features, + encoder_hidden_states=encoder_hidden_states, + ) + + # Predict instance masks via dot product between query embeddings and pixel embeddings + instance_embeds = self.instance_projection(pixel_embed) + mask_embeddings = self.mask_embedder(decoder_queries) + pred_masks = torch.einsum("bqc,bchw->bqhw", mask_embeddings, instance_embeds) + + # Generate semantic segmentation + semantic_seg = self.semantic_projection(pixel_embed) + + return Sam3MaskDecoderOutput( + pred_masks=pred_masks, + semantic_seg=semantic_seg, + ) + + def _embed_pixels( + self, + backbone_features: list[torch.Tensor], + encoder_hidden_states: torch.Tensor, + ) -> torch.Tensor: + """ + Embed pixels by combining backbone FPN features with encoder vision features. + The encoder vision features replace the finest-resolution backbone feature. + + Args: + backbone_features: List of backbone features [batch_size, C, H_i, W_i] + encoder_hidden_states: Encoder outputs [batch_size, seq_len, hidden_size] + + Returns: + Pixel embeddings [batch_size, hidden_size, H, W] + """ + backbone_visual_feats = [feat.clone() for feat in backbone_features] + + # Extract vision features from encoder output and reshape to spatial format + spatial_dim = backbone_features[-1].shape[-2] * backbone_features[-1].shape[-1] + encoder_visual_embed = encoder_hidden_states[:, :spatial_dim, :] + batch_size, _, hidden_size = encoder_visual_embed.shape + height, width = backbone_features[-1].shape[-2:] + encoder_visual_embed = encoder_visual_embed.transpose(1, 2).reshape(batch_size, hidden_size, height, width) + + # Replace finest backbone feature with encoder vision features + backbone_visual_feats[-1] = encoder_visual_embed + + # Process through FPN decoder + pixel_embed = self.pixel_decoder(backbone_visual_feats) + + return pixel_embed + + +class Sam3Model(Sam3PreTrainedModel): + input_modalities = ["image", "text"] + base_model_prefix = "detector_model" + _keys_to_ignore_on_load_unexpected = [ + r"^tracker_model.", + r"^tracker_neck.", + ] + + def __init__(self, config: Sam3Config): + # loading from a sam3_video config + if hasattr(config, "detector_config") and config.detector_config is not None: + detector_config = config.detector_config + if isinstance(detector_config, dict): + detector_config = Sam3Config(**detector_config) + config = detector_config + super().__init__(config) + self.vision_encoder = Sam3VisionModel(config.vision_config) + self.text_encoder = CLIPTextModelWithProjection(config.text_config) + self.vocab_size = config.text_config.vocab_size + + # Project text features from text encoder hidden size to model hidden size + # CLIP text encoder outputs 1024-dim features, but we need 256-dim for DETR + self.text_projection = nn.Linear(config.text_config.hidden_size, config.detr_encoder_config.hidden_size) + + # Pass _attn_implementation to subconfigs BEFORE creating modules + config.geometry_encoder_config._attn_implementation = config._attn_implementation + config.detr_encoder_config._attn_implementation = config._attn_implementation + config.detr_decoder_config._attn_implementation = config._attn_implementation + config.mask_decoder_config._attn_implementation = config._attn_implementation + + self.geometry_encoder = Sam3GeometryEncoder(config.geometry_encoder_config) + self.detr_encoder = Sam3DetrEncoder(config.detr_encoder_config) + self.detr_decoder = Sam3DetrDecoder(config.detr_decoder_config) + self.mask_decoder = Sam3MaskDecoder(config.mask_decoder_config) + + # Dot product scoring to compute classification scores + self.dot_product_scoring = Sam3DotProductScoring(config) + + self.post_init() + + @can_return_tuple + @auto_docstring + def get_text_features( + self, + input_ids: torch.LongTensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + Example: + + ```python + >>> from transformers import Sam3Model, Sam3Processor + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> model = Sam3Model.from_pretrained("facebook/sam3") + >>> processor = Sam3Processor.from_pretrained("facebook/sam3") + + >>> # Pre-compute text embeddings + >>> text_inputs = processor(text="cat", return_tensors="pt") + >>> text_embeds = model.get_text_features(**text_inputs).pooler_output + + >>> # Reuse text embeddings for multiple images + >>> url = "http://images.cocodataset.org/val2017/000000077595.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> img_inputs = processor(images=image, return_tensors="pt") + >>> outputs = model(pixel_values=img_inputs.pixel_values, text_embeds=text_embeds) + ``` + """ + text_outputs = self.text_encoder( + input_ids=input_ids, attention_mask=attention_mask, return_dict=True, **kwargs + ) + last_hidden_state = text_outputs.last_hidden_state + text_outputs.pooler_output = self.text_projection(last_hidden_state) + + return text_outputs + + @auto_docstring + def get_vision_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam3VisionEncoderOutput: + r""" + Example: + + ```python + >>> from transformers import Sam3Model, Sam3Processor + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> model = Sam3Model.from_pretrained("facebook/sam3") + >>> processor = Sam3Processor.from_pretrained("facebook/sam3") + + >>> # Pre-compute vision embeddings + >>> url = "http://images.cocodataset.org/val2017/000000077595.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> img_inputs = processor(images=image, return_tensors="pt") + >>> vision_embeds = model.get_vision_features(pixel_values=img_inputs.pixel_values) + + >>> # Reuse vision embeddings for multiple text prompts + >>> text_inputs = processor(text="cat", return_tensors="pt") + >>> outputs = model(vision_embeds=vision_embeds, input_ids=text_inputs.input_ids) + ``` + """ + vision_outputs = self.vision_encoder(pixel_values, **kwargs) + return vision_outputs + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + vision_embeds: Sam3VisionEncoderOutput | None = None, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + text_embeds: torch.FloatTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_boxes_labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam3ImageSegmentationOutput: + r""" + vision_embeds (`Sam3VisionEncoderOutput`, *optional*): + Pre-computed vision embeddings. Can be used to easily reuse vision embeddings. If provided, `pixel_values` + should not be passed. Mutually exclusive with `pixel_values`. + text_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Pre-computed text embeddings. Can be used to easily reuse text embeddings. If provided, `input_ids` + should not be passed. Mutually exclusive with `input_ids`. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`, *optional*): + Normalized box coordinates in [0, 1] range, in (cx, cy, w, h) format. + input_boxes_labels (`torch.LongTensor` of shape `(batch_size, num_boxes)`, *optional*): + Labels for boxes: 1 (positive), 0 (negative). + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoModel, AutoProcessor + + >>> model = AutoModel.from_pretrained("facebook/sam3") + >>> processor = AutoProcessor.from_pretrained("facebook/sam3") + + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())).convert("RGB") + >>> text = "car" + >>> inputs = processor(images=image, text=text, return_tensors="pt") + + >>> # Get segmentation output + >>> outputs = model(**inputs) + >>> pred_masks = outputs.pred_masks + >>> pred_boxes = outputs.pred_boxes + ``` + """ + if (pixel_values is None) == (vision_embeds is None): + raise ValueError("You must specify exactly one of pixel_values or vision_embeds") + + if (input_ids is None) == (text_embeds is None): + raise ValueError("You must specify exactly one of input_ids or text_embeds") + + if pixel_values is not None: + batch_size = pixel_values.shape[0] + device = pixel_values.device + else: + batch_size = vision_embeds.fpn_hidden_states[0].shape[0] + device = vision_embeds.fpn_hidden_states[0].device + + if vision_embeds is None: + vision_outputs = self.vision_encoder(pixel_values, **kwargs) + else: + vision_outputs = vision_embeds + + fpn_hidden_states = vision_outputs.fpn_hidden_states[:-1] + fpn_position_encoding = vision_outputs.fpn_position_encoding[:-1] + + if text_embeds is None: + text_features = self.get_text_features( + input_ids=input_ids, attention_mask=attention_mask, return_dict=True + ).pooler_output + else: + text_features = text_embeds + + text_mask = attention_mask.bool() if attention_mask is not None else None + has_geometry_prompts = input_boxes is not None and input_boxes.numel() > 0 + + geometry_prompt_features = None + geometry_prompt_mask = None + + if has_geometry_prompts: + if input_boxes is not None and input_boxes.numel() > 0: + box_embeddings = input_boxes # [batch_size, num_boxes, 4] + box_labels = ( + input_boxes_labels + if input_boxes_labels is not None + else torch.ones_like(box_embeddings[..., 0], dtype=torch.long) + ) + box_mask = ( + (input_boxes_labels != -10) + if input_boxes_labels is not None + else torch.ones(batch_size, input_boxes.shape[1], dtype=torch.bool, device=device) + ) + box_labels = torch.where(box_labels == -10, 0, box_labels) + else: + box_embeddings = torch.zeros(batch_size, 0, 4, dtype=text_features.dtype, device=device) + box_labels = torch.zeros(batch_size, 0, dtype=torch.long, device=device) + box_mask = torch.zeros(batch_size, 0, dtype=torch.bool, device=device) + + geometry_outputs = self.geometry_encoder( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + img_feats=fpn_hidden_states, + img_pos_embeds=fpn_position_encoding, + ) + + geometry_prompt_features = geometry_outputs.last_hidden_state + geometry_prompt_mask = geometry_outputs.attention_mask + + if geometry_prompt_features is not None: + # Repeat text_features for all geometry prompts + if text_features.shape[0] == 1 and geometry_prompt_features.shape[0] > 1: + text_features = text_features.repeat(geometry_prompt_features.shape[0], 1, 1) + combined_prompt_features = torch.cat([text_features, geometry_prompt_features], dim=1) + if text_mask is not None and text_mask.shape[0] == 1 and geometry_prompt_mask.shape[0] > 1: + text_mask = text_mask.repeat(geometry_prompt_mask.shape[0], 1) + + if text_mask is not None and geometry_prompt_mask is not None: + combined_prompt_mask = torch.cat([text_mask, geometry_prompt_mask], dim=1) + elif text_mask is not None: + geo_valid_mask = torch.ones( + batch_size, geometry_prompt_features.shape[1], dtype=torch.bool, device=device + ) + combined_prompt_mask = torch.cat([text_mask, geo_valid_mask], dim=1) + elif geometry_prompt_mask is not None: + text_valid_mask = torch.ones(batch_size, text_features.shape[1], dtype=torch.bool, device=device) + combined_prompt_mask = torch.cat([text_valid_mask, geometry_prompt_mask], dim=1) + else: + combined_prompt_mask = None + else: + combined_prompt_features = text_features + combined_prompt_mask = text_mask + + encoder_outputs = self.detr_encoder( + vision_features=[fpn_hidden_states[-1]], + text_features=combined_prompt_features, + vision_pos_embeds=[fpn_position_encoding[-1]], + text_mask=combined_prompt_mask, + **kwargs, + ) + + decoder_outputs = self.detr_decoder( + vision_features=encoder_outputs.last_hidden_state, + text_features=encoder_outputs.text_features, + vision_pos_encoding=encoder_outputs.pos_embeds_flattened, + text_mask=combined_prompt_mask, + spatial_shapes=encoder_outputs.spatial_shapes, + **kwargs, + ) + + # Refine boxes from decoder + all_box_offsets = self.detr_decoder.box_head(decoder_outputs.intermediate_hidden_states) + reference_boxes_inv_sig = inverse_sigmoid(decoder_outputs.reference_boxes) + all_pred_boxes_cxcywh = (reference_boxes_inv_sig + all_box_offsets).sigmoid() + all_pred_boxes = box_cxcywh_to_xyxy(all_pred_boxes_cxcywh) + + all_pred_logits = self.dot_product_scoring( + decoder_hidden_states=decoder_outputs.intermediate_hidden_states, + text_features=encoder_outputs.text_features, + text_mask=combined_prompt_mask, + ).squeeze(-1) + + pred_logits = all_pred_logits[-1] + pred_boxes = all_pred_boxes[-1] + decoder_hidden_states = decoder_outputs.intermediate_hidden_states[-1] + presence_logits = decoder_outputs.presence_logits[-1] + + mask_outputs = self.mask_decoder( + decoder_queries=decoder_hidden_states, + backbone_features=list(fpn_hidden_states), + encoder_hidden_states=encoder_outputs.last_hidden_state, + prompt_features=combined_prompt_features, + prompt_mask=combined_prompt_mask, + **kwargs, + ) + + return Sam3ImageSegmentationOutput( + pred_masks=mask_outputs.pred_masks, + pred_boxes=pred_boxes, + pred_logits=pred_logits, + presence_logits=presence_logits, + semantic_seg=mask_outputs.semantic_seg, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_reference_boxes=decoder_outputs.reference_boxes, + encoder_hidden_states=encoder_outputs.hidden_states, + vision_hidden_states=vision_outputs.hidden_states, + vision_attentions=vision_outputs.attentions, + detr_encoder_attentions=encoder_outputs.attentions, + detr_decoder_attentions=decoder_outputs.attentions, + mask_decoder_attentions=mask_outputs.attentions, + ) + + +__all__ = ["Sam3Model", "Sam3VisionModel", "Sam3ViTModel", "Sam3PreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/modular_sam3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/modular_sam3.py new file mode 100644 index 0000000000000000000000000000000000000000..8efd5e421d42ca0600012320be18a53c58c4a1e8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/modular_sam3.py @@ -0,0 +1,252 @@ +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch + +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, +) +from ..sam2.image_processing_sam2 import Sam2ImageProcessor + + +def _scale_boxes(boxes, target_sizes): + """ + Scale batch of bounding boxes to the target sizes. + + Args: + boxes (`torch.Tensor` of shape `(batch_size, num_boxes, 4)`): + Bounding boxes to scale. Each box is expected to be in (x1, y1, x2, y2) format. + target_sizes (`list[tuple[int, int]]` or `torch.Tensor` of shape `(batch_size, 2)`): + Target sizes to scale the boxes to. Each target size is expected to be in (height, width) format. + + Returns: + `torch.Tensor` of shape `(batch_size, num_boxes, 4)`: Scaled bounding boxes. + """ + + if isinstance(target_sizes, (list, tuple)): + image_height = torch.tensor([i[0] for i in target_sizes]) + image_width = torch.tensor([i[1] for i in target_sizes]) + elif isinstance(target_sizes, torch.Tensor): + image_height, image_width = target_sizes.unbind(1) + else: + raise TypeError("`target_sizes` must be a list, tuple or torch.Tensor") + + scale_factor = torch.stack([image_width, image_height, image_width, image_height], dim=1) + scale_factor = scale_factor.unsqueeze(1).to(boxes.device) + boxes = boxes * scale_factor + return boxes + + +class Sam3ImageProcessor(Sam2ImageProcessor): + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 1008, "width": 1008} + mask_size = {"height": 288, "width": 288} + + def post_process_semantic_segmentation( + self, outputs, target_sizes: list[tuple] | None = None, threshold: float = 0.5 + ): + """ + Converts the output of [`Sam3Model`] into semantic segmentation maps. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing semantic_seg. + target_sizes (`list[tuple]` of length `batch_size`, *optional*): + List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, + predictions will not be resized. + threshold (`float`, *optional*, defaults to 0.5): + Threshold for binarizing the semantic segmentation masks. + + Returns: + semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic + segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is + specified). Each entry is a binary mask (0 or 1). + """ + # Get semantic segmentation output + # semantic_seg has shape (batch_size, 1, height, width) + semantic_logits = outputs.semantic_seg + + if semantic_logits is None: + raise ValueError( + "Semantic segmentation output is not available in the model outputs. " + "Make sure the model was run with semantic segmentation enabled." + ) + + # Apply sigmoid to convert logits to probabilities + semantic_probs = semantic_logits.sigmoid() + + # Resize and binarize semantic segmentation maps + if target_sizes is not None: + if len(semantic_logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + + semantic_segmentation = [] + + for idx in range(len(semantic_logits)): + resized_probs = torch.nn.functional.interpolate( + semantic_probs[idx].unsqueeze(dim=0), + size=target_sizes[idx], + mode="bilinear", + align_corners=False, + ) + # Binarize: values > threshold become 1, otherwise 0 + semantic_map = (resized_probs[0, 0] > threshold).to(torch.long) + semantic_segmentation.append(semantic_map) + else: + # Binarize without resizing + semantic_segmentation = (semantic_probs[:, 0] > threshold).to(torch.long) + semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] + + return semantic_segmentation + + def post_process_object_detection(self, outputs, threshold: float = 0.3, target_sizes: list[tuple] | None = None): + """ + Converts the raw output of [`Sam3Model`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing pred_boxes, pred_logits, and optionally presence_logits. + threshold (`float`, *optional*, defaults to 0.3): + Score threshold to keep object detection predictions. + target_sizes (`list[tuple[int, int]]`, *optional*): + List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the + batch. If unset, predictions will not be resized. + + Returns: + `list[dict]`: A list of dictionaries, each dictionary containing the following keys: + - **scores** (`torch.Tensor`): The confidence scores for each predicted box on the image. + - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, + bottom_right_y) format. + """ + pred_logits = outputs.pred_logits # (batch_size, num_queries) + pred_boxes = outputs.pred_boxes # (batch_size, num_queries, 4) in xyxy format + presence_logits = outputs.presence_logits # (batch_size, 1) or None + + batch_size = pred_logits.shape[0] + + if target_sizes is not None and len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as images") + + # Compute scores: combine pred_logits with presence_logits if available + batch_scores = pred_logits.sigmoid() + if presence_logits is not None: + presence_scores = presence_logits.sigmoid() # (batch_size, 1) + batch_scores = batch_scores * presence_scores # Broadcast multiplication + + # Boxes are already in xyxy format from the model + batch_boxes = pred_boxes + + # Convert from relative [0, 1] to absolute [0, height/width] coordinates + if target_sizes is not None: + batch_boxes = _scale_boxes(batch_boxes, target_sizes) + + results = [] + for scores, boxes in zip(batch_scores, batch_boxes): + keep = scores > threshold + scores = scores[keep] + boxes = boxes[keep] + results.append({"scores": scores, "boxes": boxes}) + + return results + + def post_process_instance_segmentation( + self, + outputs, + threshold: float = 0.3, + mask_threshold: float = 0.5, + target_sizes: list[tuple] | None = None, + ): + """ + Converts the raw output of [`Sam3Model`] into instance segmentation predictions with bounding boxes and masks. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing pred_boxes, pred_logits, pred_masks, and optionally + presence_logits. + threshold (`float`, *optional*, defaults to 0.3): + Score threshold to keep instance predictions. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold for binarizing the predicted masks. + target_sizes (`list[tuple[int, int]]`, *optional*): + List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the + batch. If unset, predictions will not be resized. + + Returns: + `list[dict]`: A list of dictionaries, each dictionary containing the following keys: + - **scores** (`torch.Tensor`): The confidence scores for each predicted instance on the image. + - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, + bottom_right_y) format. + - **masks** (`torch.Tensor`): Binary segmentation masks for each instance, shape (num_instances, + height, width). + """ + pred_logits = outputs.pred_logits # (batch_size, num_queries) + pred_boxes = outputs.pred_boxes # (batch_size, num_queries, 4) in xyxy format + pred_masks = outputs.pred_masks # (batch_size, num_queries, height, width) + presence_logits = outputs.presence_logits # (batch_size, 1) or None + + batch_size = pred_logits.shape[0] + + if target_sizes is not None and len(target_sizes) != batch_size: + raise ValueError("Make sure that you pass in as many target sizes as images") + + # Compute scores: combine pred_logits with presence_logits if available + batch_scores = pred_logits.sigmoid() + if presence_logits is not None: + presence_scores = presence_logits.sigmoid() # (batch_size, 1) + batch_scores = batch_scores * presence_scores # Broadcast multiplication + + # Apply sigmoid to mask logits + batch_masks = pred_masks.sigmoid() + + # Boxes are already in xyxy format from the model + batch_boxes = pred_boxes + + # Scale boxes to target sizes if provided + if target_sizes is not None: + batch_boxes = _scale_boxes(batch_boxes, target_sizes) + + results = [] + for idx, (scores, boxes, masks) in enumerate(zip(batch_scores, batch_boxes, batch_masks)): + # Filter by score threshold + keep = scores > threshold + scores = scores[keep] + boxes = boxes[keep] + masks = masks[keep] # (num_keep, height, width) + + # Resize masks to target size if provided + if target_sizes is not None: + target_size = target_sizes[idx] + if len(masks) > 0: + masks = torch.nn.functional.interpolate( + masks.unsqueeze(0), # (1, num_keep, height, width) + size=target_size, + mode="bilinear", + align_corners=False, + ).squeeze(0) # (num_keep, target_height, target_width) + + # Binarize masks + masks = (masks > mask_threshold).to(torch.long) + + results.append({"scores": scores, "boxes": boxes, "masks": masks}) + + return results + + +__all__ = ["Sam3ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/processing_sam3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/processing_sam3.py new file mode 100644 index 0000000000000000000000000000000000000000..4293f14fe95c1369d34f6fcc58f12238de42c1e1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3/processing_sam3.py @@ -0,0 +1,665 @@ +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for SAM3. +""" + +from copy import deepcopy + +import numpy as np + +from ...image_utils import ImageInput +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput +from ...utils import TensorType, auto_docstring, is_torch_available, logging +from ...utils.import_utils import requires + + +logger = logging.get_logger(__name__) + +if is_torch_available(): + import torch + + +def box_cxcywh_to_xyxy(x): + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +def box_cxcywh_to_xywh(x): + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (w), (h)] + return torch.stack(b, dim=-1) + + +def box_xywh_to_xyxy(x): + x, y, w, h = x.unbind(-1) + b = [(x), (y), (x + w), (y + h)] + return torch.stack(b, dim=-1) + + +def box_xywh_to_cxcywh(x): + x, y, w, h = x.unbind(-1) + b = [(x + 0.5 * w), (y + 0.5 * h), (w), (h)] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_xywh(x): + x, y, X, Y = x.unbind(-1) + b = [(x), (y), (X - x), (Y - y)] + return torch.stack(b, dim=-1) + + +def box_xyxy_to_cxcywh(x): + x0, y0, x1, y1 = x.unbind(-1) + b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)] + return torch.stack(b, dim=-1) + + +def box_area(boxes): + """ + Batched version of box area. Boxes should be in [x0, y0, x1, y1] format. + + Inputs: + - boxes: Tensor of shape (..., 4) + + Returns: + - areas: Tensor of shape (...,) + """ + x0, y0, x1, y1 = boxes.unbind(-1) + return (x1 - x0) * (y1 - y0) + + +@requires(backends=("torch",)) +@auto_docstring +class Sam3Processor(ProcessorMixin): + def __init__( + self, image_processor, tokenizer, target_size: int | None = None, point_pad_value: int = -10, **kwargs + ): + r""" + target_size (`int`, *optional*): + The target size (target_size, target_size) to which the image will be resized. + point_pad_value (`int`, *optional*, defaults to -10): + The value used for padding input boxes. + """ + super().__init__(image_processor, tokenizer, **kwargs) + self.point_pad_value = point_pad_value + self.target_size = target_size if target_size is not None else self.image_processor.size["height"] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + segmentation_maps: ImageInput | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + input_boxes_labels: list[list[list[int]]] | torch.Tensor | None = None, + original_sizes: list[list[float]] | torch.Tensor | None = None, + return_tensors: str | TensorType | None = None, + **kwargs, + ) -> BatchEncoding: + r""" + images (`ImageInput`, *optional*): + The image(s) to process. + text (`str`, `list[str]`, `list[list[str]]`, *optional*): + The text to process. + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to process. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to process. + input_boxes_labels (`list[list[int]]`, `torch.Tensor`, *optional*): + The labels for the bounding boxes. + original_sizes (`list[list[float]]`, `torch.Tensor`, *optional*): + The original sizes of the images. + + Returns: + A [`BatchEncoding`] with the following fields: + - `pixel_values` (`torch.Tensor`): The processed image(s). + - `original_sizes` (`list[list[float]]`): The original sizes of the images. + - `labels` (`torch.Tensor`): The processed segmentation maps (if provided). + - `input_boxes_labels` (`torch.Tensor`): The processed labels for the bounding boxes. + - `input_boxes` (`torch.Tensor`): The processed bounding boxes. + """ + encoding = None + if images is not None: + encoding = self.image_processor( + images, + segmentation_maps=segmentation_maps, + return_tensors=return_tensors, + **kwargs, + ) + elif original_sizes is not None: + if isinstance(original_sizes, torch.Tensor): + original_sizes = original_sizes.cpu().tolist() + encoding = BatchEncoding({"original_sizes": original_sizes}, tensor_type=return_tensors) + elif input_boxes is not None: + raise ValueError("Either images or original_sizes must be provided if input_boxes is not None") + + text = self._resolve_text_prompts(text, input_boxes) + if text is not None: + text_inputs = self.tokenizer(text, return_tensors=return_tensors, padding="max_length", max_length=32) + if encoding is not None: + encoding.update(text_inputs) + else: + encoding = text_inputs + + # Process input boxes if provided + if input_boxes is not None: + original_sizes = encoding["original_sizes"] + # Validate and convert inputs to standardized format + processed_boxes = self._validate_single_input( + input_boxes, + expected_depth=3, + input_name="boxes", + expected_format="[image level, box level, box coordinates]", + expected_coord_size=4, + ) + processed_boxes_labels = self._validate_single_input( + input_boxes_labels, + expected_depth=2, + input_name="labels", + expected_format="[image level, box level]", + ) + + # Auto-generate labels so padded None entries are masked out in the geometry encoder (#45059). + if processed_boxes is not None and processed_boxes_labels is None: + processed_boxes_labels = self._generate_default_box_labels(processed_boxes) + + # Get padding requirements for all inputs + if processed_boxes is not None: + boxes_max_dims = self._get_nested_dimensions(processed_boxes)[:2] + if processed_boxes_labels is not None: + boxes_labels_max_dims = self._get_nested_dimensions(processed_boxes_labels)[:2] + + # Ensure boxes and labels have consistent dimensions + if processed_boxes is not None and processed_boxes_labels is not None: + if boxes_max_dims != boxes_labels_max_dims: + raise ValueError( + "Input boxes and labels have inconsistent dimensions. Please ensure they have the same dimensions." + ) + + # Pad and normalize all inputs to final tensor format + if processed_boxes is not None: + padded_boxes = self._pad_nested_list(processed_boxes, boxes_max_dims + [4]) + final_boxes = torch.tensor(padded_boxes, dtype=torch.float32) + self._normalize_tensor_coordinates( + final_boxes, original_sizes, is_bounding_box=True, preserve_padding=True + ) + final_boxes = box_xyxy_to_cxcywh(final_boxes) + encoding.update({"input_boxes": final_boxes}) + + if processed_boxes_labels is not None: + padded_boxes_labels = self._pad_nested_list(processed_boxes_labels, boxes_labels_max_dims) + final_boxes_labels = torch.tensor(padded_boxes_labels, dtype=torch.int64) + encoding.update({"input_boxes_labels": final_boxes_labels}) + + return encoding + + def _normalize_coordinates(self, coords: "torch.Tensor", original_size, is_bounding_box=False) -> "torch.Tensor": + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format. + + Args: + target_size (`int`): + The target size of the image. + coords (`torch.Tensor`): + The coordinates to be normalized. + original_size (`tuple`): + The original size of the image. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether the coordinates are bounding boxes. + """ + old_h, old_w = original_size + coords = deepcopy(coords).float() + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + coords[..., 0] = coords[..., 0] / old_w + coords[..., 1] = coords[..., 1] / old_h + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + def _generate_default_box_labels(self, processed_boxes): + """Generate default box labels: `point_pad_value` for None (padded) entries, 1 for real boxes.""" + return [None if image_boxes is None else [1] * len(image_boxes) for image_boxes in processed_boxes] + + def _convert_to_nested_list(self, data, expected_depth, current_depth=0): + """ + Recursively convert various input formats (tensors, numpy arrays, lists) to nested lists. + Preserves None values within lists. + + Args: + data: Input data in any format (may be None or contain None values) + expected_depth: Expected nesting depth + current_depth: Current depth in recursion + + Returns: + Nested list representation of the data (or None) + """ + if data is None: + return None + + # Convert tensor/numpy to list if we're at a leaf level or if it's a multi-dimensional array + if isinstance(data, torch.Tensor): # PyTorch tensor + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small tensor + return data.numpy().tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, np.ndarray): # NumPy array + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small array + return data.tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, list): + if current_depth == expected_depth: + # We've reached the expected depth, return as is + return data + else: + # Continue recursion, preserving None values + return [ + self._convert_to_nested_list(item, expected_depth, current_depth + 1) if item is not None else None + for item in data + ] + elif isinstance(data, (int, float)): + return data + else: + raise ValueError(f"Unsupported data type: {type(data)}") + + def _resolve_text_prompts(self, text, input_boxes): + """ + Resolve text prompts by setting defaults based on prompt types. + """ + # If no text provided, infer default based on prompt type + if text is None: + return "visual" if input_boxes else None + + if not isinstance(text, (list, tuple)): + return text + + # Validate list/tuple length matches both prompt types if provided + text = list(text) # Convert to list to allow modification + + if input_boxes and len(text) != len(input_boxes): + raise ValueError( + f"The number of text prompts must match the number of input boxes. " + f"Got {len(text)} text prompts and {len(input_boxes)} input boxes." + ) + + # Fill in None values with defaults based on corresponding prompt + for i, text_value in enumerate(text): + if text_value is None and input_boxes and input_boxes[i] is not None: + text[i] = "visual" + + return text + + def _get_nested_dimensions(self, nested_list, max_dims=None): + """ + Get the maximum dimensions at each level of nesting, skipping None values. + + Args: + nested_list (`list`): + Nested list structure (may contain None values). + max_dims (`list`, *optional*): + Current maximum dimensions (for recursion). + + Returns: + `list`: A list of maximum dimensions for each nesting level. + """ + if max_dims is None: + max_dims = [] + + if not isinstance(nested_list, list): + return max_dims + + if len(max_dims) == 0: + max_dims.append(len(nested_list)) + else: + max_dims[0] = max(max_dims[0], len(nested_list)) + + if len(nested_list) > 0: + for item in nested_list: + # Skip None values + if item is None: + continue + if isinstance(item, list): + sub_dims = self._get_nested_dimensions(item) + # Merge sub_dims into max_dims + for i, dim in enumerate(sub_dims): + if i + 1 >= len(max_dims): + max_dims.append(dim) + else: + max_dims[i + 1] = max(max_dims[i + 1], dim) + + return max_dims + + def _pad_nested_list(self, nested_list, target_dims, current_level=0, pad_value=None): + """ + Recursively pad a nested list to match target dimensions. Replaces None values with padded structures. + + Args: + nested_list (`list`): + Nested list to pad (may contain None values). + target_dims (`list`): + Target dimensions for each level. + current_level (`int`, *optional*, defaults to 0): + Current nesting level. + pad_value (`int`, *optional*): + Value to use for padding. + + Returns: + `list`: The padded nested list. + """ + if pad_value is None: + pad_value = self.point_pad_value + + if current_level >= len(target_dims): + return nested_list + + # Ensure we have a list + if not isinstance(nested_list, list): + nested_list = [nested_list] + + # Pad current level + current_size = len(nested_list) + target_size = target_dims[current_level] + + # Pad with appropriate values + if current_level == len(target_dims) - 1: + # At the coordinate level, pad with pad_value + nested_list.extend([pad_value] * (target_size - current_size)) + else: + # At higher levels, pad with nested structures + if current_size > 0: + # Create appropriately sized template + if current_level < len(target_dims) - 2: + # For non-coordinate levels, create empty nested structure + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + else: + # For coordinate level, create list of pad_values + template = [pad_value] * target_dims[current_level + 1] + + nested_list.extend([deepcopy(template) for _ in range(target_size - current_size)]) + else: + # Create from scratch + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + nested_list.extend([deepcopy(template) for _ in range(target_size)]) + + # Recursively pad sublists, replacing None with padded structures + if current_level < len(target_dims) - 1: + for i in range(len(nested_list)): + if nested_list[i] is None: + # Replace None with fully padded structure + template_dims = target_dims[current_level + 1 :] + nested_list[i] = self._create_empty_nested_structure(template_dims, pad_value) + elif isinstance(nested_list[i], list): + nested_list[i] = self._pad_nested_list(nested_list[i], target_dims, current_level + 1, pad_value) + + return nested_list + + def _create_empty_nested_structure(self, dims, pad_value): + """ + Create an empty nested structure with given dimensions filled with pad_value. + + Args: + dims (`list`): + The dimensions of the nested structure. + pad_value (`int`): + The value to fill the structure with. + """ + if len(dims) == 1: + return [pad_value] * dims[0] + else: + return [self._create_empty_nested_structure(dims[1:], pad_value) for _ in range(dims[0])] + + def _get_nesting_level(self, input_list): + """ + Get the nesting level of a list structure, skipping None values. + + Args: + input_list (`list`): + The list to get the nesting level of. + """ + if isinstance(input_list, list): + if len(input_list) == 0: + return 1 + # Find first non-None element to determine nesting level + for item in input_list: + if item is not None: + return 1 + self._get_nesting_level(item) + # All elements are None, treat as single level + return 1 + elif isinstance(input_list, (np.ndarray, torch.Tensor)): + # For arrays/tensors, the nesting level is the number of dimensions + return len(input_list.shape) + return 0 + + def _validate_single_input( + self, + data: torch.Tensor | np.ndarray | list, + expected_depth: int, + input_name: str, + expected_format: str, + expected_coord_size: int | None = None, + ) -> list: + """ + Validate a single input by ensuring proper nesting and raising an error if the input is not valid. + + Args: + data (`torch.Tensor`, `np.ndarray`, or `list`): + Input data to process. + expected_depth (`int`): + Expected nesting depth. + input_name (`str`): + Name of the input for error messages. + expected_format (`str`): + The expected format of the input. + expected_coord_size (`int`, *optional*): + Expected coordinate size (4 for boxes, None for labels). + . + """ + if data is None: + return None + + # Handle tensors and numpy arrays first + if isinstance(data, (torch.Tensor, np.ndarray)): + # For tensors/arrays, we can directly check the number of dimensions + if data.ndim != expected_depth: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_depth} dimensions. The expected nesting format is {expected_format}. Got {data.ndim} dimensions." + ) + elif expected_coord_size is not None: + if data.shape[-1] != expected_coord_size: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_coord_size} as the last dimension, got {data.shape[-1]}." + ) + return self._convert_to_nested_list(data, expected_depth) + + # Handle nested lists + if isinstance(data, list): + current_depth = self._get_nesting_level(data) + if current_depth != expected_depth: + raise ValueError( + f"Input {input_name} must be a nested list with {expected_depth} levels. The expected nesting format is {expected_format}. Got {current_depth} levels." + ) + return self._convert_to_nested_list(data, expected_depth) + + def _normalize_tensor_coordinates(self, tensor, original_sizes, is_bounding_box=False, preserve_padding=False): + """ + Helper method to normalize coordinates in a tensor across multiple images. + + Args: + tensor (`torch.Tensor`): + Input tensor with coordinates. + original_sizes (`list`): + Original image sizes. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether coordinates are bounding boxes. + preserve_padding (`bool`, *optional*, defaults to `False`): + Whether to preserve padding values (for boxes). + """ + if preserve_padding: + # For boxes: avoid normalizing pad values + mask = tensor != self.point_pad_value + coord_mask = mask.all(dim=-1, keepdim=True) + + for img_idx in range(len(original_sizes)): + if img_idx < tensor.shape[0]: + original_size = original_sizes[img_idx] if img_idx < len(original_sizes) else original_sizes[0] + normalized_coords = self._normalize_coordinates( + tensor[img_idx], original_size, is_bounding_box=is_bounding_box + ) + + if preserve_padding: + # Only update non-padded values + img_mask = coord_mask[img_idx] + tensor[img_idx] = torch.where( + img_mask.expand_as(tensor[img_idx]), normalized_coords, tensor[img_idx] + ) + else: + tensor[img_idx] = normalized_coords + + def post_process_semantic_segmentation(self, outputs, target_sizes=None, threshold=0.5): + """ + Converts the output of [`Sam3Model`] into semantic segmentation maps. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing semantic_seg. + target_sizes (`list[tuple]` of length `batch_size`, *optional*): + List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, + predictions will not be resized. + threshold (`float`, *optional*, defaults to 0.5): + Threshold for binarizing the semantic segmentation masks. + + Returns: + semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic + segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is + specified). Each entry is a binary mask (0 or 1). + """ + return self.image_processor.post_process_semantic_segmentation(outputs, target_sizes, threshold) + + def post_process_object_detection(self, outputs, threshold=0.3, target_sizes=None): + """ + Converts the raw output of [`Sam3Model`] into final bounding boxes in (top_left_x, top_left_y, + bottom_right_x, bottom_right_y) format. This is a convenience wrapper around the image processor method. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing pred_boxes, pred_logits, and optionally presence_logits. + threshold (`float`, *optional*, defaults to 0.3): + Score threshold to keep object detection predictions. + target_sizes (`list[tuple[int, int]]`, *optional*): + List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the + batch. If unset, predictions will not be resized. + + Returns: + `list[dict]`: A list of dictionaries, each dictionary containing the following keys: + - **scores** (`torch.Tensor`): The confidence scores for each predicted box on the image. + - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, + bottom_right_y) format. + + Example: + + ```python + >>> from transformers import AutoModel, AutoProcessor + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> model = AutoModel.from_pretrained("facebook/sam3-base") + >>> processor = AutoProcessor.from_pretrained("facebook/sam3-base") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> inputs = processor(images=image, text="cat", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> # Post-process to get bounding boxes + >>> results = processor.post_process_object_detection(outputs, threshold=0.3, target_sizes=[image.size[::-1]]) + >>> boxes = results[0]["boxes"] + >>> scores = results[0]["scores"] + ``` + """ + return self.image_processor.post_process_object_detection(outputs, threshold, target_sizes) + + def post_process_instance_segmentation( + self, + outputs, + threshold=0.3, + mask_threshold=0.5, + target_sizes=None, + ): + """ + Converts the raw output of [`Sam3Model`] into instance segmentation predictions with bounding boxes and masks. + This is a convenience wrapper around the image processor method. + + Args: + outputs ([`Sam3ImageSegmentationOutput`]): + Raw outputs of the model containing pred_boxes, pred_logits, pred_masks, and optionally + presence_logits. + threshold (`float`, *optional*, defaults to 0.3): + Score threshold to keep instance predictions. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold for binarizing the predicted masks. + target_sizes (`list[tuple[int, int]]`, *optional*): + List of tuples (`tuple[int, int]`) containing the target size `(height, width)` of each image in the + batch. If unset, predictions will not be resized. + + Returns: + `list[dict]`: A list of dictionaries, each dictionary containing the following keys: + - **scores** (`torch.Tensor`): The confidence scores for each predicted instance on the image. + - **boxes** (`torch.Tensor`): Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, + bottom_right_y) format. + - **masks** (`torch.Tensor`): Binary segmentation masks for each instance, shape (num_instances, + height, width). + + Example: + + ```python + >>> from transformers import AutoModel, AutoProcessor + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> model = AutoModel.from_pretrained("facebook/sam3-base") + >>> processor = AutoProcessor.from_pretrained("facebook/sam3-base") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> inputs = processor(images=image, text="cat", return_tensors="pt") + >>> outputs = model(**inputs) + + >>> # Post-process to get instance segmentation + >>> results = processor.post_process_instance_segmentation( + ... outputs, threshold=0.3, target_sizes=[image.size[::-1]] + ... ) + >>> masks = results[0]["masks"] + >>> boxes = results[0]["boxes"] + >>> scores = results[0]["scores"] + ``` + """ + return self.image_processor.post_process_instance_segmentation( + outputs, threshold, mask_threshold, target_sizes + ) + + +__all__ = ["Sam3Processor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..753f93a87c0e66a57fd7213c43af58c4a7826355 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam3_lite_text import * + from .modeling_sam3_lite_text import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/configuration_sam3_lite_text.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/configuration_sam3_lite_text.py new file mode 100644 index 0000000000000000000000000000000000000000..f77fa99677f41494aa186a371a8f046e7d5d5aae --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/configuration_sam3_lite_text.py @@ -0,0 +1,239 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3_lite_text/modular_sam3_lite_text.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3_lite_text.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextGeometryEncoderConfig(PreTrainedConfig): + r""" + roi_size (`int`, *optional*, defaults to 7): + ROI size for box pooling operations. + """ + + model_type = "sam3_lite_text_geometry_encoder" + + hidden_size: int = 256 + num_layers: int = 3 + num_attention_heads: int = 8 + intermediate_size: int = 2048 + dropout: float | int = 0.1 + hidden_act: str = "relu" + hidden_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + roi_size: int = 7 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextDETREncoderConfig(PreTrainedConfig): + r""" + hidden_dropout (`float`, *optional*, defaults to 0.0): + Dropout probability for hidden states. + """ + + model_type = "sam3_lite_text_detr_encoder" + + hidden_size: int = 256 + num_layers: int = 6 + num_attention_heads: int = 8 + intermediate_size: int = 2048 + dropout: float | int = 0.1 + hidden_act: str = "relu" + hidden_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextDETRDecoderConfig(PreTrainedConfig): + r""" + num_queries (`int`, *optional*, defaults to 200): + Number of object queries. + """ + + model_type = "sam3_lite_text_detr_decoder" + + hidden_size: int = 256 + num_layers: int = 6 + num_queries: int = 200 + num_attention_heads: int = 8 + intermediate_size: int = 2048 + dropout: float | int = 0.1 + hidden_act: str = "relu" + hidden_dropout: float | int = 0.0 + layer_norm_eps: float = 1e-6 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextMaskDecoderConfig(PreTrainedConfig): + r""" + num_upsampling_stages (`int`, *optional*, defaults to 3): + Number of upsampling stages in the pixel decoder (FPN). + """ + + model_type = "sam3_lite_text_mask_decoder" + + hidden_size: int = 256 + num_upsampling_stages: int = 3 + layer_norm_eps: float = 1e-6 + dropout: float | int = 0.0 + num_attention_heads: int = 8 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextTextConfig(PreTrainedConfig): + r""" + use_repmixer_blocks (`bool`, *optional*, defaults to `True`): + Whether to use RepMixer blocks (MobileCLIP-style) for the first and last encoder layers. + When `False`, all layers are standard Transformer encoder layers. + layer_scale_init_value (`float`, *optional*, defaults to `1e-5`): + Initial value for the learnable layer-scale parameters in RepMixer blocks (residual branches). + repmixer_kernel_size (`int`, *optional*, defaults to `11`): + Kernel size for depthwise convolutions in RepMixer blocks (token mixer and convolutional feed-forward path). + """ + + model_type = "sam3_lite_text_text_model" + + vocab_size: int = 49408 + hidden_size: int = 512 + intermediate_size: int = 2048 + projection_dim: int = 512 + num_hidden_layers: int = 12 + num_attention_heads: int = 8 + max_position_embeddings: int = 77 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-5 + attention_dropout: float = 0.0 + use_repmixer_blocks: bool = True + layer_scale_init_value: float = 1e-5 + repmixer_kernel_size: int = 11 + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextConfig(PreTrainedConfig): + r""" + geometry_encoder_config (`dict` or `Sam3LiteTextGeometryEncoderConfig`, *optional*): + Configuration for the geometry encoder. + detr_encoder_config (`dict` or `Sam3LiteTextDETREncoderConfig`, *optional*): + Configuration for the DETR encoder. + detr_decoder_config (`dict` or `Sam3LiteTextDETRDecoderConfig`, *optional*): + Configuration for the DETR decoder. + mask_decoder_config (`dict` or `Sam3LiteTextMaskDecoderConfig`, *optional*): + Configuration for the mask decoder. + + Example: + ```python + >>> from transformers import Sam3LiteTextConfig, Sam3LiteTextModel + + >>> # Initializing a SAM3_LITE_TEXT configuration + >>> configuration = Sam3LiteTextConfig() + + >>> # Initializing a model from the configuration + >>> model = Sam3LiteTextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "sam3_lite_text" + sub_configs = { + "vision_config": AutoConfig, + "text_config": Sam3LiteTextTextConfig, + "geometry_encoder_config": Sam3LiteTextGeometryEncoderConfig, + "detr_encoder_config": Sam3LiteTextDETREncoderConfig, + "detr_decoder_config": Sam3LiteTextDETRDecoderConfig, + "mask_decoder_config": Sam3LiteTextMaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + geometry_encoder_config: dict | PreTrainedConfig | None = None + detr_encoder_config: dict | PreTrainedConfig | None = None + detr_decoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam3_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam3_vision_model"]() + + if self.text_config is None: + self.text_config = Sam3LiteTextTextConfig() + if isinstance(self.text_config, dict): + self.text_config = Sam3LiteTextTextConfig(**self.text_config) + + if self.geometry_encoder_config is None: + self.geometry_encoder_config = Sam3LiteTextGeometryEncoderConfig() + if isinstance(self.geometry_encoder_config, dict): + self.geometry_encoder_config = Sam3LiteTextGeometryEncoderConfig(**self.geometry_encoder_config) + + if self.detr_encoder_config is None: + self.detr_encoder_config = Sam3LiteTextDETREncoderConfig() + if isinstance(self.detr_encoder_config, dict): + self.detr_encoder_config = Sam3LiteTextDETREncoderConfig(**self.detr_encoder_config) + + if self.detr_decoder_config is None: + self.detr_decoder_config = Sam3LiteTextDETRDecoderConfig() + if isinstance(self.detr_decoder_config, dict): + self.detr_decoder_config = Sam3LiteTextDETRDecoderConfig(**self.detr_decoder_config) + + if self.mask_decoder_config is None: + self.mask_decoder_config = Sam3LiteTextMaskDecoderConfig() + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam3LiteTextMaskDecoderConfig(**self.mask_decoder_config) + + super().__post_init__(**kwargs) + + @property + def image_size(self): + """Image size for the SAM3_LITE_TEXT model.""" + return self.vision_config.image_size + + @image_size.setter + def image_size(self, value): + """Set the image size and propagate to vision config.""" + self.vision_config.image_size = value + + +__all__ = [ + "Sam3LiteTextConfig", + "Sam3LiteTextTextConfig", + "Sam3LiteTextGeometryEncoderConfig", + "Sam3LiteTextDETREncoderConfig", + "Sam3LiteTextDETRDecoderConfig", + "Sam3LiteTextMaskDecoderConfig", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/modeling_sam3_lite_text.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/modeling_sam3_lite_text.py new file mode 100644 index 0000000000000000000000000000000000000000..b79759b4f2ef315e15f96ad2da8ea8877809ff44 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/modeling_sam3_lite_text.py @@ -0,0 +1,2201 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3_lite_text/modular_sam3_lite_text.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3_lite_text.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ... import initialization as init +from ...activations import ACT2FN +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutputWithPooling, ModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import auto_docstring, can_return_tuple, is_torchvision_available, logging +from ...utils.generic import TransformersKwargs, is_flash_attention_requested, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from .configuration_sam3_lite_text import ( + Sam3LiteTextConfig, + Sam3LiteTextDETRDecoderConfig, + Sam3LiteTextDETREncoderConfig, + Sam3LiteTextGeometryEncoderConfig, + Sam3LiteTextMaskDecoderConfig, + Sam3LiteTextTextConfig, +) + + +if is_torchvision_available(): + import torchvision + + +logger = logging.get_logger(__name__) + + +@dataclass +class Sam3LiteTextTextEncoderOutput(BaseModelOutputWithPooling): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Full sequence of hidden states from the text encoder. + pooler_output (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): + EOT-pooled output projected to `projection_dim` via the internal CLIP-style projection. + hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of hidden states at each layer, returned when `output_hidden_states=True`. + attentions (`tuple(torch.FloatTensor)`, *optional*): + Tuple of attention weights at each transformer layer, returned when `output_attentions=True`. + """ + + +class Sam3LiteTextTextPositionEmbedding(nn.Module): + """Learnable positional embedding with bilinear interpolation for variable sequence lengths.""" + + def __init__(self, max_position_embeddings: int, hidden_size: int): + super().__init__() + self.position_embedding = nn.Parameter(torch.empty(1, 1, max_position_embeddings, hidden_size)) + + def forward(self, seq_len: int) -> torch.Tensor: + position_embedding = self.position_embedding + if seq_len != position_embedding.shape[2]: + position_embedding = F.interpolate( + position_embedding, + size=(seq_len, position_embedding.shape[-1]), + mode="bilinear", + ) + return position_embedding.reshape(1, seq_len, -1) + + +class Sam3LiteTextMobileOneBlock(nn.Module): + """Depthwise conv branch with batch norm on the skip path and after the conv (MobileOne-style).""" + + def __init__(self, hidden_size: int, kernel_size: int = 3): + super().__init__() + self.batchnorm_skip = nn.BatchNorm2d(hidden_size) + self.conv = nn.Conv2d( + hidden_size, + hidden_size, + kernel_size=(1, kernel_size), + stride=1, + padding=(0, kernel_size // 2), + groups=hidden_size, + bias=False, + ) + self.batchnorm_conv = nn.BatchNorm2d(hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + residual = hidden_states + hidden_states = self.batchnorm_conv(self.conv(hidden_states)) + hidden_states = hidden_states + self.batchnorm_skip(residual) + return hidden_states + + +class Sam3LiteTextConvMLP(nn.Module): + """Pointwise MLP using 1×1 convolutions, compatible with 4-D (B, C, H, W) feature maps.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__() + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Conv2d(config.hidden_size, config.intermediate_size, kernel_size=1) + self.fc2 = nn.Conv2d(config.intermediate_size, config.hidden_size, kernel_size=1) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class Sam3LiteTextConvolutionalFeedForward(nn.Module): + """Convolutional feed-forward network: depthwise conv + two pointwise projections.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__() + self.depthwise_conv = nn.Conv2d( + config.hidden_size, + config.hidden_size, + kernel_size=(1, config.repmixer_kernel_size), + padding=(0, config.repmixer_kernel_size // 2), + groups=config.hidden_size, + bias=False, + ) + self.depthwise_batchnorm = nn.BatchNorm2d(config.hidden_size) + self.mlp = Sam3LiteTextConvMLP(config) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.depthwise_batchnorm(self.depthwise_conv(hidden_states)) + return self.mlp(hidden_states) + + +class Sam3LiteTextLayerScaledResidual(nn.Module): + """Common layer-scale residual pattern shared by the RepMixer and feed-forward branches.""" + + def __init__(self, hidden_size: int, layer_scale_init_value: float): + super().__init__() + self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((hidden_size, 1, 1)), requires_grad=True) + + def layer_scale_residual(self, hidden_states: torch.Tensor, update: torch.Tensor) -> torch.Tensor: + return hidden_states + self.layer_scale * update + + +class Sam3LiteTextRepMixer(Sam3LiteTextLayerScaledResidual): + """Re-parameterisable depthwise-conv token mixer operating on 1D sequence data.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__(config.hidden_size, config.layer_scale_init_value) + self.reference_batchnorm = nn.BatchNorm2d(config.hidden_size) + self.mixer = Sam3LiteTextMobileOneBlock(config.hidden_size, kernel_size=config.repmixer_kernel_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return self.layer_scale_residual( + hidden_states, self.mixer(hidden_states) - self.reference_batchnorm(hidden_states) + ) + + +class Sam3LiteTextRepMixerBlock(Sam3LiteTextLayerScaledResidual): + """Token-mixing RepMixer plus a convolutional feed-forward path, each with layer scale.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__(config.hidden_size, config.layer_scale_init_value) + self.token_mixer = Sam3LiteTextRepMixer(config) + self.conv_feed_forward = Sam3LiteTextConvolutionalFeedForward(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + hidden_states = hidden_states.transpose(1, 2).unsqueeze(2) + hidden_states = self.token_mixer(hidden_states) + hidden_states = self.layer_scale_residual(hidden_states, self.conv_feed_forward(hidden_states)) + return hidden_states.squeeze(2).transpose(1, 2) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Sam3LiteTextTextAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + queries = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + keys = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + values = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + queries, + keys, + values, + attention_mask, + is_causal=self.is_causal, + scaling=self.scale, + dropout=0.0 if not self.training else self.dropout, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class Sam3LiteTextTextMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class Sam3LiteTextTextEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.self_attn = Sam3LiteTextTextAttention(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = Sam3LiteTextTextMLP(config) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class Sam3LiteTextTextEmbeddings(nn.Module): + """Token embedding + interpolatable positional embedding for the text encoder.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__() + self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size) + self.position_embedding = Sam3LiteTextTextPositionEmbedding(config.max_position_embeddings, config.hidden_size) + + def forward(self, input_ids: torch.LongTensor) -> torch.Tensor: + hidden_states = self.token_embedding(input_ids) + hidden_states = hidden_states + self.position_embedding(input_ids.shape[1]).to(hidden_states.dtype) + return hidden_states + + +@auto_docstring +class Sam3LiteTextPreTrainedModel(PreTrainedModel): + config_class = Sam3LiteTextConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ["image", "text"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_attention_backend = True + supports_gradient_checkpointing = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Sam3LiteTextTextPositionEmbedding): + init.normal_(module.position_embedding, std=module.position_embedding.shape[-1] ** -0.5) + elif isinstance(module, Sam3LiteTextTextModel): + init.normal_(module.projection.weight, std=module.config.hidden_size**-0.5) + + +@auto_docstring( + custom_intro=""" + MobileCLIP MCT text encoder used in EfficientSAM3 LiteText. + + When `config.use_repmixer_blocks` is `True`, the first and last layers are + `Sam3LiteTextRepMixerBlock` modules; the rest are standard `Sam3LiteTextTextEncoderLayer` layers. +""" +) +class Sam3LiteTextTextModel(Sam3LiteTextPreTrainedModel): + config_class = Sam3LiteTextTextConfig + config: Sam3LiteTextTextConfig + _can_record_outputs = { + "hidden_states": Sam3LiteTextTextEncoderLayer, + "attentions": Sam3LiteTextTextAttention, + } + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__(config) + self.embeddings = Sam3LiteTextTextEmbeddings(config) + repmixer_positions = {0, config.num_hidden_layers - 1} if config.use_repmixer_blocks else set() + self.layers = nn.ModuleList( + [ + Sam3LiteTextRepMixerBlock(config) if i in repmixer_positions else Sam3LiteTextTextEncoderLayer(config) + for i in range(config.num_hidden_layers) + ] + ) + self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False) + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3LiteTextTextEncoderOutput: + hidden_states = self.embeddings(input_ids) + attention_mask = create_bidirectional_mask(self.config, hidden_states, attention_mask) + + for layer in self.layers: + hidden_states = layer(hidden_states, attention_mask=attention_mask, **kwargs) + + hidden_states = self.final_layer_norm(hidden_states) + + pooled = hidden_states[ + torch.arange(hidden_states.shape[0], device=hidden_states.device), input_ids.argmax(dim=-1) + ] + pooled = self.projection(pooled) + return Sam3LiteTextTextEncoderOutput( + last_hidden_state=hidden_states, + pooler_output=pooled, + ) + + +@auto_docstring +@dataclass +class Sam3LiteTextVisionEncoderOutput(BaseModelOutputWithPooling): + r""" + fpn_hidden_states (`tuple[torch.FloatTensor]`): + Tuple of multi-level FPN feature maps. + fpn_position_encoding (`tuple[torch.FloatTensor]`): + Tuple of position encodings for each FPN level. + """ + + fpn_hidden_states: tuple[torch.FloatTensor, ...] = None + fpn_position_encoding: tuple[torch.FloatTensor, ...] = None + + +@dataclass +@auto_docstring +class Sam3LiteTextGeometryEncoderOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_prompts, hidden_size)`): + Encoded geometry prompt features (boxes). + attention_mask (`torch.BoolTensor` of shape `(batch_size, num_prompts)`, *optional*): + Attention mask for geometry prompts where True indicates valid positions and False indicates padding. + """ + + last_hidden_state: torch.FloatTensor = None + attention_mask: torch.BoolTensor | None = None + + +@dataclass +@auto_docstring +class Sam3LiteTextDETREncoderOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Encoded vision features (flattened from multi-level features). + pos_embeds_flattened (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Flattened position embeddings for the vision features. + text_features (`torch.FloatTensor` of shape `(batch_size, text_seq_len, hidden_size)`, *optional*): + Text features (may be pooled after encoder processing). + spatial_shapes (`torch.LongTensor` of shape `(num_levels, 2)`, *optional*): + Spatial shapes (height, width) for each feature pyramid level. + hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all encoder layers. + attentions (`tuple[torch.FloatTensor]`, *optional*): + Tuple of attention weights from all encoder layers. + """ + + last_hidden_state: torch.FloatTensor = None + pos_embeds_flattened: torch.FloatTensor | None = None + text_features: torch.FloatTensor | None = None + spatial_shapes: torch.LongTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@dataclass +@auto_docstring +class Sam3LiteTextDETRDecoderOutput(ModelOutput): + r""" + intermediate_hidden_states (`torch.FloatTensor` of shape `(num_layers, batch_size, num_queries, hidden_size)`): + Decoder hidden states from all layers. + reference_boxes (`torch.FloatTensor` of shape `(num_layers, batch_size, num_queries, 4)`): + Predicted reference boxes from all decoder layers in (cx, cy, w, h) format. + presence_logits (`torch.FloatTensor` of shape `(num_layers, batch_size, 1)`): + Presence logits from all decoder layers indicating object presence confidence. + hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all decoder layers. + attentions (`tuple[torch.FloatTensor]`, *optional*): + Tuple of attention weights from all decoder layers (self-attention and cross-attention). + """ + + intermediate_hidden_states: torch.FloatTensor = None + reference_boxes: torch.FloatTensor = None + presence_logits: torch.FloatTensor = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@dataclass +@auto_docstring +class Sam3LiteTextMaskDecoderOutput(ModelOutput): + r""" + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`): + Predicted segmentation masks for each query. + semantic_seg (`torch.FloatTensor` of shape `(batch_size, 1, height, width)`, *optional*): + Semantic segmentation output. + attentions (`tuple[torch.FloatTensor]`, *optional*): + Tuple of attention weights from mask decoder cross-attention layers. + """ + + pred_masks: torch.FloatTensor = None + semantic_seg: torch.FloatTensor | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +@dataclass +@auto_docstring +class Sam3LiteTextImageSegmentationOutput(ModelOutput): + r""" + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height, width)`): + Predicted segmentation masks for each query. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Predicted bounding boxes in (x1, y1, x2, y2) format. + pred_logits (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*): + Classification confidence scores for each query, computed via dot product between + decoder query features and text features. + presence_logits (`torch.FloatTensor` of shape `(batch_size, 1)`, *optional*): + Presence logits from the DETR decoder presence token (last layer only). These indicate whether objects + are present in the scene. Can be used to compute final scores by multiplying with pred_logits: + `final_scores = pred_logits.sigmoid() * presence_logits.sigmoid()`. + semantic_seg (`torch.FloatTensor` of shape `(batch_size, 1, height, width)`, *optional*): + Semantic segmentation output. + decoder_hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all DETR decoder layers. Each tensor has shape `(batch_size, num_queries, hidden_size)`. + decoder_reference_boxes (`torch.FloatTensor` of shape `(num_layers, batch_size, num_queries, 4)`, *optional*): + Reference boxes from all DETR decoder layers. + encoder_hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all DETR encoder layers. + vision_hidden_states (`tuple[torch.FloatTensor]`, *optional*): + Tuple of hidden states from all vision encoder (ViT) layers. + vision_attentions (`tuple[torch.FloatTensor]`, *optional*): + Attention weights from vision encoder (ViT) layers. + detr_encoder_attentions (`tuple[torch.FloatTensor]`, *optional*): + Attention weights from DETR encoder layers. + detr_decoder_attentions (`tuple[torch.FloatTensor]`, *optional*): + Attention weights from DETR decoder layers (self-attention and cross-attention). + mask_decoder_attentions (`tuple[torch.FloatTensor]`, *optional*): + Attention weights from mask decoder layers. + """ + + pred_masks: torch.FloatTensor = None + pred_boxes: torch.FloatTensor = None + pred_logits: torch.FloatTensor | None = None + presence_logits: torch.FloatTensor | None = None + semantic_seg: torch.FloatTensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_reference_boxes: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + vision_hidden_states: tuple[torch.FloatTensor] | None = None + vision_attentions: tuple[torch.FloatTensor] | None = None + detr_encoder_attentions: tuple[torch.FloatTensor] | None = None + detr_decoder_attentions: tuple[torch.FloatTensor] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor] | None = None + + +class Sam3LiteTextMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class Sam3LiteTextAttention(nn.Module): + """ + Multi-head attention. + Handles standard [batch_size, seq_len, hidden_size] tensors. + """ + + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.hidden_size // config.num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.q_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.k_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.v_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.o_proj = nn.Linear(self.hidden_size, self.hidden_size) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Args: + query: [batch_size, query_len, hidden_size] + key: [batch_size, key_len, hidden_size] + value: [batch_size, value_len, hidden_size] + attention_mask: [batch_size, num_heads, query_len, key_len] or broadcastable + + Returns: + Tuple of (output, attention_weights) + output: [batch_size, query_len, hidden_size] + attention_weights: [batch_size, num_heads, query_len, key_len] + """ + batch_size = query.shape[0] + query_len = query.shape[1] + key_len = key.shape[1] + + query = self.q_proj(query).view(batch_size, query_len, self.num_attention_heads, self.head_dim).transpose(1, 2) + key = self.k_proj(key).view(batch_size, key_len, self.num_attention_heads, self.head_dim).transpose(1, 2) + value = self.v_proj(value).view(batch_size, key_len, self.num_attention_heads, self.head_dim).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if ( + is_flash_attention_requested(self.config) + and attention_mask is not None + and attention_mask.dtype != torch.bool + ): + # Relative position bias tensors are represented as float masks and are incompatible with Flash Attention + # Fallback to SDPA for this call only so the rest of the model can still benefit from FA + attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"] + logger.warning_once( + "Sam3LiteTextAttention: falling back to SDPA for relative-position cross-attention because " + "Flash Attention does not support additive bias masks." + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_mask, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = attn_output.reshape(batch_size, query_len, self.num_attention_heads * self.head_dim).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +class Sam3LiteTextSinePositionEmbedding(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one used by the Attention is all you + need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: float | None = None + ): + super().__init__() + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + self.scale = 2 * math.pi if scale is None else scale + + def encode_1d_positions(self, x: torch.Tensor, y: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """ + Encode 1D coordinate pairs using sine/cosine positional embeddings. + + Args: + x: 1D tensor of x coordinates (flattened) + y: 1D tensor of y coordinates (flattened) + + Returns: + Tuple of (pos_x, pos_y) positional embeddings + """ + x_embed = x * self.scale + y_embed = y * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=x.device).to(x.dtype) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, None] / dim_t + pos_y = y_embed[:, None] / dim_t + pos_x = torch.stack((pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2).flatten(1) + pos_y = torch.stack((pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2).flatten(1) + return pos_x, pos_y + + def encode_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """ + Encode 4D box coordinates (x, y, w, h) for decoder conditioning using sine/cosine embeddings. + + Args: + boxes: Box coordinates [batch_size, num_queries, 4] in (x, y, w, h) format + + Returns: + Position embeddings [batch_size, num_queries, num_pos_feats*4] + """ + assert boxes.size(-1) == 4, f"Expected 4D box coordinates (x, y, w, h), got shape {boxes.shape}" + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=boxes.device).to(boxes.dtype) + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) + + x_embed = boxes[:, :, 0] * self.scale + y_embed = boxes[:, :, 1] * self.scale + w_embed = boxes[:, :, 2] * self.scale + h_embed = boxes[:, :, 3] * self.scale + + pos_x = x_embed[:, :, None] / dim_t + pos_y = y_embed[:, :, None] / dim_t + pos_w = w_embed[:, :, None] / dim_t + pos_h = h_embed[:, :, None] / dim_t + + pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) + pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2) + pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2) + pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2) + + pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2) + + return pos + + @compile_compatible_method_lru_cache(maxsize=4) + def forward( + self, + shape: torch.Size, + device: torch.device | str, + dtype: torch.dtype, + mask: Tensor | None = None, + ) -> Tensor: + if mask is None: + mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool) + not_mask = (~mask).to(dtype) + y_embed = not_mask.cumsum(1) + x_embed = not_mask.cumsum(2) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype) + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +class Sam3LiteTextGeometryEncoderLayer(nn.Module): + def __init__(self, config: Sam3LiteTextGeometryEncoderConfig): + super().__init__() + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + self.self_attn = Sam3LiteTextAttention(config) + self.dropout = nn.Dropout(config.dropout) + + self.cross_attn = Sam3LiteTextAttention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam3LiteTextMLP(config) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + def forward( + self, + prompt_feats: Tensor, + vision_feats: Tensor, + vision_pos_encoding: Tensor, + prompt_mask: Tensor, + **kwargs: Unpack[TransformersKwargs], + ): + residual = prompt_feats + hidden_states = self.layer_norm1(prompt_feats) + hidden_states, _ = self.self_attn( + query=hidden_states, key=hidden_states, value=hidden_states, attention_mask=prompt_mask, **kwargs + ) + hidden_states = self.dropout(hidden_states) + residual + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + key = vision_feats + vision_pos_encoding + hidden_states, _ = self.cross_attn(query=hidden_states, key=key, value=vision_feats, **kwargs) + hidden_states = self.dropout(hidden_states) + residual + residual = hidden_states + hidden_states = self.layer_norm3(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = self.dropout(hidden_states) + residual + + return hidden_states + + +def concat_padded_sequences(seq1, mask1, seq2, mask2, return_index: bool = False): + """ + Concatenates two right-padded sequences, such that the resulting sequence + is contiguous and also right-padded. + + Tensors are batch-first, masks are batch-first with True=valid, False=padding. + + Args: + seq1: A tensor of shape (batch_size, seq1_length, hidden_size). + mask1: A tensor of shape (batch_size, seq1_length) with True=valid, False=padding. + seq2: A tensor of shape (batch_size, seq2_length, hidden_size). + mask2: A tensor of shape (batch_size, seq2_length) with True=valid, False=padding. + return_index: If True, also returns the index of the ids of the element of seq2 + in the concatenated sequence. This can be used to retrieve the elements of seq2. + + Returns: + A tuple (concatenated_sequence, concatenated_mask) if return_index is False, + otherwise (concatenated_sequence, concatenated_mask, index). + The concatenated_mask uses True=valid, False=padding convention. + """ + batch_size, seq1_length, hidden_size = seq1.shape + batch_size2, seq2_length, hidden_size2 = seq2.shape + + assert batch_size == batch_size2 == mask1.size(0) == mask2.size(0) + assert hidden_size == hidden_size2 + assert seq1_length == mask1.size(1) + assert seq2_length == mask2.size(1) + + actual_seq1_lengths = mask1.sum(dim=-1) + actual_seq2_lengths = mask2.sum(dim=-1) + + final_lengths = actual_seq1_lengths + actual_seq2_lengths + max_length = seq1_length + seq2_length + + concatenated_mask = ( + torch.arange(max_length, device=seq2.device)[None].repeat(batch_size, 1) < final_lengths[:, None] + ) + + concatenated_sequence = torch.zeros((batch_size, max_length, hidden_size), device=seq2.device, dtype=seq2.dtype) + concatenated_sequence[:, :seq1_length, :] = seq1 + + # Shift seq2 elements to start at the end of valid seq1 + index = torch.arange(seq2_length, device=seq2.device)[None].repeat(batch_size, 1) + index = index + actual_seq1_lengths[:, None] + + # Scatter seq2 into the right positions + concatenated_sequence = concatenated_sequence.scatter(1, index[:, :, None].expand(-1, -1, hidden_size), seq2) + + if return_index: + return concatenated_sequence, concatenated_mask, index + + return concatenated_sequence, concatenated_mask + + +def box_cxcywh_to_xyxy(x): + """Convert boxes from (cx, cy, w, h) format to (x1, y1, x2, y2) format.""" + x_c, y_c, w, h = x.unbind(-1) + b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)] + return torch.stack(b, dim=-1) + + +class Sam3LiteTextGeometryEncoder(nn.Module): + """ + Encoder for geometric prompts (boxes). + + Boxes are encoded using three approaches: + - Direct projection: linear projection from coordinate space to hidden_size + - Pooling: pool features from the backbone at the specified location (ROI align for boxes) + - Position encoding: use position encoding of the box center + + These encodings are combined additively and further processed with transformer layers. + """ + + def __init__(self, config: Sam3LiteTextGeometryEncoderConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.roi_size = config.roi_size + + self.position_encoding = Sam3LiteTextSinePositionEmbedding( + num_pos_feats=config.hidden_size // 2, normalize=True + ) + self.label_embed = nn.Embedding(2, self.hidden_size) + self.cls_embed = nn.Embedding(1, self.hidden_size) + + # Box encoding layers + self.boxes_direct_project = nn.Linear(4, self.hidden_size) + self.boxes_pool_project = nn.Conv2d(self.hidden_size, self.hidden_size, self.roi_size) + self.boxes_pos_enc_project = nn.Linear(self.hidden_size + 2, self.hidden_size) + + # Image feature normalization + self.vision_layer_norm = nn.LayerNorm(self.hidden_size) + + # Prompt projection and normalization + self.final_proj = nn.Linear(self.hidden_size, self.hidden_size) + self.prompt_layer_norm = nn.LayerNorm(self.hidden_size) + + # Transformer layers + self.layers = nn.ModuleList([Sam3LiteTextGeometryEncoderLayer(config) for _ in range(config.num_layers)]) + self.output_layer_norm = nn.LayerNorm(self.hidden_size) + + def _encode_box_coordinates( + self, center_x: torch.Tensor, center_y: torch.Tensor, width: torch.Tensor, height: torch.Tensor + ) -> torch.Tensor: + """ + Encode box coordinates by combining position-encoded centers with raw width/height. + + Args: + center_x: 1D tensor of box center x coordinates + center_y: 1D tensor of box center y coordinates + width: 1D tensor of box widths + height: 1D tensor of box heights + + Returns: + Encoded box coordinates [N, embedding_dim] + """ + pos_x, pos_y = self.position_encoding.encode_1d_positions(center_x, center_y) + pos = torch.cat((pos_y, pos_x, height[:, None], width[:, None]), dim=1) + return pos + + def _encode_boxes(self, boxes, boxes_mask, boxes_labels, vision_features): + """Encode box prompts. Mask convention: True=valid, False=padding.""" + batch_size, num_boxes = boxes.shape[:2] + height, width = vision_features.shape[-2:] + boxes_embed = self.boxes_direct_project(boxes) + + # Pool features using ROI align + # Convert boxes from CxCyWH to xyxy format and denormalize + boxes_xyxy = box_cxcywh_to_xyxy(boxes) + scale = torch.tensor([width, height, width, height], dtype=boxes_xyxy.dtype, device=boxes_xyxy.device) + scale = scale.view(1, 1, 4) + boxes_xyxy = boxes_xyxy * scale + # ROI align expects list of boxes per batch element, + # convert from bfloat16 to float16 as roi_align only supports float16 and float32 + dtype = torch.float16 if vision_features.dtype == torch.bfloat16 else vision_features.dtype + sampled_features = torchvision.ops.roi_align( + vision_features.to(dtype), boxes_xyxy.to(dtype).unbind(0), self.roi_size + ).to(vision_features.dtype) + + pooled_projection = self.boxes_pool_project(sampled_features) + pooled_projection = pooled_projection.view(batch_size, num_boxes, self.hidden_size) + boxes_embed = boxes_embed + pooled_projection + + # Add position encoding + center_x, center_y, box_width, box_height = boxes.unbind(-1) + pos_enc = self._encode_box_coordinates( + center_x.flatten(), center_y.flatten(), box_width.flatten(), box_height.flatten() + ) + pos_enc = pos_enc.view(batch_size, num_boxes, pos_enc.shape[-1]) + pos_projection = self.boxes_pos_enc_project(pos_enc) + boxes_embed = boxes_embed + pos_projection + + # Add label embeddings (positive/negative) + label_embed = self.label_embed(boxes_labels.long()) + return label_embed + boxes_embed, boxes_mask + + def forward( + self, + box_embeddings: torch.Tensor, + box_mask: torch.Tensor, + box_labels: torch.Tensor, + img_feats: tuple[torch.Tensor, ...], + img_pos_embeds: tuple[torch.Tensor, ...] | None = None, + ): + """ + Forward pass for encoding geometric prompts. + + Args: + box_embeddings: Box coordinates in CxCyWH format [batch_size, num_boxes, 4] + box_mask: Attention mask for boxes [batch_size, num_boxes] + box_labels: Labels for boxes (positive/negative) [batch_size, num_boxes] + img_feats: Image features from vision encoder + img_pos_embeds: Optional position embeddings for image features + + Returns: + Sam3LiteTextGeometryEncoderOutput containing encoded geometry features and attention mask. + """ + batch_size = box_embeddings.shape[0] + + # Prepare vision features for cross-attention: flatten spatial dimensions + vision_feats = img_feats[-1] # [B, C, H, W] + vision_pos_embeds = img_pos_embeds[-1] if img_pos_embeds is not None else torch.zeros_like(vision_feats) + vision_feats_flat = vision_feats.flatten(2).transpose(1, 2) # [B, H*W, C] + vision_pos_embeds_flat = vision_pos_embeds.flatten(2).transpose(1, 2) # [B, H*W, C] + + # Normalize image features for pooling operations + img_feats_last = img_feats[-1] # [B, C, H, W] + img_feats_last = img_feats_last.permute(0, 2, 3, 1) # [B, H, W, C] + normalized_img_feats = self.vision_layer_norm(img_feats_last) + normalized_img_feats = normalized_img_feats.permute(0, 3, 1, 2) # [B, C, H, W] + + prompt_embeds, prompt_mask = self._encode_boxes(box_embeddings, box_mask, box_labels, normalized_img_feats) + + # Add CLS token (always valid) + cls_embed = self.cls_embed.weight.view(1, self.hidden_size).unsqueeze(0).expand(batch_size, -1, -1) + cls_mask = torch.ones(batch_size, 1, dtype=prompt_mask.dtype, device=prompt_mask.device) + prompt_embeds, prompt_mask = concat_padded_sequences(prompt_embeds, prompt_mask, cls_embed, cls_mask) + + prompt_embeds = self.prompt_layer_norm(self.final_proj(prompt_embeds)) + + # Create bidirectional attention mask for transformer layers + prompt_attention_mask = None + if prompt_mask is not None: + prompt_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=prompt_embeds, + attention_mask=prompt_mask, + ) + + # Apply transformer layers with cross-attention to vision features + for layer in self.layers: + prompt_embeds = layer( + prompt_feats=prompt_embeds, + vision_feats=vision_feats_flat, + vision_pos_encoding=vision_pos_embeds_flat, + prompt_mask=prompt_attention_mask, + ) + + # Final output normalization + prompt_embeds = self.output_layer_norm(prompt_embeds) + + return Sam3LiteTextGeometryEncoderOutput( + last_hidden_state=prompt_embeds, + attention_mask=prompt_mask, + ) + + +class Sam3LiteTextDetrEncoderLayer(nn.Module): + """DETR encoder layer with self-attention and cross-attention.""" + + def __init__(self, config: Sam3LiteTextDETREncoderConfig): + super().__init__() + self.config = config + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + self.self_attn = Sam3LiteTextAttention(config) + self.dropout = nn.Dropout(config.dropout) + + self.cross_attn = Sam3LiteTextAttention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam3LiteTextMLP(config) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + def forward( + self, + vision_feats: Tensor, + prompt_feats: Tensor, + vision_pos_encoding: Tensor, + prompt_cross_attn_mask: Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ): + """ + Forward pass for DETR encoder layer. + + Args: + vision_feats: Vision features [batch_size, vision_len, hidden_size] (main hidden states) + prompt_feats: Text prompt features [batch_size, text_len, hidden_size] + vision_pos_encoding: Position encoding for vision [batch_size, vision_len, hidden_size] + prompt_cross_attn_mask: Cross-attention mask for prompt features + + Returns: + Updated vision features [batch_size, vision_len, hidden_size] + """ + # Self-attention on vision features with position encoding + residual = vision_feats + hidden_states = self.layer_norm1(vision_feats) + hidden_states_with_pos = hidden_states + vision_pos_encoding + hidden_states, _ = self.self_attn( + query=hidden_states_with_pos, + key=hidden_states_with_pos, + value=hidden_states, + **kwargs, + ) + hidden_states = self.dropout(hidden_states) + residual + + # Cross-attention: vision queries attend to text/prompt features + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + + hidden_states, _ = self.cross_attn( + query=hidden_states, + key=prompt_feats, + value=prompt_feats, + attention_mask=prompt_cross_attn_mask, + **kwargs, + ) + hidden_states = self.dropout(hidden_states) + residual + + # MLP + residual = hidden_states + hidden_states = self.layer_norm3(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = self.dropout(hidden_states) + residual + + return hidden_states + + +class Sam3LiteTextDetrEncoder(Sam3LiteTextPreTrainedModel): + """ + DETR-style encoder that processes multi-level vision features with text fusion. + + This encoder processes vision features from multiple levels (e.g., FPN features at different + resolutions) and fuses them with text prompts through a stack of transformer encoder layers. + """ + + _can_record_outputs = { + "hidden_states": Sam3LiteTextDetrEncoderLayer, + "attentions": Sam3LiteTextAttention, + } + + def __init__(self, config: Sam3LiteTextDETREncoderConfig): + super().__init__(config) + self.config = config + self.hidden_size = config.hidden_size + + self.layers = nn.ModuleList([Sam3LiteTextDetrEncoderLayer(config) for _ in range(config.num_layers)]) + + self.post_init() + + def _prepare_multilevel_features( + self, + vision_features: list[torch.Tensor], + vision_pos_embeds: list[torch.Tensor], + ): + """ + Prepare multi-level vision features by flattening spatial dimensions and adding level embeddings. + + Args: + vision_features: List of vision features at different levels [batch_size, channels, height, width] + vision_pos_embeds: List of position embeddings for each level [batch_size, channels, height, width] + + Returns: + Tuple containing flattened features, position embeddings, and spatial metadata + """ + features_flattened = [] + pos_embeds_flattened = [] + spatial_shapes = [] + + for features, pos_embed in zip(vision_features, vision_pos_embeds): + height, width = features.shape[-2:] + spatial_shapes.append((height, width)) + + # Flatten spatial dimensions: [batch_size, channels, height, width] -> [batch_size, height*width, channels] + features = features.flatten(2).transpose(1, 2) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + + features_flattened.append(features) + pos_embeds_flattened.append(pos_embed) + + # Concatenate all levels into single sequence + features_flattened = torch.cat(features_flattened, dim=1) + pos_embeds_flattened = torch.cat(pos_embeds_flattened, dim=1) + + spatial_shapes = torch.tensor(spatial_shapes, dtype=torch.long, device=features_flattened.device) + + return ( + features_flattened, + pos_embeds_flattened, + spatial_shapes, + ) + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + vision_features: list[torch.Tensor], + text_features: torch.Tensor, + vision_pos_embeds: list[torch.Tensor] | None = None, + text_mask: torch.Tensor | None = None, + spatial_sizes: list[tuple[int, int]] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3LiteTextDETREncoderOutput: + """ + Forward pass for the DETR encoder. + + Args: + vision_features: List of vision features at different levels + text_features: Text prompt features [batch_size, seq_len, hidden_size] + vision_pos_embeds: Optional list of position embeddings for each level + text_mask: Optional text padding mask [batch_size, seq_len] + spatial_sizes: Optional list of (height, width) tuples for reshaping + + Returns: + Sam3LiteTextDETREncoderOutput containing encoded features and metadata. + """ + batch_size = vision_features[0].shape[0] if vision_features[0].dim() == 4 else vision_features[0].shape[1] + + # TODO: See if we can remove that reshaping and just use the features as is. + if spatial_sizes is not None: + for i, (height, width) in enumerate(spatial_sizes): + # Reshape from [height*width, batch_size, channels] to [batch_size, channels, height, width] + vision_features[i] = vision_features[i].reshape(height, width, batch_size, -1).permute(2, 3, 0, 1) + vision_pos_embeds[i] = vision_pos_embeds[i].reshape(height, width, batch_size, -1).permute(2, 3, 0, 1) + + # Flatten multi-level features for encoder processing + ( + features_flattened, + pos_embeds_flattened, + spatial_shapes, + ) = self._prepare_multilevel_features(vision_features, vision_pos_embeds) + + prompt_cross_attn_mask = None + if text_mask is not None: + prompt_cross_attn_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=features_flattened, + attention_mask=text_mask, + encoder_hidden_states=text_features, + ) + + hidden_states = features_flattened + for layer in self.layers: + hidden_states = layer( + hidden_states, + prompt_feats=text_features, + vision_pos_encoding=pos_embeds_flattened, + prompt_cross_attn_mask=prompt_cross_attn_mask, + **kwargs, + ) + return Sam3LiteTextDETREncoderOutput( + last_hidden_state=hidden_states, + pos_embeds_flattened=pos_embeds_flattened, + text_features=text_features, + spatial_shapes=spatial_shapes, + ) + + +class Sam3LiteTextDecoderMLP(nn.Module): + """Simple 2 or 3-layer MLP for decoder components.""" + + def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 2): + super().__init__() + if num_layers == 2: + self.layer1 = nn.Linear(input_dim, hidden_dim) + self.layer2 = nn.Linear(hidden_dim, output_dim) + self.layer3 = None + elif num_layers == 3: + self.layer1 = nn.Linear(input_dim, hidden_dim) + self.layer2 = nn.Linear(hidden_dim, hidden_dim) + self.layer3 = nn.Linear(hidden_dim, output_dim) + else: + raise ValueError(f"Only 2 or 3 layers supported, got {num_layers}") + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = F.relu(self.layer1(x)) + if self.layer3 is not None: + x = F.relu(self.layer2(x)) + x = self.layer3(x) + else: + x = self.layer2(x) + return x + + +class Sam3LiteTextDetrDecoderLayer(nn.Module): + """DETR decoder layer with self-attention, text cross-attention, and vision cross-attention.""" + + def __init__(self, config: Sam3LiteTextDETRDecoderConfig): + super().__init__() + self.config = config + self.self_attn = Sam3LiteTextAttention(config) + self.self_attn_dropout = nn.Dropout(config.dropout) + self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size) + + self.text_cross_attn = Sam3LiteTextAttention(config) + self.text_cross_attn_dropout = nn.Dropout(config.dropout) + self.text_cross_attn_layer_norm = nn.LayerNorm(config.hidden_size) + + self.vision_cross_attn = Sam3LiteTextAttention(config) + self.vision_cross_attn_dropout = nn.Dropout(config.dropout) + self.vision_cross_attn_layer_norm = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam3LiteTextMLP(config) + self.mlp_layer_norm = nn.LayerNorm(config.hidden_size) + self.mlp_dropout = nn.Dropout(config.dropout) + + def forward( + self, + hidden_states: torch.Tensor, + query_pos: torch.Tensor, + text_features: torch.Tensor, + vision_features: torch.Tensor, + vision_pos_encoding: torch.Tensor, + text_cross_attn_mask: torch.Tensor | None = None, + vision_cross_attn_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + """ + Forward pass for decoder layer. + + Args: + hidden_states: Query features [batch_size, num_queries + 1, hidden_size] (includes presence token at position 0) + query_pos: Query position embeddings [batch_size, num_queries, hidden_size] + text_features: Text features [batch_size, seq_len, hidden_size] + vision_features: Vision features [batch_size, height*width, hidden_size] + vision_pos_encoding: Vision position encoding [batch_size, height*width, hidden_size] + text_cross_attn_mask: Text cross-attention mask + vision_cross_attn_mask: Vision cross-attention mask, already expanded for presence token + + Returns: + Updated hidden states (including presence token at position 0) + """ + # Prepend zeros to query_pos for presence token + query_pos = F.pad(query_pos, (0, 0, 1, 0), mode="constant", value=0) + + # Self-attention with query position encoding + residual = hidden_states + query_with_pos = hidden_states + query_pos + attn_output, _ = self.self_attn( + query=query_with_pos, + key=query_with_pos, + value=hidden_states, + attention_mask=None, + **kwargs, + ) + hidden_states = residual + self.self_attn_dropout(attn_output) + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Text cross-attention: queries attend to text features + residual = hidden_states + query_with_pos = hidden_states + query_pos + + attn_output, _ = self.text_cross_attn( + query=query_with_pos, + key=text_features, + value=text_features, + attention_mask=text_cross_attn_mask, + **kwargs, + ) + hidden_states = residual + self.text_cross_attn_dropout(attn_output) + hidden_states = self.text_cross_attn_layer_norm(hidden_states) + + # Vision cross-attention: queries attend to vision features (with RPB) + residual = hidden_states + query_with_pos = hidden_states + query_pos + key_with_pos = vision_features + vision_pos_encoding + attn_output, _ = self.vision_cross_attn( + query=query_with_pos, + key=key_with_pos, + value=vision_features, + attention_mask=vision_cross_attn_mask, + **kwargs, + ) + hidden_states = residual + self.vision_cross_attn_dropout(attn_output) + hidden_states = self.vision_cross_attn_layer_norm(hidden_states) + + # MLP + residual = hidden_states + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.mlp_dropout(hidden_states) + hidden_states = self.mlp_layer_norm(hidden_states) + + return hidden_states + + +def inverse_sigmoid(x: torch.Tensor, eps: float = 1e-3) -> torch.Tensor: + """The inverse function for sigmoid activation function.""" + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +class Sam3LiteTextDetrDecoder(Sam3LiteTextPreTrainedModel): + """ + DETR-style decoder with box refinement and presence token. + + Simplified version that assumes: + - Box refinement is always enabled + - Intermediate outputs are always returned + - BoxRPB (relative position bias) with log-scale encoding + - Presence token is used + """ + + _can_record_outputs = { + "hidden_states": Sam3LiteTextDetrDecoderLayer, + "attentions": Sam3LiteTextAttention, + } + + def __init__( + self, + config: Sam3LiteTextDETRDecoderConfig, + ): + super().__init__(config) + self.config = config + self.hidden_size = config.hidden_size + + self.layers = nn.ModuleList([Sam3LiteTextDetrDecoderLayer(config) for _ in range(config.num_layers)]) + + self.output_layer_norm = nn.LayerNorm(config.hidden_size) + + self.box_head = Sam3LiteTextDecoderMLP(config.hidden_size, config.hidden_size, 4, 3) + + self.query_embed = nn.Embedding(config.num_queries, config.hidden_size) + self.reference_points = nn.Embedding(config.num_queries, 4) + + self.presence_token = nn.Embedding(1, config.hidden_size) + self.presence_head = Sam3LiteTextDecoderMLP(config.hidden_size, config.hidden_size, 1, 3) + self.presence_layer_norm = nn.LayerNorm(config.hidden_size) + self.clamp_presence_logit_max_val = 10.0 + + self.ref_point_head = Sam3LiteTextDecoderMLP(2 * config.hidden_size, config.hidden_size, config.hidden_size, 2) + + self.box_rpb_embed_x = Sam3LiteTextDecoderMLP(2, config.hidden_size, config.num_attention_heads, 2) + self.box_rpb_embed_y = Sam3LiteTextDecoderMLP(2, config.hidden_size, config.num_attention_heads, 2) + + self.position_encoding = Sam3LiteTextSinePositionEmbedding( + num_pos_feats=config.hidden_size // 2, normalize=False + ) + + self.post_init() + + @compile_compatible_method_lru_cache(maxsize=1) + def _get_coords( + self, height: torch.Tensor, width: torch.Tensor, dtype: torch.dtype, device: torch.device + ) -> tuple[torch.Tensor, torch.Tensor]: + """Generate normalized coordinate grids.""" + coords_h = torch.arange(0, height, device=device, dtype=dtype) / height + coords_w = torch.arange(0, width, device=device, dtype=dtype) / width + return coords_h, coords_w + + def _get_rpb_matrix( + self, reference_boxes: torch.Tensor, spatial_shape: tuple[torch.Tensor, torch.Tensor] + ) -> torch.Tensor: + """ + Compute box relative position bias (RPB) matrix using log-scale encoding. + RPB helps the decoder attend to relevant spatial locations based on predicted box positions. + + Args: + reference_boxes: Reference boxes [batch_size, num_queries, 4] in sigmoid space + spatial_shape: (height, width) of the vision features as tensors + + Returns: + RPB matrix [batch_size, num_heads, num_queries, height*width] + """ + height, width = spatial_shape + boxes_xyxy = box_cxcywh_to_xyxy(reference_boxes) + batch_size, num_queries, _ = boxes_xyxy.shape + + # Generate coordinate grids + coords_h, coords_w = self._get_coords( + height, width, dtype=reference_boxes.dtype, device=reference_boxes.device + ) + + # Compute deltas between coordinates and box boundaries + deltas_y = coords_h.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 1:4:2] + deltas_y = deltas_y.view(batch_size, num_queries, -1, 2) + deltas_x = coords_w.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 0:3:2] + deltas_x = deltas_x.view(batch_size, num_queries, -1, 2) + + # Apply log-scale encoding + deltas_x_log = deltas_x * 8 + deltas_x_log = torch.sign(deltas_x_log) * torch.log2(torch.abs(deltas_x_log) + 1.0) / math.log2(8) + deltas_y_log = deltas_y * 8 + deltas_y_log = torch.sign(deltas_y_log) * torch.log2(torch.abs(deltas_y_log) + 1.0) / math.log2(8) + + # Embed deltas + deltas_x = self.box_rpb_embed_x(deltas_x_log) # [batch_size, num_queries, width, num_heads] + deltas_y = self.box_rpb_embed_y(deltas_y_log) # [batch_size, num_queries, height, num_heads] + + # Combine into 2D bias matrix + rpb_matrix = deltas_y.unsqueeze(3) + deltas_x.unsqueeze( + 2 + ) # [batch_size, num_queries, height, width, num_heads] + rpb_matrix = rpb_matrix.flatten(2, 3) # [batch_size, num_queries, height*width, num_heads] + rpb_matrix = rpb_matrix.permute(0, 3, 1, 2).contiguous() # [batch_size, num_heads, num_queries, height*width] + return rpb_matrix + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + vision_features: torch.Tensor, + text_features: torch.Tensor, + vision_pos_encoding: torch.Tensor, + text_mask: torch.Tensor | None = None, + spatial_shapes: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3LiteTextDETRDecoderOutput: + """ + Forward pass for the DETR decoder. + + Args: + vision_features: Vision features [batch_size, height*width, hidden_size] + text_features: Text features [batch_size, seq_len, hidden_size] + vision_pos_encoding: Vision position encoding [batch_size, height*width, hidden_size] + text_mask: Text padding mask [batch_size, seq_len] where True=valid, False=padding + spatial_shapes: Spatial shapes [num_levels, 2] + + Returns: + Sam3LiteTextDETRDecoderOutput containing decoder outputs from all layers. + """ + batch_size = vision_features.shape[0] + + query_embeds = self.query_embed.weight.unsqueeze(0).expand(batch_size, -1, -1) + reference_boxes = self.reference_points.weight.unsqueeze(0).expand(batch_size, -1, -1) + reference_boxes = reference_boxes.sigmoid() + presence_token = self.presence_token.weight.unsqueeze(0).expand(batch_size, -1, -1) + + # Concatenate presence token with query embeddings + hidden_states = torch.cat([presence_token, query_embeds], dim=1) + + text_cross_attn_mask = None + if text_mask is not None: + text_cross_attn_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=text_mask, + encoder_hidden_states=text_features, + ) + + intermediate_outputs = [] + intermediate_boxes = [reference_boxes] + intermediate_presence_logits = [] + + for layer in self.layers: + # Generate sine embeddings for conditional queries + reference_points_input = reference_boxes.unsqueeze(2) + query_sine_embed = self.position_encoding.encode_boxes(reference_points_input[:, :, 0, :]) + query_pos = self.ref_point_head(query_sine_embed) + + # Compute box relative position bias (RPB) attention mask + vision_cross_attn_mask = None + if spatial_shapes is not None and spatial_shapes.shape[0] == 1: + spatial_shape = (spatial_shapes[0, 0], spatial_shapes[0, 1]) + rpb_matrix = self._get_rpb_matrix(reference_boxes, spatial_shape) + # Prepend zeros row for presence token (it attends to all vision tokens equally) + vision_cross_attn_mask = F.pad(rpb_matrix, (0, 0, 1, 0), mode="constant", value=0) + + hidden_states = layer( + hidden_states, + query_pos=query_pos, + text_features=text_features, + vision_features=vision_features, + vision_pos_encoding=vision_pos_encoding, + text_cross_attn_mask=text_cross_attn_mask, + vision_cross_attn_mask=vision_cross_attn_mask, + **kwargs, + ) + + # Extract query hidden states (without presence token) for box refinement + query_hidden_states = hidden_states[:, 1:] + + # Box refinement: predict delta and update reference boxes + reference_boxes_before_sigmoid = inverse_sigmoid(reference_boxes) + delta_boxes = self.box_head(self.output_layer_norm(query_hidden_states)) + new_reference_boxes = (delta_boxes + reference_boxes_before_sigmoid).sigmoid() + reference_boxes = new_reference_boxes.detach() + + intermediate_outputs.append(self.output_layer_norm(query_hidden_states)) + intermediate_boxes.append(new_reference_boxes) + + # Process presence token + presence_hidden = hidden_states[:, :1] + presence_logits = self.presence_head(self.presence_layer_norm(presence_hidden)).squeeze(-1) + presence_logits = presence_logits.clamp( + min=-self.clamp_presence_logit_max_val, max=self.clamp_presence_logit_max_val + ) + intermediate_presence_logits.append(presence_logits) + + # Stack outputs from all layers + intermediate_outputs = torch.stack(intermediate_outputs) + intermediate_boxes = torch.stack(intermediate_boxes[:-1]) + intermediate_presence_logits = torch.stack(intermediate_presence_logits) + + return Sam3LiteTextDETRDecoderOutput( + intermediate_hidden_states=intermediate_outputs, + reference_boxes=intermediate_boxes, + presence_logits=intermediate_presence_logits, + ) + + +class Sam3LiteTextDotProductScoring(nn.Module): + """ + Computes classification scores by computing dot product between projected decoder queries and pooled text features. + This is used to determine confidence/presence scores for each query. + """ + + def __init__(self, config: Sam3LiteTextConfig): + super().__init__() + self.config = config + hidden_size = config.detr_decoder_config.hidden_size + projection_dim = config.detr_decoder_config.hidden_size + + self.text_mlp = Sam3LiteTextDecoderMLP( + input_dim=hidden_size, + hidden_dim=config.detr_decoder_config.intermediate_size, + output_dim=hidden_size, + num_layers=2, + ) + self.text_mlp_dropout = nn.Dropout(config.detr_decoder_config.dropout) + self.text_mlp_out_norm = nn.LayerNorm(hidden_size) + + # Projections for text and query features + self.text_proj = nn.Linear(hidden_size, projection_dim) + self.query_proj = nn.Linear(hidden_size, projection_dim) + + # Scale factor for dot product + self.scale = float(1.0 / np.sqrt(projection_dim)) + + # Clamping to avoid numerical issues + self.clamp_logits = True + self.clamp_max_val = 12.0 + + def _pool_text_features(self, text_features: torch.Tensor, text_mask: torch.Tensor | None) -> torch.Tensor: + """ + Mean pool text features, accounting for padding. + + Args: + text_features: [batch_size, seq_len, hidden_size] + text_mask: [batch_size, seq_len] where True indicates valid tokens, False indicates padding + + Returns: + pooled_text: [batch_size, hidden_size] + """ + if text_mask is None: + # No padding, simple mean + return text_features.mean(dim=1) + + is_valid = text_mask.to(text_features.dtype).unsqueeze(-1) # [batch_size, seq_len, 1] + + # Count valid tokens per batch + num_valid = is_valid.sum(dim=1).clamp(min=1.0) # [batch_size, 1] + + # Mean pool only over valid tokens + pooled_text = (text_features * is_valid).sum(dim=1) / num_valid # [batch_size, hidden_size] + + return pooled_text + + def forward( + self, + decoder_hidden_states: torch.Tensor, + text_features: torch.Tensor, + text_mask: torch.Tensor | None = None, + ) -> torch.Tensor: + """ + Compute classification scores via dot product. + + Args: + decoder_hidden_states: [num_layers, batch_size, num_queries, hidden_size] + text_features: [batch_size, seq_len, hidden_size] + text_mask: [batch_size, seq_len] where True=valid, False=padding + + Returns: + scores: [num_layers, batch_size, num_queries, 1] + """ + orig_text_features = text_features + text_features = self.text_mlp(text_features) + text_features = self.text_mlp_dropout(text_features) + text_features = text_features + orig_text_features + text_features = self.text_mlp_out_norm(text_features) + + pooled_text = self._pool_text_features(text_features, text_mask) + + proj_text = self.text_proj(pooled_text) + proj_queries = self.query_proj(decoder_hidden_states) + + proj_text = proj_text.unsqueeze(-1) + scores = torch.matmul(proj_queries, proj_text.unsqueeze(0)) + scores = scores * self.scale + if self.clamp_logits: + scores = scores.clamp(min=-self.clamp_max_val, max=self.clamp_max_val) + + return scores + + +class Sam3LiteTextMaskEmbedder(nn.Module): + """ + MLP that embeds object queries for mask prediction. + Similar to MaskFormer's mask embedder. + """ + + def __init__(self, config: Sam3LiteTextMaskDecoderConfig): + super().__init__() + self.config = config + hidden_size = config.hidden_size + + self.layers = nn.ModuleList( + [ + nn.Linear(hidden_size, hidden_size), + nn.Linear(hidden_size, hidden_size), + nn.Linear(hidden_size, hidden_size), + ] + ) + self.activation = nn.ReLU() + + def forward(self, queries: torch.Tensor) -> torch.Tensor: + """ + Args: + queries: Query embeddings [batch_size, num_queries, hidden_size] + + Returns: + Mask embeddings [batch_size, num_queries, hidden_size] + """ + hidden_states = queries + for i, layer in enumerate(self.layers): + hidden_states = layer(hidden_states) + if i < len(self.layers) - 1: + hidden_states = self.activation(hidden_states) + return hidden_states + + +class Sam3LiteTextPixelDecoder(nn.Module): + """ + Feature Pyramid Network (FPN) decoder that generates pixel-level features. + Inspired by MaskFormer's pixel decoder. + """ + + def __init__(self, config: Sam3LiteTextMaskDecoderConfig): + super().__init__() + self.config = config + hidden_size = config.hidden_size + num_upsampling_stages = config.num_upsampling_stages + + # Create conv layers and norms for FPN + self.conv_layers = nn.ModuleList( + [ + nn.Conv2d(hidden_size, hidden_size, kernel_size=3, stride=1, padding=1) + for _ in range(num_upsampling_stages) + ] + ) + self.norms = nn.ModuleList([nn.GroupNorm(8, hidden_size) for _ in range(num_upsampling_stages)]) + + self.out_channels = hidden_size + + def forward(self, backbone_features: list[torch.Tensor]) -> torch.Tensor: + """ + Args: + backbone_features: List of backbone features [batch_size, hidden_size, H_i, W_i] + from low to high resolution (assumes already projected to hidden_size) + + Returns: + Pixel embeddings [batch_size, hidden_size, H, W] at the finest resolution + """ + # Start from the coarsest feature (last in list) + prev_fpn = backbone_features[-1] + # Iterate through features from coarse to fine (excluding the last which we started with) + for layer_idx, backbone_feat in enumerate(reversed(backbone_features[:-1])): + # Upsample previous FPN output to match current backbone feature size + prev_fpn = F.interpolate(prev_fpn, size=backbone_feat.shape[-2:], mode="nearest") + + # Add skip connection + prev_fpn = prev_fpn + backbone_feat + + # Apply conv and norm + prev_fpn = self.conv_layers[layer_idx](prev_fpn) + prev_fpn = self.norms[layer_idx](prev_fpn) + prev_fpn = F.relu(prev_fpn) + + return prev_fpn + + +class Sam3LiteTextMaskDecoder(Sam3LiteTextPreTrainedModel): + """ + Mask decoder that combines object queries with pixel-level features to predict instance masks. + Also produces a semantic segmentation output and supports cross-attention to prompts. + """ + + _can_record_outputs = { + "attentions": Sam3LiteTextAttention, + } + + def __init__(self, config: Sam3LiteTextMaskDecoderConfig): + super().__init__(config) + self.config = config + hidden_size = config.hidden_size + + # Pixel decoder (FPN) + self.pixel_decoder = Sam3LiteTextPixelDecoder(config) + + # Mask embedder (MLP to transform queries) + self.mask_embedder = Sam3LiteTextMaskEmbedder(config) + + # Projection from pixel decoder output to mask embedding space + self.instance_projection = nn.Conv2d(self.pixel_decoder.out_channels, hidden_size, kernel_size=1) + + # Semantic segmentation head (always present in UniversalSegmentationHead) + self.semantic_projection = nn.Conv2d(self.pixel_decoder.out_channels, 1, kernel_size=1) + + self.prompt_cross_attn = Sam3LiteTextAttention(config) + self.prompt_cross_attn_norm = nn.LayerNorm(hidden_size) + self.prompt_cross_attn_dropout = nn.Dropout(config.dropout) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + def forward( + self, + decoder_queries: torch.Tensor, + backbone_features: list[torch.Tensor], + encoder_hidden_states: torch.Tensor, + prompt_features: torch.Tensor | None = None, + prompt_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3LiteTextMaskDecoderOutput: + """ + Args: + decoder_queries: Decoder output queries [batch_size, num_queries, hidden_size] + backbone_features: List of backbone features to process through FPN + encoder_hidden_states: Encoder outputs [batch_size, seq_len, hidden_size] + prompt_features: Prompt features (text + geometry) for cross-attention [batch_size, prompt_len, hidden_size] + prompt_mask: Padding mask [batch_size, prompt_len] where True=valid, False=padding + + Returns: + Sam3LiteTextMaskDecoderOutput containing predicted masks and semantic segmentation. + """ + if prompt_features is not None: + # Cross-attention: encoder features attend to prompt features + residual = encoder_hidden_states + normed_hidden_states = self.prompt_cross_attn_norm(encoder_hidden_states) + + cross_attn_mask = None + if prompt_mask is not None: + cross_attn_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=normed_hidden_states, + encoder_hidden_states=prompt_features, + attention_mask=prompt_mask, + ) + + attn_output, _ = self.prompt_cross_attn( + query=normed_hidden_states, + key=prompt_features, + value=prompt_features, + attention_mask=cross_attn_mask, + **kwargs, + ) + encoder_hidden_states = residual + self.prompt_cross_attn_dropout(attn_output) + + # Process backbone features through FPN to get pixel embeddings + pixel_embed = self._embed_pixels( + backbone_features=backbone_features, + encoder_hidden_states=encoder_hidden_states, + ) + + # Predict instance masks via dot product between query embeddings and pixel embeddings + instance_embeds = self.instance_projection(pixel_embed) + mask_embeddings = self.mask_embedder(decoder_queries) + pred_masks = torch.einsum("bqc,bchw->bqhw", mask_embeddings, instance_embeds) + + # Generate semantic segmentation + semantic_seg = self.semantic_projection(pixel_embed) + + return Sam3LiteTextMaskDecoderOutput( + pred_masks=pred_masks, + semantic_seg=semantic_seg, + ) + + def _embed_pixels( + self, + backbone_features: list[torch.Tensor], + encoder_hidden_states: torch.Tensor, + ) -> torch.Tensor: + """ + Embed pixels by combining backbone FPN features with encoder vision features. + The encoder vision features replace the finest-resolution backbone feature. + + Args: + backbone_features: List of backbone features [batch_size, C, H_i, W_i] + encoder_hidden_states: Encoder outputs [batch_size, seq_len, hidden_size] + + Returns: + Pixel embeddings [batch_size, hidden_size, H, W] + """ + backbone_visual_feats = [feat.clone() for feat in backbone_features] + + # Extract vision features from encoder output and reshape to spatial format + spatial_dim = backbone_features[-1].shape[-2] * backbone_features[-1].shape[-1] + encoder_visual_embed = encoder_hidden_states[:, :spatial_dim, :] + batch_size, _, hidden_size = encoder_visual_embed.shape + height, width = backbone_features[-1].shape[-2:] + encoder_visual_embed = encoder_visual_embed.transpose(1, 2).reshape(batch_size, hidden_size, height, width) + + # Replace finest backbone feature with encoder vision features + backbone_visual_feats[-1] = encoder_visual_embed + + # Process through FPN decoder + pixel_embed = self.pixel_decoder(backbone_visual_feats) + + return pixel_embed + + +class Sam3LiteTextModel(Sam3LiteTextPreTrainedModel): + input_modalities = ["image", "text"] + base_model_prefix = "detector_model" + _keys_to_ignore_on_load_unexpected = [ + r"^tracker_model.", + r"^tracker_neck.", + ] + # DETR components create float masks from features, so flash/flex attention cannot be dispatched safely. + _supports_flash_attn = False + _supports_flex_attn = False + + def __init__(self, config: Sam3LiteTextConfig): + # loading from a sam3_lite_text_video config + if hasattr(config, "detector_config") and config.detector_config is not None: + detector_config = config.detector_config + if isinstance(detector_config, dict): + detector_config = Sam3LiteTextConfig(**detector_config) + config = detector_config + super().__init__(config) + self.vision_encoder = AutoModel.from_config(config.vision_config) + self.text_encoder = Sam3LiteTextTextModel(config.text_config) + self.vocab_size = config.text_config.vocab_size + + # Project text features from text encoder hidden size to model hidden size + # CLIP text encoder outputs 1024-dim features, but we need 256-dim for DETR + self.text_projection = nn.Linear(config.text_config.hidden_size, config.detr_encoder_config.hidden_size) + + # Pass _attn_implementation to subconfigs BEFORE creating modules + config.geometry_encoder_config._attn_implementation = config._attn_implementation + config.detr_encoder_config._attn_implementation = config._attn_implementation + config.detr_decoder_config._attn_implementation = config._attn_implementation + config.mask_decoder_config._attn_implementation = config._attn_implementation + + self.geometry_encoder = Sam3LiteTextGeometryEncoder(config.geometry_encoder_config) + self.detr_encoder = Sam3LiteTextDetrEncoder(config.detr_encoder_config) + self.detr_decoder = Sam3LiteTextDetrDecoder(config.detr_decoder_config) + self.mask_decoder = Sam3LiteTextMaskDecoder(config.mask_decoder_config) + + # Dot product scoring to compute classification scores + self.dot_product_scoring = Sam3LiteTextDotProductScoring(config) + + self.post_init() + + @can_return_tuple + @auto_docstring + def get_text_features( + self, + input_ids: torch.LongTensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + Example: + + ```python + >>> from transformers import Sam3LiteTextModel, Sam3LiteTextProcessor + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> model = Sam3LiteTextModel.from_pretrained("facebook/sam3_lite_text") + >>> processor = Sam3LiteTextProcessor.from_pretrained("facebook/sam3_lite_text") + + >>> # Pre-compute text embeddings + >>> text_inputs = processor(text="cat", return_tensors="pt") + >>> text_embeds = model.get_text_features(**text_inputs).pooler_output + + >>> # Reuse text embeddings for multiple images + >>> url = "http://images.cocodataset.org/val2017/000000077595.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> img_inputs = processor(images=image, return_tensors="pt") + >>> outputs = model(pixel_values=img_inputs.pixel_values, text_embeds=text_embeds) + ``` + """ + text_outputs = self.text_encoder( + input_ids=input_ids, attention_mask=attention_mask, return_dict=True, **kwargs + ) + last_hidden_state = text_outputs.last_hidden_state + text_outputs.pooler_output = self.text_projection(last_hidden_state) + + return text_outputs + + @auto_docstring + def get_vision_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam3LiteTextVisionEncoderOutput: + r""" + Example: + + ```python + >>> from transformers import Sam3LiteTextModel, Sam3LiteTextProcessor + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> model = Sam3LiteTextModel.from_pretrained("facebook/sam3_lite_text") + >>> processor = Sam3LiteTextProcessor.from_pretrained("facebook/sam3_lite_text") + + >>> # Pre-compute vision embeddings + >>> url = "http://images.cocodataset.org/val2017/000000077595.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + >>> img_inputs = processor(images=image, return_tensors="pt") + >>> vision_embeds = model.get_vision_features(pixel_values=img_inputs.pixel_values) + + >>> # Reuse vision embeddings for multiple text prompts + >>> text_inputs = processor(text="cat", return_tensors="pt") + >>> outputs = model(vision_embeds=vision_embeds, input_ids=text_inputs.input_ids) + ``` + """ + vision_outputs = self.vision_encoder(pixel_values, **kwargs) + return vision_outputs + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + vision_embeds: Sam3LiteTextVisionEncoderOutput | None = None, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + text_embeds: torch.FloatTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_boxes_labels: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam3LiteTextImageSegmentationOutput: + r""" + vision_embeds (`Sam3LiteTextVisionEncoderOutput`, *optional*): + Pre-computed vision embeddings. Can be used to easily reuse vision embeddings. If provided, `pixel_values` + should not be passed. Mutually exclusive with `pixel_values`. + text_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Pre-computed text embeddings. Can be used to easily reuse text embeddings. If provided, `input_ids` + should not be passed. Mutually exclusive with `input_ids`. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`, *optional*): + Normalized box coordinates in [0, 1] range, in (cx, cy, w, h) format. + input_boxes_labels (`torch.LongTensor` of shape `(batch_size, num_boxes)`, *optional*): + Labels for boxes: 1 (positive), 0 (negative). + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoModel, AutoProcessor + + >>> model = AutoModel.from_pretrained("facebook/sam3_lite_text") + >>> processor = AutoProcessor.from_pretrained("facebook/sam3_lite_text") + + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())).convert("RGB") + >>> text = "car" + >>> inputs = processor(images=image, text=text, return_tensors="pt") + + >>> # Get segmentation output + >>> outputs = model(**inputs) + >>> pred_masks = outputs.pred_masks + >>> pred_boxes = outputs.pred_boxes + ``` + """ + if (pixel_values is None) == (vision_embeds is None): + raise ValueError("You must specify exactly one of pixel_values or vision_embeds") + + if (input_ids is None) == (text_embeds is None): + raise ValueError("You must specify exactly one of input_ids or text_embeds") + + if pixel_values is not None: + batch_size = pixel_values.shape[0] + device = pixel_values.device + else: + batch_size = vision_embeds.fpn_hidden_states[0].shape[0] + device = vision_embeds.fpn_hidden_states[0].device + + if vision_embeds is None: + vision_outputs = self.vision_encoder(pixel_values, **kwargs) + else: + vision_outputs = vision_embeds + + fpn_hidden_states = vision_outputs.fpn_hidden_states[:-1] + fpn_position_encoding = vision_outputs.fpn_position_encoding[:-1] + + if text_embeds is None: + text_features = self.get_text_features( + input_ids=input_ids, attention_mask=attention_mask, return_dict=True + ).pooler_output + else: + text_features = text_embeds + + text_mask = attention_mask.bool() if attention_mask is not None else None + has_geometry_prompts = input_boxes is not None and input_boxes.numel() > 0 + + geometry_prompt_features = None + geometry_prompt_mask = None + + if has_geometry_prompts: + if input_boxes is not None and input_boxes.numel() > 0: + box_embeddings = input_boxes # [batch_size, num_boxes, 4] + box_labels = ( + input_boxes_labels + if input_boxes_labels is not None + else torch.ones_like(box_embeddings[..., 0], dtype=torch.long) + ) + box_mask = ( + (input_boxes_labels != -10) + if input_boxes_labels is not None + else torch.ones(batch_size, input_boxes.shape[1], dtype=torch.bool, device=device) + ) + box_labels = torch.where(box_labels == -10, 0, box_labels) + else: + box_embeddings = torch.zeros(batch_size, 0, 4, dtype=text_features.dtype, device=device) + box_labels = torch.zeros(batch_size, 0, dtype=torch.long, device=device) + box_mask = torch.zeros(batch_size, 0, dtype=torch.bool, device=device) + + geometry_outputs = self.geometry_encoder( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + img_feats=fpn_hidden_states, + img_pos_embeds=fpn_position_encoding, + ) + + geometry_prompt_features = geometry_outputs.last_hidden_state + geometry_prompt_mask = geometry_outputs.attention_mask + + if geometry_prompt_features is not None: + # Repeat text_features for all geometry prompts + if text_features.shape[0] == 1 and geometry_prompt_features.shape[0] > 1: + text_features = text_features.repeat(geometry_prompt_features.shape[0], 1, 1) + combined_prompt_features = torch.cat([text_features, geometry_prompt_features], dim=1) + if text_mask is not None and text_mask.shape[0] == 1 and geometry_prompt_mask.shape[0] > 1: + text_mask = text_mask.repeat(geometry_prompt_mask.shape[0], 1) + + if text_mask is not None and geometry_prompt_mask is not None: + combined_prompt_mask = torch.cat([text_mask, geometry_prompt_mask], dim=1) + elif text_mask is not None: + geo_valid_mask = torch.ones( + batch_size, geometry_prompt_features.shape[1], dtype=torch.bool, device=device + ) + combined_prompt_mask = torch.cat([text_mask, geo_valid_mask], dim=1) + elif geometry_prompt_mask is not None: + text_valid_mask = torch.ones(batch_size, text_features.shape[1], dtype=torch.bool, device=device) + combined_prompt_mask = torch.cat([text_valid_mask, geometry_prompt_mask], dim=1) + else: + combined_prompt_mask = None + else: + combined_prompt_features = text_features + combined_prompt_mask = text_mask + + encoder_outputs = self.detr_encoder( + vision_features=[fpn_hidden_states[-1]], + text_features=combined_prompt_features, + vision_pos_embeds=[fpn_position_encoding[-1]], + text_mask=combined_prompt_mask, + **kwargs, + ) + + decoder_outputs = self.detr_decoder( + vision_features=encoder_outputs.last_hidden_state, + text_features=encoder_outputs.text_features, + vision_pos_encoding=encoder_outputs.pos_embeds_flattened, + text_mask=combined_prompt_mask, + spatial_shapes=encoder_outputs.spatial_shapes, + **kwargs, + ) + + # Refine boxes from decoder + all_box_offsets = self.detr_decoder.box_head(decoder_outputs.intermediate_hidden_states) + reference_boxes_inv_sig = inverse_sigmoid(decoder_outputs.reference_boxes) + all_pred_boxes_cxcywh = (reference_boxes_inv_sig + all_box_offsets).sigmoid() + all_pred_boxes = box_cxcywh_to_xyxy(all_pred_boxes_cxcywh) + + all_pred_logits = self.dot_product_scoring( + decoder_hidden_states=decoder_outputs.intermediate_hidden_states, + text_features=encoder_outputs.text_features, + text_mask=combined_prompt_mask, + ).squeeze(-1) + + pred_logits = all_pred_logits[-1] + pred_boxes = all_pred_boxes[-1] + decoder_hidden_states = decoder_outputs.intermediate_hidden_states[-1] + presence_logits = decoder_outputs.presence_logits[-1] + + mask_outputs = self.mask_decoder( + decoder_queries=decoder_hidden_states, + backbone_features=list(fpn_hidden_states), + encoder_hidden_states=encoder_outputs.last_hidden_state, + prompt_features=combined_prompt_features, + prompt_mask=combined_prompt_mask, + **kwargs, + ) + + return Sam3LiteTextImageSegmentationOutput( + pred_masks=mask_outputs.pred_masks, + pred_boxes=pred_boxes, + pred_logits=pred_logits, + presence_logits=presence_logits, + semantic_seg=mask_outputs.semantic_seg, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_reference_boxes=decoder_outputs.reference_boxes, + encoder_hidden_states=encoder_outputs.hidden_states, + vision_hidden_states=vision_outputs.hidden_states, + vision_attentions=vision_outputs.attentions, + detr_encoder_attentions=encoder_outputs.attentions, + detr_decoder_attentions=decoder_outputs.attentions, + mask_decoder_attentions=mask_outputs.attentions, + ) + + +__all__ = ["Sam3LiteTextModel", "Sam3LiteTextPreTrainedModel", "Sam3LiteTextTextModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/modular_sam3_lite_text.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/modular_sam3_lite_text.py new file mode 100644 index 0000000000000000000000000000000000000000..4e830a6d5ec335b4b307c7d33ad741c61e62dc27 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_lite_text/modular_sam3_lite_text.py @@ -0,0 +1,442 @@ +# Copyright 2026 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict + +from ... import initialization as init +from ...activations import ACT2FN +from ...configuration_utils import PreTrainedConfig +from ...masking_utils import create_bidirectional_mask +from ...modeling_outputs import BaseModelOutputWithPooling +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import auto_docstring +from ...utils.generic import TransformersKwargs, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel +from ..sam3.configuration_sam3 import ( + Sam3DETRDecoderConfig, + Sam3DETREncoderConfig, + Sam3GeometryEncoderConfig, + Sam3MaskDecoderConfig, +) +from ..sam3.modeling_sam3 import Sam3Model, Sam3PreTrainedModel +from ..siglip.modeling_siglip import SiglipAttention, SiglipEncoderLayer, SiglipMLP + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextGeometryEncoderConfig(Sam3GeometryEncoderConfig): + pass + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextDETREncoderConfig(Sam3DETREncoderConfig): + pass + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextDETRDecoderConfig(Sam3DETRDecoderConfig): + pass + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextMaskDecoderConfig(Sam3MaskDecoderConfig): + pass + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextTextConfig(PreTrainedConfig): + r""" + use_repmixer_blocks (`bool`, *optional*, defaults to `True`): + Whether to use RepMixer blocks (MobileCLIP-style) for the first and last encoder layers. + When `False`, all layers are standard Transformer encoder layers. + layer_scale_init_value (`float`, *optional*, defaults to `1e-5`): + Initial value for the learnable layer-scale parameters in RepMixer blocks (residual branches). + repmixer_kernel_size (`int`, *optional*, defaults to `11`): + Kernel size for depthwise convolutions in RepMixer blocks (token mixer and convolutional feed-forward path). + """ + + model_type = "sam3_lite_text_text_model" + + vocab_size: int = 49408 + hidden_size: int = 512 + intermediate_size: int = 2048 + projection_dim: int = 512 + num_hidden_layers: int = 12 + num_attention_heads: int = 8 + max_position_embeddings: int = 77 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-5 + attention_dropout: float = 0.0 + use_repmixer_blocks: bool = True + layer_scale_init_value: float = 1e-5 + repmixer_kernel_size: int = 11 + + +@auto_docstring(checkpoint="yonigozlan/sam3-litetext-s0") +@strict +class Sam3LiteTextConfig(PreTrainedConfig): + r""" + geometry_encoder_config (`dict` or `Sam3LiteTextGeometryEncoderConfig`, *optional*): + Configuration for the geometry encoder. + detr_encoder_config (`dict` or `Sam3LiteTextDETREncoderConfig`, *optional*): + Configuration for the DETR encoder. + detr_decoder_config (`dict` or `Sam3LiteTextDETRDecoderConfig`, *optional*): + Configuration for the DETR decoder. + mask_decoder_config (`dict` or `Sam3LiteTextMaskDecoderConfig`, *optional*): + Configuration for the mask decoder. + + Example: + ```python + >>> from transformers import Sam3LiteTextConfig, Sam3LiteTextModel + + >>> # Initializing a SAM3_LITE_TEXT configuration + >>> configuration = Sam3LiteTextConfig() + + >>> # Initializing a model from the configuration + >>> model = Sam3LiteTextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "sam3_lite_text" + sub_configs = { + "vision_config": AutoConfig, + "text_config": Sam3LiteTextTextConfig, + "geometry_encoder_config": Sam3LiteTextGeometryEncoderConfig, + "detr_encoder_config": Sam3LiteTextDETREncoderConfig, + "detr_decoder_config": Sam3LiteTextDETRDecoderConfig, + "mask_decoder_config": Sam3LiteTextMaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + geometry_encoder_config: dict | PreTrainedConfig | None = None + detr_encoder_config: dict | PreTrainedConfig | None = None + detr_decoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam3_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam3_vision_model"]() + + if self.text_config is None: + self.text_config = Sam3LiteTextTextConfig() + if isinstance(self.text_config, dict): + self.text_config = Sam3LiteTextTextConfig(**self.text_config) + + if self.geometry_encoder_config is None: + self.geometry_encoder_config = Sam3LiteTextGeometryEncoderConfig() + if isinstance(self.geometry_encoder_config, dict): + self.geometry_encoder_config = Sam3LiteTextGeometryEncoderConfig(**self.geometry_encoder_config) + + if self.detr_encoder_config is None: + self.detr_encoder_config = Sam3LiteTextDETREncoderConfig() + if isinstance(self.detr_encoder_config, dict): + self.detr_encoder_config = Sam3LiteTextDETREncoderConfig(**self.detr_encoder_config) + + if self.detr_decoder_config is None: + self.detr_decoder_config = Sam3LiteTextDETRDecoderConfig() + if isinstance(self.detr_decoder_config, dict): + self.detr_decoder_config = Sam3LiteTextDETRDecoderConfig(**self.detr_decoder_config) + + if self.mask_decoder_config is None: + self.mask_decoder_config = Sam3LiteTextMaskDecoderConfig() + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam3LiteTextMaskDecoderConfig(**self.mask_decoder_config) + + super().__post_init__(**kwargs) + + @property + def image_size(self): + """Image size for the SAM3_LITE_TEXT model.""" + return self.vision_config.image_size + + @image_size.setter + def image_size(self, value): + """Set the image size and propagate to vision config.""" + self.vision_config.image_size = value + + +@dataclass +class Sam3LiteTextTextEncoderOutput(BaseModelOutputWithPooling): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Full sequence of hidden states from the text encoder. + pooler_output (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): + EOT-pooled output projected to `projection_dim` via the internal CLIP-style projection. + hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of hidden states at each layer, returned when `output_hidden_states=True`. + attentions (`tuple(torch.FloatTensor)`, *optional*): + Tuple of attention weights at each transformer layer, returned when `output_attentions=True`. + """ + + +class Sam3LiteTextTextPositionEmbedding(nn.Module): + """Learnable positional embedding with bilinear interpolation for variable sequence lengths.""" + + def __init__(self, max_position_embeddings: int, hidden_size: int): + super().__init__() + self.position_embedding = nn.Parameter(torch.empty(1, 1, max_position_embeddings, hidden_size)) + + def forward(self, seq_len: int) -> torch.Tensor: + position_embedding = self.position_embedding + if seq_len != position_embedding.shape[2]: + position_embedding = F.interpolate( + position_embedding, + size=(seq_len, position_embedding.shape[-1]), + mode="bilinear", + ) + return position_embedding.reshape(1, seq_len, -1) + + +class Sam3LiteTextMobileOneBlock(nn.Module): + """Depthwise conv branch with batch norm on the skip path and after the conv (MobileOne-style).""" + + def __init__(self, hidden_size: int, kernel_size: int = 3): + super().__init__() + self.batchnorm_skip = nn.BatchNorm2d(hidden_size) + self.conv = nn.Conv2d( + hidden_size, + hidden_size, + kernel_size=(1, kernel_size), + stride=1, + padding=(0, kernel_size // 2), + groups=hidden_size, + bias=False, + ) + self.batchnorm_conv = nn.BatchNorm2d(hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + residual = hidden_states + hidden_states = self.batchnorm_conv(self.conv(hidden_states)) + hidden_states = hidden_states + self.batchnorm_skip(residual) + return hidden_states + + +class Sam3LiteTextConvMLP(SiglipMLP): + """Pointwise MLP using 1×1 convolutions, compatible with 4-D (B, C, H, W) feature maps.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + nn.Module.__init__(self) + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Conv2d(config.hidden_size, config.intermediate_size, kernel_size=1) + self.fc2 = nn.Conv2d(config.intermediate_size, config.hidden_size, kernel_size=1) + + +class Sam3LiteTextConvolutionalFeedForward(nn.Module): + """Convolutional feed-forward network: depthwise conv + two pointwise projections.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__() + self.depthwise_conv = nn.Conv2d( + config.hidden_size, + config.hidden_size, + kernel_size=(1, config.repmixer_kernel_size), + padding=(0, config.repmixer_kernel_size // 2), + groups=config.hidden_size, + bias=False, + ) + self.depthwise_batchnorm = nn.BatchNorm2d(config.hidden_size) + self.mlp = Sam3LiteTextConvMLP(config) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.depthwise_batchnorm(self.depthwise_conv(hidden_states)) + return self.mlp(hidden_states) + + +class Sam3LiteTextLayerScaledResidual(nn.Module): + """Common layer-scale residual pattern shared by the RepMixer and feed-forward branches.""" + + def __init__(self, hidden_size: int, layer_scale_init_value: float): + super().__init__() + self.layer_scale = nn.Parameter(layer_scale_init_value * torch.ones((hidden_size, 1, 1)), requires_grad=True) + + def layer_scale_residual(self, hidden_states: torch.Tensor, update: torch.Tensor) -> torch.Tensor: + return hidden_states + self.layer_scale * update + + +class Sam3LiteTextRepMixer(Sam3LiteTextLayerScaledResidual): + """Re-parameterisable depthwise-conv token mixer operating on 1D sequence data.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__(config.hidden_size, config.layer_scale_init_value) + self.reference_batchnorm = nn.BatchNorm2d(config.hidden_size) + self.mixer = Sam3LiteTextMobileOneBlock(config.hidden_size, kernel_size=config.repmixer_kernel_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return self.layer_scale_residual( + hidden_states, self.mixer(hidden_states) - self.reference_batchnorm(hidden_states) + ) + + +class Sam3LiteTextRepMixerBlock(Sam3LiteTextLayerScaledResidual): + """Token-mixing RepMixer plus a convolutional feed-forward path, each with layer scale.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__(config.hidden_size, config.layer_scale_init_value) + self.token_mixer = Sam3LiteTextRepMixer(config) + self.conv_feed_forward = Sam3LiteTextConvolutionalFeedForward(config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + hidden_states = hidden_states.transpose(1, 2).unsqueeze(2) + hidden_states = self.token_mixer(hidden_states) + hidden_states = self.layer_scale_residual(hidden_states, self.conv_feed_forward(hidden_states)) + return hidden_states.squeeze(2).transpose(1, 2) + + +class Sam3LiteTextTextAttention(SiglipAttention): + pass + + +class Sam3LiteTextTextMLP(SiglipMLP): + pass + + +class Sam3LiteTextTextEncoderLayer(SiglipEncoderLayer): + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__(config) + self.self_attn = Sam3LiteTextTextAttention(config) + self.mlp = Sam3LiteTextTextMLP(config) + + +class Sam3LiteTextTextEmbeddings(nn.Module): + """Token embedding + interpolatable positional embedding for the text encoder.""" + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__() + self.token_embedding = nn.Embedding(config.vocab_size, config.hidden_size) + self.position_embedding = Sam3LiteTextTextPositionEmbedding(config.max_position_embeddings, config.hidden_size) + + def forward(self, input_ids: torch.LongTensor) -> torch.Tensor: + hidden_states = self.token_embedding(input_ids) + hidden_states = hidden_states + self.position_embedding(input_ids.shape[1]).to(hidden_states.dtype) + return hidden_states + + +@auto_docstring +class Sam3LiteTextPreTrainedModel(Sam3PreTrainedModel): + config_class = Sam3LiteTextConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + + @torch.no_grad() + def _init_weights(self, module): + PreTrainedModel._init_weights(module) + if isinstance(module, Sam3LiteTextTextPositionEmbedding): + init.normal_(module.position_embedding, std=module.position_embedding.shape[-1] ** -0.5) + elif isinstance(module, Sam3LiteTextTextModel): + init.normal_(module.projection.weight, std=module.config.hidden_size**-0.5) + + +@auto_docstring( + custom_intro=""" + MobileCLIP MCT text encoder used in EfficientSAM3 LiteText. + + When `config.use_repmixer_blocks` is `True`, the first and last layers are + `Sam3LiteTextRepMixerBlock` modules; the rest are standard `Sam3LiteTextTextEncoderLayer` layers. +""" +) +class Sam3LiteTextTextModel(Sam3LiteTextPreTrainedModel): + config_class = Sam3LiteTextTextConfig + config: Sam3LiteTextTextConfig + _can_record_outputs = { + "hidden_states": Sam3LiteTextTextEncoderLayer, + "attentions": Sam3LiteTextTextAttention, + } + + def __init__(self, config: Sam3LiteTextTextConfig): + super().__init__(config) + self.embeddings = Sam3LiteTextTextEmbeddings(config) + repmixer_positions = {0, config.num_hidden_layers - 1} if config.use_repmixer_blocks else set() + self.layers = nn.ModuleList( + [ + Sam3LiteTextRepMixerBlock(config) if i in repmixer_positions else Sam3LiteTextTextEncoderLayer(config) + for i in range(config.num_hidden_layers) + ] + ) + self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.projection = nn.Linear(config.hidden_size, config.projection_dim, bias=False) + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3LiteTextTextEncoderOutput: + hidden_states = self.embeddings(input_ids) + attention_mask = create_bidirectional_mask(self.config, hidden_states, attention_mask) + + for layer in self.layers: + hidden_states = layer(hidden_states, attention_mask=attention_mask, **kwargs) + + hidden_states = self.final_layer_norm(hidden_states) + + pooled = hidden_states[ + torch.arange(hidden_states.shape[0], device=hidden_states.device), input_ids.argmax(dim=-1) + ] + pooled = self.projection(pooled) + return Sam3LiteTextTextEncoderOutput( + last_hidden_state=hidden_states, + pooler_output=pooled, + ) + + +class Sam3LiteTextModel(Sam3Model): + # DETR components create float masks from features, so flash/flex attention cannot be dispatched safely. + _supports_flash_attn = False + _supports_flex_attn = False + + def __init__(self, config: Sam3LiteTextConfig): + super().__init__(config) + self.text_encoder = Sam3LiteTextTextModel(config.text_config) + self.vision_encoder = AutoModel.from_config(config.vision_config) + + +__all__ = [ + "Sam3LiteTextConfig", + "Sam3LiteTextTextConfig", + "Sam3LiteTextGeometryEncoderConfig", + "Sam3LiteTextDETREncoderConfig", + "Sam3LiteTextDETRDecoderConfig", + "Sam3LiteTextMaskDecoderConfig", + "Sam3LiteTextModel", + "Sam3LiteTextPreTrainedModel", + "Sam3LiteTextTextModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dce9c5ca2c60c735e87adf73bce69c996f4a1241 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam3_tracker import * + from .modeling_sam3_tracker import * + from .processing_sam3_tracker import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/configuration_sam3_tracker.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/configuration_sam3_tracker.py new file mode 100644 index 0000000000000000000000000000000000000000..237fbc402ce3f4ab8f957ed1de3309b596a68204 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/configuration_sam3_tracker.py @@ -0,0 +1,164 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3_tracker/modular_sam3_tracker.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3_tracker.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerPromptEncoderConfig(PreTrainedConfig): + r""" + mask_input_channels (`int`, *optional*, defaults to 16): + The number of channels to be fed to the `MaskDecoder` module. + num_point_embeddings (`int`, *optional*, defaults to 4): + The number of point embeddings to be used. + scale (`float`, *optional*, defaults to 1): + The scale factor for the prompt encoder. + """ + + base_config_key = "prompt_encoder_config" + + hidden_size: int = 256 + + image_size: int | list[int] | tuple[int, int] = 1008 + patch_size: int | list[int] | tuple[int, int] = 14 + mask_input_channels: int = 16 + num_point_embeddings: int = 4 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + scale: int = 1 + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerMaskDecoderConfig(PreTrainedConfig): + r""" + mlp_dim (`int`, *optional*, defaults to 2048): + The dimension of the MLP in the two-way transformer. + attention_downsample_rate (`int`, *optional*, defaults to 2): + The downsample rate for the attention layers. + num_multimask_outputs (`int`, *optional*, defaults to 3): + The number of multimask outputs. + iou_head_depth (`int`, *optional*, defaults to 3): + The depth of the IoU head. + iou_head_hidden_dim (`int`, *optional*, defaults to 256): + The hidden dimension of the IoU head. + dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`): + Whether to use dynamic multimask via stability. + dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05): + The stability delta for the dynamic multimask. + dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98): + The stability threshold for the dynamic multimask. + """ + + base_config_key = "mask_decoder_config" + + hidden_size: int = 256 + hidden_act: str = "gelu" + mlp_dim: int = 2048 + num_hidden_layers: int = 2 + num_attention_heads: int = 8 + attention_downsample_rate: int = 2 + num_multimask_outputs: int = 3 + iou_head_depth: int = 3 + iou_head_hidden_dim: int = 256 + dynamic_multimask_via_stability: bool = True + dynamic_multimask_stability_delta: float = 0.05 + dynamic_multimask_stability_thresh: float = 0.98 + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerConfig(PreTrainedConfig): + r""" + prompt_encoder_config (Union[`dict`, `Sam3TrackerPromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam3TrackerPromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `Sam3TrackerMaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam3TrackerMaskDecoderConfig`]. + + Example: + + ```python + >>> from transformers import ( + ... Sam3TrackerVisionConfig, + ... Sam3TrackerPromptEncoderConfig, + ... Sam3TrackerMaskDecoderConfig, + ... Sam3TrackerModel, + ... ) + + >>> # Initializing a Sam3TrackerConfig with `"facebook/sam3_tracker.1_hiera_tiny"` style configuration + >>> configuration = Sam3TrackerConfig() + + >>> # Initializing a Sam3TrackerModel (with random weights) from the `"facebook/sam3_tracker.1_hiera_tiny"` style configuration + >>> model = Sam3TrackerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Sam3TrackerConfig from a Sam3TrackerVisionConfig, Sam3TrackerPromptEncoderConfig, and Sam3TrackerMaskDecoderConfig + >>> # Initializing SAM3_TRACKER vision encoder, memory attention, and memory encoder configurations + >>> vision_config = Sam3TrackerVisionConfig() + >>> prompt_encoder_config = Sam3TrackerPromptEncoderConfig() + >>> mask_decoder_config = Sam3TrackerMaskDecoderConfig() + + >>> config = Sam3TrackerConfig(vision_config, prompt_encoder_config, mask_decoder_config) + ``` + """ + + model_type = "sam3_tracker" + sub_configs = { + "vision_config": AutoConfig, + "prompt_encoder_config": Sam3TrackerPromptEncoderConfig, + "mask_decoder_config": Sam3TrackerMaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + prompt_encoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam3_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam3_vision_model"]( + backbone_feature_sizes=[[288, 288], [144, 144], [72, 72]] + ) + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = Sam3TrackerPromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = Sam3TrackerPromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam3TrackerMaskDecoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = Sam3TrackerMaskDecoderConfig() + + super().__post_init__(**kwargs) + + +__all__ = ["Sam3TrackerConfig", "Sam3TrackerPromptEncoderConfig", "Sam3TrackerMaskDecoderConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/modeling_sam3_tracker.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/modeling_sam3_tracker.py new file mode 100644 index 0000000000000000000000000000000000000000..28b3b83f4afe457665db29071e8a6033d9ea3f99 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/modeling_sam3_tracker.py @@ -0,0 +1,1106 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3_tracker/modular_sam3_tracker.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3_tracker.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging +from ...utils.generic import TransformersKwargs, is_flash_attention_requested, merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from ..auto import AutoModel +from .configuration_sam3_tracker import Sam3TrackerConfig, Sam3TrackerMaskDecoderConfig, Sam3TrackerPromptEncoderConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring(custom_intro="Base class for the Sam3Tracker model's output.") +@dataclass +class Sam3TrackerImageSegmentationOutput(ModelOutput): + r""" + iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`): + The Intersection over Union (IoU) scores of the predicted masks. + pred_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, height, width)`): + The predicted low-resolution masks. This is an alias for `low_res_masks`. These masks need to be post-processed + by the processor to be brought to the original image size. + object_score_logits (`torch.FloatTensor` of shape `(batch_size, point_batch_size, 1)`): + Logits for the object score, indicating if an object is present. + image_embeddings (`tuple(torch.FloatTensor)`): + The features from the FPN, which are used by the mask decoder. This is a tuple of `torch.FloatTensor` where each + tensor has shape `(batch_size, channels, height, width)`. + vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. + Hidden-states of the vision model at the output of each stage. + vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the vision model. + mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the mask decoder. + """ + + iou_scores: torch.FloatTensor | None = None + pred_masks: torch.FloatTensor | None = None + object_score_logits: torch.FloatTensor | None = None + image_embeddings: tuple[torch.FloatTensor, ...] = None + vision_hidden_states: tuple[torch.FloatTensor, ...] | None = None + vision_attentions: tuple[torch.FloatTensor, ...] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None + + +class Sam3TrackerFeedForward(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + activation: str = "relu", + sigmoid_output: bool = False, + ): + super().__init__() + self.num_layers = num_layers + self.activation = ACT2FN[activation] + self.proj_in = nn.Linear(input_dim, hidden_dim) + self.proj_out = nn.Linear(hidden_dim, output_dim) + self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)]) + self.sigmoid_output = sigmoid_output + + def forward(self, hidden_states): + hidden_states = self.proj_in(hidden_states) + hidden_states = self.activation(hidden_states) + for layer in self.layers: + hidden_states = self.activation(layer(hidden_states)) + + hidden_states = self.proj_out(hidden_states) + if self.sigmoid_output: + hidden_states = F.sigmoid(hidden_states) + return hidden_states + + +@auto_docstring( + custom_intro=""" + Segment Anything Model 3 (SAM 3) for generating segmentation masks, given an input image and + input points and labels, boxes, or masks. + """ +) +class Sam3TrackerPreTrainedModel(PreTrainedModel): + config_class = Sam3TrackerConfig + base_model_prefix = "sam3_tracker" + main_input_name = "pixel_values" + input_modalities = ("image",) + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + _keys_to_ignore_on_load_unexpected = [ + r"^memory_.*", + r"^mask_downsample.*", + r"^object_pointer_proj.*", + r"^temporal_positional_encoding_projection_layer.*", + "no_memory_positional_encoding", + "no_object_pointer", + "occlusion_spatial_embedding_parameter", + ] + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Sam3TrackerModel): + if module.no_memory_embedding is not None: + init.zeros_(module.no_memory_embedding) + elif isinstance(module, Sam3TrackerPositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + + +class Sam3TrackerPositionalEmbedding(nn.Module): + def __init__(self, config: Sam3TrackerPromptEncoderConfig): + super().__init__() + self.scale = config.scale + positional_embedding = self.scale * torch.randn((2, config.hidden_size // 2)) + self.register_buffer("positional_embedding", positional_embedding) + + def forward(self, input_coords, input_shape=None): + """Positionally encode points that are normalized to [0,1].""" + coordinates = input_coords.clone() + + if input_shape is not None: + coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1] + coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0] + coordinates.to(torch.float32) + + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coordinates = 2 * coordinates - 1 + coordinates = coordinates.to(self.positional_embedding.dtype) + coordinates = coordinates @ self.positional_embedding + coordinates = 2 * np.pi * coordinates + # outputs d_1 x ... x d_n x channel shape + return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1) + + +class Sam3TrackerMaskEmbedding(nn.Module): + def __init__(self, config: Sam3TrackerPromptEncoderConfig): + super().__init__() + self.mask_input_channels = config.mask_input_channels // 4 + self.activation = ACT2FN[config.hidden_act] + self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2) + self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2) + self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1) + self.layer_norm1 = Sam3TrackerLayerNorm( + self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first" + ) + self.layer_norm2 = Sam3TrackerLayerNorm( + self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first" + ) + + def forward(self, masks): + hidden_states = self.conv1(masks) + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.activation(hidden_states) + dense_embeddings = self.conv3(hidden_states) + return dense_embeddings + + +class Sam3TrackerPromptEncoder(nn.Module): + def __init__(self, config: Sam3TrackerPromptEncoderConfig): + super().__init__() + self.shared_embedding = Sam3TrackerPositionalEmbedding(config) + self.mask_embed = Sam3TrackerMaskEmbedding(config) + self.no_mask_embed = nn.Embedding(1, config.hidden_size) + + self.image_embedding_size = (config.image_size // config.patch_size, config.image_size // config.patch_size) + self.mask_input_size = (4 * config.image_size // config.patch_size, 4 * config.image_size // config.patch_size) + self.input_image_size = config.image_size + + self.point_embed = nn.Embedding(config.num_point_embeddings, config.hidden_size) + self.hidden_size = config.hidden_size + self.not_a_point_embed = nn.Embedding(1, config.hidden_size) + + def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + points = torch.nn.functional.pad(points, (0, 0, 0, 1), mode="constant", value=0) + labels = torch.nn.functional.pad(labels, (0, 1), mode="constant", value=-1) + input_shape = (self.input_image_size, self.input_image_size) + point_embedding = self.shared_embedding(points, input_shape) + + # torch.where and expanding the labels tensor is required by the ONNX export + point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding) + + # This is required for the ONNX export. The dtype, device need to be explicitly + # specified as otherwise torch.onnx.export interprets as double + point_embedding = torch.where( + labels[..., None] != -10, + point_embedding, + torch.zeros_like(point_embedding), + ) + + # Add point embeddings for labels >= 0 + point_embedding = point_embedding + self.point_embed(labels.clamp(min=0)) * (labels >= 0).unsqueeze(-1) + + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.view(*boxes.shape[:2], 2, 2) + # add padding point for consistency with the original implementation + coords = torch.nn.functional.pad(coords, (0, 0, 0, 1), mode="constant", value=0) + corner_embedding = self.shared_embedding(coords, (self.input_image_size, self.input_image_size)) + corner_embedding[:, :, 0, :] += self.point_embed.weight[2] + corner_embedding[:, :, 1, :] += self.point_embed.weight[3] + corner_embedding[:, :, 2, :] = self.not_a_point_embed.weight.expand_as(corner_embedding[:, :, 2, :]) + return corner_embedding + + def forward( + self, + input_points: tuple[torch.Tensor, torch.Tensor] | None, + input_labels: torch.Tensor | None, + input_boxes: torch.Tensor | None, + input_masks: torch.Tensor | None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense embeddings. + + Args: + points (`torch.Tensor`, *optional*): + point coordinates and labels to embed. + boxes (`torch.Tensor`, *optional*): + boxes to embed + masks (`torch.Tensor`, *optional*): + masks to embed + """ + sparse_embeddings = None + batch_size = 1 + if input_points is not None: + batch_size = input_points.shape[0] + if input_labels is None: + raise ValueError("If points are provided, labels must also be provided.") + point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None)) + sparse_embeddings = point_embeddings + if input_boxes is not None: + batch_size = input_boxes.shape[0] + box_embeddings = self._embed_boxes(input_boxes) + if sparse_embeddings is None: + sparse_embeddings = box_embeddings + else: + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2) + if input_masks is not None: + dense_embeddings = self.mask_embed(input_masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Sam3TrackerAttention(nn.Module): + """ + SAM3_TRACKER's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and + values. + """ + + def __init__(self, config, downsample_rate=None): + super().__init__() + downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate + self.config = config + self.hidden_size = config.hidden_size + self.internal_dim = config.hidden_size // downsample_rate + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.internal_dim // config.num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.v_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.o_proj = nn.Linear(self.internal_dim, self.hidden_size) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_similarity: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # Input projections + batch_size, point_batch_size = query.shape[:2] + new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim) + + query = self.q_proj(query).view(*new_shape).transpose(1, 2) + key = self.k_proj(key).view(*new_shape).transpose(1, 2) + value = self.v_proj(value).view(*new_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config) and attention_similarity is not None: + # Target guided masks are represented as float masks and are incompatible with Flash Attention + # Fallback to SDPA for this call only so the rest of the model can still benefit from FA + attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"] + logger.warning_once( + "Falling back to SDPA for target-guided attention because " + "Flash Attention does not support additive bias masks." + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_similarity, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = attn_output.reshape( + batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +class Sam3TrackerTwoWayAttentionBlock(GradientCheckpointingLayer): + def __init__(self, config: Sam3TrackerMaskDecoderConfig, skip_first_layer_pe: bool = False): + """ + A transformer block with four layers: + (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on + sparse inputs (4) cross attention of dense inputs -> sparse inputs + + Arguments: + config (`Sam3TrackerMaskDecoderConfig`): + The configuration file used to instantiate the block + attention_downsample_rate (*optionalk*, int, defaults to 2): + The downsample ratio of the block used to reduce the inner dim of the attention. + skip_first_layer_pe (*optional*, bool, defaults to `False`): + Whether or not to skip the addition of the query_point_embedding on the first layer. + """ + super().__init__() + self.self_attn = Sam3TrackerAttention(config, downsample_rate=1) + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + + self.cross_attn_token_to_image = Sam3TrackerAttention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam3TrackerFeedForward( + config.hidden_size, config.mlp_dim, config.hidden_size, num_layers=config.num_hidden_layers + ) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + self.layer_norm4 = nn.LayerNorm(config.hidden_size) + self.cross_attn_image_to_token = Sam3TrackerAttention(config) + + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, + queries: Tensor, + keys: Tensor, + query_point_embedding: Tensor, + key_point_embedding: Tensor, + attention_similarity: Tensor, + **kwargs: Unpack[TransformersKwargs], + ): + # Self attention block + if self.skip_first_layer_pe: + queries, _ = self.self_attn(query=queries, key=queries, value=queries) + else: + query = queries + query_point_embedding + attn_out, _ = self.self_attn(query=query, key=query, value=queries) + queries = queries + attn_out + queries = self.layer_norm1(queries) + + # Cross attention block, tokens attending to image embedding + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_token_to_image( + query=query, key=key, value=keys, attention_similarity=attention_similarity + ) + queries = queries + attn_out + + queries = self.layer_norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.layer_norm3(queries) + + # Cross attention block, image embedding attending to tokens + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_image_to_token(query=key, key=query, value=queries) + keys = keys + attn_out + + keys = self.layer_norm4(keys) + return queries, keys, attn_out + + +class Sam3TrackerTwoWayTransformer(nn.Module): + def __init__(self, config: Sam3TrackerMaskDecoderConfig): + super().__init__() + self.config = config + + self.num_hidden_layers = config.num_hidden_layers + self.layers = nn.ModuleList() + + for i in range(self.num_hidden_layers): + self.layers.append(Sam3TrackerTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0))) + + self.final_attn_token_to_image = Sam3TrackerAttention(config) + self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size) + + def forward( + self, + point_embeddings: Tensor, + image_embeddings: Tensor, + image_positional_embeddings: Tensor, + attention_similarity: Tensor, + target_embedding=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + if image_embeddings is None: + raise ValueError("You have to specify an image_embedding") + + image_embeddings = image_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + image_positional_embeddings = image_positional_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + + # Prepare queries + queries = point_embeddings + keys = image_embeddings + + # Apply transformer blocks and final layernorm + for layer in self.layers: + if target_embedding is not None: + queries += target_embedding + + queries, keys, _ = layer( + queries=queries, + keys=keys, + query_point_embedding=point_embeddings, + key_point_embedding=image_positional_embeddings, + attention_similarity=attention_similarity, + **kwargs, + ) + # Apply the final attention layer from the points to the image + query = queries + point_embeddings + key = keys + image_positional_embeddings + + attn_out, _ = self.final_attn_token_to_image(query=query, key=key, value=keys) + + queries = queries + attn_out + queries = self.layer_norm_final_attn(queries) + return queries, keys + + +class Sam3TrackerLayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +class Sam3TrackerMaskDecoder(nn.Module): + def __init__(self, config: Sam3TrackerMaskDecoderConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + + self.num_multimask_outputs = config.num_multimask_outputs + self.num_mask_tokens = config.num_multimask_outputs + 1 + + self.iou_token = nn.Embedding(1, self.hidden_size) + self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size) + + self.transformer = Sam3TrackerTwoWayTransformer(config) + + # should we create a new class for this? + self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + self.upscale_layer_norm = Sam3TrackerLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.activation = nn.GELU() + + mlps_list = [] + for _ in range(self.num_mask_tokens): + mlps_list += [Sam3TrackerFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)] + self.output_hypernetworks_mlps = nn.ModuleList(mlps_list) + self.iou_prediction_head = Sam3TrackerFeedForward( + self.hidden_size, + config.iou_head_hidden_dim, + self.num_mask_tokens, + config.iou_head_depth, + sigmoid_output=True, + ) + + self.conv_s0 = nn.Conv2d(config.hidden_size, config.hidden_size // 8, kernel_size=1, stride=1) + self.conv_s1 = nn.Conv2d(config.hidden_size, config.hidden_size // 4, kernel_size=1, stride=1) + + self.obj_score_token = nn.Embedding(1, self.hidden_size) + self.pred_obj_score_head = Sam3TrackerFeedForward(self.hidden_size, self.hidden_size, 1, 3) + + self.dynamic_multimask_via_stability = config.dynamic_multimask_via_stability + self.dynamic_multimask_stability_delta = config.dynamic_multimask_stability_delta + self.dynamic_multimask_stability_thresh = config.dynamic_multimask_stability_thresh + + def forward( + self, + image_embeddings: torch.Tensor, + image_positional_embeddings: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + high_resolution_features: list[torch.Tensor], + attention_similarity: torch.Tensor | None = None, + target_embedding: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Args: + image_embeddings (`torch.Tensor`): + The embeddings from the image encoder. + image_positional_embeddings (`torch.Tensor`): + Positional encoding with the shape of image_embeddings. + sparse_prompt_embeddings (`torch.Tensor`): + The embeddings of the points and boxes. + dense_prompt_embeddings (`torch.Tensor`): + The embeddings of the mask inputs. + multimask_output (`bool`): + Whether to return multiple masks or a single mask. + high_resolution_features (`list[torch.Tensor]`, *optional*): + The high-resolution features from the vision encoder. + attention_similarity (`torch.Tensor`, *optional*): + The attention similarity tensor. + target_embedding (`torch.Tensor`, *optional*): + The target embedding. + """ + batch_size, num_channels, height, width = image_embeddings.shape + point_batch_size = sparse_prompt_embeddings.shape[1] + # Concatenate output tokens + output_tokens = torch.cat( + [ + self.obj_score_token.weight, + self.iou_token.weight, + self.mask_tokens.weight, + ], + dim=0, + ) + output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) + + if sparse_prompt_embeddings.shape[0] != 0: + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2) + else: + tokens = output_tokens + point_embeddings = tokens.to(self.iou_token.weight.dtype) + + # Expand per-image data in batch direction to be per-mask + image_embeddings = image_embeddings + dense_prompt_embeddings + image_embeddings = image_embeddings.repeat_interleave(point_batch_size, dim=0) + image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) + # Run the transformer + point_embeddings, image_embeddings = self.transformer( + point_embeddings=point_embeddings, + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + iou_token_out = point_embeddings[:, :, 1, :] + mask_tokens_out = point_embeddings[:, :, 2 : (2 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + image_embeddings = image_embeddings.transpose(2, 3).view( + batch_size * point_batch_size, num_channels, height, width + ) + + feat_s0, feat_s1 = high_resolution_features + feat_s0 = feat_s0.repeat_interleave(point_batch_size, dim=0) + feat_s1 = feat_s1.repeat_interleave(point_batch_size, dim=0) + upscaled_embedding = self.upscale_conv1(image_embeddings) + feat_s1 + upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) + upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding) + feat_s0) + + hyper_in_list: list[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + current_mlp = self.output_hypernetworks_mlps[i] + hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])] + hyper_in = torch.stack(hyper_in_list, dim=2) + + _, num_channels, height, width = upscaled_embedding.shape + upscaled_embedding = upscaled_embedding.view(batch_size, point_batch_size, num_channels, height * width) + masks = (hyper_in @ upscaled_embedding).view(batch_size, point_batch_size, -1, height, width) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + object_score_logits = self.pred_obj_score_head(point_embeddings[:, :, 0, :]) + + # Select the correct mask or masks for output + if multimask_output: + mask_slice = slice(1, None) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + elif self.dynamic_multimask_via_stability and not self.training: + mask_slice = slice(0, 1) + masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred) + else: + mask_slice = slice(0, 1) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + + sam_tokens_out = mask_tokens_out[:, :, mask_slice] # [b, 3, c] shape + + return masks, iou_pred, sam_tokens_out, object_score_logits + + def _get_stability_scores(self, mask_logits): + """ + Compute stability scores of the mask logits based on the IoU between upper and + lower thresholds. + """ + mask_logits = mask_logits.flatten(-2) + stability_delta = self.dynamic_multimask_stability_delta + area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() + area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() + stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) + return stability_scores + + def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): + """ + When outputting a single mask, if the stability score from the current single-mask + output (based on output token 0) falls below a threshold, we instead select from + multi-mask outputs (based on output token 1~3) the mask with the highest predicted + IoU score. This is intended to ensure a valid mask for both clicking and tracking. + """ + # The best mask from multimask output tokens (1~3) + multimask_logits = all_mask_logits[:, :, 1:, :, :] + multimask_iou_scores = all_iou_scores[:, :, 1:] + best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1) # [B, P] + best_scores_inds_expanded = best_scores_inds.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) + best_scores_inds_expanded = best_scores_inds_expanded.expand( + -1, -1, 1, multimask_logits.size(-2), multimask_logits.size(-1) + ) + best_multimask_logits = torch.gather(multimask_logits, 2, best_scores_inds_expanded) # [B, P, 1, H, W] + best_multimask_iou_scores = torch.gather(multimask_iou_scores, 2, best_scores_inds.unsqueeze(-1)) # [B, P, 1] + + # The mask from singlemask output token 0 and its stability score + singlemask_logits = all_mask_logits[:, :, 0:1, :, :] + singlemask_iou_scores = all_iou_scores[:, :, 0:1] + stability_scores = self._get_stability_scores(singlemask_logits) + is_stable = stability_scores >= self.dynamic_multimask_stability_thresh + + # Dynamically fall back to best multimask output upon low stability scores. + mask_logits_out = torch.where( + is_stable[..., None, None].expand_as(singlemask_logits), + singlemask_logits, + best_multimask_logits, + ) + iou_scores_out = torch.where( + is_stable.expand_as(singlemask_iou_scores), + singlemask_iou_scores, + best_multimask_iou_scores, + ) + return mask_logits_out, iou_scores_out + + +@dataclass +@auto_docstring(custom_intro="Base class for the vision encoder's outputs.") +class Sam3TrackerVisionEncoderOutput(BaseModelOutputWithPooling): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the + model at the output of each stage. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + fpn_hidden_states (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Feature maps from the Feature Pyramid Network neck. + fpn_position_encoding (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Positional encodings corresponding to the `fpn_hidden_states`. + """ + + fpn_hidden_states: torch.FloatTensor | None = None + fpn_position_encoding: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Segment Anything Model 2 (SAM 2) for generating segmentation masks, given an input image and + input points and labels, boxes, or masks. + """ +) +class Sam3TrackerModel(Sam3TrackerPreTrainedModel): + input_modalities = ("image", "text") + _can_record_outputs = {"mask_decoder_attentions": OutputRecorder(Sam3TrackerTwoWayAttentionBlock, index=2)} + _tied_weights_keys = {} + _base_model_prefix = "tracker_model" + _keys_to_ignore_on_load_unexpected = [ + r"^detector_model.", + r"^memory_.*", + r"^mask_downsample.*", + r"^object_pointer_proj.*", + r"^temporal_positional_encoding_projection_layer.*", + "no_memory_positional_encoding", + "no_object_pointer", + "occlusion_spatial_embedding_parameter", + ] + + def __init__(self, config: Sam3TrackerConfig): + # loading from a sam3_video config + if hasattr(config, "tracker_config") and config.tracker_config is not None: + if isinstance(config.tracker_config, dict): + config.tracker_config = Sam3TrackerConfig(**config.tracker_config) + config = config.tracker_config + super().__init__(config) + self.shared_image_embedding = Sam3TrackerPositionalEmbedding(config.prompt_encoder_config) + self.vision_encoder = AutoModel.from_config(config.vision_config) + self.prompt_encoder = Sam3TrackerPromptEncoder(config.prompt_encoder_config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + self.mask_decoder = Sam3TrackerMaskDecoder(config.mask_decoder_config) + + self.backbone_feature_sizes = config.vision_config.backbone_feature_sizes + # a single token to indicate no memory embedding from previous frames + self.hidden_dim = config.vision_config.fpn_hidden_size + self.no_memory_embedding = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + + self.post_init() + + def get_input_embeddings(self): + return self.vision_encoder.get_input_embeddings() + + def get_image_wide_positional_embeddings(self) -> torch.Tensor: + size = self.prompt_encoder.image_embedding_size + target_device = self.shared_image_embedding.positional_embedding.device + target_dtype = self.shared_image_embedding.positional_embedding.dtype + grid = torch.ones(size, device=target_device, dtype=target_dtype) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / size[0] + x_embed = x_embed / size[1] + + positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1)) + return positional_embedding.permute(2, 0, 1).unsqueeze(0) # channel x height x width + + @torch.no_grad() + def get_image_embeddings( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> list[torch.Tensor]: + r""" + Returns the image embeddings by passing the pixel values through the vision encoder. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Input pixel values + """ + batch_size = pixel_values.shape[0] + image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + return image_embeddings + + @torch.no_grad() + def get_prompt_embeddings( + self, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + ): + r""" + Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder. + + Args: + input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`): + Optional input points for the prompt encoder. The padding of the point is automatically done by the + processor. `point_batch_size` refers to the number of masks that we want the model to predict per + point. The model will output `point_batch_size` times 3 masks in total. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points_per_image)`): + Optional input labels for the prompt encoder. The padding of the labels is automatically done by the + processor, or can be fed by the user. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes_per_image, 4)`): + Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the + processor. users can also pass manually the input boxes. + input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`): + Optional input masks for the prompt encoder. + """ + prompt_output = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + return prompt_output + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam3TrackerImageSegmentationOutput: + r""" + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decoder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoModel, AutoProcessor + + >>> model = AutoModel.from_pretrained("danelcsb/sam3_tracker.1_hiera_tiny") + >>> processor = AutoProcessor.from_pretrained("danelcsb/sam3_tracker.1_hiera_tiny") + + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" + >>> with httpx.stream("GET", url) as response: + ... raw_image = Image.open(BytesIO(response.read())).convert("RGB") + >>> input_points = [[[400, 650]]] # 2D location of a window on the car + >>> inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt") + + >>> # Get segmentation mask + >>> outputs = model(**inputs) + + >>> # Postprocess masks + >>> masks = processor.post_process_masks( + ... outputs.pred_masks, inputs["original_sizes"] + ... ) + ``` + """ + if not ((pixel_values is None) ^ (image_embeddings is None)): + raise ValueError("Exactly one of pixel_values or image_embeddings must be provided.") + if input_points is not None and input_boxes is not None: + if input_points.shape[1] != input_boxes.shape[1]: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {input_points.shape[1]} and {input_boxes.shape[1]}." + ) + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings[-1].shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + vision_attentions = None + vision_hidden_states = None + + if pixel_values is not None: + image_outputs: Sam3TrackerVisionEncoderOutput = self.get_image_features( + pixel_values, return_dict=True, **kwargs + ) + feature_maps = image_outputs.fpn_hidden_states + vision_hidden_states = image_outputs.hidden_states + vision_attentions = image_outputs.attentions + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + if input_points is None and input_boxes is None: + # If no points are provide, pad with an empty point (with label -1) + input_points = torch.zeros( + batch_size, 1, 1, 2, dtype=image_embeddings[-1].dtype, device=image_embeddings[-1].device + ) + input_labels = -torch.ones(batch_size, 1, 1, dtype=torch.int32, device=image_embeddings[-1].device) + + if input_masks is not None: + # If mask_inputs is provided, downsize it into low-res mask input if needed + # and feed it as a dense mask prompt into the SAM mask encoder + if input_masks.shape[-2:] != self.prompt_encoder.mask_input_size: + input_masks = F.interpolate( + input_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(input_masks.dtype) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + low_res_multimasks, iou_scores, _, object_score_logits = self.mask_decoder( + image_embeddings=image_embeddings[-1], + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + high_resolution_features=image_embeddings[:-1], + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + + return Sam3TrackerImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_multimasks, + object_score_logits=object_score_logits, + image_embeddings=image_embeddings, + vision_hidden_states=vision_hidden_states, + vision_attentions=vision_attentions, + ) + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3TrackerVisionEncoderOutput: + r""" + pixel_values (`torch.FloatTensor`): + Input pixel values of shape `(batch_size, num_channels, height, width)`. + """ + vision_outputs: Sam3TrackerVisionEncoderOutput = self.vision_encoder(pixel_values, return_dict=True, **kwargs) + + feature_maps = vision_outputs.fpn_hidden_states + feature_maps_position_embeddings = vision_outputs.fpn_position_encoding + + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + feature_maps = list(feature_maps) + feature_maps[0] = self.mask_decoder.conv_s0(feature_maps[0]) + feature_maps[1] = self.mask_decoder.conv_s1(feature_maps[1]) + + # flatten NxCxHxW to HWxNxC + feature_maps = [feature_map.flatten(2).permute(2, 0, 1) for feature_map in feature_maps] + feature_maps_position_embeddings = [ + feature_map_position_embedding.flatten(2).permute(2, 0, 1) + for feature_map_position_embedding in feature_maps_position_embeddings + ] + vision_outputs.fpn_hidden_states = feature_maps + vision_outputs.fpn_position_encoding = feature_maps_position_embeddings + + return vision_outputs + + +__all__ = ["Sam3TrackerModel", "Sam3TrackerPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/modular_sam3_tracker.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/modular_sam3_tracker.py new file mode 100644 index 0000000000000000000000000000000000000000..1da111af856ff0adfd1a35d0cad6e48780f81762 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/modular_sam3_tracker.py @@ -0,0 +1,233 @@ +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +from huggingface_hub.dataclasses import strict + +from ... import initialization as init +from ...configuration_utils import PreTrainedConfig +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoModel +from ..sam2.configuration_sam2 import ( + Sam2Config, + Sam2MaskDecoderConfig, + Sam2PromptEncoderConfig, +) +from ..sam2.modeling_sam2 import ( + Sam2Attention, + Sam2FeedForward, + Sam2ImageSegmentationOutput, + Sam2LayerNorm, + Sam2MaskDecoder, + Sam2MaskEmbedding, + Sam2Model, + Sam2PositionalEmbedding, + Sam2PreTrainedModel, + Sam2PromptEncoder, + Sam2TwoWayAttentionBlock, + Sam2TwoWayTransformer, +) +from ..sam2.processing_sam2 import Sam2Processor + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerPromptEncoderConfig(Sam2PromptEncoderConfig): + r""" + mask_input_channels (`int`, *optional*, defaults to 16): + The number of channels to be fed to the `MaskDecoder` module. + num_point_embeddings (`int`, *optional*, defaults to 4): + The number of point embeddings to be used. + scale (`float`, *optional*, defaults to 1): + The scale factor for the prompt encoder. + """ + + base_config_key = "prompt_encoder_config" + + image_size: int | list[int] | tuple[int, int] = 1008 + patch_size: int | list[int] | tuple[int, int] = 14 + + +class Sam3TrackerProcessor(Sam2Processor): + pass + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerMaskDecoderConfig(Sam2MaskDecoderConfig): + pass + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerConfig(Sam2Config): + r""" + prompt_encoder_config (Union[`dict`, `Sam3TrackerPromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam3TrackerPromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `Sam3TrackerMaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam3TrackerMaskDecoderConfig`]. + + Example: + + ```python + >>> from transformers import ( + ... Sam3TrackerVisionConfig, + ... Sam3TrackerPromptEncoderConfig, + ... Sam3TrackerMaskDecoderConfig, + ... Sam3TrackerModel, + ... ) + + >>> # Initializing a Sam3TrackerConfig with `"facebook/sam3_tracker.1_hiera_tiny"` style configuration + >>> configuration = Sam3TrackerConfig() + + >>> # Initializing a Sam3TrackerModel (with random weights) from the `"facebook/sam3_tracker.1_hiera_tiny"` style configuration + >>> model = Sam3TrackerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Sam3TrackerConfig from a Sam3TrackerVisionConfig, Sam3TrackerPromptEncoderConfig, and Sam3TrackerMaskDecoderConfig + >>> # Initializing SAM3_TRACKER vision encoder, memory attention, and memory encoder configurations + >>> vision_config = Sam3TrackerVisionConfig() + >>> prompt_encoder_config = Sam3TrackerPromptEncoderConfig() + >>> mask_decoder_config = Sam3TrackerMaskDecoderConfig() + + >>> config = Sam3TrackerConfig(vision_config, prompt_encoder_config, mask_decoder_config) + ``` + """ + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam3_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam3_vision_model"]( + backbone_feature_sizes=[[288, 288], [144, 144], [72, 72]] + ) + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = Sam3TrackerPromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = Sam3TrackerPromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam3TrackerMaskDecoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = Sam3TrackerMaskDecoderConfig() + + PreTrainedConfig.__post_init__(**kwargs) + + +class Sam3TrackerImageSegmentationOutput(Sam2ImageSegmentationOutput): + pass + + +class Sam3TrackerFeedForward(Sam2FeedForward): + pass + + +@auto_docstring( + custom_intro=""" + Segment Anything Model 3 (SAM 3) for generating segmentation masks, given an input image and + input points and labels, boxes, or masks. + """ +) +class Sam3TrackerPreTrainedModel(Sam2PreTrainedModel): + @torch.no_grad() + def _init_weights(self, module): + PreTrainedModel._init_weights(module) + if isinstance(module, Sam3TrackerModel): + if module.no_memory_embedding is not None: + init.zeros_(module.no_memory_embedding) + elif isinstance(module, Sam3TrackerPositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + + +class Sam3TrackerPositionalEmbedding(Sam2PositionalEmbedding): + pass + + +class Sam3TrackerMaskEmbedding(Sam2MaskEmbedding): + pass + + +class Sam3TrackerPromptEncoder(Sam2PromptEncoder): + pass + + +class Sam3TrackerAttention(Sam2Attention): + pass + + +class Sam3TrackerTwoWayAttentionBlock(Sam2TwoWayAttentionBlock): + pass + + +class Sam3TrackerTwoWayTransformer(Sam2TwoWayTransformer): + pass + + +class Sam3TrackerLayerNorm(Sam2LayerNorm): + pass + + +class Sam3TrackerMaskDecoder(Sam2MaskDecoder): + pass + + +class Sam3TrackerModel(Sam2Model): + _base_model_prefix = "tracker_model" + _keys_to_ignore_on_load_unexpected = [ + r"^detector_model.", + r"^memory_.*", + r"^mask_downsample.*", + r"^object_pointer_proj.*", + r"^temporal_positional_encoding_projection_layer.*", + "no_memory_positional_encoding", + "no_object_pointer", + "occlusion_spatial_embedding_parameter", + ] + + def __init__(self, config: Sam3TrackerConfig): + # loading from a sam3_video config + if hasattr(config, "tracker_config") and config.tracker_config is not None: + if isinstance(config.tracker_config, dict): + config.tracker_config = Sam3TrackerConfig(**config.tracker_config) + config = config.tracker_config + Sam3TrackerPreTrainedModel.__init__(config) + self.shared_image_embedding = Sam3TrackerPositionalEmbedding(config.prompt_encoder_config) + self.vision_encoder = AutoModel.from_config(config.vision_config) + self.prompt_encoder = Sam3TrackerPromptEncoder(config.prompt_encoder_config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + self.mask_decoder = Sam3TrackerMaskDecoder(config.mask_decoder_config) + + self.backbone_feature_sizes = config.vision_config.backbone_feature_sizes + # a single token to indicate no memory embedding from previous frames + self.hidden_dim = config.vision_config.fpn_hidden_size + self.no_memory_embedding = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + + self.post_init() + + +__all__ = [ + "Sam3TrackerConfig", + "Sam3TrackerPromptEncoderConfig", + "Sam3TrackerMaskDecoderConfig", + "Sam3TrackerProcessor", + "Sam3TrackerModel", + "Sam3TrackerPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/processing_sam3_tracker.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/processing_sam3_tracker.py new file mode 100644 index 0000000000000000000000000000000000000000..d9beb6ee65da1f07747b8db9544e7838d08924d9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker/processing_sam3_tracker.py @@ -0,0 +1,510 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3_tracker/modular_sam3_tracker.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3_tracker.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from copy import deepcopy + +import numpy as np +import torch + +from ...image_utils import ImageInput +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires + + +@requires(backends=("torch",)) +@auto_docstring +class Sam3TrackerProcessor(ProcessorMixin): + def __init__(self, image_processor, target_size: int | None = None, point_pad_value: int = -10, **kwargs): + r""" + target_size (`int`, *optional*): + The target size (in pixels) for normalizing input points and bounding boxes. If not provided, defaults + to the image processor's size configuration. All input coordinates (points and boxes) are normalized + to this size before being passed to the model. This ensures consistent coordinate representation + regardless of the original image dimensions. + point_pad_value (`int`, *optional*, defaults to -10): + The value used for padding input points when batching sequences of different lengths. This value is + used to mark padded positions and is preserved during coordinate normalization. + """ + super().__init__(image_processor, **kwargs) + self.point_pad_value = point_pad_value + self.target_size = target_size if target_size is not None else self.image_processor.size["height"] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + segmentation_maps: ImageInput | None = None, + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + original_sizes: list[list[float]] | torch.Tensor | None = None, + return_tensors: str | TensorType | None = None, + **kwargs, + ) -> BatchEncoding: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to process. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + original_sizes (`list[list[float]]`, `torch.Tensor`, *optional*): + The original sizes of the images. + + Returns: + A [`BatchEncoding`] with the following fields: + - `pixel_values` (`torch.Tensor`): The processed image(s). + - `original_sizes` (`list[list[float]]`): The original sizes of the images. + - `labels` (`torch.Tensor`): The processed segmentation maps (if provided). + - `input_points` (`torch.Tensor`): The processed points. + - `input_labels` (`torch.Tensor`): The processed labels. + - `input_boxes` (`torch.Tensor`): The processed bounding boxes. + """ + if images is not None: + encoding_image_processor = self.image_processor( + images, + segmentation_maps=segmentation_maps, + return_tensors=return_tensors, + **kwargs, + ) + elif original_sizes is not None: + if isinstance(original_sizes, torch.Tensor): + original_sizes = original_sizes.cpu().tolist() + encoding_image_processor = BatchEncoding({"original_sizes": original_sizes}, tensor_type=return_tensors) + else: + raise ValueError("Either images or original_sizes must be provided") + + # pop arguments that are not used in the forward but used nevertheless + original_sizes = encoding_image_processor["original_sizes"] + # Check original_sizes is of length 1 or len(images) + if images is not None and len(original_sizes) != 1 and len(original_sizes) != len(images): + raise ValueError( + "original_sizes must be of length 1 or len(images). If you are passing a single image, you must pass a single original_size." + ) + + # Process input points, labels, and boxes if provided + if input_points is not None or input_labels is not None or input_boxes is not None: + # Validate and convert inputs to standardized format + processed_points = self._validate_single_input( + input_points, + expected_depth=4, + input_name="points", + expected_format="[image level, object level, point level, point coordinates]", + expected_coord_size=2, + ) + processed_labels = self._validate_single_input( + input_labels, + expected_depth=3, + input_name="labels", + expected_format="[image level, object level, point level]", + ) + processed_boxes = self._validate_single_input( + input_boxes, + expected_depth=3, + input_name="boxes", + expected_format="[image level, box level, box coordinates]", + expected_coord_size=4, + ) + + # Get padding requirements for all inputs + if processed_points is not None: + points_max_dims = self._get_nested_dimensions(processed_points)[:3] + if processed_labels is not None: + labels_max_dims = self._get_nested_dimensions(processed_labels)[:3] + if processed_boxes is not None: + boxes_max_dims = self._get_nested_dimensions(processed_boxes)[:2] + + # Ensure points and labels have consistent dimensions + if processed_points is not None and processed_labels is not None: + if points_max_dims != labels_max_dims: + raise ValueError( + "Input points and labels have inconsistent dimensions. Please ensure they have the same dimensions." + ) + + # Check that boxes don't need padding (model limitation) + if processed_boxes is not None and len(processed_boxes) >= 2: + if any(len(img_boxes) < boxes_max_dims[1] for img_boxes in processed_boxes): + raise ValueError( + "Input boxes have inconsistent dimensions that would require padding, " + "but boxes cannot be padded due to model limitations. " + "Please ensure all images have the same number of boxes." + ) + + # Pad and normalize all inputs to final tensor format + if processed_points is not None: + padded_points = self._pad_nested_list(processed_points, points_max_dims + [2]) + final_points = torch.tensor(padded_points, dtype=torch.float32) + self._normalize_tensor_coordinates(final_points, original_sizes, preserve_padding=True) + encoding_image_processor.update({"input_points": final_points}) + + if processed_labels is not None: + padded_labels = self._pad_nested_list(processed_labels, labels_max_dims) + final_labels = torch.tensor(padded_labels, dtype=torch.int64) + encoding_image_processor.update({"input_labels": final_labels}) + + if processed_boxes is not None: + final_boxes = torch.tensor(processed_boxes, dtype=torch.float32) + self._normalize_tensor_coordinates(final_boxes, original_sizes, is_bounding_box=True) + encoding_image_processor.update({"input_boxes": final_boxes}) + + return encoding_image_processor + + def _normalize_coordinates( + self, target_size: int, coords: "torch.Tensor", original_size, is_bounding_box=False + ) -> "torch.Tensor": + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format. + + Args: + target_size (`int`): + The target size of the image. + coords (`torch.Tensor`): + The coordinates to be normalized. + original_size (`tuple`): + The original size of the image. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether the coordinates are bounding boxes. + """ + old_h, old_w = original_size + new_h, new_w = target_size, target_size + coords = deepcopy(coords).float() + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + def _convert_to_nested_list(self, data, expected_depth, current_depth=0): + """ + Recursively convert various input formats (tensors, numpy arrays, lists) to nested lists. + + Args: + data: Input data in any format + expected_depth: Expected nesting depth + current_depth: Current depth in recursion + + Returns: + Nested list representation of the data + """ + if data is None: + return None + + # Convert tensor/numpy to list if we're at a leaf level or if it's a multi-dimensional array + if isinstance(data, torch.Tensor): # PyTorch tensor + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small tensor + return data.numpy().tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, np.ndarray): # NumPy array + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small array + return data.tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, list): + if current_depth == expected_depth: + # We've reached the expected depth, return as is + return data + else: + # Continue recursion + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, (int, float)): + return data + else: + raise TypeError(f"Unsupported data type: {type(data)}") + + def _get_nested_dimensions(self, nested_list, max_dims=None): + """ + Get the maximum dimensions at each level of nesting. + + Args: + nested_list (`list`): + Nested list structure. + max_dims (`list`, *optional*): + Current maximum dimensions (for recursion). + + Returns: + `list`: A list of maximum dimensions for each nesting level. + """ + if max_dims is None: + max_dims = [] + + if not isinstance(nested_list, list): + return max_dims + + if len(max_dims) == 0: + max_dims.append(len(nested_list)) + else: + max_dims[0] = max(max_dims[0], len(nested_list)) + + if len(nested_list) > 0: + for item in nested_list: + if isinstance(item, list): + sub_dims = self._get_nested_dimensions(item) + # Merge sub_dims into max_dims + for i, dim in enumerate(sub_dims): + if i + 1 >= len(max_dims): + max_dims.append(dim) + else: + max_dims[i + 1] = max(max_dims[i + 1], dim) + + return max_dims + + def _pad_nested_list(self, nested_list, target_dims, current_level=0, pad_value=None): + """ + Recursively pad a nested list to match target dimensions. + + Args: + nested_list (`list`): + Nested list to pad. + target_dims (`list`): + Target dimensions for each level. + current_level (`int`, *optional*, defaults to 0): + Current nesting level. + pad_value (`int`, *optional*): + Value to use for padding. + + Returns: + `list`: The padded nested list. + """ + if pad_value is None: + pad_value = self.point_pad_value + + if current_level >= len(target_dims): + return nested_list + + # Ensure we have a list + if not isinstance(nested_list, list): + nested_list = [nested_list] + + # Pad current level + current_size = len(nested_list) + target_size = target_dims[current_level] + + # Pad with appropriate values + if current_level == len(target_dims) - 1: + # At the coordinate level, pad with pad_value + nested_list.extend([pad_value] * (target_size - current_size)) + else: + # At higher levels, pad with nested structures + if current_size > 0: + # Create appropriately sized template + if current_level < len(target_dims) - 2: + # For non-coordinate levels, create empty nested structure + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + else: + # For coordinate level, create list of pad_values + template = [pad_value] * target_dims[current_level + 1] + + nested_list.extend([deepcopy(template) for _ in range(target_size - current_size)]) + else: + # Create from scratch + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + nested_list.extend([deepcopy(template) for _ in range(target_size)]) + + # Recursively pad sublists + if current_level < len(target_dims) - 1: + for i in range(len(nested_list)): + if isinstance(nested_list[i], list): + nested_list[i] = self._pad_nested_list(nested_list[i], target_dims, current_level + 1, pad_value) + + return nested_list + + def _create_empty_nested_structure(self, dims, pad_value): + """ + Create an empty nested structure with given dimensions filled with pad_value. + + Args: + dims (`list`): + The dimensions of the nested structure. + pad_value (`int`): + The value to fill the structure with. + """ + if len(dims) == 1: + return [pad_value] * dims[0] + else: + return [self._create_empty_nested_structure(dims[1:], pad_value) for _ in range(dims[0])] + + def _get_nesting_level(self, input_list): + """ + Get the nesting level of a list structure. + + Args: + input_list (`list`): + The list to get the nesting level of. + """ + if isinstance(input_list, list): + if len(input_list) == 0: + return 1 + return 1 + self._get_nesting_level(input_list[0]) + elif isinstance(input_list, (np.ndarray, torch.Tensor)): + # For arrays/tensors, the nesting level is the number of dimensions + return len(input_list.shape) + return 0 + + def _validate_single_input( + self, + data: torch.Tensor | np.ndarray | list, + expected_depth: int, + input_name: str, + expected_format: str, + expected_coord_size: int | None = None, + ) -> list: + """ + Validate a single input by ensuring proper nesting and raising an error if the input is not valid. + + Args: + data (`torch.Tensor`, `np.ndarray`, or `list`): + Input data to process. + expected_depth (`int`): + Expected nesting depth. + input_name (`str`): + Name of the input for error messages. + expected_format (`str`): + The expected format of the input. + expected_coord_size (`int`, *optional*): + Expected coordinate size (2 for points, 4 for boxes, None for labels). + . + """ + if data is None: + return None + + # Handle tensors and numpy arrays first + if isinstance(data, (torch.Tensor, np.ndarray)): + # For tensors/arrays, we can directly check the number of dimensions + if data.ndim != expected_depth: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_depth} dimensions. The expected nesting format is {expected_format}. Got {data.ndim} dimensions." + ) + elif expected_coord_size is not None: + if data.shape[-1] != expected_coord_size: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_coord_size} as the last dimension, got {data.shape[-1]}." + ) + return self._convert_to_nested_list(data, expected_depth) + + # Handle nested lists + if isinstance(data, list): + current_depth = self._get_nesting_level(data) + if current_depth != expected_depth: + raise ValueError( + f"Input {input_name} must be a nested list with {expected_depth} levels. The expected nesting format is {expected_format}. Got {current_depth} levels." + ) + return self._convert_to_nested_list(data, expected_depth) + + def _normalize_tensor_coordinates(self, tensor, original_sizes, is_bounding_box=False, preserve_padding=False): + """ + Helper method to normalize coordinates in a tensor across multiple images. + + Args: + tensor (`torch.Tensor`): + Input tensor with coordinates. + original_sizes (`list`): + Original image sizes. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether coordinates are bounding boxes. + preserve_padding (`bool`, *optional*, defaults to `False`): + Whether to preserve padding values (for points). + """ + if preserve_padding: + # For points: avoid normalizing pad values + mask = tensor != self.point_pad_value + coord_mask = mask.all(dim=-1, keepdim=True) + + for img_idx in range(len(original_sizes)): + if img_idx < tensor.shape[0]: + original_size = original_sizes[img_idx] if img_idx < len(original_sizes) else original_sizes[0] + normalized_coords = self._normalize_coordinates( + self.target_size, tensor[img_idx], original_size, is_bounding_box=is_bounding_box + ) + + if preserve_padding: + # Only update non-padded values + img_mask = coord_mask[img_idx] + tensor[img_idx] = torch.where( + img_mask.expand_as(tensor[img_idx]), normalized_coords, tensor[img_idx] + ) + else: + tensor[img_idx] = normalized_coords + + def post_process_masks( + self, + masks, + original_sizes, + mask_threshold=0.0, + binarize=True, + max_hole_area=0.0, + max_sprinkle_area=0.0, + apply_non_overlapping_constraints=False, + **kwargs, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[List[torch.Tensor], List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + mask_threshold (`float`, *optional*, defaults to 0.0): + Threshold for binarization and post-processing operations. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + max_hole_area (`float`, *optional*, defaults to 0.0): + The maximum area of a hole to fill. + max_sprinkle_area (`float`, *optional*, defaults to 0.0): + The maximum area of a sprinkle to fill. + apply_non_overlapping_constraints (`bool`, *optional*, defaults to `False`): + Whether to apply non-overlapping constraints to the masks. + + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + return self.image_processor.post_process_masks( + masks, + original_sizes, + mask_threshold, + binarize, + max_hole_area, + max_sprinkle_area, + apply_non_overlapping_constraints, + **kwargs, + ) + + @property + def model_input_names(self): + image_processor_input_names = self.image_processor.model_input_names + return list(image_processor_input_names + ["original_sizes"]) + + +__all__ = ["Sam3TrackerProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..864afc45a4ef233dc76c32dd835a6a7fabdfe181 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam3_tracker_video import * + from .modeling_sam3_tracker_video import * + from .processing_sam3_tracker_video import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/configuration_sam3_tracker_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/configuration_sam3_tracker_video.py new file mode 100644 index 0000000000000000000000000000000000000000..99359da979d277d79f0b384ed4993f7a0e08a6a1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/configuration_sam3_tracker_video.py @@ -0,0 +1,304 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3_tracker_video/modular_sam3_tracker_video.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3_tracker_video.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerVideoPromptEncoderConfig(PreTrainedConfig): + r""" + mask_input_channels (`int`, *optional*, defaults to 16): + The number of channels to be fed to the `MaskDecoder` module. + num_point_embeddings (`int`, *optional*, defaults to 4): + The number of point embeddings to be used. + scale (`float`, *optional*, defaults to 1): + The scale factor for the prompt encoder. + """ + + base_config_key = "prompt_encoder_config" + + hidden_size: int = 256 + + image_size: int | list[int] | tuple[int, int] = 1008 + patch_size: int | list[int] | tuple[int, int] = 14 + mask_input_channels: int = 16 + num_point_embeddings: int = 4 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + scale: int = 1 + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerVideoMaskDecoderConfig(PreTrainedConfig): + r""" + mlp_dim (`int`, *optional*, defaults to 2048): + The dimension of the MLP in the two-way transformer. + attention_downsample_rate (`int`, *optional*, defaults to 2): + The downsample rate for the attention layers. + num_multimask_outputs (`int`, *optional*, defaults to 3): + The number of multimask outputs. + iou_head_depth (`int`, *optional*, defaults to 3): + The depth of the IoU head. + iou_head_hidden_dim (`int`, *optional*, defaults to 256): + The hidden dimension of the IoU head. + dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`): + Whether to use dynamic multimask via stability. + dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05): + The stability delta for the dynamic multimask. + dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98): + The stability threshold for the dynamic multimask. + """ + + base_config_key = "mask_decoder_config" + + hidden_size: int = 256 + hidden_act: str = "gelu" + mlp_dim: int = 2048 + num_hidden_layers: int = 2 + num_attention_heads: int = 8 + attention_downsample_rate: int = 2 + num_multimask_outputs: int = 3 + iou_head_depth: int = 3 + iou_head_hidden_dim: int = 256 + dynamic_multimask_via_stability: bool = True + dynamic_multimask_stability_delta: float = 0.05 + dynamic_multimask_stability_thresh: float = 0.98 + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerVideoConfig(PreTrainedConfig): + r""" + prompt_encoder_config (Union[`dict`, `Sam3TrackerVideoPromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam3TrackerVideoPromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `Sam3TrackerVideoMaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam3TrackerVideoMaskDecoderConfig`]. + initializer_range (`float`, *optional*, defaults to 0.02): + Standard deviation for parameter initialization. + num_maskmem (`int`, *optional*, defaults to 7): + The number of memory slots for the mask memory. + sigmoid_scale_for_mem_enc (`float`, *optional*, defaults to 20.0): + Scale factor for the sigmoid function in the memory encoder. + sigmoid_bias_for_mem_enc (`float`, *optional*, defaults to -10.0): + Bias for the sigmoid function in the memory encoder. + enable_occlusion_spatial_embedding (`bool`, *optional*, defaults to `True`): + Whether to enable spatial embedding for occlusions. + multimask_output_in_sam (`bool`, *optional*, defaults to `True`): + Whether to output multiple masks from the SAM head. + multimask_min_pt_num (`int`, *optional*, defaults to 0): + The minimum number of points to trigger multimask output. + multimask_max_pt_num (`int`, *optional*, defaults to 1): + The maximum number of points to trigger multimask output. + multimask_output_for_tracking (`bool`, *optional*, defaults to `True`): + Whether to use multimask output for tracking. + max_object_pointers_in_encoder (`int`, *optional*, defaults to 16): + The maximum number of object pointers in the encoder. + max_cond_frame_num (`int`, *optional*, defaults to 4): + Maximum number of conditioning frames to use in memory attention. + enable_temporal_pos_encoding_for_object_pointers (`bool`, *optional*, defaults to `True`): + Whether to enable temporal positional encoding for object pointers. + memory_attention_hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the memory attention hidden states. + memory_attention_num_layers (`int`, *optional*, defaults to 4): + The number of layers in the memory attention module. + memory_attention_num_attention_heads (`int`, *optional*, defaults to 1): + Number of attention heads for each attention layer in the memory attention. + memory_attention_downsample_rate (`int`, *optional*, defaults to 1): + The downsample rate for the attention layers. + memory_attention_feed_forward_hidden_size (`int`, *optional*, defaults to 2048): + The dimension of the feedforward network in the memory attention module. + memory_attention_feed_forward_hidden_act (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function in the feedforward network in the memory attention module. + memory_attention_dropout (`float`, *optional*, defaults to 0.1): + The dropout rate for the memory attention module. + memory_attention_rope_theta (`float`, *optional*, defaults to 10000): + The Rope theta parameter. + memory_attention_rope_feat_sizes (`list[int]`, *optional*, defaults to `[72, 72]`): + The feature sizes for the Rope positional encoding. + memory_attention_rope_dropout (`float`, *optional*, defaults to 0.1): + The dropout rate for the Rope positional encoding. + memory_encoder_hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the memory encoder hidden states. + memory_encoder_output_channels (`int`, *optional*, defaults to 64): + The number of output channels for the memory encoder. + mask_downsampler_embed_dim (`int`, *optional*, defaults to 256): + The dimension of the mask downsampler embedding. + mask_downsampler_kernel_size (`int`, *optional*, defaults to 3): + The kernel size for the mask downsampler. + mask_downsampler_stride (`int`, *optional*, defaults to 2): + The stride for the mask downsampler. + mask_downsampler_padding (`int`, *optional*, defaults to 1): + The padding for the mask downsampler. + mask_downsampler_total_stride (`int`, *optional*, defaults to 16): + The total stride for the mask downsampler. + mask_downsampler_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function in the mask downsampler. + memory_fuser_num_layers (`int`, *optional*, defaults to 2): + The number of layers in the memory fuser. + memory_fuser_embed_dim (`int`, *optional*, defaults to 256): + The dimension of the embedding layer in the memory fuser. + memory_fuser_intermediate_dim (`int`, *optional*, defaults to 1024): + The dimension of the intermediate layer in the memory fuser. + memory_fuser_kernel_size (`int`, *optional*, defaults to 7): + The kernel size for the memory fuser. + memory_fuser_padding (`int`, *optional*, defaults to 3): + The padding for the memory fuser. + memory_fuser_layer_scale_init_value (`float`, *optional*, defaults to 1e-06): + The initial value for the layer scale in the memory fuser. + memory_fuser_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function in the memory fuser. + + Example: + + ```python + >>> from transformers import ( + ... Sam3VisionConfig, + ... Sam3TrackerVideoPromptEncoderConfig, + ... Sam3TrackerVideoMaskDecoderConfig, + ... Sam3TrackerVideoModel, + ... ) + + >>> # Initializing a Sam3TrackerVideoConfig with `"facebook/sam3"` style configuration + >>> configuration = Sam3TrackerVideoConfig() + + >>> # Initializing a Sam3TrackerVideoModel (with random weights) from the `"facebook/sam3"` style configuration + >>> model = Sam3TrackerVideoModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Sam3TrackerVideoConfig from a Sam3TrackerVideoVisionConfig, Sam3TrackerVideoPromptEncoderConfig, and Sam3TrackerVideoMaskDecoderConfig + + >>> # Initializing SAM3 tracker video vision encoder, memory attention, and memory encoder configurations + >>> vision_config = Sam3TrackerVideoVisionConfig() + >>> prompt_encoder_config = Sam3TrackerVideoPromptEncoderConfig() + >>> mask_decoder_config = Sam3TrackerVideoMaskDecoderConfig() + + >>> config = Sam3TrackerVideoConfig(vision_config, prompt_encoder_config, mask_decoder_config) + ```""" + + model_type = "sam3_tracker_video" + sub_configs = { + "vision_config": AutoConfig, + "prompt_encoder_config": Sam3TrackerVideoPromptEncoderConfig, + "mask_decoder_config": Sam3TrackerVideoMaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + prompt_encoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + num_maskmem: int = 7 + sigmoid_scale_for_mem_enc: float = 20.0 + sigmoid_bias_for_mem_enc: float = -10.0 + enable_occlusion_spatial_embedding: bool = True + multimask_output_in_sam: bool = True + multimask_min_pt_num: int = 0 + multimask_max_pt_num: int = 1 + multimask_output_for_tracking: bool = True + max_object_pointers_in_encoder: int = 16 + max_cond_frame_num: int = 4 + enable_temporal_pos_encoding_for_object_pointers: bool = True + memory_attention_hidden_size: int = 256 + memory_attention_num_layers: int = 4 + memory_attention_num_attention_heads: int = 1 + memory_attention_downsample_rate: int = 1 + memory_attention_feed_forward_hidden_size: int = 2048 + memory_attention_feed_forward_hidden_act: str = "relu" + memory_attention_dropout: float | int = 0.1 + memory_attention_rope_theta: int = 10000 + memory_attention_rope_feat_sizes: list | None = None + memory_attention_rope_dropout: float | int = 0.1 + memory_encoder_hidden_size: int = 256 + memory_encoder_output_channels: int = 64 + mask_downsampler_embed_dim: int = 256 + mask_downsampler_kernel_size: int = 3 + mask_downsampler_stride: int = 2 + mask_downsampler_padding: int = 1 + mask_downsampler_total_stride: int = 16 + mask_downsampler_hidden_act: str = "gelu" + memory_fuser_num_layers: int = 2 + memory_fuser_embed_dim: int = 256 + memory_fuser_intermediate_dim: int = 1024 + memory_fuser_kernel_size: int = 7 + memory_fuser_padding: int = 3 + memory_fuser_layer_scale_init_value: float = 1e-6 + memory_fuser_hidden_act: str = "gelu" + + def __post_init__(self, **kwargs): + self.memory_attention_rope_feat_sizes = ( + [72, 72] if self.memory_attention_rope_feat_sizes is None else self.memory_attention_rope_feat_sizes + ) + + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam3_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam3_vision_model"]( + backbone_feature_sizes=[[288, 288], [144, 144], [72, 72]] + ) + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = Sam3TrackerVideoPromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = Sam3TrackerVideoPromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam3TrackerVideoMaskDecoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = Sam3TrackerVideoMaskDecoderConfig() + + self.image_size = kwargs.pop("image_size", 1008) + super().__post_init__(**kwargs) + + @property + def image_size(self): + """Image size for the tracker video model.""" + return self.vision_config.image_size + + @image_size.setter + def image_size(self, value): + """Set the image size and propagate to sub-configs. Calculates feature sizes based on patch_size.""" + self.prompt_encoder_config.image_size = value + self.vision_config.image_size = value + + patch_size = self.vision_config.backbone_config.patch_size + self.vision_config.backbone_feature_sizes = [ + [4 * value // patch_size, 4 * value // patch_size], + [2 * value // patch_size, 2 * value // patch_size], + [value // patch_size, value // patch_size], + ] + self.memory_attention_rope_feat_sizes = [ + value // patch_size, + value // patch_size, + ] + + # keep the image_size in the __dict__ to save the value in the config file (backward compatibility) + self.__dict__["image_size"] = value + + +__all__ = ["Sam3TrackerVideoMaskDecoderConfig", "Sam3TrackerVideoPromptEncoderConfig", "Sam3TrackerVideoConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/modeling_sam3_tracker_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/modeling_sam3_tracker_video.py new file mode 100644 index 0000000000000000000000000000000000000000..786e5b5b6769126a362925a44e4e630490a25ac3 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/modeling_sam3_tracker_video.py @@ -0,0 +1,2815 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3_tracker_video/modular_sam3_tracker_video.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3_tracker_video.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from collections import OrderedDict +from collections.abc import Callable, Iterator +from dataclasses import dataclass +from typing import Any + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor +from tqdm import tqdm + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...pytorch_utils import compile_compatible_method_lru_cache +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, logging +from ...utils.generic import is_flash_attention_requested +from ...utils.output_capturing import OutputRecorder +from ..auto import AutoModel +from .configuration_sam3_tracker_video import ( + Sam3TrackerVideoConfig, + Sam3TrackerVideoMaskDecoderConfig, + Sam3TrackerVideoPromptEncoderConfig, +) + + +logger = logging.get_logger(__name__) + + +class Sam3TrackerVideoInferenceCache: + """Cache for vision features and model constants.""" + + def __init__( + self, + inference_device: torch.device | str = "cpu", + inference_state_device: torch.device | str = "cpu", + max_vision_features_cache_size: int = 1, + ): + self.inference_device = inference_device + self.inference_state_device = inference_state_device + self.max_vision_features_cache_size = max_vision_features_cache_size + + self._vision_features = {} + + def cache_vision_features(self, frame_idx: int, features: dict): + """Cache vision features with automatic device management.""" + cached = {} + if len(self._vision_features) >= self.max_vision_features_cache_size: + # remove the oldest frame + self._vision_features.pop(min(self._vision_features.keys())) + + for key, value in features.items(): + if isinstance(value, torch.Tensor): + cached[key] = value.to(self.inference_state_device, non_blocking=True) + elif isinstance(value, (list, tuple)) and value and isinstance(value[0], torch.Tensor): + cached[key] = [v.to(self.inference_state_device, non_blocking=True) for v in value] + else: + cached[key] = value + self._vision_features[frame_idx] = cached + + def get_vision_features(self, frame_idx: int) -> dict | None: + """Get cached vision features, automatically moved to inference device.""" + if frame_idx not in self._vision_features: + return None + + cached = self._vision_features[frame_idx] + moved = {} + for key, value in cached.items(): + if isinstance(value, torch.Tensor): + moved[key] = value.to(self.inference_device, non_blocking=True) + elif isinstance(value, (list, tuple)) and value and isinstance(value[0], torch.Tensor): + moved[key] = [v.to(self.inference_device, non_blocking=True) for v in value] + else: + moved[key] = value + return moved + + def clear_all(self): + """Clear all cached data.""" + self._vision_features.clear() + + +class Sam3TrackerVideoInferenceSession: + r""" + Manages video inference session parameters, state and cache. + + Args: + video (`torch.FloatTensor`, *optional*): + The video to process. No need to provide when streaming. + video_height (`int`, *optional*): + The height of the video. + video_width (`int`, *optional*): + The width of the video. + inference_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to use for inference. + inference_state_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to store the inference state on. + video_storage_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to store the video on. + dtype (`torch.dtype`, *optional*, defaults to `"float32"`): + The dtype to use for the video. + max_vision_features_cache_size (`int`, *optional*, defaults to 1): + The maximum number of vision features to cache. + """ + + def __init__( + self, + video: torch.FloatTensor | None = None, + video_height: int | None = None, + video_width: int | None = None, + inference_device: torch.device | str = "cpu", + inference_state_device: torch.device | str = "cpu", + video_storage_device: torch.device | str = "cpu", + dtype: torch.dtype | str = "float32", + max_vision_features_cache_size: int = 1, + ): + # store as a dictionary to avoid double memory allocation with torch.cat when adding new frames + self.processed_frames = ( + dict(enumerate(video.to(video_storage_device, dtype=dtype))) if video is not None else None + ) + self.video_height = video_height + self.video_width = video_width + + self.inference_device = inference_device + self.inference_state_device = inference_state_device + self.video_storage_device = video_storage_device + self.dtype = dtype + self.max_vision_features_cache_size = max_vision_features_cache_size + + # Cache for computed features + self.cache = Sam3TrackerVideoInferenceCache( + inference_device=self.inference_device, + inference_state_device=self.inference_state_device, + max_vision_features_cache_size=self.max_vision_features_cache_size, + ) + + # Persistent object tracking state + self._obj_id_to_idx = OrderedDict() + self._obj_idx_to_id = OrderedDict() + self.obj_ids = [] + + # Persistent user inputs + self.point_inputs_per_obj = {} + self.mask_inputs_per_obj = {} + + # Persistent model outputs/history + self.output_dict_per_obj = {} + self.frames_tracked_per_obj = {} + + # Session state flags + self.obj_with_new_inputs = [] + + @property + def num_frames(self) -> int | None: + return len(self.processed_frames) if self.processed_frames is not None else None + + # Object management + def obj_id_to_idx(self, obj_id: int) -> int: + """Map object ID to index, creating new entry if needed.""" + obj_idx = self._obj_id_to_idx.get(obj_id, None) + if obj_idx is not None: + return obj_idx + + obj_idx = len(self._obj_id_to_idx) + self._obj_id_to_idx[obj_id] = obj_idx + self._obj_idx_to_id[obj_idx] = obj_id + self.obj_ids = list(self._obj_id_to_idx) + + self.point_inputs_per_obj[obj_idx] = {} + self.mask_inputs_per_obj[obj_idx] = {} + self.output_dict_per_obj[obj_idx] = { + "cond_frame_outputs": {}, + "non_cond_frame_outputs": {}, + } + self.frames_tracked_per_obj[obj_idx] = {} + + return obj_idx + + # Video Inference specific functions + def obj_idx_to_id(self, obj_idx: int) -> int: + """Map model-side object index to client-side object id.""" + return self._obj_idx_to_id[obj_idx] + + def get_obj_num(self) -> int: + """Get the total number of unique object ids received so far in this session.""" + return len(self._obj_idx_to_id) + + # Input management with device handling + def add_point_inputs(self, obj_idx: int, frame_idx: int, inputs: dict): + """Add point inputs with automatic device placement.""" + device_inputs = {} + for key, value in inputs.items(): + if isinstance(value, torch.Tensor): + device_inputs[key] = value.to(self.inference_device, non_blocking=False) + else: + device_inputs[key] = value + self.point_inputs_per_obj[obj_idx][frame_idx] = device_inputs + + def remove_point_inputs(self, obj_idx: int, frame_idx: int): + """Remove point inputs.""" + self.point_inputs_per_obj[obj_idx].pop(frame_idx, None) + + def add_mask_inputs(self, obj_idx: int, frame_idx: int, inputs: torch.Tensor): + """Add mask inputs with automatic device placement.""" + self.mask_inputs_per_obj[obj_idx][frame_idx] = inputs.to( + self.inference_device, dtype=self.dtype, non_blocking=True + ) + + def remove_mask_inputs(self, obj_idx: int, frame_idx: int): + """Remove mask inputs.""" + self.mask_inputs_per_obj[obj_idx].pop(frame_idx, None) + + # Output management with smart device placement + def store_output( + self, + obj_idx: int, + frame_idx: int, + output_key: str | None = None, + output_value: torch.Tensor | dict | None = None, + is_conditioning_frame: bool = True, + ): + """ + Store output with smart device management. + If output_key is None, the output is stored as a dictionary. + + Args: + obj_idx (int): The index of the object. + frame_idx (int): The index of the frame. + output_key (Optional[str]): The key of the output. If None, the output is stored as a dictionary. + output_value (Optional[Union[torch.Tensor, dict]]): The value of the output. + is_conditioning_frame (bool): Whether the output is for a conditioning frame. + """ + storage_key = "cond_frame_outputs" if is_conditioning_frame else "non_cond_frame_outputs" + + if output_key is None and isinstance(output_value, dict): + self.output_dict_per_obj[obj_idx][storage_key][frame_idx] = {} + for key, value in output_value.items(): + self.store_output(obj_idx, frame_idx, key, value, is_conditioning_frame) + return + + # Device placement: small tensors stay on inference device, large ones go to inference state device + if output_key in ["object_pointer", "object_score_logits"]: # Small tensors + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value + elif isinstance(output_value, torch.Tensor): # Large tensors like masks, features + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value.to( + self.inference_state_device, non_blocking=True + ) + else: + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value + + def get_output( + self, + obj_idx: int, + frame_idx: int, + output_key: str, + is_conditioning_frame: bool = True, + ): + """ + Get output with smart device management. + + Args: + obj_idx (int): The index of the object. + frame_idx (int): The index of the frame. + output_key (str): The key of the output. + is_conditioning_frame (bool): Whether the output is for a conditioning frame. + """ + storage_key = "cond_frame_outputs" if is_conditioning_frame else "non_cond_frame_outputs" + out = self.output_dict_per_obj[obj_idx][storage_key].get(frame_idx, None) + # move to inference device if needed + if out is None: + return None + value = out[output_key] + if isinstance(value, torch.Tensor): + value = value.to(self.inference_device, non_blocking=True) + return value + + # Video frame management + def add_new_frame(self, pixel_values: torch.Tensor, frame_idx: int | None = None) -> int: + """Add new frame with automatic device placement.""" + pixel_values = pixel_values.to(self.video_storage_device, dtype=self.dtype, non_blocking=True) + if pixel_values.dim() == 4: + pixel_values = pixel_values.squeeze(0) + + if frame_idx is None: + frame_idx = len(self.processed_frames) if self.processed_frames is not None else 0 + + if self.processed_frames is None: + self.processed_frames = {frame_idx: pixel_values} + else: + self.processed_frames[frame_idx] = pixel_values + + return frame_idx + + def get_frame(self, frame_idx: int) -> torch.Tensor: + """Get frame from video.""" + return self.processed_frames[frame_idx].to(self.inference_device, non_blocking=True) + + def reset_tracking_data(self): + """Reset tracking data but keep cache.""" + self._obj_id_to_idx.clear() + self._obj_idx_to_id.clear() + self.obj_ids.clear() + self.point_inputs_per_obj.clear() + self.mask_inputs_per_obj.clear() + self.output_dict_per_obj.clear() + self.frames_tracked_per_obj.clear() + self.obj_with_new_inputs = [] + # Note: cache and video data are preserved + + def reset_inference_session(self): + """Reset tracking data and cache.""" + self._obj_id_to_idx.clear() + self._obj_idx_to_id.clear() + self.obj_ids.clear() + self.point_inputs_per_obj.clear() + self.mask_inputs_per_obj.clear() + self.output_dict_per_obj.clear() + self.frames_tracked_per_obj.clear() + self.obj_with_new_inputs = [] + self.cache.clear_all() + + +class Sam3TrackerVideoLayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +# copied and adapted from original implementation, also practically equal to DetrSinePositionEmbedding +class Sam3TrackerVideoPositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one used by the Attention is all you + need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats: int = 64, temperature: int = 10000, normalize: bool = False, scale: float | None = None + ): + super().__init__() + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + self.scale = 2 * math.pi if scale is None else scale + + @compile_compatible_method_lru_cache(maxsize=1) + def forward( + self, + shape: torch.Size, + device: torch.device | str, + dtype: torch.dtype, + mask: Tensor | None = None, + ) -> Tensor: + if mask is None: + mask = torch.zeros((shape[0], shape[2], shape[3]), device=device, dtype=torch.bool) + not_mask = (~mask).to(dtype) + y_embed = not_mask.cumsum(1) + x_embed = not_mask.cumsum(2) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.int64, device=device).to(dtype) + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Sam3TrackerVideoAttention(nn.Module): + """ + SAM3_TRACKER_VIDEO's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and + values. + """ + + def __init__(self, config, downsample_rate=None): + super().__init__() + downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate + self.config = config + self.hidden_size = config.hidden_size + self.internal_dim = config.hidden_size // downsample_rate + self.num_attention_heads = config.num_attention_heads + self.head_dim = self.internal_dim // config.num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.v_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.o_proj = nn.Linear(self.internal_dim, self.hidden_size) + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_similarity: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + # Input projections + batch_size, point_batch_size = query.shape[:2] + new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim) + + query = self.q_proj(query).view(*new_shape).transpose(1, 2) + key = self.k_proj(key).view(*new_shape).transpose(1, 2) + value = self.v_proj(value).view(*new_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + if is_flash_attention_requested(self.config) and attention_similarity is not None: + # Target guided masks are represented as float masks and are incompatible with Flash Attention + # Fallback to SDPA for this call only so the rest of the model can still benefit from FA + attention_interface = ALL_ATTENTION_FUNCTIONS["sdpa"] + logger.warning_once( + "Falling back to SDPA for target-guided attention because " + "Flash Attention does not support additive bias masks." + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_similarity, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = attn_output.reshape( + batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights + + +class Sam3TrackerVideoTwoWayAttentionBlock(GradientCheckpointingLayer): + def __init__(self, config: Sam3TrackerVideoMaskDecoderConfig, skip_first_layer_pe: bool = False): + """ + A transformer block with four layers: + (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on + sparse inputs (4) cross attention of dense inputs -> sparse inputs + + Arguments: + config (`Sam3TrackerVideoMaskDecoderConfig`): + The configuration file used to instantiate the block + attention_downsample_rate (*optionalk*, int, defaults to 2): + The downsample ratio of the block used to reduce the inner dim of the attention. + skip_first_layer_pe (*optional*, bool, defaults to `False`): + Whether or not to skip the addition of the query_point_embedding on the first layer. + """ + super().__init__() + self.self_attn = Sam3TrackerVideoAttention(config, downsample_rate=1) + self.layer_norm1 = nn.LayerNorm(config.hidden_size) + + self.cross_attn_token_to_image = Sam3TrackerVideoAttention(config) + self.layer_norm2 = nn.LayerNorm(config.hidden_size) + + self.mlp = Sam3TrackerVideoFeedForward( + config.hidden_size, config.mlp_dim, config.hidden_size, num_layers=config.num_hidden_layers + ) + self.layer_norm3 = nn.LayerNorm(config.hidden_size) + + self.layer_norm4 = nn.LayerNorm(config.hidden_size) + self.cross_attn_image_to_token = Sam3TrackerVideoAttention(config) + + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, + queries: Tensor, + keys: Tensor, + query_point_embedding: Tensor, + key_point_embedding: Tensor, + attention_similarity: Tensor, + **kwargs: Unpack[TransformersKwargs], + ): + # Self attention block + if self.skip_first_layer_pe: + queries, _ = self.self_attn(query=queries, key=queries, value=queries) + else: + query = queries + query_point_embedding + attn_out, _ = self.self_attn(query=query, key=query, value=queries) + queries = queries + attn_out + queries = self.layer_norm1(queries) + + # Cross attention block, tokens attending to image embedding + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_token_to_image( + query=query, key=key, value=keys, attention_similarity=attention_similarity + ) + queries = queries + attn_out + + queries = self.layer_norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.layer_norm3(queries) + + # Cross attention block, image embedding attending to tokens + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_image_to_token(query=key, key=query, value=queries) + keys = keys + attn_out + + keys = self.layer_norm4(keys) + return queries, keys, attn_out + + +class Sam3TrackerVideoFeedForward(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + activation: str = "relu", + sigmoid_output: bool = False, + ): + super().__init__() + self.num_layers = num_layers + self.activation = ACT2FN[activation] + self.proj_in = nn.Linear(input_dim, hidden_dim) + self.proj_out = nn.Linear(hidden_dim, output_dim) + self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)]) + self.sigmoid_output = sigmoid_output + + def forward(self, hidden_states): + hidden_states = self.proj_in(hidden_states) + hidden_states = self.activation(hidden_states) + for layer in self.layers: + hidden_states = self.activation(layer(hidden_states)) + + hidden_states = self.proj_out(hidden_states) + if self.sigmoid_output: + hidden_states = F.sigmoid(hidden_states) + return hidden_states + + +@auto_docstring(custom_intro="Base class for the Sam3TrackerVideo model's output.") +@dataclass +class Sam3TrackerVideoImageSegmentationOutput(ModelOutput): + r""" + iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`): + The Intersection over Union (IoU) scores of the predicted masks. + pred_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, height, width)`): + The predicted low-resolution masks. This is an alias for `low_res_masks`. These masks need to be post-processed + by the processor to be brought to the original image size. + object_score_logits (`torch.FloatTensor` of shape `(batch_size, point_batch_size, 1)`): + Logits for the object score, indicating if an object is present. + image_embeddings (`tuple(torch.FloatTensor)`): + The features from the FPN, which are used by the mask decoder. This is a tuple of `torch.FloatTensor` where each + tensor has shape `(batch_size, channels, height, width)`. + vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. + Hidden-states of the vision model at the output of each stage. + vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the vision model. + mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. + Attentions weights of the mask decoder. + high_res_masks (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks, image_size, image_size)`, *optional*): + The predicted masks, upscaled to the original image size. Only used for Sam3TrackerVideoModel. + object_pointer (`torch.FloatTensor` of shape `(batch_size, point_batch_size, hidden_size)`, *optional*): + A tensor representing the object pointer, used for tracking in videos. Only used for Sam3TrackerVideoModel. + """ + + iou_scores: torch.FloatTensor | None = None + pred_masks: torch.FloatTensor | None = None + object_score_logits: torch.FloatTensor | None = None + image_embeddings: tuple[torch.FloatTensor, ...] = None + vision_hidden_states: tuple[torch.FloatTensor, ...] | None = None + vision_attentions: tuple[torch.FloatTensor, ...] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None + + high_res_masks: torch.FloatTensor | None = None + object_pointer: torch.FloatTensor | None = None + + +@auto_docstring(custom_intro="Base class for the Sam2 model's output.") +@dataclass +class Sam3TrackerVideoSegmentationOutput(ModelOutput): + r""" + object_ids (`list[int]`, *optional*): + List of object IDs being tracked in the current frame. + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_masks, height, width)`): + The predicted masks stored at the model's resolution. + object_score_logits (`torch.FloatTensor` of shape `(batch_size,)`, *optional*): + Logits for the object scores, indicating if objects are present. + frame_idx (`int`): + The frame index of the video. + """ + + object_ids: list[int] | None = None + pred_masks: torch.FloatTensor | None = None + object_score_logits: torch.FloatTensor | None = None + frame_idx: int | None = None + + +@auto_docstring +class Sam3TrackerVideoPreTrainedModel(PreTrainedModel): + config_class = Sam3TrackerVideoConfig + base_model_prefix = "tracker_model" + main_input_name = "pixel_values" + input_modalities = "video" + _supports_sdpa = True + _supports_flash_attn = True + _supports_attention_backend = True + + @torch.no_grad() + def _init_weights(self, module): + super()._init_weights(module) + if isinstance(module, Sam3TrackerVideoModel): + if module.no_memory_positional_encoding is not None: + init.zeros_(module.no_memory_positional_encoding) + if module.memory_temporal_positional_encoding is not None: + init.zeros_(module.memory_temporal_positional_encoding) + if module.no_object_pointer is not None: + init.zeros_(module.no_object_pointer) + if module.occlusion_spatial_embedding_parameter is not None: + init.zeros_(module.occlusion_spatial_embedding_parameter) + if isinstance(module, Sam3TrackerVideoMemoryFuserCXBlock): + if module.scale is not None: + init.zeros_(module.scale) + elif isinstance(module, Sam3TrackerVideoVisionRotaryEmbedding): + inv_freq = module.create_inv_freq() + init.copy_(module.rope_embeddings_cos, inv_freq.cos()) + init.copy_(module.rope_embeddings_sin, inv_freq.sin()) + elif isinstance(module, Sam3TrackerVideoPositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + + +class Sam3TrackerVideoVisionRotaryEmbedding(nn.Module): + """ + Vision Rotary Position Embedding for SAM2, following transformers library standards. + Supports 2D (axial) rotary embeddings for spatial dimensions. + """ + + def __init__(self, config: Sam3TrackerVideoConfig): + super().__init__() + self.dim = config.memory_attention_hidden_size // ( + config.memory_attention_downsample_rate * config.memory_attention_num_attention_heads + ) + # Ensure even dimension for proper axial splitting + if self.dim % 4 != 0: + raise ValueError("Dimension must be divisible by 4 for axial RoPE") + self.end_x, self.end_y = config.memory_attention_rope_feat_sizes + self.memory_attention_rope_theta = config.memory_attention_rope_theta + + # directly register the cos and sin embeddings as we have a fixed feature shape + inv_freq = self.create_inv_freq() + self.register_buffer("rope_embeddings_cos", inv_freq.cos(), persistent=False) + self.register_buffer("rope_embeddings_sin", inv_freq.sin(), persistent=False) + + @torch.no_grad() + def forward(self) -> tuple[torch.Tensor, torch.Tensor]: + # As the feature map size is fixed, we can just return the pre-computed embeddings. + return self.rope_embeddings_cos, self.rope_embeddings_sin + + def create_inv_freq(self): + freqs = 1.0 / ( + self.memory_attention_rope_theta ** (torch.arange(0, self.dim, 4)[: (self.dim // 4)].float() / self.dim) + ) + # Generate 2D position indices for axial rotary embedding + flattened_indices = torch.arange(self.end_x * self.end_y, dtype=torch.long) + x_positions = flattened_indices % self.end_x + y_positions = torch.div(flattened_indices, self.end_x, rounding_mode="floor") + freqs_x = torch.outer(x_positions, freqs).float() + freqs_y = torch.outer(y_positions, freqs).float() + inv_freq = torch.cat([freqs_x, freqs_y], dim=-1) + inv_freq = inv_freq.repeat_interleave(2, dim=-1) + return inv_freq + + +def rotate_pairwise(x): + """ + pairwise rotation of the hidden dims of the input. Differerent from Llama Half-Tensor Rotation. + + This is an optimized version of the following more explicit implementation: + ```python + x_rotated = torch.zeros_like(x, dtype=x.dtype, device=x.device) + x_rotated[..., ::2] = -x[..., 1::2] + x_rotated[..., 1::2] = x[..., ::2] + return x_rotated + ``` + """ + x = x.view(*x.shape[:-1], -1, 2) + x1, x2 = x.unbind(dim=-1) + x = torch.stack((-x2, x1), dim=-1) + return x.flatten(start_dim=-2) + + +# TODO: This leads to ~1e-07 max diff and ~1e-09 avg diff for q_embed and k_embed from the original implementation, most likely due to the use of complex tensors in the original implementation. +def apply_rotary_pos_emb_2d( + q: torch.Tensor, + k: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + num_k_exclude_rope: int = 0, + repeat_freqs_k: bool = False, +) -> tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary position embedding to query and key tensors for vision models. + Follows the standard transformers library pattern. + + Args: + q: Query tensor of shape (..., seq_len, head_dim) + k: Key tensor of shape (..., seq_len, head_dim) + cos: Cosine position embedding of shape (seq_len, head_dim) + sin: Sine position embedding of shape (seq_len, head_dim) + repeat_freqs_k: Whether to repeat frequencies for keys (for cross-attention) + + Returns: + Rotated (q, k) tensors + """ + k_rot, k_pass = k[..., : k.shape[-2] - num_k_exclude_rope, :], k[..., k.shape[-2] - num_k_exclude_rope :, :] + q_embed = q.float() # force upscale to float32 as in the original implementation + q_embed = (q_embed * cos) + (rotate_pairwise(q_embed) * sin) + if k_rot.shape[-2] == 0: + # Handle case where keys might be empty due to dropout + return q_embed.type_as(q), torch.cat([k_rot, k_pass], dim=-2) + + # Handle key tensor - may need to repeat frequencies if different sequence length + if repeat_freqs_k and k_rot.shape[-2] != q.shape[-2]: + # Repeat cos/sin to match key sequence length + repeat_factor = k_rot.shape[-2] // q.shape[-2] + cos_k = cos.repeat(1, 1, repeat_factor, 1) + sin_k = sin.repeat(1, 1, repeat_factor, 1) + else: + cos_k = cos + sin_k = sin + + # Apply rotary embedding to keys + k_embed = k_rot.float() # force upscale to float32 as in the original implementation + k_embed = (k_embed * cos_k) + (rotate_pairwise(k_embed) * sin_k) + # Concatenate back to full shape + k_embed = torch.cat([k_embed.type_as(k), k_pass], dim=-2) + return q_embed.type_as(q), k_embed + + +class Sam3TrackerVideoRoPEAttention(nn.Module): + """Attention with rotary position encoding.""" + + def __init__( + self, + config: Sam3TrackerVideoConfig, + kv_in_dim: int | None = None, + rope_k_repeat=False, + ): + super().__init__() + self.config = config + self.hidden_size = config.memory_attention_hidden_size + self.internal_dim = self.hidden_size // config.memory_attention_downsample_rate + self.num_attention_heads = config.memory_attention_num_attention_heads + self.head_dim = self.internal_dim // config.memory_attention_num_attention_heads + self.scaling = self.head_dim**-0.5 + self.is_causal = False + + self.kv_in_dim = kv_in_dim if kv_in_dim is not None else self.hidden_size + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.o_proj = nn.Linear(self.internal_dim, self.hidden_size) + + self.rope_k_repeat = rope_k_repeat + self.dropout_p = config.memory_attention_rope_dropout + + def forward( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + num_k_exclude_rope: int = 0, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> Tensor: + # Input projections + batch_size, point_batch_size = query.shape[:2] + new_shape = (batch_size * point_batch_size, -1, self.num_attention_heads, self.head_dim) + + query = self.q_proj(query).view(*new_shape).transpose(1, 2) + key = self.k_proj(key).view(*new_shape).transpose(1, 2) + value = self.v_proj(value).view(*new_shape).transpose(1, 2) + + cos, sin = position_embeddings + # Apply rotary position encoding, excluding some keys if specified + query, key = apply_rotary_pos_emb_2d( + query, key, cos, sin, repeat_freqs_k=self.rope_k_repeat, num_k_exclude_rope=num_k_exclude_rope + ) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=None, + dropout=0.0 if not self.training else self.dropout_p, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + attn_output = attn_output.reshape( + batch_size, point_batch_size, -1, self.num_attention_heads * self.head_dim + ).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class Sam3TrackerVideoMemoryAttentionLayer(nn.Module): + def __init__(self, config: Sam3TrackerVideoConfig): + super().__init__() + hidden_size = config.memory_attention_hidden_size + self.self_attn = Sam3TrackerVideoRoPEAttention(config) + self.cross_attn_image = Sam3TrackerVideoRoPEAttention(config, kv_in_dim=64, rope_k_repeat=True) + + # Implementation of Feedforward model + self.linear1 = nn.Linear(hidden_size, config.memory_attention_feed_forward_hidden_size) + self.dropout = nn.Dropout(config.memory_attention_dropout) + self.linear2 = nn.Linear(config.memory_attention_feed_forward_hidden_size, hidden_size) + + self.layer_norm1 = nn.LayerNorm(hidden_size) + self.layer_norm2 = nn.LayerNorm(hidden_size) + self.layer_norm3 = nn.LayerNorm(hidden_size) + self.dropout1 = nn.Dropout(config.memory_attention_dropout) + self.dropout2 = nn.Dropout(config.memory_attention_dropout) + self.dropout3 = nn.Dropout(config.memory_attention_dropout) + + self.activation = ACT2FN[config.memory_attention_feed_forward_hidden_act] + + def forward( + self, + queries: Tensor, + keys: Tensor, + key_point_embedding: Tensor, + rope_position_embeddings: tuple[Tensor, Tensor], + num_k_exclude_rope: int = 0, + ) -> torch.Tensor: + # Self-Attention + query = self.layer_norm1(queries) + query, _ = self.self_attn(query=query, key=query, value=query, position_embeddings=rope_position_embeddings) + queries = queries + self.dropout1(query) + + # Cross-Attention + query = self.layer_norm2(queries) + query, _ = self.cross_attn_image( + query=query, + key=keys + key_point_embedding, + value=keys, + position_embeddings=rope_position_embeddings, + num_k_exclude_rope=num_k_exclude_rope, + ) + queries = queries + self.dropout2(query) + # MLP + query = self.layer_norm3(queries) + query = self.linear2(self.dropout(self.activation(self.linear1(query)))) + queries = queries + self.dropout3(query) + return queries + + +class Sam3TrackerVideoMemoryAttention(nn.Module): + def __init__(self, config: Sam3TrackerVideoConfig): + super().__init__() + self.layers = nn.ModuleList( + [Sam3TrackerVideoMemoryAttentionLayer(config) for _ in range(config.memory_attention_num_layers)] + ) + self.layer_norm = nn.LayerNorm(config.memory_attention_hidden_size) + self.rotary_emb = Sam3TrackerVideoVisionRotaryEmbedding(config=config) + + def forward( + self, + current_vision_features: torch.Tensor, + memory: torch.Tensor, + current_vision_position_embeddings: Tensor | None = None, + memory_posision_embeddings: Tensor | None = None, + num_object_pointer_tokens: int = 0, + ): + """ + Args: + current_vision_features (`torch.FloatTensor`): + The current vision features used for self-attention. + memory (`torch.FloatTensor`): + The memory features used for cross-attention. + current_vision_position_embeddings (`torch.FloatTensor`, *optional*): + The position embeddings for the current vision features. + memory_posision_embeddings (`torch.FloatTensor`, *optional*): + The position embeddings for the memory features. + num_object_pointer_tokens (`int`, *optional*, defaults to 0): + The number of object pointer tokens. + """ + output = current_vision_features + if current_vision_position_embeddings is not None: + output = output + 0.1 * current_vision_position_embeddings + + # Convert to batch first + output = output.transpose(0, 1) + memory = memory.transpose(0, 1).unsqueeze(1) + memory_posision_embeddings = memory_posision_embeddings.transpose(0, 1).unsqueeze(1) + rope_position_embeddings = self.rotary_emb() + for layer in self.layers: + output = layer( + queries=output.unsqueeze(1) if output.ndim == 3 else output, + keys=memory, + key_point_embedding=memory_posision_embeddings, + rope_position_embeddings=rope_position_embeddings, + num_k_exclude_rope=num_object_pointer_tokens, + ) + + normed_output = self.layer_norm(output) + + # Convert back to seq first + normed_output = normed_output.transpose(0, 1) + + return normed_output + + +# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt) +class Sam3TrackerVideoMemoryFuserCXBlock(GradientCheckpointingLayer): + def __init__(self, config: Sam3TrackerVideoConfig): + super().__init__() + self.depthwise_conv = nn.Conv2d( + config.memory_fuser_embed_dim, + config.memory_fuser_embed_dim, + kernel_size=config.memory_fuser_kernel_size, + padding=config.memory_fuser_padding, + groups=config.memory_fuser_embed_dim, + ) # depthwise conv + self.layer_norm = Sam3TrackerVideoLayerNorm( + config.memory_fuser_embed_dim, eps=1e-6, data_format="channels_first" + ) + self.activation = ACT2FN[config.memory_fuser_hidden_act] + self.pointwise_conv1 = nn.Linear( + config.memory_fuser_embed_dim, config.memory_fuser_intermediate_dim + ) # pointwise/1x1 convs, implemented with linear layers + self.pointwise_conv2 = nn.Linear(config.memory_fuser_intermediate_dim, config.memory_fuser_embed_dim) + self.scale = nn.Parameter( + config.memory_fuser_layer_scale_init_value * torch.ones(config.memory_fuser_embed_dim), + requires_grad=True, + ) + + def forward(self, hidden_states): + input = hidden_states + hidden_states = self.depthwise_conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + hidden_states = self.pointwise_conv1(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.pointwise_conv2(hidden_states) + hidden_states = self.scale * hidden_states + hidden_states = hidden_states.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + hidden_states = input + hidden_states + return hidden_states + + +class Sam3TrackerVideoMemoryFuser(nn.Module): + def __init__(self, config: Sam3TrackerVideoConfig): + super().__init__() + self.layers = nn.ModuleList( + [Sam3TrackerVideoMemoryFuserCXBlock(config) for _ in range(config.memory_fuser_num_layers)] + ) + + def forward(self, hidden_states): + # normally hidden_states: (N, C, H, W) + for layer in self.layers: + hidden_states = layer(hidden_states) + return hidden_states + + +class Sam3TrackerVideoMaskDownSamplerLayer(nn.Module): + def __init__(self, config: Sam3TrackerVideoConfig, in_channels: int, out_channels: int): + super().__init__() + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=config.mask_downsampler_kernel_size, + stride=config.mask_downsampler_stride, + padding=config.mask_downsampler_padding, + ) + self.layer_norm = Sam3TrackerVideoLayerNorm(out_channels, eps=1e-6, data_format="channels_first") + self.activation = ACT2FN[config.mask_downsampler_hidden_act] + + def forward(self, x): + return self.activation(self.layer_norm(self.conv(x))) + + +class Sam3TrackerVideoMaskDownSampler(nn.Module): + """ + Progressively downsample a mask by total_stride, each time by stride. + Note that LayerNorm is applied per *token*, like in ViT. + + With each downsample (by a factor stride**2), channel capacity increases by the same factor. + In the end, we linearly project to embed_dim channels. + """ + + def __init__(self, config: Sam3TrackerVideoConfig): + super().__init__() + + num_layers = int(math.log2(config.mask_downsampler_total_stride) // math.log2(config.mask_downsampler_stride)) + + self.layers = nn.ModuleList() + self.activation = ACT2FN[config.mask_downsampler_hidden_act] + mask_in_chans, mask_out_chans = 1, 1 + for _ in range(num_layers): + mask_out_chans = mask_in_chans * (config.mask_downsampler_stride**2) + self.layers.append(Sam3TrackerVideoMaskDownSamplerLayer(config, mask_in_chans, mask_out_chans)) + mask_in_chans = mask_out_chans + + self.final_conv = nn.Conv2d(mask_out_chans, config.mask_downsampler_embed_dim, kernel_size=1) + + def forward(self, x): + for layer in self.layers: + x = layer(x) + x = self.final_conv(x) + return x + + +class Sam3TrackerVideoMemoryEncoder(nn.Module): + def __init__(self, config: Sam3TrackerVideoConfig): + super().__init__() + + hidden_size = config.memory_encoder_hidden_size + output_channels = config.memory_encoder_output_channels + self.mask_downsampler = Sam3TrackerVideoMaskDownSampler(config) + self.feature_projection = nn.Conv2d(hidden_size, hidden_size, kernel_size=1) + self.memory_fuser = Sam3TrackerVideoMemoryFuser(config) + self.position_encoding = Sam3TrackerVideoPositionEmbeddingSine( + num_pos_feats=output_channels // 2, normalize=True + ) + self.projection = nn.Conv2d(hidden_size, output_channels, kernel_size=1) + + def forward( + self, + vision_features: torch.Tensor, + masks: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor]: + ## Process masks + masks = self.mask_downsampler(masks) + ## Fuse pixel_features and downsampled masks + + vision_features = self.feature_projection(vision_features) + vision_features = vision_features + masks + vision_features = self.memory_fuser(vision_features) + vision_features = self.projection(vision_features) + + vision_pos_enc = self.position_encoding(vision_features.shape, vision_features.device, vision_features.dtype) + + return vision_features, vision_pos_enc + + +@dataclass +@auto_docstring(custom_intro="Base class for the vision encoder's outputs.") +class Sam3TrackerVideoVisionEncoderOutput(BaseModelOutputWithPooling): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each stage) of shape `(batch_size, height, width, hidden_size)`. Hidden-states of the + model at the output of each stage. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + fpn_hidden_states (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Feature maps from the Feature Pyramid Network neck. + fpn_position_encoding (`tuple(torch.FloatTensor)`): + Tuple of `torch.FloatTensor` (one for each feature level, from high to low resolution) of shape + `(batch_size, hidden_size, height, width)`. Positional encodings corresponding to the `fpn_hidden_states`. + """ + + fpn_hidden_states: torch.FloatTensor | None = None + fpn_position_encoding: torch.FloatTensor | None = None + + +class Sam3TrackerVideoPositionalEmbedding(nn.Module): + def __init__(self, config: Sam3TrackerVideoPromptEncoderConfig): + super().__init__() + self.scale = config.scale + positional_embedding = self.scale * torch.randn((2, config.hidden_size // 2)) + self.register_buffer("positional_embedding", positional_embedding) + + def forward(self, input_coords, input_shape=None): + """Positionally encode points that are normalized to [0,1].""" + coordinates = input_coords.clone() + + if input_shape is not None: + coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1] + coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0] + coordinates.to(torch.float32) + + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coordinates = 2 * coordinates - 1 + coordinates = coordinates.to(self.positional_embedding.dtype) + coordinates = coordinates @ self.positional_embedding + coordinates = 2 * np.pi * coordinates + # outputs d_1 x ... x d_n x channel shape + return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1) + + +class Sam3TrackerVideoMaskEmbedding(nn.Module): + def __init__(self, config: Sam3TrackerVideoPromptEncoderConfig): + super().__init__() + self.mask_input_channels = config.mask_input_channels // 4 + self.activation = ACT2FN[config.hidden_act] + self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2) + self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2) + self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1) + self.layer_norm1 = Sam3TrackerVideoLayerNorm( + self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first" + ) + self.layer_norm2 = Sam3TrackerVideoLayerNorm( + self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first" + ) + + def forward(self, masks): + hidden_states = self.conv1(masks) + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.activation(hidden_states) + dense_embeddings = self.conv3(hidden_states) + return dense_embeddings + + +class Sam3TrackerVideoPromptEncoder(nn.Module): + def __init__(self, config: Sam3TrackerVideoPromptEncoderConfig): + super().__init__() + self.shared_embedding = Sam3TrackerVideoPositionalEmbedding(config) + self.mask_embed = Sam3TrackerVideoMaskEmbedding(config) + self.no_mask_embed = nn.Embedding(1, config.hidden_size) + + self.image_embedding_size = (config.image_size // config.patch_size, config.image_size // config.patch_size) + self.mask_input_size = (4 * config.image_size // config.patch_size, 4 * config.image_size // config.patch_size) + self.input_image_size = config.image_size + + self.point_embed = nn.Embedding(config.num_point_embeddings, config.hidden_size) + self.hidden_size = config.hidden_size + self.not_a_point_embed = nn.Embedding(1, config.hidden_size) + + def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + points = torch.nn.functional.pad(points, (0, 0, 0, 1), mode="constant", value=0) + labels = torch.nn.functional.pad(labels, (0, 1), mode="constant", value=-1) + input_shape = (self.input_image_size, self.input_image_size) + point_embedding = self.shared_embedding(points, input_shape) + + # torch.where and expanding the labels tensor is required by the ONNX export + point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding) + + # This is required for the ONNX export. The dtype, device need to be explicitly + # specified as otherwise torch.onnx.export interprets as double + point_embedding = torch.where( + labels[..., None] != -10, + point_embedding, + torch.zeros_like(point_embedding), + ) + + # Add point embeddings for labels >= 0 + point_embedding = point_embedding + self.point_embed(labels.clamp(min=0)) * (labels >= 0).unsqueeze(-1) + + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.view(*boxes.shape[:2], 2, 2) + # add padding point for consistency with the original implementation + coords = torch.nn.functional.pad(coords, (0, 0, 0, 1), mode="constant", value=0) + corner_embedding = self.shared_embedding(coords, (self.input_image_size, self.input_image_size)) + corner_embedding[:, :, 0, :] += self.point_embed.weight[2] + corner_embedding[:, :, 1, :] += self.point_embed.weight[3] + corner_embedding[:, :, 2, :] = self.not_a_point_embed.weight.expand_as(corner_embedding[:, :, 2, :]) + return corner_embedding + + def forward( + self, + input_points: tuple[torch.Tensor, torch.Tensor] | None, + input_labels: torch.Tensor | None, + input_boxes: torch.Tensor | None, + input_masks: torch.Tensor | None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense embeddings. + + Args: + points (`torch.Tensor`, *optional*): + point coordinates and labels to embed. + boxes (`torch.Tensor`, *optional*): + boxes to embed + masks (`torch.Tensor`, *optional*): + masks to embed + """ + sparse_embeddings = None + batch_size = 1 + if input_points is not None: + batch_size = input_points.shape[0] + if input_labels is None: + raise ValueError("If points are provided, labels must also be provided.") + point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None)) + sparse_embeddings = point_embeddings + if input_boxes is not None: + batch_size = input_boxes.shape[0] + box_embeddings = self._embed_boxes(input_boxes) + if sparse_embeddings is None: + sparse_embeddings = box_embeddings + else: + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2) + if input_masks is not None: + dense_embeddings = self.mask_embed(input_masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings + + +class Sam3TrackerVideoTwoWayTransformer(nn.Module): + def __init__(self, config: Sam3TrackerVideoMaskDecoderConfig): + super().__init__() + self.config = config + + self.num_hidden_layers = config.num_hidden_layers + self.layers = nn.ModuleList() + + for i in range(self.num_hidden_layers): + self.layers.append(Sam3TrackerVideoTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0))) + + self.final_attn_token_to_image = Sam3TrackerVideoAttention(config) + self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size) + + def forward( + self, + point_embeddings: Tensor, + image_embeddings: Tensor, + image_positional_embeddings: Tensor, + attention_similarity: Tensor, + target_embedding=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + if image_embeddings is None: + raise ValueError("You have to specify an image_embedding") + + image_embeddings = image_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + image_positional_embeddings = image_positional_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + + # Prepare queries + queries = point_embeddings + keys = image_embeddings + + # Apply transformer blocks and final layernorm + for layer in self.layers: + if target_embedding is not None: + queries += target_embedding + + queries, keys, _ = layer( + queries=queries, + keys=keys, + query_point_embedding=point_embeddings, + key_point_embedding=image_positional_embeddings, + attention_similarity=attention_similarity, + **kwargs, + ) + # Apply the final attention layer from the points to the image + query = queries + point_embeddings + key = keys + image_positional_embeddings + + attn_out, _ = self.final_attn_token_to_image(query=query, key=key, value=keys) + + queries = queries + attn_out + queries = self.layer_norm_final_attn(queries) + return queries, keys + + +class Sam3TrackerVideoMaskDecoder(nn.Module): + def __init__(self, config: Sam3TrackerVideoMaskDecoderConfig): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + + self.num_multimask_outputs = config.num_multimask_outputs + self.num_mask_tokens = config.num_multimask_outputs + 1 + + self.iou_token = nn.Embedding(1, self.hidden_size) + self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size) + + self.transformer = Sam3TrackerVideoTwoWayTransformer(config) + + # should we create a new class for this? + self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + self.upscale_layer_norm = Sam3TrackerVideoLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.activation = nn.GELU() + + mlps_list = [] + for _ in range(self.num_mask_tokens): + mlps_list += [Sam3TrackerVideoFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)] + self.output_hypernetworks_mlps = nn.ModuleList(mlps_list) + self.iou_prediction_head = Sam3TrackerVideoFeedForward( + self.hidden_size, + config.iou_head_hidden_dim, + self.num_mask_tokens, + config.iou_head_depth, + sigmoid_output=True, + ) + + self.conv_s0 = nn.Conv2d(config.hidden_size, config.hidden_size // 8, kernel_size=1, stride=1) + self.conv_s1 = nn.Conv2d(config.hidden_size, config.hidden_size // 4, kernel_size=1, stride=1) + + self.obj_score_token = nn.Embedding(1, self.hidden_size) + self.pred_obj_score_head = Sam3TrackerVideoFeedForward(self.hidden_size, self.hidden_size, 1, 3) + + self.dynamic_multimask_via_stability = config.dynamic_multimask_via_stability + self.dynamic_multimask_stability_delta = config.dynamic_multimask_stability_delta + self.dynamic_multimask_stability_thresh = config.dynamic_multimask_stability_thresh + + def forward( + self, + image_embeddings: torch.Tensor, + image_positional_embeddings: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + high_resolution_features: list[torch.Tensor], + attention_similarity: torch.Tensor | None = None, + target_embedding: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Args: + image_embeddings (`torch.Tensor`): + The embeddings from the image encoder. + image_positional_embeddings (`torch.Tensor`): + Positional encoding with the shape of image_embeddings. + sparse_prompt_embeddings (`torch.Tensor`): + The embeddings of the points and boxes. + dense_prompt_embeddings (`torch.Tensor`): + The embeddings of the mask inputs. + multimask_output (`bool`): + Whether to return multiple masks or a single mask. + high_resolution_features (`list[torch.Tensor]`, *optional*): + The high-resolution features from the vision encoder. + attention_similarity (`torch.Tensor`, *optional*): + The attention similarity tensor. + target_embedding (`torch.Tensor`, *optional*): + The target embedding. + """ + batch_size, num_channels, height, width = image_embeddings.shape + point_batch_size = sparse_prompt_embeddings.shape[1] + # Concatenate output tokens + output_tokens = torch.cat( + [ + self.obj_score_token.weight, + self.iou_token.weight, + self.mask_tokens.weight, + ], + dim=0, + ) + output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) + + if sparse_prompt_embeddings.shape[0] != 0: + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=2) + else: + tokens = output_tokens + point_embeddings = tokens.to(self.iou_token.weight.dtype) + + # Expand per-image data in batch direction to be per-mask + image_embeddings = image_embeddings + dense_prompt_embeddings + image_embeddings = image_embeddings.repeat_interleave(point_batch_size, dim=0) + image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) + # Run the transformer + point_embeddings, image_embeddings = self.transformer( + point_embeddings=point_embeddings, + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + iou_token_out = point_embeddings[:, :, 1, :] + mask_tokens_out = point_embeddings[:, :, 2 : (2 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + image_embeddings = image_embeddings.transpose(2, 3).view( + batch_size * point_batch_size, num_channels, height, width + ) + + feat_s0, feat_s1 = high_resolution_features + feat_s0 = feat_s0.repeat_interleave(point_batch_size, dim=0) + feat_s1 = feat_s1.repeat_interleave(point_batch_size, dim=0) + upscaled_embedding = self.upscale_conv1(image_embeddings) + feat_s1 + upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) + upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding) + feat_s0) + + hyper_in_list: list[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + current_mlp = self.output_hypernetworks_mlps[i] + hyper_in_list += [current_mlp(mask_tokens_out[:, :, i, :])] + hyper_in = torch.stack(hyper_in_list, dim=2) + + _, num_channels, height, width = upscaled_embedding.shape + upscaled_embedding = upscaled_embedding.view(batch_size, point_batch_size, num_channels, height * width) + masks = (hyper_in @ upscaled_embedding).view(batch_size, point_batch_size, -1, height, width) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + object_score_logits = self.pred_obj_score_head(point_embeddings[:, :, 0, :]) + + # Select the correct mask or masks for output + if multimask_output: + mask_slice = slice(1, None) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + elif self.dynamic_multimask_via_stability and not self.training: + mask_slice = slice(0, 1) + masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred) + else: + mask_slice = slice(0, 1) + masks = masks[:, :, mask_slice, :, :] + iou_pred = iou_pred[:, :, mask_slice] + + sam_tokens_out = mask_tokens_out[:, :, mask_slice] # [b, 3, c] shape + + return masks, iou_pred, sam_tokens_out, object_score_logits + + def _get_stability_scores(self, mask_logits): + """ + Compute stability scores of the mask logits based on the IoU between upper and + lower thresholds. + """ + mask_logits = mask_logits.flatten(-2) + stability_delta = self.dynamic_multimask_stability_delta + area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() + area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() + stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) + return stability_scores + + def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): + """ + When outputting a single mask, if the stability score from the current single-mask + output (based on output token 0) falls below a threshold, we instead select from + multi-mask outputs (based on output token 1~3) the mask with the highest predicted + IoU score. This is intended to ensure a valid mask for both clicking and tracking. + """ + # The best mask from multimask output tokens (1~3) + multimask_logits = all_mask_logits[:, :, 1:, :, :] + multimask_iou_scores = all_iou_scores[:, :, 1:] + best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1) # [B, P] + best_scores_inds_expanded = best_scores_inds.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1) + best_scores_inds_expanded = best_scores_inds_expanded.expand( + -1, -1, 1, multimask_logits.size(-2), multimask_logits.size(-1) + ) + best_multimask_logits = torch.gather(multimask_logits, 2, best_scores_inds_expanded) # [B, P, 1, H, W] + best_multimask_iou_scores = torch.gather(multimask_iou_scores, 2, best_scores_inds.unsqueeze(-1)) # [B, P, 1] + + # The mask from singlemask output token 0 and its stability score + singlemask_logits = all_mask_logits[:, :, 0:1, :, :] + singlemask_iou_scores = all_iou_scores[:, :, 0:1] + stability_scores = self._get_stability_scores(singlemask_logits) + is_stable = stability_scores >= self.dynamic_multimask_stability_thresh + + # Dynamically fall back to best multimask output upon low stability scores. + mask_logits_out = torch.where( + is_stable[..., None, None].expand_as(singlemask_logits), + singlemask_logits, + best_multimask_logits, + ) + iou_scores_out = torch.where( + is_stable.expand_as(singlemask_iou_scores), + singlemask_iou_scores, + best_multimask_iou_scores, + ) + return mask_logits_out, iou_scores_out + + +# a large negative value as a placeholder score for missing objects +NO_OBJ_SCORE = -1024.0 + + +def get_1d_sine_pe(pos_inds, dim, temperature=10000): + """ + Get 1D sine positional embedding as in the original Transformer paper. + """ + pe_dim = dim // 2 + dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device) + dim_t = temperature ** (2 * (dim_t // 2) / pe_dim) + + pos_embed = pos_inds.unsqueeze(-1) / dim_t + pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1) + return pos_embed + + +@auto_docstring +class Sam3TrackerVideoModel(Sam3TrackerVideoPreTrainedModel): + input_modalities = ("video", "text") + _can_record_outputs = {"mask_decoder_attentions": OutputRecorder(Sam3TrackerVideoTwoWayAttentionBlock, index=2)} + _tied_weights_keys = {} + _keys_to_ignore_on_load_unexpected = [r"^detector_model."] + + def __init__(self, config: Sam3TrackerVideoConfig, remove_vision_encoder: bool = False): + r""" + remove_vision_encoder (`bool`, *optional*, defaults to `False`): + Whether to remove the vision encoder. If True, the vision encoder will be set to None. + """ + # loading from a sam3_video config + if hasattr(config, "tracker_config") and config.tracker_config is not None: + tracker_config = config.tracker_config + if isinstance(tracker_config, dict): + tracker_config = Sam3TrackerVideoConfig(**tracker_config) + config = tracker_config + super().__init__(config) + self.shared_image_embedding = Sam3TrackerVideoPositionalEmbedding(config.prompt_encoder_config) + self.vision_encoder = AutoModel.from_config(config.vision_config) if not remove_vision_encoder else None + self.prompt_encoder = Sam3TrackerVideoPromptEncoder(config.prompt_encoder_config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + self.mask_decoder = Sam3TrackerVideoMaskDecoder(config.mask_decoder_config) + + self.backbone_feature_sizes = config.vision_config.backbone_feature_sizes + # a single token to indicate no memory embedding from previous frames + self.hidden_dim = config.vision_config.fpn_hidden_size + self.no_memory_embedding = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + self.config = config + # For video sequence inference + self.image_size = config.image_size + self.memory_attention = Sam3TrackerVideoMemoryAttention(config) + self.memory_encoder = Sam3TrackerVideoMemoryEncoder(config) + self.no_memory_positional_encoding = torch.nn.Parameter( + torch.zeros(1, 1, config.vision_config.fpn_hidden_size) + ) + self.mem_dim = config.memory_encoder_output_channels + self.num_maskmem = config.num_maskmem # Number of memories accessible + # Temporal encoding of the memories + self.memory_temporal_positional_encoding = torch.nn.Parameter( + torch.zeros(self.num_maskmem, 1, 1, self.mem_dim) + ) + + self.no_object_pointer = torch.nn.Parameter(torch.zeros(1, self.hidden_dim)) + # A conv layer to downsample the mask prompt to stride 4 (the same stride as + # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale, + # so that it can be fed into the SAM mask decoder to generate a pointer. + self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4) + # a feedforward layer on SAM output tokens to turn them into object pointers + self.object_pointer_proj = Sam3TrackerVideoFeedForward(self.hidden_dim, self.hidden_dim, self.hidden_dim, 3) + + if self.config.enable_temporal_pos_encoding_for_object_pointers: + # a linear projection on temporal positional encoding in object pointers to + # avoid potential interference with spatial positional encoding + self.temporal_positional_encoding_projection_layer = torch.nn.Linear(self.hidden_dim, self.mem_dim) + else: + self.temporal_positional_encoding_projection_layer = torch.nn.Identity() + + self.occlusion_spatial_embedding_parameter = None # compatibility with Sam2 + if config.enable_occlusion_spatial_embedding: + self.occlusion_spatial_embedding_parameter = torch.nn.Parameter(torch.zeros(1, self.mem_dim)) + + self.post_init() + + def get_input_embeddings(self): + return self.vision_encoder.get_input_embeddings() + + def get_image_wide_positional_embeddings(self) -> torch.Tensor: + size = self.prompt_encoder.image_embedding_size + target_device = self.shared_image_embedding.positional_embedding.device + target_dtype = self.shared_image_embedding.positional_embedding.dtype + grid = torch.ones(size, device=target_device, dtype=target_dtype) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / size[0] + x_embed = x_embed / size[1] + + positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1)) + return positional_embedding.permute(2, 0, 1).unsqueeze(0) # channel x height x width + + @torch.no_grad() + def get_image_embeddings( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> list[torch.Tensor]: + r""" + Returns the image embeddings by passing the pixel values through the vision encoder. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Input pixel values + """ + batch_size = pixel_values.shape[0] + image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + return image_embeddings + + @torch.no_grad() + def get_prompt_embeddings( + self, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + r""" + Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder. + + Args: + input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`): + Optional input points for the prompt encoder. The padding of the point is automatically done by the + processor. `point_batch_size` refers to the number of masks that we want the model to predict per + point. The model will output `point_batch_size` times 3 masks in total. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points_per_image)`): + Optional input labels for the prompt encoder. The padding of the labels is automatically done by the + processor, or can be fed by the user. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes_per_image, 4)`): + Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the + processor. users can also pass manually the input boxes. + input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`): + Optional input masks for the prompt encoder. + """ + prompt_output = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + return prompt_output + + @torch.inference_mode() + @auto_docstring(custom_intro="Propagate the objects through a streamed video frame.") + def forward( + self, + inference_session: Sam3TrackerVideoInferenceSession, + frame_idx: int | None = None, + frame: torch.Tensor | None = None, + reverse: bool = False, + run_mem_encoder: bool = True, + **kwargs, + ) -> Sam3TrackerVideoSegmentationOutput: + r""" + inference_session (`Sam3TrackerVideoInferenceSession`): + The video inference session object. + frame_idx (`int`, *optional*): + The index of the frame on which to run inference. No need to provide when inferring + on a new streamed frame. + frame (`torch.Tensor`, *optional*): + The frame to process. Provide when streaming. + reverse (`bool`, *optional*, defaults to `False`): + Whether to propagate in reverse. + run_mem_encoder (`bool`, *optional*, defaults to `True`): + Whether to run the memory encoder on predicted masks. The memory encoder is batched across all objects for efficiency. + """ + if frame is not None: + frame_idx = inference_session.add_new_frame(frame, frame_idx) + + if frame is not None and inference_session.get_obj_num() == 0: + raise ValueError("No objects are provided for tracking; please add inputs first.") + + num_objects = inference_session.get_obj_num() + pred_masks_per_obj = [None] * num_objects + object_score_logits_per_obj = [None] * num_objects + + # Collect data for batched memory encoding + objects_needing_memory_encoding = [] + high_res_masks_for_memory = [] + object_score_logits_for_memory = [] + is_mask_from_pts_per_obj = [] + + # Note: We avoid batched inference here because per-object inputs (clicks/masks) + # can differ across objects. + for obj_idx in range(num_objects): + obj_id = inference_session.obj_idx_to_id(obj_idx) + has_new_inputs = obj_id in inference_session.obj_with_new_inputs + has_cond_output = frame_idx in inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + # If this object has no new inputs and this frame already has a + # conditioning output, reuse the cached masks instead of recomputing. + if (not has_new_inputs) and has_cond_output: + pred_masks = inference_session.get_output(obj_idx, frame_idx, "pred_masks", is_conditioning_frame=True) + object_score_logits = inference_session.get_output( + obj_idx, frame_idx, "object_score_logits", is_conditioning_frame=True + ) + is_init_cond_frame = True + else: + # Defaults when there are no new inputs + is_init_cond_frame = False + point_inputs = None + mask_inputs = None + + if has_new_inputs: + is_init_cond_frame = frame_idx not in inference_session.frames_tracked_per_obj[obj_idx] + if is_init_cond_frame: + reverse = False + point_inputs = inference_session.point_inputs_per_obj[obj_idx].get(frame_idx, None) + mask_inputs = inference_session.mask_inputs_per_obj[obj_idx].get(frame_idx, None) + if point_inputs is not None or mask_inputs is not None: + inference_session.obj_with_new_inputs.remove(obj_id) + + current_out = self._run_single_frame_inference( + inference_session=inference_session, + obj_idx=obj_idx, + frame_idx=frame_idx, + batch_size=1, # run on the slice of a single object + is_init_cond_frame=is_init_cond_frame, + point_inputs=point_inputs, + mask_inputs=mask_inputs, + reverse=reverse, + streaming=frame is not None, + ) + inference_session.store_output( + obj_idx, frame_idx, output_value=current_out, is_conditioning_frame=is_init_cond_frame + ) + pred_masks = current_out["pred_masks"] + object_score_logits = current_out["object_score_logits"] + + # Collect data for batched memory encoding + if run_mem_encoder and self.num_maskmem > 0: + objects_needing_memory_encoding.append(obj_idx) + high_res_masks_for_memory.append(current_out["high_res_masks"]) + object_score_logits_for_memory.append(object_score_logits) + is_mask_from_pts_per_obj.append(point_inputs is not None or mask_inputs is not None) + + pred_masks_per_obj[obj_idx] = pred_masks + object_score_logits_per_obj[obj_idx] = object_score_logits.squeeze(-1) + if not is_init_cond_frame: + # only for tracked frames, not for initial conditioning frames + inference_session.frames_tracked_per_obj[obj_idx][frame_idx] = {"reverse": reverse} + + # Batch encode memories for all objects at once + self._batch_encode_memories( + inference_session=inference_session, + frame_idx=frame_idx, + objects_needing_memory_encoding=objects_needing_memory_encoding, + high_res_masks_for_memory=high_res_masks_for_memory, + object_score_logits_for_memory=object_score_logits_for_memory, + is_mask_from_pts_per_obj=is_mask_from_pts_per_obj, + ) + + # Resize the output mask to the original video resolution (we directly use + # the mask scores on GPU for output to avoid any CPU conversion in between) + if len(pred_masks_per_obj) > 1: + all_pred_masks = torch.cat(pred_masks_per_obj, dim=0) + all_object_score_logits = torch.cat(object_score_logits_per_obj, dim=0) + else: + all_pred_masks = pred_masks_per_obj[0] + all_object_score_logits = object_score_logits_per_obj[0] + + return Sam3TrackerVideoSegmentationOutput( + object_ids=inference_session.obj_ids.copy(), + pred_masks=all_pred_masks, + object_score_logits=all_object_score_logits, + frame_idx=frame_idx, + ) + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3TrackerVideoVisionEncoderOutput: + r""" + pixel_values (`torch.FloatTensor`): + Input pixel values of shape `(batch_size, num_channels, height, width)`. + """ + vision_outputs: Sam3TrackerVideoVisionEncoderOutput = self.vision_encoder( + pixel_values, return_dict=True, **kwargs + ) + + feature_maps = vision_outputs.fpn_hidden_states + feature_maps_position_embeddings = vision_outputs.fpn_position_encoding + + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + feature_maps = list(feature_maps[:-1]) + feature_maps[0] = self.mask_decoder.conv_s0(feature_maps[0]) + feature_maps[1] = self.mask_decoder.conv_s1(feature_maps[1]) + + # flatten NxCxHxW to HWxNxC + feature_maps = [feature_map.flatten(2).permute(2, 0, 1) for feature_map in feature_maps] + feature_maps_position_embeddings = [ + feature_map_position_embedding.flatten(2).permute(2, 0, 1) + for feature_map_position_embedding in feature_maps_position_embeddings[:-1] + ] + vision_outputs.fpn_hidden_states = feature_maps + vision_outputs.fpn_position_encoding = feature_maps_position_embeddings + + return vision_outputs + + def _prepare_vision_features( + self, + inference_session: Sam3TrackerVideoInferenceSession, + frame_idx: int, + batch_size: int, + ) -> tuple[torch.Tensor, list[torch.Tensor]]: + """Prepare vision features for a frame.""" + + # Check if features are cached + if cached_features := inference_session.cache.get_vision_features(frame_idx): + vision_feats = cached_features["vision_feats"] + vision_pos_embeds = cached_features["vision_pos_embeds"] + else: + # Compute features using image encoder + image_batch = inference_session.get_frame(frame_idx).unsqueeze(0) # Add batch dimension + image_outputs = self.get_image_features(image_batch, return_dict=True) + vision_feats = image_outputs.fpn_hidden_states + vision_pos_embeds = image_outputs.fpn_position_encoding + # Cache features + inference_session.cache.cache_vision_features( + frame_idx, {"vision_feats": vision_feats, "vision_pos_embeds": vision_pos_embeds} + ) + + # Expand to batch size if needed + if batch_size > 1: + vision_feats = vision_feats.expand(batch_size, -1, -1, -1) + vision_pos_embeds = [pe.expand(batch_size, -1, -1, -1) for pe in vision_pos_embeds] + + return vision_feats, vision_pos_embeds + + def _single_frame_forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Sam3TrackerVideoImageSegmentationOutput: + """ + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decoder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + """ + if not ((pixel_values is None) ^ (image_embeddings is None)): + raise ValueError("Exactly one of pixel_values or image_embeddings must be provided.") + if input_points is not None and input_boxes is not None: + if input_points.shape[1] != input_boxes.shape[1]: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {input_points.shape[1]} and {input_boxes.shape[1]}." + ) + elif input_points is not None: + num_objects = input_points.shape[1] + elif input_boxes is not None: + num_objects = input_boxes.shape[1] + elif input_masks is not None: + num_objects = input_masks.shape[1] + else: + num_objects = 1 + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings[-1].shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + vision_attentions = None + vision_hidden_states = None + + if pixel_values is not None: + image_outputs = self.get_image_features(pixel_values, return_dict=True, **kwargs) + feature_maps = image_outputs.fpn_hidden_states + vision_hidden_states = image_outputs.hidden_states + vision_attentions = image_outputs.attentions + + # add no memory embedding to the last feature map + feature_maps[-1] = feature_maps[-1] + self.no_memory_embedding + + # reshape feature maps to the same shape as the backbone feature sizes + image_embeddings = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(feature_maps, self.backbone_feature_sizes) + ] + + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + if input_points is None and input_boxes is None: + # If no points are provide, pad with an empty point (with label -1) + input_points = torch.zeros( + batch_size, 1, 1, 2, dtype=image_embeddings[-1].dtype, device=image_embeddings[-1].device + ) + input_labels = -torch.ones(batch_size, 1, 1, dtype=torch.int32, device=image_embeddings[-1].device) + + if input_masks is not None: + # If mask_inputs is provided, downsize it into low-res mask input if needed + # and feed it as a dense mask prompt into the SAM mask encoder + if input_masks.shape[-2:] != self.prompt_encoder.mask_input_size: + input_masks = F.interpolate( + input_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(input_masks.dtype) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + low_res_multimasks, iou_scores, sam_output_tokens, object_score_logits = self.mask_decoder( + image_embeddings=image_embeddings[-1], + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + high_resolution_features=image_embeddings[:-1], + attention_similarity=attention_similarity, + target_embedding=target_embedding, + **kwargs, + ) + + is_obj_appearing = object_score_logits > 0 + # Mask used for spatial memories is always a *hard* choice between obj and no obj, + # consistent with the actual mask prediction + low_res_multimasks = torch.where( + is_obj_appearing[:, None, None], + low_res_multimasks, + NO_OBJ_SCORE, + ) + + # convert masks from possibly bfloat16 (or float16) to float32 + # (older PyTorch versions before 2.1 don't support `interpolate` on bf16) + high_res_multimasks = ( + F.interpolate( + low_res_multimasks.squeeze(1).float(), + size=(self.image_size, self.image_size), + mode="bilinear", + align_corners=False, + ) + .unsqueeze(1) + .to(low_res_multimasks.dtype) + ) + sam_output_token = sam_output_tokens[:, :, 0] + if multimask_output: + # take the best mask prediction (with the highest IoU estimation) + best_iou_inds = torch.argmax(iou_scores, dim=-1) + batch_inds = torch.arange(batch_size, device=high_res_multimasks.device) + object_batch_inds = torch.arange(num_objects, device=high_res_multimasks.device) + low_res_masks = low_res_multimasks[batch_inds, object_batch_inds, best_iou_inds] + high_res_masks = high_res_multimasks[batch_inds, object_batch_inds, best_iou_inds] + if sam_output_tokens.size(2) > 1: + sam_output_token = sam_output_tokens[batch_inds, object_batch_inds, best_iou_inds] + else: + low_res_masks, high_res_masks = low_res_multimasks[:, :, 0], high_res_multimasks[:, :, 0] + + # Extract object pointer from the SAM output token (with occlusion handling) + object_pointer = self.object_pointer_proj(sam_output_token) + lambda_is_obj_appearing = is_obj_appearing.to(object_pointer.dtype) + + object_pointer = lambda_is_obj_appearing * object_pointer + object_pointer = object_pointer + (1 - lambda_is_obj_appearing) * self.no_object_pointer + + return Sam3TrackerVideoImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_masks, + high_res_masks=high_res_masks, + object_pointer=object_pointer, + object_score_logits=object_score_logits, + image_embeddings=image_embeddings, + vision_hidden_states=vision_hidden_states, + vision_attentions=vision_attentions, + ) + + def _use_mask_as_output( + self, + backbone_features: torch.Tensor, + high_res_features: list[torch.Tensor], + mask_inputs: torch.Tensor, + ) -> Sam3TrackerVideoImageSegmentationOutput: + """ + Directly turn binary `mask_inputs` into a output mask logits without using SAM. + (same input and output shapes as in forward above). + """ + # Use -10/+20 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid). + out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05 + mask_inputs_float = mask_inputs.to(backbone_features[0].dtype) + + # Ensure mask is at self.image_size resolution for consistency + if mask_inputs_float.shape[-2:] != (self.image_size, self.image_size): + mask_inputs_float = F.interpolate( + mask_inputs_float.float(), + size=(self.image_size, self.image_size), + align_corners=False, + mode="bilinear", + antialias=True, + ).to(mask_inputs.dtype) + + high_res_masks = mask_inputs_float * out_scale + out_bias + low_res_masks = F.interpolate( + high_res_masks.float(), + size=self.prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(backbone_features[0].dtype) + # a dummy IoU prediction of all 1's under mask input + iou_scores = mask_inputs.new_ones(mask_inputs.size(0), 1).to(backbone_features[0].dtype) + # produce an object pointer using the SAM decoder from the mask input + object_pointer = self._single_frame_forward( + input_masks=self.mask_downsample(mask_inputs_float.to(backbone_features[0].dtype)), + image_embeddings=high_res_features + [backbone_features], + ).object_pointer + # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem; + # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying + # on the object_scores from the SAM decoder. + is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1) + is_obj_appearing = is_obj_appearing[..., None] + lambda_is_obj_appearing = is_obj_appearing.to(backbone_features[0].dtype) + object_score_logits = out_scale * lambda_is_obj_appearing + out_bias + object_pointer = lambda_is_obj_appearing * object_pointer + object_pointer = object_pointer + (1 - lambda_is_obj_appearing) * self.no_object_pointer + return Sam3TrackerVideoImageSegmentationOutput( + iou_scores=iou_scores, + pred_masks=low_res_masks, + high_res_masks=high_res_masks, + object_pointer=object_pointer, + object_score_logits=object_score_logits.unsqueeze(-1), + image_embeddings=high_res_features + [backbone_features], + ) + + def _select_closest_cond_frames(self, frame_idx, cond_frame_outputs, max_cond_frame_num): + """ + Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs` + that are temporally closest to the current frame at `frame_idx`. Here, we take + - a) the closest conditioning frame before `frame_idx` (if any); + - b) the closest conditioning frame after `frame_idx` (if any); + - c) any other temporally closest conditioning frames until reaching a total + of `max_cond_frame_num` conditioning frames. + + Outputs: + - selected_outputs: selected items (keys & values) from `cond_frame_outputs`. + - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`. + """ + if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num: + selected_outputs = cond_frame_outputs + unselected_outputs = {} + else: + selected_outputs = {} + # the closest conditioning frame before `frame_idx` (if any) + idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None) + if idx_before is not None: + selected_outputs[idx_before] = cond_frame_outputs[idx_before] + + # the closest conditioning frame after `frame_idx` (if any) + idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None) + if idx_after is not None: + selected_outputs[idx_after] = cond_frame_outputs[idx_after] + + # add other temporally closest conditioning frames until reaching a total + # of `max_cond_frame_num` conditioning frames. + num_remain = max_cond_frame_num - len(selected_outputs) + inds_remain = sorted( + (t for t in cond_frame_outputs if t not in selected_outputs), + key=lambda x: abs(x - frame_idx), + )[:num_remain] + selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain) + unselected_outputs = {t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs} + + return selected_outputs, unselected_outputs + + def _gather_memory_frame_outputs( + self, + inference_session: Sam3TrackerVideoInferenceSession, + obj_idx: int, + frame_idx: int, + track_in_reverse_time: bool = False, + ) -> list[tuple[int, dict]]: + """ + Get memory frames from conditioning and non-conditioning outputs. + + Returns: + List of (relative_temporal_offset, output_data) tuples. + """ + temporal_positions_and_previous_outputs = [] + + # Add conditioning frame outputs (limited by max_cond_frame_num) + conditioning_outputs = inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + if not conditioning_outputs: + raise ValueError( + "maskmem_features in conditioning outputs cannot be empty when not is_initial_conditioning_frame" + ) + conditioning_outputs, unselected_conditioning_outputs = self._select_closest_cond_frames( + frame_idx, conditioning_outputs, max_cond_frame_num=self.config.max_cond_frame_num + ) + + # Store (temporal_position, output_data) tuples + temporal_positions_and_previous_outputs = [(0, out) for out in conditioning_outputs.values()] + + # Add non-conditioning memory frames (up to self.num_maskmem - 1) + # These are typically frames tracked by the model without direct user input. + # Frames are selected with a stride, prioritizing the most recent ones. Here we only support stride = 1 for simplicity. + for relative_temporal_offset in range(self.num_maskmem - 1, 0, -1): + # relative_temporal_offset: how many frames before (or after if reversing) the current frame + if not track_in_reverse_time: + previous_frame_idx = frame_idx - relative_temporal_offset + else: + previous_frame_idx = frame_idx + relative_temporal_offset + + # check if the output is already stored without using get_output to avoid unnecessary memory transfers between CPU and GPU + output_data = inference_session.output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].get( + previous_frame_idx, unselected_conditioning_outputs.get(previous_frame_idx, None) + ) + + temporal_positions_and_previous_outputs.append((relative_temporal_offset, output_data)) + + return temporal_positions_and_previous_outputs + + def _build_memory_attention_inputs( + self, + temporal_positions_and_previous_outputs: list[tuple[int, dict]], + device: torch.device, + ) -> tuple[list[torch.Tensor], list[torch.Tensor]]: + """ + Concatenate memory features and positional embeddings from previous frames. + + Returns: + Tuple of (memories_to_concatenate, memory_positional_embeddings_to_concatenate). + """ + memories_to_concatenate = [] + memory_positional_embeddings_to_concatenate = [] + + for relative_temporal_offset, prev_output_data in temporal_positions_and_previous_outputs: + if prev_output_data is None: + continue # Skip if no output data for this temporal position (e.g., padding frames) + + # Load memory features (potentially from CPU to GPU) + # Features are flattened: (Batch, Channels, H, W) -> (H*W, Batch, Channels) + memory_features = prev_output_data["maskmem_features"].to(device, non_blocking=True) + memories_to_concatenate.append(memory_features) + + # Spatial positional encoding (potentially from CPU to GPU) + spatial_memory_pos_embed = prev_output_data["maskmem_pos_enc"].to(device, non_blocking=True) + + # Add temporal positional encoding + # self.memory_temporal_positional_encoding shape: (NumMaskMem, 1, 1, MemDim) + combined_memory_pos_embed = ( + spatial_memory_pos_embed + self.memory_temporal_positional_encoding[relative_temporal_offset - 1] + ) + memory_positional_embeddings_to_concatenate.append(combined_memory_pos_embed) + + return memories_to_concatenate, memory_positional_embeddings_to_concatenate + + def _get_object_pointers( + self, + inference_session: Sam3TrackerVideoInferenceSession, + obj_idx: int, + frame_idx: int, + num_total_frames: int, + device: torch.device, + track_in_reverse_time: bool = False, + streaming: bool = False, + ) -> tuple[list[int], list[torch.Tensor], int]: + """ + Get object pointers and their positional embeddings from past frames. + + Returns: + Tuple of (temporal_offsets, pointer_tokens, max_object_pointers_to_use). + """ + temporal_position_sign_multiplier = -1 if track_in_reverse_time else 1 + + # Determine max object pointers to use + if streaming: + max_object_pointers_to_use = self.config.max_object_pointers_in_encoder + else: + max_object_pointers_to_use = min(num_total_frames, self.config.max_object_pointers_in_encoder) + + temporal_offsets: list[int] = [] + pointer_tokens: list[torch.Tensor] = [] + + # Add object pointers from selected conditioning frames + # Optionally, only include pointers from past frames during evaluation + conditioning_outputs = inference_session.output_dict_per_obj[obj_idx]["cond_frame_outputs"] + eligible_conditioning_outputs = conditioning_outputs + if not self.training: + eligible_conditioning_outputs = { + temporal_idx: out + for temporal_idx, out in conditioning_outputs.items() + if (temporal_idx >= frame_idx if track_in_reverse_time else temporal_idx <= frame_idx) + } + + for temporal_idx, out_data in eligible_conditioning_outputs.items(): + temporal_difference = (frame_idx - temporal_idx) * temporal_position_sign_multiplier + temporal_offsets.append(temporal_difference) + pointer_tokens.append(out_data["object_pointer"].to(device)) + + # Add object pointers from non-conditioning frames (up to max_object_pointers_to_use - 1) + for t_diff_offset in range(1, max_object_pointers_to_use): + ref_frame_idx = frame_idx + t_diff_offset if track_in_reverse_time else frame_idx - t_diff_offset + if ref_frame_idx < 0 or ( + not streaming and num_total_frames is not None and ref_frame_idx >= num_total_frames + ): + break # Stop if frame index is out of bounds + + # check if the output is already stored without using get_output to avoid unnecessary memory transfers between CPU and GPU + out_data = inference_session.output_dict_per_obj[obj_idx]["non_cond_frame_outputs"].get( + ref_frame_idx, None + ) + if out_data is not None: + temporal_offsets.append(t_diff_offset) + pointer_tokens.append(out_data["object_pointer"].to(device)) + + return temporal_offsets, pointer_tokens, max_object_pointers_to_use + + def _process_object_pointers( + self, + temporal_offsets: list[int], + pointer_tokens: list[torch.Tensor], + max_object_pointers_to_use: int, + batch_size: int, + num_channels: int, + device: torch.device, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Process object pointers and compute their positional embeddings. + + Returns: + Tuple of (object_pointers, object_pointers_pos_embed). + """ + if not pointer_tokens: + return None, None + + # Stack object pointers: List of (Batch, Channels) -> (SeqLen_ptr, Batch, Channels) + object_pointers = torch.stack(pointer_tokens, dim=0) + + if self.config.enable_temporal_pos_encoding_for_object_pointers: + max_temporal_diff = float(max_object_pointers_to_use - 1) + # Determine dimensionality for temporal positional encoding of pointers + pointer_tpos_dim = num_channels + + # Normalize temporal differences before sine PE calculation + normalized_temporal_diffs = ( + torch.tensor(temporal_offsets, device=device, dtype=torch.float32) / max_temporal_diff + ) + sine_pe = get_1d_sine_pe(normalized_temporal_diffs, dim=pointer_tpos_dim).to(object_pointers.dtype) + projected_sine_pe = self.temporal_positional_encoding_projection_layer(sine_pe) + object_pointers_pos_embed = projected_sine_pe.unsqueeze(1).expand(-1, batch_size, self.mem_dim) + else: + object_pointers_pos_embed = object_pointers.new_zeros( + len(temporal_offsets), batch_size, self.mem_dim, dtype=object_pointers.dtype + ) + + if self.mem_dim < num_channels: + # If memory dimension is smaller, reshape/split pointers and repeat positional encoding + num_splits = num_channels // self.mem_dim + object_pointers = object_pointers.reshape(-1, batch_size, num_splits, self.mem_dim) + object_pointers = object_pointers.permute(0, 2, 1, 3).flatten( + 0, 1 + ) # (SeqLen_ptr*num_splits, Batch, MemDim) + object_pointers_pos_embed = object_pointers_pos_embed.repeat_interleave(num_splits, dim=0) + + return object_pointers, object_pointers_pos_embed + + def _prepare_memory_conditioned_features( + self, + inference_session: Sam3TrackerVideoInferenceSession, + frame_idx: int, + obj_idx: int, + is_initial_conditioning_frame: bool, + current_vision_features: list[torch.Tensor], + current_vision_positional_embeddings: list[torch.Tensor], + num_total_frames: int, + track_in_reverse_time: bool = False, + streaming: bool = False, + ) -> torch.Tensor: + """ + Fuse current frame's visual features with memory from previous frames for enhanced object tracking. + + This method conditions the current frame's visual features on temporal memory from previous frames, + enabling consistent object tracking across video sequences. For initial conditioning frames, it uses + no-memory embeddings. For subsequent frames, it retrieves and integrates memory features from both + conditioning frames (user interactions) and non-conditioning frames (tracked results) via cross-attention. + + Args: + inference_session (`Sam3TrackerVideoInferenceSession`): + The video inference session object. + frame_idx (`int`): + Index of the current frame being processed. + obj_idx (`int`): + Index of the object being processed. + is_initial_conditioning_frame (`bool`): + Whether this is an initial conditioning frame with user inputs (True) or a subsequent + tracking frame (False). + current_vision_features (`torch.Tensor`): + Highest-level vision features of shape `(seq_len, batch_size, channels)`. + current_vision_positional_embeddings (`torch.Tensor`): + Positional embedding tensors corresponding to the highest-level vision features. + num_total_frames (`int`): + Total number of frames in the video sequence. + track_in_reverse_time (`bool`, *optional*, defaults to `False`): + Whether tracking is performed in reverse temporal order. + streaming (`bool`, *optional*, defaults to `False`): + Whether this is streaming inference mode. + + Returns: + `torch.Tensor`: Memory-conditioned feature tensor of shape `(batch_size, channels, height, width)` + suitable for input to the SAM decoder. + """ + # Get dimensions from the highest-level (lowest-resolution) feature map + batch_size = current_vision_features.size(1) + num_channels = self.hidden_dim + height, width = self.backbone_feature_sizes[-1] + device = current_vision_features.device + + # If memory is disabled (e.g., for single image SAM), return current features directly. + if self.num_maskmem == 0: + # Permute (SeqLen, Batch, Channels) -> (Batch, Channels, SeqLen) then view as (Batch, Channels, Height, Width) + # Assuming SeqLen = Height * Width for the last feature map + current_feature_map = current_vision_features.permute(1, 2, 0).view( + batch_size, num_channels, height, width + ) + return current_feature_map + + # Step 1: Handle initial conditioning frames + if is_initial_conditioning_frame: + # For initial conditioning frames, no prior memory is used directly in this block. + # If configured, directly add a learnable "no memory" embedding. + # current_vision_features has shape (SeqLen, Batch, Channels) + conditioned_feature_map_flat = current_vision_features + self.no_memory_embedding + # Reshape to (Batch, Channels, Height, Width) + conditioned_feature_map = conditioned_feature_map_flat.permute(1, 2, 0).view( + batch_size, num_channels, height, width + ) + return conditioned_feature_map + + # Step 2: Get memory frames and concatenate their features + temporal_positions_and_previous_outputs = self._gather_memory_frame_outputs( + inference_session, obj_idx, frame_idx, track_in_reverse_time + ) + + memories_to_concatenate, memory_positional_embeddings_to_concatenate = self._build_memory_attention_inputs( + temporal_positions_and_previous_outputs, device + ) + + # Step 3: Get and process object pointers + temporal_offsets, pointer_tokens, max_object_pointers_to_use = self._get_object_pointers( + inference_session, obj_idx, frame_idx, num_total_frames, device, track_in_reverse_time, streaming + ) + + num_object_pointer_tokens = 0 + if pointer_tokens: + object_pointers, object_pointers_pos_embed = self._process_object_pointers( + temporal_offsets, pointer_tokens, max_object_pointers_to_use, batch_size, num_channels, device + ) + + if object_pointers is not None: + memories_to_concatenate.append(object_pointers) + memory_positional_embeddings_to_concatenate.append(object_pointers_pos_embed) + num_object_pointer_tokens = object_pointers.shape[0] + + # Step 4: Concatenate all retrieved memories and their positional embeddings + combined_memory = torch.cat(memories_to_concatenate, dim=0).to(dtype=inference_session.dtype) + combined_memory_positional_embeddings = torch.cat(memory_positional_embeddings_to_concatenate, dim=0) + + # Step 5: Forward through the memory attention mechanism + conditioned_feature_map_flat = self.memory_attention( + current_vision_features=current_vision_features, + current_vision_position_embeddings=current_vision_positional_embeddings, + memory=combined_memory, + memory_posision_embeddings=combined_memory_positional_embeddings, # Corrected typo from API + num_object_pointer_tokens=num_object_pointer_tokens, + ) + + # Reshape from (Batch, H*W, Channels) to (Batch, Channels, Height, Width) + conditioned_feature_map = ( + conditioned_feature_map_flat.squeeze(1).permute(0, 2, 1).view(batch_size, num_channels, height, width) + ) + return conditioned_feature_map + + def _use_multimask(self, is_init_cond_frame: bool, point_inputs: dict | None) -> bool: + """Whether to use multimask output in the SAM head.""" + num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(2) + multimask_output = ( + self.config.multimask_output_in_sam + and (is_init_cond_frame or self.config.multimask_output_for_tracking) + and (self.config.multimask_min_pt_num <= num_pts <= self.config.multimask_max_pt_num) + ) + return multimask_output + + def _run_single_frame_inference( + self, + inference_session: Sam3TrackerVideoInferenceSession, + frame_idx: int, + obj_idx: int, + batch_size: int, + is_init_cond_frame: bool, + point_inputs: torch.Tensor | None, + mask_inputs: torch.Tensor | None, + reverse: bool, + prev_sam_mask_logits: torch.Tensor | None = None, + streaming: bool = False, + ) -> dict[str, Any]: + """ + Perform a single tracking step for video object segmentation. + + Args: + inference_session (`Sam3TrackerVideoInferenceSession`): + The video inference session object. + frame_idx (`int`): + Index of the current frame. + obj_idx (`int`): + Index of the current object. + batch_size (`int`): + Batch size of the current frame. + is_init_cond_frame (`bool`): + Whether this is an initial conditioning frame with user inputs. + point_inputs (`dict`, *optional*): + Point prompt inputs for the current frame. + mask_inputs (`torch.Tensor`, *optional*): + Mask prompt inputs for the current frame. + reverse (`bool`, *optional*, defaults to `False`): + Whether to track in reverse time order. + prev_sam_mask_logits (`torch.Tensor`, *optional*): + Previously predicted SAM mask logits that can be fed with new clicks. + streaming (`bool`, *optional*, defaults to `False`): + Whether this is streaming inference. + + Returns: + `dict`: Dictionary containing the tracking results for the current frame, including: + - pred_masks: Predicted low-resolution masks. + - object_pointer: Object pointer for memory. + - high_res_masks: High-resolution masks for batched memory encoding. + - object_score_logits: Object score logits (inference only). + """ + # Retrieve correct image features + current_vision_feats, current_vision_pos_embeds = self._prepare_vision_features( + inference_session, frame_idx, batch_size + ) + # point and mask should not appear as input simultaneously on the same frame + if point_inputs is not None and mask_inputs is not None: + raise ValueError( + "point_inputs and mask_inputs should not appear as input simultaneously on the same frame" + ) + # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW + if len(current_vision_feats) > 1: + high_res_features = [ + x.permute(1, 2, 0).view(x.size(1), x.size(2), *s) + for x, s in zip(current_vision_feats[:-1], self.backbone_feature_sizes[:-1]) + ] + else: + high_res_features = None + if mask_inputs is not None: + # We directly output the mask input (see it as a GT mask) without using a SAM prompt encoder + mask decoder. + pix_feat = current_vision_feats[-1].permute(1, 2, 0) + pix_feat = pix_feat.view(-1, self.hidden_dim, *self.backbone_feature_sizes[-1]) + sam_outputs = self._use_mask_as_output(pix_feat, high_res_features, mask_inputs) + else: + # fused the visual feature with previous memory features in the memory bank + pix_feat = self._prepare_memory_conditioned_features( + inference_session=inference_session, + frame_idx=frame_idx, + obj_idx=obj_idx, + is_initial_conditioning_frame=is_init_cond_frame, + current_vision_features=current_vision_feats[-1], + current_vision_positional_embeddings=current_vision_pos_embeds[-1], + num_total_frames=inference_session.num_frames, + track_in_reverse_time=reverse, + streaming=streaming, + ) + # apply SAM-style segmentation head + # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder, + # e.g. in demo where such logits come from earlier interaction instead of correction sampling + # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead) + if prev_sam_mask_logits is not None: + mask_inputs = prev_sam_mask_logits + multimask_output = self._use_multimask(is_init_cond_frame, point_inputs) + sam_outputs = self._single_frame_forward( + pixel_values=None, # Vision features already computed + input_points=point_inputs["point_coords"] if point_inputs is not None else None, + input_labels=point_inputs["point_labels"] if point_inputs is not None else None, + input_masks=mask_inputs, + image_embeddings=high_res_features + [pix_feat], + multimask_output=multimask_output, + ) + + # Memory encoding is now handled in batch by the caller (forward method) + current_out = { + "pred_masks": sam_outputs.pred_masks, + "object_pointer": sam_outputs.object_pointer, + "high_res_masks": sam_outputs.high_res_masks, # Needed for batched memory encoding + } + if not self.training: + current_out["object_score_logits"] = sam_outputs.object_score_logits + + return current_out + + def _encode_new_memory( + self, + current_vision_feats: torch.Tensor, + pred_masks_high_res: torch.Tensor, + object_score_logits: torch.Tensor, + is_mask_from_pts: bool, + ) -> tuple[torch.Tensor, list[torch.Tensor]]: + """Encode the current image and its prediction into a memory feature.""" + batch_size = current_vision_feats.size(1) # batch size on this frame + channels = self.hidden_dim + height, width = self.backbone_feature_sizes[-1] # top-level (lowest-resolution) feature size + + mask_input_size_h, mask_input_size_w = self.prompt_encoder.mask_input_size + mask_mem_size_h = mask_input_size_h * 4 + mask_mem_size_w = mask_input_size_w * 4 + if pred_masks_high_res.shape[2:] != (mask_mem_size_h, mask_mem_size_w): + # downsample the predicted high-res masks into the mask encoder input size + pred_masks_high_res = F.interpolate( + pred_masks_high_res.float(), + size=(mask_mem_size_h, mask_mem_size_w), + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ).to(pred_masks_high_res.dtype) + + # top-level feature, (HW)BC => BCHW + pix_feat = current_vision_feats.permute(1, 2, 0).view(batch_size, channels, height, width) + if is_mask_from_pts and not self.training: + # binarize the mask logits + mask_for_mem = (pred_masks_high_res > 0).to(pred_masks_high_res.dtype) + else: + # apply sigmoid on the raw mask logits to turn them into range (0, 1) + mask_for_mem = torch.sigmoid(pred_masks_high_res) + # apply scale and bias terms to the sigmoid probabilities + mask_for_mem = mask_for_mem * self.config.sigmoid_scale_for_mem_enc + mask_for_mem = mask_for_mem + self.config.sigmoid_bias_for_mem_enc + + maskmem_features, maskmem_pos_enc = self.memory_encoder( + pix_feat, + mask_for_mem, + ) + # add a no-object embedding to the spatial memory to indicate that the frame + # is predicted to be occluded (i.e. no object is appearing in the frame) + if self.occlusion_spatial_embedding_parameter is not None: + is_obj_appearing = (object_score_logits > 0).float() + maskmem_features += (1 - is_obj_appearing[..., None]) * self.occlusion_spatial_embedding_parameter[ + ..., None, None + ].expand(*maskmem_features.shape) + + # convert to bfloat16 to save memory, and for consistency with the original implementation + maskmem_features = maskmem_features.to(torch.bfloat16).flatten(2).permute(2, 0, 1) + maskmem_pos_enc = maskmem_pos_enc.to(pred_masks_high_res.dtype).flatten(2).permute(2, 0, 1) + + return maskmem_features, maskmem_pos_enc + + def _batch_encode_memories( + self, + inference_session: Sam3TrackerVideoInferenceSession, + frame_idx: int, + objects_needing_memory_encoding: list[int], + high_res_masks_for_memory: list[torch.Tensor], + object_score_logits_for_memory: list[torch.Tensor], + is_mask_from_pts_per_obj: list[bool], + ): + """ + Batch encode memories for multiple objects at once. + + Args: + inference_session: The video inference session object + frame_idx: Index of the current frame + objects_needing_memory_encoding: List of object indices that need memory encoding + high_res_masks_for_memory: List of high-resolution masks for each object + object_score_logits_for_memory: List of object score logits for each object + is_mask_from_pts_per_obj: List of booleans indicating if mask is from points for each object + """ + if not objects_needing_memory_encoding: + return + + # Get vision features once for all objects + current_vision_feats, _ = self._prepare_vision_features(inference_session, frame_idx, batch_size=1) + + # Stack all high-res masks and object scores + high_res_masks_batched = torch.cat(high_res_masks_for_memory, dim=0) + object_score_logits_batched = torch.cat(object_score_logits_for_memory, dim=0) + + # Expand vision features to match batch size + expanded_vision_feats = current_vision_feats[-1].expand(-1, len(objects_needing_memory_encoding), -1) + + # Encode all memories in one batch call + maskmem_features_batched, maskmem_pos_enc_batched = self._encode_new_memory( + current_vision_feats=expanded_vision_feats, + pred_masks_high_res=high_res_masks_batched, + object_score_logits=object_score_logits_batched, + is_mask_from_pts=any(is_mask_from_pts_per_obj), + ) + + # Split and store encoded memories per object + for i, obj_idx in enumerate(objects_needing_memory_encoding): + # Extract per-object memory from batched result + maskmem_features = maskmem_features_batched[:, i : i + 1] + maskmem_pos_enc = maskmem_pos_enc_batched[:, i : i + 1] + + # Update the stored output with memory features + output_dict = inference_session.output_dict_per_obj[obj_idx] + # Determine if this was a conditioning frame + storage_key = ( + "cond_frame_outputs" if frame_idx in output_dict["cond_frame_outputs"] else "non_cond_frame_outputs" + ) + if frame_idx in output_dict[storage_key]: + output_dict[storage_key][frame_idx]["maskmem_features"] = maskmem_features + output_dict[storage_key][frame_idx]["maskmem_pos_enc"] = maskmem_pos_enc + + @torch.inference_mode() + @auto_docstring( + custom_intro=""" + Propagate the objects through the video frames. Used when initializing an inference session with a whole video. + Yields Sam3TrackerVideoSegmentationOutput for each frame. + """ + ) + def propagate_in_video_iterator( + self, + inference_session: Sam3TrackerVideoInferenceSession, + start_frame_idx: int | None = None, + max_frame_num_to_track: int | None = None, + reverse: bool = False, + show_progress_bar: bool = False, + ) -> Iterator[Sam3TrackerVideoSegmentationOutput]: + r""" + inference_session (`Sam3TrackerVideoInferenceSession`): + The video inference session object. + start_frame_idx (`int`, *optional*): + The starting frame index for propagation. + Need to be provided if `forward` hasn't been called on new inputs yet. + If not provided, the starting frame index will be the earliest frame with input points. + max_frame_num_to_track (`int`, *optional*): + The maximum number of frames to track. + reverse (`bool`, *optional*, defaults to `False`): + Whether to propagate in reverse. + show_progress_bar (`bool`, *optional*, defaults to `False`): + Whether to show a progress bar during propagation. + """ + num_frames = inference_session.num_frames + + # set start index, end index, and processing order + if start_frame_idx is None: + # default: start from the earliest frame with input points + frames_with_inputs = [ + frame_idx + for obj_output_dict in inference_session.output_dict_per_obj.values() + for frame_idx in obj_output_dict["cond_frame_outputs"] + ] + if not frames_with_inputs: + raise ValueError( + "Cannot determine the starting frame index; please specify it manually, or run inference on a frame with inputs first." + ) + start_frame_idx = min(frames_with_inputs) + if max_frame_num_to_track is None: + # default: track all the frames in the video + max_frame_num_to_track = num_frames + if reverse: + end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0) + if start_frame_idx > 0: + processing_order = range(start_frame_idx, end_frame_idx - 1, -1) + else: + processing_order = [] # skip reverse tracking if starting from frame 0 + else: + end_frame_idx = min(start_frame_idx + max_frame_num_to_track, num_frames - 1) + processing_order = range(start_frame_idx, end_frame_idx + 1) + + for frame_idx in tqdm(processing_order, desc="propagate in video", disable=not show_progress_bar): + sam3_tracker_video_output = self(inference_session, frame_idx=frame_idx, reverse=reverse) + yield sam3_tracker_video_output + + +__all__ = ["Sam3TrackerVideoModel", "Sam3TrackerVideoInferenceSession", "Sam3TrackerVideoPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/modular_sam3_tracker_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/modular_sam3_tracker_video.py new file mode 100644 index 0000000000000000000000000000000000000000..e214448b63990b489f77665619f3c7364a875b73 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/modular_sam3_tracker_video.py @@ -0,0 +1,501 @@ +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel +from ..sam2_video.configuration_sam2_video import Sam2VideoMaskDecoderConfig, Sam2VideoPromptEncoderConfig +from ..sam2_video.modeling_sam2_video import ( + Sam2VideoAttention, + Sam2VideoFeedForward, + Sam2VideoImageSegmentationOutput, + Sam2VideoInferenceCache, + Sam2VideoInferenceSession, + Sam2VideoLayerNorm, + Sam2VideoMaskDecoder, + Sam2VideoMaskDownSampler, + Sam2VideoMaskDownSamplerLayer, + Sam2VideoMaskEmbedding, + Sam2VideoMemoryAttention, + Sam2VideoMemoryAttentionLayer, + Sam2VideoMemoryEncoder, + Sam2VideoMemoryFuser, + Sam2VideoMemoryFuserCXBlock, + Sam2VideoModel, + Sam2VideoPositionalEmbedding, + Sam2VideoPositionEmbeddingSine, + Sam2VideoPreTrainedModel, + Sam2VideoPromptEncoder, + Sam2VideoRoPEAttention, + Sam2VideoSegmentationOutput, + Sam2VideoTwoWayAttentionBlock, + Sam2VideoTwoWayTransformer, + Sam2VideoVisionEncoderOutput, + Sam2VideoVisionRotaryEmbedding, +) +from ..sam2_video.processing_sam2_video import Sam2VideoProcessor + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerVideoPromptEncoderConfig(Sam2VideoPromptEncoderConfig): + r""" + mask_input_channels (`int`, *optional*, defaults to 16): + The number of channels to be fed to the `MaskDecoder` module. + num_point_embeddings (`int`, *optional*, defaults to 4): + The number of point embeddings to be used. + scale (`float`, *optional*, defaults to 1): + The scale factor for the prompt encoder. + """ + + base_config_key = "prompt_encoder_config" + + image_size: int | list[int] | tuple[int, int] = 1008 + patch_size: int | list[int] | tuple[int, int] = 14 + + +class Sam3TrackerVideoProcessor(Sam2VideoProcessor): + pass + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerVideoMaskDecoderConfig(Sam2VideoMaskDecoderConfig): + pass + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3TrackerVideoConfig(PreTrainedConfig): + r""" + prompt_encoder_config (Union[`dict`, `Sam3TrackerVideoPromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam3TrackerVideoPromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `Sam3TrackerVideoMaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`Sam3TrackerVideoMaskDecoderConfig`]. + initializer_range (`float`, *optional*, defaults to 0.02): + Standard deviation for parameter initialization. + num_maskmem (`int`, *optional*, defaults to 7): + The number of memory slots for the mask memory. + sigmoid_scale_for_mem_enc (`float`, *optional*, defaults to 20.0): + Scale factor for the sigmoid function in the memory encoder. + sigmoid_bias_for_mem_enc (`float`, *optional*, defaults to -10.0): + Bias for the sigmoid function in the memory encoder. + enable_occlusion_spatial_embedding (`bool`, *optional*, defaults to `True`): + Whether to enable spatial embedding for occlusions. + multimask_output_in_sam (`bool`, *optional*, defaults to `True`): + Whether to output multiple masks from the SAM head. + multimask_min_pt_num (`int`, *optional*, defaults to 0): + The minimum number of points to trigger multimask output. + multimask_max_pt_num (`int`, *optional*, defaults to 1): + The maximum number of points to trigger multimask output. + multimask_output_for_tracking (`bool`, *optional*, defaults to `True`): + Whether to use multimask output for tracking. + max_object_pointers_in_encoder (`int`, *optional*, defaults to 16): + The maximum number of object pointers in the encoder. + max_cond_frame_num (`int`, *optional*, defaults to 4): + Maximum number of conditioning frames to use in memory attention. + enable_temporal_pos_encoding_for_object_pointers (`bool`, *optional*, defaults to `True`): + Whether to enable temporal positional encoding for object pointers. + memory_attention_hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the memory attention hidden states. + memory_attention_num_layers (`int`, *optional*, defaults to 4): + The number of layers in the memory attention module. + memory_attention_num_attention_heads (`int`, *optional*, defaults to 1): + Number of attention heads for each attention layer in the memory attention. + memory_attention_downsample_rate (`int`, *optional*, defaults to 1): + The downsample rate for the attention layers. + memory_attention_feed_forward_hidden_size (`int`, *optional*, defaults to 2048): + The dimension of the feedforward network in the memory attention module. + memory_attention_feed_forward_hidden_act (`str`, *optional*, defaults to `"relu"`): + The non-linear activation function in the feedforward network in the memory attention module. + memory_attention_dropout (`float`, *optional*, defaults to 0.1): + The dropout rate for the memory attention module. + memory_attention_rope_theta (`float`, *optional*, defaults to 10000): + The Rope theta parameter. + memory_attention_rope_feat_sizes (`list[int]`, *optional*, defaults to `[72, 72]`): + The feature sizes for the Rope positional encoding. + memory_attention_rope_dropout (`float`, *optional*, defaults to 0.1): + The dropout rate for the Rope positional encoding. + memory_encoder_hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the memory encoder hidden states. + memory_encoder_output_channels (`int`, *optional*, defaults to 64): + The number of output channels for the memory encoder. + mask_downsampler_embed_dim (`int`, *optional*, defaults to 256): + The dimension of the mask downsampler embedding. + mask_downsampler_kernel_size (`int`, *optional*, defaults to 3): + The kernel size for the mask downsampler. + mask_downsampler_stride (`int`, *optional*, defaults to 2): + The stride for the mask downsampler. + mask_downsampler_padding (`int`, *optional*, defaults to 1): + The padding for the mask downsampler. + mask_downsampler_total_stride (`int`, *optional*, defaults to 16): + The total stride for the mask downsampler. + mask_downsampler_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function in the mask downsampler. + memory_fuser_num_layers (`int`, *optional*, defaults to 2): + The number of layers in the memory fuser. + memory_fuser_embed_dim (`int`, *optional*, defaults to 256): + The dimension of the embedding layer in the memory fuser. + memory_fuser_intermediate_dim (`int`, *optional*, defaults to 1024): + The dimension of the intermediate layer in the memory fuser. + memory_fuser_kernel_size (`int`, *optional*, defaults to 7): + The kernel size for the memory fuser. + memory_fuser_padding (`int`, *optional*, defaults to 3): + The padding for the memory fuser. + memory_fuser_layer_scale_init_value (`float`, *optional*, defaults to 1e-06): + The initial value for the layer scale in the memory fuser. + memory_fuser_hidden_act (`str`, *optional*, defaults to `"gelu"`): + The non-linear activation function in the memory fuser. + + Example: + + ```python + >>> from transformers import ( + ... Sam3VisionConfig, + ... Sam3TrackerVideoPromptEncoderConfig, + ... Sam3TrackerVideoMaskDecoderConfig, + ... Sam3TrackerVideoModel, + ... ) + + >>> # Initializing a Sam3TrackerVideoConfig with `"facebook/sam3"` style configuration + >>> configuration = Sam3TrackerVideoConfig() + + >>> # Initializing a Sam3TrackerVideoModel (with random weights) from the `"facebook/sam3"` style configuration + >>> model = Sam3TrackerVideoModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Sam3TrackerVideoConfig from a Sam3TrackerVideoVisionConfig, Sam3TrackerVideoPromptEncoderConfig, and Sam3TrackerVideoMaskDecoderConfig + + >>> # Initializing SAM3 tracker video vision encoder, memory attention, and memory encoder configurations + >>> vision_config = Sam3TrackerVideoVisionConfig() + >>> prompt_encoder_config = Sam3TrackerVideoPromptEncoderConfig() + >>> mask_decoder_config = Sam3TrackerVideoMaskDecoderConfig() + + >>> config = Sam3TrackerVideoConfig(vision_config, prompt_encoder_config, mask_decoder_config) + ```""" + + model_type = "sam3_tracker_video" + sub_configs = { + "vision_config": AutoConfig, + "prompt_encoder_config": Sam3TrackerVideoPromptEncoderConfig, + "mask_decoder_config": Sam3TrackerVideoMaskDecoderConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + prompt_encoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + num_maskmem: int = 7 + sigmoid_scale_for_mem_enc: float = 20.0 + sigmoid_bias_for_mem_enc: float = -10.0 + enable_occlusion_spatial_embedding: bool = True + multimask_output_in_sam: bool = True + multimask_min_pt_num: int = 0 + multimask_max_pt_num: int = 1 + multimask_output_for_tracking: bool = True + max_object_pointers_in_encoder: int = 16 + max_cond_frame_num: int = 4 + enable_temporal_pos_encoding_for_object_pointers: bool = True + memory_attention_hidden_size: int = 256 + memory_attention_num_layers: int = 4 + memory_attention_num_attention_heads: int = 1 + memory_attention_downsample_rate: int = 1 + memory_attention_feed_forward_hidden_size: int = 2048 + memory_attention_feed_forward_hidden_act: str = "relu" + memory_attention_dropout: float | int = 0.1 + memory_attention_rope_theta: int = 10000 + memory_attention_rope_feat_sizes: list | None = None + memory_attention_rope_dropout: float | int = 0.1 + memory_encoder_hidden_size: int = 256 + memory_encoder_output_channels: int = 64 + mask_downsampler_embed_dim: int = 256 + mask_downsampler_kernel_size: int = 3 + mask_downsampler_stride: int = 2 + mask_downsampler_padding: int = 1 + mask_downsampler_total_stride: int = 16 + mask_downsampler_hidden_act: str = "gelu" + memory_fuser_num_layers: int = 2 + memory_fuser_embed_dim: int = 256 + memory_fuser_intermediate_dim: int = 1024 + memory_fuser_kernel_size: int = 7 + memory_fuser_padding: int = 3 + memory_fuser_layer_scale_init_value: float = 1e-6 + memory_fuser_hidden_act: str = "gelu" + + def __post_init__(self, **kwargs): + self.memory_attention_rope_feat_sizes = ( + [72, 72] if self.memory_attention_rope_feat_sizes is None else self.memory_attention_rope_feat_sizes + ) + + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "sam3_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["sam3_vision_model"]( + backbone_feature_sizes=[[288, 288], [144, 144], [72, 72]] + ) + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = Sam3TrackerVideoPromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = Sam3TrackerVideoPromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = Sam3TrackerVideoMaskDecoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = Sam3TrackerVideoMaskDecoderConfig() + + self.image_size = kwargs.pop("image_size", 1008) + super().__post_init__(**kwargs) + + @property + def image_size(self): + """Image size for the tracker video model.""" + return self.vision_config.image_size + + @image_size.setter + def image_size(self, value): + """Set the image size and propagate to sub-configs. Calculates feature sizes based on patch_size.""" + self.prompt_encoder_config.image_size = value + self.vision_config.image_size = value + + patch_size = self.vision_config.backbone_config.patch_size + self.vision_config.backbone_feature_sizes = [ + [4 * value // patch_size, 4 * value // patch_size], + [2 * value // patch_size, 2 * value // patch_size], + [value // patch_size, value // patch_size], + ] + self.memory_attention_rope_feat_sizes = [ + value // patch_size, + value // patch_size, + ] + + # keep the image_size in the __dict__ to save the value in the config file (backward compatibility) + self.__dict__["image_size"] = value + + +class Sam3TrackerVideoInferenceCache(Sam2VideoInferenceCache): + pass + + +class Sam3TrackerVideoInferenceSession(Sam2VideoInferenceSession): + pass + + +class Sam3TrackerVideoLayerNorm(Sam2VideoLayerNorm): + pass + + +class Sam3TrackerVideoPositionEmbeddingSine(Sam2VideoPositionEmbeddingSine): + pass + + +class Sam3TrackerVideoAttention(Sam2VideoAttention): + pass + + +class Sam3TrackerVideoTwoWayAttentionBlock(Sam2VideoTwoWayAttentionBlock): + pass + + +class Sam3TrackerVideoFeedForward(Sam2VideoFeedForward): + pass + + +class Sam3TrackerVideoImageSegmentationOutput(Sam2VideoImageSegmentationOutput): + pass + + +class Sam3TrackerVideoSegmentationOutput(Sam2VideoSegmentationOutput): + pass + + +class Sam3TrackerVideoPreTrainedModel(Sam2VideoPreTrainedModel): + base_model_prefix = "tracker_model" + + +class Sam3TrackerVideoVisionRotaryEmbedding(Sam2VideoVisionRotaryEmbedding): + pass + + +class Sam3TrackerVideoRoPEAttention(Sam2VideoRoPEAttention): + pass + + +class Sam3TrackerVideoMemoryAttentionLayer(Sam2VideoMemoryAttentionLayer): + pass + + +class Sam3TrackerVideoMemoryAttention(Sam2VideoMemoryAttention): + pass + + +class Sam3TrackerVideoMemoryFuserCXBlock(Sam2VideoMemoryFuserCXBlock): + pass + + +class Sam3TrackerVideoMemoryFuser(Sam2VideoMemoryFuser): + pass + + +class Sam3TrackerVideoMaskDownSamplerLayer(Sam2VideoMaskDownSamplerLayer): + pass + + +class Sam3TrackerVideoMaskDownSampler(Sam2VideoMaskDownSampler): + pass + + +class Sam3TrackerVideoMemoryEncoder(Sam2VideoMemoryEncoder): + pass + + +class Sam3TrackerVideoVisionEncoderOutput(Sam2VideoVisionEncoderOutput): + pass + + +class Sam3TrackerVideoPositionalEmbedding(Sam2VideoPositionalEmbedding): + pass + + +class Sam3TrackerVideoMaskEmbedding(Sam2VideoMaskEmbedding): + pass + + +class Sam3TrackerVideoPromptEncoder(Sam2VideoPromptEncoder): + pass + + +class Sam3TrackerVideoTwoWayTransformer(Sam2VideoTwoWayTransformer): + pass + + +class Sam3TrackerVideoMaskDecoder(Sam2VideoMaskDecoder): + pass + + +class Sam3TrackerVideoModel(Sam2VideoModel): + _keys_to_ignore_on_load_unexpected = [r"^detector_model."] + + def __init__(self, config: Sam3TrackerVideoConfig, remove_vision_encoder: bool = False): + r""" + remove_vision_encoder (`bool`, *optional*, defaults to `False`): + Whether to remove the vision encoder. If True, the vision encoder will be set to None. + """ + # loading from a sam3_video config + if hasattr(config, "tracker_config") and config.tracker_config is not None: + tracker_config = config.tracker_config + if isinstance(tracker_config, dict): + tracker_config = Sam3TrackerVideoConfig(**tracker_config) + config = tracker_config + Sam3TrackerVideoPreTrainedModel.__init__(config) + self.shared_image_embedding = Sam3TrackerVideoPositionalEmbedding(config.prompt_encoder_config) + self.vision_encoder = AutoModel.from_config(config.vision_config) if not remove_vision_encoder else None + self.prompt_encoder = Sam3TrackerVideoPromptEncoder(config.prompt_encoder_config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + self.mask_decoder = Sam3TrackerVideoMaskDecoder(config.mask_decoder_config) + + self.backbone_feature_sizes = config.vision_config.backbone_feature_sizes + # a single token to indicate no memory embedding from previous frames + self.hidden_dim = config.vision_config.fpn_hidden_size + self.no_memory_embedding = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + self.config = config + # For video sequence inference + self.image_size = config.image_size + self.memory_attention = Sam3TrackerVideoMemoryAttention(config) + self.memory_encoder = Sam3TrackerVideoMemoryEncoder(config) + self.no_memory_positional_encoding = torch.nn.Parameter( + torch.zeros(1, 1, config.vision_config.fpn_hidden_size) + ) + self.mem_dim = config.memory_encoder_output_channels + self.num_maskmem = config.num_maskmem # Number of memories accessible + # Temporal encoding of the memories + self.memory_temporal_positional_encoding = torch.nn.Parameter( + torch.zeros(self.num_maskmem, 1, 1, self.mem_dim) + ) + + self.no_object_pointer = torch.nn.Parameter(torch.zeros(1, self.hidden_dim)) + # A conv layer to downsample the mask prompt to stride 4 (the same stride as + # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale, + # so that it can be fed into the SAM mask decoder to generate a pointer. + self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4) + # a feedforward layer on SAM output tokens to turn them into object pointers + self.object_pointer_proj = Sam3TrackerVideoFeedForward(self.hidden_dim, self.hidden_dim, self.hidden_dim, 3) + + if self.config.enable_temporal_pos_encoding_for_object_pointers: + # a linear projection on temporal positional encoding in object pointers to + # avoid potential interference with spatial positional encoding + self.temporal_positional_encoding_projection_layer = torch.nn.Linear(self.hidden_dim, self.mem_dim) + else: + self.temporal_positional_encoding_projection_layer = torch.nn.Identity() + + self.occlusion_spatial_embedding_parameter = None # compatibility with Sam2 + if config.enable_occlusion_spatial_embedding: + self.occlusion_spatial_embedding_parameter = torch.nn.Parameter(torch.zeros(1, self.mem_dim)) + + self.post_init() + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | Sam3TrackerVideoVisionEncoderOutput: + r""" + pixel_values (`torch.FloatTensor`): + Input pixel values of shape `(batch_size, num_channels, height, width)`. + """ + vision_outputs: Sam3TrackerVideoVisionEncoderOutput = self.vision_encoder( + pixel_values, return_dict=True, **kwargs + ) + + feature_maps = vision_outputs.fpn_hidden_states + feature_maps_position_embeddings = vision_outputs.fpn_position_encoding + + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + feature_maps = list(feature_maps[:-1]) + feature_maps[0] = self.mask_decoder.conv_s0(feature_maps[0]) + feature_maps[1] = self.mask_decoder.conv_s1(feature_maps[1]) + + # flatten NxCxHxW to HWxNxC + feature_maps = [feature_map.flatten(2).permute(2, 0, 1) for feature_map in feature_maps] + feature_maps_position_embeddings = [ + feature_map_position_embedding.flatten(2).permute(2, 0, 1) + for feature_map_position_embedding in feature_maps_position_embeddings[:-1] + ] + vision_outputs.fpn_hidden_states = feature_maps + vision_outputs.fpn_position_encoding = feature_maps_position_embeddings + + return vision_outputs + + +__all__ = [ + "Sam3TrackerVideoMaskDecoderConfig", + "Sam3TrackerVideoPromptEncoderConfig", + "Sam3TrackerVideoConfig", + "Sam3TrackerVideoModel", + "Sam3TrackerVideoInferenceSession", + "Sam3TrackerVideoPreTrainedModel", + "Sam3TrackerVideoProcessor", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/processing_sam3_tracker_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/processing_sam3_tracker_video.py new file mode 100644 index 0000000000000000000000000000000000000000..708be51bd5e32fb25297c130c8d5c26fb289d768 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_tracker_video/processing_sam3_tracker_video.py @@ -0,0 +1,803 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam3_tracker_video/modular_sam3_tracker_video.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam3_tracker_video.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from copy import deepcopy +from typing import Union + +import numpy as np +import torch + +from ...image_utils import ImageInput +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires +from ...video_utils import VideoInput +from .modeling_sam3_tracker_video import Sam3TrackerVideoInferenceSession + + +@requires(backends=("torch",)) +@auto_docstring +class Sam3TrackerVideoProcessor(ProcessorMixin): + def __init__( + self, image_processor, video_processor, target_size: int | None = None, point_pad_value: int = -10, **kwargs + ): + r""" + target_size (`int`, *optional*): + The target size (in pixels) for normalizing input points and bounding boxes. If not provided, defaults + to the image processor's size configuration. All input coordinates (points and boxes) are normalized + to this size before being passed to the model. This ensures consistent coordinate representation + regardless of the original image dimensions. + point_pad_value (`int`, *optional*, defaults to -10): + The value used for padding input points when batching sequences of different lengths. This value is + used to mark padded positions and is preserved during coordinate normalization. + """ + super().__init__(image_processor, video_processor, **kwargs) + self.point_pad_value = point_pad_value + self.target_size = target_size if target_size is not None else self.image_processor.size["height"] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + segmentation_maps: ImageInput | None = None, + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + original_sizes: list[list[float]] | torch.Tensor | None = None, + return_tensors: str | TensorType | None = None, + **kwargs, + ) -> BatchEncoding: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to process. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + original_sizes (`list[list[float]]`, `torch.Tensor`, *optional*): + The original sizes of the images. + + Returns: + A [`BatchEncoding`] with the following fields: + - `pixel_values` (`torch.Tensor`): The processed image(s). + - `original_sizes` (`list[list[float]]`): The original sizes of the images. + - `labels` (`torch.Tensor`): The processed segmentation maps (if provided). + - `input_points` (`torch.Tensor`): The processed points. + - `input_labels` (`torch.Tensor`): The processed labels. + - `input_boxes` (`torch.Tensor`): The processed bounding boxes. + """ + if images is not None: + encoding_image_processor = self.image_processor( + images, + segmentation_maps=segmentation_maps, + return_tensors=return_tensors, + **kwargs, + ) + elif original_sizes is not None: + if isinstance(original_sizes, torch.Tensor): + original_sizes = original_sizes.cpu().tolist() + encoding_image_processor = BatchEncoding({"original_sizes": original_sizes}, tensor_type=return_tensors) + else: + raise ValueError("Either images or original_sizes must be provided") + + # pop arguments that are not used in the forward but used nevertheless + original_sizes = encoding_image_processor["original_sizes"] + # Check original_sizes is of length 1 or len(images) + if images is not None and len(original_sizes) != 1 and len(original_sizes) != len(images): + raise ValueError( + "original_sizes must be of length 1 or len(images). If you are passing a single image, you must pass a single original_size." + ) + + # Process input points, labels, and boxes if provided + if input_points is not None or input_labels is not None or input_boxes is not None: + # Validate and convert inputs to standardized format + processed_points = self._validate_single_input( + input_points, + expected_depth=4, + input_name="points", + expected_format="[image level, object level, point level, point coordinates]", + expected_coord_size=2, + ) + processed_labels = self._validate_single_input( + input_labels, + expected_depth=3, + input_name="labels", + expected_format="[image level, object level, point level]", + ) + processed_boxes = self._validate_single_input( + input_boxes, + expected_depth=3, + input_name="boxes", + expected_format="[image level, box level, box coordinates]", + expected_coord_size=4, + ) + + # Get padding requirements for all inputs + if processed_points is not None: + points_max_dims = self._get_nested_dimensions(processed_points)[:3] + if processed_labels is not None: + labels_max_dims = self._get_nested_dimensions(processed_labels)[:3] + if processed_boxes is not None: + boxes_max_dims = self._get_nested_dimensions(processed_boxes)[:2] + + # Ensure points and labels have consistent dimensions + if processed_points is not None and processed_labels is not None: + if points_max_dims != labels_max_dims: + raise ValueError( + "Input points and labels have inconsistent dimensions. Please ensure they have the same dimensions." + ) + + # Check that boxes don't need padding (model limitation) + if processed_boxes is not None and len(processed_boxes) >= 2: + if any(len(img_boxes) < boxes_max_dims[1] for img_boxes in processed_boxes): + raise ValueError( + "Input boxes have inconsistent dimensions that would require padding, " + "but boxes cannot be padded due to model limitations. " + "Please ensure all images have the same number of boxes." + ) + + # Pad and normalize all inputs to final tensor format + if processed_points is not None: + padded_points = self._pad_nested_list(processed_points, points_max_dims + [2]) + final_points = torch.tensor(padded_points, dtype=torch.float32) + self._normalize_tensor_coordinates(final_points, original_sizes, preserve_padding=True) + encoding_image_processor.update({"input_points": final_points}) + + if processed_labels is not None: + padded_labels = self._pad_nested_list(processed_labels, labels_max_dims) + final_labels = torch.tensor(padded_labels, dtype=torch.int64) + encoding_image_processor.update({"input_labels": final_labels}) + + if processed_boxes is not None: + final_boxes = torch.tensor(processed_boxes, dtype=torch.float32) + self._normalize_tensor_coordinates(final_boxes, original_sizes, is_bounding_box=True) + encoding_image_processor.update({"input_boxes": final_boxes}) + + return encoding_image_processor + + def _normalize_coordinates( + self, target_size: int, coords: "torch.Tensor", original_size, is_bounding_box=False + ) -> "torch.Tensor": + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H, W) format. + + Args: + target_size (`int`): + The target size of the image. + coords (`torch.Tensor`): + The coordinates to be normalized. + original_size (`tuple`): + The original size of the image. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether the coordinates are bounding boxes. + """ + old_h, old_w = original_size + new_h, new_w = target_size, target_size + coords = deepcopy(coords).float() + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + def _convert_to_nested_list(self, data, expected_depth, current_depth=0): + """ + Recursively convert various input formats (tensors, numpy arrays, lists) to nested lists. + + Args: + data: Input data in any format + expected_depth: Expected nesting depth + current_depth: Current depth in recursion + + Returns: + Nested list representation of the data + """ + if data is None: + return None + + # Convert tensor/numpy to list if we're at a leaf level or if it's a multi-dimensional array + if isinstance(data, torch.Tensor): # PyTorch tensor + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small tensor + return data.numpy().tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, np.ndarray): # NumPy array + if current_depth == expected_depth - 2 or len(data.shape) <= 2: # At coordinate level or small array + return data.tolist() + else: + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, list): + if current_depth == expected_depth: + # We've reached the expected depth, return as is + return data + else: + # Continue recursion + return [self._convert_to_nested_list(item, expected_depth, current_depth + 1) for item in data] + elif isinstance(data, (int, float)): + return data + else: + raise TypeError(f"Unsupported data type: {type(data)}") + + def _get_nested_dimensions(self, nested_list, max_dims=None): + """ + Get the maximum dimensions at each level of nesting. + + Args: + nested_list (`list`): + Nested list structure. + max_dims (`list`, *optional*): + Current maximum dimensions (for recursion). + + Returns: + `list`: A list of maximum dimensions for each nesting level. + """ + if max_dims is None: + max_dims = [] + + if not isinstance(nested_list, list): + return max_dims + + if len(max_dims) == 0: + max_dims.append(len(nested_list)) + else: + max_dims[0] = max(max_dims[0], len(nested_list)) + + if len(nested_list) > 0: + for item in nested_list: + if isinstance(item, list): + sub_dims = self._get_nested_dimensions(item) + # Merge sub_dims into max_dims + for i, dim in enumerate(sub_dims): + if i + 1 >= len(max_dims): + max_dims.append(dim) + else: + max_dims[i + 1] = max(max_dims[i + 1], dim) + + return max_dims + + def _pad_nested_list(self, nested_list, target_dims, current_level=0, pad_value=None): + """ + Recursively pad a nested list to match target dimensions. + + Args: + nested_list (`list`): + Nested list to pad. + target_dims (`list`): + Target dimensions for each level. + current_level (`int`, *optional*, defaults to 0): + Current nesting level. + pad_value (`int`, *optional*): + Value to use for padding. + + Returns: + `list`: The padded nested list. + """ + if pad_value is None: + pad_value = self.point_pad_value + + if current_level >= len(target_dims): + return nested_list + + # Ensure we have a list + if not isinstance(nested_list, list): + nested_list = [nested_list] + + # Pad current level + current_size = len(nested_list) + target_size = target_dims[current_level] + + # Pad with appropriate values + if current_level == len(target_dims) - 1: + # At the coordinate level, pad with pad_value + nested_list.extend([pad_value] * (target_size - current_size)) + else: + # At higher levels, pad with nested structures + if current_size > 0: + # Create appropriately sized template + if current_level < len(target_dims) - 2: + # For non-coordinate levels, create empty nested structure + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + else: + # For coordinate level, create list of pad_values + template = [pad_value] * target_dims[current_level + 1] + + nested_list.extend([deepcopy(template) for _ in range(target_size - current_size)]) + else: + # Create from scratch + template_dims = target_dims[current_level + 1 :] + template = self._create_empty_nested_structure(template_dims, pad_value) + nested_list.extend([deepcopy(template) for _ in range(target_size)]) + + # Recursively pad sublists + if current_level < len(target_dims) - 1: + for i in range(len(nested_list)): + if isinstance(nested_list[i], list): + nested_list[i] = self._pad_nested_list(nested_list[i], target_dims, current_level + 1, pad_value) + + return nested_list + + def _create_empty_nested_structure(self, dims, pad_value): + """ + Create an empty nested structure with given dimensions filled with pad_value. + + Args: + dims (`list`): + The dimensions of the nested structure. + pad_value (`int`): + The value to fill the structure with. + """ + if len(dims) == 1: + return [pad_value] * dims[0] + else: + return [self._create_empty_nested_structure(dims[1:], pad_value) for _ in range(dims[0])] + + def _get_nesting_level(self, input_list): + """ + Get the nesting level of a list structure. + + Args: + input_list (`list`): + The list to get the nesting level of. + """ + if isinstance(input_list, list): + if len(input_list) == 0: + return 1 + return 1 + self._get_nesting_level(input_list[0]) + elif isinstance(input_list, (np.ndarray, torch.Tensor)): + # For arrays/tensors, the nesting level is the number of dimensions + return len(input_list.shape) + return 0 + + def _validate_single_input( + self, + data: torch.Tensor | np.ndarray | list, + expected_depth: int, + input_name: str, + expected_format: str, + expected_coord_size: int | None = None, + ) -> list: + """ + Validate a single input by ensuring proper nesting and raising an error if the input is not valid. + + Args: + data (`torch.Tensor`, `np.ndarray`, or `list`): + Input data to process. + expected_depth (`int`): + Expected nesting depth. + input_name (`str`): + Name of the input for error messages. + expected_format (`str`): + The expected format of the input. + expected_coord_size (`int`, *optional*): + Expected coordinate size (2 for points, 4 for boxes, None for labels). + . + """ + if data is None: + return None + + # Handle tensors and numpy arrays first + if isinstance(data, (torch.Tensor, np.ndarray)): + # For tensors/arrays, we can directly check the number of dimensions + if data.ndim != expected_depth: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_depth} dimensions. The expected nesting format is {expected_format}. Got {data.ndim} dimensions." + ) + elif expected_coord_size is not None: + if data.shape[-1] != expected_coord_size: + raise ValueError( + f"Input {input_name} must be a tensor/array with {expected_coord_size} as the last dimension, got {data.shape[-1]}." + ) + return self._convert_to_nested_list(data, expected_depth) + + # Handle nested lists + if isinstance(data, list): + current_depth = self._get_nesting_level(data) + if current_depth != expected_depth: + raise ValueError( + f"Input {input_name} must be a nested list with {expected_depth} levels. The expected nesting format is {expected_format}. Got {current_depth} levels." + ) + return self._convert_to_nested_list(data, expected_depth) + + def _normalize_tensor_coordinates(self, tensor, original_sizes, is_bounding_box=False, preserve_padding=False): + """ + Helper method to normalize coordinates in a tensor across multiple images. + + Args: + tensor (`torch.Tensor`): + Input tensor with coordinates. + original_sizes (`list`): + Original image sizes. + is_bounding_box (`bool`, *optional*, defaults to `False`): + Whether coordinates are bounding boxes. + preserve_padding (`bool`, *optional*, defaults to `False`): + Whether to preserve padding values (for points). + """ + if preserve_padding: + # For points: avoid normalizing pad values + mask = tensor != self.point_pad_value + coord_mask = mask.all(dim=-1, keepdim=True) + + for img_idx in range(len(original_sizes)): + if img_idx < tensor.shape[0]: + original_size = original_sizes[img_idx] if img_idx < len(original_sizes) else original_sizes[0] + normalized_coords = self._normalize_coordinates( + self.target_size, tensor[img_idx], original_size, is_bounding_box=is_bounding_box + ) + + if preserve_padding: + # Only update non-padded values + img_mask = coord_mask[img_idx] + tensor[img_idx] = torch.where( + img_mask.expand_as(tensor[img_idx]), normalized_coords, tensor[img_idx] + ) + else: + tensor[img_idx] = normalized_coords + + def post_process_masks( + self, + masks, + original_sizes, + mask_threshold=0.0, + binarize=True, + max_hole_area=0.0, + max_sprinkle_area=0.0, + apply_non_overlapping_constraints=False, + **kwargs, + ): + """ + Remove padding and upscale masks to the original image size. + + Args: + masks (`Union[List[torch.Tensor], List[np.ndarray]]`): + Batched masks from the mask_decoder in (batch_size, num_channels, height, width) format. + original_sizes (`Union[torch.Tensor, List[Tuple[int,int]]]`): + The original sizes of each image before it was resized to the model's expected input shape, in (height, + width) format. + mask_threshold (`float`, *optional*, defaults to 0.0): + Threshold for binarization and post-processing operations. + binarize (`bool`, *optional*, defaults to `True`): + Whether to binarize the masks. + max_hole_area (`float`, *optional*, defaults to 0.0): + The maximum area of a hole to fill. + max_sprinkle_area (`float`, *optional*, defaults to 0.0): + The maximum area of a sprinkle to fill. + apply_non_overlapping_constraints (`bool`, *optional*, defaults to `False`): + Whether to apply non-overlapping constraints to the masks. + + Returns: + (`torch.Tensor`): Batched masks in batch_size, num_channels, height, width) format, where (height, width) + is given by original_size. + """ + return self.image_processor.post_process_masks( + masks, + original_sizes, + mask_threshold, + binarize, + max_hole_area, + max_sprinkle_area, + apply_non_overlapping_constraints, + **kwargs, + ) + + @property + def model_input_names(self): + image_processor_input_names = self.image_processor.model_input_names + return list(image_processor_input_names + ["original_sizes"]) + + def init_video_session( + self, + video: VideoInput | None = None, + inference_device: Union[str, "torch.device"] = "cpu", + inference_state_device: Union[str, "torch.device"] | None = None, + processing_device: Union[str, "torch.device"] | None = None, + video_storage_device: Union[str, "torch.device"] | None = None, + max_vision_features_cache_size: int = 1, + dtype: torch.dtype = torch.float32, + ): + """ + Initializes a video session for inference. + If a video is provided (async inference), the video will be processed and stored on the `video_storage_device`. + + Args: + video (`VideoInput`, *optional*): + The video to process. No need to provide when streaming. + inference_device (`str` or `torch.device`, *optional*, defaults to "cpu"): + The device to use for inference. + inference_state_device (`str` or `torch.device`, *optional*): + The device to store the inference state on. + processing_device (`str` or `torch.device`, *optional*): + The device to use for video processing. + video_storage_device (`str` or `torch.device`, *optional*): + The device to store the processed video frames on. + max_vision_features_cache_size (`int`, *optional*, defaults to 1): + The maximum number of vision features to cache. + dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): + The torch dtype to use for the whole session. + """ + video_storage_device = video_storage_device if video_storage_device is not None else inference_device + inference_state_device = inference_state_device if inference_state_device is not None else inference_device + processing_device = processing_device if processing_device is not None else inference_device + pixel_values_video = None + video_height = None + video_width = None + if video is not None: + processed_video = self.video_processor(videos=video, device=processing_device, return_tensors="pt") + pixel_values_video = processed_video.pixel_values_videos[0] + video_height = processed_video.original_sizes[0][0] + video_width = processed_video.original_sizes[0][1] + inference_session = Sam3TrackerVideoInferenceSession( + video=pixel_values_video, + video_height=video_height, + video_width=video_width, + inference_device=inference_device, + video_storage_device=video_storage_device, + inference_state_device=inference_state_device, + dtype=dtype, + max_vision_features_cache_size=max_vision_features_cache_size, + ) + return inference_session + + def add_inputs_to_inference_session( + self, + inference_session: Sam3TrackerVideoInferenceSession, + frame_idx: int, + obj_ids: list[int] | int, + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + input_masks: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor] | None = None, + original_size: tuple[int, int] | None = None, + clear_old_inputs: bool = True, + ) -> Sam3TrackerVideoInferenceSession: + """ + Process new points, boxes, or masks for a video frame and add them to the inference session. + + Args: + inference_session (`Sam3TrackerVideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]` or `int`): + The object ID(s) to associate with the points or box. + These can be any integers and can be reused later on to specify an object. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + input_masks (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, or `list[torch.Tensor]`, *optional*): + The mask(s) to add to the frame. + original_size (`tuple[int, int]`, *optional*): + The original size of the video. Provide when streaming. + clear_old_inputs (`bool`, *optional*, defaults to `True`): + Whether to clear old inputs for the object. + """ + + if isinstance(obj_ids, int): + obj_ids = [obj_ids] + + # Validate inputs + if (input_points is not None) != (input_labels is not None): + raise ValueError("points and labels must be provided together") + if input_points is None and input_boxes is None and input_masks is None: + raise ValueError("at least one of points, boxes, or masks must be provided as input") + if input_masks is not None and (input_points is not None or input_boxes is not None): + raise ValueError("masks cannot be provided together with points or boxes") + + if input_masks is not None: + return self.process_new_mask_for_video_frame(inference_session, frame_idx, obj_ids, input_masks) + else: + return self.process_new_points_or_boxes_for_video_frame( + inference_session, + frame_idx, + obj_ids, + input_points, + input_labels, + input_boxes, + original_size, + clear_old_inputs, + ) + + def process_new_points_or_boxes_for_video_frame( + self, + inference_session: Sam3TrackerVideoInferenceSession, + frame_idx: int, + obj_ids: list[int], + input_points: list[list[list[list[float]]]] | torch.Tensor | None = None, + input_labels: list[list[list[int]]] | torch.Tensor | None = None, + input_boxes: list[list[list[float]]] | torch.Tensor | None = None, + original_size: tuple[int, int] | None = None, + clear_old_inputs: bool = True, + ) -> Sam3TrackerVideoInferenceSession: + """ + Process new points or boxes for a video frame and add them to the inference session. + + Args: + inference_session (`Sam3TrackerVideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]`): + The object ID(s) to associate with the points or box. + These can be any integers and can be reused later on to specify an object. + input_points (`list[list[list[list[float]]]]`, `torch.Tensor`, *optional*): + The points to add to the frame. + input_labels (`list[list[list[int]]]`, `torch.Tensor`, *optional*): + The labels for the points. + input_boxes (`list[list[list[float]]]`, `torch.Tensor`, *optional*): + The bounding boxes to add to the frame. + original_size (`tuple[int, int]`, *optional*): + The original size of the video. Provide when streaming. + clear_old_inputs (`bool`, *optional*, defaults to `True`): + Whether to clear old inputs for the object. + """ + if original_size is not None: + inference_session.video_height = original_size[0] + inference_session.video_width = original_size[1] + elif inference_session.video_height is None or inference_session.video_width is None: + raise ValueError("original_size must be provided when adding points or boxes on a first streamed frame") + + original_sizes = [[inference_session.video_height, inference_session.video_width]] + + encoded_inputs = self( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + original_sizes=original_sizes, + return_tensors="pt", + ) + input_points = encoded_inputs.get("input_points", None) + input_labels = encoded_inputs.get("input_labels", None) + input_boxes = encoded_inputs.get("input_boxes", None) + + if input_points is not None: + if input_points.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of points ({input_points.shape[1]})" + ) + else: + input_points = torch.zeros(1, len(obj_ids), 0, 2, dtype=torch.float32) + if input_labels is not None: + if input_labels.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of labels ({input_labels.shape[1]})" + ) + else: + input_labels = torch.zeros(1, len(obj_ids), 0, dtype=torch.int32) + if input_boxes is not None: + if input_boxes.shape[1] != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of boxes ({input_boxes.shape[1]})" + ) + + if input_boxes is not None: + if not clear_old_inputs: + raise ValueError( + "cannot add box without clearing old points, since " + "box prompt must be provided before any point prompt " + "(please use clear_old_points=True instead)" + ) + box_coords = input_boxes.reshape(1, -1, 2, 2) + box_labels = torch.tensor([2, 3], dtype=torch.int32).repeat(1, box_coords.shape[1], 1) + input_points = torch.cat([box_coords, input_points], dim=2) + input_labels = torch.cat([box_labels, input_labels], dim=2) + + for obj_id, idx in zip(obj_ids, range(len(obj_ids))): + obj_idx = inference_session.obj_id_to_idx(obj_id) + input_points_for_obj = input_points[:, idx, :, :].unsqueeze(1) + input_labels_for_obj = input_labels[:, idx, :].unsqueeze(1) + # Handle existing points + if not clear_old_inputs: + existing_points = inference_session.point_inputs_per_obj[obj_idx].get(frame_idx, None) + if existing_points is not None: + # Concatenate with existing points + input_points_for_obj = torch.cat( + [existing_points["point_coords"].to(input_points_for_obj.device), input_points_for_obj], dim=2 + ) + input_labels_for_obj = torch.cat( + [existing_points["point_labels"].to(input_labels_for_obj.device), input_labels_for_obj], dim=2 + ) + point_inputs = { + "point_coords": input_points_for_obj, + "point_labels": input_labels_for_obj, + } + + inference_session.add_point_inputs(obj_idx, frame_idx, point_inputs) + inference_session.remove_mask_inputs(obj_idx, frame_idx) # Clear any mask inputs + + inference_session.obj_with_new_inputs = obj_ids + + def process_new_mask_for_video_frame( + self, + inference_session: Sam3TrackerVideoInferenceSession, + frame_idx: int, + obj_ids: list[int], + input_masks: np.ndarray | torch.Tensor | list[np.ndarray] | list[torch.Tensor], + ): + """ + Add new mask to a frame and add them to the inference session. + + Args: + inference_session (`Sam3TrackerVideoInferenceSession`): + The inference session for the video. + frame_idx (`int`): + The index of the frame to process. + obj_ids (`list[int]`): + The object ID(s) to associate with the mask. + These can be any integers and can be reused later on to specify an object. + input_masks (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, or `list[torch.Tensor]`): + The mask(s) to add to the frame. + """ + if not isinstance(input_masks, list): + input_masks = [input_masks] + if len(input_masks) != len(obj_ids): + raise ValueError( + f"Number of object ids ({len(obj_ids)}) does not match number of masks ({len(input_masks)})" + ) + + for obj_id, mask in zip(obj_ids, input_masks): + obj_idx = inference_session.obj_id_to_idx(obj_id) + + device = inference_session.inference_device + + # Process mask + if not isinstance(mask, torch.Tensor): + mask = torch.tensor(mask, dtype=torch.bool) + nb_dim = mask.dim() + if nb_dim > 4 or nb_dim < 2: + raise ValueError(f"Mask has an unsupported number of dimensions: {nb_dim}") + for i in range(4 - nb_dim): + mask = mask.unsqueeze(0) + + mask_H, mask_W = mask.shape[-2:] + mask_inputs_orig = mask.to(device) + mask_inputs_orig = mask_inputs_orig.float().to(device) + + # Resize mask if needed + if mask_H != self.target_size or mask_W != self.target_size: + mask_inputs = torch.nn.functional.interpolate( + mask_inputs_orig, + size=(self.target_size, self.target_size), + align_corners=False, + mode="bilinear", + antialias=True, + ) + mask_inputs = (mask_inputs >= 0.5).float() + else: + mask_inputs = mask_inputs_orig + + inference_session.add_mask_inputs(obj_idx, frame_idx, mask_inputs) + inference_session.remove_point_inputs(obj_idx, frame_idx) # Clear any point inputs + + inference_session.obj_with_new_inputs = obj_ids + + +__all__ = ["Sam3TrackerVideoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..97943d102b6ed94e328e4ff49162894fec0a38f5 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam3_video import * + from .modeling_sam3_video import * + from .processing_sam3_video import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/configuration_sam3_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/configuration_sam3_video.py new file mode 100644 index 0000000000000000000000000000000000000000..3ecd4bc626fe3431f46fa94c0cc1c7e8a1adb46e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/configuration_sam3_video.py @@ -0,0 +1,177 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SAM3 Video model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging +from ..auto import CONFIG_MAPPING, AutoConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="facebook/sam3") +@strict +class Sam3VideoConfig(PreTrainedConfig): + r""" + detector_config (`dict` or `Sam3Config`, *optional*): + Configuration for the Sam3 detector model. If not provided, default Sam3Config will be used. + tracker_config (`dict` or `Sam2VideoConfig`, *optional*): + Configuration for the Sam2Video tracker model. If not provided, default Sam2VideoConfig will be used. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing weight matrices. + low_res_mask_size (`int`, *optional*, defaults to 288): + Size (height and width) of the low-resolution mask outputs from the tracker before upsampling to video resolution. + score_threshold_detection (`float`, *optional*, defaults to 0.5): + Probability threshold for detection outputs - only keep detections above this threshold. + det_nms_thresh (`float`, *optional*, defaults to 0.1): + IoU threshold for detection NMS (Non-Maximum Suppression). + assoc_iou_thresh (`float`, *optional*, defaults to 0.1): + IoU threshold for detection-to-track matching. A detection is considered "matched" to a tracklet if + it overlaps with the tracklet above this threshold. Often a loose threshold like 0.1. + trk_assoc_iou_thresh (`float`, *optional*, defaults to 0.5): + IoU threshold for detection-to-track matching, used to determine whether a masklet is "unmatched" + by any detections. Often a stricter threshold like 0.5. + new_det_thresh (`float`, *optional*, defaults to 0.7): + Probability threshold for a detection to be added as a new object. + recondition_on_trk_masks (`bool`, *optional*, defaults to `True`): + Whether to use tracked masks (True) or detection masks (False) for reconditioning. Use True when tracked + masks are higher quality and detector serves as validation signal to strengthen memory and prevent drift. + hotstart_delay (`int`, *optional*, defaults to 15): + Number of frames to buffer outputs during hotstart. We hold off the outputs for `hotstart_delay` + frames and remove tracklets based on hotstart heuristics. + hotstart_unmatch_thresh (`int`, *optional*, defaults to 8): + Number of unmatched frames required to remove a tracklet during hotstart period. + hotstart_dup_thresh (`int`, *optional*, defaults to 8): + Number of overlapping frames required to remove a duplicate tracklet during hotstart period. + suppress_unmatched_only_within_hotstart (`bool`, *optional*, defaults to `True`): + Whether to suppress masks only within hotstart period. If False, we can suppress masks even if + they start before hotstart period. + init_trk_keep_alive (`int`, *optional*, defaults to 30): + Initial keep-alive counter for new tracks. + max_trk_keep_alive (`int`, *optional*, defaults to 30): + Maximum keep-alive counter value. Tracks with matched detections get their counter increased up to this value. + min_trk_keep_alive (`int`, *optional*, defaults to -1): + Minimum keep-alive counter value. Tracks with unmatched detections get their counter decreased to this value. + suppress_overlapping_based_on_recent_occlusion_threshold (`float`, *optional*, defaults to 0.7): + Threshold for suppressing overlapping objects based on recent occlusion. Overlapping masks with + IoU above this threshold are suppressed based on which was most recently occluded. + decrease_trk_keep_alive_for_empty_masklets (`bool`, *optional*, defaults to `False`): + Whether to decrease keep-alive counter for masklets with zero area in SAM2 prediction. + fill_hole_area (`int`, *optional*, defaults to 16): + Minimum area (in pixels) for filling holes in masks and removing small sprinkles. + max_num_objects (`int`, *optional*, defaults to 10000): + Maximum number of objects to track. Default 10000 effectively turns off this limit. + recondition_every_nth_frame (`int`, *optional*, defaults to 16): + Frequency of mask reconditioning (in frames). Set to 0 to disable reconditioning. + high_conf_thresh (`float`, *optional*, defaults to 0.8): + High confidence threshold for reconditioning. Only detections above this threshold can recondition tracklets. + high_iou_thresh (`float`, *optional*, defaults to 0.8): + High IoU threshold for reconditioning. Only detections with IoU above this threshold can recondition tracklets. + + Example: + ```python + >>> from transformers import Sam3VideoConfig, Sam3VideoModel + + >>> # Initializing a SAM3 Video configuration with default detector and tracker + >>> configuration = Sam3VideoConfig() + + >>> # Changing image size for custom resolution inference (automatically propagates to all nested configs) + >>> configuration.image_size = 560 + + >>> # Initializing a model from the configuration + >>> model = Sam3VideoModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + >>> detector_config = configuration.detector_config + >>> tracker_config = configuration.tracker_config + ``` + """ + + model_type = "sam3_video" + is_composition = True + sub_configs = { + "detector_config": AutoConfig, + "tracker_config": AutoConfig, + } + + detector_config: dict | PreTrainedConfig | None = None + tracker_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + low_res_mask_size: int = 288 + score_threshold_detection: float = 0.5 + det_nms_thresh: float = 0.1 + assoc_iou_thresh: float = 0.1 + trk_assoc_iou_thresh: float = 0.5 + new_det_thresh: float = 0.7 + recondition_on_trk_masks: bool = True + hotstart_delay: int = 15 + hotstart_unmatch_thresh: int = 8 + hotstart_dup_thresh: int = 8 + suppress_unmatched_only_within_hotstart: bool = True + init_trk_keep_alive: int = 30 + max_trk_keep_alive: int = 30 + min_trk_keep_alive: int = -1 + suppress_overlapping_based_on_recent_occlusion_threshold: float = 0.7 + decrease_trk_keep_alive_for_empty_masklets: bool = False + fill_hole_area: int = 16 + max_num_objects: int = 10000 + recondition_every_nth_frame: int = 16 + high_conf_thresh: float = 0.8 + high_iou_thresh: float = 0.8 + + def __post_init__(self, **kwargs): + if self.detector_config is None: + self.detector_config = CONFIG_MAPPING["sam3"]() + logger.info("detector_config is None. Initializing the Sam3Config with default values.") + if isinstance(self.detector_config, dict): + self.detector_config["model_type"] = self.detector_config.get("model_type", "sam3") + self.detector_config = CONFIG_MAPPING[self.detector_config["model_type"]](**self.detector_config) + + if self.tracker_config is None: + self.tracker_config = CONFIG_MAPPING["sam3_tracker_video"]() + logger.info("tracker_config is None. Initializing the Sam3TrackerVideoConfig with default values.") + if isinstance(self.tracker_config, dict): + self.tracker_config["model_type"] = self.tracker_config.get("model_type", "sam3_tracker_video") + self.tracker_config = CONFIG_MAPPING[self.tracker_config["model_type"]](**self.tracker_config) + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.hotstart_delay > 0: + if self.hotstart_unmatch_thresh > self.hotstart_delay: + raise ValueError( + f"hotstart_unmatch_thresh ({self.hotstart_unmatch_thresh}) must be <= hotstart_delay ({self.hotstart_delay})" + ) + if self.hotstart_dup_thresh > self.hotstart_delay: + raise ValueError( + f"hotstart_dup_thresh ({self.hotstart_dup_thresh}) must be <= hotstart_delay ({self.hotstart_delay})" + ) + + @property + def image_size(self): + """Image size for the video model.""" + return self.detector_config.image_size + + @image_size.setter + def image_size(self, value): + """Recursively propagate the image size to detector and tracker configs.""" + self.detector_config.image_size = value + self.tracker_config.image_size = value + + +__all__ = ["Sam3VideoConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/modeling_sam3_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/modeling_sam3_video.py new file mode 100644 index 0000000000000000000000000000000000000000..98d65489d7874182f2ac7cd420440f524aa0201c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/modeling_sam3_video.py @@ -0,0 +1,2012 @@ +# Copyright 2025 The Meta AI Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections import OrderedDict, defaultdict +from collections.abc import Iterator +from copy import deepcopy +from dataclasses import dataclass +from typing import Any + +import torch +import torch.nn.functional as F +from torch import Tensor +from tqdm.auto import tqdm + +from transformers.models.sam3.modeling_sam3 import Sam3VisionNeck + +from ...modeling_utils import PreTrainedModel +from ...utils import ModelOutput, auto_docstring, is_kernels_available, logging +from ..auto import AutoModel +from .configuration_sam3_video import Sam3VideoConfig + + +if is_kernels_available(): + from ...integrations.hub_kernels import get_kernel + +logger = logging.get_logger(__name__) + +cv_utils_kernel = None # None = not attempted, False = failed, kernel object = success + + +def _load_cv_utils_kernel_once(): + """Load cv_utils_kernel once on first use.""" + global cv_utils_kernel + if cv_utils_kernel is not None: + return # Already attempted loading (successfully or not) + + if not is_kernels_available(): + logger.warning_once( + "kernels library is not installed. NMS post-processing, hole filling, and sprinkle removal will be skipped. " + "Install it with `pip install kernels` for better mask quality." + ) + cv_utils_kernel = False + return + + try: + cv_utils_kernel = get_kernel("kernels-community/cv-utils") + except Exception as e: + logger.warning_once( + f"Failed to load cv_utils kernel (your torch/cuda setup may not be supported): {e}. " + "NMS post-processing, hole filling, and sprinkle removal will be skipped." + ) + cv_utils_kernel = False + + +class Sam3VideoInferenceCache: + """Cache for vision features and model constants.""" + + def __init__( + self, + inference_device: torch.device | str = "cpu", + inference_state_device: torch.device | str = "cpu", + max_vision_features_cache_size: int = 1, + ): + self.inference_device = inference_device + self.inference_state_device = inference_state_device + self.max_vision_features_cache_size = max_vision_features_cache_size + + self._vision_features = {} + + def cache_vision_features(self, frame_idx: int, features: dict): + """Cache vision features with automatic device management.""" + cached = {} + if len(self._vision_features) >= self.max_vision_features_cache_size: + # remove the oldest frame + self._vision_features.pop(min(self._vision_features.keys())) + + for key, value in features.items(): + if isinstance(value, torch.Tensor): + cached[key] = value.to(self.inference_state_device, non_blocking=True) + elif isinstance(value, (list, tuple)) and value and isinstance(value[0], torch.Tensor): + cached[key] = [v.to(self.inference_state_device, non_blocking=True) for v in value] + else: + cached[key] = value + self._vision_features[frame_idx] = cached + + def get_vision_features(self, frame_idx: int) -> dict | None: + """Get cached vision features, automatically moved to inference device.""" + if frame_idx not in self._vision_features: + return None + + cached = self._vision_features[frame_idx] + moved = {} + for key, value in cached.items(): + if isinstance(value, torch.Tensor): + moved[key] = value.to(self.inference_device, non_blocking=True) + elif isinstance(value, (list, tuple)) and value and isinstance(value[0], torch.Tensor): + moved[key] = [v.to(self.inference_device, non_blocking=True) for v in value] + else: + moved[key] = value + return moved + + def clear_all(self): + """Clear all cached data.""" + self._vision_features.clear() + + +class Sam3VideoInferenceSession: + r""" + Manages video inference session parameters, state and cache. + + Args: + video (`torch.FloatTensor`, *optional*): + The video to process. No need to provide when streaming. + video_height (`int`, *optional*): + The height of the video. + video_width (`int`, *optional*): + The width of the video. + inference_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to use for inference. + inference_state_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to store the inference state on. + video_storage_device (`torch.device`, *optional*, defaults to `"cpu"`): + The device to store the video on. + dtype (`torch.dtype`, *optional*, defaults to `"float32"`): + The dtype to use for the video. + max_vision_features_cache_size (`int`, *optional*, defaults to 1): + The maximum number of vision features to cache. + """ + + def __init__( + self, + video: torch.FloatTensor | None = None, + video_height: int | None = None, + video_width: int | None = None, + inference_device: torch.device | str = "cpu", + inference_state_device: torch.device | str = "cpu", + video_storage_device: torch.device | str = "cpu", + dtype: torch.dtype | str = "float32", + max_vision_features_cache_size: int = 1, + ): + # store as a dictionary to avoid double memory allocation with torch.cat when adding new frames + self.processed_frames = ( + dict(enumerate(video.to(video_storage_device, dtype=dtype))) if video is not None else None + ) + self.video_height = video_height + self.video_width = video_width + + self.inference_device = inference_device + self.inference_state_device = inference_state_device + self.video_storage_device = video_storage_device + self.dtype = dtype + self.max_vision_features_cache_size = max_vision_features_cache_size + + # Cache for computed features + self.cache = Sam3VideoInferenceCache( + inference_device=self.inference_device, + inference_state_device=self.inference_state_device, + max_vision_features_cache_size=self.max_vision_features_cache_size, + ) + + # Persistent object tracking state + self._obj_id_to_idx = OrderedDict() + self._obj_idx_to_id = OrderedDict() + self.obj_ids = [] + + self.mask_inputs_per_obj = {} + self.point_inputs_per_obj = {} + + # Persistent model outputs/history + self.output_dict_per_obj = {} + self.frames_tracked_per_obj = {} + + # Multi-prompt support + self.prompts = {} # prompt_id -> prompt_text + self.prompt_input_ids = {} # prompt_id -> input_ids + self.prompt_embeddings = {} # prompt_id -> text embeddings + self.prompt_attention_masks = {} # prompt_id -> attention_mask + self.obj_id_to_prompt_id = {} # obj_id -> prompt_id (assigned at detection time) + + # Tracking metadata for detection-tracking fusion + self.obj_id_to_score = {} # Detection scores per object + self.obj_id_to_tracker_score_frame_wise = defaultdict(dict) # Frame-wise tracker scores + self.obj_id_to_last_occluded = {} # Last occlusion frame per object + self.max_obj_id = -1 # Maximum object ID assigned so far (-1 means no object has been assigned yet) + + # Hotstart metadata + self.obj_first_frame_idx = {} # First frame index per object + self.unmatched_frame_inds = defaultdict(list) # Unmatched frame indices per object + self.overlap_pair_to_frame_inds = defaultdict(list) # Overlap tracking for duplicate detection + self.trk_keep_alive = {} # Keep-alive counters per object + self.removed_obj_ids = set() # Set of removed object IDs + self.suppressed_obj_ids = defaultdict(set) # Suppressed object IDs per frame + self.hotstart_removed_obj_ids = set() # Set of removed object IDs during hotstart + + # Output buffering for hotstart delay + self.output_buffer = [] + + @property + def num_frames(self) -> int | None: + """Number of frames in the video.""" + return len(self.processed_frames) if self.processed_frames is not None else None + + def add_prompt(self, prompt_text: str) -> int: + """ + Add a text prompt to the session and return its unique ID. + If the prompt already exists, returns the existing ID. + """ + for prompt_id, text in self.prompts.items(): + if text == prompt_text: + return prompt_id + + prompt_id = len(self.prompts) + self.prompts[prompt_id] = prompt_text + return prompt_id + + # Object management + def obj_id_to_idx(self, obj_id: int) -> int: + """Map object ID to index, creating new entry if needed.""" + if obj_id not in self._obj_id_to_idx: + obj_idx = len(self._obj_id_to_idx) + self._obj_id_to_idx[obj_id] = obj_idx + self._obj_idx_to_id[obj_idx] = obj_id + self.obj_ids.append(obj_id) + + self.mask_inputs_per_obj[obj_idx] = {} + self.point_inputs_per_obj[obj_idx] = {} + self.output_dict_per_obj[obj_idx] = { + "cond_frame_outputs": {}, + "non_cond_frame_outputs": {}, + } + self.frames_tracked_per_obj[obj_idx] = {} + return self._obj_id_to_idx[obj_id] + + # Video Inference specific functions + def obj_idx_to_id(self, obj_idx: int) -> int: + """Map model-side object index to client-side object id.""" + return self._obj_idx_to_id[obj_idx] + + def get_obj_num(self) -> int: + """Get the total number of unique object ids received so far in this session.""" + return len(self._obj_idx_to_id) + + def add_mask_inputs(self, obj_idx: int, frame_idx: int, inputs: torch.Tensor): + """Add mask inputs with automatic device placement.""" + self.mask_inputs_per_obj[obj_idx][frame_idx] = inputs.to( + self.inference_device, dtype=self.dtype, non_blocking=True + ) + + def remove_mask_inputs(self, obj_idx: int, frame_idx: int): + """Remove mask inputs.""" + self.mask_inputs_per_obj[obj_idx].pop(frame_idx, None) + + def remove_object(self, obj_id: int, strict: bool = False): + """ + Remove an object from the inference session. This would remove the object from + all frames in the video. + + Args: + obj_id (`int`): The object ID to remove. + strict (`bool`, *optional*, defaults to `False`): Whether to raise an error if the object doesn't exist. + """ + old_obj_idx_to_rm = self._obj_id_to_idx.get(obj_id, None) + # Check whether this object_id to remove actually exists and possibly raise an error. + if old_obj_idx_to_rm is None: + if not strict: + return + raise RuntimeError( + f"Cannot remove object id {obj_id} as it doesn't exist. All existing object ids: {self.obj_ids}." + ) + + # Clean up prompt mapping + self.obj_id_to_prompt_id.pop(obj_id, None) + + # If this is the only remaining object id, we simply reset the state. + if len(self._obj_id_to_idx) == 1: + self.reset_inference_session() + return + + # Step 1: Update the object id mapping (note that it must be done after Step 0, + # since Step 0 still requires the old object id mappings in inference_state) + old_obj_ids = self.obj_ids + old_obj_inds = list(range(len(old_obj_ids))) + remain_old_obj_inds = old_obj_inds.copy() + remain_old_obj_inds.remove(old_obj_idx_to_rm) + new_obj_ids = [old_obj_ids[old_idx] for old_idx in remain_old_obj_inds] + new_obj_inds = list(range(len(new_obj_ids))) + # build new mappings + old_idx_to_new_idx = dict(zip(remain_old_obj_inds, new_obj_inds)) + self._obj_id_to_idx = dict(zip(new_obj_ids, new_obj_inds)) + self._obj_idx_to_id = dict(zip(new_obj_inds, new_obj_ids)) + self.obj_ids = new_obj_ids + + # Step 2: For per-object tensor storage, we shift their obj_idx in the dict keys. + def _map_keys(container): + new_kvs = [] + for k in old_obj_inds: + v = container.pop(k) + if k in old_idx_to_new_idx: + new_kvs.append((old_idx_to_new_idx[k], v)) + container.update(new_kvs) + + _map_keys(self.point_inputs_per_obj) + _map_keys(self.mask_inputs_per_obj) + _map_keys(self.output_dict_per_obj) + _map_keys(self.frames_tracked_per_obj) + + # Output management with smart device placement + def store_output( + self, + obj_idx: int, + frame_idx: int, + output_key: str | None = None, + output_value: torch.Tensor | dict | None = None, + is_conditioning_frame: bool = True, + ): + """ + Store output with smart device management. + If output_key is None, the output is stored as a dictionary. + + Args: + obj_idx (int): The index of the object. + frame_idx (int): The index of the frame. + output_key (Optional[str]): The key of the output. If None, the output is stored as a dictionary. + output_value (Optional[Union[torch.Tensor, dict]]): The value of the output. + is_conditioning_frame (bool): Whether the output is for a conditioning frame. + """ + storage_key = "cond_frame_outputs" if is_conditioning_frame else "non_cond_frame_outputs" + + if output_key is None and isinstance(output_value, dict): + self.output_dict_per_obj[obj_idx][storage_key][frame_idx] = {} + for key, value in output_value.items(): + self.store_output(obj_idx, frame_idx, key, value, is_conditioning_frame) + return + + # Device placement: small tensors stay on inference device, large ones go to inference state device + if output_key in ["object_pointer", "object_score_logits"]: # Small tensors + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value + elif isinstance(output_value, torch.Tensor): # Large tensors like masks, features + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value.to( + self.inference_state_device, non_blocking=True + ) + else: + self.output_dict_per_obj[obj_idx][storage_key][frame_idx][output_key] = output_value + + def get_output( + self, + obj_idx: int, + frame_idx: int, + output_key: str, + is_conditioning_frame: bool = True, + ): + """ + Get output with smart device management. + + Args: + obj_idx (int): The index of the object. + frame_idx (int): The index of the frame. + output_key (str): The key of the output. + is_conditioning_frame (bool): Whether the output is for a conditioning frame. + """ + storage_key = "cond_frame_outputs" if is_conditioning_frame else "non_cond_frame_outputs" + out = self.output_dict_per_obj[obj_idx][storage_key].get(frame_idx, None) + # move to inference device if needed + if out is None: + return None + value = out[output_key] + if isinstance(value, torch.Tensor): + value = value.to(self.inference_device, non_blocking=True) + return value + + # Video frame management + def add_new_frame(self, pixel_values: torch.Tensor, frame_idx: int | None = None) -> int: + """Add new frame with automatic device placement.""" + pixel_values = pixel_values.to(self.video_storage_device, dtype=self.dtype, non_blocking=True) + if pixel_values.dim() == 4: + pixel_values = pixel_values.squeeze(0) + + if frame_idx is None: + frame_idx = len(self.processed_frames) if self.processed_frames is not None else 0 + + if self.processed_frames is None: + self.processed_frames = {frame_idx: pixel_values} + else: + self.processed_frames[frame_idx] = pixel_values + + return frame_idx + + def get_frame(self, frame_idx: int) -> torch.Tensor: + """Get frame from video.""" + return self.processed_frames[frame_idx].to(self.inference_device, non_blocking=True) + + def reset_tracking_data(self): + """Reset tracking data but keep cache.""" + self._obj_id_to_idx.clear() + self._obj_idx_to_id.clear() + self.obj_ids.clear() + self.output_dict_per_obj.clear() + self.frames_tracked_per_obj.clear() + # Note: cache and video data are preserved + + # Reset prompt mappings for objects (but keep prompts themselves) + self.obj_id_to_prompt_id.clear() + + def reset_inference_session(self): + """Reset tracking data and cache.""" + self._obj_id_to_idx.clear() + self._obj_idx_to_id.clear() + self.obj_ids.clear() + self.output_dict_per_obj.clear() + self.frames_tracked_per_obj.clear() + self.cache.clear_all() + + # Reset prompt mappings for objects (but keep prompts themselves) + self.obj_id_to_prompt_id.clear() + + def reset_state(self): + """Reset the inference session state.""" + self._obj_id_to_idx = OrderedDict() + self._obj_idx_to_id = OrderedDict() + self.obj_ids = [] + self.output_dict_per_obj = {} + self.frames_tracked_per_obj = {} + + # Reset detection-tracking fusion state + self.obj_id_to_score = {} + self.obj_id_to_tracker_score_frame_wise = defaultdict(dict) + self.obj_id_to_last_occluded = {} + self.max_obj_id = 0 + self.obj_first_frame_idx = {} + self.unmatched_frame_inds = defaultdict(list) + self.overlap_pair_to_frame_inds = defaultdict(list) + self.trk_keep_alive = {} + self.removed_obj_ids = set() + self.suppressed_obj_ids = defaultdict(set) + self.output_buffer = [] + + # Reset multi-prompt state + self.prompts.clear() + self.prompt_input_ids.clear() + self.prompt_embeddings.clear() + self.prompt_attention_masks.clear() + self.obj_id_to_prompt_id.clear() + + # Clear cache + self.cache.clear_all() + + +@auto_docstring(custom_intro="Base class for the Sam3Video model's output.") +@dataclass +class Sam3VideoSegmentationOutput(ModelOutput): + r""" + object_ids (`list[int]`, *optional*): + List of object IDs being tracked in the current frame. + obj_id_to_mask (`dict[int, torch.FloatTensor]`, *optional*): + Dictionary mapping object IDs to their predicted low-resolution masks. + Each mask has shape `(1, H_low, W_low)`. + obj_id_to_score (`dict[int, float]`, *optional*): + Dictionary mapping object IDs to their detection scores. + obj_id_to_tracker_score (`dict[int, float]`, *optional*): + Dictionary mapping object IDs to their tracker scores for the current frame. + removed_obj_ids (`set[int]`, *optional*): + Set of object IDs that have been removed (e.g., via hotstart heuristics). + suppressed_obj_ids (`set[int]`, *optional*): + Set of object IDs that have been suppressed in the current frame. + frame_idx (`int`, *optional*): + The frame index of the video. + """ + + object_ids: list[int] | None = None + obj_id_to_mask: dict[int, torch.FloatTensor] | None = None + obj_id_to_score: dict[int, float] | None = None + obj_id_to_tracker_score: dict[int, float] | None = None + removed_obj_ids: set[int] | None = None + suppressed_obj_ids: set[int] | None = None + frame_idx: int | None = None + + +class Sam3VideoPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = Sam3VideoConfig + base_model_prefix = "sam3_video" + main_input_name = "pixel_values" + input_modalities = ["video", "text"] + _supports_sdpa = True + _supports_flash_attn = True + _supports_flex_attn = True + _supports_attention_backend = True + + +@auto_docstring +class Sam3VideoModel(Sam3VideoPreTrainedModel): + def __init__(self, config: Sam3VideoConfig): + super().__init__(config) + self.config = config + self.detector_model = AutoModel.from_config(config.detector_config) + self.tracker_model = AutoModel.from_config(config.tracker_config, remove_vision_encoder=True) + self.low_res_mask_size = config.low_res_mask_size + self.score_threshold_detection = config.score_threshold_detection + self.det_nms_thresh = config.det_nms_thresh + self.assoc_iou_thresh = config.assoc_iou_thresh + self.trk_assoc_iou_thresh = config.trk_assoc_iou_thresh + self.new_det_thresh = config.new_det_thresh + self.recondition_on_trk_masks = config.recondition_on_trk_masks + # hotstart parameters + self.hotstart_delay = config.hotstart_delay + self.hotstart_unmatch_thresh = config.hotstart_unmatch_thresh + self.hotstart_dup_thresh = config.hotstart_dup_thresh + self.suppress_unmatched_only_within_hotstart = config.suppress_unmatched_only_within_hotstart + self.init_trk_keep_alive = config.init_trk_keep_alive + self.max_trk_keep_alive = config.max_trk_keep_alive + self.min_trk_keep_alive = config.min_trk_keep_alive + self.suppress_overlapping_based_on_recent_occlusion_threshold = ( + config.suppress_overlapping_based_on_recent_occlusion_threshold + ) + self.decrease_trk_keep_alive_for_empty_masklets = config.decrease_trk_keep_alive_for_empty_masklets + self.fill_hole_area = config.fill_hole_area + self.eval() + + # the maximum object number + self.max_num_objects = config.max_num_objects + self.recondition_every_nth_frame = config.recondition_every_nth_frame + self.high_conf_thresh = config.high_conf_thresh + self.high_iou_thresh = config.high_iou_thresh + + self.tracker_neck = Sam3VisionNeck(config.detector_config.vision_config) + + self.post_init() + + def get_vision_features_for_tracker(self, vision_embeds: torch.Tensor): + hidden_states = vision_embeds.last_hidden_state + batch_size = hidden_states.shape[0] + height, width = self.tracker_model.prompt_encoder.image_embedding_size + hidden_states_spatial = hidden_states.view(batch_size, height, width, -1).permute(0, 3, 1, 2) + + fpn_hidden_states, fpn_position_encoding = self.tracker_neck(hidden_states_spatial) + + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + feature_maps = list(fpn_hidden_states[:-1]) + feature_maps[0] = self.tracker_model.mask_decoder.conv_s0(feature_maps[0]) + feature_maps[1] = self.tracker_model.mask_decoder.conv_s1(feature_maps[1]) + + # flatten NxCxHxW to HWxNxC + feature_maps = [feature_map.flatten(2).permute(2, 0, 1) for feature_map in feature_maps] + feature_maps_position_embeddings = [ + feature_map_position_embedding.flatten(2).permute(2, 0, 1) + for feature_map_position_embedding in fpn_position_encoding[:-1] + ] + return feature_maps, feature_maps_position_embeddings + + def run_detection( + self, + inference_session: Sam3VideoInferenceSession, + vision_embeds: torch.Tensor, + ): + """ + Run detection for all prompts efficiently by reusing vision embeddings. + + Args: + inference_session: The inference session containing prompts and state + vision_embeds: Pre-computed vision embeddings to reuse across prompts + + Returns: + Dictionary mapping prompt_id to detection outputs + """ + prompt_ids = list(inference_session.prompts.keys()) + if not prompt_ids: + raise ValueError("No prompts available for detection. Please add prompts to the session first.") + + all_detections = {} + + for prompt_id in prompt_ids: + # Get or compute text embeddings for this prompt + if prompt_id not in inference_session.prompt_embeddings: + text_embeds = self.detector_model.get_text_features( + input_ids=inference_session.prompt_input_ids[prompt_id], + attention_mask=inference_session.prompt_attention_masks[prompt_id], + return_dict=True, + ).pooler_output + inference_session.prompt_embeddings[prompt_id] = text_embeds + else: + text_embeds = inference_session.prompt_embeddings[prompt_id] + + # Run detector with cached vision features (efficient!) + detector_outputs = self.detector_model( + vision_embeds=vision_embeds, + text_embeds=text_embeds, + attention_mask=inference_session.prompt_attention_masks[prompt_id], + ) + + pred_logits = detector_outputs.pred_logits + presence_logits = detector_outputs.presence_logits + + pred_probs = pred_logits.sigmoid() + presence_scores = presence_logits.sigmoid() + pred_probs = pred_probs * presence_scores + + run_nms = self.det_nms_thresh > 0.0 + if run_nms: + keep = nms_masks( + pred_probs=pred_probs[0], + pred_masks=detector_outputs.pred_masks[0], + prob_threshold=self.score_threshold_detection, + iou_threshold=self.det_nms_thresh, + ) + # Set suppressed detections' probabilities to 0 + pred_probs[0][~keep] = 0.0 + + pred_boxes_xyxy = detector_outputs.pred_boxes + pred_masks = detector_outputs.pred_masks + # get the positive detection outputs above threshold + pos_pred_idx = torch.where(pred_probs > self.score_threshold_detection) + det_out = { + "bbox": pred_boxes_xyxy[pos_pred_idx[0], pos_pred_idx[1]], + "mask": pred_masks[pos_pred_idx[0], pos_pred_idx[1]], + "scores": pred_probs[pos_pred_idx[0], pos_pred_idx[1]], + } + + all_detections[prompt_id] = det_out + + return all_detections + + def run_tracker_propagation( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + reverse: bool, + ): + low_res_masks_list = [] + obj_scores_list = [] + if len(inference_session.obj_ids) > 0: + # propagate one frame + out = self.tracker_model( + inference_session=inference_session, + frame_idx=frame_idx, + reverse=reverse, + run_mem_encoder=False, + ) + out_low_res_masks = out.pred_masks + out_obj_scores = out.object_score_logits + + # only 1 frames should be propagated + low_res_masks_list.append(out_low_res_masks.squeeze(1)) + obj_scores_list.append(out_obj_scores.squeeze(1)) + + # concatenate the output masklets from all local inference states + H_mask = W_mask = self.low_res_mask_size + if len(low_res_masks_list) > 0: + low_res_masks = torch.cat(low_res_masks_list, dim=0) + obj_scores = torch.cat(obj_scores_list, dim=0) + + # Apply hole filling to the masks + low_res_masks = fill_holes_in_mask_scores( + low_res_masks.unsqueeze(1), + max_area=self.fill_hole_area, + fill_holes=True, + remove_sprinkles=True, + ) + low_res_masks = low_res_masks.squeeze(1) + else: + low_res_masks = torch.zeros(0, H_mask, W_mask, device=self.device) + obj_scores = torch.zeros(0, device=self.device) + + return low_res_masks, obj_scores + + def _associate_det_trk( + self, + det_masks: Tensor, + det_scores: Tensor, + trk_masks: Tensor, + trk_obj_ids: list[int], + det_prompt_ids: torch.Tensor, + trk_prompt_ids: torch.Tensor, + ): + """ + Match detections on the current frame with the existing masklets. + + Args: + - det_masks: (N, H, W) tensor of predicted masks + - det_scores: (N,) tensor of detection scores + - trk_masks: (M, H, W) tensor of track masks + - trk_obj_ids: (M,) list of object IDs corresponding to trk_masks + - det_prompt_ids: (N,) tensor of prompt IDs for each detection. Prevents cross-prompt + associations by zeroing IoUs between detections and tracks from different prompts. + - trk_prompt_ids: (M,) tensor of prompt IDs for each tracked object. Prevents cross-prompt + associations by zeroing IoUs between detections and tracks from different prompts. + + Returns: + - new_det_out_inds: list of new object indices among in FA detection outputs + - unmatched_trk_obj_ids: list of existing masklet object IDs that are not matched + to any detections on this frame (for unmatched, we only count masklets with >0 area) + - det_to_matched_trk_obj_ids: dict[int, list[int]]: mapping from FA detection indices + to the list of matched tracklet object IDs + - empty_trk_obj_ids: list of existing masklet object IDs with zero area in SAM2 prediction + """ + iou_threshold = self.assoc_iou_thresh + iou_threshold_trk = self.trk_assoc_iou_thresh + new_det_thresh = self.new_det_thresh + + trk_obj_ids_tensor = ( + torch.tensor(trk_obj_ids, dtype=torch.long, device=det_masks.device) + if trk_obj_ids + else torch.empty(0, dtype=torch.long, device=det_masks.device) + ) + if trk_masks.size(0) == 0: + # all detections are new + new_det_out_inds = list(range(det_masks.size(0))) + unmatched_trk_obj_ids = [] + empty_trk_obj_ids = [] + det_to_matched_trk_obj_ids = {} + trk_id_to_max_iou_high_conf_det = {} + return ( + new_det_out_inds, + unmatched_trk_obj_ids, + det_to_matched_trk_obj_ids, + trk_id_to_max_iou_high_conf_det, + empty_trk_obj_ids, + ) + elif det_masks.size(0) == 0: + # all previous tracklets are unmatched if they have a non-zero area + new_det_out_inds = [] + trk_is_nonempty = (trk_masks > 0).any(dim=(1, 2)) # (M,) tensor + # Use tensor boolean indexing - elegant and avoids intermediate conversions + unmatched_trk_obj_ids = trk_obj_ids_tensor[trk_is_nonempty].tolist() + empty_trk_obj_ids = trk_obj_ids_tensor[~trk_is_nonempty].tolist() + det_to_matched_trk_obj_ids = {} + trk_id_to_max_iou_high_conf_det = {} + return ( + new_det_out_inds, + unmatched_trk_obj_ids, + det_to_matched_trk_obj_ids, + trk_id_to_max_iou_high_conf_det, + empty_trk_obj_ids, + ) + + det_masks_binary = det_masks > 0 + trk_masks_binary = trk_masks > 0 + ious = mask_iou(det_masks_binary, trk_masks_binary) # (N, M) tensor + + # Prevent cross-prompt associations by zeroing IoUs between different prompt groups. + prompt_match = det_prompt_ids.unsqueeze(1) == trk_prompt_ids.unsqueeze(0) + ious = torch.where(prompt_match, ious, torch.zeros_like(ious)) + + # trk_is_matched: for each track, True if matched to any detection above threshold + trk_is_matched = (ious >= iou_threshold_trk).any(dim=0) # (M,) + # Non-empty tracks not matched by Hungarian assignment above threshold are unmatched + trk_is_nonempty = trk_masks_binary.any(dim=(1, 2)) # (M,) + trk_is_unmatched = trk_is_nonempty & ~trk_is_matched # (M,) + # Use tensor boolean indexing directly - no intermediate conversions + unmatched_trk_obj_ids = trk_obj_ids_tensor[trk_is_unmatched].tolist() + empty_trk_obj_ids = trk_obj_ids_tensor[~trk_is_nonempty].tolist() + + # For detections: allow many tracks to match to the same detection (many-to-one) + # So, a detection is 'new' if it does not match any track above threshold + det_matches_any_trk = (ious >= iou_threshold).any(dim=1) # (N,) + is_new_det = (det_scores >= new_det_thresh) & ~det_matches_any_trk # (N,) + new_det_out_inds = torch.where(is_new_det)[0].tolist() + + # Build detection-to-track mappings using tensor operations + det_to_matched_trk_obj_ids = {} + trk_id_to_max_iou_high_conf_det = {} # trk id --> exactly one detection idx + det_to_max_iou_trk_idx = ious.argmax(dim=1) # (N,) + det_is_high_conf = (det_scores >= self.high_conf_thresh) & ~is_new_det # (N,) + det_max_iou = ious.max(dim=1)[0] # (N,) + det_is_high_iou = det_max_iou >= self.high_iou_thresh # (N,) + det_is_high_conf_and_iou = det_is_high_conf & det_is_high_iou # (N,) + high_conf_and_iou_mask = det_is_high_conf_and_iou # Keep as tensor + + for det_idx in range(det_masks.size(0)): + # Find which tracks match this detection using tensor boolean indexing + matched_trk_mask = ious[det_idx] >= iou_threshold # (M,) + det_to_matched_trk_obj_ids[det_idx] = trk_obj_ids_tensor[matched_trk_mask].tolist() + + if high_conf_and_iou_mask[det_idx].item(): + trk_idx = det_to_max_iou_trk_idx[det_idx].item() + trk_obj_id = trk_obj_ids_tensor[trk_idx].item() + trk_id_to_max_iou_high_conf_det[trk_obj_id] = det_idx + + return ( + new_det_out_inds, + unmatched_trk_obj_ids, + det_to_matched_trk_obj_ids, + trk_id_to_max_iou_high_conf_det, + empty_trk_obj_ids, + ) + + def _process_hotstart( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + reverse: bool, + det_to_matched_trk_obj_ids: dict[int, list[int]], + new_det_obj_ids: list[int], + empty_trk_obj_ids: list[int], + unmatched_trk_obj_ids: list[int], + extra_metadata: dict[str, Any], + streaming: bool = False, + ): + """ + Handle hotstart heuristics to remove unmatched or duplicated objects. + + In streaming mode, hotstart removal logic is disabled since we don't have + future frames to make informed decisions about object removal. + """ + # obj_id --> first frame index where the object was detected + obj_first_frame_idx = extra_metadata["obj_first_frame_idx"] + # obj_id --> [mismatched frame indices] + unmatched_frame_inds = extra_metadata["unmatched_frame_inds"] + trk_keep_alive = extra_metadata["trk_keep_alive"] + # (first_appear_obj_id, obj_id) --> [overlap frame indices] + overlap_pair_to_frame_inds = extra_metadata["overlap_pair_to_frame_inds"] + # removed_obj_ids: object IDs that are suppressed via hot-start + removed_obj_ids = extra_metadata["removed_obj_ids"] + suppressed_obj_ids = extra_metadata["suppressed_obj_ids"][frame_idx] + + obj_ids_newly_removed = set() # object IDs to be newly removed on this frame + hotstart_diff = frame_idx - self.hotstart_delay if not reverse else frame_idx + self.hotstart_delay + + # Step 1: log the frame index where each object ID first appears + for obj_id in new_det_obj_ids: + if obj_id not in obj_first_frame_idx: + obj_first_frame_idx[obj_id] = frame_idx + trk_keep_alive[int(obj_id)] = self.init_trk_keep_alive + + matched_trks = set() + # We use the det-->tracks list to check for matched objects. Otherwise, we need to compute areas to decide whether they're occluded + for matched_trks_per_det in det_to_matched_trk_obj_ids.values(): + matched_trks.update({int(obj_id) for obj_id in matched_trks_per_det}) + for obj_id in matched_trks: + # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the max value of trk_keep_alive + trk_keep_alive[int(obj_id)] = min(self.max_trk_keep_alive, trk_keep_alive[int(obj_id)] + 1) + for obj_id in unmatched_trk_obj_ids: + unmatched_frame_inds[obj_id].append(frame_idx) + # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the min value of trk_keep_alive + # The max keep alive is 2x the min, means the model prefers to keep the prediction rather than suppress it if it was matched long enough. + trk_keep_alive[int(obj_id)] = max(self.min_trk_keep_alive, trk_keep_alive[int(obj_id)] - 1) + if self.decrease_trk_keep_alive_for_empty_masklets: + for obj_id in empty_trk_obj_ids: + # NOTE: To minimize number of configurable params, we use the hotstart_unmatch_thresh to set the min value of trk_keep_alive + trk_keep_alive[int(obj_id)] = max(self.min_trk_keep_alive, trk_keep_alive[int(obj_id)] - 1) + + # Step 2: removed tracks that has not matched with detections for `hotstart_unmatch_thresh` frames with hotstart period + # a) add unmatched frame indices for each existing object ID + # note that `unmatched_trk_obj_ids` contains those frames where the SAM2 output mask + # doesn't match any FA detection; it excludes those frames where SAM2 gives an empty mask + # b) remove a masklet if it first appears after `hotstart_diff` and is unmatched for more + # than `self.hotstart_unmatch_thresh` frames + # NOTE: In streaming mode, we skip hotstart removal logic since we don't have future frames + if not streaming: + for obj_id, frame_indices in unmatched_frame_inds.items(): + if obj_id in removed_obj_ids or obj_id in obj_ids_newly_removed: + continue # skip if the object is already removed + if len(frame_indices) >= self.hotstart_unmatch_thresh: + is_within_hotstart = (obj_first_frame_idx[obj_id] > hotstart_diff and not reverse) or ( + obj_first_frame_idx[obj_id] < hotstart_diff and reverse + ) + if is_within_hotstart: + obj_ids_newly_removed.add(obj_id) + logger.info( + f"Removing object {obj_id} at frame {frame_idx} " + f"since it is unmatched for frames: {frame_indices}" + ) + if ( + trk_keep_alive[obj_id] <= 0 # Object has not been matched for too long + and not self.suppress_unmatched_only_within_hotstart + and obj_id not in removed_obj_ids + and obj_id not in obj_ids_newly_removed + ): + logger.debug(f"Suppressing object {obj_id} at frame {frame_idx}, due to being unmatched") + suppressed_obj_ids.add(obj_id) + + # Step 3: removed tracks that overlaps with another track for `hotstart_dup_thresh` frames + # a) find overlaps tracks -- we consider overlap if they match to the same detection + # NOTE: In streaming mode, we still track overlaps for metadata but skip removal logic + for matched_trk_obj_ids in det_to_matched_trk_obj_ids.values(): + if len(matched_trk_obj_ids) < 2: + continue # only count detections that are matched to multiple (>=2) masklets + # if there are multiple matched track ids, we need to find the one that appeared first; + # these later appearing ids may be removed since they may be considered as duplicates + first_appear_obj_id = ( + min(matched_trk_obj_ids, key=lambda x: obj_first_frame_idx[x]) + if not reverse + else max(matched_trk_obj_ids, key=lambda x: obj_first_frame_idx[x]) + ) + for obj_id in matched_trk_obj_ids: + if obj_id != first_appear_obj_id: + key = (first_appear_obj_id, obj_id) + overlap_pair_to_frame_inds[key].append(frame_idx) + + # b) remove a masklet if it first appears after `hotstart_diff` and it overlaps with another + # masklet (that appears earlier) for more than `self.hotstart_dup_thresh` frames + # NOTE: In streaming mode, we skip hotstart removal logic since we don't have future frames + if not streaming: + for (first_obj_id, obj_id), frame_indices in overlap_pair_to_frame_inds.items(): + if obj_id in removed_obj_ids or obj_id in obj_ids_newly_removed: + continue # skip if the object is already removed + if (obj_first_frame_idx[obj_id] > hotstart_diff and not reverse) or ( + obj_first_frame_idx[obj_id] < hotstart_diff and reverse + ): + if len(frame_indices) >= self.hotstart_dup_thresh: + obj_ids_newly_removed.add(obj_id) + logger.info( + f"Removing object {obj_id} at frame {frame_idx} " + f"since it overlaps with another track {first_obj_id} at frames: {frame_indices}" + ) + + removed_obj_ids.update(obj_ids_newly_removed) + return obj_ids_newly_removed, extra_metadata + + def run_memory_encoder( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + high_res_masks: torch.Tensor, + object_score_logits: torch.Tensor, + ): + """ + Run the memory encoder on `high_res_masks`. This is usually after applying + non-overlapping constraints to object scores. Since their scores changed, their + memory also need to be computed again with the memory encoder. + """ + # Retrieve correct image features + cached_features = inference_session.cache.get_vision_features(frame_idx) + current_vision_feats = cached_features["vision_feats"] + maskmem_features, maskmem_pos_enc = self.tracker_model._encode_new_memory( + current_vision_feats=current_vision_feats[-1], + pred_masks_high_res=high_res_masks, + object_score_logits=object_score_logits, + is_mask_from_pts=False, + ) + return maskmem_features, maskmem_pos_enc + + def _prepare_recondition_masks( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + det_out: dict[str, Tensor], + trk_masks: Tensor, + trk_id_to_max_iou_high_conf_det: dict[int, int], + tracker_obj_scores_global: Tensor, + ) -> dict[int, Tensor]: + """ + Prepare high-resolution masks for reconditioned objects. + Returns a dict of obj_idx -> high_res_mask for objects that should be reconditioned. + + When recondition_on_trk_masks=True, uses detector as validation signal to strengthen tracker memory. + When False, uses detector to correct tracker drift by replacing with detection masks. + """ + reconditioned_masks = {} + reconditioned_obj_ids = set() + + for trk_obj_id, det_idx in trk_id_to_max_iou_high_conf_det.items(): + obj_idx = inference_session.obj_id_to_idx(trk_obj_id) + obj_score = tracker_obj_scores_global[obj_idx] + if obj_score <= self.high_conf_thresh: + continue + + if self.recondition_on_trk_masks: + # Validation mode: detector confirms tracker quality, strengthen memory with tracked mask + new_mask = trk_masks[obj_idx : obj_idx + 1].unsqueeze(1) + reconditioned_masks[obj_idx] = new_mask + reconditioned_obj_ids.add(trk_obj_id) + else: + # Correction mode: detector corrects drift, replace tracker mask with detection mask + new_mask = det_out["mask"][det_idx : det_idx + 1].unsqueeze(1) + reconditioned_masks[obj_idx] = new_mask >= 0.5 + reconditioned_obj_ids.add(trk_obj_id) + + return reconditioned_masks, reconditioned_obj_ids + + def _get_objects_to_suppress_based_on_most_recently_occluded( + self, + binary_low_res_masks: Tensor, + last_occluded: list[int], + obj_ids: list[int], + reverse: bool = False, + ): + # Suppress overlapping masks for objects that were most recently occluded + to_suppress = torch.zeros( + binary_low_res_masks.size(0), + device=binary_low_res_masks.device, + dtype=torch.bool, + ) + if len(obj_ids) <= 1: + return to_suppress + + iou = mask_iou(binary_low_res_masks, binary_low_res_masks) # [N,N] + + # Create masks for upper triangular matrix (i < j) and IoU threshold + mask_iou_thresh = iou >= self.suppress_overlapping_based_on_recent_occlusion_threshold + overlapping_pairs = torch.triu(mask_iou_thresh, diagonal=1) # [N,N] + + last_occ_expanded_i = last_occluded.unsqueeze(1) # (N, 1) + last_occ_expanded_j = last_occluded.unsqueeze(0) # (1, N) + # Suppress most recently occluded + cmp_op = torch.gt if not reverse else torch.lt + suppress_i_mask = ( + overlapping_pairs + & cmp_op(last_occ_expanded_i, last_occ_expanded_j) # (last_occ_expanded_i > last_occ_expanded_j) + & (last_occ_expanded_j > -1) # j can suppress i only if i was previously occluded + ) + suppress_j_mask = ( + overlapping_pairs + & cmp_op(last_occ_expanded_j, last_occ_expanded_i) + & (last_occ_expanded_i > -1) # i can suppress j only if j was previously occluded + ) + # Apply suppression + to_suppress = suppress_i_mask.any(dim=1) | suppress_j_mask.any(dim=0) + + return to_suppress + + def _suppress_overlapping_based_on_recent_occlusion( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + tracker_low_res_masks_global: Tensor, + tracker_metadata_new: dict[str, Any], + obj_ids_newly_removed: set[int], + reverse: bool = False, + ): + """ + Suppress overlapping masks based on the most recent occlusion information. If an object is removed by hotstart, we always suppress it if it overlaps with any other object. + Args: + frame_idx (int): The current frame index. + tracker_low_res_masks_global (Tensor): The low-resolution masks for the current frame. + tracker_metadata_prev (Dict[str, Any]): The metadata from the previous frame. + tracker_metadata_new (Dict[str, Any]): The metadata for the current frame. + obj_ids_newly_removed (Set[int]): The object IDs that have been removed. + Return: + Tensor: The updated low-resolution masks with some objects suppressed. + """ + obj_ids_global = inference_session.obj_ids + binary_tracker_low_res_masks_global = tracker_low_res_masks_global > 0 + batch_size = tracker_low_res_masks_global.size(0) + if batch_size > 0: + NEVER_OCCLUDED = -1 + ALWAYS_OCCLUDED = 100000 # This value should be larger than any possible frame index, indicates that the object was removed by hotstart logic + last_occluded_prev = torch.cat( + [ + inference_session.obj_id_to_last_occluded.get( + obj_id, + torch.full( + (1,), + fill_value=(NEVER_OCCLUDED if obj_id not in obj_ids_newly_removed else ALWAYS_OCCLUDED), + device=binary_tracker_low_res_masks_global.device, + dtype=torch.long, + ), + ) + for obj_id in obj_ids_global + ], + dim=0, + ) + + prompt_ids_global = torch.tensor( + [inference_session.obj_id_to_prompt_id[obj_id] for obj_id in obj_ids_global], + device=binary_tracker_low_res_masks_global.device, + dtype=torch.long, + ) + to_suppress = torch.zeros( + batch_size, + device=binary_tracker_low_res_masks_global.device, + dtype=torch.bool, + ) + + # Only suppress overlaps within the same prompt group. + unique_prompts = prompt_ids_global.unique(sorted=True) + for prompt_id in unique_prompts: + prompt_mask = prompt_ids_global == prompt_id + prompt_indices = torch.nonzero(prompt_mask, as_tuple=True)[0] + if prompt_indices.numel() <= 1: + continue + + prompt_masks = binary_tracker_low_res_masks_global[prompt_indices] + prompt_last_occ = last_occluded_prev[prompt_indices] + prompt_obj_ids = [obj_ids_global[idx] for idx in prompt_indices.tolist()] + prompt_suppress = self._get_objects_to_suppress_based_on_most_recently_occluded( + prompt_masks, + prompt_last_occ, + prompt_obj_ids, + reverse, + ) + to_suppress[prompt_indices] = prompt_suppress + + # Update metadata with occlusion information + is_obj_occluded = ~(binary_tracker_low_res_masks_global.any(dim=(-1, -2))) + is_obj_occluded_or_suppressed = is_obj_occluded | to_suppress + last_occluded_new = last_occluded_prev.clone() + last_occluded_new[is_obj_occluded_or_suppressed] = frame_idx + # Slice out the last occluded frame for each object + tracker_metadata_new["obj_id_to_last_occluded"] = { + obj_id: last_occluded_new[obj_idx : obj_idx + 1] for obj_idx, obj_id in enumerate(obj_ids_global) + } + + # Zero out suppressed masks before memory encoding + NO_OBJ_LOGIT = -10 + tracker_low_res_masks_global[to_suppress] = NO_OBJ_LOGIT + + return tracker_low_res_masks_global + + def _apply_non_overlapping_constraints(self, pred_masks): + """ + Apply non-overlapping constraints to the object scores in pred_masks. Here we + keep only the highest scoring object at each spatial location in pred_masks. + """ + batch_size = pred_masks.size(0) + if batch_size == 1: + return pred_masks + + device = pred_masks.device + # "max_obj_inds": object index of the object with the highest score at each location + max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) + # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` + batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] + keep = max_obj_inds == batch_obj_inds + # suppress overlapping regions' scores below -10.0 so that the foreground regions + # don't overlap (here sigmoid(-10.0)=4.5398e-05) + pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) + return pred_masks + + def _suppress_shrinked_masks(self, pred_masks, new_pred_masks, shrink_threshold=0.3): + area_before = (pred_masks > 0).sum(dim=(-1, -2)) + area_after = (new_pred_masks > 0).sum(dim=(-1, -2)) + area_before = torch.clamp(area_before, min=1.0) + area_ratio = area_after / area_before + keep = area_ratio >= shrink_threshold + keep_mask = keep[..., None, None].expand_as(pred_masks) + pred_masks_after = torch.where(keep_mask, pred_masks, torch.clamp(pred_masks, max=-10.0)) + return pred_masks_after + + def _suppress_object_pw_area_shrinkage( + self, + pred_masks, + prompt_ids: list[int] | None = None, + ): + """ + This function suppresses masks that shrink in area after applying pixelwise non-overlapping constraints. + When `prompt_ids` are provided, constraints are enforced independently per prompt group. + """ + if prompt_ids is None: + return self._suppress_object_pw_area_shrinkage_impl(pred_masks) + + if len(prompt_ids) != pred_masks.size(0): + raise ValueError("prompt_ids must have the same length as pred_masks") + + prompt_ids_tensor = torch.tensor(prompt_ids, device=pred_masks.device, dtype=torch.long) + pred_masks_grouped = pred_masks.clone() + for prompt_id in prompt_ids_tensor.unique(sorted=True): + indices = torch.nonzero(prompt_ids_tensor == prompt_id, as_tuple=True)[0] + if indices.numel() == 0: + continue + pred_masks_grouped[indices] = self._suppress_object_pw_area_shrinkage_impl(pred_masks_grouped[indices]) + return pred_masks_grouped + + def _suppress_object_pw_area_shrinkage_impl(self, pred_masks): + if pred_masks.size(0) <= 1: + return pred_masks + + pixel_level_non_overlapping_masks = self._apply_non_overlapping_constraints(pred_masks) + pred_masks = self._suppress_shrinked_masks(pred_masks, pixel_level_non_overlapping_masks) + return pred_masks + + def _tracker_update_memories( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + low_res_masks: Tensor, + reconditioned_masks: dict[int, Tensor] | None = None, + ): + """ + Run Sam3Tracker memory encoder, enforcing non-overlapping constraints globally. + Now with batched memory encoding for better performance. + + Args: + inference_session: The inference session state + frame_idx: Current frame index + low_res_masks: Low-resolution tracker masks for all objects + reconditioned_masks: Optional dict of obj_idx -> high_res_mask for objects that + should use detection masks instead of tracker masks + """ + if len(inference_session.obj_ids) == 0: + return + + if reconditioned_masks is None: + reconditioned_masks = {} + # Interpolate tracker masks to high resolution + high_res_masks = low_res_masks.unsqueeze(1) + + # Override with detection masks for reconditioned objects + for obj_idx, recond_mask in reconditioned_masks.items(): + high_res_masks[obj_idx] = recond_mask.float() + # Mark as conditioning frame for reconditioned objects + output_dict = inference_session.output_dict_per_obj[obj_idx] + if frame_idx in output_dict["non_cond_frame_outputs"]: + current_out = output_dict["non_cond_frame_outputs"].pop(frame_idx) + output_dict["cond_frame_outputs"][frame_idx] = current_out + + # Apply non-overlapping constraints before memory encoding. + # Constraints are enforced independently per prompt group. + # Every object ID has a prompt_id assigned when it's created. + prompt_ids_for_objects = [ + inference_session.obj_id_to_prompt_id[obj_id] for obj_id in inference_session.obj_ids + ] + high_res_masks = self._suppress_object_pw_area_shrinkage(high_res_masks, prompt_ids_for_objects) + # Use mask areas as a proxy for object scores + object_score_logits = torch.where((high_res_masks > 0).any(dim=(-1, -2)), 10.0, -10.0) + + # Run memory encoder in batch for all objects at once + num_objects = len(inference_session.obj_ids) + object_score_logits_batched = object_score_logits.unsqueeze(-1) # Shape: (num_objects, 1) + + # Encode memories for all objects in one batch call + maskmem_features_batched, maskmem_pos_enc_batched = self.run_memory_encoder( + inference_session, + frame_idx, + high_res_masks, # Shape: (num_objects, 1, H, W) + object_score_logits_batched, # Shape: (num_objects, 1) + ) + + # Split and store encoded memories per object + for obj_idx in range(num_objects): + output_dict = inference_session.output_dict_per_obj[obj_idx] + # Extract per-object memory from batched result + maskmem_features = maskmem_features_batched[:, obj_idx : obj_idx + 1] + maskmem_pos_enc = maskmem_pos_enc_batched[:, obj_idx : obj_idx + 1] + + for storage_key in ["cond_frame_outputs", "non_cond_frame_outputs"]: + if frame_idx not in output_dict[storage_key]: + continue + current_out = output_dict[storage_key][frame_idx] + current_out["maskmem_features"] = maskmem_features + current_out["maskmem_pos_enc"] = maskmem_pos_enc + + def run_tracker_update_planning_phase( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + reverse: bool, + det_out: dict[str, Tensor], + tracker_low_res_masks_global: Tensor, + tracker_obj_scores_global: Tensor, + det_idx_to_prompt_id: dict[int, int], + streaming: bool = False, + ): + # initialize new metadata from previous metadata (its values will be updated later) + tracker_metadata_new = { + "obj_ids": deepcopy(inference_session.obj_ids), + "obj_id_to_score": deepcopy(inference_session.obj_id_to_score), + "obj_id_to_tracker_score_frame_wise": deepcopy(inference_session.obj_id_to_tracker_score_frame_wise), + "obj_id_to_last_occluded": {}, # will be filled later + "max_obj_id": deepcopy(inference_session.max_obj_id), + } + + # Initialize reconditioned_obj_ids early to avoid UnboundLocalError + reconditioned_obj_ids = set() + + # Step 1: make the update plan and resolve heuristics + det_mask_preds: Tensor = det_out["mask"] # low-res mask logits + det_scores: Tensor = det_out["scores"].float() # Keep as tensor! + # det_idx_to_prompt_id maps every detection index to its prompt_id (created by _merge_detections_from_prompts). + det_prompt_ids = ( + torch.tensor( + [det_idx_to_prompt_id[idx] for idx in range(det_mask_preds.size(0))], + device=det_mask_preds.device, + dtype=torch.long, + ) + if det_mask_preds.size(0) > 0 + else torch.empty(0, device=det_mask_preds.device, dtype=torch.long) + ) + # Get prompt IDs for tracked objects. + trk_prompt_ids = ( + torch.tensor( + [inference_session.obj_id_to_prompt_id[obj_id] for obj_id in inference_session.obj_ids], + device=tracker_low_res_masks_global.device + if tracker_low_res_masks_global.numel() > 0 + else det_mask_preds.device, + dtype=torch.long, + ) + if tracker_low_res_masks_global.numel() > 0 + else torch.empty(0, device=det_mask_preds.device, dtype=torch.long) + ) + # a) match FA and SAM2 masks and find new objects + ( + new_det_out_inds, + unmatched_trk_obj_ids, + det_to_matched_trk_obj_ids, + trk_id_to_max_iou_high_conf_det, + empty_trk_obj_ids, + ) = self._associate_det_trk( + det_masks=det_mask_preds, + det_scores=det_scores, + trk_masks=tracker_low_res_masks_global, + trk_obj_ids=inference_session.obj_ids, + det_prompt_ids=det_prompt_ids, + trk_prompt_ids=trk_prompt_ids, + ) + + # check whether we've hit the maximum number of objects we can track (and if so, drop some detections) + prev_obj_num = len(inference_session.obj_ids) + new_det_num = len(new_det_out_inds) + num_obj_dropped_due_to_limit = 0 + if prev_obj_num + new_det_num > self.max_num_objects: + logger.warning(f"hitting {self.max_num_objects=} with {new_det_num=} and {prev_obj_num=}") + new_det_num_to_keep = self.max_num_objects - prev_obj_num + num_obj_dropped_due_to_limit = new_det_num - new_det_num_to_keep + # Keep top scoring detections + new_det_inds_tensor = torch.tensor(new_det_out_inds, dtype=torch.long, device=det_scores.device) + scores_for_new_dets = det_scores[new_det_inds_tensor] + _, top_inds = torch.topk(scores_for_new_dets, k=new_det_num_to_keep, largest=True) + new_det_out_inds = [new_det_out_inds[i] for i in top_inds] + new_det_num = len(new_det_out_inds) + + # assign object IDs to new detections + new_det_start_obj_id = inference_session.max_obj_id + 1 + new_det_obj_ids = list(range(new_det_start_obj_id, new_det_start_obj_id + new_det_num)) + + # Assign prompt IDs to new objects based on which prompt detected them. + for obj_id, det_idx in zip(new_det_obj_ids, new_det_out_inds): + prompt_id = det_idx_to_prompt_id[det_idx] + inference_session.obj_id_to_prompt_id[obj_id] = prompt_id + + # b) handle hotstart heuristics to remove objects + extra_metadata_new = deepcopy( + { + "obj_first_frame_idx": inference_session.obj_first_frame_idx, + "unmatched_frame_inds": inference_session.unmatched_frame_inds, + "trk_keep_alive": inference_session.trk_keep_alive, + "overlap_pair_to_frame_inds": inference_session.overlap_pair_to_frame_inds, + "removed_obj_ids": inference_session.removed_obj_ids, + "suppressed_obj_ids": inference_session.suppressed_obj_ids, + } + ) + + obj_ids_newly_removed, extra_metadata_new = self._process_hotstart( + inference_session=inference_session, + frame_idx=frame_idx, + reverse=reverse, + det_to_matched_trk_obj_ids=det_to_matched_trk_obj_ids, + new_det_obj_ids=new_det_obj_ids, + empty_trk_obj_ids=empty_trk_obj_ids, + unmatched_trk_obj_ids=unmatched_trk_obj_ids, + extra_metadata=extra_metadata_new, + streaming=streaming, + ) + tracker_metadata_new["extra_metadata"] = extra_metadata_new + + # Step 3 (optional): prepare reconditioned masks based on high-confidence detections + reconditioned_masks = {} + reconditioned_obj_ids = set() + should_recondition_periodic = ( + self.recondition_every_nth_frame > 0 + and frame_idx % self.recondition_every_nth_frame == 0 + and len(trk_id_to_max_iou_high_conf_det) > 0 + ) + + if should_recondition_periodic: + reconditioned_masks, reconditioned_obj_ids = self._prepare_recondition_masks( + inference_session=inference_session, + frame_idx=frame_idx, + det_out=det_out, + trk_masks=tracker_low_res_masks_global, + trk_id_to_max_iou_high_conf_det=trk_id_to_max_iou_high_conf_det, + tracker_obj_scores_global=tracker_obj_scores_global, + ) + + tracker_update_plan = { + "new_det_out_inds": new_det_out_inds, # List[int] + "new_det_obj_ids": new_det_obj_ids, # List[int] + "unmatched_trk_obj_ids": unmatched_trk_obj_ids, # List[int] + "det_to_matched_trk_obj_ids": det_to_matched_trk_obj_ids, # dict + "obj_ids_newly_removed": obj_ids_newly_removed, # set + "num_obj_dropped_due_to_limit": num_obj_dropped_due_to_limit, # int + "trk_id_to_max_iou_high_conf_det": trk_id_to_max_iou_high_conf_det, # dict + "reconditioned_obj_ids": reconditioned_obj_ids, # set + } + + # Step 4: Run SAM2 memory encoder on the current frame's prediction masks + # This uses tracker masks for most objects, but detection masks for reconditioned objects + batch_size = tracker_low_res_masks_global.size(0) + if batch_size > 0: + if self.suppress_overlapping_based_on_recent_occlusion_threshold > 0.0: + # NOTE: tracker_low_res_masks_global is updated in-place then returned + tracker_low_res_masks_global = self._suppress_overlapping_based_on_recent_occlusion( + inference_session=inference_session, + frame_idx=frame_idx, + tracker_low_res_masks_global=tracker_low_res_masks_global, + tracker_metadata_new=tracker_metadata_new, + obj_ids_newly_removed=obj_ids_newly_removed, + reverse=reverse, + ) + + # Unified memory encoding: uses detection masks for reconditioned objects + self._tracker_update_memories( + inference_session=inference_session, + frame_idx=frame_idx, + low_res_masks=tracker_low_res_masks_global, + reconditioned_masks=reconditioned_masks, + ) + + # Step 5: update the SAM2 metadata based on the update plan + updated_obj_ids = tracker_metadata_new["obj_ids"] + if len(new_det_obj_ids) > 0: + updated_obj_ids = updated_obj_ids + new_det_obj_ids + if len(obj_ids_newly_removed) > 0: + updated_obj_ids = [obj_id for obj_id in updated_obj_ids if obj_id not in obj_ids_newly_removed] + tracker_metadata_new["obj_ids"] = updated_obj_ids + + # update object scores and the maximum object ID assigned so far + if len(new_det_obj_ids) > 0: + # Index tensor with list of indices and convert to list + new_det_scores = det_scores[ + torch.tensor(new_det_out_inds, dtype=torch.long, device=det_scores.device) + ].tolist() + tracker_metadata_new["obj_id_to_score"].update(zip(new_det_obj_ids, new_det_scores)) + # tracker scores are not available for new objects, use det score instead. + tracker_metadata_new["obj_id_to_tracker_score_frame_wise"][frame_idx].update( + zip(new_det_obj_ids, new_det_scores) + ) + tracker_metadata_new["max_obj_id"] = max( + tracker_metadata_new["max_obj_id"], + max(new_det_obj_ids), + ) + # for removed objects, we set their scores to a very low value (-1e4) but still + # keep them in "obj_id_to_score" (it's easier to handle outputs this way) + for obj_id in obj_ids_newly_removed: + tracker_metadata_new["obj_id_to_score"][obj_id] = -1e4 + tracker_metadata_new["obj_id_to_tracker_score_frame_wise"][frame_idx][obj_id] = -1e4 + tracker_metadata_new["obj_id_to_last_occluded"].pop(obj_id, None) + + return tracker_update_plan, tracker_metadata_new + + def _tracker_add_new_objects( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + new_obj_ids: list[int], + new_obj_masks: Tensor, + reverse: bool = False, + ): + """Add a new object to SAM2 inference states.""" + new_obj_masks = new_obj_masks >= 0.5 + for obj_id, mask in zip(new_obj_ids, new_obj_masks): + obj_idx = inference_session.obj_id_to_idx(obj_id) + inference_session.add_mask_inputs(obj_idx, frame_idx, mask.unsqueeze(0).unsqueeze(0)) + + inference_session.obj_with_new_inputs = list(new_obj_ids) + + self.tracker_model( + inference_session=inference_session, + frame_idx=frame_idx, + reverse=reverse, + run_mem_encoder=True, + ) + + def run_tracker_update_execution_phase( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + det_out: dict[str, Tensor], + tracker_update_plan: dict, + reverse: bool = False, + ): + # initialize tracking scores with detection scores + new_det_out_inds: list[int] = tracker_update_plan["new_det_out_inds"] + new_det_obj_ids: list[int] = tracker_update_plan["new_det_obj_ids"] + obj_ids_newly_removed: set[int] = tracker_update_plan["obj_ids_newly_removed"] + + # Step 1: add new objects from FA detection to SAM2 inference states + if len(new_det_out_inds) > 0: + new_det_out_inds_t = torch.tensor(new_det_out_inds, dtype=torch.long) + new_det_masks: Tensor = det_out["mask"][new_det_out_inds_t] + # initialize SAM2 with new object masks + self._tracker_add_new_objects( + inference_session=inference_session, + frame_idx=frame_idx, + new_obj_ids=new_det_obj_ids, + new_obj_masks=new_det_masks, + reverse=reverse, + ) + + # Step 2: remove from SAM2 inference states those objects removed by heuristics + for obj_id in obj_ids_newly_removed: + inference_session.remove_object(obj_id, strict=False) # implement remove_object in inference_session? + + def build_outputs( + self, + inference_session: Sam3VideoInferenceSession, + det_out: dict[str, Tensor], + tracker_low_res_masks_global: Tensor, + tracker_update_plan: dict, + reconditioned_obj_ids: set | None = None, + ): + """ + Build output dictionary with low-resolution masks. + Interpolation to video resolution is handled by the processor. + + Returns: + obj_id_to_mask: dict mapping obj_id to low-res mask tensor (1, H_low, W_low) + """ + new_det_out_inds: list[int] = tracker_update_plan["new_det_out_inds"] + new_det_obj_ids: list[int] = tracker_update_plan["new_det_obj_ids"] + obj_id_to_mask = {} # obj_id --> low-res mask tensor + + # Part 1: masks from tracker propagation (existing objects) + existing_masklet_obj_ids = inference_session.obj_ids + for obj_id, mask in zip(existing_masklet_obj_ids, tracker_low_res_masks_global): + obj_id_to_mask[int(obj_id)] = mask.unsqueeze(0) # (1, H_low, W_low) + + # Part 2: masks from new detections + if len(new_det_out_inds) > 0: + new_det_out_inds_t = torch.tensor(new_det_out_inds, dtype=torch.long, device=det_out["mask"].device) + new_det_low_res_masks = det_out["mask"][new_det_out_inds_t] + # Apply hole filling to new detection masks + new_det_low_res_masks = fill_holes_in_mask_scores( + new_det_low_res_masks.unsqueeze(1), + max_area=self.fill_hole_area, + fill_holes=True, + remove_sprinkles=True, + ).squeeze(1) + + for obj_id, mask in zip(new_det_obj_ids, new_det_low_res_masks): + obj_id_to_mask[int(obj_id)] = mask.unsqueeze(0) # (1, H_low, W_low) + + # Part 3: Override masks for reconditioned objects using detection masks + if reconditioned_obj_ids is not None and len(reconditioned_obj_ids) > 0: + trk_id_to_max_iou_high_conf_det = tracker_update_plan.get("trk_id_to_max_iou_high_conf_det", {}) + + for obj_id in reconditioned_obj_ids: + det_idx = trk_id_to_max_iou_high_conf_det.get(obj_id) + if det_idx is not None: + det_mask = det_out["mask"][det_idx].unsqueeze(0) # (1, H_low, W_low) + obj_id_to_mask[int(obj_id)] = det_mask + + return obj_id_to_mask + + def _merge_detections_from_prompts( + self, + all_detections: dict[int, dict[str, Tensor]], + inference_session: Sam3VideoInferenceSession, + ) -> tuple[dict[str, Tensor], dict[int, int]]: + """ + Merge detections from multiple prompts into a single detection output. + Assigns unique object IDs and tracks which prompt detected each object. + + Args: + all_detections: Dictionary mapping prompt_id to detection outputs + inference_session: Session to track obj_id -> prompt_id mapping + + Returns: + Tuple of (merged_det_out, det_idx_to_prompt_id) where det_idx_to_prompt_id + maps detection index in the merged output to the prompt that produced it. + """ + merged_bboxes, merged_masks, merged_scores = [], [], [] + det_idx_to_prompt_id = {} + det_idx = 0 + + for prompt_id, det_out in all_detections.items(): + num_dets = len(det_out["bbox"]) + if num_dets > 0: + merged_bboxes.append(det_out["bbox"]) + merged_masks.append(det_out["mask"]) + merged_scores.append(det_out["scores"]) + for i in range(num_dets): + det_idx_to_prompt_id[det_idx + i] = prompt_id + det_idx += num_dets + + if merged_bboxes: + merged_det_out = { + "bbox": torch.cat(merged_bboxes), + "mask": torch.cat(merged_masks), + "scores": torch.cat(merged_scores), + } + else: + device = inference_session.inference_device + merged_det_out = { + "bbox": torch.zeros(0, 4, device=device), + "mask": torch.zeros(0, self.low_res_mask_size, self.low_res_mask_size, device=device), + "scores": torch.zeros(0, device=device), + } + + return merged_det_out, det_idx_to_prompt_id + + def _det_track_one_frame( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int, + reverse: bool, + streaming: bool = False, + ): + """ + This function handles one-step inference for the DenseTracking model. + + - `inference_session` contains all the information needed for inference, including the input video frames, text prompts, and any other relevant metadata + - The function processes detection and tracking for a single frame + - `streaming` indicates whether this is streaming inference mode (frames provided one at a time) + """ + + pixel_values = inference_session.get_frame(frame_idx).unsqueeze(0) + vision_embeds = self.detector_model.get_vision_features(pixel_values=pixel_values) + + # Step 1: run detection for all prompts (efficiently reusing vision embeddings) + # Returns dict mapping prompt_id to detection outputs + all_detections = self.run_detection( + inference_session=inference_session, + vision_embeds=vision_embeds, + ) + + # Merge detections from all prompts into single output for tracking + det_out, det_idx_to_prompt_id = self._merge_detections_from_prompts(all_detections, inference_session) + + # share the vision encoder outputs from the detector to the tracker + vision_feats, vision_pos_embeds = self.get_vision_features_for_tracker( + vision_embeds=vision_embeds, + ) + inference_session.cache.cache_vision_features( + frame_idx, {"vision_feats": vision_feats, "vision_pos_embeds": vision_pos_embeds} + ) + + # Step 2: propagate SAM2 states to get the SAM2 prediction masks. + # The returned `tracker_low_res_masks_global` contains the masklet predictions. + # Note that this step only runs the SAM2 propagation step, but doesn't encode new memory for the predicted masks; + # we defer memory encoding to `run_tracker_update_execution_phase` after resolving all heuristics. + tracker_low_res_masks_global, tracker_obj_scores_global = self.run_tracker_propagation( + inference_session=inference_session, frame_idx=frame_idx, reverse=reverse + ) + + # Step 3: based on detection outputs and the propagated SAM2 prediction masks, we make plans + # for SAM2 masklet updates (i.e. which objects to add and remove, etc). + # We also run SAM2 memory encoder in this step to resolve non-overlapping constraints. + # **This step should involve all the heuristics needed for any updates.** + # This step also generates the new masklet metadata `tracker_metadata_new` (based on its previous version). + tracker_update_plan, tracker_metadata_new = self.run_tracker_update_planning_phase( + inference_session=inference_session, + frame_idx=frame_idx, + reverse=reverse, + det_out=det_out, + tracker_low_res_masks_global=tracker_low_res_masks_global, + tracker_obj_scores_global=tracker_obj_scores_global, + det_idx_to_prompt_id=det_idx_to_prompt_id, + streaming=streaming, + ) + + # Step 4: based on `tracker_update_plan`, execute the update w.r.t. the tracker states + self.run_tracker_update_execution_phase( + inference_session=inference_session, + frame_idx=frame_idx, + reverse=reverse, + det_out=det_out, + tracker_update_plan=tracker_update_plan, + ) + + # Step 5: finally, build the outputs for this frame + reconditioned_obj_ids = tracker_update_plan["reconditioned_obj_ids"] + obj_id_to_mask = self.build_outputs( + inference_session=inference_session, + det_out=det_out, + tracker_low_res_masks_global=tracker_low_res_masks_global, + tracker_update_plan=tracker_update_plan, + reconditioned_obj_ids=reconditioned_obj_ids, + ) + obj_id_to_score = tracker_metadata_new["obj_id_to_score"] + # add tracker scores to metadata, it should be fired for frames except the first frame + if tracker_obj_scores_global.shape[0] > 0: + # Convert tracker_obj_scores_global to sigmoid scores before updating + tracker_obj_scores_global = tracker_obj_scores_global.sigmoid().tolist() + tracker_obj_ids = inference_session.obj_ids + tracker_metadata_new["obj_id_to_tracker_score_frame_wise"][frame_idx].update( + dict(zip(tracker_obj_ids, tracker_obj_scores_global)) + ) + + return ( + obj_id_to_mask, # a dict: obj_id --> output mask + obj_id_to_score, # a dict: obj_id --> output score (prob) + tracker_metadata_new, + tracker_obj_scores_global, # a dict: obj_id --> tracker frame-level scores + ) + + @torch.inference_mode() + @auto_docstring(custom_intro="Propagate the objects through a streamed video frame.") + def forward( + self, + inference_session: Sam3VideoInferenceSession, + frame_idx: int | None = None, + frame: torch.Tensor | None = None, + reverse: bool = False, + **kwargs, + ): + r""" + inference_session (`Sam3VideoInferenceSession`): + The video inference session object. + frame_idx (`int`, *optional*): + The index of the frame on which to run inference. No need to provide when inferring + on a new streamed frame. + frame (`torch.Tensor`, *optional*): + The frame to process. Provide when streaming. + reverse (`bool`, *optional*, defaults to `False`): + Whether to propagate in reverse. + """ + if frame is not None: + frame_idx = inference_session.add_new_frame(frame, frame_idx) + + if frame_idx is None: + raise ValueError("frame_idx must be provided when frame is not provided for streaming.") + + ( + obj_id_to_mask, + obj_id_to_score, + tracker_metadata_new, + _, + ) = self._det_track_one_frame( + inference_session=inference_session, + frame_idx=frame_idx, + reverse=reverse, + streaming=frame is not None, + ) + # use a dummy string in "previous_stages_out" to indicate this frame has outputs + # inference_session.previous_stages_out[frame_idx] = "_THIS_FRAME_HAS_OUTPUTS_" + + extra_metadata = tracker_metadata_new["extra_metadata"] + removed_obj_ids = extra_metadata["removed_obj_ids"] + + # Update inference session state + inference_session.obj_id_to_score = obj_id_to_score + inference_session.obj_id_to_tracker_score_frame_wise = tracker_metadata_new[ + "obj_id_to_tracker_score_frame_wise" + ] + inference_session.obj_id_to_last_occluded = tracker_metadata_new["obj_id_to_last_occluded"] + inference_session.max_obj_id = tracker_metadata_new["max_obj_id"] + inference_session.obj_ids = list(tracker_metadata_new["obj_ids"]) + + inference_session.obj_first_frame_idx = extra_metadata["obj_first_frame_idx"] + inference_session.unmatched_frame_inds = extra_metadata["unmatched_frame_inds"] + inference_session.trk_keep_alive = extra_metadata["trk_keep_alive"] + inference_session.overlap_pair_to_frame_inds = extra_metadata["overlap_pair_to_frame_inds"] + inference_session.removed_obj_ids = removed_obj_ids + inference_session.suppressed_obj_ids[frame_idx] = extra_metadata["suppressed_obj_ids"][frame_idx] + + return Sam3VideoSegmentationOutput( + object_ids=list(tracker_metadata_new["obj_ids"]), + obj_id_to_mask=obj_id_to_mask, + obj_id_to_score=obj_id_to_score, + obj_id_to_tracker_score=tracker_metadata_new["obj_id_to_tracker_score_frame_wise"][frame_idx], + removed_obj_ids=removed_obj_ids, + suppressed_obj_ids=extra_metadata["suppressed_obj_ids"][frame_idx], + frame_idx=frame_idx, + ) + + def _get_processing_order( + self, + inference_session: Sam3VideoInferenceSession, + start_frame_idx: int, + max_frame_num_to_track: int | None = None, + reverse: bool = False, + ): + num_frames = inference_session.num_frames + if max_frame_num_to_track is None: + # default: track all the frames in the video + max_frame_num_to_track = num_frames + if reverse: + end_frame_idx = start_frame_idx - max_frame_num_to_track + end_frame_idx = max(end_frame_idx, 0) + processing_order = range(start_frame_idx - 1, end_frame_idx - 1, -1) + else: + end_frame_idx = start_frame_idx + max_frame_num_to_track + end_frame_idx = min(end_frame_idx, num_frames - 1) + processing_order = range(start_frame_idx, end_frame_idx + 1) + return processing_order, end_frame_idx + + @torch.inference_mode() + @auto_docstring( + custom_intro=""" + Propagate the prompts to get grounding results for the entire video. Used when initializing an inference session with a whole video. + Yields Sam3VideoSegmentationOutput for each frame. + """ + ) + def propagate_in_video_iterator( + self, + inference_session: Sam3VideoInferenceSession, + start_frame_idx: int = 0, + max_frame_num_to_track: int | None = None, + reverse: bool = False, + show_progress_bar: bool = False, + ) -> Iterator[Sam3VideoSegmentationOutput]: + r""" + inference_session (`Sam3VideoInferenceSession`): + The video inference session object. + start_frame_idx (`int`, *optional*, defaults to `0`): + The starting frame index for propagation. + max_frame_num_to_track (`int`, *optional*): + The maximum number of frames to track. If not provided, all frames in the video will be tracked. + reverse (`bool`, *optional*, defaults to `False`): + Whether to propagate in reverse. + show_progress_bar (`bool`, *optional*, defaults to `False`): + Whether to show a progress bar during propagation. + """ + processing_order, end_frame_idx = self._get_processing_order( + inference_session, + start_frame_idx, + max_frame_num_to_track, + reverse=reverse, + ) + + hotstart_buffer = [] + for frame_idx in tqdm(processing_order, desc="propagate in video", disable=not show_progress_bar): + out = self(inference_session=inference_session, frame_idx=frame_idx, reverse=reverse) + + if self.hotstart_delay > 0: + # accumulate the outputs for the first `hotstart_delay` frames + hotstart_buffer.append(out) + # update the object IDs removed by hotstart so that we don't output them + inference_session.hotstart_removed_obj_ids.update(out.removed_obj_ids) + + if frame_idx == end_frame_idx: + # we reached the end of propagation -- yield all frames in the buffer + yield_list = hotstart_buffer + hotstart_buffer = [] + elif len(hotstart_buffer) >= self.hotstart_delay: + # we have enough frames -- yield and remove the first (oldest) frame from the buffer + yield_list = hotstart_buffer[:1] + hotstart_buffer = hotstart_buffer[1:] + else: + # not enough frames yet -- skip yielding + yield_list = [] + else: + yield_list = [out] # output the current frame + + yield from yield_list + + +@torch.jit.script +def fast_diag_box_iou(boxes1, boxes2): + box1_xy = boxes1[:, 2:] + box1_XY = boxes1[:, :2] + box2_xy = boxes2[:, 2:] + box2_XY = boxes2[:, :2] + area1 = (box1_xy - box1_XY).prod(-1) + area2 = (box2_xy - box2_XY).prod(-1) + + lt = torch.max(box1_XY, box2_XY) + rb = torch.min(box1_xy, box2_xy) + + inter = (rb - lt).clamp(min=0).prod(-1) + union = area1 + area2 - inter + iou = inter / union + return iou + + +def mask_iou(pred_masks: torch.Tensor, gt_masks: torch.Tensor) -> torch.Tensor: + """ + Compute the IoU (Intersection over Union) between predicted masks and ground truth masks. + Args: + - pred_masks: (N, H, W) bool Tensor, containing binary predicted segmentation masks + - gt_masks: (M, H, W) bool Tensor, containing binary ground truth segmentation masks + Returns: + - ious: (N, M) float Tensor, containing IoUs for each pair of predicted and ground truth masks + """ + N, H, W = pred_masks.shape + M, _, _ = gt_masks.shape + + # Flatten masks: (N, 1, H*W) and (1, M, H*W) + pred_flat = pred_masks.view(N, 1, H * W) + gt_flat = gt_masks.view(1, M, H * W) + + # Compute intersection and union: (N, M) + intersection = (pred_flat & gt_flat).sum(dim=2).float() + union = (pred_flat | gt_flat).sum(dim=2).float() + ious = intersection / union.clamp(min=1) + return ious # shape: (N, M) + + +def nms_masks( + pred_probs: torch.Tensor, + pred_masks: torch.Tensor, + prob_threshold: float, + iou_threshold: float, +) -> torch.Tensor: + """ + Args: + - pred_probs: (num_det,) float Tensor, containing the score (probability) of each detection + - pred_masks: (num_det, H_mask, W_mask) float Tensor, containing the binary segmentation mask of each detection + - prob_threshold: float, score threshold to prefilter detections (NMS is performed on detections above threshold) + - iou_threshold: float, mask IoU threshold for NMS + + Returns: + - keep: (num_det,) bool Tensor, indicating whether each detection is kept after score thresholding + NMS + """ + # prefilter the detections with prob_threshold ("valid" are those above prob_threshold) + is_valid = pred_probs > prob_threshold # (num_det,) + probs = pred_probs[is_valid] # (num_valid,) + masks_binary = pred_masks[is_valid] > 0 # (num_valid, H_mask, W_mask) + if probs.numel() == 0: + return is_valid # no valid detection, return empty keep mask + + ious = mask_iou(masks_binary, masks_binary) # (num_valid, num_valid) + + # Try to use kernels for NMS, fallback to keeping all valid detections if unavailable + _load_cv_utils_kernel_once() + if not cv_utils_kernel: + return is_valid # Fallback: keep all valid detections without NMS + + try: + kept_inds = cv_utils_kernel.generic_nms(ious, probs, iou_threshold, use_iou_matrix=True) + except Exception as e: + logger.warning_once(f"Failed to run NMS using kernels library: {e}. NMS post-processing will be skipped.") + return is_valid # Fallback: keep all valid detections without NMS + + # valid_inds are the indices among `probs` of valid detections before NMS (or -1 for invalid) + valid_inds = torch.where(is_valid, is_valid.cumsum(dim=0) - 1, -1) # (num_det,) + keep = torch.isin(valid_inds, kept_inds) # (num_det,) + return keep + + +def fill_holes_in_mask_scores(mask, max_area, fill_holes=True, remove_sprinkles=True): + """ + A post processor to fill small holes in mask scores with area under `max_area`. + Holes are those small connected components in either background or foreground. + + Note that it relies on the "cc_torch" package to find connected components fast. You can + install it via the following command (`TORCH_CUDA_ARCH_LIST=8.0` is for A100 GPUs): + ``` + pip uninstall -y cc_torch; TORCH_CUDA_ARCH_LIST=8.0 9.0 pip install git+https://github.com/ronghanghu/cc_torch + ``` + Otherwise, it will fallback to a slightly slower triton implementation, or skimage if the tensor is on cpu + """ + + if max_area <= 0: + return mask # nothing to fill in this case + + if fill_holes: + # We remove small connected components in background by changing them to foreground + # with a small positive mask score (0.1). + mask_bg = mask <= 0 + bg_area_thresh = max_area + _, areas_bg = _get_connected_components_with_padding(mask_bg) + small_components_bg = mask_bg & (areas_bg <= bg_area_thresh) + mask = torch.where(small_components_bg, 0.1, mask) + + if remove_sprinkles: + # We remove small connected components in foreground by changing them to background + # with a small negative mask score (-0.1). Here we only remove connected components + # whose areas are under both `max_area` and half of the entire mask's area. This + # removes sprinkles while avoids filtering out tiny objects that we want to track. + mask_fg = mask > 0 + fg_area_thresh = torch.sum(mask_fg, dim=(2, 3), keepdim=True, dtype=torch.int32) + fg_area_thresh.floor_divide_(2).clamp_(max=max_area) + _, areas_fg = _get_connected_components_with_padding(mask_fg) + small_components_fg = mask_fg & (areas_fg <= fg_area_thresh) + mask = torch.where(small_components_fg, -0.1, mask) + return mask + + +def _get_connected_components_with_padding(mask): + """Get connected components from masks (possibly padding them to an even size).""" + mask = mask.to(torch.uint8) + _, _, H, W = mask.shape + + # Try to use kernels for connected components, fallback if unavailable + _load_cv_utils_kernel_once() + if not cv_utils_kernel: + # Fallback: return dummy labels and counts that won't trigger filtering + labels = torch.zeros_like(mask, dtype=torch.int32) + counts = torch.full_like(mask, fill_value=mask.shape[2] * mask.shape[3] + 1, dtype=torch.int32) + return labels, counts + + # make sure both height and width are even (to be compatible with cc_torch) + pad_h = H % 2 + pad_w = W % 2 + + try: + if pad_h == 0 and pad_w == 0: + labels, counts = cv_utils_kernel.cc_2d(mask.contiguous(), get_counts=True) + else: + # pad the mask to make its height and width even + # padding format is (padding_left,padding_right,padding_top,padding_bottom) + mask_pad = F.pad(mask, (0, pad_w, 0, pad_h), mode="constant", value=0) + labels, counts = cv_utils_kernel.cc_2d(mask_pad.contiguous(), get_counts=True) + labels = labels[:, :, :H, :W] + counts = counts[:, :, :H, :W] + except Exception as e: + logger.warning_once( + f"Failed to compute connected components using kernels library: {e}. " + "Hole filling and sprinkle removal will be skipped." + ) + # Fallback: return dummy labels and counts that won't trigger filtering + labels = torch.zeros_like(mask, dtype=torch.int32) + counts = torch.full_like(mask, fill_value=H * W + 1, dtype=torch.int32) + return labels, counts + + return labels, counts + + +__all__ = [ + "Sam3VideoModel", + "Sam3VideoPreTrainedModel", + "Sam3VideoInferenceSession", + "Sam3VideoSegmentationOutput", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/processing_sam3_video.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/processing_sam3_video.py new file mode 100644 index 0000000000000000000000000000000000000000..4d8277095dc11fd738afe58d573a48afde8cb033 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam3_video/processing_sam3_video.py @@ -0,0 +1,374 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import Union + +from ...utils import is_torch_available + + +if is_torch_available(): + import torch + +from ...utils import is_torchvision_available + + +if is_torchvision_available(): + from torchvision.ops import masks_to_boxes + +from ...image_utils import ImageInput +from ...processing_utils import ProcessorMixin +from ...tokenization_utils_base import BatchEncoding +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires +from ...video_utils import VideoInput +from .modeling_sam3_video import Sam3VideoInferenceSession + + +@requires(backends=("torch", "torchvision")) +@auto_docstring +class Sam3VideoProcessor(ProcessorMixin): + def __init__( + self, + image_processor, + video_processor, + tokenizer, + target_size: int | None = None, + **kwargs, + ): + r""" + target_size (`int`, *optional*): + The target size (target_size, target_size) to which the image will be resized. + """ + super().__init__(image_processor, video_processor, tokenizer, **kwargs) + self.target_size = target_size if target_size is not None else self.image_processor.size["height"] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + segmentation_maps: ImageInput | None = None, + original_sizes: list[list[float]] | torch.Tensor | None = None, + return_tensors: str | TensorType | None = None, + **kwargs, + ) -> BatchEncoding: + r""" + images (`ImageInput`, *optional*): + The image(s) to process. + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to process (optional, for image processor). + original_sizes (`list[list[float]]`, `torch.Tensor`, *optional*): + The original sizes of the images. Only used when images is not provided. + + Returns: + A [`BatchEncoding`] with the following fields: + - `pixel_values` (`torch.Tensor`): The processed image(s). + - `original_sizes` (`list[list[float]]`): The original sizes of the images. + - `labels` (`torch.Tensor`, *optional*): The processed segmentation maps (if provided). + """ + if images is not None: + encoding_image_processor = self.image_processor( + images, + segmentation_maps=segmentation_maps, + return_tensors=return_tensors, + **kwargs, + ) + elif original_sizes is not None: + if isinstance(original_sizes, torch.Tensor): + original_sizes = original_sizes.cpu().tolist() + encoding_image_processor = BatchEncoding({"original_sizes": original_sizes}, tensor_type=return_tensors) + else: + raise ValueError("Either images or original_sizes must be provided") + + original_sizes = encoding_image_processor["original_sizes"] + # Check original_sizes is of length 1 or len(images) + if images is not None and len(original_sizes) != 1 and len(original_sizes) != len(images): + raise ValueError( + "original_sizes must be of length 1 or len(images). If you are passing a single image, you must pass a single original_size." + ) + + return encoding_image_processor + + def add_text_prompt(self, inference_session: Sam3VideoInferenceSession, text: str | list[str]): + """ + Add text prompt(s) to the inference session. + + Args: + inference_session (`Sam3VideoInferenceSession`): The inference session. + text (`str` or `list[str]`): The text prompt(s) to add. + + Returns: + `Sam3VideoInferenceSession`: The inference session with the added text prompt(s). + """ + if isinstance(text, str): + text = [text] + + prompt_ids = [] + for prompt_text in text: + # Add prompt and get its ID (reuses existing if duplicate) + prompt_id = inference_session.add_prompt(prompt_text) + + # Only encode if this is a new prompt (not already in prompt_input_ids) + if prompt_id not in inference_session.prompt_input_ids: + encoded_text = self.tokenizer( + prompt_text, return_tensors="pt", padding="max_length", max_length=32 + ).to(inference_session.inference_device) + + inference_session.prompt_input_ids[prompt_id] = encoded_text.input_ids + inference_session.prompt_attention_masks[prompt_id] = encoded_text.attention_mask + + prompt_ids.append(prompt_id) + + return inference_session + + def init_video_session( + self, + video: VideoInput | None = None, + inference_device: Union[str, "torch.device"] = "cpu", + inference_state_device: Union[str, "torch.device"] | None = None, + processing_device: Union[str, "torch.device"] | None = None, + video_storage_device: Union[str, "torch.device"] | None = None, + max_vision_features_cache_size: int = 1, + dtype: torch.dtype = torch.float32, + ): + """ + Initializes a video session for inference. + If a video is provided (async inference), the video will be processed and stored on the `video_storage_device`. + + Args: + video (`VideoInput`, *optional*): + The video to process. No need to provide when streaming. + inference_device (`str` or `torch.device`, *optional*, defaults to "cpu"): + The device to use for inference. + inference_state_device (`str` or `torch.device`, *optional*): + The device to store the inference state on. + processing_device (`str` or `torch.device`, *optional*): + The device to use for video processing. + video_storage_device (`str` or `torch.device`, *optional*): + The device to store the processed video frames on. + max_vision_features_cache_size (`int`, *optional*, defaults to 1): + The maximum number of vision features to cache. + dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): + The torch dtype to use for the whole session. + """ + video_storage_device = video_storage_device if video_storage_device is not None else inference_device + inference_state_device = inference_state_device if inference_state_device is not None else inference_device + processing_device = processing_device if processing_device is not None else inference_device + pixel_values_video = None + video_height = None + video_width = None + if video is not None: + processed_video = self.video_processor(videos=video, device=processing_device, return_tensors="pt") + pixel_values_video = processed_video.pixel_values_videos[0] + video_height = processed_video.original_sizes[0][0] + video_width = processed_video.original_sizes[0][1] + inference_session = Sam3VideoInferenceSession( + video=pixel_values_video, + video_height=video_height, + video_width=video_width, + inference_device=inference_device, + video_storage_device=video_storage_device, + inference_state_device=inference_state_device, + dtype=dtype, + max_vision_features_cache_size=max_vision_features_cache_size, + ) + return inference_session + + def _apply_non_overlapping_constraints(self, pred_masks): + """ + Apply non-overlapping constraints to the object scores in pred_masks. Here we + keep only the highest scoring object at each spatial location in pred_masks. + """ + batch_size = pred_masks.size(0) + if batch_size == 1: + return pred_masks + + device = pred_masks.device + # "max_obj_inds": object index of the object with the highest score at each location + max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) + # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` + batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] + keep = max_obj_inds == batch_obj_inds + # suppress overlapping regions' scores below -10.0 so that the foreground regions + # don't overlap (here sigmoid(-10.0)=4.5398e-05) + pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) + return pred_masks + + def _apply_object_wise_non_overlapping_constraints( + self, + pred_masks, + obj_scores, + background_value=-10.0, + prompt_ids=None, + ): + """ + Applies non-overlapping constraints object wise (i.e. only one object can claim the overlapping region). + Constraints are enforced independently for each prompt group when `prompt_ids` are provided. + """ + if prompt_ids is None: + return self._apply_object_wise_non_overlapping_constraints_impl(pred_masks, obj_scores, background_value) + + if len(prompt_ids) != pred_masks.size(0): + raise ValueError("prompt_ids must have the same length as pred_masks") + + pred_masks_grouped = pred_masks.clone() + prompt_ids_tensor = torch.tensor(prompt_ids, device=pred_masks.device, dtype=torch.long) + for prompt_id in prompt_ids_tensor.unique(sorted=True): + indices = torch.nonzero(prompt_ids_tensor == prompt_id, as_tuple=True)[0] + if indices.numel() == 0: + continue + prompt_masks = self._apply_object_wise_non_overlapping_constraints_impl( + pred_masks_grouped[indices], + obj_scores[indices], + background_value, + ) + pred_masks_grouped[indices] = prompt_masks.to(pred_masks_grouped.dtype) + return pred_masks_grouped + + def _apply_object_wise_non_overlapping_constraints_impl(self, pred_masks, obj_scores, background_value=-10.0): + pred_masks_single_score = torch.where(pred_masks > 0, obj_scores[..., None, None], background_value) + pixel_level_non_overlapping_masks = self._apply_non_overlapping_constraints(pred_masks_single_score) + pred_masks = torch.where( + pixel_level_non_overlapping_masks > 0, + pred_masks, + torch.clamp(pred_masks, max=background_value), + ) + return pred_masks.to(pred_masks_single_score.dtype) + + def postprocess_outputs( + self, + inference_session, + model_outputs, + original_sizes: list[list[float]] | torch.Tensor | None = None, + ): + """ + Post-process model outputs to get final masks, boxes, and scores. + + Args: + inference_session (`Sam3VideoInferenceSession`): + The inference session object. + model_outputs (`Sam3VideoSegmentationOutput`): + The raw model output from `Sam3VideoModel.forward()`. + original_sizes (`list[list[float]]` or `torch.Tensor`, *optional*): + Optional original frame sizes [height, width]. Required for streaming inference + when video_height/video_width are not set in the session. + + Returns: + `dict`: A dictionary containing the following keys: + - **object_ids** (`torch.Tensor` of shape `(num_objects,)`): Object IDs for each detected object. + - **scores** (`torch.Tensor` of shape `(num_objects,)`): Detection scores for each object. + - **boxes** (`torch.Tensor` of shape `(num_objects, 4)`): Bounding boxes in XYXY format + (top_left_x, top_left_y, bottom_right_x, bottom_right_y). + - **masks** (`torch.Tensor` of shape `(num_objects, height, width)`): Binary segmentation masks + for each object at the original video resolution. + - **prompt_to_obj_ids** (`dict[str, list[int]]`): Mapping from prompt text to list of + object IDs detected by that prompt. + """ + obj_id_to_mask = model_outputs["obj_id_to_mask"] # low res masks (1, H_low, W_low) + curr_obj_ids = sorted(obj_id_to_mask.keys()) + + # Get video dimensions - use original_sizes for streaming inference if session doesn't have them + if inference_session.video_height is not None and inference_session.video_width is not None: + H_video, W_video = inference_session.video_height, inference_session.video_width + elif original_sizes is not None: + if isinstance(original_sizes, torch.Tensor): + original_sizes = original_sizes.cpu().tolist() + # original_sizes is a list of [height, width] pairs, take the first one + if isinstance(original_sizes[0], list): + H_video, W_video = int(original_sizes[0][0]), int(original_sizes[0][1]) + else: + H_video, W_video = int(original_sizes[0]), int(original_sizes[1]) + else: + raise ValueError( + "Either inference_session.video_height/video_width must be set, " + "or original_sizes must be provided for streaming inference." + ) + if len(curr_obj_ids) == 0: + out_obj_ids = torch.zeros(0, dtype=torch.int64) + out_probs = torch.zeros(0, dtype=torch.float32) + out_binary_masks = torch.zeros(0, H_video, W_video, dtype=torch.bool) + out_boxes_xyxy = torch.zeros(0, 4, dtype=torch.float32) + else: + out_obj_ids = torch.tensor(curr_obj_ids, dtype=torch.int64) + out_probs = torch.tensor([model_outputs["obj_id_to_score"][obj_id] for obj_id in curr_obj_ids]) + out_tracker_probs = torch.tensor( + [model_outputs["obj_id_to_tracker_score"].get(obj_id, 0.0) for obj_id in curr_obj_ids] + ) + + # Interpolate low-res masks to video resolution + low_res_masks = torch.cat([obj_id_to_mask[obj_id] for obj_id in curr_obj_ids], dim=0) # (N, H_low, W_low) + # Add channel dimension for interpolation: (N, H, W) -> (N, 1, H, W) + out_binary_masks = torch.nn.functional.interpolate( + low_res_masks.unsqueeze(1), + size=(H_video, W_video), + mode="bilinear", + align_corners=False, + ).squeeze(1) # (N, H_video, W_video) + out_binary_masks = out_binary_masks > 0 + + assert out_binary_masks.dtype == torch.bool + keep = out_binary_masks.any(dim=(1, 2)).cpu() # remove masks with 0 areas + # hide outputs for those object IDs in `obj_ids_to_hide` + obj_ids_to_hide = [] + if model_outputs["suppressed_obj_ids"] is not None: + obj_ids_to_hide.extend(list(model_outputs["suppressed_obj_ids"])) + if len(inference_session.hotstart_removed_obj_ids) > 0: + obj_ids_to_hide.extend(list(inference_session.hotstart_removed_obj_ids)) + if len(obj_ids_to_hide) > 0: + obj_ids_to_hide_t = torch.tensor(obj_ids_to_hide, dtype=torch.int64) + keep &= ~torch.isin(out_obj_ids, obj_ids_to_hide_t) + + # slice those valid entries from the original outputs + keep_idx = torch.nonzero(keep, as_tuple=True)[0] + keep_idx_gpu = keep_idx.to(device=out_binary_masks.device, non_blocking=True) + + out_obj_ids = torch.index_select(out_obj_ids, 0, keep_idx) + out_probs = torch.index_select(out_probs, 0, keep_idx) + out_tracker_probs = torch.index_select(out_tracker_probs, 0, keep_idx) + out_binary_masks = torch.index_select(out_binary_masks, 0, keep_idx_gpu) + + out_boxes_xyxy = masks_to_boxes(out_binary_masks) + + # Apply non-overlapping constraints on the existing masklets. + # Constraints are enforced independently per prompt group. + if out_binary_masks.shape[0] > 1: + assert len(out_binary_masks) == len(out_tracker_probs) + prompt_ids_filtered = [ + inference_session.obj_id_to_prompt_id[int(obj_id)] for obj_id in out_obj_ids.tolist() + ] + out_binary_masks = ( + self._apply_object_wise_non_overlapping_constraints( + out_binary_masks.unsqueeze(1), + out_tracker_probs.unsqueeze(1).to(out_binary_masks.device), + background_value=0, + prompt_ids=prompt_ids_filtered, + ).squeeze(1) + ) > 0 + + # Build prompt_to_obj_ids mapping: group object IDs by their associated prompt text. + prompt_to_obj_ids = {} + for obj_id in out_obj_ids.tolist(): + prompt_id = inference_session.obj_id_to_prompt_id[obj_id] + prompt_text = inference_session.prompts[prompt_id] + prompt_to_obj_ids.setdefault(prompt_text, []).append(obj_id) + + outputs = { + "object_ids": out_obj_ids, + "scores": out_probs, + "boxes": out_boxes_xyxy, + "masks": out_binary_masks, + "prompt_to_obj_ids": prompt_to_obj_ids, + } + return outputs + + +__all__ = ["Sam3VideoProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f56ba4c932c5fc6a011cb876a48964f2322ef1b7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sam_hq import * + from .modeling_sam_hq import * + from .processing_sam_hq import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/configuration_sam_hq.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/configuration_sam_hq.py new file mode 100644 index 0000000000000000000000000000000000000000..a6dfa78ce2e4ed91013a23470c6920dadf85fab8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/configuration_sam_hq.py @@ -0,0 +1,193 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam_hq/modular_sam_hq.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam_hq.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="syscv-community/sam-hq-vit-base") +@strict +class SamHQPromptEncoderConfig(PreTrainedConfig): + r""" + mask_input_channels (`int`, *optional*, defaults to 16): + The number of channels to be fed to the `MaskDecoder` module. + num_point_embeddings (`int`, *optional*, defaults to 4): + The number of point embeddings to be used. + """ + + base_config_key = "prompt_encoder_config" + + hidden_size: int = 256 + image_size: int | list[int] | tuple[int, int] = 1024 + patch_size: int | list[int] | tuple[int, int] = 16 + mask_input_channels: int = 16 + num_point_embeddings: int = 4 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-6 + + def __post_init__(self, **kwargs): + self.image_embedding_size = self.image_size // self.patch_size + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="syscv-community/sam-hq-vit-base") +@strict +class SamHQVisionConfig(PreTrainedConfig): + r""" + output_channels (`int`, *optional*, defaults to 256): + Dimensionality of the output channels in the Patch Encoder. + use_rel_pos (`bool`, *optional*, defaults to `True`): + Whether to use relative position embedding. + window_size (`int`, *optional*, defaults to 14): + Window size for relative position. + global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`): + The indexes of the global attention layers. + num_pos_feats (`int`, *optional*, defaults to 128): + The dimensionality of the position embedding. + mlp_dim (`int`, *optional*): + The dimensionality of the MLP layer in the Transformer encoder. If `None`, defaults to `mlp_ratio * + hidden_size`. + + Example: + + ```python + >>> from transformers import ( + ... SamHQVisionConfig, + ... SamHQVisionModel, + ... ) + + >>> # Initializing a SamHQVisionConfig with `"facebook/sam_hq-vit-huge"` style configuration + >>> configuration = SamHQVisionConfig() + + >>> # Initializing a SamHQVisionModel (with random weights) from the `"facebook/sam_hq-vit-huge"` style configuration + >>> model = SamHQVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + base_config_key = "vision_config" + model_type = "sam_hq_vision_model" + + hidden_size: int = 768 + output_channels: int = 256 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 1024 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-06 + attention_dropout: float | int = 0.0 + initializer_range: float = 1e-10 + qkv_bias: bool = True + mlp_ratio: float = 4.0 + use_abs_pos: bool = True + use_rel_pos: bool = True + window_size: int = 14 + global_attn_indexes: list[int] | tuple[int, ...] = (2, 5, 8, 11) + num_pos_feats: int = 128 + mlp_dim: int | None = None + + def __post_init__(self, **kwargs): + self.mlp_dim = int(self.hidden_size * self.mlp_ratio) if self.mlp_dim is None else self.mlp_dim + self.scale = self.hidden_size // 2 + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="syscv-community/sam-hq-vit-base") +@strict +class SamHQMaskDecoderConfig(PreTrainedConfig): + r""" + mlp_dim (`int`, *optional*, defaults to 2048): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + attention_downsample_rate (`int`, *optional*, defaults to 2): + The downsampling rate of the attention layer. + num_multimask_outputs (`int`, *optional*, defaults to 3): + The number of outputs from the `SamMaskDecoder` module. In the Segment Anything paper, this is set to 3. + iou_head_depth (`int`, *optional*, defaults to 3): + The number of layers in the IoU head module. + iou_head_hidden_dim (`int`, *optional*, defaults to 256): + The dimensionality of the hidden states in the IoU head module. + vit_dim (`int`, *optional*, defaults to 768): + Dimensionality of the Vision Transformer (ViT) used in the `SamHQMaskDecoder` module. + """ + + base_config_key = "mask_decoder_config" + + hidden_size: int = 256 + hidden_act: str = "relu" + mlp_dim: int = 2048 + num_hidden_layers: int = 2 + num_attention_heads: int = 8 + attention_downsample_rate: int = 2 + num_multimask_outputs: int = 3 + iou_head_depth: int = 3 + iou_head_hidden_dim: int = 256 + layer_norm_eps: float = 1e-6 + + vit_dim: int = 768 + + +@auto_docstring(checkpoint="syscv-community/sam-hq-vit-base") +@strict +class SamHQConfig(PreTrainedConfig): + r""" + prompt_encoder_config (Union[`dict`, `SamHQPromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`SamHQPromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `SamHQMaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`SamHQMaskDecoderConfig`]. + """ + + model_type = "sam_hq" + sub_configs = { + "prompt_encoder_config": SamHQPromptEncoderConfig, + "mask_decoder_config": SamHQMaskDecoderConfig, + "vision_config": SamHQVisionConfig, + } + + vision_config: dict | PreTrainedConfig | None = None + prompt_encoder_config: dict | PreTrainedConfig | None = None + mask_decoder_config: dict | PreTrainedConfig | None = None + initializer_range: float = 0.02 + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config = SamHQVisionConfig(**self.vision_config) + elif self.vision_config is None: + self.vision_config = SamHQVisionConfig() + + if isinstance(self.prompt_encoder_config, dict): + self.prompt_encoder_config = SamHQPromptEncoderConfig(**self.prompt_encoder_config) + elif self.prompt_encoder_config is None: + self.prompt_encoder_config = SamHQPromptEncoderConfig() + + if isinstance(self.mask_decoder_config, dict): + self.mask_decoder_config = SamHQMaskDecoderConfig(**self.mask_decoder_config) + elif self.mask_decoder_config is None: + self.mask_decoder_config = SamHQMaskDecoderConfig() + + super().__post_init__(**kwargs) + + +__all__ = ["SamHQVisionConfig", "SamHQMaskDecoderConfig", "SamHQPromptEncoderConfig", "SamHQConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/modeling_sam_hq.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/modeling_sam_hq.py new file mode 100644 index 0000000000000000000000000000000000000000..02987f052f593f5e924c379d7d6b00647c8ac1cb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/modeling_sam_hq.py @@ -0,0 +1,1490 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sam_hq/modular_sam_hq.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sam_hq.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +import collections +from collections.abc import Callable +from dataclasses import dataclass + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import auto_docstring, logging +from ...utils.generic import ModelOutput, TransformersKwargs, merge_with_config_defaults +from ...utils.output_capturing import OutputRecorder, capture_outputs +from .configuration_sam_hq import SamHQConfig, SamHQMaskDecoderConfig, SamHQPromptEncoderConfig, SamHQVisionConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring( + custom_intro=""" + Base class for sam_hq vision model's outputs that also contains image embeddings obtained by applying the projection + layer to the pooler_output. + """ +) +@dataclass +class SamHQVisionEncoderOutput(ModelOutput): + r""" + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + intermediate_embeddings (`list(torch.FloatTensor)`, *optional*): + A list of intermediate embeddings collected from certain blocks within the model, typically those without + windowed attention. Each element in the list is of shape `(batch_size, sequence_length, hidden_size)`. + This is specific to SAM-HQ and not present in base SAM. + """ + + image_embeds: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + intermediate_embeddings: list[torch.FloatTensor] | None = None + + +@dataclass +class SamHQMMaskDecoderOutputs(ModelOutput): + r""" + masks (`torch.FloatTensor` of shape `(batch_size, num_prompts, num_masks, height, width)`): + The predicted masks for the input image. The masks are of shape `(batch_size, num_prompts, num_masks, height, width)`. + iou_scores (`torch.FloatTensor` of shape `(batch_size, num_prompts, num_masks)`): + The predicted IoU scores for each mask. The scores are of shape `(batch_size, num_prompts, num_masks)`. + mask_decoder_attentions (`torch.FloatTensor`, *optional*): + The attention weights from the mask decoder, if `output_attentions=True` was passed during the forward pass. + This is specific to SAM-HQ and not present in base SAM. + """ + + masks: torch.FloatTensor + iou_scores: torch.FloatTensor | None = None + mask_decoder_attentions: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + Base class for Segment-Anything model's output + """ +) +@dataclass +class SamHQImageSegmentationOutput(ModelOutput): + r""" + iou_scores (`torch.FloatTensor` of shape `(batch_size, num_masks)`): + The iou scores of the predicted masks. + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_masks, height, width)`): + The predicted low resolutions masks. Needs to be post-processed by the processor + vision_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the vision model at the output of each layer plus the optional initial embedding outputs. + vision_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + mask_decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + iou_scores: torch.FloatTensor | None = None + pred_masks: torch.FloatTensor | None = None + vision_hidden_states: tuple[torch.FloatTensor, ...] | None = None + vision_attentions: tuple[torch.FloatTensor, ...] | None = None + mask_decoder_attentions: tuple[torch.FloatTensor, ...] | None = None + + +class SamHQVisionAttention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__(self, config, window_size): + super().__init__() + input_size = ( + (config.image_size // config.patch_size, config.image_size // config.patch_size) + if window_size == 0 + else (window_size, window_size) + ) + + self.num_attention_heads = config.num_attention_heads + head_dim = config.hidden_size // config.num_attention_heads + self.scale = head_dim**-0.5 + self.dropout = config.attention_dropout + + self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias) + self.proj = nn.Linear(config.hidden_size, config.hidden_size) + + self.use_rel_pos = config.use_rel_pos + if self.use_rel_pos: + if input_size is None: + raise ValueError("Input size must be provided if using relative positional encoding.") + + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + + Args: + q_size (int): + size of the query. + k_size (int): + size of key k. + rel_pos (`torch.Tensor`): + relative position embeddings (L, channel). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + def get_decomposed_rel_pos( + self, + query: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: tuple[int, int], + k_size: tuple[int, int], + ) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py + + Args: + query (`torch.Tensor`): + query q in the attention layer with shape (batch_size, query_height * query_width, channel). + rel_pos_h (`torch.Tensor`): + relative position embeddings (Lh, channel) for height axis. + rel_pos_w (`torch.Tensor`): + relative position embeddings (Lw, channel) for width axis. + q_size (tuple): + spatial sequence size of query q with (query_height, query_width). + k_size (tuple): + spatial sequence size of key k with (key_height, key_width). + + Returns: + decomposed_rel_pos (`torch.Tensor`): + decomposed relative position embeddings. + """ + query_height, query_width = q_size + key_height, key_width = k_size + relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h) + relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w) + + batch_size, _, dim = query.shape + reshaped_query = query.reshape(batch_size, query_height, query_width, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height) + rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width) + + decomposed_rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + + return decomposed_rel_pos + + def forward(self, hidden_states: torch.Tensor, output_attentions=None) -> tuple[torch.Tensor, torch.Tensor]: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, batch_size, nHead, height * width, channel) + qkv = ( + self.qkv(hidden_states) + .reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + .permute(2, 0, 3, 1, 4) + ) + # q, k, v with shape (batch_size * nHead, height * width, channel) + query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0) + + attn_weights = (query * self.scale) @ key.transpose(-2, -1) + + if self.use_rel_pos: + decomposed_rel_pos = self.get_decomposed_rel_pos( + query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width) + ) + decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights) + attn_weights = attn_weights + decomposed_rel_pos + + attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype) + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1) + attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + return attn_output, attn_weights + + +class SamHQMLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim) + self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size) + self.act = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.lin1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.lin2(hidden_states) + return hidden_states + + +class SamHQVisionSdpaAttention(SamHQVisionAttention): + """ + Multi-head Attention block with relative position embeddings. + Using SDPA instead of the default attention. + """ + + def __init__(self, config, window_size): + super().__init__(config, window_size) + + def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor: + if output_attentions: + logger.warning_once( + f"{self.__class__.__name__} does not support `output_attentions=True`. The returned attention weights will " + "be `None`. If you want to get attention weights, please set `attn_implementation='eager'` when loading the model." + ) + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, B, nHead, H * W, C) + qkv = ( + self.qkv(hidden_states) + .reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + .permute(2, 0, 3, 1, 4) + ) + # q, k, v with shape (B * nHead, H * W, C) + query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0) + + attn_bias = None + if self.use_rel_pos: + decomposed_rel_pos = self.get_decomposed_rel_pos( + query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width) + ) + decomposed_rel_pos = decomposed_rel_pos.reshape( + batch_size, self.num_attention_heads, height * width, height * width + ) + attn_bias = decomposed_rel_pos + + query = query.view(batch_size, self.num_attention_heads, height * width, -1) + key = key.view(batch_size, self.num_attention_heads, height * width, -1) + value = value.view(batch_size, self.num_attention_heads, height * width, -1) + + attn_output = torch.nn.functional.scaled_dot_product_attention(query, key, value, attn_mask=attn_bias) + + attn_output = ( + attn_output.view(batch_size, self.num_attention_heads, height, width, -1) + .permute(0, 2, 3, 1, 4) + .reshape(batch_size, height, width, -1) + ) + + attn_output = self.proj(attn_output) + return attn_output, None + + +SAM_HQ_VISION_ATTENTION_CLASSES = { + "eager": SamHQVisionAttention, + "sdpa": SamHQVisionSdpaAttention, +} + + +class SamHQVisionLayer(GradientCheckpointingLayer): + def __init__(self, config, window_size): + super().__init__() + self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attn = SAM_HQ_VISION_ATTENTION_CLASSES[config._attn_implementation](config, window_size) + self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = SamHQMLPBlock(config) + self.window_size = window_size + + def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> tuple[torch.Tensor, tuple[int, int]]: + """ + Args: + Partition into non-overlapping windows with padding if needed. + hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window + size. + + Returns: + windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel]. + (pad_height, pad_width): padded height and width before partition + """ + batch_size, height, width, channel = hidden_states.shape + + pad_h = (window_size - height % window_size) % window_size + pad_w = (window_size - width % window_size) % window_size + hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h)) + pad_height, pad_width = height + pad_h, width + pad_w + + hidden_states = hidden_states.reshape( + batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel + ) + windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel) + return windows, (pad_height, pad_width) + + def window_unpartition( + self, windows: torch.Tensor, window_size: int, padding_shape: tuple[int, int], original_shape: tuple[int, int] + ) -> torch.Tensor: + """ + Args: + Window unpartition into original sequences and removing padding. + hidden_states (tensor): + input tokens with [batch_size * num_windows, window_size, window_size, channel]. + window_size (int): + window size. + padding_shape (Tuple): + padded height and width (pad_height, pad_width). + original_shape (Tuple): original height and width (height, width) before padding. + + Returns: + hidden_states: unpartitioned sequences with [batch_size, height, width, channel]. + """ + pad_height, pad_width = padding_shape + height, width = original_shape + batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size) + hidden_states = windows.reshape( + batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1 + ) + hidden_states = ( + hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1) + ) + + hidden_states = hidden_states[:, :height, :width, :].contiguous() + return hidden_states + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.FloatTensor]: + residual = hidden_states + hidden_states = self.layer_norm1(hidden_states) + # Window partition + if self.window_size > 0: + height, width = hidden_states.shape[1], hidden_states.shape[2] + hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size) + + hidden_states, attn_weights = self.attn( + hidden_states=hidden_states, + ) + # Reverse window partition + if self.window_size > 0: + hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width)) + + hidden_states = residual + hidden_states + layernorm_output = self.layer_norm2(hidden_states) + hidden_states = hidden_states + self.mlp(layernorm_output) + return hidden_states + + +class SamHQPositionalEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.scale = config.scale + self.positional_embedding = nn.Parameter(self.scale * torch.randn((2, config.num_pos_feats))) + + def forward(self, input_coords, input_shape=None): + """Positionally encode points that are normalized to [0,1].""" + coordinates = input_coords.clone() + + if input_shape is not None: + coordinates[:, :, :, 0] = coordinates[:, :, :, 0] / input_shape[1] + coordinates[:, :, :, 1] = coordinates[:, :, :, 1] / input_shape[0] + + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coordinates = 2 * coordinates - 1 + coordinates = coordinates.to(self.positional_embedding.dtype) + coordinates = coordinates @ self.positional_embedding + coordinates = 2 * np.pi * coordinates + # outputs d_1 x ... x d_n x channel shape + return torch.cat([torch.sin(coordinates), torch.cos(coordinates)], dim=-1) + + +@auto_docstring +class SamHQPreTrainedModel(PreTrainedModel): + config: SamHQConfig + base_model_prefix = "sam_hq" + main_input_name = "pixel_values" + input_modalities = ("image",) + _no_split_modules = ["SamHQVisionAttention"] + supports_gradient_checkpointing = True + _supports_sdpa = True + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + super()._init_weights(module) + if isinstance(module, SamHQVisionAttention): + if module.use_rel_pos: + init.zeros_(module.rel_pos_h) + init.zeros_(module.rel_pos_w) + elif isinstance(module, SamHQVisionEncoder): + if self.config.use_abs_pos: + init.zeros_(module.pos_embed) + elif isinstance(module, SamHQPositionalEmbedding): + init.normal_(module.positional_embedding, std=module.scale) + + +class SamHQPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values): + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).permute(0, 2, 3, 1) + return embeddings + + +class SamHQVisionNeck(nn.Module): + def __init__(self, config: SamHQVisionConfig): + super().__init__() + self.config = config + + self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False) + self.layer_norm1 = SamHQLayerNorm(config.output_channels, data_format="channels_first") + self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False) + self.layer_norm2 = SamHQLayerNorm(config.output_channels, data_format="channels_first") + + def forward(self, hidden_states): + hidden_states = hidden_states.permute(0, 3, 1, 2) + hidden_states = self.conv1(hidden_states) + hidden_states = self.layer_norm1(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + return hidden_states + + +class SamHQVisionEncoder(SamHQPreTrainedModel): + _can_record_outputs = { + "hidden_states": SamHQVisionLayer, + "attentions": SamHQVisionAttention, + } + + def __init__(self, config: SamHQVisionConfig): + super().__init__(config) + self.config = config + self.image_size = config.image_size + self.patch_embed = SamHQPatchEmbeddings(config) + + self.pos_embed = None + if config.use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros( + 1, + config.image_size // config.patch_size, + config.image_size // config.patch_size, + config.hidden_size, + ) + ) + + self.layers = nn.ModuleList() + for i in range(config.num_hidden_layers): + layer = SamHQVisionLayer( + config, + window_size=config.window_size if i not in config.global_attn_indexes else 0, + ) + self.layers.append(layer) + + self.neck = SamHQVisionNeck(config) + + self.gradient_checkpointing = False + self.post_init() + + def get_input_embeddings(self): + return self.patch_embed + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, pixel_values: torch.FloatTensor | None = None, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | SamHQVisionEncoderOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.patch_embed(pixel_values) + if self.pos_embed is not None: + hidden_states = hidden_states + self.pos_embed + + intermediate_embeddings = [] + + for layer_module in self.layers: + hidden_states = layer_module(hidden_states) + + # Collect embeddings from non-windowed blocks + if hasattr(layer_module, "window_size") and layer_module.window_size == 0: + intermediate_embeddings.append(hidden_states) + + hidden_states = self.neck(hidden_states) + + return SamHQVisionEncoderOutput( + last_hidden_state=hidden_states, + intermediate_embeddings=intermediate_embeddings, + ) + + +class SamHQLayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class SamHQAttention(nn.Module): + """ + SAM_HQ's attention layer that allows for downscaling the size of the embedding after projection to queries, keys, and + values. + """ + + def __init__(self, config, downsample_rate=None): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + + downsample_rate = config.attention_downsample_rate if downsample_rate is None else downsample_rate + + self.internal_dim = config.hidden_size // downsample_rate + self.num_attention_heads = config.num_attention_heads + if self.internal_dim % config.num_attention_heads != 0: + raise ValueError("num_attention_heads must divide hidden_size.") + self.scaling = (self.internal_dim // config.num_attention_heads) ** -0.5 + + self.q_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.k_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.v_proj = nn.Linear(self.hidden_size, self.internal_dim) + self.out_proj = nn.Linear(self.internal_dim, self.hidden_size) + + self.is_causal = False + + def _separate_heads(self, hidden_states: Tensor, num_attention_heads: int) -> Tensor: + batch, point_batch_size, n_tokens, channel = hidden_states.shape + c_per_head = channel // num_attention_heads + hidden_states = hidden_states.reshape(batch * point_batch_size, n_tokens, num_attention_heads, c_per_head) + return hidden_states.transpose(1, 2) + + def _recombine_heads(self, hidden_states: Tensor, point_batch_size: int) -> Tensor: + batch, n_tokens, n_heads, c_per_head = hidden_states.shape + return hidden_states.reshape(batch // point_batch_size, point_batch_size, n_tokens, n_heads * c_per_head) + + def forward( + self, + query: Tensor, + key: Tensor, + value: Tensor, + attention_similarity: Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Tensor: + # Input projections + query = self.q_proj(query) + key = self.k_proj(key) + value = self.v_proj(value) + + point_batch_size = query.shape[1] + # Separate into heads + query = self._separate_heads(query, self.num_attention_heads) + key = self._separate_heads(key, self.num_attention_heads) + value = self._separate_heads(value, self.num_attention_heads) + + # SamHQAttention + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query, + key, + value, + attention_mask=attention_similarity, + dropout=0.0, + scaling=self.scaling, + is_causal=self.is_causal, + **kwargs, + ) + + attn_output = self._recombine_heads(attn_output, point_batch_size) + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class SamHQTwoWayAttentionBlock(nn.Module): + def __init__(self, config, attention_downsample_rate: int = 2, skip_first_layer_pe: bool = False): + """ + A transformer block with four layers: + (1) self-attention of sparse inputs (2) cross attention of sparse inputs -> dense inputs (3) mlp block on + sparse inputs (4) cross attention of dense inputs -> sparse inputs + + Arguments: + config (`SamHQMaskDecoderConfig`): + The configuration file used to instantiate the block + attention_downsample_rate (*optionalk*, int, defaults to 2): + The downsample ratio of the block used to reduce the inner dim of the attention. + skip_first_layer_pe (*optional*, bool, defaults to `False`): + Whether or not to skip the addition of the query_point_embedding on the first layer. + """ + super().__init__() + + self.hidden_size = config.hidden_size + self.layer_norm_eps = config.layer_norm_eps + + self.self_attn = SamHQAttention(config, downsample_rate=1) + self.layer_norm1 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps) + + self.cross_attn_token_to_image = SamHQAttention(config, downsample_rate=attention_downsample_rate) + self.layer_norm2 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps) + + self.mlp = SamHQMLPBlock(config) + self.layer_norm3 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps) + + self.layer_norm4 = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps) + self.cross_attn_image_to_token = SamHQAttention(config, downsample_rate=attention_downsample_rate) + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, + queries: Tensor, + keys: Tensor, + query_point_embedding: Tensor, + key_point_embedding: Tensor, + attention_similarity: Tensor, + **kwargs: Unpack[TransformersKwargs], + ): + # Self attention block + if self.skip_first_layer_pe: + queries, _ = self.self_attn(query=queries, key=queries, value=queries) + else: + query = queries + query_point_embedding + attn_out, _ = self.self_attn(query=query, key=query, value=queries) + queries = queries + attn_out + queries = self.layer_norm1(queries) + + # Cross attention block, tokens attending to image embedding + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_token_to_image( + query=query, key=key, value=keys, attention_similarity=attention_similarity + ) + queries = queries + attn_out + + queries = self.layer_norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.layer_norm3(queries) + + # Cross attention block, image embedding attending to tokens + query = queries + query_point_embedding + key = keys + key_point_embedding + + attn_out, _ = self.cross_attn_image_to_token(query=key, key=query, value=queries) + keys = keys + attn_out + + keys = self.layer_norm4(keys) + return queries, keys, attn_out + + +class SamHQTwoWayTransformer(nn.Module): + def __init__(self, config: SamHQMaskDecoderConfig): + super().__init__() + self.config = config + + self.num_hidden_layers = config.num_hidden_layers + self.layers = nn.ModuleList() + + for i in range(self.num_hidden_layers): + self.layers.append(SamHQTwoWayAttentionBlock(config, skip_first_layer_pe=(i == 0))) + + self.final_attn_token_to_image = SamHQAttention(config) + self.layer_norm_final_attn = nn.LayerNorm(config.hidden_size) + + def forward( + self, + point_embeddings: Tensor, + image_embeddings: Tensor, + image_positional_embeddings: Tensor, + attention_similarity: Tensor, + target_embedding=None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + if image_embeddings is None: + raise ValueError("You have to specify an image_embedding") + + image_embeddings = image_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + image_positional_embeddings = image_positional_embeddings.flatten(2).permute(0, 2, 1).unsqueeze(1) + + # Prepare queries + queries = point_embeddings + keys = image_embeddings + + # Apply transformer blocks and final layernorm + for layer in self.layers: + if target_embedding is not None: + queries += target_embedding + + queries, keys, _ = layer( + queries=queries, + keys=keys, + query_point_embedding=point_embeddings, + key_point_embedding=image_positional_embeddings, + attention_similarity=attention_similarity, + **kwargs, + ) + # Apply the final attention layer from the points to the image + query = queries + point_embeddings + key = keys + image_positional_embeddings + + attn_out, _ = self.final_attn_token_to_image(query=query, key=key, value=keys) + + queries = queries + attn_out + queries = self.layer_norm_final_attn(queries) + return queries, keys + + +class SamHQFeedForward(nn.Module): + def __init__( + self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int, sigmoid_output: bool = False + ): + super().__init__() + self.num_layers = num_layers + self.activation = nn.ReLU() + self.proj_in = nn.Linear(input_dim, hidden_dim) + self.proj_out = nn.Linear(hidden_dim, output_dim) + self.layers = nn.ModuleList([nn.Linear(hidden_dim, hidden_dim) for _ in range(num_layers - 2)]) + self.sigmoid_output = sigmoid_output + + def forward(self, hidden_states): + hidden_states = self.proj_in(hidden_states) + hidden_states = self.activation(hidden_states) + for layer in self.layers: + hidden_states = self.activation(layer(hidden_states)) + + hidden_states = self.proj_out(hidden_states) + if self.sigmoid_output: + hidden_states = F.sigmoid(hidden_states) + return hidden_states + + +class SamHQMaskDecoder(nn.Module): + def __init__(self, config: SamHQMaskDecoderConfig): + super().__init__() + self.hidden_size = config.hidden_size + + self.num_multimask_outputs = config.num_multimask_outputs + self.num_mask_tokens = config.num_multimask_outputs + 1 + + self.iou_token = nn.Embedding(1, self.hidden_size) + self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size) + + self.transformer = SamHQTwoWayTransformer(config) + + self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + self.upscale_layer_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.activation = nn.GELU() + + mlps_list = [] + for _ in range(self.num_mask_tokens): + mlps_list += [SamHQFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)] + self.output_hypernetworks_mlps = nn.ModuleList(mlps_list) + + self.iou_prediction_head = SamHQFeedForward( + self.hidden_size, config.iou_head_hidden_dim, self.num_mask_tokens, config.iou_head_depth + ) + + self.hq_token = nn.Embedding(1, self.hidden_size) + self.hq_mask_mlp = SamHQFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3) + self.num_mask_tokens = self.num_mask_tokens + 1 + + # Compress ViT features + self.compress_vit_conv1 = nn.ConvTranspose2d(config.vit_dim, self.hidden_size, kernel_size=2, stride=2) + self.compress_vit_norm = SamHQLayerNorm(self.hidden_size, data_format="channels_first") + self.compress_vit_conv2 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 8, kernel_size=2, stride=2) + + # Embedding encoder + self.encoder_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.encoder_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.encoder_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + + # Embedding mask feature + self.mask_conv1 = nn.Conv2d(self.hidden_size // 8, self.hidden_size // 4, kernel_size=3, stride=1, padding=1) + self.mask_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.mask_conv2 = nn.Conv2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=3, stride=1, padding=1) + + def forward( + self, + image_embeddings: torch.Tensor, + image_positional_embeddings: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + hq_token_only: bool, + intermediate_embeddings: list[torch.Tensor] | None = None, + attention_similarity: torch.Tensor | None = None, + target_embedding: torch.Tensor | None = None, + ) -> SamHQMMaskDecoderOutputs: + """ + Predict high-quality masks given image and prompt embeddings. + + Args: + image_embeddings (`torch.Tensor`): + The embeddings from the image encoder. + image_positional_embedding (`torch.Tensor`): + Positional encoding with the shape of image_embeddings. + sparse_prompt_embeddings (`torch.Tensor`): + The embeddings of the points and boxes. + dense_prompt_embeddings (`torch.Tensor`): + The embeddings of the mask inputs. + multimask_output (bool): + Whether to return multiple masks or a single mask. + hq_token_only (bool): + Whether to use only the high-quality token output or combine with SAM output. + intermediate_embeddings (`torch.Tensor`): + Intermediate embeddings from the vision encoder for feature fusion. + attention_similarity (`torch.Tensor`, *optional*): + Optional tensor for attention similarity computation. + target_embedding (`torch.Tensor`, *optional*): + Optional target embedding for transformer processing. + + Returns: + `Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]`: A tuple of tensors containing: + - A tensor of shape `(batch_size, num_prompts, num_masks, height, width)` containing the output masks. + - A tensor of shape `(batch_size, num_prompts, num_masks)` containing the iou predictions for each mask. + - (Optional) A tuple containing attention tensors if output_attentions is True. + """ + batch_size, num_channels, height, width = image_embeddings.shape + point_batch_size = sparse_prompt_embeddings.shape[1] if sparse_prompt_embeddings is not None else 1 + + has_intermediate = intermediate_embeddings is not None and len(intermediate_embeddings) > 0 + + if has_intermediate: + vit_features = intermediate_embeddings[0].permute(0, 3, 1, 2).contiguous() + + embed_encode = self.encoder_conv1(image_embeddings) + embed_encode = self.activation(self.encoder_norm(embed_encode)) + embed_encode = self.encoder_conv2(embed_encode) + + if has_intermediate: + compressed_vit_features = self.compress_vit_conv1(vit_features) + compressed_vit_features = self.activation(self.compress_vit_norm(compressed_vit_features)) + compressed_vit_features = self.compress_vit_conv2(compressed_vit_features) + + hq_features = embed_encode + compressed_vit_features + else: + hq_features = embed_encode + + output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight, self.hq_token.weight], dim=0) + output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) + + if sparse_prompt_embeddings is not None: + tokens = torch.cat([output_tokens, sparse_prompt_embeddings], dim=2) + else: + tokens = output_tokens + point_embeddings = tokens.to(self.iou_token.weight.dtype) + image_embeddings = image_embeddings + dense_prompt_embeddings + image_embeddings = image_embeddings.repeat_interleave(point_batch_size, 0) + image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) + + point_embedding, iou_token_out = self.transformer( + point_embeddings=point_embeddings, + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + ) + iou_token_out = point_embedding[:, :, 0, :] + mask_tokens_out = point_embedding[:, :, 1 : (1 + self.num_mask_tokens), :] + + image_embeddings = image_embeddings.transpose(2, 3).reshape( + batch_size * point_batch_size, num_channels, height, width + ) + + upscaled_embedding = self.upscale_conv1(image_embeddings) + upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) + upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding)) + + upscaled_embedding_hq = self.mask_conv1(upscaled_embedding) + upscaled_embedding_hq = self.activation(self.mask_norm(upscaled_embedding_hq)) + upscaled_embedding_hq = self.mask_conv2(upscaled_embedding_hq) + + if hq_features.shape[0] == 1: + hq_features = hq_features.repeat(batch_size * point_batch_size, 1, 1, 1) + elif hq_features.shape[0] == batch_size and batch_size * point_batch_size != batch_size: + hq_features = hq_features.repeat_interleave(point_batch_size, 0) + upscaled_embedding_hq = upscaled_embedding_hq + hq_features + + hyper_in_list = [] + for mask_token_index in range(self.num_mask_tokens): + if mask_token_index < self.num_mask_tokens - 1: + current_mlp = self.output_hypernetworks_mlps[mask_token_index] + else: + current_mlp = self.hq_mask_mlp + hyper_in_list += [current_mlp(mask_tokens_out[:, :, mask_token_index, :])] + + hyper_in = torch.stack(hyper_in_list, dim=2) + _, num_channels, height, width = upscaled_embedding.shape + upscaled_embedding = upscaled_embedding.reshape(batch_size, point_batch_size, num_channels, height * width) + upscaled_embedding_hq = upscaled_embedding_hq.reshape( + batch_size, point_batch_size, num_channels, height * width + ) + + masks_sam = (hyper_in[:, :, : self.num_mask_tokens - 1] @ upscaled_embedding).reshape( + batch_size, point_batch_size, -1, height, width + ) + masks_hq = (hyper_in[:, :, self.num_mask_tokens - 1 :] @ upscaled_embedding_hq).reshape( + batch_size, point_batch_size, -1, height, width + ) + masks = torch.cat([masks_sam, masks_hq], dim=2) + + iou_pred = self.iou_prediction_head(iou_token_out) + + if multimask_output: + mask_slice = slice(1, self.num_mask_tokens - 1) + iou_pred = iou_pred[:, :, mask_slice] + # Sort the IoU scores in descending order and get indices + iou_pred_sorted, sort_indices = torch.sort(iou_pred, dim=2, descending=True) + # Reorder the masks according to sorted scores + masks_sam = masks[:, :, mask_slice, :, :] + masks_sam = torch.gather( + masks_sam, + 2, + sort_indices[..., None, None].expand(-1, -1, -1, masks_sam.shape[3], masks_sam.shape[4]), + ) + # Update iou_pred with sorted scores + iou_pred = iou_pred_sorted + else: + mask_slice = slice(0, 1) + iou_pred = iou_pred[:, :, mask_slice] + masks_sam = masks[:, :, mask_slice, :, :] + + masks_hq = masks[:, :, slice(self.num_mask_tokens - 1, self.num_mask_tokens), :, :] + if hq_token_only: + masks = masks_hq + else: + masks = masks_sam + masks_hq + + return masks, iou_pred + + +@auto_docstring( + custom_intro=""" + The vision model from SamHQ without any head or projection on top. + """ +) +class SamHQVisionModel(SamHQPreTrainedModel): + config: SamHQVisionConfig + main_input_name = "pixel_values" + + def __init__(self, config: SamHQVisionConfig): + super().__init__(config) + self.vision_encoder = SamHQVisionEncoder(config) + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_encoder.patch_embed + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | SamHQVisionEncoderOutput: + return self.vision_encoder(pixel_values, **kwargs) + + +class SamHQMaskEmbedding(nn.Module): + def __init__(self, config: SamHQPromptEncoderConfig): + super().__init__() + self.mask_input_channels = config.mask_input_channels // 4 + self.activation = ACT2FN[config.hidden_act] + self.conv1 = nn.Conv2d(1, self.mask_input_channels, kernel_size=2, stride=2) + self.conv2 = nn.Conv2d(self.mask_input_channels, config.mask_input_channels, kernel_size=2, stride=2) + self.conv3 = nn.Conv2d(config.mask_input_channels, config.hidden_size, kernel_size=1) + self.layer_norm1 = SamHQLayerNorm( + self.mask_input_channels, eps=config.layer_norm_eps, data_format="channels_first" + ) + self.layer_norm2 = SamHQLayerNorm( + self.mask_input_channels * 4, eps=config.layer_norm_eps, data_format="channels_first" + ) + + def forward(self, masks): + hidden_states = self.conv1(masks) + hidden_states = self.layer_norm1(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.activation(hidden_states) + dense_embeddings = self.conv3(hidden_states) + return dense_embeddings + + +class SamHQPromptEncoder(nn.Module): + def __init__(self, config: SamHQConfig): + super().__init__() + self.shared_embedding = SamHQPositionalEmbedding(config.vision_config) + config = config.prompt_encoder_config + self.mask_embed = SamHQMaskEmbedding(config) + self.no_mask_embed = nn.Embedding(1, config.hidden_size) + + self.image_embedding_size = (config.image_embedding_size, config.image_embedding_size) + self.input_image_size = config.image_size + + self.point_embed = nn.ModuleList( + [nn.Embedding(1, config.hidden_size) for i in range(config.num_point_embeddings)] + ) + self.hidden_size = config.hidden_size + self.not_a_point_embed = nn.Embedding(1, config.hidden_size) + + def _embed_points(self, points: torch.Tensor, labels: torch.Tensor, pad: bool) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + target_point_shape = (points.shape[0], points.shape[1], 1, points.shape[-1]) + target_labels_shape = (points.shape[0], points.shape[1], 1) + padding_point = torch.zeros(target_point_shape, device=points.device) + padding_label = -torch.ones(target_labels_shape, device=labels.device) + points = torch.cat([points, padding_point], dim=2) + labels = torch.cat([labels, padding_label], dim=2) + input_shape = (self.input_image_size, self.input_image_size) + point_embedding = self.shared_embedding(points, input_shape) + + # torch.where and expanding the labels tensor is required by the ONNX export + point_embedding = torch.where(labels[..., None] == -1, self.not_a_point_embed.weight, point_embedding) + + # This is required for the ONNX export. The dtype, device need to be explicitly + # specified as otherwise torch.onnx.export interprets as double + point_embedding = torch.where(labels[..., None] != -10, point_embedding, torch.zeros_like(point_embedding)) + + point_embedding = torch.where( + (labels == 0)[:, :, :, None], + point_embedding + self.point_embed[0].weight[None, None, :, :], + point_embedding, + ) + + point_embedding = torch.where( + (labels == 1)[:, :, :, None], + point_embedding + self.point_embed[1].weight[None, None, :, :], + point_embedding, + ) + + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + batch_size, nb_boxes = boxes.shape[:2] + coords = boxes.reshape(batch_size, nb_boxes, 2, 2) + input_shape = (self.input_image_size, self.input_image_size) + corner_embedding = self.shared_embedding(coords, input_shape) + corner_embedding[:, :, 0, :] += self.point_embed[2].weight + corner_embedding[:, :, 1, :] += self.point_embed[3].weight + return corner_embedding + + def forward( + self, + input_points: tuple[torch.Tensor, torch.Tensor] | None, + input_labels: torch.Tensor | None, + input_boxes: torch.Tensor | None, + input_masks: torch.Tensor | None, + ) -> tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense embeddings. + + Args: + points (`torch.Tensor`, *optional*): + point coordinates and labels to embed. + boxes (`torch.Tensor`, *optional*): + boxes to embed + masks (`torch.Tensor`, *optional*): + masks to embed + """ + sparse_embeddings = None + batch_size = 1 + if input_points is not None: + batch_size = input_points.shape[0] + if input_labels is None: + raise ValueError("If points are provided, labels must also be provided.") + point_embeddings = self._embed_points(input_points, input_labels, pad=(input_boxes is None)) + sparse_embeddings = point_embeddings + if input_boxes is not None: + batch_size = input_boxes.shape[0] + box_embeddings = self._embed_boxes(input_boxes) + if sparse_embeddings is None: + sparse_embeddings = box_embeddings + else: + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=2) + if input_masks is not None: + dense_embeddings = self.mask_embed(input_masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + batch_size, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings + + +@auto_docstring( + custom_intro=""" + Segment Anything Model HQ (SAM-HQ) for generating masks, given an input image and optional 2D location and bounding boxes. + """ +) +class SamHQModel(SamHQPreTrainedModel): + input_modalities = ("image", "text") + _can_record_outputs = {"mask_decoder_attentions": OutputRecorder(SamHQTwoWayAttentionBlock, index=2)} + _tied_weights_keys = { + "prompt_encoder.shared_embedding.positional_embedding": "shared_image_embedding.positional_embedding" + } + + def __init__(self, config): + super().__init__(config) + self.shared_image_embedding = SamHQPositionalEmbedding(config.vision_config) + self.vision_encoder = SamHQVisionEncoder(config.vision_config) + self.prompt_encoder = SamHQPromptEncoder(config) + # The module using it is not a PreTrainedModel subclass so we need this + config.mask_decoder_config._attn_implementation = config._attn_implementation + + self.mask_decoder = SamHQMaskDecoder(config.mask_decoder_config) + self.post_init() + + def get_input_embeddings(self): + return self.vision_encoder.get_input_embeddings() + + def get_image_wide_positional_embeddings(self): + size = self.config.prompt_encoder_config.image_embedding_size + target_device = self.shared_image_embedding.positional_embedding.device + target_dtype = self.shared_image_embedding.positional_embedding.dtype + grid = torch.ones((size, size), device=target_device, dtype=target_dtype) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / size + x_embed = x_embed / size + + positional_embedding = self.shared_image_embedding(torch.stack([x_embed, y_embed], dim=-1)) + return positional_embedding.permute(2, 0, 1).unsqueeze(0) # channel x height x width + + @torch.no_grad() + def get_image_embeddings( + self, + pixel_values, + ): + r""" + Returns the image embeddings by passing the pixel values through the vision encoder. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Input pixel values + """ + vision_output = self.vision_encoder(pixel_values=pixel_values) + image_embeddings = vision_output[0] + intermediate_embeddings = vision_output[1] + return image_embeddings, intermediate_embeddings + + @torch.no_grad() + def get_prompt_embeddings( + self, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + ): + r""" + Returns the prompt embeddings by passing the input points, labels, boxes and masks through the prompt encoder. + + Args: + input_points (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_points_per_image, 2)`): + Optional input points for the prompt encoder. The padding of the point is automatically done by the + processor. `point_batch_size` refers to the number of masks that we want the model to predict per + point. The model will output `point_batch_size` times 3 masks in total. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points_per_image)`): + Optional input labels for the prompt encoder. The padding of the labels is automatically done by the + processor, or can be fed by the user. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes_per_image, 4)`): + Optional input boxes for the prompt encoder. The padding of the boxes is automatically done by the + processor. users can also pass manually the input boxes. + input_masks (`torch.LongTensor` of shape `(batch_size, image_size, image_size)`): + Optional input masks for the prompt encoder. + """ + prompt_output = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + return prompt_output + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + hq_token_only: bool = False, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + intermediate_embeddings: list[torch.FloatTensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> list[dict[str, torch.Tensor]]: + r""" + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM_HQ model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + hq_token_only (`bool`, *optional*, defaults to `False`): + Whether to use only the HQ token path for mask generation. When False, combines both standard and HQ paths. + This is specific to SAM-HQ's architecture. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + intermediate_embeddings (`List[torch.FloatTensor]`, *optional*): + Intermediate embeddings from vision encoder's non-windowed blocks, used by SAM-HQ for enhanced mask quality. + Required when providing pre-computed image_embeddings instead of pixel_values. + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoModel, AutoProcessor + + >>> model = AutoModel.from_pretrained("sushmanth/sam_hq_vit_b") + >>> processor = AutoProcessor.from_pretrained("sushmanth/sam_hq_vit_b") + + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())).convert("RGB") + >>> input_points = [[[400, 650]]] # 2D location of a window on the car + >>> inputs = processor(images=image, input_points=input_points, return_tensors="pt") + + >>> # Get high-quality segmentation mask + >>> outputs = model(**inputs) + + >>> # For high-quality mask only + >>> outputs = model(**inputs, hq_token_only=True) + + >>> # Postprocess masks + >>> masks = processor.post_process_masks( + ... outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"] + ... ) + ``` + """ + if pixel_values is None and image_embeddings is None: + raise ValueError("Either pixel_values or image_embeddings must be provided.") + + if pixel_values is not None and image_embeddings is not None: + raise ValueError("Only one of pixel_values and image_embeddings can be provided.") + + if input_points is not None and len(input_points.shape) != 4: + raise ValueError( + "The input_points must be a 4D tensor. Of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`." + f" got {input_points.shape}." + ) + + if input_boxes is not None and len(input_boxes.shape) != 3: + raise ValueError( + "The input_boxes must be a 3D tensor. Of shape `batch_size`, `nb_boxes`, `4`." + f" got {input_boxes.shape}." + ) + + # Add validation for point and box batch sizes + if input_points is not None and input_boxes is not None: + point_batch_size = input_points.shape[1] + box_batch_size = input_boxes.shape[1] + if point_batch_size != box_batch_size: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {point_batch_size} and {box_batch_size}." + ) + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings.shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + if pixel_values is not None: + vision_outputs = self.vision_encoder(pixel_values, **kwargs) + image_embeddings = vision_outputs.last_hidden_state + intermediate_embeddings = vision_outputs.intermediate_embeddings + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + + # Predict masks + mask_decoder_output = self.mask_decoder( + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + hq_token_only=hq_token_only, + intermediate_embeddings=intermediate_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + ) + return SamHQImageSegmentationOutput( + iou_scores=mask_decoder_output[1], + pred_masks=mask_decoder_output[0], + vision_hidden_states=vision_outputs.hidden_states if pixel_values is not None else None, + vision_attentions=vision_outputs.attentions if pixel_values is not None else None, + ) + + +__all__ = ["SamHQModel", "SamHQPreTrainedModel", "SamHQVisionModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/modular_sam_hq.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/modular_sam_hq.py new file mode 100644 index 0000000000000000000000000000000000000000..5122ed9da2f6a7381f1af70659fc991f96e107fd --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/modular_sam_hq.py @@ -0,0 +1,595 @@ +# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass + +import torch +from huggingface_hub.dataclasses import strict +from torch import nn + +from ...processing_utils import Unpack +from ...utils import auto_docstring, logging +from ...utils.generic import ModelOutput, TransformersKwargs, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..sam.configuration_sam import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig +from ..sam.modeling_sam import ( + SamFeedForward, + SamImageSegmentationOutput, + SamLayerNorm, + SamModel, + SamPreTrainedModel, + SamTwoWayTransformer, + SamVisionAttention, + SamVisionEncoder, + SamVisionEncoderOutput, + SamVisionLayer, + SamVisionModel, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="syscv-community/sam-hq-vit-base") +@strict +class SamHQPromptEncoderConfig(SamPromptEncoderConfig): + pass + + +@auto_docstring(checkpoint="syscv-community/sam-hq-vit-base") +@strict +class SamHQVisionConfig(SamVisionConfig): + pass + + +@auto_docstring(checkpoint="syscv-community/sam-hq-vit-base") +@strict +class SamHQMaskDecoderConfig(SamMaskDecoderConfig): + r""" + mlp_dim (`int`, *optional*, defaults to 2048): + Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. + attention_downsample_rate (`int`, *optional*, defaults to 2): + The downsampling rate of the attention layer. + num_multimask_outputs (`int`, *optional*, defaults to 3): + The number of outputs from the `SamMaskDecoder` module. In the Segment Anything paper, this is set to 3. + iou_head_depth (`int`, *optional*, defaults to 3): + The number of layers in the IoU head module. + iou_head_hidden_dim (`int`, *optional*, defaults to 256): + The dimensionality of the hidden states in the IoU head module. + vit_dim (`int`, *optional*, defaults to 768): + Dimensionality of the Vision Transformer (ViT) used in the `SamHQMaskDecoder` module. + """ + + vit_dim: int = 768 + + +@auto_docstring(checkpoint="syscv-community/sam-hq-vit-base") +@strict +class SamHQConfig(SamConfig): + r""" + prompt_encoder_config (Union[`dict`, `SamHQPromptEncoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`SamHQPromptEncoderConfig`]. + mask_decoder_config (Union[`dict`, `SamHQMaskDecoderConfig`], *optional*): + Dictionary of configuration options used to initialize [`SamHQMaskDecoderConfig`]. + """ + + +class SamHQVisionEncoderOutput(SamVisionEncoderOutput): + r""" + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + intermediate_embeddings (`list(torch.FloatTensor)`, *optional*): + A list of intermediate embeddings collected from certain blocks within the model, typically those without + windowed attention. Each element in the list is of shape `(batch_size, sequence_length, hidden_size)`. + This is specific to SAM-HQ and not present in base SAM. + """ + + intermediate_embeddings: list[torch.FloatTensor] | None = None + + +@dataclass +class SamHQMMaskDecoderOutputs(ModelOutput): + r""" + masks (`torch.FloatTensor` of shape `(batch_size, num_prompts, num_masks, height, width)`): + The predicted masks for the input image. The masks are of shape `(batch_size, num_prompts, num_masks, height, width)`. + iou_scores (`torch.FloatTensor` of shape `(batch_size, num_prompts, num_masks)`): + The predicted IoU scores for each mask. The scores are of shape `(batch_size, num_prompts, num_masks)`. + mask_decoder_attentions (`torch.FloatTensor`, *optional*): + The attention weights from the mask decoder, if `output_attentions=True` was passed during the forward pass. + This is specific to SAM-HQ and not present in base SAM. + """ + + masks: torch.FloatTensor + iou_scores: torch.FloatTensor | None = None + mask_decoder_attentions: torch.FloatTensor | None = None + + +class SamHQImageSegmentationOutput(SamImageSegmentationOutput): + pass + + +class SamHQVisionAttention(SamVisionAttention): + pass + + +class SamHQVisionLayer(SamVisionLayer): + pass + + +class SamHQPreTrainedModel(SamPreTrainedModel): + pass + + +class SamHQVisionEncoder(SamVisionEncoder, SamHQPreTrainedModel): + _can_record_outputs = { + "hidden_states": SamHQVisionLayer, + "attentions": SamHQVisionAttention, + } + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, pixel_values: torch.FloatTensor | None = None, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | SamHQVisionEncoderOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.patch_embed(pixel_values) + if self.pos_embed is not None: + hidden_states = hidden_states + self.pos_embed + + intermediate_embeddings = [] + + for layer_module in self.layers: + hidden_states = layer_module(hidden_states) + + # Collect embeddings from non-windowed blocks + if hasattr(layer_module, "window_size") and layer_module.window_size == 0: + intermediate_embeddings.append(hidden_states) + + hidden_states = self.neck(hidden_states) + + return SamHQVisionEncoderOutput( + last_hidden_state=hidden_states, + intermediate_embeddings=intermediate_embeddings, + ) + + +class SamHQLayerNorm(SamLayerNorm): + pass + + +class SamHQTwoWayTransformer(SamTwoWayTransformer): + pass + + +class SamHQFeedForward(SamFeedForward): + pass + + +class SamHQMaskDecoder(nn.Module): + def __init__(self, config: SamHQMaskDecoderConfig): + super().__init__() + self.hidden_size = config.hidden_size + + self.num_multimask_outputs = config.num_multimask_outputs + self.num_mask_tokens = config.num_multimask_outputs + 1 + + self.iou_token = nn.Embedding(1, self.hidden_size) + self.mask_tokens = nn.Embedding(self.num_mask_tokens, self.hidden_size) + + self.transformer = SamHQTwoWayTransformer(config) + + self.upscale_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.upscale_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + self.upscale_layer_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.activation = nn.GELU() + + mlps_list = [] + for _ in range(self.num_mask_tokens): + mlps_list += [SamHQFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3)] + self.output_hypernetworks_mlps = nn.ModuleList(mlps_list) + + self.iou_prediction_head = SamHQFeedForward( + self.hidden_size, config.iou_head_hidden_dim, self.num_mask_tokens, config.iou_head_depth + ) + + self.hq_token = nn.Embedding(1, self.hidden_size) + self.hq_mask_mlp = SamHQFeedForward(self.hidden_size, self.hidden_size, self.hidden_size // 8, 3) + self.num_mask_tokens = self.num_mask_tokens + 1 + + # Compress ViT features + self.compress_vit_conv1 = nn.ConvTranspose2d(config.vit_dim, self.hidden_size, kernel_size=2, stride=2) + self.compress_vit_norm = SamHQLayerNorm(self.hidden_size, data_format="channels_first") + self.compress_vit_conv2 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 8, kernel_size=2, stride=2) + + # Embedding encoder + self.encoder_conv1 = nn.ConvTranspose2d(self.hidden_size, self.hidden_size // 4, kernel_size=2, stride=2) + self.encoder_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.encoder_conv2 = nn.ConvTranspose2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=2, stride=2) + + # Embedding mask feature + self.mask_conv1 = nn.Conv2d(self.hidden_size // 8, self.hidden_size // 4, kernel_size=3, stride=1, padding=1) + self.mask_norm = SamHQLayerNorm(self.hidden_size // 4, data_format="channels_first") + self.mask_conv2 = nn.Conv2d(self.hidden_size // 4, self.hidden_size // 8, kernel_size=3, stride=1, padding=1) + + def forward( + self, + image_embeddings: torch.Tensor, + image_positional_embeddings: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + hq_token_only: bool, + intermediate_embeddings: list[torch.Tensor] | None = None, + attention_similarity: torch.Tensor | None = None, + target_embedding: torch.Tensor | None = None, + ) -> SamHQMMaskDecoderOutputs: + """ + Predict high-quality masks given image and prompt embeddings. + + Args: + image_embeddings (`torch.Tensor`): + The embeddings from the image encoder. + image_positional_embedding (`torch.Tensor`): + Positional encoding with the shape of image_embeddings. + sparse_prompt_embeddings (`torch.Tensor`): + The embeddings of the points and boxes. + dense_prompt_embeddings (`torch.Tensor`): + The embeddings of the mask inputs. + multimask_output (bool): + Whether to return multiple masks or a single mask. + hq_token_only (bool): + Whether to use only the high-quality token output or combine with SAM output. + intermediate_embeddings (`torch.Tensor`): + Intermediate embeddings from the vision encoder for feature fusion. + attention_similarity (`torch.Tensor`, *optional*): + Optional tensor for attention similarity computation. + target_embedding (`torch.Tensor`, *optional*): + Optional target embedding for transformer processing. + + Returns: + `Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]`: A tuple of tensors containing: + - A tensor of shape `(batch_size, num_prompts, num_masks, height, width)` containing the output masks. + - A tensor of shape `(batch_size, num_prompts, num_masks)` containing the iou predictions for each mask. + - (Optional) A tuple containing attention tensors if output_attentions is True. + """ + batch_size, num_channels, height, width = image_embeddings.shape + point_batch_size = sparse_prompt_embeddings.shape[1] if sparse_prompt_embeddings is not None else 1 + + has_intermediate = intermediate_embeddings is not None and len(intermediate_embeddings) > 0 + + if has_intermediate: + vit_features = intermediate_embeddings[0].permute(0, 3, 1, 2).contiguous() + + embed_encode = self.encoder_conv1(image_embeddings) + embed_encode = self.activation(self.encoder_norm(embed_encode)) + embed_encode = self.encoder_conv2(embed_encode) + + if has_intermediate: + compressed_vit_features = self.compress_vit_conv1(vit_features) + compressed_vit_features = self.activation(self.compress_vit_norm(compressed_vit_features)) + compressed_vit_features = self.compress_vit_conv2(compressed_vit_features) + + hq_features = embed_encode + compressed_vit_features + else: + hq_features = embed_encode + + output_tokens = torch.cat([self.iou_token.weight, self.mask_tokens.weight, self.hq_token.weight], dim=0) + output_tokens = output_tokens.repeat(batch_size, point_batch_size, 1, 1) + + if sparse_prompt_embeddings is not None: + tokens = torch.cat([output_tokens, sparse_prompt_embeddings], dim=2) + else: + tokens = output_tokens + point_embeddings = tokens.to(self.iou_token.weight.dtype) + image_embeddings = image_embeddings + dense_prompt_embeddings + image_embeddings = image_embeddings.repeat_interleave(point_batch_size, 0) + image_positional_embeddings = image_positional_embeddings.repeat_interleave(point_batch_size, 0) + + point_embedding, iou_token_out = self.transformer( + point_embeddings=point_embeddings, + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + ) + iou_token_out = point_embedding[:, :, 0, :] + mask_tokens_out = point_embedding[:, :, 1 : (1 + self.num_mask_tokens), :] + + image_embeddings = image_embeddings.transpose(2, 3).reshape( + batch_size * point_batch_size, num_channels, height, width + ) + + upscaled_embedding = self.upscale_conv1(image_embeddings) + upscaled_embedding = self.activation(self.upscale_layer_norm(upscaled_embedding)) + upscaled_embedding = self.activation(self.upscale_conv2(upscaled_embedding)) + + upscaled_embedding_hq = self.mask_conv1(upscaled_embedding) + upscaled_embedding_hq = self.activation(self.mask_norm(upscaled_embedding_hq)) + upscaled_embedding_hq = self.mask_conv2(upscaled_embedding_hq) + + if hq_features.shape[0] == 1: + hq_features = hq_features.repeat(batch_size * point_batch_size, 1, 1, 1) + elif hq_features.shape[0] == batch_size and batch_size * point_batch_size != batch_size: + hq_features = hq_features.repeat_interleave(point_batch_size, 0) + upscaled_embedding_hq = upscaled_embedding_hq + hq_features + + hyper_in_list = [] + for mask_token_index in range(self.num_mask_tokens): + if mask_token_index < self.num_mask_tokens - 1: + current_mlp = self.output_hypernetworks_mlps[mask_token_index] + else: + current_mlp = self.hq_mask_mlp + hyper_in_list += [current_mlp(mask_tokens_out[:, :, mask_token_index, :])] + + hyper_in = torch.stack(hyper_in_list, dim=2) + _, num_channels, height, width = upscaled_embedding.shape + upscaled_embedding = upscaled_embedding.reshape(batch_size, point_batch_size, num_channels, height * width) + upscaled_embedding_hq = upscaled_embedding_hq.reshape( + batch_size, point_batch_size, num_channels, height * width + ) + + masks_sam = (hyper_in[:, :, : self.num_mask_tokens - 1] @ upscaled_embedding).reshape( + batch_size, point_batch_size, -1, height, width + ) + masks_hq = (hyper_in[:, :, self.num_mask_tokens - 1 :] @ upscaled_embedding_hq).reshape( + batch_size, point_batch_size, -1, height, width + ) + masks = torch.cat([masks_sam, masks_hq], dim=2) + + iou_pred = self.iou_prediction_head(iou_token_out) + + if multimask_output: + mask_slice = slice(1, self.num_mask_tokens - 1) + iou_pred = iou_pred[:, :, mask_slice] + # Sort the IoU scores in descending order and get indices + iou_pred_sorted, sort_indices = torch.sort(iou_pred, dim=2, descending=True) + # Reorder the masks according to sorted scores + masks_sam = masks[:, :, mask_slice, :, :] + masks_sam = torch.gather( + masks_sam, + 2, + sort_indices[..., None, None].expand(-1, -1, -1, masks_sam.shape[3], masks_sam.shape[4]), + ) + # Update iou_pred with sorted scores + iou_pred = iou_pred_sorted + else: + mask_slice = slice(0, 1) + iou_pred = iou_pred[:, :, mask_slice] + masks_sam = masks[:, :, mask_slice, :, :] + + masks_hq = masks[:, :, slice(self.num_mask_tokens - 1, self.num_mask_tokens), :, :] + if hq_token_only: + masks = masks_hq + else: + masks = masks_sam + masks_hq + + return masks, iou_pred + + +class SamHQVisionModel(SamVisionModel): + pass + + +@auto_docstring( + custom_intro=""" + Segment Anything Model HQ (SAM-HQ) for generating masks, given an input image and optional 2D location and bounding boxes. + """ +) +class SamHQModel(SamModel): + def __init__(self, config): + super().__init__(config) + self.vision_encoder = SamHQVisionEncoder(config.vision_config) + + self.mask_decoder = SamHQMaskDecoder(config.mask_decoder_config) + + self.post_init() + + @torch.no_grad() + def get_image_embeddings( + self, + pixel_values, + ): + r""" + Returns the image embeddings by passing the pixel values through the vision encoder. + + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Input pixel values + """ + vision_output = self.vision_encoder(pixel_values=pixel_values) + image_embeddings = vision_output[0] + intermediate_embeddings = vision_output[1] + return image_embeddings, intermediate_embeddings + + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + input_points: torch.FloatTensor | None = None, + input_labels: torch.LongTensor | None = None, + input_boxes: torch.FloatTensor | None = None, + input_masks: torch.LongTensor | None = None, + image_embeddings: torch.FloatTensor | None = None, + multimask_output: bool = True, + hq_token_only: bool = False, + attention_similarity: torch.FloatTensor | None = None, + target_embedding: torch.FloatTensor | None = None, + intermediate_embeddings: list[torch.FloatTensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> list[dict[str, torch.Tensor]]: + r""" + input_points (`torch.FloatTensor` of shape `(batch_size, num_points, 2)`): + Input 2D spatial points, this is used by the prompt encoder to encode the prompt. Generally yields to much + better results. The points can be obtained by passing a list of list of list to the processor that will + create corresponding `torch` tensors of dimension 4. The first dimension is the image batch size, the + second dimension is the point batch size (i.e. how many segmentation masks do we want the model to predict + per input point), the third dimension is the number of points per segmentation mask (it is possible to pass + multiple points for a single mask), and the last dimension is the x (vertical) and y (horizontal) + coordinates of the point. If a different number of points is passed either for each image, or for each + mask, the processor will create "PAD" points that will correspond to the (0, 0) coordinate, and the + computation of the embedding will be skipped for these points using the labels. + input_labels (`torch.LongTensor` of shape `(batch_size, point_batch_size, num_points)`): + Input labels for the points, this is used by the prompt encoder to encode the prompt. According to the + official implementation, there are 3 types of labels + + - `1`: the point is a point that contains the object of interest + - `0`: the point is a point that does not contain the object of interest + - `-1`: the point corresponds to the background + + We added the label: + + - `-10`: the point is a padding point, thus should be ignored by the prompt encoder + + The padding labels should be automatically done by the processor. + input_boxes (`torch.FloatTensor` of shape `(batch_size, num_boxes, 4)`): + Input boxes for the points, this is used by the prompt encoder to encode the prompt. Generally yields to + much better generated masks. The boxes can be obtained by passing a list of list of list to the processor, + that will generate a `torch` tensor, with each dimension corresponding respectively to the image batch + size, the number of boxes per image and the coordinates of the top left and bottom right point of the box. + In the order (`x1`, `y1`, `x2`, `y2`): + + - `x1`: the x coordinate of the top left point of the input box + - `y1`: the y coordinate of the top left point of the input box + - `x2`: the x coordinate of the bottom right point of the input box + - `y2`: the y coordinate of the bottom right point of the input box + input_masks (`torch.FloatTensor` of shape `(batch_size, image_size, image_size)`): + SAM_HQ model also accepts segmentation masks as input. The mask will be embedded by the prompt encoder to + generate a corresponding embedding, that will be fed later on to the mask decoder. These masks needs to be + manually fed by the user, and they need to be of shape (`batch_size`, `image_size`, `image_size`). + image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_channels, window_size, window_size)`): + Image embeddings, this is used by the mask decder to generate masks and iou scores. For more memory + efficient computation, users can first retrieve the image embeddings using the `get_image_embeddings` + method, and then feed them to the `forward` method instead of feeding the `pixel_values`. + multimask_output (`bool`, *optional*): + In the original implementation and paper, the model always outputs 3 masks per image (or per point / per + bounding box if relevant). However, it is possible to just output a single mask, that corresponds to the + "best" mask, by specifying `multimask_output=False`. + hq_token_only (`bool`, *optional*, defaults to `False`): + Whether to use only the HQ token path for mask generation. When False, combines both standard and HQ paths. + This is specific to SAM-HQ's architecture. + attention_similarity (`torch.FloatTensor`, *optional*): + Attention similarity tensor, to be provided to the mask decoder for target-guided attention in case the + model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + target_embedding (`torch.FloatTensor`, *optional*): + Embedding of the target concept, to be provided to the mask decoder for target-semantic prompting in case + the model is used for personalization as introduced in [PerSAM](https://huggingface.co/papers/2305.03048). + intermediate_embeddings (`List[torch.FloatTensor]`, *optional*): + Intermediate embeddings from vision encoder's non-windowed blocks, used by SAM-HQ for enhanced mask quality. + Required when providing pre-computed image_embeddings instead of pixel_values. + + Example: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoModel, AutoProcessor + + >>> model = AutoModel.from_pretrained("sushmanth/sam_hq_vit_b") + >>> processor = AutoProcessor.from_pretrained("sushmanth/sam_hq_vit_b") + + >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-car.png" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())).convert("RGB") + >>> input_points = [[[400, 650]]] # 2D location of a window on the car + >>> inputs = processor(images=image, input_points=input_points, return_tensors="pt") + + >>> # Get high-quality segmentation mask + >>> outputs = model(**inputs) + + >>> # For high-quality mask only + >>> outputs = model(**inputs, hq_token_only=True) + + >>> # Postprocess masks + >>> masks = processor.post_process_masks( + ... outputs.pred_masks, inputs["original_sizes"], inputs["reshaped_input_sizes"] + ... ) + ``` + """ + if pixel_values is None and image_embeddings is None: + raise ValueError("Either pixel_values or image_embeddings must be provided.") + + if pixel_values is not None and image_embeddings is not None: + raise ValueError("Only one of pixel_values and image_embeddings can be provided.") + + if input_points is not None and len(input_points.shape) != 4: + raise ValueError( + "The input_points must be a 4D tensor. Of shape `batch_size`, `point_batch_size`, `nb_points_per_image`, `2`." + f" got {input_points.shape}." + ) + + if input_boxes is not None and len(input_boxes.shape) != 3: + raise ValueError( + "The input_boxes must be a 3D tensor. Of shape `batch_size`, `nb_boxes`, `4`." + f" got {input_boxes.shape}." + ) + + # Add validation for point and box batch sizes + if input_points is not None and input_boxes is not None: + point_batch_size = input_points.shape[1] + box_batch_size = input_boxes.shape[1] + if point_batch_size != box_batch_size: + raise ValueError( + f"You should provide as many bounding boxes as input points per box. Got {point_batch_size} and {box_batch_size}." + ) + + image_positional_embeddings = self.get_image_wide_positional_embeddings() + # repeat with batch size + batch_size = pixel_values.shape[0] if pixel_values is not None else image_embeddings.shape[0] + image_positional_embeddings = image_positional_embeddings.repeat(batch_size, 1, 1, 1) + + if pixel_values is not None: + vision_outputs = self.vision_encoder(pixel_values, **kwargs) + image_embeddings = vision_outputs.last_hidden_state + intermediate_embeddings = vision_outputs.intermediate_embeddings + if input_points is not None and input_labels is None: + input_labels = torch.ones_like(input_points[:, :, :, 0], dtype=torch.int, device=input_points.device) + + sparse_embeddings, dense_embeddings = self.prompt_encoder( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + input_masks=input_masks, + ) + + # Predict masks + mask_decoder_output = self.mask_decoder( + image_embeddings=image_embeddings, + image_positional_embeddings=image_positional_embeddings, + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + hq_token_only=hq_token_only, + intermediate_embeddings=intermediate_embeddings, + attention_similarity=attention_similarity, + target_embedding=target_embedding, + ) + return SamHQImageSegmentationOutput( + iou_scores=mask_decoder_output[1], + pred_masks=mask_decoder_output[0], + vision_hidden_states=vision_outputs.hidden_states if pixel_values is not None else None, + vision_attentions=vision_outputs.attentions if pixel_values is not None else None, + ) + + +__all__ = [ + "SamHQVisionConfig", + "SamHQMaskDecoderConfig", + "SamHQPromptEncoderConfig", + "SamHQConfig", + "SamHQModel", + "SamHQPreTrainedModel", + "SamHQVisionModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/processing_sam_hq.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/processing_sam_hq.py new file mode 100644 index 0000000000000000000000000000000000000000..8bd9d814a9759b731bc9fa23437ced568122b9b9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sam_hq/processing_sam_hq.py @@ -0,0 +1,327 @@ +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for SAMHQ. +""" + +from copy import deepcopy +from typing import Union + +import numpy as np + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack +from ...utils import auto_docstring, is_torch_available + + +if is_torch_available(): + import torch + +NestedList = list[Union[float | int | None, "NestedList"]] + + +class SamHQImagesKwargs(ImagesKwargs, total=False): + """ + segmentation_maps (`ImageInput`, *optional*): + Ground truth segmentation maps to process alongside the input images. These maps are used for training + or evaluation purposes and are resized and normalized to match the processed image dimensions. + input_points (`NestedList`, *optional*): + Input points for prompt-based segmentation. Should be a nested list with structure + `[image_level, object_level, point_level, [x, y]]` where each point is specified as `[x, y]` coordinates + in the original image space. Points are normalized to the target image size before being passed to the model. + input_labels (`NestedList`, *optional*): + Labels for the input points, indicating whether each point is a foreground (1) or background (0) point. + Should be a nested list with structure `[image_level, object_level, point_level]`. Must have the same + structure as `input_points` (excluding the coordinate dimension). + input_boxes (`NestedList`, *optional*): + Bounding boxes for prompt-based segmentation. Should be a nested list with structure + `[image_level, box_level, [x1, y1, x2, y2]]` where each box is specified as `[x1, y1, x2, y2]` coordinates + in the original image space. Boxes are normalized to the target image size before being passed to the model. + point_pad_value (`int`, *optional*, defaults to `None`): + The value used for padding input points when batching sequences of different lengths. This value marks + padded positions and is preserved during coordinate normalization to distinguish real points from padding. + If `None`, the default pad value from the processor configuration is used. + mask_size (`dict[str, int]`, *optional*): + Dictionary specifying the target mask size with keys `"height"` and `"width"`. This determines the + resolution of the output segmentation masks generated by the model. + mask_pad_size (`dict[str, int]`, *optional*): + Dictionary specifying the padding size for masks with keys `"height"` and `"width"`. This is used when + batching masks of different sizes to ensure consistent dimensions. + """ + + segmentation_maps: ImageInput | None + input_points: "NestedList | torch.Tensor | None" + input_labels: "NestedList | int | torch.Tensor | None" + input_boxes: "NestedList | torch.Tensor | None" + point_pad_value: int | None + mask_size: dict[str, int] + mask_pad_size: dict[str, int] + + +class SamHQProcessorKwargs(ProcessingKwargs, total=False): + images_kwargs: SamHQImagesKwargs + _defaults = { + "images_kwargs": { + "point_pad_value": None, + } + } + + +@auto_docstring +class SamHQProcessor(ProcessorMixin): + def __init__(self, image_processor): + super().__init__(image_processor) + # Ensure image_processor is properly initialized + if not hasattr(self, "image_processor"): + raise ValueError("image_processor was not properly initialized") + if not hasattr(self.image_processor, "size"): + raise ValueError("image_processor.size is not set") + self.target_size = self.image_processor.size["longest_edge"] + + @auto_docstring + def __call__( + self, + images: ImageInput | None = None, + **kwargs: Unpack[SamHQProcessorKwargs], + ) -> BatchFeature: + output_kwargs = self._merge_kwargs( + SamHQProcessorKwargs, + tokenizer_init_kwargs={}, + **kwargs, + ) + + input_points = output_kwargs["images_kwargs"].pop("input_points", None) + input_labels = output_kwargs["images_kwargs"].pop("input_labels", None) + input_boxes = output_kwargs["images_kwargs"].pop("input_boxes", None) + point_pad_value = output_kwargs["images_kwargs"].pop("point_pad_value", None) + + encoding_image_processor = self.image_processor( + images, + **output_kwargs["images_kwargs"], + ) + + original_sizes = encoding_image_processor["original_sizes"] + + if hasattr(original_sizes, "numpy"): + original_sizes = original_sizes.numpy() + + input_points, input_labels, input_boxes = self._check_and_preprocess_points( + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + ) + + encoding_image_processor = self._normalize_and_convert( + encoding_image_processor, + original_sizes, + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + return_tensors=output_kwargs["images_kwargs"].get("return_tensors"), + point_pad_value=point_pad_value, + ) + + return encoding_image_processor + + def _normalize_and_convert( + self, + encoding_image_processor, + original_sizes, + input_points=None, + input_labels=None, + input_boxes=None, + return_tensors="pt", + point_pad_value=-10, + ): + """ + Normalize and convert the image processor output to the expected format. + """ + # Process input points + if input_points is not None: + input_points = self._normalize_batch_coordinates(input_points, original_sizes) + + if not all(point.shape == input_points[0].shape for point in input_points): + if input_labels is not None: + input_points, input_labels = self._pad_points_and_labels( + input_points, input_labels, point_pad_value + ) + + input_points = np.array(input_points) + + # Process input labels + if input_labels is not None: + input_labels = np.array(input_labels) + + # Process input boxes + if input_boxes is not None: + input_boxes = self._normalize_batch_coordinates(input_boxes, original_sizes, is_bounding_box=True) + input_boxes = np.array(input_boxes) + + # Update processor with converted inputs + if input_boxes is not None: + encoding_image_processor["input_boxes"] = self._to_tensor(input_boxes, 3, return_tensors) + if input_points is not None: + encoding_image_processor["input_points"] = self._to_tensor(input_points, 4, return_tensors) + if input_labels is not None: + encoding_image_processor["input_labels"] = self._to_tensor(input_labels, 3, return_tensors) + + return encoding_image_processor + + def _pad_points_and_labels(self, input_points, input_labels, point_pad_value): + r""" + The method pads the 2D points and labels to the maximum number of points in the batch. + """ + expected_nb_points = max(point.shape[0] for point in input_points) + processed_input_points = [] + for i, point in enumerate(input_points): + if point.shape[0] != expected_nb_points: + point = np.concatenate( + [point, np.zeros((expected_nb_points - point.shape[0], 2)) + point_pad_value], axis=0 + ) + input_labels[i] = np.append(input_labels[i], [point_pad_value]) + processed_input_points.append(point) + input_points = processed_input_points + return input_points, input_labels + + def _normalize_coordinates( + self, target_size: int, coords: np.ndarray, original_size, is_bounding_box=False + ) -> np.ndarray: + """ + Expects a numpy array of length 2 in the final dimension. Requires the original image size in (H,W) format. + """ + old_h, old_w = original_size + new_h, new_w = self.image_processor._get_preprocess_shape(original_size, longest_edge=target_size) + coords = deepcopy(coords).astype(float) + + if is_bounding_box: + coords = coords.reshape(-1, 2, 2) + + coords[..., 0] = coords[..., 0] * (new_w / old_w) + coords[..., 1] = coords[..., 1] * (new_h / old_h) + + if is_bounding_box: + coords = coords.reshape(-1, 4) + + return coords + + def _preprocess_input(self, inputs, error_message, expected_nesting=1, dtype=None): + """ + Preprocess input by converting torch tensors to numpy arrays and validating structure. + + Args: + inputs: The input to process + error_message: Error message if validation fails + expected_nesting: Expected nesting level (1 for points/labels, 2 for boxes) + dtype: Optional data type for numpy array conversion + + Returns: + Processed input as list of numpy arrays or None + """ + if inputs is None: + return None + + # Convert torch tensor to list if applicable + if hasattr(inputs, "numpy"): + inputs = inputs.numpy().tolist() + + # Validate structure based on expected nesting + valid = isinstance(inputs, list) + current = inputs + + for _ in range(expected_nesting): + if not valid or not current: + break + valid = valid and isinstance(current[0], list) + current = current[0] if current else None + + if not valid: + raise ValueError(error_message) + + # Convert to numpy arrays + return [np.array(item, dtype=dtype) for item in inputs] + + def _check_and_preprocess_points( + self, + input_points=None, + input_labels=None, + input_boxes=None, + ): + r""" + Check and preprocesses the 2D points, labels and bounding boxes. It checks if the input is valid and if they + are, it converts the coordinates of the points and bounding boxes. If a user passes directly a `torch.Tensor`, + it is converted to a `numpy.ndarray` and then to a `list`. + """ + # Process each input type + input_points = self._preprocess_input(input_points, "Input points must be a list of list of floating points.") + + input_labels = self._preprocess_input(input_labels, "Input labels must be a list of list integers.") + + input_boxes = self._preprocess_input( + input_boxes, + "Input boxes must be a list of list of list of floating points.", + expected_nesting=2, + dtype=np.float32, + ) + + return input_points, input_labels, input_boxes + + @property + def model_input_names(self): + image_processor_input_names = self.image_processor.model_input_names + return list(image_processor_input_names + ["original_sizes", "reshaped_input_sizes"]) + + def post_process_masks(self, *args, **kwargs): + return self.image_processor.post_process_masks(*args, **kwargs) + + def _to_tensor(self, array, min_dim, return_tensors): + """ + Convert numpy array to tensor and ensure proper dimensionality. + Args: + array: The numpy array to convert + min_dim: The minimum number of dimensions the result should have + return_tensors: The type of tensors to return (e.g., "pt" for PyTorch tensors) + Returns: + The converted array or tensor with proper dimensions + """ + if return_tensors == "pt": + array = torch.from_numpy(array) + return array.unsqueeze(1) if array.ndim < min_dim else array + return array + + def _normalize_batch_coordinates(self, inputs, original_sizes, is_bounding_box=False): + """ + Normalize coordinates based on original sizes. + Args: + inputs: List of coordinate arrays + original_sizes: Original sizes of the images + is_bounding_box: Whether inputs are bounding boxes + Returns: + Normalized coordinates as list + """ + if len(original_sizes) != len(inputs): + # Use first original size for all inputs + return [ + self._normalize_coordinates(self.target_size, item, original_sizes[0], is_bounding_box=is_bounding_box) + for item in inputs + ] + else: + # Use paired original sizes for each input + return [ + self._normalize_coordinates(self.target_size, item, size, is_bounding_box=is_bounding_box) + for item, size in zip(inputs, original_sizes) + ] + + +__all__ = ["SamHQProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..27c08de501c141e44e7e101b79f228e826396ff7 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_seamless_m4t import * + from .feature_extraction_seamless_m4t import * + from .modeling_seamless_m4t import * + from .processing_seamless_m4t import * + from .tokenization_seamless_m4t import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/configuration_seamless_m4t.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/configuration_seamless_m4t.py new file mode 100644 index 0000000000000000000000000000000000000000..50912ebf1a3199cd3e344eec5d7c471e285390da --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/configuration_seamless_m4t.py @@ -0,0 +1,235 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SeamlessM4T model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="facebook/hf-seamless-m4t-medium") +@strict +class SeamlessM4TConfig(PreTrainedConfig): + r""" + t2u_vocab_size (`int`, *optional*, defaults to 10082): + Unit vocabulary size of the SeamlessM4T model. Defines the number of different unit tokens that can be + represented by the `inputs_ids` passed when calling the Text-To-Units sub-model of [`~SeamlessM4TModel`], + [`~SeamlessM4TForSpeechToSpeech`] or [`~SeamlessM4TForTextToSpeech`]. + max_new_tokens (`int`, *optional*, defaults to 256): + The maximum numbers of text tokens to generate, ignoring the number of tokens in the prompt. + speech_encoder_layers (`int`, *optional*, defaults to 24): + Number of hidden layers in the Transformer speech encoder. + speech_encoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer speech encoder. + speech_encoder_intermediate_size (`int`, *optional*, defaults to 4096): + Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer speech encoder. + speech_encoder_hidden_act (`str` or `function`, *optional*, defaults to `"swish"`): + The non-linear activation function (function or string) in the speech encoder. If string, `"gelu"`, + `"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported. + speech_encoder_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for all layers in the speech encoder. + add_adapter (`bool`, *optional*, defaults to `True`): + Add an adapter layer on top of the speech encoder. + speech_encoder_layerdrop (`float`, *optional*, defaults to 0.1): + The LayerDrop probability for the speech encoder. See the [LayerDrop paper](see + https://huggingface.co/papers/1909.11556) for more details. + feature_projection_input_dim (`int`, *optional*, defaults to 160): + Input dimension of the input feature projection of the speech encoder, i.e the dimension after processing + input audios with [`SeamlessM4TFeatureExtractor`]. + num_conv_pos_embeddings (`int`, *optional*, defaults to 128): + Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional + embeddings layer of the speech encoder. + num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): + Number of groups of 1D convolutional positional embeddings layer of the speech encoder. + adaptor_kernel_size (`int`, *optional*, defaults to 8): + Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. + adaptor_stride (`int`, *optional*, defaults to 8): + Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. + adaptor_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all layers in the speech adapter. + num_adapter_layers (`int`, *optional*, defaults to 1): + Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is + True`. + position_embeddings_type (`str`, *optional*, defaults to `"relative"`): + Can be specified to `relative` or `rotary` for relative or rotary position embeddings respectively. If left + `None` no relative position embedding is applied. Only applied to the speech encoder. + rotary_embedding_base (`int`, *optional*, defaults to 10000): + If `"rotary"` position embeddings are used, defines the size of the embedding base. Only applied to the + speech encoder. + max_source_positions (`int`, *optional*, defaults to 4096): + if `"relative"` position embeddings are used, defines the maximum source input positions. Only applied to + the speech encoder. + conv_depthwise_kernel_size (`int`, *optional*, defaults to 31): + Kernel size of convolutional depthwise 1D layer in Conformer blocks. Only applied to the speech encoder. + t2u_bos_token_id (`int`, *optional*, defaults to 0): + The id of the _beginning-of-stream_ unit token. Only applied to the text-to-unit seq2seq model. + t2u_pad_token_id (`int`, *optional*, defaults to 1): + The id of the _padding_ unit token. Only applied to the text-to-unit seq2seq model. + t2u_eos_token_id (`int`, *optional*, defaults to 2): + The id of the _end-of-stream_ unit token. Only applied to the text-to-unit seq2seq model. + t2u_decoder_start_token_id (`int`, *optional*, defaults to 2): + If an encoder-decoder model starts decoding with a different token than _bos_, the id of that token. Only + applied to the text-to-unit seq2seq model. + t2u_max_new_tokens (`int`, *optional*, defaults to 1024): + The maximum numbers of unit tokens to generate, ignoring the number of tokens in the prompt. Only applied + to the text-to-unit seq2seq model. + t2u_encoder_layers (`int`, *optional*, defaults to 6): + Number of hidden layers in the Transformer text-to-unit encoder. + t2u_encoder_ffn_dim (`int`, *optional*, defaults to 8192): + Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text-to-unit encoder. + t2u_encoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer text-to-unit encoder. + t2u_decoder_layers (`int`, *optional*, defaults to 6): + Number of hidden layers in the Transformer text-to-unit decoder. + t2u_decoder_ffn_dim (`int`, *optional*, defaults to 8192): + Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text-to-unit decoder. + t2u_decoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer text-to-unit decoder. + t2u_max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model text-to-unit component might ever be used with. Typically set + this to something large just in case (e.g., 512 or 1024 or 2048). + upsample_initial_channel (`int`, *optional*, defaults to 512): + The number of input channels into the hifi-gan upsampling network. Applies to the vocoder only. + upsample_rates (`tuple[int]` or `list[int]`, *optional*, defaults to `[5, 4, 4, 2, 2]`): + A tuple of integers defining the stride of each 1D convolutional layer in the vocoder upsampling network. + The length of *upsample_rates* defines the number of convolutional layers and has to match the length of + *upsample_kernel_sizes*. Applies to the vocoder only. + upsample_kernel_sizes (`tuple[int]` or `list[int]`, *optional*, defaults to `[11, 8, 8, 4, 4]`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the vocoder upsampling + network. The length of *upsample_kernel_sizes* defines the number of convolutional layers and has to match + the length of *upsample_rates*. Applies to the vocoder only. + resblock_kernel_sizes (`tuple[int]` or `list[int]`, *optional*, defaults to `[3, 7, 11]`): + A tuple of integers defining the kernel sizes of the vocoder 1D convolutional layers in the multi-receptive + field fusion (MRF) module. Applies to the vocoder only. + resblock_dilation_sizes (`tuple[tuple[int]]` or `list[list[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): + A nested tuple of integers defining the dilation rates of the vocoder dilated 1D convolutional layers in + the multi-receptive field fusion (MRF) module. Applies to the vocoder only. + leaky_relu_slope (`float`, *optional*, defaults to 0.1): + The angle of the negative slope used by the leaky ReLU activation in the vocoder. Applies to the vocoder + only. + unit_hifi_gan_vocab_size (`int`, *optional*, defaults to 10000): + Vocabulary size of the SeamlessM4T vocoder. Defines the number of different unit tokens that can be + represented by the `inputs_ids` passed when calling the vocoder of [`~SeamlessM4TModel`], + [`~SeamlessM4TForSpeechToSpeech`] or [`~SeamlessM4TForTextToSpeech`]. + unit_embed_dim (`int`, *optional*, defaults to 1280): + The projection dimension of the input ids given to the hifi-gan vocoder. Applies to the vocoder only. + lang_embed_dim (`int`, *optional*, defaults to 256): + The projection dimension of the target language given to the hifi-gan vocoder. Applies to the vocoder only. + spkr_embed_dim (`int`, *optional*, defaults to 256): + The projection dimension of the speaker id given to the hifi-gan vocoder. Applies to the vocoder only. + vocoder_num_langs (`int`, *optional*, defaults to 36): + Number of langs supported by the vocoder. Might be different from `t2u_num_langs`. + vocoder_num_spkrs (`int`, *optional*, defaults to 200): + Number of speakers supported by the vocoder. + variance_predictor_kernel_size (`int`, *optional*, defaults to 3): + Kernel size of the duration predictor. Applies to the vocoder only. + var_pred_dropout (`float`, *optional*, defaults to 0.5): + The dropout probability of the duration predictor. Applies to the vocoder only. + vocoder_offset (`int`, *optional*, defaults to 4): + Offset the unit token ids by this number to account for symbol tokens. Applies to the vocoder only. + + ```python + >>> from transformers import SeamlessM4TModel, SeamlessM4TConfig + + >>> # Initializing a SeamlessM4T "facebook/hf-seamless-m4t-medium" style configuration + >>> configuration = SeamlessM4TConfig() + + >>> # Initializing a model from the "facebook/hf-seamless-m4t-medium" style configuration + >>> model = SeamlessM4TModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "seamless_m4t" + attribute_map = {"num_hidden_layers": "decoder_layers", "num_attention_heads": "decoder_attention_heads"} + + vocab_size: int = 256102 + t2u_vocab_size: int = 10082 + hidden_size: int = 1024 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-5 + use_cache: bool = True + max_position_embeddings: int = 1024 + is_encoder_decoder: bool = True + encoder_layerdrop: float | int = 0.05 + decoder_layerdrop: float | int = 0.05 + activation_function: str = "relu" + dropout: float | int = 0.1 + attention_dropout: float | int = 0.1 + activation_dropout: float | int = 0.0 + scale_embedding: bool = True + encoder_layers: int = 24 + encoder_ffn_dim: int = 8192 + encoder_attention_heads: int = 16 + decoder_layers: int = 24 + decoder_ffn_dim: int = 8192 + decoder_attention_heads: int = 16 + decoder_start_token_id: int = 3 + max_new_tokens: int | None = 256 + pad_token_id: int | None = 0 + bos_token_id: int | None = 2 + eos_token_id: int | list[int] | None = 3 + speech_encoder_layers: int = 24 + speech_encoder_attention_heads: int = 16 + speech_encoder_intermediate_size: int = 4096 + speech_encoder_hidden_act: str = "swish" + speech_encoder_dropout: float | int = 0.0 + add_adapter: bool = True + speech_encoder_layerdrop: float | int = 0.1 + feature_projection_input_dim: int = 160 + num_conv_pos_embeddings: int = 128 + num_conv_pos_embedding_groups: int = 16 + adaptor_kernel_size: int = 8 + adaptor_stride: int = 8 + adaptor_dropout: float | int = 0.1 + num_adapter_layers: int = 1 + position_embeddings_type: str = "relative" + rotary_embedding_base: int = 10000 + max_source_positions: int = 4096 + conv_depthwise_kernel_size: int = 31 + t2u_bos_token_id: int | None = 0 + t2u_pad_token_id: int | None = 1 + t2u_eos_token_id: int | list[int] | None = 2 + t2u_decoder_start_token_id: int = 2 + t2u_max_new_tokens: int = 1024 + t2u_encoder_layers: int = 6 + t2u_encoder_ffn_dim: int = 8192 + t2u_encoder_attention_heads: int = 16 + t2u_decoder_layers: int = 6 + t2u_decoder_ffn_dim: int = 8192 + t2u_decoder_attention_heads: int = 16 + t2u_max_position_embeddings: int = 2048 + sampling_rate: int = 16000 + upsample_initial_channel: int = 512 + upsample_rates: list[int] | tuple[int, ...] = (5, 4, 4, 2, 2) + upsample_kernel_sizes: list[int] | tuple[int, ...] = (11, 8, 8, 4, 4) + resblock_kernel_sizes: list[int] | tuple[int, ...] = (3, 7, 11) + resblock_dilation_sizes: list | tuple = ((1, 3, 5), (1, 3, 5), (1, 3, 5)) + leaky_relu_slope: float = 0.1 + unit_hifi_gan_vocab_size: int = 10000 + unit_embed_dim: int = 1280 + lang_embed_dim: int = 256 + spkr_embed_dim: int = 256 + vocoder_num_langs: int = 36 + vocoder_num_spkrs: int = 200 + variance_predictor_kernel_size: int = 3 + var_pred_dropout: float | int = 0.5 + vocoder_offset: int = 4 + tie_word_embeddings: bool = True + + +__all__ = ["SeamlessM4TConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py new file mode 100644 index 0000000000000000000000000000000000000000..1b18dcc33404223471e7fbd16f4e1210faed512e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/feature_extraction_seamless_m4t.py @@ -0,0 +1,305 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Feature extractor class for SeamlessM4T +""" + +import numpy as np + +from ...utils import is_torch_available + + +if is_torch_available(): + import torch + +from ...audio_utils import mel_filter_bank, spectrogram, window_function +from ...feature_extraction_sequence_utils import SequenceFeatureExtractor +from ...feature_extraction_utils import BatchFeature +from ...utils import PaddingStrategy, TensorType, logging + + +logger = logging.get_logger(__name__) + + +class SeamlessM4TFeatureExtractor(SequenceFeatureExtractor): + r""" + Constructs a SeamlessM4T feature extractor. + + This feature extractor inherits from [`SequenceFeatureExtractor`] which contains most of the main methods. Users + should refer to this superclass for more information regarding those methods. + + This class extracts mel-filter bank features from raw speech. + + Args: + feature_size (`int`, *optional*, defaults to 80): + The feature dimension of the extracted features. + sampling_rate (`int`, *optional*, defaults to 16000): + The sampling rate at which the audio files should be digitalized expressed in hertz (Hz). + num_mel_bins (`int`, *optional*, defaults to 80): + Number of Mel-frequency bins. + padding_value (`float`, *optional*, defaults to 0.0): + The value that is used to fill the padding vectors. + stride (`int`, *optional*, defaults to 2): + Stride used to reshape audios from shape (batch_size,num_frames,num_mel_bins) to + (batch_size,num_frames//stride,num_mel_bins*stride). + """ + + model_input_names = ["input_features", "attention_mask"] + + def __init__( + self, + feature_size=80, + sampling_rate=16000, + num_mel_bins=80, + padding_value=0.0, + stride=2, + **kwargs, + ): + self.num_mel_bins = num_mel_bins + self.return_attention_mask = True + self.stride = stride + + mel_filters = mel_filter_bank( + num_frequency_bins=257, + num_mel_filters=self.num_mel_bins, + min_frequency=20, + max_frequency=sampling_rate // 2, + sampling_rate=sampling_rate, + norm=None, + mel_scale="kaldi", + triangularize_in_mel_space=True, + ) + + self.mel_filters = mel_filters + self.window = window_function(400, "povey", periodic=False) + + super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs) + + @staticmethod + # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm + def zero_mean_unit_var_norm( + input_values: list[np.ndarray], attention_mask: list[np.ndarray], padding_value: float = 0.0 + ) -> list[np.ndarray]: + """ + Every array in the list is normalized to have zero mean and unit variance + """ + if attention_mask is not None: + attention_mask = np.array(attention_mask, np.int32) + normed_input_values = [] + + for vector, length in zip(input_values, attention_mask.sum(-1)): + normed_slice = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7) + if length < normed_slice.shape[0]: + normed_slice[length:] = padding_value + + normed_input_values.append(normed_slice) + else: + normed_input_values = [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values] + + return normed_input_values + + def _extract_fbank_features( + self, + waveform: np.ndarray, + ) -> np.ndarray: + """ + Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs + and hence the waveform should not be normalized before feature extraction. + """ + # by default, it extracts the left channel if stereo + if len(waveform.shape) == 2: + waveform = waveform[0] + + waveform = np.squeeze(waveform) * (2**15) # Kaldi compliance: 16-bit signed integers + features = spectrogram( + waveform, + self.window, + frame_length=400, + hop_length=160, + fft_length=512, + power=2.0, + center=False, + preemphasis=0.97, + mel_filters=self.mel_filters, + log_mel="log", + mel_floor=1.192092955078125e-07, + remove_dc_offset=True, + ).T + return features + + def __call__( + self, + raw_speech: np.ndarray | list[float] | list[np.ndarray] | list[list[float]], + padding: bool | str | PaddingStrategy = True, + pad_to_multiple_of: int | None = 2, + max_length: int | None = None, + truncation: bool = False, + return_tensors: str | TensorType | None = None, + sampling_rate: int | None = None, + return_attention_mask: bool | None = None, + do_normalize_per_mel_bins: bool | None = True, + **kwargs, + ) -> BatchFeature: + """ + Main method to featurize and prepare for the model one or several sequence(s). + + Args: + raw_speech (`np.ndarray`, `torch.Tensor`, `list[float]`, `list[np.ndarray]`, `list[torch.Tensor]`, + `list[list[float]]`, `list[list[list[float]]]`): + The sequence or batch of sequences to be padded. Each sequence can be a numpy array, + a torch tensor, a list of float values, a list of numpy arrays, a list of torch tensors, + a list of list of float values or a list of a list of list of float values. + If `raw_speech` is a one-dimensional `np.ndarray`, `torch.Tensor` or a `list[float]`, `raw_speech` is + considered a single-channel, single-sample sound. In all other cases, the first dimension of + `raw_speech`, whether from an `np.ndarray`, a `torch.Tensor` or a `list[...]`, + corresponds to the number of samples in the batch, and the number of channels + (i.e. mono or stereo character) is derived from the other dimensions + (1D -> single-channel waveform batches; 2D-> stereo-channel waveform batches). + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding + index) among: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + pad_to_multiple_of (`int`, *optional*, defaults to 2): + If set will pad the sequence to a multiple of the provided value. + + This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability + `>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128. + max_length (`int`, *optional*): + Maximum length of the returned list and optionally padding length (see above). + truncation (`bool`): + Activates truncation to cut input sequences longer than *max_length* to *max_length*. + return_attention_mask (`bool`, *optional*): + Whether to return the attention mask. If left to the default, will return the attention mask according + to the specific feature_extractor's default. + + [What are attention masks?](../glossary#attention-mask) + + + + For SeamlessM4T models, `attention_mask` should always be passed for batched inference, to avoid subtle + bugs. + + + + return_tensors (`str` or [`~utils.TensorType`], *optional*): + If set, will return tensors instead of list of python integers. Acceptable values are: + + - `'pt'`: Return PyTorch `torch.Tensor` objects. + - `'np'`: Return Numpy `np.ndarray` objects. + sampling_rate (`int`, *optional*): + The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass + `sampling_rate` at the forward call to prevent silent errors. + do_normalize_per_mel_bins (`bool`, *optional*, defaults to `True`): + Whether or not to zero-mean unit-variance normalize the input per mel-channel. + kwargs (*optional*): + Remaining dictionary of keyword arguments that will be passed to the tokenizer or the feature + extractor. + """ + if sampling_rate is not None: + if sampling_rate != self.sampling_rate: + raise ValueError( + f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of" + f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with" + f" {self.sampling_rate} and not {sampling_rate}." + ) + else: + logger.warning( + f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. " + "Failing to do so can result in silent errors that might be hard to debug." + ) + + return_attention_mask = ( + return_attention_mask if return_attention_mask is not None else self.return_attention_mask + ) + + is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1 + if is_batched_numpy and len(raw_speech.shape) > 3: + raise ValueError(f"Only mono-channel or stereo-channel audio is supported for input to {self}") + + acceptable_types = ( + (torch.Tensor, np.ndarray, tuple, list) if is_torch_available() else (np.ndarray, tuple, list) + ) + is_batched = is_batched_numpy or ( + isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], acceptable_types)) + ) + + if is_batched: + raw_speech = [np.asarray(speech, dtype=np.float32) for speech in raw_speech] + elif not is_batched and not isinstance(raw_speech, np.ndarray): + raw_speech = np.asarray(raw_speech, dtype=np.float32) + elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64): + raw_speech = raw_speech.astype(np.float32) + + # always return batch + if not is_batched: + raw_speech = [raw_speech] + + # extract fbank features + features = [self._extract_fbank_features(waveform) for waveform in raw_speech] + + if do_normalize_per_mel_bins: + # torch defaults to ddof=1, and numpy defaults to ddof=0 + features = [ + (x - np.expand_dims(x.mean(0), 0)) / np.sqrt(np.expand_dims(x.var(0, ddof=1), 0) + 1e-7) + for x in features + ] + + # convert into correct format for padding + encoded_inputs = BatchFeature({"input_features": features}) + + padded_inputs = self.pad( + encoded_inputs, + padding=padding, + max_length=max_length, + truncation=truncation, + pad_to_multiple_of=pad_to_multiple_of, + return_attention_mask=True, + return_tensors="np", + ) + + # SeamlessM4T needs to process extracted features + input_features = padded_inputs.get("input_features") + attention_mask = padded_inputs.pop("attention_mask") + + batch_size, num_frames, num_channels = input_features.shape + + remainder = num_frames % self.stride + if remainder != 0: + input_features = input_features[:, : num_frames - remainder, :] + attention_mask = attention_mask[:, : num_frames - remainder] + + input_features = np.reshape( + input_features, (batch_size, num_frames // self.stride, num_channels * self.stride) + ) + + indices = np.arange(0, num_frames - remainder) + attention_mask = attention_mask[:, indices % self.stride == 1] + + padded_inputs["input_features"] = input_features + if return_attention_mask: + padded_inputs["attention_mask"] = attention_mask + + if return_tensors is not None: + padded_inputs = padded_inputs.convert_to_tensors(return_tensors) + + return padded_inputs + + +__all__ = ["SeamlessM4TFeatureExtractor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/modeling_seamless_m4t.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/modeling_seamless_m4t.py new file mode 100644 index 0000000000000000000000000000000000000000..9795a52dce970101e4ae75cfb3ae879c1a1311d9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/modeling_seamless_m4t.py @@ -0,0 +1,4036 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SeamlessM4T model.""" + +import copy +import math +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from torch.nn import CrossEntropyLoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...integrations.fsdp import is_fsdp_managed_module +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, + Wav2Vec2BaseModelOutput, +) +from ...modeling_utils import PreTrainedModel +from ...utils import ModelOutput, auto_docstring, logging +from ...utils.deprecation import deprecate_kwarg +from .configuration_seamless_m4t import SeamlessM4TConfig + + +logger = logging.get_logger(__name__) + +SEAMLESS_M4T_COMMON_CUSTOM_ARGS = r""" + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`): + Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the + [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details. + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` + is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + If you want to change padding behavior, you should read [`modeling_bart._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more + information on the default strategy. + inputs_embeds (`torch.FloatTensor` of shape`(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. +""" + + +@auto_docstring( + custom_intro=""" + Class defining the generated outputs from [`SeamlessM4TModel`], [`SeamlessM4TForTextToText`], + [`SeamlessM4TForTextToSpeech`], [`SeamlessM4TForSpeechToSpeech`] and [`SeamlessM4TForTextToSpeech`]. + """ +) +@dataclass +class SeamlessM4TGenerationOutput(ModelOutput): + r""" + waveform (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + The final audio waveform predicted by the model. + waveform_lengths (`torch.IntTensor` of shape `(batch_size,)`, *optional*): + The length in samples of each element in the `waveform` batch. + sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + The generated translated sequences. This is the output of the text-to-text or the speech-to-text models. + The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished + early due to the `eos_token_id`. + unit_sequences (`torch.LongTensor` of shape `(batch_size, unit_sequence_length)`, *optional*): + The generated translated unit sequences. This is the output of the text-to-units model. The second + dimension (unit_sequence_length) is either equal to `t2u_max_length` or shorter if all batches finished + early due to the `t2u_eos_token_id`. + """ + + waveform: torch.FloatTensor | None = None + waveform_lengths: torch.IntTensor | None = None + sequences: tuple[torch.FloatTensor] | None = None + unit_sequences: tuple[torch.FloatTensor] | None = None + + +############ UTILS ################ + + +# Copied from transformers.models.bart.modeling_bart.shift_tokens_right +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +def _compute_new_attention_mask(hidden_states: torch.Tensor, seq_lens: torch.Tensor): + """ + Computes an attention mask of the form `(batch, seq_len)` with an attention for each element in the batch that + stops at the corresponding element in `seq_lens`. + + Args: + hidden_states (`torch.FloatTensor` of shape `(batch, seq_len, *)`): + The sequences to mask, where `*` is any number of sequence-specific dimensions including none. + seq_lens (`torch.Tensor` of shape `(batch)`: + Each element represents the length of the sequence at the same index in `hidden_states` + + Returns: + `torch.FloatTensor`: The float attention mask of shape `(batch, seq_len)` + """ + batch_size, mask_seq_len = hidden_states.shape[:2] + + indices = torch.arange(mask_seq_len, device=seq_lens.device).expand(batch_size, -1) + + bool_mask = indices >= seq_lens.unsqueeze(1).expand(-1, mask_seq_len) + + mask = hidden_states.new_ones((batch_size, mask_seq_len)) + + mask = mask.masked_fill(bool_mask, 0) + + return mask + + +def format_speech_generation_kwargs(kwargs): + """ + Format kwargs for SeamlessM4T models that generate speech, attribute kwargs to either the text generation or the + speech generation models. + + Args: + kwargs (`dict`)`: + Keyword arguments are of two types: + + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model, + except for `decoder_input_ids` which will only be passed through the text components. + - With a *text_* or *speech_* prefix, they will be input for the `generate` method of the + text model and speech model respectively. It has the priority over the keywords without a prefix. + + This means you can, for example, specify a generation strategy for one generation but not for the + other. + """ + # attribute kwargs to models + kwargs_text = {} + kwargs_speech = {} + for key, value in kwargs.items(): + if key.startswith("text_"): + key = key[len("text_") :] + kwargs_text[key] = value + elif key.startswith("speech_"): + key = key[len("speech_") :] + kwargs_speech[key] = value + elif key == "generation_config": + kwargs_text[key] = value + else: + # If the key is already in a specific config, then it's been set with a + # submodules specific value and we don't override + if key not in kwargs_text: + kwargs_text[key] = value + if key not in kwargs_speech: + kwargs_speech[key] = value + return kwargs_text, kwargs_speech + + +############ SPEECH ENCODER related code ################ + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2PositionalConvEmbedding with Wav2Vec2->SeamlessM4TConformer, feat_extract_activation->speech_encoder_hidden_act +class SeamlessM4TConformerPositionalConvEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.conv = nn.Conv1d( + config.hidden_size, + config.hidden_size, + kernel_size=config.num_conv_pos_embeddings, + padding=config.num_conv_pos_embeddings // 2, + groups=config.num_conv_pos_embedding_groups, + ) + + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + if is_deepspeed_zero3_enabled(): + import deepspeed + + with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0): + self.conv = weight_norm(self.conv, name="weight", dim=2) + if hasattr(self.conv, "parametrizations"): + weight_g = self.conv.parametrizations.weight.original0 + weight_v = self.conv.parametrizations.weight.original1 + else: + weight_g = self.conv.weight_g + weight_v = self.conv.weight_v + deepspeed.zero.register_external_parameter(self, weight_v) + deepspeed.zero.register_external_parameter(self, weight_g) + else: + self.conv = weight_norm(self.conv, name="weight", dim=2) + + self.padding = SeamlessM4TConformerSamePadLayer(config.num_conv_pos_embeddings) + self.activation = ACT2FN[config.speech_encoder_hidden_act] + + def forward(self, hidden_states): + hidden_states = hidden_states.transpose(1, 2) + + hidden_states = self.conv(hidden_states) + hidden_states = self.padding(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = hidden_states.transpose(1, 2) + return hidden_states + + +# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerRotaryPositionalEmbedding with Wav2Vec2->SeamlessM4T, num_attention_heads->speech_encoder_attention_heads +class SeamlessM4TConformerRotaryPositionalEmbedding(nn.Module): + """Rotary positional embedding + Reference : https://blog.eleuther.ai/rotary-embeddings/ Paper: https://huggingface.co/papers/2104.09864 + """ + + def __init__(self, config): + super().__init__() + dim = config.hidden_size // config.speech_encoder_attention_heads + base = config.rotary_embedding_base + + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim)) + self.register_buffer("inv_freq", inv_freq) + self.cached_sequence_length = None + self.cached_rotary_positional_embedding = None + + def forward(self, hidden_states): + sequence_length = hidden_states.shape[1] + + if sequence_length == self.cached_sequence_length and self.cached_rotary_positional_embedding is not None: + return self.cached_rotary_positional_embedding + + self.cached_sequence_length = sequence_length + # Embeddings are computed in the dtype of the inv_freq constant + time_stamps = torch.arange(sequence_length).type_as(self.inv_freq) + freqs = torch.einsum("i,j->ij", time_stamps, self.inv_freq) + embeddings = torch.cat((freqs, freqs), dim=-1) + + cos_embeddings = embeddings.cos()[:, None, None, :] + sin_embeddings = embeddings.sin()[:, None, None, :] + # Computed embeddings are cast to the dtype of the hidden state inputs + self.cached_rotary_positional_embedding = torch.stack([cos_embeddings, sin_embeddings]).type_as(hidden_states) + return self.cached_rotary_positional_embedding + + +# Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerRelPositionalEmbedding with Wav2Vec2->SeamlessM4T +class SeamlessM4TConformerRelPositionalEmbedding(nn.Module): + """Relative positional encoding module.""" + + def __init__(self, config): + super().__init__() + self.max_len = config.max_source_positions + self.d_model = config.hidden_size + self.register_buffer("pe", self.extend_pe(torch.tensor(0.0).expand(1, self.max_len)), persistent=False) + + def extend_pe(self, x, pe=None): + # Reset the positional encodings + if pe is not None: + # self.pe contains both positive and negative parts + # the length of self.pe is 2 * input_len - 1 + if pe.size(1) >= x.size(1) * 2 - 1: + if pe.dtype != x.dtype or pe.device != x.device: + pe = pe.to(dtype=x.dtype, device=x.device) + return pe + # Suppose `i` is the position of query vector and `j` is the + # position of key vector. We use positive relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (iSeamlessM4T +class SeamlessM4TConformerSamePadLayer(nn.Module): + def __init__(self, num_conv_pos_embeddings): + super().__init__() + self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0 + + def forward(self, hidden_states): + if self.num_pad_remove > 0: + hidden_states = hidden_states[:, :, : -self.num_pad_remove] + return hidden_states + + +class SeamlessM4TConformerFeatureProjection(nn.Module): + def __init__(self, config): + super().__init__() + self.layer_norm = nn.LayerNorm(config.feature_projection_input_dim, eps=config.layer_norm_eps) + self.projection = nn.Linear(config.feature_projection_input_dim, config.hidden_size) + self.dropout = nn.Dropout(config.speech_encoder_dropout) + + def forward(self, hidden_states): + # non-projected hidden states are needed for quantization + norm_hidden_states = self.layer_norm(hidden_states) + hidden_states = self.projection(norm_hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class SeamlessM4TConformerFeedForward(nn.Module): + def __init__(self, config, act_fn=None, dropout=None): + super().__init__() + dropout = dropout if dropout is not None else config.speech_encoder_dropout + act_fn = act_fn if act_fn is not None else config.speech_encoder_hidden_act + + self.intermediate_dropout = nn.Dropout(dropout) + self.intermediate_dense = nn.Linear(config.hidden_size, config.speech_encoder_intermediate_size) + self.intermediate_act_fn = ACT2FN[act_fn] if isinstance(act_fn, str) else act_fn + + self.output_dense = nn.Linear(config.speech_encoder_intermediate_size, config.hidden_size) + self.output_dropout = nn.Dropout(dropout) + + def forward(self, hidden_states): + hidden_states = self.intermediate_dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.intermediate_dropout(hidden_states) + + hidden_states = self.output_dense(hidden_states) + hidden_states = self.output_dropout(hidden_states) + return hidden_states + + +class SeamlessM4TConformerConvolutionModule(nn.Module): + """Convolution block used in the conformer block""" + + def __init__(self, config): + super().__init__() + if (config.conv_depthwise_kernel_size - 1) % 2 == 1: + raise ValueError("`config.conv_depthwise_kernel_size` should be a odd number for 'SAME' padding") + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.pointwise_conv1 = nn.Conv1d( + config.hidden_size, + 2 * config.hidden_size, + kernel_size=1, + stride=1, + padding=0, + bias=False, + ) + self.glu = nn.GLU(dim=1) + self.depthwise_conv = nn.Conv1d( + config.hidden_size, + config.hidden_size, + config.conv_depthwise_kernel_size, + stride=1, + padding="same", + groups=config.hidden_size, + bias=False, + ) + self.batch_norm = nn.BatchNorm1d(config.hidden_size) + self.activation = ACT2FN[config.speech_encoder_hidden_act] + self.pointwise_conv2 = nn.Conv1d( + config.hidden_size, + config.hidden_size, + kernel_size=1, + stride=1, + padding=0, + bias=False, + ) + self.dropout = nn.Dropout(config.speech_encoder_dropout) + + def forward(self, hidden_states, attention_mask=None): + hidden_states = self.layer_norm(hidden_states) + + # Ensure that we do not leak padded positions in depthwise convolution. + # Put 0 where necessary + if attention_mask is not None: + hidden_states = hidden_states.masked_fill(~attention_mask.bool().unsqueeze(-1), 0.0) + + # exchange the temporal dimension and the feature dimension + hidden_states = hidden_states.transpose(1, 2) + + # GLU mechanism + # => (batch, 2*channel, dim) + hidden_states = self.pointwise_conv1(hidden_states) + # => (batch, channel, dim) + hidden_states = self.glu(hidden_states) + + # 1D Depthwise Conv + hidden_states = self.depthwise_conv(hidden_states) + hidden_states = self.batch_norm(hidden_states) + hidden_states = self.activation(hidden_states) + + hidden_states = self.pointwise_conv2(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = hidden_states.transpose(1, 2) + return hidden_states + + +class SeamlessM4TConformerSelfAttention(nn.Module): + """Construct a SeamlessM4TConformerSelfAttention object. + Can be enhanced with rotary or relative position embeddings. + """ + + def __init__(self, config, use_position_embeddings=True): + super().__init__() + + self.head_size = config.hidden_size // config.speech_encoder_attention_heads + self.num_heads = config.speech_encoder_attention_heads + self.position_embeddings_type = config.position_embeddings_type if use_position_embeddings else None + + self.linear_q = nn.Linear(config.hidden_size, config.hidden_size) + self.linear_k = nn.Linear(config.hidden_size, config.hidden_size) + self.linear_v = nn.Linear(config.hidden_size, config.hidden_size) + self.linear_out = nn.Linear(config.hidden_size, config.hidden_size) + + self.dropout = nn.Dropout(p=config.speech_encoder_dropout) + + if self.position_embeddings_type == "relative": + # linear transformation for positional encoding + self.linear_pos = nn.Linear(config.hidden_size, config.hidden_size, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://huggingface.co/papers/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.zeros(self.num_heads, self.head_size)) + self.pos_bias_v = nn.Parameter(torch.zeros(self.num_heads, self.head_size)) + + # Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerSelfAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + relative_position_embeddings: torch.Tensor | None = None, + output_attentions: bool = False, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + # self-attention mechanism + batch_size, sequence_length, hidden_size = hidden_states.size() + + # make sure query/key states can be != value states + query_key_states = hidden_states + value_states = hidden_states + + if self.position_embeddings_type == "rotary": + if relative_position_embeddings is None: + raise ValueError( + "`relative_position_embeddings` has to be defined when `self.position_embeddings_type == 'rotary'" + ) + query_key_states = self._apply_rotary_embedding(query_key_states, relative_position_embeddings) + + # project query_key_states and value_states + query = self.linear_q(query_key_states).view(batch_size, -1, self.num_heads, self.head_size) + key = self.linear_k(query_key_states).view(batch_size, -1, self.num_heads, self.head_size) + value = self.linear_v(value_states).view(batch_size, -1, self.num_heads, self.head_size) + + # => (batch, head, time1, d_k) + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = value.transpose(1, 2) + + if self.position_embeddings_type == "relative": + if relative_position_embeddings is None: + raise ValueError( + "`relative_position_embeddings` has to be defined when `self.position_embeddings_type ==" + " 'relative'" + ) + # apply relative_position_embeddings to qk scores + # as proposed in Transformer_XL: https://huggingface.co/papers/1901.02860 + scores = self._apply_relative_embeddings( + query=query, key=key, relative_position_embeddings=relative_position_embeddings + ) + else: + scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_size) + + # apply attention_mask if necessary + if attention_mask is not None: + scores = scores + attention_mask + + # => (batch, head, time1, time2) + probs = torch.softmax(scores, dim=-1) + probs = self.dropout(probs) + + # => (batch, head, time1, d_k) + hidden_states = torch.matmul(probs, value) + + # => (batch, time1, hidden_size) + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_size) + hidden_states = self.linear_out(hidden_states) + + return hidden_states, probs + + # Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerSelfAttention._apply_rotary_embedding + def _apply_rotary_embedding(self, hidden_states, relative_position_embeddings): + batch_size, sequence_length, hidden_size = hidden_states.size() + hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads, self.head_size) + + cos = relative_position_embeddings[0, :sequence_length, ...] + sin = relative_position_embeddings[1, :sequence_length, ...] + + # rotate hidden_states with rotary embeddings + hidden_states = hidden_states.transpose(0, 1) + rotated_states_begin = hidden_states[..., : self.head_size // 2] + rotated_states_end = hidden_states[..., self.head_size // 2 :] + rotated_states = torch.cat((-rotated_states_end, rotated_states_begin), dim=rotated_states_begin.ndim - 1) + hidden_states = (hidden_states * cos) + (rotated_states * sin) + hidden_states = hidden_states.transpose(0, 1) + + hidden_states = hidden_states.view(batch_size, sequence_length, self.num_heads * self.head_size) + + return hidden_states + + # Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerSelfAttention._apply_relative_embeddings + def _apply_relative_embeddings(self, query, key, relative_position_embeddings): + # 1. project positional embeddings + # => (batch, head, 2*time1-1, d_k) + proj_relative_position_embeddings = self.linear_pos(relative_position_embeddings) + proj_relative_position_embeddings = proj_relative_position_embeddings.view( + relative_position_embeddings.size(0), -1, self.num_heads, self.head_size + ) + proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(1, 2) + proj_relative_position_embeddings = proj_relative_position_embeddings.transpose(2, 3) + + # 2. Add bias to query + # => (batch, head, time1, d_k) + query = query.transpose(1, 2) + q_with_bias_u = (query + self.pos_bias_u).transpose(1, 2) + q_with_bias_v = (query + self.pos_bias_v).transpose(1, 2) + + # 3. attention score: first compute matrix a and matrix c + # as described in https://huggingface.co/papers/1901.02860 Section 3.3 + # => (batch, head, time1, time2) + scores_ac = torch.matmul(q_with_bias_u, key.transpose(-2, -1)) + + # 4. then compute matrix b and matrix d + # => (batch, head, time1, 2*time1-1) + scores_bd = torch.matmul(q_with_bias_v, proj_relative_position_embeddings) + + # 5. shift matrix b and matrix d + zero_pad = torch.zeros((*scores_bd.size()[:3], 1), device=scores_bd.device, dtype=scores_bd.dtype) + scores_bd_padded = torch.cat([zero_pad, scores_bd], dim=-1) + scores_bd_padded_shape = scores_bd.size()[:2] + (scores_bd.shape[3] + 1, scores_bd.shape[2]) + scores_bd_padded = scores_bd_padded.view(*scores_bd_padded_shape) + scores_bd = scores_bd_padded[:, :, 1:].view_as(scores_bd) + scores_bd = scores_bd[:, :, :, : scores_bd.size(-1) // 2 + 1] + + # 6. sum matrices + # => (batch, head, time1, time2) + scores = (scores_ac + scores_bd) / math.sqrt(self.head_size) + + return scores + + +class SeamlessM4TConformerEncoderLayer(GradientCheckpointingLayer): + """Conformer block based on https://huggingface.co/papers/2005.08100.""" + + # Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerEncoderLayer.__init__ with Wav2Vec2->SeamlessM4T, attention_dropout->speech_encoder_dropout, torch.nn->nn + def __init__(self, config): + super().__init__() + embed_dim = config.hidden_size + dropout = config.speech_encoder_dropout + + # Feed-forward 1 + self.ffn1_layer_norm = nn.LayerNorm(embed_dim) + self.ffn1 = SeamlessM4TConformerFeedForward(config) + + # Self-Attention + self.self_attn_layer_norm = nn.LayerNorm(embed_dim) + self.self_attn_dropout = nn.Dropout(dropout) + self.self_attn = SeamlessM4TConformerSelfAttention(config) + + # Conformer Convolution + self.conv_module = SeamlessM4TConformerConvolutionModule(config) + + # Feed-forward 2 + self.ffn2_layer_norm = nn.LayerNorm(embed_dim) + self.ffn2 = SeamlessM4TConformerFeedForward(config) + self.final_layer_norm = nn.LayerNorm(embed_dim) + + def forward( + self, + hidden_states, + attention_mask: torch.Tensor | None = None, + relative_position_embeddings: torch.Tensor | None = None, + output_attentions: bool = False, + conv_attention_mask: torch.Tensor | None = None, + ): + # 1. Feed-Forward 1 layer + residual = hidden_states + hidden_states = self.ffn1_layer_norm(hidden_states) + hidden_states = self.ffn1(hidden_states) + hidden_states = hidden_states * 0.5 + residual + residual = hidden_states + + # 2. Self-Attention layer + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, attn_weigts = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + relative_position_embeddings=relative_position_embeddings, + output_attentions=output_attentions, + ) + hidden_states = self.self_attn_dropout(hidden_states) + hidden_states = hidden_states + residual + + # 3. Convolutional Layer + residual = hidden_states + hidden_states = self.conv_module(hidden_states, attention_mask=conv_attention_mask) + hidden_states = residual + hidden_states + + # 4. Feed-Forward 2 Layer + residual = hidden_states + hidden_states = self.ffn2_layer_norm(hidden_states) + hidden_states = self.ffn2(hidden_states) + hidden_states = hidden_states * 0.5 + residual + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states, attn_weigts + + +class SeamlessM4TConformerEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + + if config.position_embeddings_type == "relative": + self.embed_positions = SeamlessM4TConformerRelPositionalEmbedding(config) + elif config.position_embeddings_type == "rotary": + self.embed_positions = SeamlessM4TConformerRotaryPositionalEmbedding(config) + else: + self.embed_positions = None + + self.dropout = nn.Dropout(config.speech_encoder_dropout) + self.layers = nn.ModuleList( + [SeamlessM4TConformerEncoderLayer(config) for _ in range(config.speech_encoder_layers)] + ) + + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + conv_attention_mask = attention_mask + if attention_mask is not None: + # make sure padded tokens output 0 + hidden_states = hidden_states.masked_fill(~attention_mask.bool().unsqueeze(-1), 0.0) + # extend attention_mask + attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) + attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min + attention_mask = attention_mask.expand( + attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] + ) + + hidden_states = self.dropout(hidden_states) + + if self.embed_positions is not None: + relative_position_embeddings = self.embed_positions(hidden_states) + else: + relative_position_embeddings = None + + synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self) + + for i, layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + dropout_probability = torch.rand([]) + + skip_the_layer = self.training and dropout_probability < self.config.speech_encoder_layerdrop + if not skip_the_layer or synced_gpus: + # under fsdp or deepspeed zero3 all gpus must run in sync + layer_outputs = layer( + hidden_states, + attention_mask=attention_mask, + relative_position_embeddings=relative_position_embeddings, + output_attentions=output_attentions, + conv_attention_mask=conv_attention_mask, + ) + hidden_states = layer_outputs[0] + + if skip_the_layer: + layer_outputs = (None, None) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +class SeamlessM4TConformerAdapterLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + + embed_dim = config.hidden_size + dropout = config.adaptor_dropout + + self.kernel_size = config.adaptor_kernel_size + self.stride = config.adaptor_stride + + # 1. residual convolution + self.residual_layer_norm = nn.LayerNorm(embed_dim) + self.residual_conv = nn.Conv1d( + embed_dim, + 2 * embed_dim, + self.kernel_size, + stride=self.stride, + padding=self.stride // 2, + ) + self.activation = nn.GLU(dim=1) + + # Self-Attention + self.self_attn_layer_norm = nn.LayerNorm(embed_dim) + self.self_attn_conv = nn.Conv1d( + embed_dim, + 2 * embed_dim, + self.kernel_size, + stride=self.stride, + padding=self.stride // 2, + ) + self.self_attn = SeamlessM4TConformerSelfAttention(config, use_position_embeddings=False) + self.self_attn_dropout = nn.Dropout(dropout) + + # Feed-forward + self.ffn_layer_norm = nn.LayerNorm(embed_dim) + self.ffn = SeamlessM4TConformerFeedForward(config, act_fn="relu", dropout=dropout) + + def _compute_sub_sample_lengths_from_attention_mask(self, attention_mask): + pad = self.kernel_size // 2 + seq_lens = attention_mask.size(1) - (1 - attention_mask.int()).sum(1) + + seq_lens = ((seq_lens + 2 * pad - self.kernel_size) / self.stride) + 1 + + return seq_lens.floor() + + def forward( + self, + hidden_states, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + ): + residual = self.residual_layer_norm(hidden_states) + + # Apply pooling to the residual to match the sequence length of the + # multi-head attention output. + # (batch, seq_len, feature_dim) -> (batch, feature_dim, seq_len) + residual = residual.transpose(1, 2) + residual = self.residual_conv(residual) + residual = self.activation(residual) + # (batch, feature_dim, seq_len) -> (batch, seq_len, feature_dim) + residual = residual.transpose(1, 2) + + hidden_states = self.self_attn_layer_norm(hidden_states) + # Apply pooling before feeding to the multihead-attention layer. + # (batch, seq_len, feature_dim) -> (batch, feature_dim, seq_len) + hidden_states = hidden_states.transpose(1, 2) + hidden_states = self.self_attn_conv(hidden_states) + hidden_states = self.activation(hidden_states) + # (batch, feature_dim, seq_len) -> (batch, seq_len, feature_dim) + hidden_states = hidden_states.transpose(1, 2) + + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + hidden_states.device + ) + attention_mask = _compute_new_attention_mask(hidden_states=hidden_states, seq_lens=sub_sampled_lengths) + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + ) + + # The rest of the computation is identical to a vanilla Transformer + # encoder layer. + hidden_states, attn_weights = self.self_attn( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.self_attn_dropout(hidden_states) + hidden_states = hidden_states + residual + + residual = hidden_states + + hidden_states = self.ffn_layer_norm(hidden_states) + hidden_states = self.ffn(hidden_states) + residual + + return hidden_states + + +class SeamlessM4TConformerAdapter(nn.Module): + def __init__(self, config): + super().__init__() + + self.layers = nn.ModuleList(SeamlessM4TConformerAdapterLayer(config) for _ in range(config.num_adapter_layers)) + + def forward(self, hidden_states, attention_mask): + # down project hidden_states if necessary + + for layer in self.layers: + hidden_states = layer(hidden_states, attention_mask) + + return hidden_states + + +############ TEXT / UNITS related code ################ + + +# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100ScaledWordEmbedding with M2M100->SeamlessM4T +class SeamlessM4TScaledWordEmbedding(nn.Embedding): + """ + This module overrides nn.Embeddings' forward by multiplying with embeddings scale. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float | None = 1.0): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.embed_scale = embed_scale + + def forward(self, input_ids: torch.Tensor): + return super().forward(input_ids) * self.embed_scale + + +# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding with M2M100->SeamlessM4T +class SeamlessM4TSinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length.""" + + def __init__(self, num_positions: int, embedding_dim: int, padding_idx: int | None = None): + super().__init__() + self.offset = 2 + self.num_positions = num_positions + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.make_weights(num_positions + self.offset, embedding_dim, padding_idx) + + def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: int | None = None): + emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx) + if hasattr(self, "weights"): + # in forward put the weights on the correct dtype and device of the param + emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device) + + self.register_buffer("weights", emb_weights, persistent=False) + + @staticmethod + def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: int | None = None): + """ + Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of + "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb) + emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + + return emb.to(torch.get_default_dtype()) + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values_length: int = 0, + ): + if input_ids is not None: + bsz, seq_len = input_ids.size() + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ).to(input_ids.device) + else: + bsz, seq_len = inputs_embeds.size()[:-1] + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds, past_key_values_length, self.padding_idx + ) + + # expand embeddings if needed + max_pos = self.padding_idx + 1 + seq_len + past_key_values_length + if max_pos > self.weights.size(0): + self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx) + + return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach() + + @staticmethod + def create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length, padding_idx): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length + + @staticmethod + # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_input_ids + def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx + + +class SeamlessM4TAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + # Copied from transformers.models.bart.modeling_bart.BartAttention.__init__ with Bart->SeamlessM4T + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: SeamlessM4TConfig | None = None, + layer_idx: int | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + if layer_idx is None and self.is_decoder: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + # if encoder_hidden_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = encoder_hidden_states is not None + + bsz, tgt_len, _ = hidden_states.size() + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = encoder_hidden_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.k_proj(current_states) + value_states = self.v_proj(current_states) + key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if past_key_values is not None: + # save all key/value_states to cache to be re-used for fast auto-regressive generation + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + proj_shape = (bsz * self.num_heads, -1, self.head_dim) + query_states = query_states.view(bsz, tgt_len, self.num_heads, self.head_dim).transpose(1, 2) + query_states = query_states.reshape(*proj_shape) + key_states = key_states.reshape(*proj_shape) + value_states = value_states.reshape(*proj_shape) + + src_len = key_states.size(1) + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, tgt_len, src_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask + attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to be reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz * self.num_heads, tgt_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + + # Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be + # partitioned across GPUs when using tensor-parallelism. + attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped + + +# Copied from transformers.models.nllb_moe.modeling_nllb_moe.NllbMoeDenseActDense with NllbMoe->SeamlessM4T,DenseActDense->FeedForwardNetwork, d_model->hidden_size +class SeamlessM4TFeedForwardNetwork(nn.Module): + def __init__(self, config: SeamlessM4TConfig, ffn_dim: int): + super().__init__() + self.fc1 = nn.Linear(config.hidden_size, ffn_dim) + self.fc2 = nn.Linear(ffn_dim, config.hidden_size) + self.dropout = nn.Dropout(config.activation_dropout) + self.act = ACT2FN[config.activation_function] + + def forward(self, hidden_states: torch.Tensor): + hidden_states = self.fc1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.dropout(hidden_states) + if ( + isinstance(self.fc2.weight, torch.Tensor) + and hidden_states.dtype != self.fc2.weight.dtype + and (self.fc2.weight.dtype != torch.int8 and self.fc2.weight.dtype != torch.uint8) + ): + hidden_states = hidden_states.to(self.fc2.weight.dtype) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class SeamlessM4TEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: SeamlessM4TConfig, encoder_ffn_dim=None, encoder_attention_heads=None): + super().__init__() + encoder_ffn_dim = config.encoder_ffn_dim if encoder_ffn_dim is None else encoder_ffn_dim + encoder_attention_heads = ( + config.encoder_attention_heads if encoder_attention_heads is None else encoder_attention_heads + ) + + self.embed_dim = config.hidden_size + self.self_attn = SeamlessM4TAttention( + embed_dim=self.embed_dim, + num_heads=encoder_attention_heads, + dropout=config.attention_dropout, + ) + self.attn_dropout = nn.Dropout(config.dropout) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + self.ffn = SeamlessM4TFeedForwardNetwork(config, ffn_dim=encoder_ffn_dim) + + self.ffn_layer_norm = nn.LayerNorm(config.hidden_size) + self.ffn_dropout = nn.Dropout(config.activation_dropout) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + output_attentions: bool = False, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): + attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very + large negative values. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + + residual = hidden_states + + hidden_states = self.ffn_layer_norm(hidden_states) + + hidden_states = self.ffn(hidden_states) + hidden_states = self.ffn_dropout(hidden_states) + + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class SeamlessM4TDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: SeamlessM4TConfig, decoder_ffn_dim=None, decoder_attention_heads=None, layer_idx=None): + super().__init__() + decoder_ffn_dim = config.decoder_ffn_dim if decoder_ffn_dim is None else decoder_ffn_dim + decoder_attention_heads = ( + config.decoder_attention_heads if decoder_attention_heads is None else decoder_attention_heads + ) + + self.embed_dim = config.hidden_size + self.self_attn = SeamlessM4TAttention( + embed_dim=self.embed_dim, + num_heads=decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.attn_dropout = nn.Dropout(config.dropout) + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.cross_attention = SeamlessM4TAttention( + self.embed_dim, + decoder_attention_heads, + config.attention_dropout, + is_decoder=True, + layer_idx=layer_idx, + ) + self.cross_attention_layer_norm = nn.LayerNorm(self.embed_dim) + + self.ffn = SeamlessM4TFeedForwardNetwork(config, ffn_dim=decoder_ffn_dim) + + self.ffn_layer_norm = nn.LayerNorm(config.hidden_size) + self.ffn_dropout = nn.Dropout(config.activation_dropout) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool | None = False, + use_cache: bool | None = True, + **kwargs, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): + attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very + large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): + encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by + very large negative values. + past_key_values (`Cache`): + cached past key and value projection states + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + + # Cross-Attention Block + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.cross_attention_layer_norm(hidden_states) + + hidden_states, cross_attn_weights = self.cross_attention( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + + hidden_states = self.ffn_layer_norm(hidden_states) + + hidden_states = self.ffn(hidden_states) + hidden_states = self.ffn_dropout(hidden_states) + + hidden_states = residual + hidden_states + + return hidden_states, self_attn_weights, cross_attn_weights + + +############ SUB-MODELS related code ################ + + +@auto_docstring +class SeamlessM4TPreTrainedModel(PreTrainedModel): + config: SeamlessM4TConfig + base_model_prefix = "seamless_m4t" + supports_gradient_checkpointing = True + _no_split_modules = ["SeamlessM4TEncoderLayer", "SeamlessM4TDecoderLayer", "SeamlessM4TConformerEncoderLayer"] + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + """Initialize the weights""" + std = self.config.initializer_range + if isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=std) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=std) + # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag + if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): + init.zeros_(module.weight[module.padding_idx]) + elif isinstance(module, SeamlessM4TConformerSelfAttention): + if hasattr(module, "pos_bias_u"): + init.xavier_uniform_(module.pos_bias_u) + if hasattr(module, "pos_bias_v"): + init.xavier_uniform_(module.pos_bias_v) + elif isinstance(module, SeamlessM4TConformerPositionalConvEmbedding): + init.normal_( + module.conv.weight, + mean=0, + std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)), + ) + init.constant_(module.conv.bias, 0) + elif isinstance(module, SeamlessM4TConformerFeatureProjection): + k = math.sqrt(1 / module.projection.in_features) + init.uniform_(module.projection.weight, a=-k, b=k) + init.uniform_(module.projection.bias, a=-k, b=k) + elif isinstance(module, (nn.LayerNorm, nn.BatchNorm1d)): + init.zeros_(module.bias) + init.ones_(module.weight) + if getattr(module, "running_mean", None) is not None: + init.zeros_(module.running_mean) + init.ones_(module.running_var) + init.zeros_(module.num_batches_tracked) + elif isinstance(module, nn.Conv1d): + init.kaiming_normal_(module.weight) + if module.bias is not None: + k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0])) + init.uniform_(module.bias, a=-k, b=k) + elif isinstance(module, SeamlessM4TSinusoidalPositionalEmbedding): + emb_weights = module.get_embedding( + module.num_positions + module.offset, module.embedding_dim, module.padding_idx + ) + init.copy_(module.weights, emb_weights) + elif isinstance(module, SeamlessM4TConformerRotaryPositionalEmbedding): + dim = self.config.hidden_size // self.config.speech_encoder_attention_heads + base = self.config.rotary_embedding_base + inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim)) + init.copy_(module.inv_freq, inv_freq) + elif isinstance(module, SeamlessM4TConformerRelPositionalEmbedding): + init.copy_(module.pe, module.extend_pe(torch.tensor(0.0).expand(1, module.max_len))) + + def _compute_sub_sample_lengths_from_attention_mask(self, attention_mask): + kernel_size, stride = self.config.adaptor_kernel_size, self.config.adaptor_stride + pad = kernel_size // 2 + seq_lens = attention_mask.size(1) - (1 - attention_mask.int()).sum(1) + + seq_lens = ((seq_lens + 2 * pad - kernel_size) / stride) + 1 + + return seq_lens.floor() + + def compute_last_hidden_states_per_sample( + self, + hidden_states: tuple[tuple[torch.Tensor]], + beam_indices: torch.Tensor | None = None, + ) -> torch.Tensor: + """ + Computes the last hidden states. + + Parameters: + hidden_states (`tuple[tuple[torch.Tensor]]`): + The generated hidden states. Tuple (one element for each generated token) of tuples (one element for + each layer of the decoder) of torch.FloatTensor of shape (batch_size*num_beams*num_return_sequences, + generated_length, hidden_size). + beam_indices (`torch.LongTensor`, *optional*): + Beam indices of generated token id at each generation step. `torch.LongTensor` of shape + `(batch_size*num_return_sequences, sequence_length)`. Only required if a `num_beams>1` at + generate-time. + + Return: + `torch.Tensor`: A `torch.Tensor` of shape `(batch_size*num_return_sequences, sequence_length, hidden_size)` + containing + the last hidden states. + ```""" + # 1. First, let's compute last_hidden_states from hidden_states. + # For each generation step, takes the hidden state from the last layer. + # shape: (batch_size*vocab_size*num_return_sequences, # generation_steps, hidden_dim) + last_hidden_states = torch.concat([hidden_states[-1] for hidden_states in hidden_states], dim=1) + + # 2. In absence of `beam_indices`, we can assume that we come from e.g. greedy search, which is equivalent + # to a beam search approach were the first (and only) beam is always selected + # in that case, return directly last_hidden_states + if beam_indices is None: + return last_hidden_states + + # 3. cut beam_indices to longest beam length + beam_indices_mask = beam_indices < 0 + max_beam_length = (1 - beam_indices_mask.long()).sum(-1).max() + beam_indices = beam_indices.clone()[:, :max_beam_length] + beam_indices_mask = beam_indices_mask[:, :max_beam_length] + + # 4. Set indices of beams that finished early to 0; such indices will be masked correctly afterwards anyways + beam_indices[beam_indices_mask] = 0 + + # 5. expand beam_indices to last_hidden_states dim + beam_indices = beam_indices.unsqueeze(-1) + beam_indices = beam_indices.expand(-1, -1, last_hidden_states.shape[-1]) + + # 6. select the right candidate for each beam + # in other words, new_last_hidden_states[i,j,k] = last_hidden_states[beam_indices[i,j,k], j, k] for all i, j, k + last_hidden_states = torch.gather(last_hidden_states, 0, beam_indices) + + return last_hidden_states + + +@auto_docstring( + custom_intro=""" + Transformer speech encoder consisting of *config.speech_encoder_layers* conformer self attention layers. + Each layer is a [`SeamlessM4TConformerEncoderLayer`]. + """ +) +class SeamlessM4TSpeechEncoder(SeamlessM4TPreTrainedModel): + main_input_name = "input_features" + input_modalities = "audio" + + def __init__(self, config: SeamlessM4TConfig): + super().__init__(config) + + self.feature_projection = SeamlessM4TConformerFeatureProjection(config) + self.encoder = SeamlessM4TConformerEncoder(config) + self.intermediate_ffn = SeamlessM4TConformerFeedForward(config, act_fn="relu", dropout=0.0) + self.adapter = SeamlessM4TConformerAdapter(config) if config.add_adapter else None + self.inner_layer_norm = nn.LayerNorm(config.hidden_size) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_features: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | Wav2Vec2BaseModelOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_features is None: + raise ValueError( + """Both `input_features` and `inputs_embeds` are `None` in `SeamlessM4TSpeechEncoder.forward`. + Make sure one of them is not `None`.""" + ) + + hidden_states = self.feature_projection(input_features) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + expanded_hidden_states = self.intermediate_ffn(hidden_states) + hidden_states = hidden_states + 0.5 * expanded_hidden_states + + if self.adapter is not None: + hidden_states = self.adapter(hidden_states, attention_mask=attention_mask) + + hidden_states = self.inner_layer_norm(hidden_states) + + if not return_dict: + return (hidden_states,) + encoder_outputs[1:] + + return Wav2Vec2BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +# inspired from MBart and NllbMoe +@auto_docstring( + custom_intro=""" + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`SeamlessM4TEncoderLayer`]. + """ +) +class SeamlessM4TEncoder(SeamlessM4TPreTrainedModel): + def __init__( + self, + config: SeamlessM4TConfig, + embed_tokens: nn.Embedding | None = None, + is_t2u_encoder: bool = False, + ): + r""" + embed_tokens (`nn.Embedding`, *optional*): + Input embedding + is_t2u_encoder (`bool`, *optional*, defaults to `False`): + indicates if it belongs to the text-to-units model, in which case it won't have input embeddings + """ + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + self.padding_idx = config.pad_token_id + embed_dim = config.hidden_size + + self.is_t2u_encoder = is_t2u_encoder + self.max_source_positions = config.max_position_embeddings + + if not self.is_t2u_encoder: + embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = SeamlessM4TScaledWordEmbedding( + config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale + ) + + if embed_tokens is not None: + self.embed_tokens.weight = embed_tokens.weight + + self.embed_positions = SeamlessM4TSinusoidalPositionalEmbedding( + self.max_source_positions, + embed_dim, + self.padding_idx, + ) + + layers = [] + for _ in range(config.encoder_layers): + layers.append( + SeamlessM4TEncoderLayer( + config, + encoder_attention_heads=config.encoder_attention_heads, + encoder_ffn_dim=config.encoder_ffn_dim, + ) + ) + + self.layers = nn.ModuleList(layers) + + self.layer_norm = nn.LayerNorm(config.hidden_size) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_ids is not None and self.is_t2u_encoder: + raise ValueError( + "You cannot pass input_ids to the encoder of the text_to_units model. Pass inputs_embeds instead." + ) + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_shape = input.shape + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if not self.is_t2u_encoder: + embed_pos = self.embed_positions(input) + + hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device) + else: + hidden_states = inputs_embeds + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # expand attention_mask + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +@auto_docstring( + custom_intro=""" + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`SeamlessM4TDecoderLayer`]. + """ +) +class SeamlessM4TDecoder(SeamlessM4TPreTrainedModel): + def __init__( + self, + config: SeamlessM4TConfig, + embed_tokens: nn.Embedding | None = None, + ): + r""" + embed_tokens (`nn.Embedding`, *optional*): + Input embedding + """ + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.max_target_positions = config.max_position_embeddings + embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 + + if embed_tokens is not None: + # if embed_tokens defined, use its shape instead + self.embed_tokens = SeamlessM4TScaledWordEmbedding( + embed_tokens.num_embeddings, embed_tokens.embedding_dim, self.padding_idx, embed_scale=embed_scale + ) + self.embed_tokens.weight = embed_tokens.weight + else: + self.embed_tokens = SeamlessM4TScaledWordEmbedding( + self.vocab_size, config.hidden_size, self.padding_idx, embed_scale=embed_scale + ) + + self.embed_positions = SeamlessM4TSinusoidalPositionalEmbedding( + self.max_target_positions, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + layers = [] + for i in range(config.decoder_layers): + layers.append( + SeamlessM4TDecoderLayer( + config, + decoder_attention_heads=config.decoder_attention_heads, + decoder_ffn_dim=config.decoder_ffn_dim, + layer_idx=i, + ) + ) + self.layers = nn.ModuleList(layers) + self.layer_norm = nn.LayerNorm(config.hidden_size) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPastAndCrossAttentions: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_shape = input.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing`. Setting `use_cache=False`..." + ) + use_cache = False + + # initialize `past_key_values` + if use_cache and past_key_values is None: + past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + + # expand encoder attention mask + if encoder_hidden_states is not None and encoder_attention_mask is not None: + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + # embed positions + positions = self.embed_positions(input, past_key_values_length=past_key_values_length) + + hidden_states = inputs_embeds + positions.to(inputs_embeds.device) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + layer_outputs = decoder_layer( + hidden_states, + attention_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + hidden_states = self.layer_norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Transformer bare text-to-unit encoder-decoder. The encoder is a [`SeamlessM4TEncoder`] without embeddings and the decoder is a [`SeamlessM4TDecoder`]. + """ +) +class SeamlessM4TTextToUnitModel(SeamlessM4TPreTrainedModel): + def __init__( + self, + config: SeamlessM4TConfig, + embed_tokens_decoder: nn.Embedding | None = None, + ): + r""" + embed_tokens_decoder (`nn.Embedding`, *optional*): + input embedding of the decoder. + """ + super().__init__(config) + + self.encoder = SeamlessM4TEncoder(config, is_t2u_encoder=True) + self.decoder = SeamlessM4TDecoder(config, embed_tokens_decoder) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | Seq2SeqModelOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return Seq2SeqModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + Transformer text-to-unit encoder-decoder with a language model head. The base encoder-decoder model is a [`SeamlessM4TTextToUnit`]. + """ +) +class SeamlessM4TTextToUnitForConditionalGeneration(SeamlessM4TPreTrainedModel, GenerationMixin): + _keys_to_ignore_on_load_missing = [ + "vocoder", + "speech_encoder", + "text_encoder", + "text_decoder", + ] + _tied_weights_keys = {"lm_head.weight": "model.decoder.embed_tokens.weight"} + + def __init__( + self, + config: SeamlessM4TConfig, + embed_tokens_decoder: nn.Embedding | None = None, + ): + r""" + embed_tokens_decoder (`nn.Embedding`, *optional*): + input embedding of the decoder. + """ + # update config - used principality for bos_token_id etc. + config = copy.deepcopy(config) + for param, val in config.to_dict().items(): + if param.startswith("t2u_"): + config.__setattr__(param[4:], val) + super().__init__(config) + + self.model = SeamlessM4TTextToUnitModel(config, embed_tokens_decoder) + + self.lm_head = nn.Linear(config.hidden_size, config.t2u_vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.model.encoder + + def get_decoder(self): + return self.model.decoder + + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + @auto_docstring(custom_args=SEAMLESS_M4T_COMMON_CUSTOM_ARGS) + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.t2u_pad_token_id, self.config.t2u_decoder_start_token_id + ) + + outputs = self.model( + input_ids, + attention_mask=attention_mask, + decoder_input_ids=decoder_input_ids, + encoder_outputs=encoder_outputs, + decoder_attention_mask=decoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + lm_logits = self.lm_head(outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=outputs.past_key_values, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor): + return shift_tokens_right(labels, self.config.t2u_pad_token_id, self.config.t2u_decoder_start_token_id) + + +############ VOCODER related code ################ + + +# Copied from transformers.models.speecht5.modeling_speecht5.HifiGanResidualBlock +class HifiGanResidualBlock(nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1): + super().__init__() + self.leaky_relu_slope = leaky_relu_slope + + self.convs1 = nn.ModuleList( + [ + nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=dilation[i], + padding=self.get_padding(kernel_size, dilation[i]), + ) + for i in range(len(dilation)) + ] + ) + self.convs2 = nn.ModuleList( + [ + nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=1, + padding=self.get_padding(kernel_size, 1), + ) + for _ in range(len(dilation)) + ] + ) + + def get_padding(self, kernel_size, dilation=1): + return (kernel_size * dilation - dilation) // 2 + + def apply_weight_norm(self): + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + for layer in self.convs1: + weight_norm(layer) + for layer in self.convs2: + weight_norm(layer) + + def remove_weight_norm(self): + for layer in self.convs1: + nn.utils.remove_weight_norm(layer) + for layer in self.convs2: + nn.utils.remove_weight_norm(layer) + + def forward(self, hidden_states): + for conv1, conv2 in zip(self.convs1, self.convs2): + residual = hidden_states + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = conv1(hidden_states) + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = conv2(hidden_states) + hidden_states = hidden_states + residual + return hidden_states + + +class SeamlessM4TVariancePredictor(nn.Module): + def __init__(self, config): + super().__init__() + + embed_dim = config.unit_embed_dim + kernel_size = config.variance_predictor_kernel_size + var_pred_dropout = config.var_pred_dropout + + self.conv1 = nn.Conv1d( + embed_dim, + embed_dim, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + ) + self.activation_function = nn.ReLU() + self.ln1 = nn.LayerNorm(embed_dim) + self.dropout_module = nn.Dropout(p=var_pred_dropout) + self.conv2 = nn.Conv1d( + embed_dim, + embed_dim, + kernel_size=kernel_size, + padding=1, + ) + self.ln2 = nn.LayerNorm(embed_dim) + self.proj = nn.Linear(embed_dim, 1) + + def forward(self, hidden_states: Tensor) -> Tensor: + # Input: B x T x C; Output: B x T + hidden_states = self.conv1(hidden_states.transpose(1, 2)) + hidden_states = self.activation_function(hidden_states).transpose(1, 2) + hidden_states = self.dropout_module(self.ln1(hidden_states)) + hidden_states = self.conv2(hidden_states.transpose(1, 2)) + hidden_states = self.activation_function(hidden_states).transpose(1, 2) + hidden_states = self.dropout_module(self.ln2(hidden_states)) + return self.proj(hidden_states).squeeze(dim=2) + + +class SeamlessM4THifiGan(nn.Module): + def __init__(self, config: SeamlessM4TConfig): + super().__init__() + model_in_dim = config.unit_embed_dim + config.lang_embed_dim + config.spkr_embed_dim + self.leaky_relu_slope = config.leaky_relu_slope + self.num_kernels = len(config.resblock_kernel_sizes) + self.num_upsamples = len(config.upsample_rates) + self.conv_pre = nn.Conv1d( + model_in_dim, + config.upsample_initial_channel, + kernel_size=7, + stride=1, + padding=3, + ) + + self.upsampler = nn.ModuleList() + for i, (upsample_rate, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes)): + self.upsampler.append( + nn.ConvTranspose1d( + config.upsample_initial_channel // (2**i), + config.upsample_initial_channel // (2 ** (i + 1)), + kernel_size=kernel_size, + stride=upsample_rate, + padding=(kernel_size - upsample_rate) // 2, + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.upsampler)): + channels = config.upsample_initial_channel // (2 ** (i + 1)) + for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes): + self.resblocks.append(HifiGanResidualBlock(channels, kernel_size, dilation, config.leaky_relu_slope)) + + self.conv_post = nn.Conv1d(channels, 1, kernel_size=7, stride=1, padding=3) + + @deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds") + def forward(self, inputs_embeds: torch.FloatTensor) -> torch.FloatTensor: + r""" + Converts a log-mel spectrogram into a speech waveform. Passing a batch of log-mel spectrograms returns a batch + of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a single, un-batched speech + waveform. + + Args: + spectrogram (`torch.FloatTensor`): + Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length, + model_in_dim)`, or un-batched and of shape `(sequence_length, model_in_dim)`. Note that `model_in_dim` + is the sum of `config.unit_embed_dim`, `config.lang_embed_dim` and `config.spkr_embed_dim`. + + Returns: + `torch.FloatTensor`: Tensor containing the speech waveform. If the input spectrogram is batched, will be of + shape `(batch_size, num_frames,)`. If un-batched, will be of shape `(num_frames,)`. + """ + + hidden_states = self.conv_pre(inputs_embeds) + for i in range(self.num_upsamples): + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = self.upsampler[i](hidden_states) + + res_state = self.resblocks[i * self.num_kernels](hidden_states) + for j in range(1, self.num_kernels): + res_state += self.resblocks[i * self.num_kernels + j](hidden_states) + hidden_states = res_state / self.num_kernels + + hidden_states = nn.functional.leaky_relu(hidden_states) + hidden_states = self.conv_post(hidden_states) + hidden_states = torch.tanh(hidden_states) + + # remove seq-len dim since this collapses to 1 + waveform = hidden_states.squeeze(1) + + return waveform + + +@auto_docstring( + custom_intro=""" + Code HiFi-GAN vocoder as described in this [repository](https://github.com/facebookresearch/speech-resynthesis). + """ +) +class SeamlessM4TCodeHifiGan(PreTrainedModel): + config: SeamlessM4TConfig + main_input_name = "inputs_embeds" + input_modalities = "audio" + _no_split_modules = [] + + def __init__(self, config): + super().__init__(config) + + self.pad_token_id = config.t2u_pad_token_id + self.dur_predictor = SeamlessM4TVariancePredictor(config) + + self.unit_embedding = nn.Embedding(config.unit_hifi_gan_vocab_size, config.unit_embed_dim) + self.speaker_embedding = nn.Embedding(config.vocoder_num_spkrs, config.spkr_embed_dim) + self.language_embedding = nn.Embedding(config.vocoder_num_langs, config.lang_embed_dim) + + self.hifi_gan = SeamlessM4THifiGan(config) + + # Initialize weights and apply final processing + self.post_init() + + def _get_dur_output_lengths(self, input_ids, dur_out): + """ + Computes the output length after the duration layer. + """ + unit_lengths = (input_ids != self.pad_token_id).sum(1) + + # take care of edge cases where no padding or too many padding + unit_lengths = torch.clamp(unit_lengths, 0, dur_out.shape[1] - 1) + + cumulative_dur_out = torch.cumsum(dur_out, dim=1) + unit_lengths = cumulative_dur_out.gather(dim=1, index=unit_lengths.unsqueeze(1)).squeeze() + + return unit_lengths + + def _get_output_hifigan_lengths(self, input_lengths: torch.LongTensor | int): + """ + Computes the output length of the hifigan convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride, pad, dilation=1): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return ( + torch.div(input_length + 2 * pad - dilation * (kernel_size - 1) - 1, stride, rounding_mode="floor") + 1 + ) + + def _transpose_conv_out_length(input_length, kernel_size, stride, pad, dilation=1): + return (input_length - 1) * stride - 2 * pad + dilation * (kernel_size - 1) + 1 + + # conv_pre + input_lengths = _conv_out_length(input_lengths, 7, 1, 3) + + # upsampler + for i, (upsample_rate, kernel_size) in enumerate( + zip(self.config.upsample_rates, self.config.upsample_kernel_sizes) + ): + input_lengths = _transpose_conv_out_length( + input_lengths, kernel_size, upsample_rate, (kernel_size - upsample_rate) // 2 + ) + + # resblock + for i in range(len(self.config.upsample_rates)): + for kernel_size, dilation in zip(self.config.resblock_kernel_sizes, self.config.resblock_dilation_sizes): + for dil in dilation: + input_lengths = _conv_out_length( + input_lengths, kernel_size, 1, (kernel_size - 1) * dil // 2, dilation=dil + ) + + for dil in dilation: + input_lengths = _conv_out_length(input_lengths, kernel_size, 1, (kernel_size - 1) // 2, dilation=1) + + # conv_post + input_lengths = _conv_out_length(input_lengths, 7, 1, 3) + + return input_lengths + + def forward( + self, input_ids: torch.LongTensor, spkr_id: torch.Tensor, lang_id: torch.Tensor, **kwargs + ) -> tuple[torch.Tensor]: + """ + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`SeamlessM4TTextToUnitForConditionalGeneration`]. [What are input + IDs?](../glossary#input-ids) + spkr_id (`int`, *optional*): + The id of the speaker used for speech synthesis. Must be lower than `config.vocoder_num_spkrs`. + tgt_lang (`str`, *optional*): + The language id to use as target language for translation. + """ + hidden_states = self.unit_embedding(input_ids).transpose(1, 2) + spkr = self.speaker_embedding(spkr_id).transpose(1, 2) + lang = self.language_embedding(lang_id).transpose(1, 2) + + log_dur_pred = self.dur_predictor(hidden_states.transpose(1, 2)) + dur_out = torch.clamp(torch.round(torch.expm1(log_dur_pred)).long(), min=1) + # B x C x T + if hidden_states.size(0) == 1: + hidden_states = torch.repeat_interleave(hidden_states, dur_out.view(-1), dim=2) + else: + # if batched sample, need to interleave per sample, and pad -> loss of parallelism + if hidden_states.shape[0] > 1 and self.training: + logger.warning( + """`self.training=True` and you use batching. You lose parallelism during the hifigan + forward pass because the samples are interleaved.""" + ) + hidden_states = [ + torch.repeat_interleave(hidden_state, duration, dim=-1).transpose(0, 1) + for (hidden_state, duration) in zip(hidden_states, dur_out) + ] + + hidden_states = nn.utils.rnn.pad_sequence(hidden_states, batch_first=True).transpose(1, 2) + + spkr = spkr.repeat(1, 1, hidden_states.shape[-1]) + lang = lang.repeat(1, 1, hidden_states.shape[-1]) + hidden_states = torch.cat([lang, hidden_states, spkr], dim=1) + + hidden_states = self.hifi_gan(hidden_states) + + unit_lengths = self._get_dur_output_lengths(input_ids, dur_out) + lengths = self._get_output_hifigan_lengths(unit_lengths) + + return hidden_states, lengths + + def apply_weight_norm(self): + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + weight_norm(self.hifi_gan.conv_pre) + for layer in self.hifi_gan.upsampler: + weight_norm(layer) + for layer in self.hifi_gan.resblocks: + layer.apply_weight_norm() + weight_norm(self.hifi_gan.conv_post) + + def remove_weight_norm(self): + nn.utils.remove_weight_norm(self.hifi_gan.conv_pre) + for layer in self.hifi_gan.upsampler: + nn.utils.remove_weight_norm(layer) + for layer in self.hifi_gan.resblocks: + layer.remove_weight_norm() + nn.utils.remove_weight_norm(self.hifi_gan.conv_post) + + +############ WHOLE MODEL related code ################ + + +@auto_docstring( + custom_intro=""" + The text-to-text SeamlessM4T Model transformer which can be used for T2TT. + """ +) +class SeamlessM4TForTextToText(SeamlessM4TPreTrainedModel, GenerationMixin): + _keys_to_ignore_on_load_missing = ["speech_encoder", "t2u_model", "vocoder"] + main_input_name = "input_ids" + + _tied_weights_keys = { + "lm_head.weight": "shared.weight", + "text_encoder.embed_tokens.weight": "shared.weight", + "text_decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: SeamlessM4TConfig): + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + + self.text_encoder = SeamlessM4TEncoder(config) + self.text_decoder = SeamlessM4TDecoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.text_encoder + + def get_decoder(self): + return self.text_decoder + + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + def set_input_embeddings(self, value): + self.text_encoder.embed_tokens = value + self.text_decoder.embed_tokens = value + self.shared = value + + @auto_docstring(custom_args=SEAMLESS_M4T_COMMON_CUSTOM_ARGS) + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + encoder_outputs = self.text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + def generate( + self, + input_ids=None, + tgt_lang=None, + generation_config=None, + logits_processor=None, + stopping_criteria=None, + prefix_allowed_tokens_fn=None, + synced_gpus=False, + **kwargs, + ): + """ + Generates sequences of token ids. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Parameters: + input_ids (`torch.Tensor` of varying shape depending on the modality, *optional*): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`SeamlessM4TTokenizer`] or [`SeamlessM4TProcessor`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + logits_processor (`LogitsProcessorList`, *optional*): + Custom logits processors that complement the default logits processors built from arguments and + generation config. If a logit processor is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + stopping_criteria (`StoppingCriteriaList`, *optional*): + Custom stopping criteria that complement the default stopping criteria built from arguments and a + generation config. If a stopping criteria is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], list[int]]`, *optional*): + If provided, this function constraints the beam search to allowed tokens only at each step. If not + provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and + `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned + on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful + for constrained generation conditioned on the prefix, as described in [Autoregressive Entity + Retrieval](https://huggingface.co/papers/2010.00904). + synced_gpus (`bool`, *optional*, defaults to `False`): + Whether to continue running the while loop until max_length (needed to avoid deadlocking with + `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). + kwargs (`dict[str, Any]`, *optional*): + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. + + Return: + [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` + or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. The possible + [`~utils.ModelOutput`] types are: + - [`~generation.GenerateEncoderDecoderOutput`], + - [`~generation.GenerateBeamEncoderDecoderOutput`] + """ + # prepare text_decoder_input_ids + text_decoder_input_ids = kwargs.pop("decoder_input_ids", None) + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + if tgt_lang is not None: + batch_size = len(input_ids) if input_ids is not None else len(kwargs.get("inputs_embeds")) + + if hasattr(self.generation_config, "text_decoder_lang_to_code_id"): + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + if tgt_lang not in self.generation_config.text_decoder_lang_to_code_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. Please specify a `tgt_lang` in + {", ".join(self.generation_config.text_decoder_lang_to_code_id.keys())}""" + ) + # tgt_lang gets priority over decoder input ids + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + else: + raise ValueError( + """This model generation config doesn't have a `text_decoder_lang_to_code_id` key which maps + the target language to the right token id. Make sure to load the right generation config.""" + ) + else: + # only a warning, otherwise errors appear in the tests + logger.warning( + """You must either specify a `tgt_lang` or pass a correct `text_decoder_input_ids` to get + a correct generation, otherwise the generation will probably make no sense.""" + ) + + return super().generate( + input_ids, + generation_config, + logits_processor, + stopping_criteria, + prefix_allowed_tokens_fn, + synced_gpus, + decoder_input_ids=text_decoder_input_ids, + **kwargs, + ) + + +@auto_docstring( + custom_intro=""" + The speech-to-text SeamlessM4T Model transformer which can be used for S2TT. + """ +) +class SeamlessM4TForSpeechToText(SeamlessM4TPreTrainedModel, GenerationMixin): + input_modalities = "audio" + _keys_to_ignore_on_load_missing = ["text_encoder", "t2u_model", "vocoder"] + main_input_name = "input_features" + + _tied_weights_keys = { + "lm_head.weight": "shared.weight", + "text_decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: SeamlessM4TConfig): + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + self.speech_encoder = SeamlessM4TSpeechEncoder(config) + self.text_decoder = SeamlessM4TDecoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.speech_encoder + + def get_decoder(self): + return self.text_decoder + + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + def set_input_embeddings(self, value): + self.text_decoder.embed_tokens = value + + @auto_docstring(custom_args=SEAMLESS_M4T_COMMON_CUSTOM_ARGS) + def forward( + self, + input_features: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + encoder_outputs = self.speech_encoder( + input_features=input_features, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_outputs[0].device + ) + encoder_attention_mask = _compute_new_attention_mask( + hidden_states=encoder_outputs[0], seq_lens=sub_sampled_lengths + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + def generate( + self, + input_features=None, + tgt_lang=None, + generation_config=None, + logits_processor=None, + stopping_criteria=None, + prefix_allowed_tokens_fn=None, + synced_gpus=False, + **kwargs, + ): + """ + Generates sequences of token ids. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Parameters: + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`): + Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the + [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details. + + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + logits_processor (`LogitsProcessorList`, *optional*): + Custom logits processors that complement the default logits processors built from arguments and + generation config. If a logit processor is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + stopping_criteria (`StoppingCriteriaList`, *optional*): + Custom stopping criteria that complement the default stopping criteria built from arguments and a + generation config. If a stopping criteria is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], list[int]]`, *optional*): + If provided, this function constraints the beam search to allowed tokens only at each step. If not + provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and + `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned + on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful + for constrained generation conditioned on the prefix, as described in [Autoregressive Entity + Retrieval](https://huggingface.co/papers/2010.00904). + synced_gpus (`bool`, *optional*, defaults to `False`): + Whether to continue running the while loop until max_length (needed to avoid deadlocking with + `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). + kwargs (`dict[str, Any]`, *optional*): + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. + + Return: + [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` + or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. The possible + [`~utils.ModelOutput`] types are: + - [`~generation.GenerateEncoderDecoderOutput`], + - [`~generation.GenerateBeamEncoderDecoderOutput`] + """ + text_decoder_input_ids = kwargs.pop("decoder_input_ids", None) + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + input_features = input_features if input_features is not None else kwargs.pop("inputs") + if tgt_lang is not None: + inputs = kwargs.get("inputs_embeds") if input_features is None else input_features + inputs = ( + inputs + if inputs is not None + else kwargs.get("encoder_outputs", {"last_hidden_state": None})["last_hidden_state"] + ) + batch_size = len(inputs) + + if hasattr(self.generation_config, "text_decoder_lang_to_code_id"): + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + if tgt_lang not in self.generation_config.text_decoder_lang_to_code_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. Please specify a `tgt_lang` in + {", ".join(self.generation_config.text_decoder_lang_to_code_id.keys())}""" + ) + # tgt_lang gets priority over decoder input ids + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + else: + raise ValueError( + """This model generation config doesn't have a `text_decoder_lang_to_code_id` key which maps + the target language to the right token id. Make sure to load the right generation config.""" + ) + else: + # only a warning, otherwise errors appear in the tests + logger.warning( + """You must either specify a `tgt_lang` or pass a correct `text_decoder_input_ids` to get + a correct generation, otherwise the generation will probably make no sense.""" + ) + return super().generate( + input_features, + generation_config, + logits_processor, + stopping_criteria, + prefix_allowed_tokens_fn, + synced_gpus, + decoder_input_ids=text_decoder_input_ids, + **kwargs, + ) + + +@auto_docstring( + custom_intro=""" + The text-to-speech SeamlessM4T Model transformer which can be used for T2ST. + """ +) +class SeamlessM4TForTextToSpeech(SeamlessM4TPreTrainedModel, GenerationMixin): + output_modalities = ("audio",) + _keys_to_ignore_on_load_missing = ["speech_encoder"] + main_input_name = "input_ids" + + _tied_weights_keys = { + "lm_head.weight": "shared.weight", + "text_encoder.embed_tokens.weight": "shared.weight", + "text_decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: SeamlessM4TConfig): + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + + self.text_encoder = SeamlessM4TEncoder(config) + self.text_decoder = SeamlessM4TDecoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.t2u_model = SeamlessM4TTextToUnitForConditionalGeneration(config) + self.vocoder = SeamlessM4TCodeHifiGan(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.text_encoder + + def get_decoder(self): + return self.text_decoder + + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + def set_input_embeddings(self, value): + self.text_encoder.embed_tokens = value + self.text_decoder.embed_tokens = value + self.shared = value + + @auto_docstring(custom_args=SEAMLESS_M4T_COMMON_CUSTOM_ARGS) + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + # if encoder_outputs is not None, it's probably used within a .generate method so no need to warn + logger.warning( + "This is the same forward method as `SeamlessM4TForTextToText`." + "It doesn't use the text-to-unit model `SeamlessM4TTextToUnitForConditionalGeneration`." + "If you want to generate speech, use the `.generate` method." + ) + encoder_outputs = self.text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_ids: torch.Tensor | None = None, + return_intermediate_token_ids: bool | None = None, + tgt_lang: str | None = None, + spkr_id: int | None = 0, + **kwargs, + ) -> torch.Tensor | SeamlessM4TGenerationOutput: + """ + Generates translated audio waveforms. + + + + This method successively calls the `.generate` function of two different sub-models. You can specify keyword + arguments at two different levels: general arguments that will be passed to both models, or prefixed arguments + that will be passed to one of them. + + For example, calling `.generate(input_ids, num_beams=4, speech_do_sample=True)` will successively perform + beam-search decoding on the text model, and multinomial beam-search sampling on the speech model. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`SeamlessM4TTokenizer`] or [`SeamlessM4TProcessor`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + return_intermediate_token_ids (`bool`, *optional*): + If `True`, also returns the intermediate generated text and unit tokens. Set to `True` if you also want + to get translated text alongside the audio. + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + spkr_id (`int`, *optional*, defaults to 0): + The id of the speaker used for speech synthesis. Must be lower than `config.vocoder_num_spkrs`. + kwargs (*optional*): + Remaining dictionary of keyword arguments that will be passed to [`GenerationMixin.generate`]. Keyword + arguments are of two types: + + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model, + except for `decoder_input_ids` which will only be passed through the text components. + - With a *text_* or *speech_* prefix, they will be input for the `generate` method of the + text model and speech model respectively. It has the priority over the keywords without a prefix. + + This means you can, for example, specify a generation strategy for one generation but not for the + other. + + + Returns: + `Union[SeamlessM4TGenerationOutput, tuple[Tensor]]`: + - If `return_intermediate_token_ids`, returns [`SeamlessM4TGenerationOutput`]. + - If not `return_intermediate_token_ids`, returns a tuple composed of waveforms of shape `(batch_size, + sequence_length)` and `waveform_lengths` which gives the length of each sample. + """ + batch_size = len(input_ids) if input_ids is not None else len(kwargs.get("inputs_embeds")) + + if tgt_lang is None: + raise ValueError("You must specify a `tgt_lang` to generate translated speech.") + else: + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + for key in ["text_decoder_lang_to_code_id", "t2u_lang_code_to_id", "vocoder_lang_code_to_id"]: + lang_code_to_id = getattr(self.generation_config, key, None) + if lang_code_to_id is None: + raise ValueError( + f"""This model generation config doesn't have a `{key}` key which maps the target language + to the right token id. Make sure to load the right generation config.""" + ) + elif tgt_lang not in lang_code_to_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. + Please specify a `tgt_lang` in {",".join(lang_code_to_id.keys())}. Note that SeamlessM4T supports + more languages for text translation than for speech synthesis.""" + ) + + kwargs_text, kwargs_speech = format_speech_generation_kwargs(kwargs) + kwargs_text["output_hidden_states"] = True + kwargs_text["return_dict_in_generate"] = True + kwargs_text["output_scores"] = True + + text_decoder_input_ids = kwargs_text.get("decoder_input_ids") + + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + + kwargs_text["decoder_input_ids"] = text_decoder_input_ids + + # first generation + text_generation_output = super().generate(input_ids, **kwargs_text) + sequences = text_generation_output.sequences + + # prepare second generation + num_return_sequences = len(sequences) // batch_size + attention_mask = kwargs_speech.get("attention_mask", kwargs_text.get("attention_mask", None)) + + encoder_hidden_states = text_generation_output.encoder_hidden_states[-1] + + # take care of num_return_sequences + # take most probable hidden states per batch of return_sequences + # (batch_size*num_return_sequences, ...) -> (batch_size,...) + if num_return_sequences > 1: + idx_most_probable_sequences_per_batch = text_generation_output.sequences_scores.view(batch_size, -1) + idx_most_probable_sequences_per_batch = idx_most_probable_sequences_per_batch.argmax(-1) + idx_most_probable_sequences_per_batch = ( + idx_most_probable_sequences_per_batch + + torch.arange(batch_size, device=self.device) * num_return_sequences + ) + sequences = sequences[idx_most_probable_sequences_per_batch] + + # get decoder last hidden state - must do a pass through the text decoder + t2u_input_embeds = self.text_decoder( + input_ids=sequences, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=attention_mask, + ).last_hidden_state + + pad_token_id = self.generation_config.pad_token_id + + # Compute new attention mask + seq_lens = (sequences != pad_token_id).int().sum(1) + t2u_model_attention_mask = _compute_new_attention_mask(t2u_input_embeds, seq_lens) + kwargs_speech["attention_mask"] = t2u_model_attention_mask + + # Compute t2u decoder_input_ids + t2u_decoder_input_ids = kwargs_speech.get("decoder_input_ids") + t2u_tgt_lang_id = self.generation_config.t2u_lang_code_to_id.get(tgt_lang) + t2u_decoder_input_ids = torch.tensor( + [[self.config.t2u_eos_token_id, t2u_tgt_lang_id]] * batch_size, device=self.device + ) + kwargs_speech["decoder_input_ids"] = t2u_decoder_input_ids + # second generation + unit_ids = self.t2u_model.generate(inputs_embeds=t2u_input_embeds, **kwargs_speech) + output_unit_ids = unit_ids.detach().clone() + + # get rid of t2u_decoder_input_ids + unit_ids = unit_ids[:, kwargs_speech["decoder_input_ids"].shape[1] :] + # replace eos per pad + unit_ids[unit_ids == self.config.t2u_eos_token_id] = self.config.t2u_pad_token_id + # offset of control symbols + unit_ids = torch.where( + unit_ids == self.config.t2u_pad_token_id, unit_ids, unit_ids - self.config.vocoder_offset + ) + + vocoder_tgt_lang_id = self.generation_config.vocoder_lang_code_to_id.get(tgt_lang) + vocoder_tgt_lang_id = torch.tensor([[vocoder_tgt_lang_id]] * len(unit_ids), device=self.device) + + spkr_id = torch.tensor([[spkr_id]] * len(unit_ids), device=self.device) + + waveform, waveform_lengths = self.vocoder(input_ids=unit_ids, spkr_id=spkr_id, lang_id=vocoder_tgt_lang_id) + + if return_intermediate_token_ids: + return SeamlessM4TGenerationOutput( + waveform=waveform, + waveform_lengths=waveform_lengths, + sequences=sequences, + unit_sequences=output_unit_ids, + ) + + return waveform, waveform_lengths + + +@auto_docstring( + custom_intro=""" + The speech-to-speech SeamlessM4T Model transformer which can be used for S2ST. + """ +) +class SeamlessM4TForSpeechToSpeech(SeamlessM4TPreTrainedModel, GenerationMixin): + input_modalities = "audio" + output_modalities = ("audio",) + _keys_to_ignore_on_load_missing = ["text_encoder"] + main_input_name = "input_features" + + _tied_weights_keys = {"lm_head.weight": "shared.weight", "text_decoder.embed_tokens.weight": "shared.weight"} + + def __init__(self, config): + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + self.speech_encoder = SeamlessM4TSpeechEncoder(config) + self.text_decoder = SeamlessM4TDecoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.t2u_model = SeamlessM4TTextToUnitForConditionalGeneration(config) + self.vocoder = SeamlessM4TCodeHifiGan(config) + self.post_init() + + def get_encoder(self): + return self.speech_encoder + + def get_decoder(self): + return self.text_decoder + + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + def set_input_embeddings(self, value): + self.text_decoder.embed_tokens = value + + @auto_docstring(custom_args=SEAMLESS_M4T_COMMON_CUSTOM_ARGS) + def forward( + self, + input_features: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + # if encoder_outputs is not None, it's probably used within a .generate method so no need to warn + logger.warning( + "This is the same forward method as `SeamlessM4TForSpeechToText`. It doesn't use `self.t2u_model`." + "If you want to generate speech, use the `generate` method." + ) + + encoder_outputs = self.speech_encoder( + input_features=input_features, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_outputs[0].device + ) + encoder_attention_mask = _compute_new_attention_mask( + hidden_states=encoder_outputs[0], seq_lens=sub_sampled_lengths + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_features: torch.Tensor | None = None, + return_intermediate_token_ids: bool | None = None, + tgt_lang: str | None = None, + spkr_id: int | None = 0, + **kwargs, + ) -> torch.Tensor | SeamlessM4TGenerationOutput: + """ + Generates translated audio waveforms. + + + + This method successively calls the `.generate` function of two different sub-models. You can specify keyword + arguments at two different levels: general arguments that will be passed to both models, or prefixed arguments + that will be passed to one of them. + + For example, calling `.generate(input_features, num_beams=4, speech_do_sample=True)` will successively perform + beam-search decoding on the text model, and multinomial beam-search sampling on the speech model. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Args: + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`): + Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the + [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details. + return_intermediate_token_ids (`bool`, *optional*): + If `True`, also returns the intermediate generated text and unit tokens. Set to `True` if you also want + to get translated text alongside the audio. + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + spkr_id (`int`, *optional*, defaults to 0): + The id of the speaker used for speech synthesis. Must be lower than `config.vocoder_num_spkrs`. + + kwargs (*optional*): + Remaining dictionary of keyword arguments that will be passed to [`GenerationMixin.generate`]. Keyword + arguments are of two types: + + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model, + except for `decoder_input_ids` which will only be passed through the text components. + - With a *text_* or *speech_* prefix, they will be input for the `generate` method of the + text model and speech model respectively. It has the priority over the keywords without a prefix. + + This means you can, for example, specify a generation strategy for one generation but not for the + other. + + + Returns: + `Union[SeamlessM4TGenerationOutput, tuple[Tensor]]`: + - If `return_intermediate_token_ids`, returns [`SeamlessM4TGenerationOutput`]. + - If not `return_intermediate_token_ids`, returns a tuple composed of waveforms of shape `(batch_size, + sequence_length)` and `waveform_lengths` which gives the length of each sample. + """ + batch_size = len(input_features) if input_features is not None else len(kwargs.get("inputs_embeds")) + + if tgt_lang is None: + raise ValueError("You must specify a `tgt_lang` to generate translated speech.") + else: + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + for key in ["text_decoder_lang_to_code_id", "t2u_lang_code_to_id", "vocoder_lang_code_to_id"]: + lang_code_to_id = getattr(self.generation_config, key, None) + if lang_code_to_id is None: + raise ValueError( + f"""This model generation config doesn't have a `{key}` key which maps the target language + to the right token id. Make sure to load the right generation config.""" + ) + elif tgt_lang not in lang_code_to_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. + Please specify a `tgt_lang` in {",".join(lang_code_to_id.keys())}. Note that SeamlessM4T supports + more languages for text translation than for speech synthesis.""" + ) + + kwargs_text, kwargs_speech = format_speech_generation_kwargs(kwargs) + kwargs_text["output_hidden_states"] = True + kwargs_text["return_dict_in_generate"] = True + kwargs_text["output_scores"] = True + + text_decoder_input_ids = kwargs_text.get("decoder_input_ids") + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + + kwargs_text["decoder_input_ids"] = text_decoder_input_ids + + # first generation + text_generation_output = super().generate(input_features, **kwargs_text) + sequences = text_generation_output.sequences + + # prepare second generation + num_return_sequences = len(sequences) // batch_size + attention_mask = kwargs_speech.get("attention_mask", kwargs_text.get("attention_mask", None)) + + # get last_hidden_state from encoder + encoder_hidden_states = self.speech_encoder(input_features=input_features, attention_mask=attention_mask)[0] + + # input modality = speech so new attention mask for the decoder + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_hidden_states.device + ) + attention_mask = _compute_new_attention_mask( + hidden_states=encoder_hidden_states, seq_lens=sub_sampled_lengths + ) + + # take care of num_return_sequences + # take most probable hidden states per batch of return_sequences + # (batch_size*num_return_sequences, ...) -> (batch_size,...) + if num_return_sequences > 1: + idx_most_probable_sequences_per_batch = text_generation_output.sequences_scores.view(batch_size, -1) + idx_most_probable_sequences_per_batch = idx_most_probable_sequences_per_batch.argmax(-1) + idx_most_probable_sequences_per_batch = ( + idx_most_probable_sequences_per_batch + + torch.arange(batch_size, device=self.device) * num_return_sequences + ) + sequences = sequences[idx_most_probable_sequences_per_batch] + + # get decoder last hidden state - must do a pass through the text decoder + t2u_input_embeds = self.text_decoder( + input_ids=sequences, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=attention_mask, + ).last_hidden_state + + pad_token_id = self.generation_config.pad_token_id + + # Compute new attention mask + seq_lens = (sequences != pad_token_id).int().sum(1) + t2u_model_attention_mask = _compute_new_attention_mask(t2u_input_embeds, seq_lens) + kwargs_speech["attention_mask"] = t2u_model_attention_mask + + # Compute t2u decoder_input_ids + t2u_decoder_input_ids = kwargs_speech.get("decoder_input_ids") + t2u_tgt_lang_id = self.generation_config.t2u_lang_code_to_id.get(tgt_lang) + t2u_decoder_input_ids = torch.tensor( + [[self.config.t2u_eos_token_id, t2u_tgt_lang_id]] * batch_size, device=self.device + ) + kwargs_speech["decoder_input_ids"] = t2u_decoder_input_ids + + # second generation + unit_ids = self.t2u_model.generate(inputs_embeds=t2u_input_embeds, **kwargs_speech) + output_unit_ids = unit_ids.detach().clone() + + # get rid of t2u_decoder_input_ids + unit_ids = unit_ids[:, kwargs_speech["decoder_input_ids"].shape[1] :] + # replace eos per pad + unit_ids[unit_ids == self.config.t2u_eos_token_id] = self.config.t2u_pad_token_id + # offset of control symbols + unit_ids = torch.where( + unit_ids == self.config.t2u_pad_token_id, unit_ids, unit_ids - self.config.vocoder_offset + ) + + vocoder_tgt_lang_id = self.generation_config.vocoder_lang_code_to_id.get(tgt_lang) + vocoder_tgt_lang_id = torch.tensor([[vocoder_tgt_lang_id]] * len(unit_ids), device=self.device) + + spkr_id = torch.tensor([[spkr_id]] * len(unit_ids), device=self.device) + + waveform, waveform_lengths = self.vocoder(input_ids=unit_ids, spkr_id=spkr_id, lang_id=vocoder_tgt_lang_id) + + if return_intermediate_token_ids: + return SeamlessM4TGenerationOutput( + waveform=waveform, + waveform_lengths=waveform_lengths, + sequences=sequences, + unit_sequences=output_unit_ids, + ) + + return waveform, waveform_lengths + + +@auto_docstring( + custom_intro=""" + The original SeamlessM4T Model transformer which can be used for every tasks available (S2ST, S2TT, T2TT, T2ST). + """ +) +class SeamlessM4TModel(SeamlessM4TPreTrainedModel, GenerationMixin): + input_modalities = ("audio", "text") + output_modalities = ("audio", "text") + _tied_weights_keys = { + "lm_head.weight": "shared.weight", + "text_encoder.embed_tokens.weight": "shared.weight", + "text_decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config, current_modality="text"): + r""" + current_modality (`str`, *optional*, defaults to `"text"`): + Default modality. Used to initialize the model. + """ + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + + self.text_encoder = SeamlessM4TEncoder(config) + self.speech_encoder = SeamlessM4TSpeechEncoder(config) + self.text_decoder = SeamlessM4TDecoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.current_modality = current_modality + if current_modality == "speech": + self.main_input_name = "input_features" + + # these models already call post_init in their initialization + self.t2u_model = SeamlessM4TTextToUnitForConditionalGeneration(config) + self.vocoder = SeamlessM4TCodeHifiGan(config) + + # Initialize weights and apply final processing + self.post_init() + + def set_modality(self, modality="text"): + if modality == "text": + self.main_input_name = "input_ids" + self.current_modality = "text" + elif modality == "speech": + self.main_input_name = "input_features" + self.current_modality = "speech" + else: + raise ValueError(f"`modality={modality}` is not a valid modality. It must be `text` or `speech`.") + + def get_encoder(self): + if self.current_modality == "text": + return self.text_encoder + else: + return self.speech_encoder + + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + def set_input_embeddings(self, value): + self.text_encoder.embed_tokens = value + self.text_decoder.embed_tokens = value + self.shared = value + + @auto_docstring(custom_args=SEAMLESS_M4T_COMMON_CUSTOM_ARGS) + def forward( + self, + input_ids: torch.LongTensor | None = None, + input_features: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + if input_ids is None and input_features is None and inputs_embeds is None and encoder_outputs is None: + raise ValueError( + "`input_ids`,`input_features`, `inputs_embeds` and `encoder_outputs` are all empty. Make sure at least one of them is not." + ) + elif input_features is not None: + if input_ids is not None: + logger.warning( + "`input_ids` is not `None` but `input_features` has been given." + "`input_features` will be used in priority through the `speech_encoder`. " + "Make sure that `input_features` and `input_ids` are mutually exclusive." + ) + + if inputs_embeds is not None: + logger.warning( + "`inputs_embeds` is not `None` but `input_features` has been given." + "`input_features` will be used in priority through `speech_encoder`. " + "`inputs_embeds` will be ignored." + ) + + # if encoder_outputs is not None, it's probably used within a .generate method so no need to warn + logger.warning( + "This calls the same method `forward` as `SeamlessM4TForTextToText` and `SeamlessM4TForSpeechToText`" + "depending on the input modality. If you want to generate speech, use the `generate` method." + ) + + self.set_modality("speech") + + encoder_outputs = self.speech_encoder( + input_features=input_features, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + elif input_ids is not None or inputs_embeds is not None: + # if encoder_outputs is not None, it's probably used within a .generate method so no need to warn + logger.warning( + "This calls the same method `forward` as `SeamlessM4TForTextToText` and `SeamlessM4TForSpeechToText`" + "depending on the input modality. If you want to generate speech, use the `generate` method." + ) + self.set_modality("text") + encoder_outputs = self.text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + # input modality = speech so new attention mask + if self.current_modality == "speech" and attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_outputs[0].device + ) + encoder_attention_mask = _compute_new_attention_mask( + hidden_states=encoder_outputs[0], seq_lens=sub_sampled_lengths + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_ids: torch.Tensor | None = None, + input_features: torch.Tensor | None = None, + return_intermediate_token_ids: bool | None = None, + tgt_lang: str | None = None, + spkr_id: int | None = 0, + generate_speech: bool | None = True, + **kwargs, + ) -> torch.Tensor | SeamlessM4TGenerationOutput: + """ + Generates translated token ids and/or translated audio waveforms. + + + + This method successively calls the `.generate` function of two different sub-models. You can specify keyword + arguments at two different levels: general arguments that will be passed to both models, or prefixed arguments + that will be passed to one of them. + + For example, calling `.generate(input_ids=input_ids, num_beams=4, speech_do_sample=True)` will successively + perform beam-search decoding on the text model, and multinomial beam-search sampling on the speech model. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`SeamlessM4TTokenizer`] or [`SeamlessM4TProcessor`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`, *optional*): + Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the + [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details. + return_intermediate_token_ids (`bool`, *optional*): + If `True`, also returns the intermediate generated text and unit tokens. Set to `True` if you also want + to get translated text alongside the audio. + Note that if `generate_speech=False`, this parameter will be ignored and + the text tokens are returned. + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + spkr_id (`int`, *optional*, defaults to 0): + The id of the speaker used for speech synthesis. Must be lower than `config.vocoder_num_spkrs`. + generate_speech (`bool`, *optional*, defaults to `True`): + If `False`, will only returns the text tokens and won't generate speech. + + kwargs (*optional*): + Remaining dictionary of keyword arguments that will be passed to [`GenerationMixin.generate`]. Keyword + arguments are of two types: + + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model, + except for `decoder_input_ids` which will only be passed through the text components. + - With a *text_* or *speech_* prefix, they will be input for the `generate` method of the + text model and speech model respectively. It has the priority over the keywords without a prefix. + + This means you can, for example, specify a generation strategy for one generation but not for the + other. + + Returns: + `Union[SeamlessM4TGenerationOutput, tuple[Tensor], ModelOutput]`: + - If `generate_speech` and `return_intermediate_token_ids`, returns [`SeamlessM4TGenerationOutput`]. + - If `generate_speech` and not `return_intermediate_token_ids`, returns a tuple composed of waveforms of + shape `(batch_size, sequence_length)` and `waveform_lengths` which gives the length of each sample. + - If `generate_speech=False`, it will returns `ModelOutput`. + """ + if input_ids is None and input_features is None and kwargs.get("inputs_embeds") is None: + raise ValueError( + "`input_ids`,`input_features` and `inputs_embeds` are all empty. Make sure at least one of them is not." + ) + + if generate_speech and tgt_lang is None: + raise ValueError("You must specify a `tgt_lang` to generate translated speech.") + + if tgt_lang is not None: + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + for key in ["text_decoder_lang_to_code_id", "t2u_lang_code_to_id", "vocoder_lang_code_to_id"]: + lang_code_to_id = getattr(self.generation_config, key, None) + if lang_code_to_id is None: + raise ValueError( + f"""This model generation config doesn't have a `{key}` key which maps the target language + to the right token id. Make sure to load the right generation config.""" + ) + elif tgt_lang not in lang_code_to_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. + Please specify a `tgt_lang` in {",".join(lang_code_to_id.keys())}. Note that SeamlessM4T supports + more languages for text translation than for speech synthesis.""" + ) + + batch_size = ( + len(input_features) + if input_features is not None + else (len(input_ids) if input_ids is not None else len(kwargs.get("inputs_embeds"))) + ) + + kwargs_text, kwargs_speech = format_speech_generation_kwargs(kwargs) + kwargs_text["output_hidden_states"] = True + kwargs_text["return_dict_in_generate"] = True + kwargs_text["output_scores"] = True + + text_decoder_input_ids = kwargs_text.get("decoder_input_ids") + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + if tgt_lang is not None: + # tgt_lang gets priority over decoder input ids + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + + kwargs_text["decoder_input_ids"] = text_decoder_input_ids + + # first generation + if input_features is not None: + self.set_modality("speech") + if input_ids is not None: + logger.warning( + "`input_features` and `input_ids` are both non empty. `input_features` will be used in priority " + "through the speech encoder. Make sure `input_features=None` if you want to use the text encoder." + ) + text_generation_output = super().generate(input_features=input_features, **kwargs_text) + else: + self.set_modality("text") + text_generation_output = super().generate(input_ids=input_ids, input_features=None, **kwargs_text) + sequences = text_generation_output.sequences + + if not generate_speech: + return text_generation_output + + # prepare second generation + num_return_sequences = len(sequences) // batch_size + attention_mask = kwargs_speech.get("attention_mask", kwargs_text.get("attention_mask", None)) + + # get encoder last hidden states + if self.current_modality == "speech": + # get last_hidden_state from encoder - must do a pass through the speech encoder + encoder_hidden_states = self.speech_encoder( + input_features=input_features, attention_mask=attention_mask + ).last_hidden_state + + # input modality = speech so new attention mask for the decoder + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_hidden_states.device + ) + attention_mask = _compute_new_attention_mask( + hidden_states=encoder_hidden_states, seq_lens=sub_sampled_lengths + ) + else: + encoder_hidden_states = text_generation_output.encoder_hidden_states[-1] + + # take care of num_return_sequences + # take most probable hidden states per batch of return_sequences + # (batch_size*num_return_sequences, ...) -> (batch_size,...) + if num_return_sequences > 1: + idx_most_probable_sequences_per_batch = text_generation_output.sequences_scores.view(batch_size, -1) + idx_most_probable_sequences_per_batch = idx_most_probable_sequences_per_batch.argmax(-1) + idx_most_probable_sequences_per_batch = ( + idx_most_probable_sequences_per_batch + + torch.arange(batch_size, device=self.device) * num_return_sequences + ) + sequences = sequences[idx_most_probable_sequences_per_batch] + + # get decoder last hidden state - must do a pass through the text decoder + t2u_input_embeds = self.text_decoder( + input_ids=sequences, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=attention_mask, + ).last_hidden_state + + pad_token_id = self.generation_config.pad_token_id + + # Compute new attention mask + seq_lens = (sequences != pad_token_id).int().sum(1) + t2u_model_attention_mask = _compute_new_attention_mask(t2u_input_embeds, seq_lens) + kwargs_speech["attention_mask"] = t2u_model_attention_mask + + # Compute t2u decoder_input_ids + t2u_decoder_input_ids = kwargs_speech.get("decoder_input_ids") + t2u_tgt_lang_id = self.generation_config.t2u_lang_code_to_id.get(tgt_lang) + t2u_decoder_input_ids = torch.tensor( + [[self.config.t2u_eos_token_id, t2u_tgt_lang_id]] * batch_size, device=self.device + ) + kwargs_speech["decoder_input_ids"] = t2u_decoder_input_ids + + # second generation + unit_ids = self.t2u_model.generate(inputs_embeds=t2u_input_embeds, **kwargs_speech) + output_unit_ids = unit_ids.detach().clone() + + # get rid of t2u_decoder_input_ids + unit_ids = unit_ids[:, kwargs_speech["decoder_input_ids"].shape[1] :] + # replace eos per pad + unit_ids[unit_ids == self.config.t2u_eos_token_id] = self.config.t2u_pad_token_id + # offset of control symbols + unit_ids = torch.where( + unit_ids == self.config.t2u_pad_token_id, unit_ids, unit_ids - self.config.vocoder_offset + ) + + vocoder_tgt_lang_id = self.generation_config.vocoder_lang_code_to_id.get(tgt_lang) + vocoder_tgt_lang_id = torch.tensor([[vocoder_tgt_lang_id]] * len(unit_ids), device=self.device) + + spkr_id = torch.tensor([[spkr_id]] * len(unit_ids), device=self.device) + + waveform, waveform_lengths = self.vocoder(input_ids=unit_ids, spkr_id=spkr_id, lang_id=vocoder_tgt_lang_id) + + if return_intermediate_token_ids: + return SeamlessM4TGenerationOutput( + waveform=waveform, + waveform_lengths=waveform_lengths, + sequences=sequences, + unit_sequences=output_unit_ids, + ) + + return waveform, waveform_lengths + + +__all__ = [ + "SeamlessM4TForTextToSpeech", + "SeamlessM4TForSpeechToSpeech", + "SeamlessM4TForTextToText", + "SeamlessM4TForSpeechToText", + "SeamlessM4TModel", + "SeamlessM4TPreTrainedModel", + "SeamlessM4TCodeHifiGan", + "SeamlessM4THifiGan", + "SeamlessM4TTextToUnitForConditionalGeneration", + "SeamlessM4TTextToUnitModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/processing_seamless_m4t.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/processing_seamless_m4t.py new file mode 100644 index 0000000000000000000000000000000000000000..21b5d253a4f08d2cd5f75f9af34417952dae93d4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/processing_seamless_m4t.py @@ -0,0 +1,78 @@ +# Copyright 2023 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Audio/Text processor class for SeamlessM4T +""" + +from ...audio_utils import AudioInput +from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack +from ...tokenization_utils_base import PreTokenizedInput, TextInput +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +class SeamlessM4TTextKwargs(TextKwargs): + """ + src_lang (`str`, *optional*): + The source language code for the input text (e.g., "eng" for English, "fra" for French). This is used + to set the language token at the beginning of the input sequence, which helps the model understand the + input language for translation or transcription tasks. + tgt_lang (`str`, *optional*): + The target language code for the output (e.g., "eng" for English, "fra" for French). This is used to + specify the desired output language for translation tasks. The model will generate text in this language. + """ + + src_lang: str | None + tgt_lang: str | None + + +class SeamlessM4TProcessorKwargs(ProcessingKwargs, total=False): + text_kwargs: SeamlessM4TTextKwargs + _defaults = {} + + +@auto_docstring +class SeamlessM4TProcessor(ProcessorMixin): + valid_processor_kwargs = SeamlessM4TProcessorKwargs + + def __init__(self, feature_extractor, tokenizer): + super().__init__(feature_extractor, tokenizer) + + @auto_docstring + def __call__( + self, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + audio: AudioInput | None = None, + **kwargs: Unpack[ProcessingKwargs], + ): + r""" + Returns: + [`BatchEncoding`]: A [`BatchEncoding`] with the following fields: + + - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. + - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when + `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not + `None`). + - **input_features** -- Audio input features to be fed to a model. Returned when `audios` is not `None`. + """ + if text is not None and audio is not None: + raise ValueError( + "Text and audios are mututally exclusive when passed to `SeamlessM4T`. Specify one or another." + ) + return super().__call__(text=text, audio=audio, **kwargs) + + +__all__ = ["SeamlessM4TProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/tokenization_seamless_m4t.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/tokenization_seamless_m4t.py new file mode 100644 index 0000000000000000000000000000000000000000..863f91070c5de05250b6dbc2aaa75cfde8a8e736 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t/tokenization_seamless_m4t.py @@ -0,0 +1,427 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for SeamlessM4T.""" + +from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors +from tokenizers.models import BPE + +from ...tokenization_python import ( + BatchEncoding, + PreTokenizedInput, + TextInput, +) +from ...tokenization_utils_tokenizers import TokenizersBackend +from ...utils import PaddingStrategy, logging + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"} + + +class SeamlessM4TTokenizer(TokenizersBackend): + """ + Construct a SeamlessM4T tokenizer (backed by HuggingFace's *tokenizers* library). Based on + [BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=bpe#models). + + This tokenizer inherits from [`TokenizersBackend`] which contains most of the main methods. Users should + refer to this superclass for more information regarding those methods. + + The tokenization method is ` ` for source language documents, and ` ` for target language documents. + + Examples: + + ```python + >>> from transformers import SeamlessM4TTokenizer + + >>> tokenizer = SeamlessM4TTokenizer.from_pretrained( + ... "facebook/hf-seamless-m4t-medium", src_lang="eng", tgt_lang="fra" + ... ) + >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria" + >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie." + >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt") + ``` + + Args: + vocab (`list` or `dict`, *optional*): + List of (token, score) tuples or dict mapping tokens to indices. If not provided, uses default vocab. + merges (`str` or `list`, *optional*): + List of merge rules for BPE model. If not provided, uses empty list. + bos_token (`str`, *optional*, defaults to `""`): + The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. + + + + When building a sequence using special tokens, this is not the token that is used for the beginning of + sequence. The token used is the `cls_token`. + + + + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + + + + When building a sequence using special tokens, this is not the token that is used for the end of sequence. + The token used is the `sep_token`. + + + + sep_token (`str`, *optional*, defaults to `""`): + The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for + sequence classification or for a text and a question for question answering. It is also used as the last + token of a sequence built with special tokens. + cls_token (`str`, *optional*, defaults to `""`): + The classifier token which is used when doing sequence classification (classification of the whole sequence + instead of per-token classification). It is the first token of the sequence when built with special tokens. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + src_lang (`str`, *optional*, defaults to `"eng"`): + The language to use as source language for translation. + tgt_lang (`str`, *optional*, defaults to `"fra"`): + The language to use as target language for translation. + additional_special_tokens (tuple or list of `str` or `tokenizers.AddedToken`, *optional*): + A tuple or a list of additional special tokens. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + model = BPE + + prefix_tokens: list[int] = None + suffix_tokens: list[int] = None + + def __init__( + self, + vocab: str | dict[str, int] | None = None, + merges: str | list[str] | None = None, + bos_token="", + eos_token="", + sep_token="", + cls_token="", + unk_token="", + pad_token="", + src_lang="eng", + tgt_lang="fra", + additional_special_tokens=None, + keep_accents=None, + vocab_file=None, + **kwargs, + ): + self._vocab = vocab or { + str(pad_token): 0, + str(unk_token): 1, + str(bos_token): 2, + str(eos_token): 3, + } + + self._merges = merges or [] + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + dropout=None, + unk_token=str(unk_token), + fuse_unk=True, + byte_fallback=False, + ) + ) + + self._tokenizer.normalizer = normalizers.Sequence( + [ + normalizers.Replace(Regex(r"[\n\r\t]"), " "), + normalizers.NFKC(), + normalizers.Strip(left=False, right=True), + normalizers.Replace(Regex(r" +▁"), "▁"), + normalizers.Replace(Regex(r"^▁+$"), ""), + normalizers.Replace(Regex(r" {2,}"), "▁"), + ] + ) + + self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="first", split=True) + + self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="first", split=True) + + if "__" not in src_lang: + src_lang = f"__{src_lang}__" + if "__" not in tgt_lang: + tgt_lang = f"__{tgt_lang}__" + + # V5: Convert additional_special_tokens parameter to extra_special_tokens for backward compatibility + # PreTrainedTokenizerBase.__init__() will handle the conversion, but we need to pass it via kwargs + if additional_special_tokens is not None: + kwargs.setdefault("additional_special_tokens", additional_special_tokens) + + super().__init__( + bos_token=bos_token, + eos_token=eos_token, + sep_token=sep_token, + cls_token=cls_token, + unk_token=unk_token, + pad_token=pad_token, + src_lang=src_lang, + tgt_lang=tgt_lang, + keep_accents=keep_accents, + vocab_file=vocab_file, + **kwargs, + ) + + # Build fairseq mappings + self.fairseq_offset = 1 + self.fairseq_tokens_to_ids = { + "": 0, + "": 1, + "": 2, + "": 3, + } + self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()} + + self._src_lang = src_lang + self._tgt_lang = tgt_lang + + self.set_tgt_lang_special_tokens(self._tgt_lang) + + @classmethod + def convert_from_spm_model(cls, vocab, **kwargs): + """When converting from spm, offset is needed to account for special tokens.""" + _vocab = { + "": 0, + "": 1, + "": 2, + "": 3, + } + for i, token in enumerate(list(vocab.keys())): + _vocab[token] = i + 1 # offset by 1 to account for special tokens + kwargs["vocab"] = _vocab + return kwargs + + @property + def src_lang(self) -> str: + return self._src_lang + + @src_lang.setter + def src_lang(self, new_src_lang: str) -> None: + if "__" not in new_src_lang: + self._src_lang = f"__{new_src_lang}__" + else: + self._src_lang = new_src_lang + self.set_src_lang_special_tokens(self._src_lang) + + @property + def tgt_lang(self) -> str: + return self._tgt_lang + + @tgt_lang.setter + def tgt_lang(self, new_tgt_lang: str) -> None: + if "__" not in new_tgt_lang: + self._tgt_lang = f"__{new_tgt_lang}__" + else: + self._tgt_lang = new_tgt_lang + self.set_tgt_lang_special_tokens(self._tgt_lang) + + def _build_translation_inputs( + self, raw_inputs, return_tensors: str, src_lang: str | None, tgt_lang: str | None, **extra_kwargs + ): + """Used by translation pipeline, to prepare inputs for the generate function""" + if src_lang is None or tgt_lang is None: + raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model") + self.src_lang = src_lang + inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs) + if "__" not in tgt_lang: + tgt_lang = f"__{tgt_lang}__" + tgt_lang_id = self.convert_tokens_to_ids(tgt_lang) + inputs["forced_bos_token_id"] = tgt_lang_id + return inputs + + def prepare_seq2seq_batch( + self, + src_texts: list[str], + src_lang: str = "eng", + tgt_texts: list[str] | None = None, + tgt_lang: str = "fra", + max_length: int | None = None, + max_target_length: int | None = None, + padding: str = "longest", + return_tensors: str | None = None, + truncation: bool = True, + **kwargs, + ) -> BatchEncoding: + self.src_lang = src_lang + self.tgt_lang = tgt_lang + + if max_length is None: + max_length = self.model_max_length + + model_inputs = self( + src_texts, + add_special_tokens=True, + return_tensors=return_tensors, + max_length=max_length, + padding=padding, + truncation=truncation, + **kwargs, + ) + + if tgt_texts is None: + return model_inputs + + # Process tgt_texts + if max_target_length is None: + max_target_length = max_length + + self._switch_to_target_mode() + labels = self( + tgt_texts, + add_special_tokens=True, + return_tensors=return_tensors, + padding=padding, + max_length=max_target_length, + truncation=truncation, + **kwargs, + ) + model_inputs["labels"] = labels["input_ids"] + + self._switch_to_input_mode() + + return model_inputs + + def _switch_to_input_mode(self): + return self.set_src_lang_special_tokens(self.src_lang) + + def _switch_to_target_mode(self): + return self.set_tgt_lang_special_tokens(self.tgt_lang) + + def set_src_lang_special_tokens(self, src_lang) -> None: + """Reset the special tokens to the source lang setting. + Prefix=[src_lang_code], suffix = [eos] + """ + self.cur_lang_code = self.convert_tokens_to_ids(src_lang) + + if self.cur_lang_code == self.unk_token_id: + logger.warning_once( + f"`src_lang={src_lang}` has not be found in the `vocabulary`. Behaviour will probably be unexpected because the language token id will be replaced by the unknown token id." + ) + + self.prefix_tokens = [self.cur_lang_code] + self.suffix_tokens = [self.eos_token_id] + + prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens) + suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens) + + self._tokenizer.post_processor = processors.TemplateProcessing( + single=prefix_tokens_str + ["$A"] + suffix_tokens_str, + pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str, + special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)), + ) + + def set_tgt_lang_special_tokens(self, lang: str) -> None: + """Reset the special tokens to the target lang setting. + Prefix=[eos, tgt_lang_code] and suffix=[eos]. + """ + self.cur_lang_code = self.convert_tokens_to_ids(lang) + + if self.cur_lang_code == self.unk_token_id: + logger.warning_once( + f"`tgt_lang={lang}` has not be found in the `vocabulary`. Behaviour will probably be unexpected because the language token id will be replaced by the unknown token id." + ) + + self.prefix_tokens = [self.eos_token_id, self.cur_lang_code] + self.suffix_tokens = [self.eos_token_id] + + prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens) + suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens) + + self._tokenizer.post_processor = processors.TemplateProcessing( + single=prefix_tokens_str + ["$A"] + suffix_tokens_str, + pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str, + special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)), + ) + + def __call__( + self, + text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + text_pair: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + text_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + text_pair_target: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None, + padding: bool | str | PaddingStrategy = False, + pad_to_multiple_of: int | None = None, + src_lang: str | None = None, + tgt_lang: str | None = None, + **kwargs, + ): + """ + Args: + text (`str`, `list[str]`, `list[list[str]]`, *optional*): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + text_pair (`str`, `list[str]`, `list[list[str]]`, *optional*): + The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings + (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set + `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + text_target (`str`, `list[str]`, `list[list[str]]`, *optional*): + The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a + list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), + you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + text_pair_target (`str`, `list[str]`, `list[list[str]]`, *optional*): + The sequence or batch of sequences to be encoded as target texts. Each sequence can be a string or a + list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), + you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). + padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): + Select a strategy to pad the returned sequences (according to the model's padding side and padding + index) among: + + - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single + sequence if provided). + - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum + acceptable input length for the model if that argument is not provided. + - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different + lengths). + pad_to_multiple_of (`int`, *optional*, defaults to `None`): + If set will pad the sequence to a multiple of the provided value. + + This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability + `>= 7.5` (Volta). + src_lang (`str`, *optional*): + A string representing the source language. If not specified, the last `src_lang` specified (either + during initialization or when calling this tokenizer) will be used. + tgt_lang (`str`, *optional*): + A string representing the target language. If not specified, the last `tgt_lang` specified (either + during initialization or when calling this tokenizer) will be used. + kwargs (*optional*): + Remaining dictionary of keyword arguments that will be passed to [`TokenizersBackend.__call__`]. + """ + if src_lang is not None: + self.src_lang = src_lang + if tgt_lang is not None: + self.tgt_lang = tgt_lang + + output = super().__call__( + text=text, + text_pair=text_pair, + text_target=text_target, + text_pair_target=text_pair_target, + padding=padding, + pad_to_multiple_of=pad_to_multiple_of, + **kwargs, + ) + + return output + + +__all__ = ["SeamlessM4TTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..10f256f8575595d6b3409c030e1d6c3b33c75aa9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_seamless_m4t_v2 import * + from .modeling_seamless_m4t_v2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/configuration_seamless_m4t_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/configuration_seamless_m4t_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..30a7b887659975b908ac4e36a58e2cd7e45efbd4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/configuration_seamless_m4t_v2.py @@ -0,0 +1,242 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SeamlessM4Tv2 model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="facebook/hf-seamless-m4t-medium") +@strict +class SeamlessM4Tv2Config(PreTrainedConfig): + r""" + t2u_vocab_size (`int`, *optional*, defaults to 10082): + Unit vocabulary size of the SeamlessM4Tv2 model. Defines the number of different "unit tokens" that can be + represented by the `inputs_ids` passed when calling the Text-To-Units sub-model of [`~SeamlessM4Tv2Model`], + [`~SeamlessM4Tv2ForSpeechToSpeech`] or [`~SeamlessM4Tv2ForTextToSpeech`]. + char_vocab_size (`int`, *optional*, defaults to 10943): + Character vocabulary size of the SeamlessM4Tv2 model. Defines the number of different character tokens that + can be represented by the `char_inputs_ids` passed when calling the Text-To-Units sub-model of + [`~SeamlessM4Tv2Model`], [`~SeamlessM4Tv2ForSpeechToSpeech`] or [`~SeamlessM4Tv2ForTextToSpeech`]. + max_new_tokens (`int`, *optional*, defaults to 256): + The maximum numbers of text tokens to generate, ignoring the number of tokens in the prompt. + speech_encoder_layers (`int`, *optional*, defaults to 24): + Number of hidden layers in the Transformer speech encoder. + speech_encoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer speech encoder. + speech_encoder_intermediate_size (`int`, *optional*, defaults to 4096): + Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer speech encoder. + speech_encoder_hidden_act (`str` or `function`, *optional*, defaults to `"swish"`): + The non-linear activation function (function or string) in the speech encoder. If string, `"gelu"`, + `"relu"`, `"selu"`, `"swish"` and `"gelu_new"` are supported. + speech_encoder_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for all layers in the speech encoder. + add_adapter (`bool`, *optional*, defaults to `True`): + Add an adapter layer on top of the speech encoder. + speech_encoder_layerdrop (`float`, *optional*, defaults to 0.1): + The LayerDrop probability for the speech encoder. See the [LayerDrop paper](see + https://huggingface.co/papers/1909.11556) for more details. + feature_projection_input_dim (`int`, *optional*, defaults to 160): + Input dimension of the input feature projection of the speech encoder, i.e the dimension after processing + input audios with [`SeamlessM4TFeatureExtractor`]. + adaptor_kernel_size (`int`, *optional*, defaults to 8): + Kernel size of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. + adaptor_stride (`int`, *optional*, defaults to 8): + Stride of the convolutional layers in the adapter network. Only relevant if `add_adapter is True`. + adaptor_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all layers in the speech adapter. + num_adapter_layers (`int`, *optional*, defaults to 1): + Number of convolutional layers that should be used in the adapter network. Only relevant if `add_adapter is + True`. + position_embeddings_type (`str`, *optional*, defaults to `"relative_key"`): + Can be specified to `relative_key`. If left to `None`, no relative position embedding is applied. Only + applied to the speech encoder. For more information on `"relative_key"`, please refer to [Self-Attention + with Relative Position Representations (Shaw et al.)](https://huggingface.co/papers/1803.02155). + conv_depthwise_kernel_size (`int`, *optional*, defaults to 31): + Kernel size of convolutional depthwise 1D layer in Conformer blocks. Only applied to the speech encoder. + left_max_position_embeddings (`int`, *optional*, defaults to 64): + The left clipping value for relative positions. + right_max_position_embeddings (`int`, *optional*, defaults to 8): + The right clipping value for relative positions. + speech_encoder_chunk_size (`int`, *optional*, defaults to 20000): + The size of each attention chunk. + speech_encoder_left_chunk_num (`int`, *optional*, defaults to 128): + Number of chunks on the left up to which lookahead is allowed. + t2u_bos_token_id (`int`, *optional*, defaults to 0): + The id of the _beginning-of-stream_ unit token. Only applied to the text-to-unit seq2seq model. + t2u_pad_token_id (`int`, *optional*, defaults to 1): + The id of the _padding_ unit token. Only applied to the text-to-unit seq2seq model. + t2u_eos_token_id (`int`, *optional*, defaults to 2): + The id of the _end-of-stream_ unit token. Only applied to the text-to-unit seq2seq model. + t2u_encoder_layers (`int`, *optional*, defaults to 6): + Number of hidden layers in the Transformer text-to-unit encoder. + t2u_encoder_ffn_dim (`int`, *optional*, defaults to 8192): + Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text-to-unit encoder. + t2u_encoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer text-to-unit encoder. + t2u_decoder_layers (`int`, *optional*, defaults to 6): + Number of hidden layers in the Transformer text-to-unit decoder. + t2u_decoder_ffn_dim (`int`, *optional*, defaults to 8192): + Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer text-to-unit decoder. + t2u_decoder_attention_heads (`int`, *optional*, defaults to 16): + Number of attention heads for each attention layer in the Transformer text-to-unit decoder. + t2u_max_position_embeddings (`int`, *optional*, defaults to 4096): + The maximum sequence length that this model text-to-unit component might ever be used with. Typically set + this to something large just in case (e.g., 512 or 1024 or 2048). + t2u_variance_predictor_embed_dim (`int`, *optional*, defaults to 1024): + The projection dimension of the text-to-unit's duration predictor. + t2u_variance_predictor_hidden_dim (`int`, *optional*, defaults to 256): + Internal dimension of the text-to-unit's duration predictor. + t2u_variance_predictor_kernel_size (`int`, *optional*, defaults to 3): + Kernel size of the convolutional layers of the text-to-unit's duration predictor. + t2u_variance_pred_dropout (`float`, *optional*, defaults to 0.5): + The dropout probability of the text-to-unit's duration predictor. + upsample_initial_channel (`int`, *optional*, defaults to 512): + The number of input channels into the hifi-gan upsampling network. Applies to the vocoder only. + upsample_rates (`tuple[int]` or `list[int]`, *optional*, defaults to `[5, 4, 4, 2, 2]`): + A tuple of integers defining the stride of each 1D convolutional layer in the vocoder upsampling network. + The length of *upsample_rates* defines the number of convolutional layers and has to match the length of + *upsample_kernel_sizes*. Applies to the vocoder only. + upsample_kernel_sizes (`tuple[int]` or `list[int]`, *optional*, defaults to `[11, 8, 8, 4, 4]`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the vocoder upsampling + network. The length of *upsample_kernel_sizes* defines the number of convolutional layers and has to match + the length of *upsample_rates*. Applies to the vocoder only. + resblock_kernel_sizes (`tuple[int]` or `list[int]`, *optional*, defaults to `[3, 7, 11]`): + A tuple of integers defining the kernel sizes of the vocoder 1D convolutional layers in the multi-receptive + field fusion (MRF) module. Applies to the vocoder only. + resblock_dilation_sizes (`tuple[tuple[int]]` or `list[list[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): + A nested tuple of integers defining the dilation rates of the vocoder dilated 1D convolutional layers in + the multi-receptive field fusion (MRF) module. Applies to the vocoder only. + leaky_relu_slope (`float`, *optional*, defaults to 0.1): + The angle of the negative slope used by the leaky ReLU activation in the vocoder. Applies to the vocoder + only. + unit_hifi_gan_vocab_size (`int`, *optional*, defaults to 10000): + Vocabulary size of the SeamlessM4Tv2 vocoder. Defines the number of different unit tokens that can be + represented by the `inputs_ids` passed when calling the vocoder of [`~SeamlessM4Tv2Model`], + [`~SeamlessM4Tv2ForSpeechToSpeech`] or [`~SeamlessM4Tv2ForTextToSpeech`]. + unit_embed_dim (`int`, *optional*, defaults to 1280): + The projection dimension of the input ids given to the hifi-gan vocoder. Applies to the vocoder only. + lang_embed_dim (`int`, *optional*, defaults to 256): + The projection dimension of the target language given to the hifi-gan vocoder. Applies to the vocoder only. + spkr_embed_dim (`int`, *optional*, defaults to 256): + The projection dimension of the speaker id given to the hifi-gan vocoder. Applies to the vocoder only. + vocoder_num_langs (`int`, *optional*, defaults to 36): + Number of langs supported by the vocoder. Might be different from `t2u_num_langs`. + vocoder_num_spkrs (`int`, *optional*, defaults to 200): + Number of speakers supported by the vocoder. + variance_predictor_kernel_size (`int`, *optional*, defaults to 3): + Kernel size of the duration predictor. Applies to the vocoder only. + var_pred_dropout (`float`, *optional*, defaults to 0.5): + The dropout probability of the duration predictor. Applies to the vocoder only. + vocoder_offset (`int`, *optional*, defaults to 4): + Offset the unit token ids by this number to account for symbol tokens. Applies to the vocoder only. + + ```python + >>> from transformers import SeamlessM4Tv2Model, SeamlessM4Tv2Config + + >>> # Initializing a SeamlessM4Tv2 "" style configuration + >>> configuration = SeamlessM4Tv2Config() + + >>> # Initializing a model from the "" style configuration + >>> model = SeamlessM4Tv2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "seamless_m4t_v2" + attribute_map = {"num_hidden_layers": "decoder_layers", "num_attention_heads": "decoder_attention_heads"} + + vocab_size: int = 256102 + t2u_vocab_size: int = 10082 + char_vocab_size: int = 10943 + hidden_size: int = 1024 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-5 + use_cache: bool = True + max_position_embeddings: int = 4096 + is_encoder_decoder: bool = True + encoder_layerdrop: float | int = 0.05 + decoder_layerdrop: float | int = 0.05 + activation_function: str = "relu" + dropout: float | int = 0.1 + attention_dropout: float | int = 0.1 + activation_dropout: float | int = 0.0 + scale_embedding: bool = True + encoder_layers: int = 24 + encoder_ffn_dim: int = 8192 + encoder_attention_heads: int = 16 + decoder_layers: int = 24 + decoder_ffn_dim: int = 8192 + decoder_attention_heads: int = 16 + decoder_start_token_id: int = 3 + max_new_tokens: int | None = 256 + pad_token_id: int | None = 0 + bos_token_id: int | None = 2 + eos_token_id: int | list[int] | None = 3 + speech_encoder_layers: int = 24 + speech_encoder_attention_heads: int = 16 + speech_encoder_intermediate_size: int = 4096 + speech_encoder_hidden_act: str = "swish" + speech_encoder_dropout: float | int = 0.0 + add_adapter: bool = True + speech_encoder_layerdrop: float | int = 0.1 + feature_projection_input_dim: int = 160 + adaptor_kernel_size: int = 8 + adaptor_stride: int = 8 + adaptor_dropout: float | int = 0.1 + num_adapter_layers: int = 1 + position_embeddings_type: str = "relative_key" + conv_depthwise_kernel_size: int = 31 + left_max_position_embeddings: int = 64 + right_max_position_embeddings: int = 8 + speech_encoder_chunk_size: int = 20000 + speech_encoder_left_chunk_num: int = 128 + t2u_bos_token_id: int | None = 0 + t2u_pad_token_id: int | None = 1 + t2u_eos_token_id: int | list[int] | None = 2 + t2u_encoder_layers: int = 6 + t2u_encoder_ffn_dim: int = 8192 + t2u_encoder_attention_heads: int = 16 + t2u_decoder_layers: int = 6 + t2u_decoder_ffn_dim: int = 8192 + t2u_decoder_attention_heads: int = 16 + t2u_max_position_embeddings: int = 4096 + t2u_variance_predictor_embed_dim: int = 1024 + t2u_variance_predictor_hidden_dim: int = 256 + t2u_variance_predictor_kernel_size: int = 3 + t2u_variance_pred_dropout: float | int = 0.5 + sampling_rate: int = 16000 + upsample_initial_channel: int = 512 + upsample_rates: list[int] | tuple[int, ...] = (5, 4, 4, 2, 2) + upsample_kernel_sizes: list[int] | tuple[int, ...] = (11, 8, 8, 4, 4) + resblock_kernel_sizes: list[int] | tuple[int, ...] = (3, 7, 11) + resblock_dilation_sizes: list | tuple = ((1, 3, 5), (1, 3, 5), (1, 3, 5)) + leaky_relu_slope: float = 0.1 + unit_hifi_gan_vocab_size: int = 10000 + unit_embed_dim: int = 1280 + lang_embed_dim: int = 256 + spkr_embed_dim: int = 256 + vocoder_num_langs: int = 36 + vocoder_num_spkrs: int = 200 + variance_predictor_kernel_size: int = 3 + var_pred_dropout: float | int = 0.5 + vocoder_offset: int = 4 + tie_word_embeddings: bool = True + + +__all__ = ["SeamlessM4Tv2Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py new file mode 100644 index 0000000000000000000000000000000000000000..92ef692925c47ef6ee44020d85328749052831f3 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py @@ -0,0 +1,4358 @@ +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SeamlessM4Tv2 model.""" + +import copy +import math +from dataclasses import dataclass + +import torch +from torch import Tensor, nn +from torch.nn import CrossEntropyLoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache +from ...generation import GenerationMixin +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...integrations.fsdp import is_fsdp_managed_module +from ...masking_utils import create_bidirectional_mask, create_causal_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import ( + BaseModelOutput, + BaseModelOutputWithPastAndCrossAttentions, + Seq2SeqLMOutput, + Seq2SeqModelOutput, + Wav2Vec2BaseModelOutput, +) +from ...modeling_utils import PreTrainedModel +from ...utils import ModelOutput, auto_docstring, logging +from ...utils.deprecation import deprecate_kwarg +from .configuration_seamless_m4t_v2 import SeamlessM4Tv2Config + + +logger = logging.get_logger(__name__) + +SEAMLESS_M4T_V2_COMMON_CUSTOM_ARGS = r""" + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`): + Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the + [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details. + decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Indices of decoder input sequence tokens in the vocabulary. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are decoder input IDs?](../glossary#decoder-input-ids) + + Bart uses the `eos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` + is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). + + For translation and summarization training, `decoder_input_ids` should be provided. If no + `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right + for denoising pre-training following the paper. + decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): + Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also + be used by default. + + If you want to change padding behavior, you should read [`modeling_bart._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://huggingface.co/papers/1910.13461) for more + information on the default strategy. + inputs_embeds (`torch.FloatTensor` of shape`(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. +""" + + +@dataclass +@auto_docstring( + custom_intro=""" + Class defining the generated outputs from [`SeamlessM4Tv2Model`], [`SeamlessM4Tv2ForTextToText`], + [`SeamlessM4Tv2ForTextToSpeech`], [`SeamlessM4Tv2ForSpeechToSpeech`] and [`SeamlessM4Tv2ForTextToSpeech`]. + """ +) +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TGenerationOutput with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2GenerationOutput(ModelOutput): + r""" + waveform (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + The final audio waveform predicted by the model. + waveform_lengths (`torch.IntTensor` of shape `(batch_size,)`, *optional*): + The length in samples of each element in the `waveform` batch. + sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + The generated translated sequences. This is the output of the text-to-text or the speech-to-text models. + The second dimension (sequence_length) is either equal to `max_length` or shorter if all batches finished + early due to the `eos_token_id`. + unit_sequences (`torch.LongTensor` of shape `(batch_size, unit_sequence_length)`, *optional*): + The generated translated unit sequences. This is the output of the text-to-units model. The second + dimension (unit_sequence_length) is either equal to `t2u_max_length` or shorter if all batches finished + early due to the `t2u_eos_token_id`. + """ + + waveform: torch.FloatTensor | None = None + waveform_lengths: torch.IntTensor | None = None + sequences: tuple[torch.FloatTensor] | None = None + unit_sequences: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Class defining the outputs from [`SeamlessM4Tv2TextToUnitDecoder`]. + """ +) +@dataclass +class SeamlessM4Tv2TextToUnitDecoderOutput(ModelOutput): + r""" + padding_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked* or 0 + for *masked* + """ + + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + padding_mask: torch.Tensor | None = None + + +@auto_docstring( + custom_intro=""" + Class defining the outputs from [`SeamlessM4Tv2TextToUnitForConditionalGeneration`] and + [`SeamlessM4Tv2TextToUnitModel`]. + """ +) +@dataclass +class SeamlessM4Tv2TextToUnitOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + + If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, + hidden_size)` is output. + padding_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked* or 0 + for *masked* + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss. + """ + + last_hidden_state: torch.FloatTensor | None = None + padding_mask: torch.Tensor | None = None + decoder_hidden_states: tuple[torch.FloatTensor] | None = None + decoder_attentions: tuple[torch.FloatTensor] | None = None + encoder_last_hidden_state: torch.FloatTensor | None = None + encoder_hidden_states: tuple[torch.FloatTensor] | None = None + encoder_attentions: tuple[torch.FloatTensor] | None = None + loss: torch.FloatTensor | None = None + + +############ UTILS ################ + + +# Copied from transformers.models.bart.modeling_bart.shift_tokens_right +def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): + """ + Shift input ids one token to the right. + """ + shifted_input_ids = input_ids.new_zeros(input_ids.shape) + shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() + shifted_input_ids[:, 0] = decoder_start_token_id + + if pad_token_id is None: + raise ValueError("self.model.config.pad_token_id has to be defined.") + # replace possible -100 values in labels by `pad_token_id` + shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) + + return shifted_input_ids + + +def _compute_new_attention_mask(hidden_states: torch.Tensor, seq_lens: torch.Tensor): + """ + Computes an attention mask of the form `(batch, seq_len)` with an attention for each element in the batch that + stops at the corresponding element in `seq_lens`. + + Args: + hidden_states (`torch.FloatTensor` of shape `(batch, seq_len, *)`): + The sequences to mask, where `*` is any number of sequence-specific dimensions including none. + seq_lens (`torch.Tensor` of shape `(batch)`: + Each element represents the length of the sequence at the same index in `hidden_states` + + Returns: + `torch.FloatTensor`: The float attention mask of shape `(batch, seq_len)` + """ + batch_size, mask_seq_len = hidden_states.shape[:2] + + indices = torch.arange(mask_seq_len, device=seq_lens.device).expand(batch_size, -1) + + bool_mask = indices >= seq_lens.unsqueeze(1).expand(-1, mask_seq_len) + + mask = hidden_states.new_ones((batch_size, mask_seq_len)) + + mask = mask.masked_fill(bool_mask, 0) + + return mask + + +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.format_speech_generation_kwargs with SeamlessM4T->SeamlessM4Tv2 +def format_speech_generation_kwargs(kwargs): + """ + Format kwargs for SeamlessM4Tv2 models that generate speech, attribute kwargs to either the text generation or the + speech generation models. + + Args: + kwargs (`dict`)`: + Keyword arguments are of two types: + + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model, + except for `decoder_input_ids` which will only be passed through the text components. + - With a *text_* or *speech_* prefix, they will be input for the `generate` method of the + text model and speech model respectively. It has the priority over the keywords without a prefix. + + This means you can, for example, specify a generation strategy for one generation but not for the + other. + """ + # attribute kwargs to models + kwargs_text = {} + kwargs_speech = {} + for key, value in kwargs.items(): + if key.startswith("text_"): + key = key[len("text_") :] + kwargs_text[key] = value + elif key.startswith("speech_"): + key = key[len("speech_") :] + kwargs_speech[key] = value + elif key == "generation_config": + kwargs_text[key] = value + else: + # If the key is already in a specific config, then it's been set with a + # submodules specific value and we don't override + if key not in kwargs_text: + kwargs_text[key] = value + if key not in kwargs_speech: + kwargs_speech[key] = value + return kwargs_text, kwargs_speech + + +############ SPEECH ENCODER related code ################ + + +class SeamlessM4Tv2ConformerFeatureProjection(nn.Module): + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TConformerFeatureProjection.__init__ + def __init__(self, config): + super().__init__() + self.layer_norm = nn.LayerNorm(config.feature_projection_input_dim, eps=config.layer_norm_eps) + self.projection = nn.Linear(config.feature_projection_input_dim, config.hidden_size) + self.dropout = nn.Dropout(config.speech_encoder_dropout) + + def forward(self, hidden_states): + # non-projected hidden states are needed for quantization + norm_hidden_states = self.layer_norm(hidden_states.to(self.layer_norm.weight.dtype)) + hidden_states = self.projection(norm_hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TConformerFeedForward with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2ConformerFeedForward(nn.Module): + def __init__(self, config, act_fn=None, dropout=None): + super().__init__() + dropout = dropout if dropout is not None else config.speech_encoder_dropout + act_fn = act_fn if act_fn is not None else config.speech_encoder_hidden_act + + self.intermediate_dropout = nn.Dropout(dropout) + self.intermediate_dense = nn.Linear(config.hidden_size, config.speech_encoder_intermediate_size) + self.intermediate_act_fn = ACT2FN[act_fn] if isinstance(act_fn, str) else act_fn + + self.output_dense = nn.Linear(config.speech_encoder_intermediate_size, config.hidden_size) + self.output_dropout = nn.Dropout(dropout) + + def forward(self, hidden_states): + hidden_states = self.intermediate_dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.intermediate_dropout(hidden_states) + + hidden_states = self.output_dense(hidden_states) + hidden_states = self.output_dropout(hidden_states) + return hidden_states + + +class SeamlessM4Tv2ConformerConvolutionModule(nn.Module): + """Convolution block used in the conformer block. Uses a causal depthwise convolution similar to that + described in Section 2.1 of https://huggingface.co/papers/1609.03499 + """ + + def __init__(self, config): + super().__init__() + if (config.conv_depthwise_kernel_size - 1) % 2 == 1: + raise ValueError("`config.conv_depthwise_kernel_size` should be a odd number for 'SAME' padding") + self.layer_norm = nn.LayerNorm(config.hidden_size) + self.pointwise_conv1 = nn.Conv1d( + config.hidden_size, + 2 * config.hidden_size, + kernel_size=1, + stride=1, + padding=0, + bias=False, + ) + self.glu = nn.GLU(dim=1) + self.depthwise_conv = nn.Conv1d( + config.hidden_size, + config.hidden_size, + config.conv_depthwise_kernel_size, + stride=1, + padding=0, + groups=config.hidden_size, + bias=False, + ) + self.depthwise_layer_norm = nn.LayerNorm(config.hidden_size) + self.activation = ACT2FN[config.speech_encoder_hidden_act] + self.pointwise_conv2 = nn.Conv1d( + config.hidden_size, + config.hidden_size, + kernel_size=1, + stride=1, + padding=0, + bias=False, + ) + self.dropout = nn.Dropout(config.speech_encoder_dropout) + + def forward(self, hidden_states, attention_mask=None): + hidden_states = self.layer_norm(hidden_states) + + # Ensure that we do not leak padded positions in depthwise convolution. + # Put 0 where necessary + if attention_mask is not None: + hidden_states = hidden_states.masked_fill(~attention_mask.bool().unsqueeze(-1), 0.0) + + # exchange the temporal dimension and the feature dimension + hidden_states = hidden_states.transpose(1, 2) + + # GLU mechanism + # => (batch, 2*channel, dim) + hidden_states = self.pointwise_conv1(hidden_states) + # => (batch, channel, dim) + hidden_states = self.glu(hidden_states) + + # Pad the sequence entirely on the left because of causal convolution. + hidden_states = torch.nn.functional.pad(hidden_states, (self.depthwise_conv.kernel_size[0] - 1, 0)) + + # 1D Depthwise Conv + hidden_states = self.depthwise_conv(hidden_states) + hidden_states = self.depthwise_layer_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + hidden_states = self.activation(hidden_states) + + hidden_states = self.pointwise_conv2(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = hidden_states.transpose(1, 2) + return hidden_states + + +class SeamlessM4Tv2ConformerSelfAttention(nn.Module): + """Construct a SeamlessM4Tv2ConformerSelfAttention object. + Can be enhanced with relative position embeddings. + """ + + def __init__(self, config, use_position_embeddings=True): + super().__init__() + + self.head_size = config.hidden_size // config.speech_encoder_attention_heads + self.num_heads = config.speech_encoder_attention_heads + self.position_embeddings_type = config.position_embeddings_type if use_position_embeddings else None + + self.linear_q = nn.Linear(config.hidden_size, config.hidden_size) + self.linear_k = nn.Linear(config.hidden_size, config.hidden_size) + self.linear_v = nn.Linear(config.hidden_size, config.hidden_size) + self.linear_out = nn.Linear(config.hidden_size, config.hidden_size) + + self.dropout = nn.Dropout(p=config.speech_encoder_dropout) + + if self.position_embeddings_type == "relative_key": + self.left_max_position_embeddings = config.left_max_position_embeddings + self.right_max_position_embeddings = config.right_max_position_embeddings + num_positions = self.left_max_position_embeddings + self.right_max_position_embeddings + 1 + self.distance_embedding = nn.Embedding(num_positions, self.head_size) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + # self-attention mechanism + batch_size, sequence_length, hidden_size = hidden_states.size() + + # make sure query/key states can be != value states + query_key_states = hidden_states + value_states = hidden_states + + # project query_key_states and value_states + query = self.linear_q(query_key_states).view(batch_size, -1, self.num_heads, self.head_size) + key = self.linear_k(query_key_states).view(batch_size, -1, self.num_heads, self.head_size) + value = self.linear_v(value_states).view(batch_size, -1, self.num_heads, self.head_size) + + # => (batch, head, time1, d_k) + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = value.transpose(1, 2) + + attn_weights = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_size) + + if self.position_embeddings_type == "relative_key": + query_length, key_length = query.shape[2], key.shape[2] + + position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_r - position_ids_l + distance = torch.clamp(distance, -self.left_max_position_embeddings, self.right_max_position_embeddings) + + positional_embedding = self.distance_embedding(distance + self.left_max_position_embeddings) + positional_embedding = positional_embedding.to(dtype=query.dtype) # fp16 compatibility + + relative_position_attn_weights = torch.einsum("bhld,lrd->bhlr", query, positional_embedding) + attn_weights = attn_weights + (relative_position_attn_weights / math.sqrt(self.head_size)) + + # apply attention_mask if necessary + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + # => (batch, head, time1, time2) + attn_weights = torch.softmax(attn_weights, dim=-1) + attn_weights = self.dropout(attn_weights) + + # => (batch, head, time1, d_k) + attn_output = torch.matmul(attn_weights, value) + + # => (batch, time1, hidden_size) + attn_output = attn_output.transpose(1, 2).reshape(batch_size, -1, self.num_heads * self.head_size) + attn_output = self.linear_out(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights + + +class SeamlessM4Tv2ConformerEncoderLayer(GradientCheckpointingLayer): + """Conformer block based on https://huggingface.co/papers/2005.08100.""" + + # Copied from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer.Wav2Vec2ConformerEncoderLayer.__init__ with Wav2Vec2->SeamlessM4Tv2, attention_dropout->speech_encoder_dropout, torch.nn->nn + def __init__(self, config): + super().__init__() + embed_dim = config.hidden_size + dropout = config.speech_encoder_dropout + + # Feed-forward 1 + self.ffn1_layer_norm = nn.LayerNorm(embed_dim) + self.ffn1 = SeamlessM4Tv2ConformerFeedForward(config) + + # Self-Attention + self.self_attn_layer_norm = nn.LayerNorm(embed_dim) + self.self_attn_dropout = nn.Dropout(dropout) + self.self_attn = SeamlessM4Tv2ConformerSelfAttention(config) + + # Conformer Convolution + self.conv_module = SeamlessM4Tv2ConformerConvolutionModule(config) + + # Feed-forward 2 + self.ffn2_layer_norm = nn.LayerNorm(embed_dim) + self.ffn2 = SeamlessM4Tv2ConformerFeedForward(config) + self.final_layer_norm = nn.LayerNorm(embed_dim) + + def forward( + self, + hidden_states, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + conv_attention_mask: torch.Tensor | None = None, + ): + # 1. Feed-Forward 1 layer + residual = hidden_states + hidden_states = self.ffn1_layer_norm(hidden_states) + hidden_states = self.ffn1(hidden_states) + hidden_states = hidden_states * 0.5 + residual + residual = hidden_states + + # 2. Self-Attention layer + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.self_attn_dropout(hidden_states) + hidden_states = hidden_states + residual + + # 3. Convolutional Layer + residual = hidden_states + hidden_states = self.conv_module(hidden_states, attention_mask=conv_attention_mask) + hidden_states = residual + hidden_states + + # 4. Feed-Forward 2 Layer + residual = hidden_states + hidden_states = self.ffn2_layer_norm(hidden_states) + hidden_states = self.ffn2(hidden_states) + hidden_states = hidden_states * 0.5 + residual + hidden_states = self.final_layer_norm(hidden_states) + + return hidden_states, attn_weights + + +class SeamlessM4Tv2ConformerEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + + self.dropout = nn.Dropout(config.speech_encoder_dropout) + self.layers = nn.ModuleList( + [SeamlessM4Tv2ConformerEncoderLayer(config) for _ in range(config.speech_encoder_layers)] + ) + + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + self.gradient_checkpointing = False + + def _apply_chunk_attention(self, attention_mask, hidden_states): + """ + Creates a chunk attention mask. It creates a mask to prevent attention across chunks, ensuring that each + position attends only to positions within its own chunk. If a left chunk overlap is specified + (`speech_encoder_chunk_size` in the configuration), the attention mask is adjusted accordingly to allow each + position to also attends the `speech_encoder_chunk_size - 1` previous chunks. + """ + sequence_len = hidden_states.shape[1] + + chunk_indices = torch.arange(sequence_len, device=hidden_states.device) + chunk_indices = torch.div(chunk_indices, self.config.speech_encoder_chunk_size).long() + + start_indices = torch.full_like(chunk_indices, 0) + if self.config.speech_encoder_left_chunk_num >= 0: + start_indices = (chunk_indices - self.config.speech_encoder_left_chunk_num).clamp_(min=0) + start_indices = start_indices * self.config.speech_encoder_chunk_size + start_indices = start_indices.unsqueeze(1).expand(-1, sequence_len) + + end_indices = ((chunk_indices + 1) * self.config.speech_encoder_chunk_size).clamp_(max=sequence_len) + + end_indices = end_indices.unsqueeze(1).expand(-1, sequence_len) + + indices = torch.arange(sequence_len, device=hidden_states.device).unsqueeze(0).expand(sequence_len, -1) + + chunk_mask = (indices < start_indices) | (indices >= end_indices) + chunk_mask = chunk_mask.unsqueeze(0).unsqueeze(0) + + attention_mask = chunk_mask if attention_mask is None else (attention_mask.bool() | chunk_mask) + attention_mask = attention_mask.to(dtype=hidden_states.dtype) + return attention_mask + + def forward( + self, + hidden_states, + attention_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + conv_attention_mask = attention_mask + if attention_mask is not None: + # make sure padded tokens output 0 + hidden_states = hidden_states.masked_fill(~attention_mask.bool().unsqueeze(-1), 0.0) + # extend attention_mask + attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) + attention_mask = attention_mask.expand( + attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] + ) + + if self.config.speech_encoder_chunk_size is not None: + attention_mask = self._apply_chunk_attention(attention_mask, hidden_states) + + if attention_mask is not None: + attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min + + hidden_states = self.dropout(hidden_states) + + synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self) + + for i, layer in enumerate(self.layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + dropout_probability = torch.rand([]) + + skip_the_layer = self.training and dropout_probability < self.config.speech_encoder_layerdrop + if not skip_the_layer or synced_gpus: + # under fsdp or deepspeed zero3 all gpus must run in sync + layer_outputs = layer( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + conv_attention_mask=conv_attention_mask, + ) + hidden_states = layer_outputs[0] + + if skip_the_layer: + layer_outputs = (None, None) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TConformerAdapterLayer with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2ConformerAdapterLayer(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + + embed_dim = config.hidden_size + dropout = config.adaptor_dropout + + self.kernel_size = config.adaptor_kernel_size + self.stride = config.adaptor_stride + + # 1. residual convolution + self.residual_layer_norm = nn.LayerNorm(embed_dim) + self.residual_conv = nn.Conv1d( + embed_dim, + 2 * embed_dim, + self.kernel_size, + stride=self.stride, + padding=self.stride // 2, + ) + self.activation = nn.GLU(dim=1) + + # Self-Attention + self.self_attn_layer_norm = nn.LayerNorm(embed_dim) + self.self_attn_conv = nn.Conv1d( + embed_dim, + 2 * embed_dim, + self.kernel_size, + stride=self.stride, + padding=self.stride // 2, + ) + self.self_attn = SeamlessM4Tv2ConformerSelfAttention(config, use_position_embeddings=False) + self.self_attn_dropout = nn.Dropout(dropout) + + # Feed-forward + self.ffn_layer_norm = nn.LayerNorm(embed_dim) + self.ffn = SeamlessM4Tv2ConformerFeedForward(config, act_fn="relu", dropout=dropout) + + def _compute_sub_sample_lengths_from_attention_mask(self, attention_mask): + pad = self.kernel_size // 2 + seq_lens = attention_mask.size(1) - (1 - attention_mask.int()).sum(1) + + seq_lens = ((seq_lens + 2 * pad - self.kernel_size) / self.stride) + 1 + + return seq_lens.floor() + + def forward( + self, + hidden_states, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + ): + residual = self.residual_layer_norm(hidden_states) + + # Apply pooling to the residual to match the sequence length of the + # multi-head attention output. + # (batch, seq_len, feature_dim) -> (batch, feature_dim, seq_len) + residual = residual.transpose(1, 2) + residual = self.residual_conv(residual) + residual = self.activation(residual) + # (batch, feature_dim, seq_len) -> (batch, seq_len, feature_dim) + residual = residual.transpose(1, 2) + + hidden_states = self.self_attn_layer_norm(hidden_states) + # Apply pooling before feeding to the multihead-attention layer. + # (batch, seq_len, feature_dim) -> (batch, feature_dim, seq_len) + hidden_states = hidden_states.transpose(1, 2) + hidden_states = self.self_attn_conv(hidden_states) + hidden_states = self.activation(hidden_states) + # (batch, feature_dim, seq_len) -> (batch, seq_len, feature_dim) + hidden_states = hidden_states.transpose(1, 2) + + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + hidden_states.device + ) + attention_mask = _compute_new_attention_mask(hidden_states=hidden_states, seq_lens=sub_sampled_lengths) + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + ) + + # The rest of the computation is identical to a vanilla Transformer + # encoder layer. + hidden_states, attn_weights = self.self_attn( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.self_attn_dropout(hidden_states) + hidden_states = hidden_states + residual + + residual = hidden_states + + hidden_states = self.ffn_layer_norm(hidden_states) + hidden_states = self.ffn(hidden_states) + residual + + return hidden_states + + +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TConformerAdapter with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2ConformerAdapter(nn.Module): + def __init__(self, config): + super().__init__() + + self.layers = nn.ModuleList( + SeamlessM4Tv2ConformerAdapterLayer(config) for _ in range(config.num_adapter_layers) + ) + + def forward(self, hidden_states, attention_mask): + # down project hidden_states if necessary + + for layer in self.layers: + hidden_states = layer(hidden_states, attention_mask) + + return hidden_states + + +############ TEXT / UNITS related code ################ + + +# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100ScaledWordEmbedding with M2M100->SeamlessM4Tv2 +class SeamlessM4Tv2ScaledWordEmbedding(nn.Embedding): + """ + This module overrides nn.Embeddings' forward by multiplying with embeddings scale. + """ + + def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float | None = 1.0): + super().__init__(num_embeddings, embedding_dim, padding_idx) + self.embed_scale = embed_scale + + def forward(self, input_ids: torch.Tensor): + return super().forward(input_ids) * self.embed_scale + + +# Copied from transformers.models.m2m_100.modeling_m2m_100.M2M100SinusoidalPositionalEmbedding with M2M100->SeamlessM4Tv2 +class SeamlessM4Tv2SinusoidalPositionalEmbedding(nn.Module): + """This module produces sinusoidal positional embeddings of any length.""" + + def __init__(self, num_positions: int, embedding_dim: int, padding_idx: int | None = None): + super().__init__() + self.offset = 2 + self.num_positions = num_positions + self.embedding_dim = embedding_dim + self.padding_idx = padding_idx + self.make_weights(num_positions + self.offset, embedding_dim, padding_idx) + + def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: int | None = None): + emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx) + if hasattr(self, "weights"): + # in forward put the weights on the correct dtype and device of the param + emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device) + + self.register_buffer("weights", emb_weights, persistent=False) + + @staticmethod + def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: int | None = None): + """ + Build sinusoidal embeddings. + + This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of + "Attention Is All You Need". + """ + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb) + emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0) + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1) + if embedding_dim % 2 == 1: + # zero pad + emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1) + if padding_idx is not None: + emb[padding_idx, :] = 0 + + return emb.to(torch.get_default_dtype()) + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor | None = None, + inputs_embeds: torch.Tensor | None = None, + past_key_values_length: int = 0, + ): + if input_ids is not None: + bsz, seq_len = input_ids.size() + # Create the position ids from the input token ids. Any padded tokens remain padded. + position_ids = self.create_position_ids_from_input_ids( + input_ids, self.padding_idx, past_key_values_length + ).to(input_ids.device) + else: + bsz, seq_len = inputs_embeds.size()[:-1] + position_ids = self.create_position_ids_from_inputs_embeds( + inputs_embeds, past_key_values_length, self.padding_idx + ) + + # expand embeddings if needed + max_pos = self.padding_idx + 1 + seq_len + past_key_values_length + if max_pos > self.weights.size(0): + self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx) + + return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach() + + @staticmethod + def create_position_ids_from_inputs_embeds(inputs_embeds, past_key_values_length, padding_idx): + """ + We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids. + + Args: + inputs_embeds: torch.Tensor + + Returns: torch.Tensor + """ + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + + position_ids = torch.arange( + padding_idx + 1, sequence_length + padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ) + return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length + + @staticmethod + # Copied from transformers.models.roberta.modeling_roberta.RobertaEmbeddings.create_position_ids_from_input_ids + def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0): + """ + Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols + are ignored. This is modified from fairseq's `utils.make_positions`. + + Args: + x: torch.Tensor x: + + Returns: torch.Tensor + """ + # The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA. + mask = input_ids.ne(padding_idx).int() + incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask + return incremental_indices.long() + padding_idx + + +class SeamlessM4Tv2Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + # Copied from transformers.models.bart.modeling_bart.BartAttention.__init__ with Bart->SeamlessM4Tv2 + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: SeamlessM4Tv2Config | None = None, + layer_idx: int | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + self.layer_idx = layer_idx + if layer_idx is None and self.is_decoder: + logger.warning_once( + f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and " + "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor | None = None, + past_key_values: Cache | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + is_cross_attention = encoder_hidden_states is not None + batch_size, seq_length = hidden_states.shape[:2] + + is_updated = False + if past_key_values is not None: + if isinstance(past_key_values, EncoderDecoderCache): + is_updated = past_key_values.is_updated.get(self.layer_idx) + if is_cross_attention: + # after the first generated id, we can subsequently re-use all key/value_states from cache + curr_past_key_values = past_key_values.cross_attention_cache + else: + curr_past_key_values = past_key_values.self_attention_cache + else: + curr_past_key_values = past_key_values + + current_states = encoder_hidden_states if is_cross_attention else hidden_states + if is_cross_attention and past_key_values is not None and is_updated: + # reuse k,v, cross_attentions + key_states = curr_past_key_values.layers[self.layer_idx].keys + value_states = curr_past_key_values.layers[self.layer_idx].values + else: + key_states = self.k_proj(current_states) + value_states = self.v_proj(current_states) + key_states = key_states.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2) + + if past_key_values is not None: + # save all key/value_states to cache to be re-used for fast auto-regressive generation + key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx) + # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls + if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache): + past_key_values.is_updated[self.layer_idx] = True + + query_states = self.q_proj(hidden_states) + query_states = query_states.reshape(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) + query_states = query_states * self.scaling + attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2)) + + if attention_mask is not None: + attention_scores = attention_scores + attention_mask + + # (batch_size, n_heads, seq_length, key_length) + attn_weights = nn.functional.softmax(attention_scores.float(), dim=-1).type_as(attention_scores) + attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + # attn_output = torch.bmm(attn_probs, value_states) ? + context_states = torch.matmul(attn_weights, value_states) + # attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim) ? + context_states = context_states.permute(0, 2, 1, 3).contiguous().view(batch_size, seq_length, -1) + attn_output = self.out_proj(context_states) + + return attn_output, attn_weights + + +# Copied from transformers.models.nllb_moe.modeling_nllb_moe.NllbMoeDenseActDense with NllbMoe->SeamlessM4Tv2,DenseActDense->FeedForwardNetwork, d_model->hidden_size +class SeamlessM4Tv2FeedForwardNetwork(nn.Module): + def __init__(self, config: SeamlessM4Tv2Config, ffn_dim: int): + super().__init__() + self.fc1 = nn.Linear(config.hidden_size, ffn_dim) + self.fc2 = nn.Linear(ffn_dim, config.hidden_size) + self.dropout = nn.Dropout(config.activation_dropout) + self.act = ACT2FN[config.activation_function] + + def forward(self, hidden_states: torch.Tensor): + hidden_states = self.fc1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.dropout(hidden_states) + if ( + isinstance(self.fc2.weight, torch.Tensor) + and hidden_states.dtype != self.fc2.weight.dtype + and (self.fc2.weight.dtype != torch.int8 and self.fc2.weight.dtype != torch.uint8) + ): + hidden_states = hidden_states.to(self.fc2.weight.dtype) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TEncoderLayer with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2EncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: SeamlessM4Tv2Config, encoder_ffn_dim=None, encoder_attention_heads=None): + super().__init__() + encoder_ffn_dim = config.encoder_ffn_dim if encoder_ffn_dim is None else encoder_ffn_dim + encoder_attention_heads = ( + config.encoder_attention_heads if encoder_attention_heads is None else encoder_attention_heads + ) + + self.embed_dim = config.hidden_size + self.self_attn = SeamlessM4Tv2Attention( + embed_dim=self.embed_dim, + num_heads=encoder_attention_heads, + dropout=config.attention_dropout, + ) + self.attn_dropout = nn.Dropout(config.dropout) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + self.ffn = SeamlessM4Tv2FeedForwardNetwork(config, ffn_dim=encoder_ffn_dim) + + self.ffn_layer_norm = nn.LayerNorm(config.hidden_size) + self.ffn_dropout = nn.Dropout(config.activation_dropout) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + output_attentions: bool = False, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): + attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very + large negative values. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + + residual = hidden_states + + hidden_states = self.ffn_layer_norm(hidden_states) + + hidden_states = self.ffn(hidden_states) + hidden_states = self.ffn_dropout(hidden_states) + + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TDecoderLayer with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2DecoderLayer(GradientCheckpointingLayer): + def __init__( + self, config: SeamlessM4Tv2Config, decoder_ffn_dim=None, decoder_attention_heads=None, layer_idx=None + ): + super().__init__() + decoder_ffn_dim = config.decoder_ffn_dim if decoder_ffn_dim is None else decoder_ffn_dim + decoder_attention_heads = ( + config.decoder_attention_heads if decoder_attention_heads is None else decoder_attention_heads + ) + + self.embed_dim = config.hidden_size + self.self_attn = SeamlessM4Tv2Attention( + embed_dim=self.embed_dim, + num_heads=decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + layer_idx=layer_idx, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.attn_dropout = nn.Dropout(config.dropout) + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.cross_attention = SeamlessM4Tv2Attention( + self.embed_dim, + decoder_attention_heads, + config.attention_dropout, + is_decoder=True, + layer_idx=layer_idx, + ) + self.cross_attention_layer_norm = nn.LayerNorm(self.embed_dim) + + self.ffn = SeamlessM4Tv2FeedForwardNetwork(config, ffn_dim=decoder_ffn_dim) + + self.ffn_layer_norm = nn.LayerNorm(config.hidden_size) + self.ffn_dropout = nn.Dropout(config.activation_dropout) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.Tensor | None = None, + encoder_attention_mask: torch.Tensor | None = None, + past_key_values: Cache | None = None, + output_attentions: bool | None = False, + use_cache: bool | None = True, + **kwargs, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): + attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very + large negative values. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(batch, seq_len, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): + encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by + very large negative values. + past_key_values (`Cache`): + cached past key and value projection states + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Self Attention + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + past_key_values=past_key_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + + # Cross-Attention Block + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + hidden_states = self.cross_attention_layer_norm(hidden_states) + + hidden_states, cross_attn_weights = self.cross_attention( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + past_key_values=past_key_values, + attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + ) + hidden_states = self.attn_dropout(hidden_states) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + + hidden_states = self.ffn_layer_norm(hidden_states) + + hidden_states = self.ffn(hidden_states) + hidden_states = self.ffn_dropout(hidden_states) + + hidden_states = residual + hidden_states + + return hidden_states, self_attn_weights, cross_attn_weights + + +class SeamlessM4Tv2TextToUnitDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: SeamlessM4Tv2Config, decoder_ffn_dim=None, decoder_attention_heads=None): + super().__init__() + decoder_ffn_dim = config.decoder_ffn_dim if decoder_ffn_dim is None else decoder_ffn_dim + decoder_attention_heads = ( + config.decoder_attention_heads if decoder_attention_heads is None else decoder_attention_heads + ) + self.dropout = config.dropout + self.embed_dim = config.hidden_size + + self.self_attn = SeamlessM4Tv2Attention( + embed_dim=self.embed_dim, + num_heads=decoder_attention_heads, + dropout=config.attention_dropout, + is_decoder=True, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + self.conv1 = nn.Conv1d(self.embed_dim, self.embed_dim, kernel_size=7, stride=1, padding="same") + self.activation_fn = ACT2FN[config.activation_function] + self.conv2 = nn.Conv1d(self.embed_dim, self.embed_dim, kernel_size=7, stride=1, padding="same") + + self.conv_layer_norm = nn.LayerNorm(config.hidden_size) + self.conv_dropout = nn.Dropout(self.dropout) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + padding_mask: torch.Tensor | None = None, + output_attentions: bool | None = False, + ) -> torch.Tensor: + """ + Args: + hidden_states (`torch.FloatTensor`): + input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): + attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very + large negative values. + padding_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Indicates which inputs are to be ignored due to padding, where elements are either 1 for *not masked* + or 0 for *masked* + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + residual = hidden_states + + # Self Attention + hidden_states, self_attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + ) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # Conv + residual = hidden_states + + # Apply padding mask to avoid leaking padded positions in the convolution layer + if padding_mask is not None: + hidden_states = hidden_states.masked_fill(~padding_mask.bool().unsqueeze(-1), 0.0) + hidden_states = self.conv1(hidden_states.transpose(1, 2)).transpose(1, 2) + + if padding_mask is not None: + hidden_states = hidden_states.masked_fill(~padding_mask.bool().unsqueeze(-1), 0.0) + + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.conv2(hidden_states.transpose(1, 2)).transpose(1, 2) + + hidden_states = self.conv_dropout(hidden_states) + hidden_states = residual + hidden_states + hidden_states = self.conv_layer_norm(hidden_states) + + return hidden_states, self_attn_weights + + +############ SUB-MODELS related code ################ + + +@auto_docstring +class SeamlessM4Tv2PreTrainedModel(PreTrainedModel): + config: SeamlessM4Tv2Config + base_model_prefix = "seamless_m4t_v2" + supports_gradient_checkpointing = True + _no_split_modules = [ + "SeamlessM4Tv2EncoderLayer", + "SeamlessM4Tv2DecoderLayer", + "SeamlessM4Tv2ConformerEncoderLayer", + "SeamlessM4Tv2TextToUnitDecoderLayer", + ] + + @torch.no_grad() + def _init_weights(self, module: nn.Module): + """Initialize the weights""" + std = self.config.initializer_range + if isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=std) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=std) + # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag + if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): + init.zeros_(module.weight[module.padding_idx]) + elif isinstance(module, SeamlessM4Tv2ConformerSelfAttention): + if hasattr(module, "pos_bias_u"): + init.xavier_uniform_(module.pos_bias_u) + if hasattr(module, "pos_bias_v"): + init.xavier_uniform_(module.pos_bias_v) + elif isinstance(module, SeamlessM4Tv2ConformerFeatureProjection): + k = math.sqrt(1 / module.projection.in_features) + init.uniform_(module.projection.weight, a=-k, b=k) + init.uniform_(module.projection.bias, a=-k, b=k) + elif isinstance(module, SeamlessM4Tv2TextToUnitDecoder): + init.ones_(module.pos_emb_alpha_char) + init.ones_(module.pos_emb_alpha) + elif isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, (nn.Conv1d, nn.ConvTranspose1d)): + init.kaiming_normal_(module.weight) + if module.bias is not None: + k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0])) + init.uniform_(module.bias, a=-k, b=k) + elif isinstance(module, SeamlessM4Tv2SinusoidalPositionalEmbedding): + emb_weights = module.get_embedding( + module.num_positions + module.offset, module.embedding_dim, module.padding_idx + ) + init.copy_(module.weights, emb_weights) + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TPreTrainedModel._compute_sub_sample_lengths_from_attention_mask + def _compute_sub_sample_lengths_from_attention_mask(self, attention_mask): + kernel_size, stride = self.config.adaptor_kernel_size, self.config.adaptor_stride + pad = kernel_size // 2 + seq_lens = attention_mask.size(1) - (1 - attention_mask.int()).sum(1) + + seq_lens = ((seq_lens + 2 * pad - kernel_size) / stride) + 1 + + return seq_lens.floor() + + def _indices_to_subwords(self, input_ids): + """ + Returns the corresponding text string for each input id. + """ + if not hasattr(self.generation_config, "id_to_text"): + raise ValueError( + """This model generation config doesn't have a `id_to_text` key which maps + token ids to subwords. Make sure to load the right generation config.""" + ) + batch_size, sequence_len = input_ids.shape + + subwords_batch = [] + for batch_id in range(batch_size): + subwords = [] + for i in range(sequence_len): + subword = self.generation_config.id_to_text.get(str(input_ids[batch_id, i].item())) + subwords.append(str(subword)) + subwords_batch.append(subwords) + return subwords_batch + + def _count_character_length_in_subword( + self, + input_ids, + subwords_batch, + merge_space_with_prev_subword=False, + pad_token_id=0, + unk_token_id=1, + space="▁", + ): + """ + Counts the number of characters per text string associated with the input token id. + + Args: + input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + subwords_batch (`list[list[str]]` of shape `(batch_size, sequence_length)`): + Corresponding text string for each input id. + merge_space_with_prev_subword (`bool`, *optional*, defaults to `False`): + Indicates if the space character is merged with the previous subword. If `False`, it will be merged + with the next subword. + pad_token_id (`int`, *optional*, defaults to 0): + The id of the _padding_ text token. If it is encountered when calculating the length of a subword + sample, the lengths of subsequent subwords will be set to 0. + unk_token_id (`int`, *optional*, defaults to 1): + The id of the _unknown_ text token. Associated to a subword of length 1. + space (`str`, *optional*, defaults to `"▁"`): + The space character. + """ + batch_size, _ = input_ids.shape + + char_count_per_id = input_ids.new_zeros(input_ids.size()) + + subword_lens = input_ids.ne(pad_token_id).sum(1) + + for batch_id in range(batch_size): + # We slice out the tensor till the padding index. + subword_indices = input_ids[batch_id, : subword_lens[batch_id]] + subwords = subwords_batch[batch_id][: subword_lens[batch_id]] + + is_next_start_with_space = [ + len(subwords[i + 1]) > 1 and subwords[i + 1][0] == space if i < len(subwords) - 1 else False + for i in range(len(subwords)) + ] + is_punc = [ + len(subwords[i]) == 1 + and not subwords[i].isalpha() + and not subwords[i].isnumeric() + and subwords[i] != space + for i in range(len(subwords)) + ] + for i, (subword_idx, subword) in enumerate(zip(subword_indices, subwords)): + if subword_idx == pad_token_id: + break + + if subword_idx == unk_token_id: + # We set char_len to 1 for an unk token. + char_len = 1 + + if merge_space_with_prev_subword and is_next_start_with_space[i]: + char_len += 1 + else: + # By default, spaces are merged with the next subword. + # char_len includes the space. + char_len = len(subword) + + if merge_space_with_prev_subword: + # Add the space for the next subword. + if is_next_start_with_space[i]: + char_len += 1 + # Subtract the space for the current subword. + if i > 0 and is_next_start_with_space[i - 1]: + char_len -= 1 + else: + # Merge space with punctuation mark by default. + if is_punc[i] and is_next_start_with_space[i]: + char_len += 1 + # Subtract the space for the subword succeeding the punctuation mark. + elif i > 0 and is_punc[i - 1] and is_next_start_with_space[i - 1]: + char_len -= 1 + + char_count_per_id[batch_id, i] = char_len + + return char_count_per_id + + def _get_char_input_ids(self, input_ids, subwords_batch, char_count_per_id, pad_token_id=0, unk_token_id=1): + """ + Returns the corresponding character input id for each character of `subwords_batch`. + + Args: + input_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + subwords_batch (`list[list[str]]` of shape `(batch_size, sequence_length)`): + Corresponding text string for each input id. + char_count_per_id (`torch.Tensor` of shape `(batch_size, sequence_length)`): + Number of characters per input id. + pad_token_id (`int`, *optional*, defaults to 0): + The id of the _padding_ text token. If it is encountered when calculating the length of a subword + sample, the lengths of subsequent subwords will be set to 0. + unk_token_id (`int`, *optional*, defaults to 1): + The id of the _unknown_ text token. Associated to a subword of length 1. + Returns: + `torch.Tensor`: Tensor of shape `(batch_size, char_sequence_length)` containing the id of each character. + """ + if not hasattr(self.generation_config, "char_to_id"): + raise ValueError( + """This model generation config doesn't have a `char_to_id` key which maps + characters to character ids. Make sure to load the right generation config.""" + ) + + batch_size = input_ids.shape[0] + max_len = int(char_count_per_id.sum(1).max().item()) + + char_seqs = input_ids.new_zeros((batch_size, max_len)).fill_(pad_token_id) + + subword_lens = input_ids.ne(pad_token_id).sum(1) + + for batch_id in range(batch_size): + total = 0 + subword_indices = input_ids[batch_id, : subword_lens[batch_id]] + subwords = subwords_batch[batch_id][: subword_lens[batch_id]] + for subword_idx, subword in zip(subword_indices, subwords): + if subword_idx == unk_token_id: + char_ids = [unk_token_id] + else: + # Get char token indices corresponding to the subwords. + char_ids = [self.generation_config.char_to_id.get(ch, unk_token_id) for ch in list(subword)] + char_seq_len = len(char_ids) + char_seqs[batch_id, total : total + char_seq_len] = torch.tensor(char_ids).to(char_seqs) + total += char_seq_len + return char_seqs + + def _hard_upsample(self, hidden_states, durations): + """ + Repeats the time dimension of each sample in the batch based on the corresponding duration. + + Args: + hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, *)`, *optional*): + The sequence to repeat, where `*` is any number of sequence-specific dimensions including none. + durations (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Indicates how many times to repeat time segments. + """ + if hidden_states.size(0) == 1: + hidden_states = torch.repeat_interleave(hidden_states, durations.view(-1), dim=1) + else: + # if batched sample, need to interleave per sample, and pad -> loss of parallelism + if hidden_states.shape[0] > 1 and self.training: + logger.warning_once( + """`self.training=True` and you use batching. You lose parallelism during the hifigan + forward pass because the samples are interleaved.""" + ) + hidden_states = [ + torch.repeat_interleave(hidden_state, duration, dim=0) + for (hidden_state, duration) in zip(hidden_states, durations) + ] + + hidden_states = nn.utils.rnn.pad_sequence(hidden_states, batch_first=True) + + return hidden_states + + +@auto_docstring( + custom_intro=""" + Transformer speech encoder consisting of *config.speech_encoder_layers* conformer self attention layers. + Each layer is a [`SeamlessM4Tv2ConformerEncoderLayer`]. + """ +) +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TSpeechEncoder with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2SpeechEncoder(SeamlessM4Tv2PreTrainedModel): + main_input_name = "input_features" + input_modalities = "audio" + + def __init__(self, config: SeamlessM4Tv2Config): + super().__init__(config) + + self.feature_projection = SeamlessM4Tv2ConformerFeatureProjection(config) + self.encoder = SeamlessM4Tv2ConformerEncoder(config) + self.intermediate_ffn = SeamlessM4Tv2ConformerFeedForward(config, act_fn="relu", dropout=0.0) + self.adapter = SeamlessM4Tv2ConformerAdapter(config) if config.add_adapter else None + self.inner_layer_norm = nn.LayerNorm(config.hidden_size) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_features: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | Wav2Vec2BaseModelOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_features is None: + raise ValueError( + """Both `input_features` and `inputs_embeds` are `None` in `SeamlessM4Tv2SpeechEncoder.forward`. + Make sure one of them is not `None`.""" + ) + + hidden_states = self.feature_projection(input_features) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + expanded_hidden_states = self.intermediate_ffn(hidden_states) + hidden_states = hidden_states + 0.5 * expanded_hidden_states + + if self.adapter is not None: + hidden_states = self.adapter(hidden_states, attention_mask=attention_mask) + + hidden_states = self.inner_layer_norm(hidden_states) + + if not return_dict: + return (hidden_states,) + encoder_outputs[1:] + + return Wav2Vec2BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +# inspired from MBart and NllbMoe +@auto_docstring( + custom_intro=""" + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`SeamlessM4Tv2EncoderLayer`]. + """ +) +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TEncoder with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2Encoder(SeamlessM4Tv2PreTrainedModel): + def __init__( + self, + config: SeamlessM4Tv2Config, + embed_tokens: nn.Embedding | None = None, + is_t2u_encoder: bool = False, + ): + r""" + embed_tokens (`nn.Embedding`, *optional*): + Input embedding + is_t2u_encoder (`bool`, *optional*, defaults to `False`): + indicates if it belongs to the text-to-units model, in which case it won't have input embeddings + """ + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + self.padding_idx = config.pad_token_id + embed_dim = config.hidden_size + + self.is_t2u_encoder = is_t2u_encoder + self.max_source_positions = config.max_position_embeddings + + if not self.is_t2u_encoder: + embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0 + + self.embed_tokens = SeamlessM4Tv2ScaledWordEmbedding( + config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale + ) + + if embed_tokens is not None: + self.embed_tokens.weight = embed_tokens.weight + + self.embed_positions = SeamlessM4Tv2SinusoidalPositionalEmbedding( + self.max_source_positions, + embed_dim, + self.padding_idx, + ) + + layers = [] + for _ in range(config.encoder_layers): + layers.append( + SeamlessM4Tv2EncoderLayer( + config, + encoder_attention_heads=config.encoder_attention_heads, + encoder_ffn_dim=config.encoder_ffn_dim, + ) + ) + + self.layers = nn.ModuleList(layers) + + self.layer_norm = nn.LayerNorm(config.hidden_size) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you + provide it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated vectors + than the model's internal embedding lookup matrix. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if input_ids is not None and self.is_t2u_encoder: + raise ValueError( + "You cannot pass input_ids to the encoder of the text_to_units model. Pass inputs_embeds instead." + ) + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_shape = input.shape + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if not self.is_t2u_encoder: + embed_pos = self.embed_positions(input) + + hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device) + else: + hidden_states = inputs_embeds + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # expand attention_mask + if attention_mask is not None: + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + ) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + for idx, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + hidden_states = self.layer_norm(hidden_states) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +@auto_docstring( + custom_intro=""" + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`SeamlessM4Tv2DecoderLayer`]. + """ +) +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TDecoder with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2Decoder(SeamlessM4Tv2PreTrainedModel): + def __init__( + self, + config: SeamlessM4Tv2Config, + embed_tokens: nn.Embedding | None = None, + ): + r""" + embed_tokens (`nn.Embedding`, *optional*): + Input embedding + """ + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.max_target_positions = config.max_position_embeddings + embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 + + if embed_tokens is not None: + # if embed_tokens defined, use its shape instead + self.embed_tokens = SeamlessM4Tv2ScaledWordEmbedding( + embed_tokens.num_embeddings, embed_tokens.embedding_dim, self.padding_idx, embed_scale=embed_scale + ) + self.embed_tokens.weight = embed_tokens.weight + else: + self.embed_tokens = SeamlessM4Tv2ScaledWordEmbedding( + self.vocab_size, config.hidden_size, self.padding_idx, embed_scale=embed_scale + ) + + self.embed_positions = SeamlessM4Tv2SinusoidalPositionalEmbedding( + self.max_target_positions, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + layers = [] + for i in range(config.decoder_layers): + layers.append( + SeamlessM4Tv2DecoderLayer( + config, + decoder_attention_heads=config.decoder_attention_heads, + decoder_ffn_dim=config.decoder_ffn_dim, + layer_idx=i, + ) + ) + self.layers = nn.ModuleList(layers) + self.layer_norm = nn.LayerNorm(config.hidden_size) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + encoder_attention_mask: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutputWithPastAndCrossAttentions: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # retrieve input_ids and inputs_embeds + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time") + elif input_ids is not None: + input = input_ids + input_shape = input.size() + input_ids = input_ids.view(-1, input_shape[-1]) + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + input = inputs_embeds[:, :, -1] + else: + raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing`. Setting `use_cache=False`..." + ) + use_cache = False + + # initialize `past_key_values` + if use_cache and past_key_values is None: + past_key_values = EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) + + past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 + + attention_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + ) + + # expand encoder attention mask + if encoder_hidden_states is not None and encoder_attention_mask is not None: + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=encoder_attention_mask, + encoder_hidden_states=encoder_hidden_states, + ) + + # embed positions + positions = self.embed_positions(input, past_key_values_length=past_key_values_length) + + hidden_states = inputs_embeds + positions.to(inputs_embeds.device) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + layer_outputs = decoder_layer( + hidden_states, + attention_mask, + encoder_hidden_states, # as a positional argument for gradient checkpointing + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + ) + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + hidden_states = self.layer_norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + ) + + +@auto_docstring( + custom_intro=""" + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`SeamlessM4Tv2DecoderLayer`]. + """ +) +class SeamlessM4Tv2TextToUnitDecoder(SeamlessM4Tv2PreTrainedModel): + def __init__( + self, + config: SeamlessM4Tv2Config, + embed_tokens: nn.Embedding | None = None, + ): + r""" + embed_tokens (`nn.Embedding`, *optional*): + Input embedding + """ + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + self.max_target_positions = config.max_position_embeddings + self.embed_scale = math.sqrt(config.hidden_size) if config.scale_embedding else 1.0 + + if embed_tokens is not None: + # if embed_tokens defined, use its shape instead + self.embed_tokens = nn.Embedding(embed_tokens.num_embeddings, embed_tokens.embedding_dim, self.padding_idx) + self.embed_tokens.weight = embed_tokens.weight + else: + self.embed_tokens = nn.Embedding(self.vocab_size, config.hidden_size, self.padding_idx) + + self.embed_char = nn.Embedding(config.char_vocab_size, config.hidden_size) + self.embed_char_positions = SeamlessM4Tv2SinusoidalPositionalEmbedding( + self.max_target_positions, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + self.pos_emb_alpha_char = nn.Parameter(torch.ones(1)) + self.pos_emb_alpha = nn.Parameter(torch.ones(1)) + self.duration_predictor = SeamlessM4Tv2VariancePredictor( + config.variance_predictor_embed_dim, + config.variance_predictor_hidden_dim, + config.variance_predictor_kernel_size, + config.variance_pred_dropout, + ) + + self.embed_positions = SeamlessM4Tv2SinusoidalPositionalEmbedding( + self.max_target_positions, + config.hidden_size, + padding_idx=self.padding_idx, + ) + + layers = [] + for _ in range(config.decoder_layers): + layers.append( + SeamlessM4Tv2TextToUnitDecoderLayer( + config, + decoder_attention_heads=config.decoder_attention_heads, + decoder_ffn_dim=config.decoder_ffn_dim, + ) + ) + self.layers = nn.ModuleList(layers) + self.layer_norm = nn.LayerNorm(config.hidden_size) + + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + char_input_ids: torch.LongTensor | None = None, + char_count_per_id: torch.LongTensor | None = None, + encoder_hidden_states: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | SeamlessM4Tv2TextToUnitDecoderOutput: + r""" + Args: + char_input_ids (`torch.LongTensor` of shape `(batch_size, char_sequence_length)`): + Character indices. The correspondence between characters and indices can be found in `char_to_id`, a + dictionary in the generation configuration. + char_count_per_id (`torch.Tensor` of shape `(batch_size, encoder_sequence_length)`): + Number of characters per text input id. + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + # create padding mask for character lengths + char_padding_mask = _compute_new_attention_mask(char_input_ids, char_count_per_id.sum(1)) + + # upsample hidden states according to characters sequence lengths + char_hidden_states = self._hard_upsample(encoder_hidden_states, char_count_per_id) + # embed char positions + char_positions = self.pos_emb_alpha_char * self.embed_char_positions(inputs_embeds=char_hidden_states) + # update char hidden states with positions and char embeddings + char_hidden_states = self.embed_char(char_input_ids) * self.embed_scale + char_positions + char_hidden_states + + # predict duration + log_dur_pred = self.duration_predictor(char_hidden_states, padding_mask=char_padding_mask) + dur_out = torch.clamp(torch.round(torch.expm1(log_dur_pred)).long(), min=1) + dur_out = dur_out.masked_fill(~char_padding_mask.bool(), 0.0) + + # upsample char hidden states according to predicted duration + char_hidden_states = self._hard_upsample(char_hidden_states, dur_out) + + positions = self.pos_emb_alpha * self.embed_positions(inputs_embeds=char_hidden_states) + hidden_states = char_hidden_states + positions + + padding_mask = _compute_new_attention_mask(hidden_states, dur_out.sum(1)) + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=padding_mask, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + + layer_outputs = decoder_layer( + hidden_states, + attention_mask=attention_mask, + padding_mask=padding_mask, + output_attentions=output_attentions, + ) + hidden_states = layer_outputs[0] + + if output_attentions: + all_self_attns += (layer_outputs[2],) + + hidden_states = self.layer_norm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attns, padding_mask] if v is not None) + return SeamlessM4Tv2TextToUnitDecoderOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attns, + padding_mask=padding_mask, + ) + + +@auto_docstring( + custom_intro=""" + Transformer bare text-to-unit encoder-decoder. The encoder is a [`SeamlessM4Tv2Encoder`] without embeddings and the decoder is a [`SeamlessM4Tv2TextToUnitDecoder`]. + """ +) +class SeamlessM4Tv2TextToUnitModel(SeamlessM4Tv2PreTrainedModel): + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TTextToUnitModel.__init__ with SeamlessM4T->SeamlessM4Tv2, Decoder->TextToUnitDecoder + def __init__( + self, + config: SeamlessM4Tv2Config, + embed_tokens_decoder: nn.Embedding | None = None, + ): + r""" + embed_tokens_decoder (`nn.Embedding`, *optional*): + input embedding of the decoder. + """ + super().__init__(config) + + self.encoder = SeamlessM4Tv2Encoder(config, is_t2u_encoder=True) + self.decoder = SeamlessM4Tv2TextToUnitDecoder(config, embed_tokens_decoder) + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: torch.LongTensor | None = None, + char_input_ids: torch.LongTensor | None = None, + char_count_per_id: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple[torch.Tensor] | Seq2SeqModelOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + encoder_outputs = self.encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # decoder outputs consists of (dec_features, dec_hidden, dec_attn, padding_mask) + decoder_outputs = self.decoder( + char_input_ids=char_input_ids, + char_count_per_id=char_count_per_id, + encoder_hidden_states=encoder_outputs[0], + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return SeamlessM4Tv2TextToUnitOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + padding_mask=decoder_outputs.padding_mask, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + Transformer text-to-unit encoder-decoder with a language model head. The base encoder-decoder model is a [`SeamlessM4Tv2TextToUnitModel`]. + """ +) +class SeamlessM4Tv2TextToUnitForConditionalGeneration(SeamlessM4Tv2PreTrainedModel, GenerationMixin): + _keys_to_ignore_on_load_missing = [ + "vocoder", + "speech_encoder", + "text_encoder", + "text_decoder", + ] + _tied_weights_keys = {"lm_head.weight": "model.decoder.embed_tokens.weight"} + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TTextToUnitForConditionalGeneration.__init__ with SeamlessM4T->SeamlessM4Tv2 + def __init__( + self, + config: SeamlessM4Tv2Config, + embed_tokens_decoder: nn.Embedding | None = None, + ): + r""" + embed_tokens_decoder (`nn.Embedding`, *optional*): + input embedding of the decoder. + """ + # update config - used principality for bos_token_id etc. + config = copy.deepcopy(config) + for param, val in config.to_dict().items(): + if param.startswith("t2u_"): + config.__setattr__(param[4:], val) + super().__init__(config) + + self.model = SeamlessM4Tv2TextToUnitModel(config, embed_tokens_decoder) + + self.lm_head = nn.Linear(config.hidden_size, config.t2u_vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TTextToUnitForConditionalGeneration.get_encoder + def get_encoder(self): + return self.model.encoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TTextToUnitForConditionalGeneration.get_decoder + def get_decoder(self): + return self.model.decoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TTextToUnitForConditionalGeneration.get_input_embeddings + def get_input_embeddings(self): + return self.model.decoder.embed_tokens + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TTextToUnitForConditionalGeneration.set_input_embeddings + def set_input_embeddings(self, value): + self.model.decoder.embed_tokens = value + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + char_input_ids: torch.LongTensor | None = None, + char_count_per_id: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + char_input_ids (`torch.LongTensor` of shape `(batch_size, char_sequence_length)`): + Character indices. The correspondence between characters and indices can be found in `char_to_id`, a + dictionary in the generation configuration. + char_count_per_id (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Number of characters per input id. + inputs_embeds (`torch.FloatTensor` of shape`(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.model( + input_ids, + char_input_ids=char_input_ids, + char_count_per_id=char_count_per_id, + attention_mask=attention_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + lm_logits = self.lm_head(outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return SeamlessM4Tv2TextToUnitOutput( + last_hidden_state=lm_logits, + padding_mask=outputs.padding_mask, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + loss=masked_lm_loss, + ) + + +############ VOCODER related code ################ + + +# Copied from transformers.models.speecht5.modeling_speecht5.HifiGanResidualBlock +class HifiGanResidualBlock(nn.Module): + def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1): + super().__init__() + self.leaky_relu_slope = leaky_relu_slope + + self.convs1 = nn.ModuleList( + [ + nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=dilation[i], + padding=self.get_padding(kernel_size, dilation[i]), + ) + for i in range(len(dilation)) + ] + ) + self.convs2 = nn.ModuleList( + [ + nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=1, + padding=self.get_padding(kernel_size, 1), + ) + for _ in range(len(dilation)) + ] + ) + + def get_padding(self, kernel_size, dilation=1): + return (kernel_size * dilation - dilation) // 2 + + def apply_weight_norm(self): + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + for layer in self.convs1: + weight_norm(layer) + for layer in self.convs2: + weight_norm(layer) + + def remove_weight_norm(self): + for layer in self.convs1: + nn.utils.remove_weight_norm(layer) + for layer in self.convs2: + nn.utils.remove_weight_norm(layer) + + def forward(self, hidden_states): + for conv1, conv2 in zip(self.convs1, self.convs2): + residual = hidden_states + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = conv1(hidden_states) + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = conv2(hidden_states) + hidden_states = hidden_states + residual + return hidden_states + + +class SeamlessM4Tv2VariancePredictor(nn.Module): + def __init__(self, embed_dim, hidden_dim, kernel_size, var_pred_dropout): + super().__init__() + + self.conv1 = nn.Conv1d( + embed_dim, + hidden_dim, + kernel_size=kernel_size, + padding="same", + ) + self.activation_function = nn.ReLU() + self.ln1 = nn.LayerNorm(hidden_dim) + self.dropout_module = nn.Dropout(p=var_pred_dropout) + self.conv2 = nn.Conv1d( + hidden_dim, + hidden_dim, + kernel_size=kernel_size, + padding="same", + ) + self.ln2 = nn.LayerNorm(hidden_dim) + self.proj = nn.Linear(hidden_dim, 1) + + def forward(self, hidden_states: Tensor, padding_mask: Tensor | None = None) -> Tensor: + # Input: B x T x C; Output: B x T + if padding_mask is not None: + hidden_states = hidden_states.masked_fill(~padding_mask.bool().unsqueeze(-1), 0.0) + hidden_states = self.conv1(hidden_states.transpose(1, 2)) + hidden_states = self.activation_function(hidden_states).transpose(1, 2) + hidden_states = self.dropout_module(self.ln1(hidden_states)) + if padding_mask is not None: + hidden_states = hidden_states.masked_fill(~padding_mask.bool().unsqueeze(-1), 0.0) + hidden_states = self.conv2(hidden_states.transpose(1, 2)) + hidden_states = self.activation_function(hidden_states).transpose(1, 2) + hidden_states = self.dropout_module(self.ln2(hidden_states)) + return self.proj(hidden_states).squeeze(dim=2) + + +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4THifiGan with SeamlessM4T->SeamlessM4Tv2 +class SeamlessM4Tv2HifiGan(nn.Module): + def __init__(self, config: SeamlessM4Tv2Config): + super().__init__() + model_in_dim = config.unit_embed_dim + config.lang_embed_dim + config.spkr_embed_dim + self.leaky_relu_slope = config.leaky_relu_slope + self.num_kernels = len(config.resblock_kernel_sizes) + self.num_upsamples = len(config.upsample_rates) + self.conv_pre = nn.Conv1d( + model_in_dim, + config.upsample_initial_channel, + kernel_size=7, + stride=1, + padding=3, + ) + + self.upsampler = nn.ModuleList() + for i, (upsample_rate, kernel_size) in enumerate(zip(config.upsample_rates, config.upsample_kernel_sizes)): + self.upsampler.append( + nn.ConvTranspose1d( + config.upsample_initial_channel // (2**i), + config.upsample_initial_channel // (2 ** (i + 1)), + kernel_size=kernel_size, + stride=upsample_rate, + padding=(kernel_size - upsample_rate) // 2, + ) + ) + + self.resblocks = nn.ModuleList() + for i in range(len(self.upsampler)): + channels = config.upsample_initial_channel // (2 ** (i + 1)) + for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes): + self.resblocks.append(HifiGanResidualBlock(channels, kernel_size, dilation, config.leaky_relu_slope)) + + self.conv_post = nn.Conv1d(channels, 1, kernel_size=7, stride=1, padding=3) + + @deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds") + def forward(self, inputs_embeds: torch.FloatTensor) -> torch.FloatTensor: + r""" + Converts a log-mel spectrogram into a speech waveform. Passing a batch of log-mel spectrograms returns a batch + of speech waveforms. Passing a single, un-batched log-mel spectrogram returns a single, un-batched speech + waveform. + + Args: + spectrogram (`torch.FloatTensor`): + Tensor containing the log-mel spectrograms. Can be batched and of shape `(batch_size, sequence_length, + model_in_dim)`, or un-batched and of shape `(sequence_length, model_in_dim)`. Note that `model_in_dim` + is the sum of `config.unit_embed_dim`, `config.lang_embed_dim` and `config.spkr_embed_dim`. + + Returns: + `torch.FloatTensor`: Tensor containing the speech waveform. If the input spectrogram is batched, will be of + shape `(batch_size, num_frames,)`. If un-batched, will be of shape `(num_frames,)`. + """ + + hidden_states = self.conv_pre(inputs_embeds) + for i in range(self.num_upsamples): + hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope) + hidden_states = self.upsampler[i](hidden_states) + + res_state = self.resblocks[i * self.num_kernels](hidden_states) + for j in range(1, self.num_kernels): + res_state += self.resblocks[i * self.num_kernels + j](hidden_states) + hidden_states = res_state / self.num_kernels + + hidden_states = nn.functional.leaky_relu(hidden_states) + hidden_states = self.conv_post(hidden_states) + hidden_states = torch.tanh(hidden_states) + + # remove seq-len dim since this collapses to 1 + waveform = hidden_states.squeeze(1) + + return waveform + + +@auto_docstring( + custom_intro=""" + Code HiFi-GAN vocoder as described in this [repository](https://github.com/facebookresearch/speech-resynthesis). + """ +) +class SeamlessM4Tv2CodeHifiGan(PreTrainedModel): + config: SeamlessM4Tv2Config + main_input_name = "inputs_embeds" + input_modalities = "audio" + _no_split_modules = [] + + def __init__(self, config): + super().__init__(config) + + self.pad_token_id = config.t2u_pad_token_id + embed_dim = config.unit_embed_dim + kernel_size = config.variance_predictor_kernel_size + var_pred_dropout = config.var_pred_dropout + self.dur_predictor = SeamlessM4Tv2VariancePredictor(embed_dim, embed_dim, kernel_size, var_pred_dropout) + + self.unit_embedding = nn.Embedding(config.unit_hifi_gan_vocab_size, config.unit_embed_dim) + self.speaker_embedding = nn.Embedding(config.vocoder_num_spkrs, config.spkr_embed_dim) + self.language_embedding = nn.Embedding(config.vocoder_num_langs, config.lang_embed_dim) + + self.hifi_gan = SeamlessM4Tv2HifiGan(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TCodeHifiGan._get_dur_output_lengths + def _get_dur_output_lengths(self, input_ids, dur_out): + """ + Computes the output length after the duration layer. + """ + unit_lengths = (input_ids != self.pad_token_id).sum(1) + + # take care of edge cases where no padding or too many padding + unit_lengths = torch.clamp(unit_lengths, 0, dur_out.shape[1] - 1) + + cumulative_dur_out = torch.cumsum(dur_out, dim=1) + unit_lengths = cumulative_dur_out.gather(dim=1, index=unit_lengths.unsqueeze(1)).squeeze() + + return unit_lengths + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TCodeHifiGan._get_output_hifigan_lengths + def _get_output_hifigan_lengths(self, input_lengths: torch.LongTensor | int): + """ + Computes the output length of the hifigan convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride, pad, dilation=1): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return ( + torch.div(input_length + 2 * pad - dilation * (kernel_size - 1) - 1, stride, rounding_mode="floor") + 1 + ) + + def _transpose_conv_out_length(input_length, kernel_size, stride, pad, dilation=1): + return (input_length - 1) * stride - 2 * pad + dilation * (kernel_size - 1) + 1 + + # conv_pre + input_lengths = _conv_out_length(input_lengths, 7, 1, 3) + + # upsampler + for i, (upsample_rate, kernel_size) in enumerate( + zip(self.config.upsample_rates, self.config.upsample_kernel_sizes) + ): + input_lengths = _transpose_conv_out_length( + input_lengths, kernel_size, upsample_rate, (kernel_size - upsample_rate) // 2 + ) + + # resblock + for i in range(len(self.config.upsample_rates)): + for kernel_size, dilation in zip(self.config.resblock_kernel_sizes, self.config.resblock_dilation_sizes): + for dil in dilation: + input_lengths = _conv_out_length( + input_lengths, kernel_size, 1, (kernel_size - 1) * dil // 2, dilation=dil + ) + + for dil in dilation: + input_lengths = _conv_out_length(input_lengths, kernel_size, 1, (kernel_size - 1) // 2, dilation=1) + + # conv_post + input_lengths = _conv_out_length(input_lengths, 7, 1, 3) + + return input_lengths + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TCodeHifiGan.forward with SeamlessM4T->SeamlessM4Tv2, spkr_id->speaker_id + def forward( + self, input_ids: torch.LongTensor, speaker_id: torch.Tensor, lang_id: torch.Tensor, **kwargs + ) -> tuple[torch.Tensor]: + """ + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`SeamlessM4Tv2TextToUnitForConditionalGeneration`]. [What are input + IDs?](../glossary#input-ids) + speaker_id (`int`, *optional*): + The id of the speaker used for speech synthesis. Must be lower than `config.vocoder_num_spkrs`. + tgt_lang (`str`, *optional*): + The language id to use as target language for translation. + """ + hidden_states = self.unit_embedding(input_ids).transpose(1, 2) + spkr = self.speaker_embedding(speaker_id).transpose(1, 2) + lang = self.language_embedding(lang_id).transpose(1, 2) + + log_dur_pred = self.dur_predictor(hidden_states.transpose(1, 2)) + dur_out = torch.clamp(torch.round(torch.expm1(log_dur_pred)).long(), min=1) + # B x C x T + if hidden_states.size(0) == 1: + hidden_states = torch.repeat_interleave(hidden_states, dur_out.view(-1), dim=2) + else: + # if batched sample, need to interleave per sample, and pad -> loss of parallelism + if hidden_states.shape[0] > 1 and self.training: + logger.warning( + """`self.training=True` and you use batching. You lose parallelism during the hifigan + forward pass because the samples are interleaved.""" + ) + hidden_states = [ + torch.repeat_interleave(hidden_state, duration, dim=-1).transpose(0, 1) + for (hidden_state, duration) in zip(hidden_states, dur_out) + ] + + hidden_states = nn.utils.rnn.pad_sequence(hidden_states, batch_first=True).transpose(1, 2) + + spkr = spkr.repeat(1, 1, hidden_states.shape[-1]) + lang = lang.repeat(1, 1, hidden_states.shape[-1]) + hidden_states = torch.cat([lang, hidden_states, spkr], dim=1) + + hidden_states = self.hifi_gan(hidden_states) + + unit_lengths = self._get_dur_output_lengths(input_ids, dur_out) + lengths = self._get_output_hifigan_lengths(unit_lengths) + + return hidden_states, lengths + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TCodeHifiGan.apply_weight_norm + def apply_weight_norm(self): + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + weight_norm(self.hifi_gan.conv_pre) + for layer in self.hifi_gan.upsampler: + weight_norm(layer) + for layer in self.hifi_gan.resblocks: + layer.apply_weight_norm() + weight_norm(self.hifi_gan.conv_post) + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TCodeHifiGan.remove_weight_norm + def remove_weight_norm(self): + nn.utils.remove_weight_norm(self.hifi_gan.conv_pre) + for layer in self.hifi_gan.upsampler: + nn.utils.remove_weight_norm(layer) + for layer in self.hifi_gan.resblocks: + layer.remove_weight_norm() + nn.utils.remove_weight_norm(self.hifi_gan.conv_post) + + +############ WHOLE MODEL related code ################ + + +@auto_docstring( + custom_intro=""" + The text-to-text SeamlessM4Tv2 Model transformer which can be used for T2TT. + """ +) +# Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForTextToText with SeamlessM4T->SeamlessM4Tv2,SeamlessM4Tv2Tokenizer->SeamlessM4TTokenizer, SeamlessM4Tv2Processor->SeamlessM4TProcessor, SEAMLESS_M4T->SEAMLESS_M4T_V2 +class SeamlessM4Tv2ForTextToText(SeamlessM4Tv2PreTrainedModel, GenerationMixin): + _keys_to_ignore_on_load_missing = ["speech_encoder", "t2u_model", "vocoder"] + main_input_name = "input_ids" + + _tied_weights_keys = { + "lm_head.weight": "shared.weight", + "text_encoder.embed_tokens.weight": "shared.weight", + "text_decoder.embed_tokens.weight": "shared.weight", + } + + def __init__(self, config: SeamlessM4Tv2Config): + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + + self.text_encoder = SeamlessM4Tv2Encoder(config) + self.text_decoder = SeamlessM4Tv2Decoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.text_encoder + + def get_decoder(self): + return self.text_decoder + + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + def set_input_embeddings(self, value): + self.text_encoder.embed_tokens = value + self.text_decoder.embed_tokens = value + self.shared = value + + @auto_docstring(custom_args=SEAMLESS_M4T_V2_COMMON_CUSTOM_ARGS) + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + encoder_outputs = self.text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + def generate( + self, + input_ids=None, + tgt_lang=None, + generation_config=None, + logits_processor=None, + stopping_criteria=None, + prefix_allowed_tokens_fn=None, + synced_gpus=False, + **kwargs, + ): + """ + Generates sequences of token ids. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Parameters: + input_ids (`torch.Tensor` of varying shape depending on the modality, *optional*): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`SeamlessM4TTokenizer`] or [`SeamlessM4TProcessor`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + logits_processor (`LogitsProcessorList`, *optional*): + Custom logits processors that complement the default logits processors built from arguments and + generation config. If a logit processor is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + stopping_criteria (`StoppingCriteriaList`, *optional*): + Custom stopping criteria that complement the default stopping criteria built from arguments and a + generation config. If a stopping criteria is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], list[int]]`, *optional*): + If provided, this function constraints the beam search to allowed tokens only at each step. If not + provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and + `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned + on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful + for constrained generation conditioned on the prefix, as described in [Autoregressive Entity + Retrieval](https://huggingface.co/papers/2010.00904). + synced_gpus (`bool`, *optional*, defaults to `False`): + Whether to continue running the while loop until max_length (needed to avoid deadlocking with + `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). + kwargs (`dict[str, Any]`, *optional*): + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. + + Return: + [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` + or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. The possible + [`~utils.ModelOutput`] types are: + - [`~generation.GenerateEncoderDecoderOutput`], + - [`~generation.GenerateBeamEncoderDecoderOutput`] + """ + # prepare text_decoder_input_ids + text_decoder_input_ids = kwargs.pop("decoder_input_ids", None) + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + if tgt_lang is not None: + batch_size = len(input_ids) if input_ids is not None else len(kwargs.get("inputs_embeds")) + + if hasattr(self.generation_config, "text_decoder_lang_to_code_id"): + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + if tgt_lang not in self.generation_config.text_decoder_lang_to_code_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. Please specify a `tgt_lang` in + {", ".join(self.generation_config.text_decoder_lang_to_code_id.keys())}""" + ) + # tgt_lang gets priority over decoder input ids + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + else: + raise ValueError( + """This model generation config doesn't have a `text_decoder_lang_to_code_id` key which maps + the target language to the right token id. Make sure to load the right generation config.""" + ) + else: + # only a warning, otherwise errors appear in the tests + logger.warning( + """You must either specify a `tgt_lang` or pass a correct `text_decoder_input_ids` to get + a correct generation, otherwise the generation will probably make no sense.""" + ) + + return super().generate( + input_ids, + generation_config, + logits_processor, + stopping_criteria, + prefix_allowed_tokens_fn, + synced_gpus, + decoder_input_ids=text_decoder_input_ids, + **kwargs, + ) + + +@auto_docstring( + custom_intro=""" + The speech-to-text SeamlessM4Tv2 Model transformer which can be used for S2TT. + """ +) +class SeamlessM4Tv2ForSpeechToText(SeamlessM4Tv2PreTrainedModel, GenerationMixin): + input_modalities = "audio" + _keys_to_ignore_on_load_missing = ["text_encoder", "t2u_model", "vocoder"] + main_input_name = "input_features" + + _tied_weights_keys = { + "lm_head.weight": "shared.weight", + "text_decoder.embed_tokens.weight": "shared.weight", + } + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToText.__init__ with SeamlessM4T->SeamlessM4Tv2 + def __init__(self, config: SeamlessM4Tv2Config): + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + self.speech_encoder = SeamlessM4Tv2SpeechEncoder(config) + self.text_decoder = SeamlessM4Tv2Decoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToText.get_encoder + def get_encoder(self): + return self.speech_encoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToText.get_decoder + def get_decoder(self): + return self.text_decoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToText.get_input_embeddings + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToText.set_input_embeddings + def set_input_embeddings(self, value): + self.text_decoder.embed_tokens = value + + @auto_docstring(custom_args=SEAMLESS_M4T_V2_COMMON_CUSTOM_ARGS) + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToText.forward + def forward( + self, + input_features: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + encoder_outputs = self.speech_encoder( + input_features=input_features, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_outputs[0].device + ) + encoder_attention_mask = _compute_new_attention_mask( + hidden_states=encoder_outputs[0], seq_lens=sub_sampled_lengths + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToText.generate + def generate( + self, + input_features=None, + tgt_lang=None, + generation_config=None, + logits_processor=None, + stopping_criteria=None, + prefix_allowed_tokens_fn=None, + synced_gpus=False, + **kwargs, + ): + """ + Generates sequences of token ids. + + + + Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the + model's default generation configuration. You can override any `generation_config` by passing the corresponding + parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Parameters: + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`): + Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the + [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details. + + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + generation_config (`~generation.GenerationConfig`, *optional*): + The generation configuration to be used as base parametrization for the generation call. `**kwargs` + passed to generate matching the attributes of `generation_config` will override them. If + `generation_config` is not provided, the default will be used, which had the following loading + priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model + configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s + default values, whose documentation should be checked to parameterize generation. + logits_processor (`LogitsProcessorList`, *optional*): + Custom logits processors that complement the default logits processors built from arguments and + generation config. If a logit processor is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + stopping_criteria (`StoppingCriteriaList`, *optional*): + Custom stopping criteria that complement the default stopping criteria built from arguments and a + generation config. If a stopping criteria is passed that is already created with the arguments or a + generation config an error is thrown. This feature is intended for advanced users. + prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], list[int]]`, *optional*): + If provided, this function constraints the beam search to allowed tokens only at each step. If not + provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and + `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned + on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful + for constrained generation conditioned on the prefix, as described in [Autoregressive Entity + Retrieval](https://huggingface.co/papers/2010.00904). + synced_gpus (`bool`, *optional*, defaults to `False`): + Whether to continue running the while loop until max_length (needed to avoid deadlocking with + `FullyShardedDataParallel` and DeepSpeed ZeRO Stage 3). + kwargs (`dict[str, Any]`, *optional*): + Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be + forwarded to the `forward` function of the model. + + Return: + [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True` + or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`. The possible + [`~utils.ModelOutput`] types are: + - [`~generation.GenerateEncoderDecoderOutput`], + - [`~generation.GenerateBeamEncoderDecoderOutput`] + """ + text_decoder_input_ids = kwargs.pop("decoder_input_ids", None) + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + input_features = input_features if input_features is not None else kwargs.pop("inputs") + if tgt_lang is not None: + inputs = kwargs.get("inputs_embeds") if input_features is None else input_features + inputs = ( + inputs + if inputs is not None + else kwargs.get("encoder_outputs", {"last_hidden_state": None})["last_hidden_state"] + ) + batch_size = len(inputs) + + if hasattr(self.generation_config, "text_decoder_lang_to_code_id"): + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + if tgt_lang not in self.generation_config.text_decoder_lang_to_code_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. Please specify a `tgt_lang` in + {", ".join(self.generation_config.text_decoder_lang_to_code_id.keys())}""" + ) + # tgt_lang gets priority over decoder input ids + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + else: + raise ValueError( + """This model generation config doesn't have a `text_decoder_lang_to_code_id` key which maps + the target language to the right token id. Make sure to load the right generation config.""" + ) + else: + # only a warning, otherwise errors appear in the tests + logger.warning( + """You must either specify a `tgt_lang` or pass a correct `text_decoder_input_ids` to get + a correct generation, otherwise the generation will probably make no sense.""" + ) + return super().generate( + input_features, + generation_config, + logits_processor, + stopping_criteria, + prefix_allowed_tokens_fn, + synced_gpus, + decoder_input_ids=text_decoder_input_ids, + **kwargs, + ) + + +@auto_docstring( + custom_intro=""" + The text-to-speech SeamlessM4Tv2 Model transformer which can be used for T2ST. + """ +) +class SeamlessM4Tv2ForTextToSpeech(SeamlessM4Tv2PreTrainedModel, GenerationMixin): + output_modalities = ("audio",) + _keys_to_ignore_on_load_missing = ["speech_encoder"] + main_input_name = "input_ids" + + _tied_weights_keys = { + "lm_head.weight": "shared.weight", + "text_encoder.embed_tokens.weight": "shared.weight", + "text_decoder.embed_tokens.weight": "shared.weight", + } + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForTextToSpeech.__init__ with SeamlessM4T->SeamlessM4Tv2 + def __init__(self, config: SeamlessM4Tv2Config): + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + + self.text_encoder = SeamlessM4Tv2Encoder(config) + self.text_decoder = SeamlessM4Tv2Decoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.t2u_model = SeamlessM4Tv2TextToUnitForConditionalGeneration(config) + self.vocoder = SeamlessM4Tv2CodeHifiGan(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForTextToSpeech.get_encoder + def get_encoder(self): + return self.text_encoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForTextToSpeech.get_decoder + def get_decoder(self): + return self.text_decoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForTextToSpeech.get_input_embeddings + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForTextToSpeech.set_input_embeddings + def set_input_embeddings(self, value): + self.text_encoder.embed_tokens = value + self.text_decoder.embed_tokens = value + self.shared = value + + @auto_docstring(custom_args=SEAMLESS_M4T_V2_COMMON_CUSTOM_ARGS) + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForTextToSpeech.forward with SeamlessM4T->SeamlessM4Tv2 + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + # if encoder_outputs is not None, it's probably used within a .generate method so no need to warn + logger.warning( + "This is the same forward method as `SeamlessM4Tv2ForTextToText`." + "It doesn't use the text-to-unit model `SeamlessM4Tv2TextToUnitForConditionalGeneration`." + "If you want to generate speech, use the `.generate` method." + ) + encoder_outputs = self.text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_ids: torch.Tensor | None = None, + return_intermediate_token_ids: bool | None = None, + tgt_lang: str | None = None, + speaker_id: int | None = 0, + **kwargs, + ) -> torch.Tensor | SeamlessM4Tv2GenerationOutput: + """ + Generates translated audio waveforms. + + + + This method successively calls the `.generate` function of two different sub-models. You can specify keyword + arguments at two different levels: general arguments that will be passed to both models, or prefixed arguments + that will be passed to one of them. + + For example, calling `.generate(input_ids, num_beams=4, speech_do_sample=True)` will successively perform + beam-search decoding on the text model, and multinomial beam-search sampling on the speech model. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`SeamlessM4TTokenizer`] or [`SeamlessM4TProcessor`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + return_intermediate_token_ids (`bool`, *optional*): + If `True`, also returns the intermediate generated text and unit tokens. Set to `True` if you also want + to get translated text alongside the audio. + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + speaker_id (`int`, *optional*, defaults to 0): + The id of the speaker used for speech synthesis. Must be lower than `config.vocoder_num_spkrs`. + kwargs (*optional*): + Remaining dictionary of keyword arguments that will be passed to [`GenerationMixin.generate`]. Keyword + arguments are of two types: + + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model, + except for `decoder_input_ids` which will only be passed through the text components. + - With a *text_* or *speech_* prefix, they will be input for the `generate` method of the + text model and speech model respectively. It has the priority over the keywords without a prefix. + + This means you can, for example, specify a generation strategy for one generation but not for the + other. + + + Returns: + `Union[SeamlessM4Tv2GenerationOutput, tuple[Tensor]]`: + - If `return_intermediate_token_ids`, returns [`SeamlessM4Tv2GenerationOutput`]. + - If not `return_intermediate_token_ids`, returns a tuple composed of waveforms of shape `(batch_size, + sequence_length)` and `waveform_lengths` which gives the length of each sample. + """ + batch_size = len(input_ids) if input_ids is not None else len(kwargs.get("inputs_embeds")) + + if tgt_lang is None: + raise ValueError("You must specify a `tgt_lang` to generate translated speech.") + else: + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + for key in ["text_decoder_lang_to_code_id", "t2u_lang_code_to_id", "vocoder_lang_code_to_id"]: + lang_code_to_id = getattr(self.generation_config, key, None) + if lang_code_to_id is None: + raise ValueError( + f"""This model generation config doesn't have a `{key}` key which maps the target language + to the right token id. Make sure to load the right generation config.""" + ) + elif tgt_lang not in lang_code_to_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. + Please specify a `tgt_lang` in {",".join(lang_code_to_id.keys())}. Note that SeamlessM4Tv2 supports + more languages for text translation than for speech synthesis.""" + ) + + kwargs_text, kwargs_speech = format_speech_generation_kwargs(kwargs) + kwargs_text["output_hidden_states"] = True + kwargs_text["return_dict_in_generate"] = True + kwargs_text["output_scores"] = True + + text_decoder_input_ids = kwargs_text.get("decoder_input_ids") + + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + + kwargs_text["decoder_input_ids"] = text_decoder_input_ids + + # first generation + text_generation_output = super().generate(input_ids, **kwargs_text) + sequences = text_generation_output.sequences + + # prepare second generation + num_return_sequences = len(sequences) // batch_size + attention_mask = kwargs_speech.get("attention_mask", kwargs_text.get("attention_mask", None)) + + if attention_mask is not None: + # repeat attention mask alongside batch dimension + attention_mask = torch.repeat_interleave(attention_mask, num_return_sequences, dim=0) + encoder_hidden_states = text_generation_output.encoder_hidden_states[-1] + + # repeat attention mask alongside batch dimension + encoder_hidden_states = torch.repeat_interleave(encoder_hidden_states, num_return_sequences, dim=0) + + # get decoder last hidden state - must do a pass through the text decoder + t2u_input_embeds = self.text_decoder( + input_ids=sequences[:, :-1], # Manually trim the final EOS token + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=attention_mask, + ).last_hidden_state + + pad_token_id = self.generation_config.pad_token_id + + # Compute new attention mask + seq_lens = (sequences[:, :-1] != pad_token_id).int().sum(1) + t2u_model_attention_mask = _compute_new_attention_mask(t2u_input_embeds, seq_lens) + kwargs_speech["attention_mask"] = t2u_model_attention_mask + + # REMOVE EOS and lang_id + t2u_input_ids = sequences[:, 2:-1] + # replace every other EOS + t2u_input_ids = torch.masked_fill( + t2u_input_ids, t2u_input_ids == self.generation_config.eos_token_id, pad_token_id + ) + + # compute t2u_char_input_ids + t2u_subwords = self._indices_to_subwords(t2u_input_ids) + t2u_char_count_per_id = self._count_character_length_in_subword( + t2u_input_ids, t2u_subwords, pad_token_id=pad_token_id + ) + + # Add pads for lang, EOS tokens as per NLLB "source" tokenizer mode. + pad_zero = t2u_char_count_per_id.new_zeros((t2u_char_count_per_id.shape[0], 1)) + t2u_char_count_per_id = torch.cat([pad_zero, t2u_char_count_per_id, pad_zero], dim=1) + t2u_char_input_ids = self._get_char_input_ids( + t2u_input_ids, t2u_subwords, t2u_char_count_per_id, pad_token_id=pad_token_id + ) + + # second pass + t2u_output = self.t2u_model( + inputs_embeds=t2u_input_embeds, + char_input_ids=t2u_char_input_ids, + char_count_per_id=t2u_char_count_per_id, + **kwargs_speech, + ) + + t2u_logits = t2u_output[0] + padding_mask = t2u_output[1].bool() + + # The text-to-unit model is non auto-regressive. We keep the ability to use sampling with temperature + temperature = kwargs_speech.get("temperature", None) + if (temperature is None or temperature == 1.0) or not kwargs_speech.get("do_sample", False): + unit_ids = t2u_logits.argmax(dim=-1) + else: + t2u_logits = t2u_logits / temperature + # apply softmax + probs = nn.functional.softmax(t2u_logits, dim=-1) + # reshape to 2D: (batch_size, seq_len, t2u_vocab_size) -> (batch_size*seq_len, t2u_vocab_size) + probs = probs.reshape((-1, probs.shape[2])) + # multinomial then reshape : (batch_size*seq_len)-> (batch_size,seq_len) + unit_ids = torch.multinomial(probs, num_samples=1).view(t2u_logits.shape[0], -1) + + output_unit_ids = unit_ids.detach().clone() + + replace_mask = (unit_ids == self.config.t2u_eos_token_id) | (~padding_mask) + # replace eos per pad + unit_ids = unit_ids.masked_fill(replace_mask, self.config.t2u_pad_token_id) + + # offset of control symbols + unit_ids = torch.where( + unit_ids == self.config.t2u_pad_token_id, unit_ids, unit_ids - self.config.vocoder_offset + ) + + vocoder_tgt_lang_id = self.generation_config.vocoder_lang_code_to_id.get(tgt_lang) + vocoder_tgt_lang_id = torch.tensor([[vocoder_tgt_lang_id]] * len(unit_ids), device=self.device) + + speaker_id = torch.tensor([[speaker_id]] * len(unit_ids), device=self.device) + + waveform, waveform_lengths = self.vocoder( + input_ids=unit_ids, speaker_id=speaker_id, lang_id=vocoder_tgt_lang_id + ) + + if return_intermediate_token_ids: + return SeamlessM4Tv2GenerationOutput( + waveform=waveform, + waveform_lengths=waveform_lengths, + sequences=sequences, + unit_sequences=output_unit_ids, + ) + + return waveform, waveform_lengths + + +@auto_docstring( + custom_intro=""" + The speech-to-speech SeamlessM4Tv2 Model transformer which can be used for S2ST. + """ +) +class SeamlessM4Tv2ForSpeechToSpeech(SeamlessM4Tv2PreTrainedModel, GenerationMixin): + input_modalities = "audio" + output_modalities = ("audio",) + _keys_to_ignore_on_load_missing = ["text_encoder"] + main_input_name = "input_features" + + _tied_weights_keys = {"lm_head.weight": "shared.weight", "text_decoder.embed_tokens.weight": "shared.weight"} + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToSpeech.__init__ with SeamlessM4T->SeamlessM4Tv2 + def __init__(self, config): + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + self.speech_encoder = SeamlessM4Tv2SpeechEncoder(config) + self.text_decoder = SeamlessM4Tv2Decoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.t2u_model = SeamlessM4Tv2TextToUnitForConditionalGeneration(config) + self.vocoder = SeamlessM4Tv2CodeHifiGan(config) + self.post_init() + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToSpeech.get_encoder + def get_encoder(self): + return self.speech_encoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToSpeech.get_decoder + def get_decoder(self): + return self.text_decoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToSpeech.get_input_embeddings + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToSpeech.set_input_embeddings + def set_input_embeddings(self, value): + self.text_decoder.embed_tokens = value + + @auto_docstring(custom_args=SEAMLESS_M4T_V2_COMMON_CUSTOM_ARGS) + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TForSpeechToSpeech.forward with SeamlessM4T->SeamlessM4Tv2 + def forward( + self, + input_features: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if encoder_outputs is None: + # if encoder_outputs is not None, it's probably used within a .generate method so no need to warn + logger.warning( + "This is the same forward method as `SeamlessM4Tv2ForSpeechToText`. It doesn't use `self.t2u_model`." + "If you want to generate speech, use the `generate` method." + ) + + encoder_outputs = self.speech_encoder( + input_features=input_features, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_outputs[0].device + ) + encoder_attention_mask = _compute_new_attention_mask( + hidden_states=encoder_outputs[0], seq_lens=sub_sampled_lengths + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_features: torch.Tensor | None = None, + return_intermediate_token_ids: bool | None = None, + tgt_lang: str | None = None, + speaker_id: int | None = 0, + **kwargs, + ) -> torch.Tensor | SeamlessM4Tv2GenerationOutput: + """ + Generates translated audio waveforms. + + + + This method successively calls the `.generate` function of two different sub-models. You can specify keyword + arguments at two different levels: general arguments that will be passed to both models, or prefixed arguments + that will be passed to one of them. + + For example, calling `.generate(input_features, num_beams=4, speech_do_sample=True)` will successively perform + beam-search decoding on the text model, and multinomial beam-search sampling on the speech model. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + Args: + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`): + Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the + [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details. + return_intermediate_token_ids (`bool`, *optional*): + If `True`, also returns the intermediate generated text and unit tokens. Set to `True` if you also want + to get translated text alongside the audio. + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + speaker_id (`int`, *optional*, defaults to 0): + The id of the speaker used for speech synthesis. Must be lower than `config.vocoder_num_spkrs`. + + kwargs (*optional*): + Remaining dictionary of keyword arguments that will be passed to [`GenerationMixin.generate`]. Keyword + arguments are of two types: + + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model, + except for `decoder_input_ids` which will only be passed through the text components. + - With a *text_* or *speech_* prefix, they will be input for the `generate` method of the + text model and speech model respectively. It has the priority over the keywords without a prefix. + + This means you can, for example, specify a generation strategy for one generation but not for the + other. + + + Returns: + `Union[SeamlessM4Tv2GenerationOutput, tuple[Tensor]]`: + - If `return_intermediate_token_ids`, returns [`SeamlessM4Tv2GenerationOutput`]. + - If not `return_intermediate_token_ids`, returns a tuple composed of waveforms of shape `(batch_size, + sequence_length)` and `waveform_lengths` which gives the length of each sample. + """ + batch_size = len(input_features) if input_features is not None else len(kwargs.get("inputs_embeds")) + + if tgt_lang is None: + raise ValueError("You must specify a `tgt_lang` to generate translated speech.") + else: + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + for key in ["text_decoder_lang_to_code_id", "t2u_lang_code_to_id", "vocoder_lang_code_to_id"]: + lang_code_to_id = getattr(self.generation_config, key, None) + if lang_code_to_id is None: + raise ValueError( + f"""This model generation config doesn't have a `{key}` key which maps the target language + to the right token id. Make sure to load the right generation config.""" + ) + elif tgt_lang not in lang_code_to_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. + Please specify a `tgt_lang` in {",".join(lang_code_to_id.keys())}. Note that SeamlessM4Tv2 supports + more languages for text translation than for speech synthesis.""" + ) + + kwargs_text, kwargs_speech = format_speech_generation_kwargs(kwargs) + kwargs_text["output_hidden_states"] = True + kwargs_text["return_dict_in_generate"] = True + kwargs_text["output_scores"] = True + + text_decoder_input_ids = kwargs_text.get("decoder_input_ids") + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + + kwargs_text["decoder_input_ids"] = text_decoder_input_ids + + # first generation + text_generation_output = super().generate(input_features, **kwargs_text) + sequences = text_generation_output.sequences + + # prepare second generation + num_return_sequences = len(sequences) // batch_size + attention_mask = kwargs_speech.get("attention_mask", kwargs_text.get("attention_mask", None)) + + # get last_hidden_state from encoder + encoder_hidden_states = self.speech_encoder(input_features=input_features, attention_mask=attention_mask)[0] + + # input modality = speech so new attention mask for the decoder + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_hidden_states.device + ) + attention_mask = _compute_new_attention_mask( + hidden_states=encoder_hidden_states, seq_lens=sub_sampled_lengths + ) + + # repeat attention mask alongside batch dimension + attention_mask = torch.repeat_interleave(attention_mask, num_return_sequences, dim=0) + + # repeat attention mask alongside batch dimension + encoder_hidden_states = torch.repeat_interleave(encoder_hidden_states, num_return_sequences, dim=0) + + # get decoder last hidden state - must do a pass through the text decoder + t2u_input_embeds = self.text_decoder( + input_ids=sequences[:, :-1], # Manually trim the final EOS token + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=attention_mask, + ).last_hidden_state + + pad_token_id = self.generation_config.pad_token_id + + # Compute new attention mask + seq_lens = (sequences[:, :-1] != pad_token_id).int().sum(1) + t2u_model_attention_mask = _compute_new_attention_mask(t2u_input_embeds, seq_lens) + kwargs_speech["attention_mask"] = t2u_model_attention_mask + + # REMOVE EOS and lang_id + t2u_input_ids = sequences[:, 2:-1] + # replace every other EOS + t2u_input_ids = torch.masked_fill( + t2u_input_ids, t2u_input_ids == self.generation_config.eos_token_id, pad_token_id + ) + + # compute t2u_char_input_ids + t2u_subwords = self._indices_to_subwords(t2u_input_ids) + t2u_char_count_per_id = self._count_character_length_in_subword( + t2u_input_ids, t2u_subwords, pad_token_id=pad_token_id + ) + + # Add pads for lang, EOS tokens as per NLLB "source" tokenizer mode. + pad_zero = t2u_char_count_per_id.new_zeros((t2u_char_count_per_id.shape[0], 1)) + t2u_char_count_per_id = torch.cat([pad_zero, t2u_char_count_per_id, pad_zero], dim=1) + t2u_char_input_ids = self._get_char_input_ids( + t2u_input_ids, t2u_subwords, t2u_char_count_per_id, pad_token_id=pad_token_id + ) + + # second pass + t2u_output = self.t2u_model( + inputs_embeds=t2u_input_embeds, + char_input_ids=t2u_char_input_ids, + char_count_per_id=t2u_char_count_per_id, + **kwargs_speech, + ) + + t2u_logits = t2u_output[0] + padding_mask = t2u_output[1].bool() + + # The text-to-unit model is non auto-regressive. We keep the ability to use sampling with temperature + temperature = kwargs_speech.get("temperature", None) + if (temperature is None or temperature == 1.0) or not kwargs_speech.get("do_sample", False): + unit_ids = t2u_logits.argmax(dim=-1) + else: + t2u_logits = t2u_logits / temperature + # apply softmax + probs = nn.functional.softmax(t2u_logits, dim=-1) + # reshape to 2D: (batch_size, seq_len, t2u_vocab_size) -> (batch_size*seq_len, t2u_vocab_size) + probs = probs.reshape((-1, probs.shape[2])) + # multinomial then reshape : (batch_size*seq_len)-> (batch_size,seq_len) + unit_ids = torch.multinomial(probs, num_samples=1).view(t2u_logits.shape[0], -1) + + output_unit_ids = unit_ids.detach().clone() + + replace_mask = (unit_ids == self.config.t2u_eos_token_id) | (~padding_mask) + # replace eos per pad + unit_ids = unit_ids.masked_fill(replace_mask, self.config.t2u_pad_token_id) + + # offset of control symbols + unit_ids = torch.where( + unit_ids == self.config.t2u_pad_token_id, unit_ids, unit_ids - self.config.vocoder_offset + ) + + vocoder_tgt_lang_id = self.generation_config.vocoder_lang_code_to_id.get(tgt_lang) + vocoder_tgt_lang_id = torch.tensor([[vocoder_tgt_lang_id]] * len(unit_ids), device=self.device) + + speaker_id = torch.tensor([[speaker_id]] * len(unit_ids), device=self.device) + + waveform, waveform_lengths = self.vocoder( + input_ids=unit_ids, speaker_id=speaker_id, lang_id=vocoder_tgt_lang_id + ) + + if return_intermediate_token_ids: + return SeamlessM4Tv2GenerationOutput( + waveform=waveform, + waveform_lengths=waveform_lengths, + sequences=sequences, + unit_sequences=output_unit_ids, + ) + + return waveform, waveform_lengths + + +@auto_docstring( + custom_intro=""" + The original SeamlessM4Tv2 Model transformer which can be used for every tasks available (S2ST, S2TT, T2TT, T2ST). + """ +) +class SeamlessM4Tv2Model(SeamlessM4Tv2PreTrainedModel, GenerationMixin): + input_modalities = ("audio", "text") + output_modalities = ("audio", "text") + _tied_weights_keys = { + "lm_head.weight": "shared.weight", + "text_encoder.embed_tokens.weight": "shared.weight", + "text_decoder.embed_tokens.weight": "shared.weight", + } + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TModel.__init__ with SeamlessM4T->SeamlessM4Tv2 + def __init__(self, config, current_modality="text"): + r""" + current_modality (`str`, *optional*, defaults to `"text"`): + Default modality. Used to initialize the model. + """ + super().__init__(config) + + self.shared = nn.Embedding(config.vocab_size, config.hidden_size, config.pad_token_id) + + self.text_encoder = SeamlessM4Tv2Encoder(config) + self.speech_encoder = SeamlessM4Tv2SpeechEncoder(config) + self.text_decoder = SeamlessM4Tv2Decoder(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.current_modality = current_modality + if current_modality == "speech": + self.main_input_name = "input_features" + + # these models already call post_init in their initialization + self.t2u_model = SeamlessM4Tv2TextToUnitForConditionalGeneration(config) + self.vocoder = SeamlessM4Tv2CodeHifiGan(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TModel.set_modality + def set_modality(self, modality="text"): + if modality == "text": + self.main_input_name = "input_ids" + self.current_modality = "text" + elif modality == "speech": + self.main_input_name = "input_features" + self.current_modality = "speech" + else: + raise ValueError(f"`modality={modality}` is not a valid modality. It must be `text` or `speech`.") + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TModel.get_encoder + def get_encoder(self): + if self.current_modality == "text": + return self.text_encoder + else: + return self.speech_encoder + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TModel.get_input_embeddings + def get_input_embeddings(self): + return self.text_decoder.embed_tokens + + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TModel.set_input_embeddings + def set_input_embeddings(self, value): + self.text_encoder.embed_tokens = value + self.text_decoder.embed_tokens = value + self.shared = value + + @auto_docstring(custom_args=SEAMLESS_M4T_V2_COMMON_CUSTOM_ARGS) + # Copied from transformers.models.seamless_m4t.modeling_seamless_m4t.SeamlessM4TModel.forward with SeamlessM4T->SeamlessM4Tv2 + def forward( + self, + input_ids: torch.LongTensor | None = None, + input_features: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + decoder_input_ids: torch.LongTensor | None = None, + decoder_attention_mask: torch.LongTensor | None = None, + encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + decoder_inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> Seq2SeqLMOutput | tuple[torch.FloatTensor]: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., + config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the + loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if labels is not None: + if use_cache: + logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.") + use_cache = False + if decoder_input_ids is None and decoder_inputs_embeds is None: + decoder_input_ids = shift_tokens_right( + labels, self.config.pad_token_id, self.config.decoder_start_token_id + ) + + if input_ids is None and input_features is None and inputs_embeds is None and encoder_outputs is None: + raise ValueError( + "`input_ids`,`input_features`, `inputs_embeds` and `encoder_outputs` are all empty. Make sure at least one of them is not." + ) + elif input_features is not None: + if input_ids is not None: + logger.warning( + "`input_ids` is not `None` but `input_features` has been given." + "`input_features` will be used in priority through the `speech_encoder`. " + "Make sure that `input_features` and `input_ids` are mutually exclusive." + ) + + if inputs_embeds is not None: + logger.warning( + "`inputs_embeds` is not `None` but `input_features` has been given." + "`input_features` will be used in priority through `speech_encoder`. " + "`inputs_embeds` will be ignored." + ) + + # if encoder_outputs is not None, it's probably used within a .generate method so no need to warn + logger.warning( + "This calls the same method `forward` as `SeamlessM4Tv2ForTextToText` and `SeamlessM4Tv2ForSpeechToText`" + "depending on the input modality. If you want to generate speech, use the `generate` method." + ) + + self.set_modality("speech") + + encoder_outputs = self.speech_encoder( + input_features=input_features, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + elif input_ids is not None or inputs_embeds is not None: + # if encoder_outputs is not None, it's probably used within a .generate method so no need to warn + logger.warning( + "This calls the same method `forward` as `SeamlessM4Tv2ForTextToText` and `SeamlessM4Tv2ForSpeechToText`" + "depending on the input modality. If you want to generate speech, use the `generate` method." + ) + self.set_modality("text") + encoder_outputs = self.text_encoder( + input_ids=input_ids, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + encoder_attention_mask = attention_mask + # input modality = speech so new attention mask + if self.current_modality == "speech" and attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_outputs[0].device + ) + encoder_attention_mask = _compute_new_attention_mask( + hidden_states=encoder_outputs[0], seq_lens=sub_sampled_lengths + ) + + # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn) + decoder_outputs = self.text_decoder( + input_ids=decoder_input_ids, + attention_mask=decoder_attention_mask, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + inputs_embeds=decoder_inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + lm_logits = self.lm_head(decoder_outputs[0]) + + masked_lm_loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + labels = labels.to(lm_logits.device) + masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1)) + + if not return_dict: + outputs = decoder_outputs + encoder_outputs + output = (lm_logits,) + outputs[1:] + return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output + + return Seq2SeqLMOutput( + loss=masked_lm_loss, + logits=lm_logits, + past_key_values=decoder_outputs.past_key_values, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + @torch.no_grad() + def generate( + self, + input_ids: torch.Tensor | None = None, + input_features: torch.Tensor | None = None, + return_intermediate_token_ids: bool | None = None, + tgt_lang: str | None = None, + speaker_id: int | None = 0, + generate_speech: bool | None = True, + **kwargs, + ) -> torch.Tensor | SeamlessM4Tv2GenerationOutput: + """ + Generates translated token ids and/or translated audio waveforms. + + + + This method successively calls the `.generate` function of two different sub-models. You can specify keyword + arguments at two different levels: general arguments that will be passed to both models, or prefixed arguments + that will be passed to one of them. + + For example, calling `.generate(input_ids=input_ids, num_beams=4, speech_do_sample=True)` will successively + perform beam-search decoding on the text model, and multinomial beam-search sampling on the speech model. + + For an overview of generation strategies and code examples, check out the [following + guide](./generation_strategies). + + + + + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of input sequence tokens in the vocabulary. + + Indices can be obtained using [`SeamlessM4TTokenizer`] or [`SeamlessM4TProcessor`]. See + [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + input_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_banks)`, *optional*): + Input audio features. This should be returned by the [`SeamlessM4TFeatureExtractor`] class or the + [`SeamlessM4TProcessor`] class. See [`SeamlessM4TFeatureExtractor.__call__`] for details. + return_intermediate_token_ids (`bool`, *optional*): + If `True`, also returns the intermediate generated text and unit tokens. Set to `True` if you also want + to get translated text alongside the audio. + Note that if `generate_speech=False`, this parameter will be ignored and + the text tokens are returned. + tgt_lang (`str`, *optional*): + The language to use as target language for translation. + speaker_id (`int`, *optional*, defaults to 0): + The id of the speaker used for speech synthesis. Must be lower than `config.vocoder_num_spkrs`. + generate_speech (`bool`, *optional*, defaults to `True`): + If `False`, will only returns the text tokens and won't generate speech. + + kwargs (*optional*): + Remaining dictioy of keyword arguments that will be passed to [`GenerationMixin.generate`]. Keyword + arguments are of two types: + + - Without a prefix, they will be entered as `**kwargs` for the `generate` method of each sub-model, + except for `decoder_input_ids` which will only be passed through the text components. + - With a *text_* or *speech_* prefix, they will be input for the `generate` method of the + text model and speech model respectively. It has the priority over the keywords without a prefix. + + This means you can, for example, specify a generation strategy for one generation but not for the + other. + + Returns: + `Union[SeamlessM4Tv2GenerationOutput, tuple[Tensor], ModelOutput]`: + - If `generate_speech` and `return_intermediate_token_ids`, returns [`SeamlessM4Tv2GenerationOutput`]. + - If `generate_speech` and not `return_intermediate_token_ids`, returns a tuple composed of waveforms of + shape `(batch_size, sequence_length)` and `waveform_lengths` which gives the length of each sample. + - If `generate_speech=False`, it will returns `ModelOutput`. + """ + if input_ids is None and input_features is None and kwargs.get("inputs_embeds") is None: + raise ValueError( + "`input_ids`,`input_features` and `inputs_embeds` are all empty. Make sure at least one of them is not." + ) + + if generate_speech and tgt_lang is None: + raise ValueError("You must specify a `tgt_lang` to generate translated speech.") + + if tgt_lang is not None: + # also accept __xxx__ + tgt_lang = tgt_lang.replace("__", "") + if generate_speech: + keys_to_check = ["text_decoder_lang_to_code_id", "t2u_lang_code_to_id", "vocoder_lang_code_to_id"] + else: + keys_to_check = ["text_decoder_lang_to_code_id"] + for key in keys_to_check: + lang_code_to_id = getattr(self.generation_config, key, None) + if lang_code_to_id is None: + raise ValueError( + f"""This model generation config doesn't have a `{key}` key which maps the target language + to the right token id. Make sure to load the right generation config.""" + ) + elif tgt_lang not in lang_code_to_id: + raise ValueError( + f"""`tgt_lang={tgt_lang}` is not supported by this model. + Please specify a `tgt_lang` in {",".join(lang_code_to_id.keys())}. Note that SeamlessM4Tv2 supports + more languages for text translation than for speech synthesis.""" + ) + + batch_size = ( + len(input_features) + if input_features is not None + else (len(input_ids) if input_ids is not None else len(kwargs.get("inputs_embeds"))) + ) + + kwargs_text, kwargs_speech = format_speech_generation_kwargs(kwargs) + kwargs_text["output_hidden_states"] = True + kwargs_text["return_dict_in_generate"] = True + kwargs_text["output_scores"] = True + + text_decoder_input_ids = kwargs_text.get("decoder_input_ids") + # overwrite text_decoder_input_ids if tgt_lang is passed. The latter gets priority over decoder_input_ids. + if tgt_lang is not None: + # tgt_lang gets priority over decoder input ids + text_tgt_lang_id = self.generation_config.text_decoder_lang_to_code_id.get(tgt_lang) + text_decoder_input_ids = torch.tensor([[text_tgt_lang_id]] * batch_size, device=self.device) + + kwargs_text["decoder_input_ids"] = text_decoder_input_ids + + # first generation + if input_features is not None: + self.set_modality("speech") + if input_ids is not None: + logger.warning( + "`input_features` and `input_ids` are both non empty. `input_features` will be used in priority " + "through the speech encoder. Make sure `input_features=None` if you want to use the text encoder." + ) + text_generation_output = super().generate(input_features=input_features, **kwargs_text) + else: + self.set_modality("text") + text_generation_output = super().generate(input_ids=input_ids, input_features=None, **kwargs_text) + sequences = text_generation_output.sequences + + if not generate_speech: + return text_generation_output + + # prepare second generation + num_return_sequences = len(sequences) // batch_size + attention_mask = kwargs_speech.get("attention_mask", kwargs_text.get("attention_mask", None)) + + # get encoder last hidden states + if self.current_modality == "speech": + # get last_hidden_state from encoder - must do a pass through the speech encoder + encoder_hidden_states = self.speech_encoder( + input_features=input_features, attention_mask=attention_mask + ).last_hidden_state + + # input modality = speech so new attention mask for the decoder + if attention_mask is not None: + sub_sampled_lengths = self._compute_sub_sample_lengths_from_attention_mask(attention_mask).to( + encoder_hidden_states.device + ) + attention_mask = _compute_new_attention_mask( + hidden_states=encoder_hidden_states, seq_lens=sub_sampled_lengths + ) + else: + encoder_hidden_states = text_generation_output.encoder_hidden_states[-1] + + if attention_mask is not None: + # repeat attention mask alongside batch dimension + attention_mask = torch.repeat_interleave(attention_mask, num_return_sequences, dim=0) + + # repeat attention mask alongside batch dimension + encoder_hidden_states = torch.repeat_interleave(encoder_hidden_states, num_return_sequences, dim=0) + + # get decoder last hidden state - must do a pass through the text decoder + t2u_input_embeds = self.text_decoder( + input_ids=sequences[:, :-1], # Manually trim the final EOS token + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=attention_mask, + ).last_hidden_state + + pad_token_id = self.generation_config.pad_token_id + + # Compute new attention mask + seq_lens = (sequences[:, :-1] != pad_token_id).int().sum(1) + t2u_model_attention_mask = _compute_new_attention_mask(t2u_input_embeds, seq_lens) + kwargs_speech["attention_mask"] = t2u_model_attention_mask + + # REMOVE EOS and lang_id + t2u_input_ids = sequences[:, 2:-1] + # replace every other EOS + t2u_input_ids = torch.masked_fill( + t2u_input_ids, t2u_input_ids == self.generation_config.eos_token_id, pad_token_id + ) + + # compute t2u_char_input_ids + t2u_subwords = self._indices_to_subwords(t2u_input_ids) + t2u_char_count_per_id = self._count_character_length_in_subword( + t2u_input_ids, t2u_subwords, pad_token_id=pad_token_id + ) + + # Add pads for lang, EOS tokens as per NLLB "source" tokenizer mode. + pad_zero = t2u_char_count_per_id.new_zeros((t2u_char_count_per_id.shape[0], 1)) + t2u_char_count_per_id = torch.cat([pad_zero, t2u_char_count_per_id, pad_zero], dim=1) + t2u_char_input_ids = self._get_char_input_ids( + t2u_input_ids, t2u_subwords, t2u_char_count_per_id, pad_token_id=pad_token_id + ) + + # second pass + t2u_output = self.t2u_model( + inputs_embeds=t2u_input_embeds, + char_input_ids=t2u_char_input_ids, + char_count_per_id=t2u_char_count_per_id, + **kwargs_speech, + ) + + t2u_logits = t2u_output[0] + padding_mask = t2u_output[1].bool() + + # The text-to-unit model is non auto-regressive. We keep the ability to use sampling with temperature + temperature = kwargs_speech.get("temperature", None) + if (temperature is None or temperature == 1.0) or not kwargs_speech.get("do_sample", False): + unit_ids = t2u_logits.argmax(dim=-1) + else: + t2u_logits = t2u_logits / temperature + # apply softmax + probs = nn.functional.softmax(t2u_logits, dim=-1) + # reshape to 2D: (batch_size, seq_len, t2u_vocab_size) -> (batch_size*seq_len, t2u_vocab_size) + probs = probs.reshape((-1, probs.shape[2])) + # multinomial then reshape : (batch_size*seq_len)-> (batch_size,seq_len) + unit_ids = torch.multinomial(probs, num_samples=1).view(t2u_logits.shape[0], -1) + + output_unit_ids = unit_ids.detach().clone() + + replace_mask = (unit_ids == self.config.t2u_eos_token_id) | (~padding_mask) + # replace eos per pad + unit_ids = unit_ids.masked_fill(replace_mask, self.config.t2u_pad_token_id) + + # offset of control symbols + unit_ids = torch.where( + unit_ids == self.config.t2u_pad_token_id, unit_ids, unit_ids - self.config.vocoder_offset + ) + + vocoder_tgt_lang_id = self.generation_config.vocoder_lang_code_to_id.get(tgt_lang) + vocoder_tgt_lang_id = torch.tensor([[vocoder_tgt_lang_id]] * len(unit_ids), device=self.device) + + speaker_id = torch.tensor([[speaker_id]] * len(unit_ids), device=self.device) + + waveform, waveform_lengths = self.vocoder( + input_ids=unit_ids, speaker_id=speaker_id, lang_id=vocoder_tgt_lang_id + ) + + if return_intermediate_token_ids: + return SeamlessM4Tv2GenerationOutput( + waveform=waveform, + waveform_lengths=waveform_lengths, + sequences=sequences, + unit_sequences=output_unit_ids, + ) + + return waveform, waveform_lengths + + +__all__ = [ + "SeamlessM4Tv2ForTextToSpeech", + "SeamlessM4Tv2ForSpeechToSpeech", + "SeamlessM4Tv2ForTextToText", + "SeamlessM4Tv2ForSpeechToText", + "SeamlessM4Tv2Model", + "SeamlessM4Tv2PreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ef9d0cb0f2103b2f7eed560f6835b1fd5a55b8ed --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2025 Bytedance-Seed Ltd and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_seed_oss import * + from .modeling_seed_oss import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/configuration_seed_oss.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/configuration_seed_oss.py new file mode 100644 index 0000000000000000000000000000000000000000..b1221fcf53ce9407d5038be41a348afe372f39b1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/configuration_seed_oss.py @@ -0,0 +1,94 @@ +# Copyright 2025 Bytedance-Seed Ltd and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SeedOss model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="ByteDance-Seed/Seed-OSS-36B-Instruct") +@strict +class SeedOssConfig(PreTrainedConfig): + r""" + attention_out_bias (`bool`, *optional*, defaults to `False`): + Whether to use a bias in the output projection layer during self-attention. + + ```python + >>> from transformers import SeedOssModel, SeedOssConfig + + >>> # Initializing a SeedOss-36b style configuration + >>> configuration = SeedOssConfig() + + >>> # Initializing a model from the SeedOss-36b style configuration + >>> model = SeedOssModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "seed_oss" + keys_to_ignore_at_inference = ["past_key_values"] + # Default tensor parallel plan for base model `SeedOssModel` + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 155136 + hidden_size: int = 4096 + intermediate_size: int = 27648 + num_hidden_layers: int = 64 + num_attention_heads: int = 80 + num_key_value_heads: int | None = 8 + hidden_act: str = "silu" + max_position_embeddings: int = 524288 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-6 + use_cache: bool = True + pad_token_id: int | None = 1 + bos_token_id: int | None = 0 + eos_token_id: int | list[int] | None = 2 + pretraining_tp: int = 1 + tie_word_embeddings: bool = False + rope_parameters: RopeParameters | dict | None = None + attention_bias: bool = True + attention_out_bias: bool = False + attention_dropout: float | int = 0.1 + residual_dropout: float | int = 0.1 + mlp_bias: bool = False + head_dim: int | None = 128 + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + self.head_dim = self.head_dim if self.head_dim is not None else self.hidden_size // self.num_attention_heads + super().__post_init__(**kwargs) + + +__all__ = ["SeedOssConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/modeling_seed_oss.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/modeling_seed_oss.py new file mode 100644 index 0000000000000000000000000000000000000000..1ebc8f10a272722e1b138bdeb850d700d30d1f09 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/modeling_seed_oss.py @@ -0,0 +1,527 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/seed_oss/modular_seed_oss.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_seed_oss.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 Bytedance-Seed Ltd and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable +from typing import Optional + +import torch +import torch.nn as nn + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub +from ...masking_utils import create_causal_mask +from ...modeling_layers import ( + GenericForQuestionAnswering, + GenericForSequenceClassification, + GenericForTokenClassification, + GradientCheckpointingLayer, +) +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_seed_oss import SeedOssConfig + + +@use_kernel_forward_from_hub("RMSNorm") +class SeedOssRMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + SeedOssRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class SeedOssMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + self.residual_dropout = config.residual_dropout + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + down_proj = nn.functional.dropout(down_proj, p=self.residual_dropout, training=self.training) + return down_proj + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class SeedOssAttention(nn.Module): + def __init__(self, config: SeedOssConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = config.head_dim + self.num_key_value_heads = config.num_key_value_heads + self.num_attention_heads = config.num_attention_heads + self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, self.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + self.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_out_bias + ) + + self.residual_dropout = config.residual_dropout + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + attn_output = nn.functional.dropout(attn_output, p=self.residual_dropout, training=self.training) + + return attn_output, attn_weights + + +class SeedOssDecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: SeedOssConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = SeedOssAttention(config=config, layer_idx=layer_idx) + + self.mlp = SeedOssMLP(config) + self.input_layernorm = SeedOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = SeedOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class SeedOssPreTrainedModel(PreTrainedModel): + config: SeedOssConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["SeedOssDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": SeedOssDecoderLayer, + "attentions": SeedOssAttention, + } + + +class SeedOssRotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: SeedOssConfig, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: SeedOssConfig | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +@auto_docstring +class SeedOssModel(SeedOssPreTrainedModel): + def __init__(self, config: SeedOssConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [SeedOssDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = SeedOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = SeedOssRotaryEmbedding(config=config) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + causal_mask = create_causal_mask( + config=self.config, + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + ) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids) + + for decoder_layer in self.layers[: self.config.num_hidden_layers]: + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_embeddings=position_embeddings, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values, + ) + + +@auto_docstring +class SeedOssForCausalLM(SeedOssPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = SeedOssModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, SeedOssForCausalLM + + >>> model = SeedOssForCausalLM.from_pretrained("ByteDance-Seed/Seed-OSS-36B-Instruct") + >>> tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-OSS-36B-Instruct") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class SeedOssForSequenceClassification(GenericForSequenceClassification, SeedOssPreTrainedModel): + pass + + +class SeedOssForTokenClassification(GenericForTokenClassification, SeedOssPreTrainedModel): + pass + + +class SeedOssForQuestionAnswering(GenericForQuestionAnswering, SeedOssPreTrainedModel): + base_model_prefix = "transformer" # For BC, where `transformer` was used instead of `model` + + +__all__ = [ + "SeedOssForCausalLM", + "SeedOssForQuestionAnswering", + "SeedOssPreTrainedModel", + "SeedOssModel", + "SeedOssForSequenceClassification", + "SeedOssForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/modular_seed_oss.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/modular_seed_oss.py new file mode 100644 index 0000000000000000000000000000000000000000..88e345fdcac128f1df42453a5f5efd17351d2987 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seed_oss/modular_seed_oss.py @@ -0,0 +1,201 @@ +# Copyright 2025 Bytedance-Seed Ltd and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SeedOss model.""" + +from collections.abc import Callable + +import torch +import torch.nn as nn + +from ...activations import ACT2FN +from ...cache_utils import Cache +from ...modeling_outputs import CausalLMOutputWithPast +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, logging +from ..llama.modeling_llama import ( + LlamaDecoderLayer, + LlamaForCausalLM, + LlamaForQuestionAnswering, + LlamaForSequenceClassification, + LlamaForTokenClassification, + LlamaModel, + LlamaPreTrainedModel, + LlamaRMSNorm, + apply_rotary_pos_emb, + eager_attention_forward, +) +from .configuration_seed_oss import SeedOssConfig + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "ByteDance-Seed/Seed-OSS-36B-Instruct" + + +class SeedOssRMSNorm(LlamaRMSNorm): + pass + + +class SeedOssMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + self.residual_dropout = config.residual_dropout + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + down_proj = nn.functional.dropout(down_proj, p=self.residual_dropout, training=self.training) + return down_proj + + +class SeedOssAttention(nn.Module): + def __init__(self, config: SeedOssConfig, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = config.head_dim + self.num_key_value_heads = config.num_key_value_heads + self.num_attention_heads = config.num_attention_heads + self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, self.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + self.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_out_bias + ) + + self.residual_dropout = config.residual_dropout + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple[torch.Tensor, torch.Tensor]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + attn_output = nn.functional.dropout(attn_output, p=self.residual_dropout, training=self.training) + + return attn_output, attn_weights + + +class SeedOssDecoderLayer(LlamaDecoderLayer): + pass + + +class SeedOssPreTrainedModel(LlamaPreTrainedModel): + pass + + +class SeedOssModel(LlamaModel): + pass + + +class SeedOssForCausalLM(LlamaForCausalLM): + def forward( + self, + **super_kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> from transformers import AutoTokenizer, SeedOssForCausalLM + + >>> model = SeedOssForCausalLM.from_pretrained("ByteDance-Seed/Seed-OSS-36B-Instruct") + >>> tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/Seed-OSS-36B-Instruct") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + return super().forward(**super_kwargs) + + +class SeedOssForSequenceClassification(LlamaForSequenceClassification): + pass + + +class SeedOssForTokenClassification(LlamaForTokenClassification): + pass + + +class SeedOssForQuestionAnswering(LlamaForQuestionAnswering): + pass + + +__all__ = [ + "SeedOssForCausalLM", + "SeedOssForQuestionAnswering", + "SeedOssPreTrainedModel", + "SeedOssModel", + "SeedOssForSequenceClassification", + "SeedOssForTokenClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..327d46cff948e9e52054a2c56532e3c44547d581 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_segformer import * + from .image_processing_pil_segformer import * + from .image_processing_segformer import * + from .modeling_segformer import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/configuration_segformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/configuration_segformer.py new file mode 100644 index 0000000000000000000000000000000000000000..c900ea2665e952e5def0f786c012fb36d282c6f8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/configuration_segformer.py @@ -0,0 +1,80 @@ +# Copyright 2021 NVIDIA and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SegFormer model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="ByteDance-Seed/Seed-OSS-36B-Instruct") +@strict +class SegformerConfig(PreTrainedConfig): + r""" + num_encoder_blocks (`int`, *optional*, defaults to 4): + The number of encoder blocks (i.e. stages in the Mix Transformer encoder). + depths (`list[int]`, *optional*, defaults to `[2, 2, 2, 2]`): + The number of layers in each encoder block. + sr_ratios (`list[int]`, *optional*, defaults to `[8, 4, 2, 1]`): + Sequence reduction ratios in each encoder block. + patch_sizes (`list[int]`, *optional*, defaults to `[7, 3, 3, 3]`): + Patch size before each encoder block. + strides (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`): + Stride before each encoder block. + mlp_ratios (`list[int]`, *optional*, defaults to `[4, 4, 4, 4]`): + Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the + encoder blocks. + reshape_last_stage (`bool`, *optional*, defaults to True): + Whether to reshape the last stage outputs + + Example: + + ```python + >>> from transformers import SegformerModel, SegformerConfig + + >>> # Initializing a SegFormer nvidia/segformer-b0-finetuned-ade-512-512 style configuration + >>> configuration = SegformerConfig() + + >>> # Initializing a model from the nvidia/segformer-b0-finetuned-ade-512-512 style configuration + >>> model = SegformerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "segformer" + + num_channels: int = 3 + num_encoder_blocks: int = 4 + depths: list[int] | tuple[int, ...] = (2, 2, 2, 2) + sr_ratios: list[int] | tuple[int, ...] = (8, 4, 2, 1) + hidden_sizes: list[int] | tuple[int, ...] = (32, 64, 160, 256) + patch_sizes: list[int] | tuple[int, ...] = (7, 3, 3, 3) + strides: list[int] | tuple[int, ...] = (4, 2, 2, 2) + num_attention_heads: list[int] | tuple[int, ...] = (1, 2, 5, 8) + mlp_ratios: list[int] | tuple[int, ...] = (4, 4, 4, 4) + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.0 + attention_probs_dropout_prob: float | int = 0.0 + classifier_dropout_prob: float | int = 0.1 + initializer_range: float = 0.02 + drop_path_rate: float | int = 0.1 + layer_norm_eps: float = 1e-6 + decoder_hidden_size: int = 256 + semantic_loss_ignore_index: int = 255 + reshape_last_stage: bool = True + + +__all__ = ["SegformerConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/image_processing_pil_segformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/image_processing_pil_segformer.py new file mode 100644 index 0000000000000000000000000000000000000000..f1d0bb0f627bf8d27df314dc6694325a8f39d100 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/image_processing_pil_segformer.py @@ -0,0 +1,223 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/segformer/modular_segformer.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_segformer.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_torch_available, is_torchvision_available +from ...utils.import_utils import requires + + +if is_torch_available(): + pass +if is_torchvision_available(): + import torchvision.transforms.v2.functional as tvF + + +class SegformerImageProcessorKwargs(ImagesKwargs, total=False): + r""" + do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): + Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 + is used for background, and background itself is not included in all classes of a dataset (e.g. + ADE20k). The background label will be replaced by 255. + """ + + do_reduce_labels: bool + + +@requires(backends=("torch", "torchvision")) +class SegformerImageProcessorPil(PilBackend): + """PIL backend for Segformer with reduce_label support.""" + + valid_kwargs = SegformerImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 512, "width": 512} + default_to_square = True + crop_size = None + do_resize = True + do_center_crop = None + do_rescale = True + do_normalize = True + do_reduce_labels = False + rescale_factor = 1 / 255 + + def __init__(self, **kwargs: Unpack[SegformerImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + segmentation_maps: ImageInput | None = None, + **kwargs: Unpack[SegformerImageProcessorKwargs], + ) -> BatchFeature: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to preprocess. + """ + return super().preprocess(images, segmentation_maps, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """Handle extra inputs beyond images.""" + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format + ) + images_kwargs = kwargs.copy() + images_kwargs["do_reduce_labels"] = False + data = {} + data["pixel_values"] = self._preprocess(images, **images_kwargs) + + # Prepare segmentation maps if provided + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + # Nearest resample is used for segmentation maps instead of BILINEAR. + "resample": tvF.InterpolationMode.NEAREST_EXACT, + } + ) + processed_segmentation_maps = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + + # Convert to int64 and squeeze channel dimension + processed_segmentation_maps = [ + processed_segmentation_map.squeeze(0).astype(np.int64) + for processed_segmentation_map in processed_segmentation_maps + ] + data["labels"] = processed_segmentation_maps + + return BatchFeature(data=data, tensor_type=return_tensors) + + def reduce_label(self, image: np.ndarray) -> np.ndarray: + """Reduce label values by 1, replacing 0 with 255.""" + # Avoid using underflow conversion + image[image == 0] = 255 + image = image - 1 + image[image == 254] = 255 + return image + + def _preprocess( + self, + images: list["np.ndarray"], + do_reduce_labels: bool, + resample: "PILImageResampling | None", + do_resize: bool, + do_rescale: bool, + do_normalize: bool, + size: SizeDict, + rescale_factor: float, + image_mean: float | list[float], + image_std: float | list[float], + **kwargs, + ) -> list["np.ndarray"]: + """Custom preprocessing for Segformer.""" + processed_images = [] + for image in images: + if do_reduce_labels: + image = self.reduce_label(image) + if do_resize: + image = self.resize(image, size, resample) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + + return processed_images + + @requires(backends=("torch",)) + def post_process_semantic_segmentation(self, outputs, target_sizes: list[tuple] | None = None): + """ + Converts the output of [`SegformerForSemanticSegmentation`] into semantic segmentation maps. + + Args: + outputs ([`SegformerForSemanticSegmentation`]): + Raw outputs of the model. + target_sizes (`list[Tuple]` of length `batch_size`, *optional*): + List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, + predictions will not be resized. + + Returns: + semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic + segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is + specified). Each entry of each `torch.Tensor` correspond to a semantic class id. + """ + import torch + import torch.nn.functional as F + + logits = outputs.logits + + # Resize logits and compute semantic segmentation maps + if target_sizes is not None: + if len(logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + + if isinstance(target_sizes, torch.Tensor): + target_sizes = target_sizes.numpy() + + semantic_segmentation = [] + + for idx in range(len(logits)): + resized_logits = F.interpolate( + logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False + ) + semantic_map = resized_logits[0].argmax(dim=0) + semantic_segmentation.append(semantic_map) + else: + semantic_segmentation = logits.argmax(dim=1) + semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] + + return semantic_segmentation + + +__all__ = ["SegformerImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/image_processing_segformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/image_processing_segformer.py new file mode 100644 index 0000000000000000000000000000000000000000..efc8c312953ef106bdb260ff3925194d20aaa34f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/image_processing_segformer.py @@ -0,0 +1,237 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/segformer/modular_segformer.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_segformer.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Union + +import torch +import torch.nn.functional as F +import torchvision.transforms.v2.functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, is_torch_available + + +class SegformerImageProcessorKwargs(ImagesKwargs, total=False): + r""" + do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`): + Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 + is used for background, and background itself is not included in all classes of a dataset (e.g. + ADE20k). The background label will be replaced by 255. + """ + + do_reduce_labels: bool + + +@auto_docstring +class SegformerImageProcessor(TorchvisionBackend): + """PIL backend for Segformer with reduce_label support.""" + + valid_kwargs = SegformerImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 512, "width": 512} + default_to_square = True + crop_size = None + do_resize = True + do_center_crop = None + do_rescale = True + do_normalize = True + do_reduce_labels = False + rescale_factor = 1 / 255 + + def __init__(self, **kwargs: Unpack[SegformerImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess( + self, + images: ImageInput, + segmentation_maps: ImageInput | None = None, + **kwargs: Unpack[SegformerImageProcessorKwargs], + ) -> BatchFeature: + r""" + segmentation_maps (`ImageInput`, *optional*): + The segmentation maps to preprocess. + """ + return super().preprocess(images, segmentation_maps, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + return_tensors: str | TensorType | None, + device: Union[str, "torch.device"] | None = None, + **kwargs, + ) -> BatchFeature: + """Handle extra inputs beyond images.""" + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device + ) + images_kwargs = kwargs.copy() + images_kwargs["do_reduce_labels"] = False + data = {} + data["pixel_values"] = self._preprocess(images, **images_kwargs) + + # Prepare segmentation maps if provided + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + # Nearest resample is used for segmentation maps instead of BILINEAR. + "resample": tvF.InterpolationMode.NEAREST_EXACT, + } + ) + processed_segmentation_maps = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + + # Convert to int64 and squeeze channel dimension + processed_segmentation_maps = [ + processed_segmentation_map.squeeze(0).to(torch.int64) + for processed_segmentation_map in processed_segmentation_maps + ] + data["labels"] = processed_segmentation_maps + + return BatchFeature(data=data, tensor_type=return_tensors) + + def reduce_label(self, labels: list["torch.Tensor"]) -> list["torch.Tensor"]: + """Reduce label values by 1, replacing 0 with 255.""" + for idx in range(len(labels)): + label = labels[idx] + label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype, device=label.device), label) + label = label - 1 + label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype, device=label.device), label) + labels[idx] = label + return labels + + def _preprocess( + self, + images: list["torch.Tensor"], + do_reduce_labels: bool, + resample: "PILImageResampling | None", + do_resize: bool, + do_rescale: bool, + do_normalize: bool, + size: SizeDict, + rescale_factor: float, + image_mean: float | list[float], + image_std: float | list[float], + disable_grouping: bool, + **kwargs, + ) -> BatchFeature: # Return type can be list if return_tensors=None + """Custom preprocessing for Segformer.""" + if do_reduce_labels: + images = self.reduce_label(images) # Apply reduction if needed + + # Group images by size for batched resizing + resized_images = images + if do_resize: + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + resized_stacked_images = self.resize(image=stacked_images, size=size, resample=resample) + resized_images_grouped[shape] = resized_stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing (rescale/normalize) + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return processed_images + + def post_process_semantic_segmentation(self, outputs, target_sizes: list[tuple] | None = None): + """ + Converts the output of [`SegformerForSemanticSegmentation`] into semantic segmentation maps. + + Args: + outputs ([`SegformerForSemanticSegmentation`]): + Raw outputs of the model. + target_sizes (`list[Tuple]` of length `batch_size`, *optional*): + List of tuples corresponding to the requested final size (height, width) of each prediction. If unset, + predictions will not be resized. + + Returns: + semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic + segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is + specified). Each entry of each `torch.Tensor` correspond to a semantic class id. + """ + if not is_torch_available(): + raise ImportError("PyTorch is required for post_process_semantic_segmentation") + + logits = outputs.logits + + # Resize logits and compute semantic segmentation maps + if target_sizes is not None: + if len(logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + + if isinstance(target_sizes, torch.Tensor): + target_sizes = target_sizes.numpy() + + semantic_segmentation = [] + + for idx in range(len(logits)): + resized_logits = F.interpolate( + logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False + ) + semantic_map = resized_logits[0].argmax(dim=0) + semantic_segmentation.append(semantic_map) + else: + semantic_segmentation = logits.argmax(dim=1) + semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] + + return semantic_segmentation + + +__all__ = ["SegformerImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/modeling_segformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/modeling_segformer.py new file mode 100644 index 0000000000000000000000000000000000000000..df1430d5cad9f41311de22329d515a8f25d6324d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/modeling_segformer.py @@ -0,0 +1,704 @@ +# Copyright 2021 NVIDIA The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SegFormer model.""" + +import math + +import torch +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss + +from ...activations import ACT2FN +from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput, SemanticSegmenterOutput +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring, logging +from .configuration_segformer import SegformerConfig + + +logger = logging.get_logger(__name__) + + +class SegFormerImageClassifierOutput(ImageClassifierOutput): + """ + Base class for outputs of image classification models. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Classification (or regression if config.num_labels==1) loss. + logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`): + Classification (or regression if config.num_labels==1) scores (before SoftMax). + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also + called feature maps) of the model at the output of each stage. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, patch_size, + sequence_length)`. + + Attentions weights after the attention softmax, used to compute the weighted average in the self-attention + heads. + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.convnext.modeling_convnext.ConvNextDropPath with ConvNext->Segformer +class SegformerDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float | None = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return f"p={self.drop_prob}" + + +class SegformerOverlapPatchEmbeddings(nn.Module): + """Construct the overlapping patch embeddings.""" + + def __init__(self, patch_size, stride, num_channels, hidden_size): + super().__init__() + self.proj = nn.Conv2d( + num_channels, + hidden_size, + kernel_size=patch_size, + stride=stride, + padding=patch_size // 2, + ) + + self.layer_norm = nn.LayerNorm(hidden_size) + + def forward(self, pixel_values): + embeddings = self.proj(pixel_values) + _, _, height, width = embeddings.shape + # (batch_size, num_channels, height, width) -> (batch_size, num_channels, height*width) -> (batch_size, height*width, num_channels) + # this can be fed to a Transformer layer + embeddings = embeddings.flatten(2).transpose(1, 2) + embeddings = self.layer_norm(embeddings) + return embeddings, height, width + + +class SegformerEfficientSelfAttention(nn.Module): + """SegFormer's efficient self-attention mechanism. Employs the sequence reduction process introduced in the [PvT + paper](https://huggingface.co/papers/2102.12122).""" + + def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio): + super().__init__() + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + + if self.hidden_size % self.num_attention_heads != 0: + raise ValueError( + f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention " + f"heads ({self.num_attention_heads})" + ) + + self.attention_head_size = int(self.hidden_size / self.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(self.hidden_size, self.all_head_size) + self.key = nn.Linear(self.hidden_size, self.all_head_size) + self.value = nn.Linear(self.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + + self.sr_ratio = sequence_reduction_ratio + if sequence_reduction_ratio > 1: + self.sr = nn.Conv2d( + hidden_size, hidden_size, kernel_size=sequence_reduction_ratio, stride=sequence_reduction_ratio + ) + self.layer_norm = nn.LayerNorm(hidden_size) + + def forward( + self, + hidden_states, + height, + width, + output_attentions=False, + ): + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.attention_head_size) + query_layer = self.query(hidden_states).view(hidden_shape).transpose(1, 2) + + if self.sr_ratio > 1: + batch_size, seq_len, num_channels = hidden_states.shape + # Reshape to (batch_size, num_channels, height, width) + hidden_states = hidden_states.permute(0, 2, 1).reshape(batch_size, num_channels, height, width) + # Apply sequence reduction + hidden_states = self.sr(hidden_states) + # Reshape back to (batch_size, seq_len, num_channels) + hidden_states = hidden_states.reshape(batch_size, num_channels, -1).permute(0, 2, 1) + hidden_states = self.layer_norm(hidden_states) + + kv_shape = (*hidden_states.shape[:-1], -1, self.attention_head_size) + key_layer = self.key(hidden_states).view(kv_shape).transpose(1, 2) + value_layer = self.value(hidden_states).view(kv_shape).transpose(1, 2) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + + context_layer = torch.matmul(attention_probs, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + return outputs + + +class SegformerSelfOutput(nn.Module): + def __init__(self, config, hidden_size): + super().__init__() + self.dense = nn.Linear(hidden_size, hidden_size) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class SegformerAttention(nn.Module): + def __init__(self, config, hidden_size, num_attention_heads, sequence_reduction_ratio): + super().__init__() + self.self = SegformerEfficientSelfAttention( + config=config, + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + sequence_reduction_ratio=sequence_reduction_ratio, + ) + self.output = SegformerSelfOutput(config, hidden_size=hidden_size) + + def forward(self, hidden_states, height, width, output_attentions=False): + self_outputs = self.self(hidden_states, height, width, output_attentions) + + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class SegformerDWConv(nn.Module): + def __init__(self, dim=768): + super().__init__() + self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) + + def forward(self, hidden_states, height, width): + batch_size, seq_len, num_channels = hidden_states.shape + hidden_states = hidden_states.transpose(1, 2).view(batch_size, num_channels, height, width) + hidden_states = self.dwconv(hidden_states) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + + return hidden_states + + +class SegformerMixFFN(nn.Module): + def __init__(self, config, in_features, hidden_features=None, out_features=None): + super().__init__() + out_features = out_features or in_features + self.dense1 = nn.Linear(in_features, hidden_features) + self.dwconv = SegformerDWConv(hidden_features) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + self.dense2 = nn.Linear(hidden_features, out_features) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, height, width): + hidden_states = self.dense1(hidden_states) + hidden_states = self.dwconv(hidden_states, height, width) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.dense2(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class SegformerLayer(nn.Module): + """This corresponds to the Block class in the original implementation.""" + + def __init__(self, config, hidden_size, num_attention_heads, drop_path, sequence_reduction_ratio, mlp_ratio): + super().__init__() + self.layer_norm_1 = nn.LayerNorm(hidden_size) + self.attention = SegformerAttention( + config, + hidden_size=hidden_size, + num_attention_heads=num_attention_heads, + sequence_reduction_ratio=sequence_reduction_ratio, + ) + self.drop_path = SegformerDropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.layer_norm_2 = nn.LayerNorm(hidden_size) + mlp_hidden_size = int(hidden_size * mlp_ratio) + self.mlp = SegformerMixFFN(config, in_features=hidden_size, hidden_features=mlp_hidden_size) + + def forward(self, hidden_states, height, width, output_attentions=False): + self_attention_outputs = self.attention( + self.layer_norm_1(hidden_states), # in Segformer, layernorm is applied before self-attention + height, + width, + output_attentions=output_attentions, + ) + + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + # first residual connection (with stochastic depth) + attention_output = self.drop_path(attention_output) + hidden_states = attention_output + hidden_states + + mlp_output = self.mlp(self.layer_norm_2(hidden_states), height, width) + + # second residual connection (with stochastic depth) + mlp_output = self.drop_path(mlp_output) + layer_output = mlp_output + hidden_states + + outputs = (layer_output,) + outputs + + return outputs + + +class SegformerEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + + # stochastic depth decay rule + drop_path_decays = [ + x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths), device="cpu") + ] + + # patch embeddings + embeddings = [] + for i in range(config.num_encoder_blocks): + embeddings.append( + SegformerOverlapPatchEmbeddings( + patch_size=config.patch_sizes[i], + stride=config.strides[i], + num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1], + hidden_size=config.hidden_sizes[i], + ) + ) + self.patch_embeddings = nn.ModuleList(embeddings) + + # Transformer blocks + blocks = [] + cur = 0 + for i in range(config.num_encoder_blocks): + # each block consists of layers + layers = [] + if i != 0: + cur += config.depths[i - 1] + for j in range(config.depths[i]): + layers.append( + SegformerLayer( + config, + hidden_size=config.hidden_sizes[i], + num_attention_heads=config.num_attention_heads[i], + drop_path=drop_path_decays[cur + j], + sequence_reduction_ratio=config.sr_ratios[i], + mlp_ratio=config.mlp_ratios[i], + ) + ) + blocks.append(nn.ModuleList(layers)) + + self.block = nn.ModuleList(blocks) + + # Layer norms + self.layer_norm = nn.ModuleList( + [nn.LayerNorm(config.hidden_sizes[i]) for i in range(config.num_encoder_blocks)] + ) + + def forward( + self, + pixel_values: torch.FloatTensor, + output_attentions: bool | None = False, + output_hidden_states: bool | None = False, + return_dict: bool | None = True, + ) -> tuple | BaseModelOutput: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + batch_size = pixel_values.shape[0] + + hidden_states = pixel_values + for idx, x in enumerate(zip(self.patch_embeddings, self.block, self.layer_norm)): + embedding_layer, block_layer, norm_layer = x + # first, obtain patch embeddings + hidden_states, height, width = embedding_layer(hidden_states) + # second, send embeddings through blocks + for i, blk in enumerate(block_layer): + layer_outputs = blk(hidden_states, height, width, output_attentions) + hidden_states = layer_outputs[0] + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + # third, apply layer norm + hidden_states = norm_layer(hidden_states) + # fourth, optionally reshape back to (batch_size, num_channels, height, width) + if idx != len(self.patch_embeddings) - 1 or ( + idx == len(self.patch_embeddings) - 1 and self.config.reshape_last_stage + ): + hidden_states = hidden_states.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous() + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +@auto_docstring +class SegformerPreTrainedModel(PreTrainedModel): + config: SegformerConfig + base_model_prefix = "segformer" + main_input_name = "pixel_values" + input_modalities = ("image",) + + +@auto_docstring +class SegformerModel(SegformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + + # hierarchical Transformer encoder + self.encoder = SegformerEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + encoder_outputs = self.encoder( + pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + sequence_output = encoder_outputs[0] + + if not return_dict: + return (sequence_output,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=sequence_output, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + SegFormer Model transformer with an image classification head on top (a linear layer on top of the final hidden + states) e.g. for ImageNet. + """ +) +class SegformerForImageClassification(SegformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + self.num_labels = config.num_labels + self.segformer = SegformerModel(config) + + # Classifier head + self.classifier = nn.Linear(config.hidden_sizes[-1], config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | SegFormerImageClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + outputs = self.segformer( + pixel_values, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + # convert last hidden states to (batch_size, height*width, hidden_size) + batch_size = sequence_output.shape[0] + if self.config.reshape_last_stage: + # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels) + sequence_output = sequence_output.permute(0, 2, 3, 1) + sequence_output = sequence_output.reshape(batch_size, -1, self.config.hidden_sizes[-1]) + + # global average pooling + sequence_output = sequence_output.mean(dim=1) + + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + if not return_dict: + output = (logits,) + outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SegFormerImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class SegformerMLP(nn.Module): + """ + Linear Embedding. + """ + + def __init__(self, config: SegformerConfig, input_dim): + super().__init__() + self.proj = nn.Linear(input_dim, config.decoder_hidden_size) + + def forward(self, hidden_states: torch.Tensor): + hidden_states = hidden_states.flatten(2).transpose(1, 2) + hidden_states = self.proj(hidden_states) + return hidden_states + + +class SegformerDecodeHead(nn.Module): + def __init__(self, config): + super().__init__() + # linear layers which will unify the channel dimension of each of the encoder blocks to the same config.decoder_hidden_size + mlps = [] + for i in range(config.num_encoder_blocks): + mlp = SegformerMLP(config, input_dim=config.hidden_sizes[i]) + mlps.append(mlp) + self.linear_c = nn.ModuleList(mlps) + + # the following 3 layers implement the ConvModule of the original implementation + self.linear_fuse = nn.Conv2d( + in_channels=config.decoder_hidden_size * config.num_encoder_blocks, + out_channels=config.decoder_hidden_size, + kernel_size=1, + bias=False, + ) + self.batch_norm = nn.BatchNorm2d(config.decoder_hidden_size) + self.activation = nn.ReLU() + + self.dropout = nn.Dropout(config.classifier_dropout_prob) + self.classifier = nn.Conv2d(config.decoder_hidden_size, config.num_labels, kernel_size=1) + + self.config = config + + def forward(self, encoder_hidden_states: torch.FloatTensor, **kwargs) -> torch.Tensor: + batch_size = encoder_hidden_states[-1].shape[0] + + all_hidden_states = () + for encoder_hidden_state, mlp in zip(encoder_hidden_states, self.linear_c): + if self.config.reshape_last_stage is False and encoder_hidden_state.ndim == 3: + height = width = int(math.sqrt(encoder_hidden_state.shape[-1])) + encoder_hidden_state = ( + encoder_hidden_state.reshape(batch_size, height, width, -1).permute(0, 3, 1, 2).contiguous() + ) + + # unify channel dimension + height, width = encoder_hidden_state.shape[2], encoder_hidden_state.shape[3] + encoder_hidden_state = mlp(encoder_hidden_state) + encoder_hidden_state = encoder_hidden_state.permute(0, 2, 1) + encoder_hidden_state = encoder_hidden_state.reshape(batch_size, -1, height, width) + # upsample + encoder_hidden_state = nn.functional.interpolate( + encoder_hidden_state, size=encoder_hidden_states[0].size()[2:], mode="bilinear", align_corners=False + ) + all_hidden_states += (encoder_hidden_state,) + + hidden_states = self.linear_fuse(torch.cat(all_hidden_states[::-1], dim=1)) + hidden_states = self.batch_norm(hidden_states) + hidden_states = self.activation(hidden_states) + hidden_states = self.dropout(hidden_states) + + # logits are of shape (batch_size, num_labels, height/4, width/4) + logits = self.classifier(hidden_states) + + return logits + + +@auto_docstring( + custom_intro=""" + SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes. + """ +) +class SegformerForSemanticSegmentation(SegformerPreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.segformer = SegformerModel(config) + self.decode_head = SegformerDecodeHead(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + labels: torch.LongTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | SemanticSegmenterOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): + Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, SegformerForSemanticSegmentation + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> image_processor = AutoImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") + >>> model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4) + >>> list(logits.shape) + [1, 150, 128, 128] + ```""" + return_dict = return_dict if return_dict is not None else self.config.return_dict + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + + if labels is not None and self.config.num_labels < 1: + raise ValueError(f"Number of labels should be >=0: {self.config.num_labels}") + + outputs = self.segformer( + pixel_values, + output_attentions=output_attentions, + output_hidden_states=True, # we need the intermediate hidden states + return_dict=return_dict, + ) + + encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1] + + logits = self.decode_head(encoder_hidden_states) + + loss = None + if labels is not None: + # upsample logits to the images' original size + upsampled_logits = nn.functional.interpolate( + logits, size=labels.shape[-2:], mode="bilinear", align_corners=False + ) + if self.config.num_labels > 1: + loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index) + loss = loss_fct(upsampled_logits, labels) + elif self.config.num_labels == 1: + valid_mask = ((labels >= 0) & (labels != self.config.semantic_loss_ignore_index)).float() + loss_fct = BCEWithLogitsLoss(reduction="none") + loss = loss_fct(upsampled_logits.squeeze(1), labels.float()) + loss = (loss * valid_mask).mean() + + if not return_dict: + if output_hidden_states: + output = (logits,) + outputs[1:] + else: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SemanticSegmenterOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states if output_hidden_states else None, + attentions=outputs.attentions, + ) + + +__all__ = [ + "SegformerDecodeHead", + "SegformerForImageClassification", + "SegformerForSemanticSegmentation", + "SegformerLayer", + "SegformerModel", + "SegformerPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/modular_segformer.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/modular_segformer.py new file mode 100644 index 0000000000000000000000000000000000000000..414dc58e8c5212e7ab4517e03e9de99b69acff1e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/segformer/modular_segformer.py @@ -0,0 +1,238 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Union + +import numpy as np +import torch +import torchvision.transforms.v2.functional as tvF + +from transformers.models.beit.image_processing_beit import BeitImageProcessor +from transformers.models.beit.image_processing_pil_beit import BeitImageProcessorPil + +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...utils import TensorType +from ...utils.import_utils import requires + + +class SegformerImageProcessor(BeitImageProcessor): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 512, "width": 512} + do_resize = True + do_rescale = True + rescale_factor = 1 / 255 + do_normalize = True + do_reduce_labels = False + do_center_crop = None + crop_size = None + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + return_tensors: str | TensorType | None, + device: Union[str, "torch.device"] | None = None, + **kwargs, + ) -> BatchFeature: + """Handle extra inputs beyond images.""" + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device + ) + images_kwargs = kwargs.copy() + images_kwargs["do_reduce_labels"] = False + data = {} + data["pixel_values"] = self._preprocess(images, **images_kwargs) + + # Prepare segmentation maps if provided + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + # Nearest resample is used for segmentation maps instead of BILINEAR. + "resample": tvF.InterpolationMode.NEAREST_EXACT, + } + ) + processed_segmentation_maps = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + + # Convert to int64 and squeeze channel dimension + processed_segmentation_maps = [ + processed_segmentation_map.squeeze(0).to(torch.int64) + for processed_segmentation_map in processed_segmentation_maps + ] + data["labels"] = processed_segmentation_maps + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_reduce_labels: bool, + resample: "PILImageResampling | None", + do_resize: bool, + do_rescale: bool, + do_normalize: bool, + size: SizeDict, + rescale_factor: float, + image_mean: float | list[float], + image_std: float | list[float], + disable_grouping: bool, + **kwargs, + ) -> BatchFeature: # Return type can be list if return_tensors=None + if do_reduce_labels: + images = self.reduce_label(images) # Apply reduction if needed + + # Group images by size for batched resizing + resized_images = images + if do_resize: + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + resized_stacked_images = self.resize(image=stacked_images, size=size, resample=resample) + resized_images_grouped[shape] = resized_stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing (rescale/normalize) + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + return processed_images + + +@requires(backends=("torch", "torchvision")) +class SegformerImageProcessorPil(BeitImageProcessorPil): + resample = PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 512, "width": 512} + do_resize = True + do_rescale = True + rescale_factor = 1 / 255 + do_normalize = True + do_reduce_labels = False + do_center_crop = None + crop_size = None + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + segmentation_maps: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """Handle extra inputs beyond images.""" + images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format + ) + images_kwargs = kwargs.copy() + images_kwargs["do_reduce_labels"] = False + data = {} + data["pixel_values"] = self._preprocess(images, **images_kwargs) + + # Prepare segmentation maps if provided + if segmentation_maps is not None: + processed_segmentation_maps = self._prepare_image_like_inputs( + images=segmentation_maps, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + + segmentation_maps_kwargs = kwargs.copy() + segmentation_maps_kwargs.update( + { + "do_normalize": False, + "do_rescale": False, + # Nearest resample is used for segmentation maps instead of BILINEAR. + "resample": tvF.InterpolationMode.NEAREST_EXACT, + } + ) + processed_segmentation_maps = self._preprocess( + images=processed_segmentation_maps, **segmentation_maps_kwargs + ) + + # Convert to int64 and squeeze channel dimension + processed_segmentation_maps = [ + processed_segmentation_map.squeeze(0).astype(np.int64) + for processed_segmentation_map in processed_segmentation_maps + ] + data["labels"] = processed_segmentation_maps + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _preprocess( + self, + images: list["np.ndarray"], + do_reduce_labels: bool, + resample: "PILImageResampling | None", + do_resize: bool, + do_rescale: bool, + do_normalize: bool, + size: SizeDict, + rescale_factor: float, + image_mean: float | list[float], + image_std: float | list[float], + **kwargs, + ) -> list["np.ndarray"]: + """Custom preprocessing for Segformer.""" + processed_images = [] + for image in images: + if do_reduce_labels: + image = self.reduce_label(image) + if do_resize: + image = self.resize(image, size, resample) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + + return processed_images + + +__all__ = ["SegformerImageProcessor", "SegformerImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7f9056abe47c820b3b49db9e9f1d6a43a8d52f77 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_seggpt import * + from .image_processing_pil_seggpt import * + from .image_processing_seggpt import * + from .modeling_seggpt import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/configuration_seggpt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/configuration_seggpt.py new file mode 100644 index 0000000000000000000000000000000000000000..f25b0f528ec90ffd7890fad4f471e2711ddf7ec6 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/configuration_seggpt.py @@ -0,0 +1,89 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SegGpt model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="BAAI/seggpt-vit-large") +@strict +class SegGptConfig(PreTrainedConfig): + r""" + mlp_dim (`int`, *optional*): + The dimensionality of the MLP layer in the Transformer encoder. If unset, defaults to + `hidden_size` * 4. + pretrain_image_size (`int`, *optional*, defaults to 224): + The pretrained size of the absolute position embeddings. + use_relative_position_embeddings (`bool`, *optional*, defaults to `True`): + Whether to use relative position embeddings in the attention layers. + merge_index (`int`, *optional*, defaults to 2): + The index of the encoder layer to merge the embeddings. + intermediate_hidden_state_indices (`list[int]`, *optional*, defaults to `[5, 11, 17, 23]`): + The indices of the encoder layers which we store as features for the decoder. + beta (`float`, *optional*, defaults to 0.01): + Regularization factor for SegGptLoss (smooth-l1 loss). + + Example: + + ```python + >>> from transformers import SegGptConfig, SegGptModel + + >>> # Initializing a SegGPT seggpt-vit-large style configuration + >>> configuration = SegGptConfig() + + >>> # Initializing a model (with random weights) from the seggpt-vit-large style configuration + >>> model = SegGptModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "seggpt" + + hidden_size: int = 1024 + num_hidden_layers: int = 24 + num_attention_heads: int = 16 + hidden_act: str = "gelu" + hidden_dropout_prob: float | int = 0.0 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-6 + image_size: int | list[int] | tuple[int, ...] = (896, 448) + patch_size: int | list[int] | tuple[int, int] = 16 + num_channels: int = 3 + qkv_bias: bool = True + mlp_dim: int | None = None + drop_path_rate: float | int = 0.1 + pretrain_image_size: int | list[int] | tuple[int, int] = 224 + decoder_hidden_size: int = 64 + use_relative_position_embeddings: bool = True + merge_index: int = 2 + intermediate_hidden_state_indices: list[int] | tuple[int, ...] = (5, 11, 17, 23) + beta: float = 0.01 + + def __post_init__(self, **kwargs): + self.mlp_dim = int(self.hidden_size * 4) if self.mlp_dim is None else self.mlp_dim + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if self.merge_index > min(self.intermediate_hidden_state_indices): + raise ValueError( + f"Merge index must be less than the minimum encoder output index, but got {self.merge_index=} and {self.intermediate_hidden_state_indices=}" + ) + + +__all__ = ["SegGptConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/image_processing_pil_seggpt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/image_processing_pil_seggpt.py new file mode 100644 index 0000000000000000000000000000000000000000..e9365c304e009a9722d25ef89c1e482ff100160a --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/image_processing_pil_seggpt.py @@ -0,0 +1,295 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SegGPT.""" + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring +from ...utils.import_utils import requires + + +# Adapted from transformers.models.seggpt.image_processing_seggpt.SegGptImageProcessorKwargs +class SegGptImageProcessorKwargs(ImagesKwargs, total=False): + r""" + num_labels (`int`, *optional*): + Number of classes in the segmentation task (excluding the background). If specified, a palette will be + built, assuming that class_idx 0 is the background, to map the prompt mask from a plain segmentation map + to a 3-channel RGB image. Not specifying this will result in the prompt mask being duplicated across the + channel dimension when `do_convert_rgb` is `True`. + """ + + num_labels: int + + +# Adapted from transformers.models.seggpt.image_processing_seggpt.build_palette +# See https://huggingface.co/papers/2212.02499 at 3.1 Redefining Output Spaces as "Images" - Semantic Segmentation +# Taken from https://github.com/Abdullah-Meda/Painter/blob/main/Painter/data/coco_semseg/gen_color_coco_panoptic_segm.py#L31 +def build_palette(num_labels: int) -> list[tuple[int, int, int]]: + base = int(num_labels ** (1 / 3)) + 1 + margin = 256 // base + + # class_idx 0 is the background which is mapped to black + color_list = [(0, 0, 0)] + for location in range(num_labels): + num_seq_r = location // base**2 + num_seq_g = (location % base**2) // base + num_seq_b = location % base + + R = 255 - num_seq_r * margin + G = 255 - num_seq_g * margin + B = 255 - num_seq_b * margin + + color_list.append((R, G, B)) + + return color_list + + +@auto_docstring +class SegGptImageProcessorPil(PilBackend): + valid_kwargs = SegGptImageProcessorKwargs + + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 448, "width": 448} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + num_labels = None + + def __init__(self, **kwargs: Unpack[SegGptImageProcessorKwargs]): + super().__init__(**kwargs) + + def get_palette(self, num_labels: int) -> list[tuple[int, int, int]]: + """Build a palette to map the prompt mask from a single channel to a 3-channel RGB. + + Args: + num_labels (`int`): + Number of classes in the segmentation task (excluding the background). + + Returns: + `list[tuple[int, int, int]]`: Palette to map the prompt mask to RGB. + """ + return build_palette(num_labels) + + def mask_to_rgb(self, mask: np.ndarray, palette: list[tuple[int, int, int]] | None = None) -> np.ndarray: + """Converts a segmentation map to RGB format. + + Args: + mask (`np.ndarray`): + Segmentation map with shape `(height, width)` or `(1, height, width)` where pixel values + represent the class index. + palette (`list[tuple[int, int, int]]`, *optional*): + Palette to use to convert the mask to RGB format. If unset, the mask is duplicated across + the channel dimension. + + Returns: + `np.ndarray`: The mask in RGB format with shape `(3, height, width)`. + """ + if mask.ndim == 3: + mask = mask.squeeze(0) + + height, width = mask.shape + + if palette is not None: + rgb_mask = np.zeros((3, height, width), dtype=np.uint8) + classes_in_mask = np.unique(mask) + for class_idx in classes_in_mask: + rgb_value = palette[class_idx] + class_mask = (mask == class_idx).astype(np.uint8) + class_rgb = np.array(rgb_value, dtype=np.uint8).reshape(3, 1, 1) + rgb_mask += (np.expand_dims(class_mask, 0) * class_rgb).astype(np.uint8) + rgb_mask = np.clip(rgb_mask, 0, 255).astype(np.uint8) + else: + rgb_mask = np.repeat(mask[np.newaxis, ...], 3, axis=0) + + return rgb_mask + + @auto_docstring + def preprocess( + self, + images: ImageInput | None = None, + prompt_images: ImageInput | None = None, + prompt_masks: ImageInput | None = None, + **kwargs: Unpack[SegGptImageProcessorKwargs], + ) -> BatchFeature: + r""" + prompt_images (`ImageInput`, *optional*): + Prompt images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. + prompt_masks (`ImageInput`, *optional*): + Prompt masks to preprocess. Can be in the format of segmentation maps (no channels) or RGB images. + If in the format of RGB images, `do_convert_rgb` should be set to `False`. If in the format of + segmentation maps, specifying `num_labels` is recommended to build a palette to map the prompt mask + from a single channel to a 3-channel RGB. If `num_labels` is not specified, the prompt mask will be + duplicated across the channel dimension. + """ + if all(v is None for v in [images, prompt_images, prompt_masks]): + raise ValueError("At least one of images, prompt_images, prompt_masks must be specified.") + + _images_input = images if images is not None else [] + return super().preprocess(_images_input, prompt_images, prompt_masks, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + prompt_images: ImageInput | None, + prompt_masks: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + return_tensors: str | TensorType | None, + num_labels: int | None = None, + **kwargs, + ) -> BatchFeature: + data = {} + + # Process regular images (do_convert_rgb=False: assume RGB, no mask conversion) + # Check for the empty-list sentinel passed when images=None + _images_provided = not (isinstance(images, list) and len(images) == 0) + if _images_provided: + prepared_images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=False, input_data_format=input_data_format + ) + data["pixel_values"] = self._preprocess(prepared_images, **kwargs) + + # Process prompt images (same as regular images) + if prompt_images is not None: + prepared_prompt_images = self._prepare_image_like_inputs( + images=prompt_images, do_convert_rgb=False, input_data_format=input_data_format + ) + data["prompt_pixel_values"] = self._preprocess(prepared_prompt_images, **kwargs) + + # Process prompt masks with special handling + if prompt_masks is not None: + if do_convert_rgb: + # 2D segmentation maps → convert to 3-channel RGB via palette + prepared_masks = self._prepare_image_like_inputs( + images=prompt_masks, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + ) + palette = self.get_palette(num_labels) if num_labels is not None else None + prepared_masks = [self.mask_to_rgb(mask, palette=palette) for mask in prepared_masks] + else: + # Already 3-channel RGB masks + prepared_masks = self._prepare_image_like_inputs( + images=prompt_masks, expected_ndims=3, do_convert_rgb=False, input_data_format=input_data_format + ) + + masks_kwargs = dict(kwargs) + masks_kwargs["resample"] = PILImageResampling.NEAREST + data["prompt_masks"] = self._preprocess(prepared_masks, **masks_kwargs) + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + size: SizeDict, + resample: PILImageResampling | None, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + **kwargs, + ) -> list[np.ndarray]: + processed_images = [] + for image in images: + if do_resize: + image = self.resize(image, size, resample) + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + processed_images.append(image) + + return processed_images + + @requires(backends=("torch",)) + def post_process_semantic_segmentation( + self, outputs, target_sizes: list[tuple[int, int]] | None = None, num_labels: int | None = None + ): + """ + Converts the output of [`SegGptImageSegmentationOutput`] into segmentation maps. Only supports PyTorch. + + Args: + outputs ([`SegGptImageSegmentationOutput`]): + Raw outputs of the model. + target_sizes (`list[tuple[int, int]]`, *optional*): + List of length `batch_size`, where each item corresponds to the requested final size `(height, width)` + of each prediction. If left to `None`, predictions will not be resized. + num_labels (`int`, *optional*): + Number of classes in the segmentation task (excluding the background). If specified, a palette will be + built to map prediction masks from RGB values back to class indices. Should match the value used during + preprocessing. + + Returns: + `list[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape + `(height, width)`. Each entry corresponds to a semantic class id. + """ + + import torch + + masks = outputs.pred_masks + masks = masks[:, :, masks.shape[2] // 2 :, :] + + std = torch.tensor(self.image_std).to(masks.device) + mean = torch.tensor(self.image_mean).to(masks.device) + masks = masks.permute(0, 2, 3, 1) * std + mean + masks = masks.permute(0, 3, 1, 2) + + masks = torch.clip(masks * 255, 0, 255) + + semantic_segmentation = [] + palette_tensor = None + palette = self.get_palette(num_labels) if num_labels is not None else None + if palette is not None: + palette_tensor = torch.tensor(palette).to(device=masks.device, dtype=torch.float) + _, num_channels, _, _ = masks.shape + palette_tensor = palette_tensor.view(1, 1, num_labels + 1, num_channels) + + for idx, mask in enumerate(masks): + if target_sizes is not None: + mask = torch.nn.functional.interpolate(mask.unsqueeze(0), size=target_sizes[idx], mode="nearest")[0] + + if num_labels is not None: + channels, height, width = mask.shape + dist = mask.permute(1, 2, 0).view(height, width, 1, channels) + dist = dist - palette_tensor + dist = torch.pow(dist, 2) + dist = torch.sum(dist, dim=-1) + pred = dist.argmin(dim=-1) + else: + pred = mask.mean(dim=0).int() + + semantic_segmentation.append(pred) + + return semantic_segmentation + + +__all__ = ["SegGptImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/image_processing_seggpt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/image_processing_seggpt.py new file mode 100644 index 0000000000000000000000000000000000000000..f9fbb288c4a794ea31ad9b156f448a1cd3e39b33 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/image_processing_seggpt.py @@ -0,0 +1,324 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SegGPT.""" + +from typing import Union + +import numpy as np +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + ChannelDimension, + ImageInput, + PILImageResampling, + SizeDict, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring, requires_backends + + +# See https://huggingface.co/papers/2212.02499 at 3.1 Redefining Output Spaces as "Images" - Semantic Segmentation +# Taken from https://github.com/Abdullah-Meda/Painter/blob/main/Painter/data/coco_semseg/gen_color_coco_panoptic_segm.py#L31 +def build_palette(num_labels: int) -> list[tuple[int, int, int]]: + base = int(num_labels ** (1 / 3)) + 1 + margin = 256 // base + + # class_idx 0 is the background which is mapped to black + color_list = [(0, 0, 0)] + for location in range(num_labels): + num_seq_r = location // base**2 + num_seq_g = (location % base**2) // base + num_seq_b = location % base + + R = 255 - num_seq_r * margin + G = 255 - num_seq_g * margin + B = 255 - num_seq_b * margin + + color_list.append((R, G, B)) + + return color_list + + +class SegGptImageProcessorKwargs(ImagesKwargs, total=False): + r""" + num_labels (`int`, *optional*): + Number of classes in the segmentation task (excluding the background). If specified, a palette will be + built, assuming that class_idx 0 is the background, to map the prompt mask from a plain segmentation map + to a 3-channel RGB image. Not specifying this will result in the prompt mask being duplicated across the + channel dimension when `do_convert_rgb` is `True`. + """ + + num_labels: int + + +@auto_docstring +class SegGptImageProcessor(TorchvisionBackend): + valid_kwargs = SegGptImageProcessorKwargs + + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 448, "width": 448} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + num_labels = None + + def __init__(self, **kwargs: Unpack[SegGptImageProcessorKwargs]): + super().__init__(**kwargs) + + def get_palette(self, num_labels: int) -> list[tuple[int, int, int]]: + """Build a palette to map the prompt mask from a single channel to a 3-channel RGB. + + Args: + num_labels (`int`): + Number of classes in the segmentation task (excluding the background). + + Returns: + `list[tuple[int, int, int]]`: Palette to map the prompt mask to RGB. + """ + return build_palette(num_labels) + + def mask_to_rgb(self, mask: np.ndarray, palette: list[tuple[int, int, int]] | None = None) -> np.ndarray: + """Converts a segmentation map to RGB format. + + Args: + mask (`np.ndarray`): + Segmentation map with shape `(height, width)` or `(1, height, width)` where pixel values + represent the class index. + palette (`list[tuple[int, int, int]]`, *optional*): + Palette to use to convert the mask to RGB format. If unset, the mask is duplicated across + the channel dimension. + + Returns: + `np.ndarray`: The mask in RGB format with shape `(3, height, width)`. + """ + if mask.ndim == 3: + mask = mask.squeeze(0) + + height, width = mask.shape + + if palette is not None: + rgb_mask = np.zeros((3, height, width), dtype=np.uint8) + classes_in_mask = np.unique(mask) + for class_idx in classes_in_mask: + rgb_value = palette[class_idx] + class_mask = (mask == class_idx).astype(np.uint8) + class_rgb = np.array(rgb_value, dtype=np.uint8).reshape(3, 1, 1) + rgb_mask += (np.expand_dims(class_mask, 0) * class_rgb).astype(np.uint8) + rgb_mask = np.clip(rgb_mask, 0, 255).astype(np.uint8) + else: + rgb_mask = np.repeat(mask[np.newaxis, ...], 3, axis=0) + + return rgb_mask + + @auto_docstring + def preprocess( + self, + images: ImageInput | None = None, + prompt_images: ImageInput | None = None, + prompt_masks: ImageInput | None = None, + **kwargs: Unpack[SegGptImageProcessorKwargs], + ) -> BatchFeature: + r""" + prompt_images (`ImageInput`, *optional*): + Prompt images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. + prompt_masks (`ImageInput`, *optional*): + Prompt masks to preprocess. Can be in the format of segmentation maps (no channels) or RGB images. + If in the format of RGB images, `do_convert_rgb` should be set to `False`. If in the format of + segmentation maps, specifying `num_labels` is recommended to build a palette to map the prompt mask + from a single channel to a 3-channel RGB. If `num_labels` is not specified, the prompt mask will be + duplicated across the channel dimension. + """ + if all(v is None for v in [images, prompt_images, prompt_masks]): + raise ValueError("At least one of images, prompt_images, prompt_masks must be specified.") + + # Pass an empty list as sentinel when images is None; _preprocess_image_like_inputs handles it + _images_input = images if images is not None else [] + return super().preprocess(_images_input, prompt_images, prompt_masks, **kwargs) + + def _preprocess_image_like_inputs( + self, + images: ImageInput, + prompt_images: ImageInput | None, + prompt_masks: ImageInput | None, + do_convert_rgb: bool, + input_data_format: ChannelDimension, + return_tensors: str | TensorType | None, + device: Union[str, "torch.device"] | None = None, + num_labels: int | None = None, + **kwargs, + ) -> BatchFeature: + data = {} + + # Process regular images (do_convert_rgb=False: assume RGB, no mask conversion) + # Check for the empty-list sentinel passed when images=None + _images_provided = not (isinstance(images, list) and len(images) == 0) + if _images_provided: + prepared_images = self._prepare_image_like_inputs( + images=images, do_convert_rgb=False, input_data_format=input_data_format, device=device + ) + data["pixel_values"] = self._preprocess(prepared_images, **kwargs) + + # Process prompt images (same as regular images) + if prompt_images is not None: + prepared_prompt_images = self._prepare_image_like_inputs( + images=prompt_images, do_convert_rgb=False, input_data_format=input_data_format, device=device + ) + data["prompt_pixel_values"] = self._preprocess(prepared_prompt_images, **kwargs) + + # Process prompt masks with special handling + if prompt_masks is not None: + if do_convert_rgb: + # 2D segmentation maps → convert to 3-channel RGB via palette + prepared_masks = self._prepare_image_like_inputs( + images=prompt_masks, + expected_ndims=2, + do_convert_rgb=False, + input_data_format=ChannelDimension.FIRST, + device=device, + ) + palette = self.get_palette(num_labels) if num_labels is not None else None + converted = [] + for mask_tensor in prepared_masks: + mask_np = mask_tensor.squeeze(0).numpy() + rgb_np = self.mask_to_rgb(mask_np, palette=palette) + converted.append(torch.from_numpy(rgb_np.astype(np.float32))) + prepared_masks = converted + else: + # Already 3-channel RGB masks + prepared_masks = self._prepare_image_like_inputs( + images=prompt_masks, + expected_ndims=3, + do_convert_rgb=False, + input_data_format=input_data_format, + device=device, + ) + + masks_kwargs = dict(kwargs) + masks_kwargs["resample"] = PILImageResampling.NEAREST + data["prompt_masks"] = self._preprocess(prepared_masks, **masks_kwargs) + + return BatchFeature(data=data, tensor_type=return_tensors) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + disable_grouping: bool | None, + **kwargs, + ) -> list["torch.Tensor"]: + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(stacked_images, size, resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + + return reorder_images(processed_images_grouped, grouped_images_index) + + def post_process_semantic_segmentation( + self, outputs, target_sizes: list[tuple[int, int]] | None = None, num_labels: int | None = None + ): + """ + Converts the output of [`SegGptImageSegmentationOutput`] into segmentation maps. Only supports PyTorch. + + Args: + outputs ([`SegGptImageSegmentationOutput`]): + Raw outputs of the model. + target_sizes (`list[tuple[int, int]]`, *optional*): + List of length `batch_size`, where each item corresponds to the requested final size `(height, width)` + of each prediction. If left to `None`, predictions will not be resized. + num_labels (`int`, *optional*): + Number of classes in the segmentation task (excluding the background). If specified, a palette will be + built to map prediction masks from RGB values back to class indices. Should match the value used during + preprocessing. + + Returns: + `list[torch.Tensor]` of length `batch_size`, where each item is a semantic segmentation map of shape + `(height, width)`. Each entry corresponds to a semantic class id. + """ + + requires_backends(self, ["torch"]) + + # batch_size x num_channels x 2*height x width + masks = outputs.pred_masks + + # Predicted mask and prompt are concatenated in the height dimension + # batch_size x num_channels x height x width + masks = masks[:, :, masks.shape[2] // 2 :, :] + + # Unnormalize: permute to channel-last, apply std/mean, permute back + std = torch.tensor(self.image_std).to(masks.device) + mean = torch.tensor(self.image_mean).to(masks.device) + masks = masks.permute(0, 2, 3, 1) * std + mean + masks = masks.permute(0, 3, 1, 2) + + masks = torch.clip(masks * 255, 0, 255) + + semantic_segmentation = [] + palette_tensor = None + palette = self.get_palette(num_labels) if num_labels is not None else None + if palette is not None: + palette_tensor = torch.tensor(palette).to(device=masks.device, dtype=torch.float) + _, num_channels, _, _ = masks.shape + palette_tensor = palette_tensor.view(1, 1, num_labels + 1, num_channels) + + for idx, mask in enumerate(masks): + if target_sizes is not None: + mask = torch.nn.functional.interpolate( + mask.unsqueeze(0), + size=target_sizes[idx], + mode="nearest", + )[0] + + if num_labels is not None: + channels, height, width = mask.shape + dist = mask.permute(1, 2, 0).view(height, width, 1, channels) + dist = dist - palette_tensor + dist = torch.pow(dist, 2) + dist = torch.sum(dist, dim=-1) + pred = dist.argmin(dim=-1) + else: + pred = mask.mean(dim=0).int() + + semantic_segmentation.append(pred) + + return semantic_segmentation + + +__all__ = ["SegGptImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/modeling_seggpt.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/modeling_seggpt.py new file mode 100644 index 0000000000000000000000000000000000000000..1d8b095000e313fcf34982494eae9d76413b9205 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/seggpt/modeling_seggpt.py @@ -0,0 +1,962 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SegGpt model.""" + +import collections.abc +from dataclasses import dataclass + +import torch +from torch import nn +from torch.nn import functional as F + +from ... import initialization as init +from ...activations import ACT2FN +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_utils import PreTrainedModel +from ...utils import ModelOutput, auto_docstring, logging, torch_int +from .configuration_seggpt import SegGptConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring( + custom_intro=""" + Output type of [`SegGptEncoderOutput`]. + """ +) +@dataclass +class SegGptEncoderOutput(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, patch_height, patch_width, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple[torch.FloatTensor]`, `optional`, returned when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) + of shape `(batch_size, patch_height, patch_width, hidden_size)`. + attentions (`tuple[torch.FloatTensor]`, `optional`, returned when `config.output_attentions=True`): + Tuple of *torch.FloatTensor* (one for each layer) of shape + `(batch_size, num_heads, seq_len, seq_len)`. + intermediate_hidden_states (`tuple[torch.FloatTensor]`, *optional*, returned when `config.intermediate_hidden_state_indices` is set): + Tuple of `torch.FloatTensor` of shape `(batch_size, patch_height, patch_width, hidden_size)`. + Each element in the Tuple corresponds to the output of the layer specified in `config.intermediate_hidden_state_indices`. + Additionally, each feature passes through a LayerNorm. + """ + + last_hidden_state: torch.FloatTensor + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + intermediate_hidden_states: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + Output type of [`SegGptImageSegmentationOutput`]. + """ +) +@dataclass +class SegGptImageSegmentationOutput(ModelOutput): + r""" + loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided): + The loss value. + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + The predicted masks. + hidden_states (`tuple[torch.FloatTensor]`, `optional`, returned when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) + of shape `(batch_size, patch_height, patch_width, hidden_size)`. + attentions (`tuple[torch.FloatTensor]`, `optional`, returned when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape + `(batch_size, num_heads, seq_len, seq_len)`. + """ + + loss: torch.FloatTensor | None = None + pred_masks: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + + +# Copied from transformers.models.sam.modeling_sam.SamPatchEmbeddings with Sam->SegGpt +class SegGptPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values): + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).permute(0, 2, 3, 1) + return embeddings + + +class SegGptEmbeddings(nn.Module): + """ + Construct the embeddings from patch, position embeddings for input and prompt. + """ + + def __init__(self, config: SegGptConfig) -> None: + super().__init__() + + self.mask_token = nn.Parameter(torch.zeros(1, 1, 1, config.hidden_size)) + self.segment_token_input = nn.Parameter(torch.zeros(1, 1, 1, config.hidden_size)) + self.segment_token_prompt = nn.Parameter(torch.zeros(1, 1, 1, config.hidden_size)) + # token for seg types + self.type_token_semantic = nn.Parameter(torch.zeros(1, 1, 1, config.hidden_size)) + self.type_token_instance = nn.Parameter(torch.zeros(1, 1, 1, config.hidden_size)) + + self.patch_embeddings = SegGptPatchEmbeddings(config) + + num_positions = (config.pretrain_image_size // config.patch_size) ** 2 + 1 + self.position_embeddings = nn.Parameter(torch.randn(1, num_positions, config.hidden_size)) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def interpolate_pos_encoding(self, height: int, width: int) -> torch.Tensor: + patch_pos_embed = self.position_embeddings[:, 1:] + num_patches = patch_pos_embed.shape[1] + pretrain_patch_size = torch_int(num_patches**0.5) + + # always interpolate when tracing to ensure the exported model works for dynamic input shapes + if torch.jit.is_tracing() or pretrain_patch_size != height or pretrain_patch_size != width: + patch_pos_embed = F.interpolate( + patch_pos_embed.reshape(1, pretrain_patch_size, pretrain_patch_size, -1).permute(0, 3, 1, 2), + size=(height, width), + mode="bicubic", + align_corners=False, + ) + + return patch_pos_embed.permute(0, 2, 3, 1) + else: + return patch_pos_embed.reshape(1, height, width, -1) + + def forward( + self, + pixel_values: torch.Tensor, + prompt_pixel_values: torch.Tensor, + bool_masked_pos: torch.BoolTensor | None = None, + embedding_type: str | None = None, + ) -> torch.Tensor: + input_embeddings = self.patch_embeddings(pixel_values) + prompt_embeddings = self.patch_embeddings(prompt_pixel_values) + + batch_size, patch_height, patch_width, _ = input_embeddings.shape + + mask_token = self.mask_token.expand(batch_size, patch_height, patch_width, -1) + # replace the masked visual tokens by mask_token + w = bool_masked_pos.unsqueeze(-1).type_as(mask_token).reshape(-1, patch_height, patch_width, 1) + prompt_embeddings = prompt_embeddings * (1 - w) + mask_token * w + + embedding_type = embedding_type if embedding_type is not None else "instance" + + # add positional encoding to each token + pos_embed = self.interpolate_pos_encoding(patch_height, patch_width) + + # add segment token + input_embeddings = input_embeddings + self.segment_token_input + prompt_embeddings = prompt_embeddings + self.segment_token_prompt + + # add position embedding skipping CLS + input_embeddings = input_embeddings + pos_embed + prompt_embeddings = prompt_embeddings + pos_embed + + # add type embedding to each token + if embedding_type == "semantic": + type_embedding = self.type_token_semantic + elif embedding_type == "instance": + type_embedding = self.type_token_instance + else: + raise ValueError(f"Embedding type should be either 'semantic' or 'instance', but got {embedding_type}") + + input_embeddings = input_embeddings + type_embedding + prompt_embeddings = prompt_embeddings + type_embedding + + embeddings = torch.cat((input_embeddings, prompt_embeddings), dim=0) + + return embeddings + + +class SegGptAttention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + + input_size = (image_size[0] // config.patch_size, image_size[1] // config.patch_size) + head_dim = config.hidden_size // config.num_attention_heads + + self.num_attention_heads = config.num_attention_heads + self.scale = head_dim**-0.5 + + self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias) + self.proj = nn.Linear(config.hidden_size, config.hidden_size) + + self.use_relative_position_embeddings = config.use_relative_position_embeddings + if self.use_relative_position_embeddings: + if input_size is None: + raise ValueError("Input size must be provided if using relative positional encoding.") + + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + + Args: + q_size (int): + size of the query. + k_size (int): + size of key k. + rel_pos (`torch.Tensor`): + relative position embeddings (L, channel). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + def add_decomposed_rel_pos( + self, + attn: torch.Tensor, + query: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: tuple[int, int], + k_size: tuple[int, int], + ) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py + + Args: + attn (`torch.Tensor`): + attention map. + query (`torch.Tensor`): + query q in the attention layer with shape (batch_size, query_height * query_width, channel). + rel_pos_h (`torch.Tensor`): + relative position embeddings (Lh, channel) for height axis. + rel_pos_w (`torch.Tensor`): + relative position embeddings (Lw, channel) for width axis. + q_size (tuple): + spatial sequence size of query q with (query_height, query_width). + k_size (tuple): + spatial sequence size of key k with (key_height, key_width). + + Returns: + attn (`torch.Tensor`): + attention map with added relative positional embeddings. + """ + query_height, query_width = q_size + key_height, key_width = k_size + relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h) + relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w) + + batch_size, _, dim = query.shape + reshaped_query = query.reshape(batch_size, query_height, query_width, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height) + rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width) + attn = attn.reshape(batch_size, query_height, query_width, key_height, key_width) + attn = attn + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + attn = attn.reshape(batch_size, query_height * query_width, key_height * key_width) + return attn + + def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, batch_size, nHead, height * width, channel) + qkv = ( + self.qkv(hidden_states) + .reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + .permute(2, 0, 3, 1, 4) + ) + # q, k, v with shape (batch_size * nHead, height * width, channel) + query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0) + + attn_weights = (query * self.scale) @ key.transpose(-2, -1) + + if self.use_relative_position_embeddings: + attn_weights = self.add_decomposed_rel_pos( + attn_weights, query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width) + ) + + attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(batch_size, self.num_attention_heads, height * width, -1) + attn_weights = attn_weights_reshaped.view(batch_size * self.num_attention_heads, height * width, -1) + else: + attn_weights_reshaped = None + + attn_output = (attn_weights @ value).reshape(batch_size, self.num_attention_heads, height, width, -1) + attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + + return (attn_output, attn_weights_reshaped) + + +# Copied from transformers.models.sam.modeling_sam.SamMLPBlock with SamMLPBlock->SegGptMlp +class SegGptMlp(nn.Module): + def __init__(self, config): + super().__init__() + self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim) + self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size) + self.act = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.lin1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.lin2(hidden_states) + return hidden_states + + +# Copied from transformers.models.beit.modeling_beit.drop_path +def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: + """ + Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + """ + if drop_prob == 0.0 or not training: + return input + keep_prob = 1 - drop_prob + shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) + random_tensor.floor_() # binarize + output = input.div(keep_prob) * random_tensor + return output + + +# Copied from transformers.models.beit.modeling_beit.BeitDropPath with Beit->SegGpt +class SegGptDropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float | None = None) -> None: + super().__init__() + self.drop_prob = drop_prob + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + return drop_path(hidden_states, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return f"p={self.drop_prob}" + + +class SegGptLayer(GradientCheckpointingLayer): + def __init__(self, config: SegGptConfig, drop_path_rate: float) -> None: + super().__init__() + self.attention = SegGptAttention(config) + self.mlp = SegGptMlp(config) + self.drop_path = SegGptDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity() + self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + ensemble_cond: int, + feature_ensemble: bool = False, + output_attentions: bool = False, + ) -> tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor]: + self_attention_outputs = self.attention( + self.layernorm_before(hidden_states), # in SegGpt, layernorm is applied before self-attention + output_attentions=output_attentions, + ) + attention_output = self_attention_outputs[0] + outputs = self_attention_outputs[1:] # add self attentions if we output attention weights + + if feature_ensemble and attention_output.shape[0] // 2 >= ensemble_cond: + prompt, inputs = attention_output.split(attention_output.shape[1] // 2, dim=1) + if ensemble_cond == 2: + num_prompts = attention_output.shape[0] // 2 + inputs = inputs.reshape(2, num_prompts, -1) + inputs = inputs.mean(dim=1, keepdim=True).expand_as(inputs) + inputs = inputs.reshape(*prompt.shape) + else: + inputs = inputs.mean(dim=0, keepdim=True).expand_as(inputs) + attention_output = torch.cat([prompt, inputs], dim=1) + + # first residual connection + hidden_states = self.drop_path(attention_output) + hidden_states + residual = hidden_states + + hidden_states = self.layernorm_after(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + self.drop_path(hidden_states) + + outputs = (hidden_states,) + outputs + + return outputs + + +class SegGptEncoder(nn.Module): + def __init__(self, config: SegGptConfig) -> None: + super().__init__() + self.config = config + dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers, device="cpu")] + self.layers = nn.ModuleList([SegGptLayer(config, dpr[i]) for i in range(config.num_hidden_layers)]) + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.Tensor, + feature_ensemble: bool = False, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ) -> tuple | SegGptEncoderOutput: + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + intermediate_hidden_states = [] + + for i, layer_module in enumerate(self.layers): + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # Condition to check if we have the appropriate number of prompts to ensemble + ensemble_cond = 2 if self.config.merge_index > i else 1 + + layer_outputs = layer_module(hidden_states, ensemble_cond, feature_ensemble, output_attentions) + + hidden_states = layer_outputs[0] + + if i == self.config.merge_index: + hidden_states = ( + hidden_states[: hidden_states.shape[0] // 2] + hidden_states[hidden_states.shape[0] // 2 :] + ) * 0.5 + + if i in self.config.intermediate_hidden_state_indices: + intermediate_hidden_states.append(self.layernorm(hidden_states)) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [hidden_states, all_hidden_states, all_self_attentions, intermediate_hidden_states] + if v is not None + ) + return SegGptEncoderOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + intermediate_hidden_states=intermediate_hidden_states, + ) + + +# Copied from transformers.models.convnext.modeling_convnext.ConvNextLayerNorm with ConvNext->SegGpt +class SegGptLayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +class SegGptDecoderHead(nn.Module): + def __init__(self, config): + super().__init__() + self.conv = nn.Conv2d( + config.decoder_hidden_size, + config.decoder_hidden_size, + kernel_size=3, + padding=1, + ) + self.layernorm = SegGptLayerNorm( + normalized_shape=config.decoder_hidden_size, eps=config.layer_norm_eps, data_format="channels_first" + ) + self.act_fct = ACT2FN[config.hidden_act] + self.head = nn.Conv2d(config.decoder_hidden_size, 3, kernel_size=1, bias=True) # decoder to patch + + def forward(self, hidden_states: torch.FloatTensor): + hidden_states = self.conv(hidden_states) + hidden_states = self.layernorm(hidden_states) + hidden_states = self.act_fct(hidden_states) + hidden_states = self.head(hidden_states) + + return hidden_states + + +class SegGptDecoder(nn.Module): + def __init__(self, config): + super().__init__() + self.decoder_embed = nn.Linear( + config.hidden_size * len(config.intermediate_hidden_state_indices), + config.patch_size**2 * config.decoder_hidden_size, + bias=True, + ) + self.decoder_pred = SegGptDecoderHead(config) + self.patch_size = config.patch_size + self.decoder_hidden_size = config.decoder_hidden_size + self.config = config + + def _reshape_hidden_states(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + batch_size, patch_height, patch_width, _ = hidden_states.shape + hidden_states = hidden_states.reshape( + batch_size, patch_height, patch_width, self.patch_size, self.patch_size, self.decoder_hidden_size + ) + hidden_states = hidden_states.permute(0, 5, 1, 3, 2, 4) + hidden_states = hidden_states.reshape( + shape=(batch_size, -1, patch_height * self.patch_size, patch_width * self.patch_size) + ) + + return hidden_states + + def forward(self, hidden_states: torch.FloatTensor): + hidden_states = self.decoder_embed(hidden_states) + hidden_states = self._reshape_hidden_states(hidden_states) + hidden_states = self.decoder_pred(hidden_states) + + return hidden_states + + +@auto_docstring +class SegGptPreTrainedModel(PreTrainedModel): + config: SegGptConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + _no_split_modules = ["SegGptEmbeddings", "SegGptLayer"] + + @torch.no_grad() + def _init_weights(self, module: nn.Module) -> None: + """Initialize the weights""" + std = self.config.initializer_range + if isinstance(module, (nn.Linear, nn.Conv2d)): + init.trunc_normal_(module.weight, mean=0.0, std=std) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, (nn.LayerNorm, SegGptLayerNorm)): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, SegGptAttention): + init.trunc_normal_(module.rel_pos_h, mean=0.0, std=std) + init.trunc_normal_(module.rel_pos_w, mean=0.0, std=std) + elif isinstance(module, SegGptEmbeddings): + init.trunc_normal_(module.position_embeddings, mean=0.0, std=std) + init.normal_(module.mask_token, std=std) + init.normal_(module.segment_token_input, std=std) + init.normal_(module.segment_token_prompt, std=std) + init.normal_(module.type_token_semantic, std=std) + init.normal_(module.type_token_instance, std=std) + + +@auto_docstring +class SegGptModel(SegGptPreTrainedModel): + def __init__(self, config: SegGptConfig): + super().__init__(config) + self.config = config + + self.embeddings = SegGptEmbeddings(config) + self.encoder = SegGptEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> SegGptPatchEmbeddings: + return self.embeddings.patch_embeddings + + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + prompt_pixel_values: torch.Tensor, + prompt_masks: torch.Tensor, + bool_masked_pos: torch.BoolTensor | None = None, + feature_ensemble: bool | None = None, + embedding_type: str | None = None, + labels: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | SegGptEncoderOutput: + r""" + prompt_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Prompt pixel values. Prompt pixel values can be obtained using [`AutoImageProcessor`]. See + [`SegGptImageProcessor.__call__`] for details. + prompt_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Prompt mask. Prompt mask can be obtained using [`AutoImageProcessor`]. See [`SegGptImageProcessor.__call__`] for + details. + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + feature_ensemble (`bool`, *optional*): + Boolean indicating whether to use feature ensemble or not. If `True`, the model will use feature ensemble + if we have at least two prompts. If `False`, the model will not use feature ensemble. This argument should + be considered when doing few-shot inference on an input image i.e. more than one prompt for the same image. + embedding_type (`str`, *optional*): + Embedding type. Indicates whether the prompt is a semantic or instance embedding. Can be either + instance or semantic. + labels (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, `optional`): + Ground truth mask for input images. + + Examples: + + ```python + >>> from transformers import SegGptImageProcessor, SegGptModel + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> image_input_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_2.jpg" + >>> image_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1.jpg" + >>> mask_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1_target.png" + + >>> with httpx.stream("GET", image_input_url) as response: + ... image_input = Image.open(BytesIO(response.read())) + + >>> with httpx.stream("GET", image_prompt_url) as response: + ... image_prompt = Image.open(BytesIO(response.read())) + + >>> with httpx.stream("GET", mask_prompt_url) as response: + ... mask_prompt = Image.open(BytesIO(response.read())).convert("L") + + >>> checkpoint = "BAAI/seggpt-vit-large" + >>> model = SegGptModel.from_pretrained(checkpoint) + >>> image_processor = SegGptImageProcessor.from_pretrained(checkpoint) + + >>> inputs = image_processor(images=image_input, prompt_images=image_prompt, prompt_masks=mask_prompt, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> list(outputs.last_hidden_state.shape) + [1, 56, 28, 1024] + ``` + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + feature_ensemble = feature_ensemble if feature_ensemble is not None else False + + expected_dtype = self.embeddings.patch_embeddings.projection.weight.dtype + pixel_values = pixel_values.to(expected_dtype) + prompt_pixel_values = prompt_pixel_values.to(expected_dtype) + + # Prepare inputs + pixel_values = torch.cat((prompt_pixel_values, pixel_values), dim=2) + prompt_pixel_values = ( + torch.cat((prompt_masks, prompt_masks), dim=2) + if labels is None + else torch.cat((prompt_masks, labels), dim=2) + ) + + if bool_masked_pos is None and labels is not None: + logger.warning_once( + "Labels were provided, but bool_masked_pos were not. It will be set to default value. If you're training the model, make sure to provide a bool_masked_pos." + ) + + # We concat on height axis so SegGPT can handle as a single image, hence we need to mask the portion + # of the mask prompt pixels that will be destinated to the prediction as they don't add any information. + # This is only the case for inference. In training, the model concat of prompt mask and label is masked + # and reconstructed together (In-Context Painting). + if bool_masked_pos is None: + num_patches = self.embeddings.patch_embeddings.num_patches + bool_masked_pos_zeros = torch.zeros(num_patches // 2, dtype=torch.bool, device=pixel_values.device) + bool_masked_pos_ones = torch.ones( + num_patches - num_patches // 2, dtype=torch.bool, device=pixel_values.device + ) + bool_masked_pos = torch.cat([bool_masked_pos_zeros, bool_masked_pos_ones]) + bool_masked_pos = bool_masked_pos.unsqueeze(0) + + embedding_output = self.embeddings( + pixel_values, prompt_pixel_values, embedding_type=embedding_type, bool_masked_pos=bool_masked_pos + ) + + encoder_outputs = self.encoder( + embedding_output, + feature_ensemble=feature_ensemble, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + return encoder_outputs + + +def patchify(tensor: torch.Tensor, patch_size: int) -> torch.Tensor: + batch_size, num_channels, height, width = tensor.shape + patch_height = height // patch_size + patch_width = width // patch_size + + tensor = tensor.reshape(shape=(batch_size, num_channels, patch_height, patch_size, patch_width, patch_size)) + tensor = tensor.permute(0, 2, 4, 3, 5, 1) + tensor = tensor.reshape(shape=(batch_size, patch_height * patch_width, patch_size**2 * 3)) + + return tensor + + +def unpatchify(tensor: torch.Tensor, patch_height: int, patch_width: int) -> torch.Tensor: + batch_size = tensor.shape[0] + patch_size = int((tensor.shape[-1] / 3) ** 0.5) + if patch_height * patch_width != tensor.shape[1]: + raise ValueError( + f"Number of patches {tensor.shape[1]} does not match patch height ({patch_height}) and width ({patch_width})." + ) + + tensor = tensor.reshape(shape=(batch_size, patch_height, patch_width, patch_size, patch_size, 3)) + tensor = tensor.permute(0, 5, 1, 3, 2, 4) + tensor = tensor.reshape(shape=(batch_size, 3, patch_height * patch_size, patch_width * patch_size)) + + return tensor + + +class SegGptLoss(nn.Module): + def __init__(self, config): + super().__init__() + self.beta = config.beta + self.patch_size = config.patch_size + + def forward( + self, + prompt_masks: torch.FloatTensor, + pred_masks: torch.FloatTensor, + labels: torch.FloatTensor, + bool_masked_pos: torch.BoolTensor, + ): + """Computes the L1 loss between the predicted masks and the ground truth masks. + + Args: + prompt_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values from mask prompt. + + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, 2*height, width)`): + Predicted masks. + + labels (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Ground truth mask for input images. + + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + + Returns: + `torch.FloatTensor`: The mean L1 loss between the predicted masks and the ground truth masks. + """ + ground_truth = torch.cat((prompt_masks, labels), dim=2) + + mask = bool_masked_pos[:, :, None].repeat(1, 1, self.patch_size**2 * 3) + mask = unpatchify(mask, ground_truth.shape[2] // self.patch_size, ground_truth.shape[3] // self.patch_size) + + loss = F.smooth_l1_loss(pred_masks, ground_truth, reduction="none", beta=self.beta) + loss = (loss * mask).sum() / mask.sum() # mean loss on removed patches + + return loss + + +@auto_docstring( + custom_intro=""" + SegGpt model with a decoder on top for one-shot image segmentation. + """ +) +class SegGptForImageSegmentation(SegGptPreTrainedModel): + def __init__(self, config: SegGptConfig): + super().__init__(config) + self.config = config + + self.model = SegGptModel(config) + self.decoder = SegGptDecoder(config) + + # Initialize weights and apply final processing + self.post_init() + + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor, + prompt_pixel_values: torch.Tensor, + prompt_masks: torch.Tensor, + bool_masked_pos: torch.BoolTensor | None = None, + feature_ensemble: bool | None = None, + embedding_type: str | None = None, + labels: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | SegGptImageSegmentationOutput: + r""" + prompt_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Prompt pixel values. Prompt pixel values can be obtained using [`AutoImageProcessor`]. See + [`SegGptImageProcessor.__call__`] for details. + prompt_masks (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Prompt mask. Prompt mask can be obtained using [`AutoImageProcessor`]. See [`SegGptImageProcessor.__call__`] for + details. + bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*): + Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). + feature_ensemble (`bool`, *optional*): + Boolean indicating whether to use feature ensemble or not. If `True`, the model will use feature ensemble + if we have at least two prompts. If `False`, the model will not use feature ensemble. This argument should + be considered when doing few-shot inference on an input image i.e. more than one prompt for the same image. + embedding_type (`str`, *optional*): + Embedding type. Indicates whether the prompt is a semantic or instance embedding. Can be either + instance or semantic. + labels (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, `optional`): + Ground truth mask for input images. + + Examples: + + ```python + >>> from transformers import SegGptImageProcessor, SegGptForImageSegmentation + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> image_input_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_2.jpg" + >>> image_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1.jpg" + >>> mask_prompt_url = "https://raw.githubusercontent.com/baaivision/Painter/main/SegGPT/SegGPT_inference/examples/hmbb_1_target.png" + + >>> with httpx.stream("GET", image_input_url) as response: + ... image_input = Image.open(BytesIO(response.read())) + + >>> with httpx.stream("GET", image_prompt_url) as response: + ... image_prompt = Image.open(BytesIO(response.read())) + + >>> with httpx.stream("GET", mask_prompt_url) as response: + ... mask_prompt = Image.open(BytesIO(response.read())).convert("L") + + >>> checkpoint = "BAAI/seggpt-vit-large" + >>> model = SegGptForImageSegmentation.from_pretrained(checkpoint) + >>> image_processor = SegGptImageProcessor.from_pretrained(checkpoint) + + >>> inputs = image_processor(images=image_input, prompt_images=image_prompt, prompt_masks=mask_prompt, return_tensors="pt") + >>> outputs = model(**inputs) + >>> result = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[(image_input.height, image_input.width)])[0] + >>> print(list(result.shape)) + [170, 297] + ``` + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if bool_masked_pos is None: + num_patches = self.model.embeddings.patch_embeddings.num_patches + bool_masked_pos_zeros = torch.zeros(num_patches // 2, dtype=torch.bool, device=pixel_values.device) + bool_masked_pos_ones = torch.ones( + num_patches - num_patches // 2, dtype=torch.bool, device=pixel_values.device + ) + bool_masked_pos = torch.cat([bool_masked_pos_zeros, bool_masked_pos_ones]) + bool_masked_pos = bool_masked_pos.unsqueeze(0) + + outputs = self.model( + pixel_values=pixel_values, + prompt_pixel_values=prompt_pixel_values, + prompt_masks=prompt_masks, + bool_masked_pos=bool_masked_pos, + feature_ensemble=feature_ensemble, + embedding_type=embedding_type, + labels=labels, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + intermediate_hidden_states = outputs.intermediate_hidden_states if return_dict else outputs[-1] + intermediate_hidden_states = torch.cat(intermediate_hidden_states, dim=-1) + pred_masks = self.decoder(intermediate_hidden_states) + + loss = None + if labels is not None: + loss_fn = SegGptLoss(self.config) + loss = loss_fn(prompt_masks, pred_masks, labels, bool_masked_pos) + + if not return_dict: + output = (pred_masks,) + if output_hidden_states: + output = output + (outputs[1],) + + if output_attentions: + idx = 2 if output_hidden_states else 1 + output = output + (outputs[idx],) + + if loss is not None: + output = (loss,) + output + return output + + return SegGptImageSegmentationOutput( + loss=loss, + pred_masks=pred_masks, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = ["SegGptModel", "SegGptPreTrainedModel", "SegGptForImageSegmentation"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0bd614f74a68062d842548bdcba4a65ce19fd62c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sew import * + from .modeling_sew import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/configuration_sew.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/configuration_sew.py new file mode 100644 index 0000000000000000000000000000000000000000..acb7be58a454ae8c36d820dfac5118951e1fa7b0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/configuration_sew.py @@ -0,0 +1,176 @@ +# Copyright 2021 ASAPP Inc. and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SEW model configuration""" + +import functools +import operator + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="BAAI/seggpt-vit-large") +@strict +class SEWConfig(PreTrainedConfig): + r""" + squeeze_factor (`int`, *optional*, defaults to 2): + Sequence length downsampling factor after the encoder and upsampling factor after the transformer. + feat_proj_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for output of the feature encoder. + final_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for the final projection layer of [`SEWForCTC`]. + feat_extract_norm (`str`, *optional*, defaults to `"group"`): + The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group + normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D + convolutional layers. + feat_extract_activation (`str, `optional`, defaults to `"gelu"`): + The non-linear activation function (function or string) in the 1D convolutional layers of the feature + extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. + conv_dim (`tuple[int]` or `list[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)`): + A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the + feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. + conv_stride (`tuple[int]` or `list[int]`, *optional*, defaults to `(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`): + A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length + of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. + conv_kernel (`tuple[int]` or `list[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The + length of *conv_kernel* defines the number of convolutional layers and has to match the length of + *conv_dim*. + conv_bias (`bool`, *optional*, defaults to `False`): + Whether the 1D convolutional layers have a bias. + num_conv_pos_embeddings (`int`, *optional*, defaults to 128): + Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional + embeddings layer. + num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): + Number of groups of 1D convolutional positional embeddings layer. + apply_spec_augment (`bool`, *optional*, defaults to `True`): + Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see + [SpecAugment: A Simple Data Augmentation Method for Automatic Speech + Recognition](https://huggingface.co/papers/1904.08779). + mask_time_prob (`float`, *optional*, defaults to 0.05): + Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking + procedure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If + reasoning from the probability of each feature vector to be chosen as the start of the vector span to be + masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the + actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. + mask_time_length (`int`, *optional*, defaults to 10): + Length of vector span along the time axis. + mask_time_min_masks (`int`, *optional*, defaults to 2),: + The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, + irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < + mask_time_min_masks'' + mask_feature_prob (`float`, *optional*, defaults to 0.0): + Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The + masking procedure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over + the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector + span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap + may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is + True`. + mask_feature_length (`int`, *optional*, defaults to 10): + Length of vector span along the feature axis. + mask_feature_min_masks (`int`, *optional*, defaults to 0): + The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time + step, irrespectively of `mask_feature_prob`. Only relevant if + ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' + ctc_zero_infinity (`bool`, *optional*, defaults to `False`): + Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly + occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance + of [`SEWForCTC`]. + use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): + Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an + instance of [`Wav2Vec2ForSequenceClassification`]. + classifier_proj_size (`int`, *optional*, defaults to 256): + Dimensionality of the projection before token mean-pooling for classification. + + Example: + + ```python + >>> from transformers import SEWConfig, SEWModel + + >>> # Initializing a SEW asapp/sew-tiny-100k style configuration + >>> configuration = SEWConfig() + + >>> # Initializing a model (with random weights) from the asapp/sew-tiny-100k style configuration + >>> model = SEWModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "sew" + + vocab_size: int = 32 + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + squeeze_factor: int = 2 + hidden_act: str = "gelu" + hidden_dropout: float | int = 0.1 + activation_dropout: float | int = 0.1 + attention_dropout: float | int = 0.1 + feat_proj_dropout: float | int = 0.0 + final_dropout: float | int = 0.1 + layerdrop: float | int = 0.1 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-5 + feat_extract_norm: str = "group" + feat_extract_activation: str = "gelu" + conv_dim: list[int] | tuple[int, ...] = (64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) + conv_stride: list[int] | tuple[int, ...] = (5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) + conv_kernel: list[int] | tuple[int, ...] = (10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) + conv_bias: bool = False + num_conv_pos_embeddings: int = 128 + num_conv_pos_embedding_groups: int = 16 + apply_spec_augment: bool = True + mask_time_prob: float | int = 0.05 + mask_time_length: int = 10 + mask_time_min_masks: int = 2 + mask_feature_prob: float | int = 0.0 + mask_feature_length: int = 10 + mask_feature_min_masks: int = 0 + ctc_loss_reduction: str = "mean" + ctc_zero_infinity: bool = False + use_weighted_layer_sum: bool = False + classifier_proj_size: int = 256 + pad_token_id: int | None = 0 + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 2 + + def __post_init__(self, **kwargs): + self.num_feat_extract_layers = len(self.conv_dim) + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if ( + (len(self.conv_stride) != self.num_feat_extract_layers) + or (len(self.conv_kernel) != self.num_feat_extract_layers) + or (len(self.conv_dim) != self.num_feat_extract_layers) + ): + raise ValueError( + "Configuration for convolutional layers is incorrect. " + "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, " + f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride) " + f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`." + ) + + @property + def inputs_to_logits_ratio(self): + return functools.reduce(operator.mul, self.conv_stride, 1) + + +__all__ = ["SEWConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/modeling_sew.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/modeling_sew.py new file mode 100644 index 0000000000000000000000000000000000000000..ea499e63289aa5590d3e43eadb8e58f003bcdbb3 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/modeling_sew.py @@ -0,0 +1,1080 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/sew/modular_sew.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_sew.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2021 ASAPP Inc. and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from collections.abc import Callable + +import numpy as np +import torch +from torch import nn +from torch.nn import CrossEntropyLoss + +from ... import initialization as init +from ...activations import ACT2FN +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...integrations.fsdp import is_fsdp_managed_module +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel, get_torch_context_manager_or_global_device +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, logging +from ...utils.generic import is_flash_attention_requested +from .configuration_sew import SEWConfig + + +logger = logging.get_logger(__name__) + + +class SEWNoLayerNormConvLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +class SEWLayerNormConvLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + + hidden_states = hidden_states.transpose(-2, -1) + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states.transpose(-2, -1) + + hidden_states = self.activation(hidden_states) + return hidden_states + + +class SEWGroupNormConvLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.activation = ACT2FN[config.feat_extract_activation] + + self.layer_norm = nn.GroupNorm(num_groups=self.out_conv_dim, num_channels=self.out_conv_dim, affine=True) + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +class SEWPositionalConvEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.conv = nn.Conv1d( + config.hidden_size, + config.hidden_size, + kernel_size=config.num_conv_pos_embeddings, + padding=config.num_conv_pos_embeddings // 2, + groups=config.num_conv_pos_embedding_groups, + stride=config.squeeze_factor, + ) + + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + if is_deepspeed_zero3_enabled(): + import deepspeed + + with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0): + self.conv = weight_norm(self.conv, name="weight", dim=2) + if hasattr(self.conv, "parametrizations"): + weight_g = self.conv.parametrizations.weight.original0 + weight_v = self.conv.parametrizations.weight.original1 + else: + weight_g = self.conv.weight_g + weight_v = self.conv.weight_v + deepspeed.zero.register_external_parameter(self, weight_v) + deepspeed.zero.register_external_parameter(self, weight_g) + else: + self.conv = weight_norm(self.conv, name="weight", dim=2) + + self.padding = SEWSamePadLayer(config.num_conv_pos_embeddings) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.padding(hidden_states) + hidden_states = self.activation(hidden_states) + + return hidden_states + + +class SEWSamePadLayer(nn.Module): + def __init__(self, num_conv_pos_embeddings): + super().__init__() + self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0 + + def forward(self, hidden_states): + if self.num_pad_remove > 0: + hidden_states = hidden_states[:, :, : -self.num_pad_remove] + return hidden_states + + +class SEWUpsampling(nn.Module): + def __init__(self, config): + super().__init__() + self.projection = nn.Linear(config.hidden_size, config.hidden_size * config.squeeze_factor) + self.activation = ACT2FN[config.feat_extract_activation] + self.squeeze_factor = config.squeeze_factor + + def forward(self, hidden_states): + hidden_states = self.projection(hidden_states) + hidden_states = self.activation(hidden_states) + + if self.squeeze_factor > 1: + # transform embedding channels to sequence length + bsz, src_len, src_embed_dim = hidden_states.size() + tgt_len = src_len * self.squeeze_factor + tgt_embed_dim = src_embed_dim // self.squeeze_factor + hidden_states = hidden_states.reshape(bsz, src_len, self.squeeze_factor, tgt_embed_dim) + hidden_states = hidden_states.reshape(bsz, tgt_len, tgt_embed_dim) + + return hidden_states + + +class SEWFeatureEncoder(nn.Module): + """Construct the features from raw audio waveform""" + + def __init__(self, config): + super().__init__() + + if config.feat_extract_norm == "group": + conv_layers = [SEWGroupNormConvLayer(config, layer_id=0)] + [ + SEWNoLayerNormConvLayer(config, layer_id=i + 1) for i in range(config.num_feat_extract_layers - 1) + ] + elif config.feat_extract_norm == "layer": + conv_layers = [SEWLayerNormConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)] + else: + raise ValueError( + f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']" + ) + self.conv_layers = nn.ModuleList(conv_layers) + self.gradient_checkpointing = False + self._requires_grad = True + + def _freeze_parameters(self): + for param in self.parameters(): + param.requires_grad = False + self._requires_grad = False + + def forward(self, input_values): + hidden_states = input_values[:, None] + + # make sure hidden_states require grad for gradient_checkpointing + if self._requires_grad and self.training: + hidden_states.requires_grad = True + + for conv_layer in self.conv_layers: + hidden_states = conv_layer(hidden_states) + + return hidden_states + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float | None = None, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + if scaling is None: + scaling = query.size(-1) ** -0.5 + + # Take the dot product between "query" and "key" to get the raw attention scores. + attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class SEWAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + is_decoder: bool = False, + bias: bool = True, + is_causal: bool = False, + config: SEWConfig | None = None, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + self.config = config + + if (self.head_dim * num_heads) != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" + f" and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + self.is_decoder = is_decoder + self.is_causal = is_causal + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def forward( + self, + hidden_states: torch.Tensor, + key_value_states: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = False, + # TODO: we need a refactor so that the different attention modules can get their specific kwargs + # ATM, we have mixed things encoder, decoder, and encoder-decoder attn + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]: + """Input shape: Batch x Time x Channel""" + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + + # determine input shapes + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + + # get query proj + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + current_states = key_value_states if is_cross_attention else hidden_states + kv_shape = (*current_states.shape[:-1], -1, self.head_dim) + key_states = self.k_proj(current_states).view(kv_shape).transpose(1, 2) + value_states = self.v_proj(current_states).view(kv_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.dropout, + scaling=self.scaling, + output_attentions=output_attentions, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights, None + + +class SEWFeedForward(nn.Module): + def __init__(self, config): + super().__init__() + self.intermediate_dropout = nn.Dropout(config.activation_dropout) + + self.intermediate_dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + self.output_dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.output_dropout = nn.Dropout(config.hidden_dropout) + + def forward(self, hidden_states): + hidden_states = self.intermediate_dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + hidden_states = self.intermediate_dropout(hidden_states) + + hidden_states = self.output_dense(hidden_states) + hidden_states = self.output_dropout(hidden_states) + return hidden_states + + +class SEWEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config): + super().__init__() + self.attention = SEWAttention( + embed_dim=config.hidden_size, + num_heads=config.num_attention_heads, + dropout=config.attention_dropout, + is_decoder=False, + config=config, + ) + + self.dropout = nn.Dropout(config.hidden_dropout) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.feed_forward = SEWFeedForward(config) + self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states, attention_mask=None, output_attentions=False): + attn_residual = hidden_states + hidden_states, attn_weights, _ = self.attention( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = self.dropout(hidden_states) + hidden_states = attn_residual + hidden_states + + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states + self.feed_forward(hidden_states) + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class SEWEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.pos_conv_embed = SEWPositionalConvEmbedding(config) + self.pool = nn.AvgPool1d(config.squeeze_factor, config.squeeze_factor) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layers = nn.ModuleList([SEWEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.upsample = SEWUpsampling(config) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if attention_mask is not None: + expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + if is_flash_attention_requested(self.config): + # make sure padded tokens output 0 + hidden_states[~expand_attention_mask] = 0.0 + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + else: + # make sure padded tokens output 0 + hidden_states[~expand_attention_mask] = 0.0 + input_lengths = (attention_mask.long()).sum(-1) + # apply pooling formula to get real output_lengths + output_lengths = input_lengths // self.config.squeeze_factor + max_encoder_length = hidden_states.shape[1] // self.config.squeeze_factor + attention_ids = ( + torch.arange(0, max_encoder_length, device=output_lengths.device) + .view(1, -1) + .expand(output_lengths.shape[0], -1) + ) + attention_mask = (attention_ids < output_lengths.view(-1, 1)).long() + + # extend attention_mask + attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) + attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min + attention_mask = attention_mask.expand( + attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] + ) + + n_input_timesteps = hidden_states.shape[1] + + hidden_states = hidden_states.transpose(1, 2) + position_embeddings = self.pos_conv_embed(hidden_states) + pooled_hidden_states = self.pool(hidden_states) + min_length = min(position_embeddings.size(-1), pooled_hidden_states.size(-1)) + hidden_states = pooled_hidden_states[..., :min_length] + position_embeddings[..., :min_length] + hidden_states = hidden_states.transpose(1, 2) + + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.dropout(hidden_states) + + synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self) + + for layer in self.layers: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + dropout_probability = torch.rand([]) + + skip_the_layer = self.training and dropout_probability < self.config.layerdrop + if not skip_the_layer or synced_gpus: + # under fsdp or deepspeed zero3 all gpus must run in sync + layer_outputs = layer( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = layer_outputs[0] + + if skip_the_layer: + layer_outputs = (None, None) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + hidden_states = self.upsample(hidden_states) + if hidden_states.shape[1] < n_input_timesteps: + hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, n_input_timesteps - hidden_states.shape[1])) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +@auto_docstring +class SEWPreTrainedModel(PreTrainedModel): + config: SEWConfig + base_model_prefix = "sew" + main_input_name = "input_values" + input_modalities = "audio" + supports_gradient_checkpointing = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = False # needs a proper look into the mask creation + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, SEWPositionalConvEmbedding): + init.normal_( + module.conv.weight, + mean=0, + std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)), + ) + init.constant_(module.conv.bias, 0) + elif isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, nn.Conv1d): + if is_deepspeed_zero3_enabled(): + import deepspeed + + if hasattr(module, "weight_v") and hasattr(module, "weight_g"): + with deepspeed.zero.GatheredParameters([module.weight_v, module.weight_g], modifier_rank=0): + init.kaiming_normal_(module.weight) + else: + with deepspeed.zero.GatheredParameters(module.weight, modifier_rank=0): + init.kaiming_normal_(module.weight) + else: + init.kaiming_normal_(module.weight) + + if isinstance(module, (nn.Linear, nn.Conv1d)) and module.bias is not None: + init.zeros_(module.bias) + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor | int): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + return input_lengths + + def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor): + output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long) + batch_size = attention_mask.shape[0] + + attention_mask = torch.zeros( + (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device + ) + # these two operations makes sure that all values before the output lengths idxs are attended to + attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1 + attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() + return attention_mask + + +def _compute_mask_indices( + shape: tuple[int, int], + mask_prob: float, + mask_length: int, + attention_mask: torch.LongTensor | None = None, + min_masks: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for + ASR](https://huggingface.co/papers/1904.08779). Note that this method is not optimized to run on TPU and should be run on + CPU as part of the preprocessing during training. + + Args: + shape: The shape for which to compute masks. This should be of a tuple of size 2 where + the first element is the batch size and the second element is the length of the axis to span. + mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of + independently generated mask spans of length `mask_length` is computed by + `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the + actual percentage will be smaller. + mask_length: size of the mask + min_masks: minimum number of masked spans + attention_mask: A (right-padded) attention mask which independently shortens the feature axis of + each batch dimension. + """ + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}" + f" and `sequence_length`: {sequence_length}`" + ) + + # epsilon is used for probabilistic rounding + epsilon = np.random.rand(1).item() + + def compute_num_masked_span(input_length): + """Given input length, compute how many spans should be masked""" + num_masked_span = int(mask_prob * input_length / mask_length + epsilon) + num_masked_span = max(num_masked_span, min_masks) + + # make sure num masked span <= sequence_length + if num_masked_span * mask_length > sequence_length: + num_masked_span = sequence_length // mask_length + + # make sure num_masked span is also <= input_length - (mask_length - 1) + if input_length - (mask_length - 1) < num_masked_span: + num_masked_span = max(input_length - (mask_length - 1), 0) + + return num_masked_span + + # compute number of masked spans in batch + input_lengths = ( + attention_mask.detach().sum(-1).tolist() + if attention_mask is not None + else [sequence_length for _ in range(batch_size)] + ) + + # SpecAugment mask to fill + spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) + spec_aug_mask_idxs = [] + + max_num_masked_span = compute_num_masked_span(sequence_length) + + if max_num_masked_span == 0: + return spec_aug_mask + + for input_length in input_lengths: + # compute num of masked spans for this input + num_masked_span = compute_num_masked_span(input_length) + + # get random indices to mask + spec_aug_mask_idx = np.random.choice( + np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False + ) + + # pick first sampled index that will serve as a dummy index to pad vector + # to ensure same dimension for all batches due to probabilistic rounding + # Picking first sample just pads those vectors twice. + if len(spec_aug_mask_idx) == 0: + # this case can only happen if `input_length` is strictly smaller then + # `sequence_length` in which case the last token has to be a padding + # token which we can use as a dummy mask id + dummy_mask_idx = sequence_length - 1 + else: + dummy_mask_idx = spec_aug_mask_idx[0] + + spec_aug_mask_idx = np.concatenate( + [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx] + ) + spec_aug_mask_idxs.append(spec_aug_mask_idx) + + spec_aug_mask_idxs = np.array(spec_aug_mask_idxs) + + # expand masked indices to masked spans + spec_aug_mask_idxs = np.broadcast_to( + spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length) + ) + spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length) + + # add offset to the starting indexes so that indexes now create a span + offsets = np.arange(mask_length)[None, None, :] + offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape( + batch_size, max_num_masked_span * mask_length + ) + spec_aug_mask_idxs = spec_aug_mask_idxs + offsets + + # ensure that we cannot have indices larger than sequence_length + if spec_aug_mask_idxs.max() > sequence_length - 1: + spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1 + + # scatter indices to mask + np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) + + return spec_aug_mask + + +@auto_docstring +class SEWModel(SEWPreTrainedModel): + def __init__(self, config: SEWConfig): + super().__init__(config) + self.config = config + self.feature_extractor = SEWFeatureEncoder(config) + self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps) + + self.project_features = config.conv_dim[-1] != config.hidden_size + if self.project_features: + self.feature_projection = nn.Linear(config.conv_dim[-1], config.hidden_size) + self.feature_dropout = nn.Dropout(config.feat_proj_dropout) + + if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0: + self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_()) + + self.encoder = SEWEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states + def _mask_hidden_states( + self, + hidden_states: torch.FloatTensor, + mask_time_indices: torch.FloatTensor | None = None, + attention_mask: torch.LongTensor | None = None, + ): + """ + Masks extracted features along time axis and/or along feature axis according to + [SpecAugment](https://huggingface.co/papers/1904.08779). + """ + + # `config.apply_spec_augment` can set masking to False + if not getattr(self.config, "apply_spec_augment", True): + return hidden_states + + # generate indices & apply SpecAugment along time axis + batch_size, sequence_length, hidden_size = hidden_states.size() + + if mask_time_indices is not None: + # apply SpecAugment along time axis with given mask_time_indices + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + elif self.config.mask_time_prob > 0 and self.training: + mask_time_indices = _compute_mask_indices( + (batch_size, sequence_length), + mask_prob=self.config.mask_time_prob, + mask_length=self.config.mask_time_length, + attention_mask=attention_mask, + min_masks=self.config.mask_time_min_masks, + ) + mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool) + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + + if self.config.mask_feature_prob > 0 and self.training: + # generate indices & apply SpecAugment along feature axis + mask_feature_indices = _compute_mask_indices( + (batch_size, hidden_size), + mask_prob=self.config.mask_feature_prob, + mask_length=self.config.mask_feature_length, + min_masks=self.config.mask_feature_min_masks, + ) + mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool) + mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1) + hidden_states[mask_feature_indices] = 0 + + return hidden_states + + @auto_docstring + def forward( + self, + input_values: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + mask_time_indices: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + r""" + mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict + masked extracted features in *config.proj_codevector_dim* space. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + extract_features = self.feature_extractor(input_values) + extract_features = extract_features.transpose(1, 2) + extract_features = self.layer_norm(extract_features) + + if self.project_features: + extract_features = self.feature_projection(extract_features) + hidden_states = self.feature_dropout(extract_features) + + if attention_mask is not None: + # compute reduced attention_mask corresponding to feature vectors + attention_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask) + + hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + if not return_dict: + return (hidden_states,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +_HIDDEN_STATES_START_POSITION = 1 + + +@auto_docstring( + custom_intro=""" + SEW Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). + """ +) +class SEWForCTC(SEWPreTrainedModel): + def __init__(self, config, target_lang: str | None = None): + r""" + target_lang (`str`, *optional*): + Language id of adapter weights. Adapter weights are stored in the format adapter..safetensors or + adapter..bin. Only relevant when using an instance of [`SEWForCTC`] with adapters. Uses 'eng' by + default. + """ + super().__init__(config) + + self.sew = SEWModel(config) + self.dropout = nn.Dropout(config.final_dropout) + + self.target_lang = target_lang + + if config.vocab_size is None: + raise ValueError( + f"You are trying to instantiate {self.__class__} with a configuration that " + "does not define the vocabulary size of the language model head. Please " + "instantiate the model as follows: `SEWForCTC.from_pretrained(..., vocab_size=vocab_size)`. " + "or define `vocab_size` of your model's configuration." + ) + output_hidden_size = ( + config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size + ) + self.lm_head = nn.Linear(output_hidden_size, config.vocab_size) + + # Initialize weights and apply final processing + self.post_init() + + def tie_weights(self, **kwargs): + """ + This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when + passing `target_lang=...` to `from_pretrained(...)`. + + This method is **not** supposed to be called by the user and is prone to be changed in the future. + """ + + if get_torch_context_manager_or_global_device() == torch.device("meta"): + return + + # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to + # correctly load adapter layers for SEW so that we do not have to introduce a new API to + # [`PreTrainedModel`]. While slightly hacky, SEW never has to tie input and output embeddings, so that it is + # ok to repurpose this function here. + target_lang = self.target_lang + + if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None: + raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.") + elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None: + logger.info("By default `target_lang` is set to 'eng'.") + elif target_lang is not None: + self.load_adapter(target_lang, force_load=True) + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.sew.feature_extractor._freeze_parameters() + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.sew.parameters(): + param.requires_grad = False + + @auto_docstring + def forward( + self, + input_values: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + labels: torch.Tensor | None = None, + **kwargs, + ) -> tuple | CausalLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*): + Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to + the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. + All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., + config.vocab_size - 1]`. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if labels is not None and labels.max() >= self.config.vocab_size: + raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}") + + outputs = self.sew( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + hidden_states = self.dropout(hidden_states) + + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # retrieve loss input_lengths from attention_mask + attention_mask = ( + attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long) + ) + input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long) + + # assuming that padded tokens are filled with -100 + # when not being attended to + labels_mask = labels >= 0 + target_lengths = labels_mask.sum(-1) + flattened_targets = labels.masked_select(labels_mask) + + # ctc_loss doesn't support fp16 + log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1) + + with torch.backends.cudnn.flags(enabled=False): + loss = nn.functional.ctc_loss( + log_probs, + flattened_targets, + input_lengths, + target_lengths, + blank=self.config.pad_token_id, + reduction=self.config.ctc_loss_reduction, + zero_infinity=self.config.ctc_zero_infinity, + ) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutput( + loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions + ) + + +@auto_docstring( + custom_intro=""" + SEW Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like + SUPERB Keyword Spotting. + """ +) +class SEWForSequenceClassification(SEWPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + if hasattr(config, "add_adapter") and config.add_adapter: + raise ValueError( + "Sequence classification does not support the use of SEW adapters (config.add_adapter=True)" + ) + self.sew = SEWModel(config) + num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings + if config.use_weighted_layer_sum: + self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) + self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size) + self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.sew.feature_extractor._freeze_parameters() + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.sew.parameters(): + param.requires_grad = False + + @auto_docstring + def forward( + self, + input_values: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + labels: torch.Tensor | None = None, + **kwargs, + ) -> tuple | SequenceClassifierOutput: + r""" + input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file + into an array of type `list[float]`, a `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec library + (`pip install torchcodec`) or the soundfile library (`pip install soundfile`). + To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and conversion + into a tensor of type `torch.FloatTensor`. See [`SEWProcessor.__call__`] for details. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + return_dict = return_dict if return_dict is not None else self.config.return_dict + output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states + + outputs = self.sew( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if self.config.use_weighted_layer_sum: + hidden_states = outputs[_HIDDEN_STATES_START_POSITION] + hidden_states = torch.stack(hidden_states, dim=1) + norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) + hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) + else: + hidden_states = outputs[0] + + hidden_states = self.projector(hidden_states) + if attention_mask is None: + pooled_output = hidden_states.mean(dim=1) + else: + padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask) + expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + hidden_states[~expand_padding_mask] = 0.0 + pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1) + + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = ["SEWForCTC", "SEWForSequenceClassification", "SEWModel", "SEWPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/modular_sew.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/modular_sew.py new file mode 100644 index 0000000000000000000000000000000000000000..312419793a3461f00dc5c9d16d83b106294f403d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew/modular_sew.py @@ -0,0 +1,449 @@ +# Copyright 2021 ASAPP Inc. and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SEW model.""" + +import math + +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...integrations.fsdp import is_fsdp_managed_module +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...utils import auto_docstring +from ...utils.generic import is_flash_attention_requested +from ..wav2vec2.modeling_wav2vec2 import ( + Wav2Vec2Attention, + Wav2Vec2EncoderLayer, + Wav2Vec2FeatureEncoder, + Wav2Vec2FeedForward, + Wav2Vec2ForCTC, + Wav2Vec2ForSequenceClassification, + Wav2Vec2GroupNormConvLayer, + Wav2Vec2LayerNormConvLayer, + Wav2Vec2NoLayerNormConvLayer, + Wav2Vec2SamePadLayer, + _compute_mask_indices, +) +from .configuration_sew import SEWConfig + + +_HIDDEN_STATES_START_POSITION = 1 + + +class SEWNoLayerNormConvLayer(Wav2Vec2NoLayerNormConvLayer): + pass + + +class SEWLayerNormConvLayer(Wav2Vec2LayerNormConvLayer): + pass + + +class SEWGroupNormConvLayer(Wav2Vec2GroupNormConvLayer): + pass + + +class SEWPositionalConvEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.conv = nn.Conv1d( + config.hidden_size, + config.hidden_size, + kernel_size=config.num_conv_pos_embeddings, + padding=config.num_conv_pos_embeddings // 2, + groups=config.num_conv_pos_embedding_groups, + stride=config.squeeze_factor, + ) + + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + if is_deepspeed_zero3_enabled(): + import deepspeed + + with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0): + self.conv = weight_norm(self.conv, name="weight", dim=2) + if hasattr(self.conv, "parametrizations"): + weight_g = self.conv.parametrizations.weight.original0 + weight_v = self.conv.parametrizations.weight.original1 + else: + weight_g = self.conv.weight_g + weight_v = self.conv.weight_v + deepspeed.zero.register_external_parameter(self, weight_v) + deepspeed.zero.register_external_parameter(self, weight_g) + else: + self.conv = weight_norm(self.conv, name="weight", dim=2) + + self.padding = SEWSamePadLayer(config.num_conv_pos_embeddings) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.padding(hidden_states) + hidden_states = self.activation(hidden_states) + + return hidden_states + + +class SEWSamePadLayer(Wav2Vec2SamePadLayer): + pass + + +class SEWUpsampling(nn.Module): + def __init__(self, config): + super().__init__() + self.projection = nn.Linear(config.hidden_size, config.hidden_size * config.squeeze_factor) + self.activation = ACT2FN[config.feat_extract_activation] + self.squeeze_factor = config.squeeze_factor + + def forward(self, hidden_states): + hidden_states = self.projection(hidden_states) + hidden_states = self.activation(hidden_states) + + if self.squeeze_factor > 1: + # transform embedding channels to sequence length + bsz, src_len, src_embed_dim = hidden_states.size() + tgt_len = src_len * self.squeeze_factor + tgt_embed_dim = src_embed_dim // self.squeeze_factor + hidden_states = hidden_states.reshape(bsz, src_len, self.squeeze_factor, tgt_embed_dim) + hidden_states = hidden_states.reshape(bsz, tgt_len, tgt_embed_dim) + + return hidden_states + + +class SEWFeatureEncoder(Wav2Vec2FeatureEncoder): + pass + + +class SEWAttention(Wav2Vec2Attention): + pass + + +class SEWFeedForward(Wav2Vec2FeedForward): + pass + + +class SEWEncoderLayer(Wav2Vec2EncoderLayer): + pass + + +class SEWEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.pos_conv_embed = SEWPositionalConvEmbedding(config) + self.pool = nn.AvgPool1d(config.squeeze_factor, config.squeeze_factor) + self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout) + self.layers = nn.ModuleList([SEWEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.upsample = SEWUpsampling(config) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + + if attention_mask is not None: + expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + if is_flash_attention_requested(self.config): + # make sure padded tokens output 0 + hidden_states[~expand_attention_mask] = 0.0 + # 2d mask is passed through the layers + attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None + else: + # make sure padded tokens output 0 + hidden_states[~expand_attention_mask] = 0.0 + input_lengths = (attention_mask.long()).sum(-1) + # apply pooling formula to get real output_lengths + output_lengths = input_lengths // self.config.squeeze_factor + max_encoder_length = hidden_states.shape[1] // self.config.squeeze_factor + attention_ids = ( + torch.arange(0, max_encoder_length, device=output_lengths.device) + .view(1, -1) + .expand(output_lengths.shape[0], -1) + ) + attention_mask = (attention_ids < output_lengths.view(-1, 1)).long() + + # extend attention_mask + attention_mask = 1.0 - attention_mask[:, None, None, :].to(dtype=hidden_states.dtype) + attention_mask = attention_mask * torch.finfo(hidden_states.dtype).min + attention_mask = attention_mask.expand( + attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1] + ) + + n_input_timesteps = hidden_states.shape[1] + + hidden_states = hidden_states.transpose(1, 2) + position_embeddings = self.pos_conv_embed(hidden_states) + pooled_hidden_states = self.pool(hidden_states) + min_length = min(position_embeddings.size(-1), pooled_hidden_states.size(-1)) + hidden_states = pooled_hidden_states[..., :min_length] + position_embeddings[..., :min_length] + hidden_states = hidden_states.transpose(1, 2) + + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.dropout(hidden_states) + + synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self) + + for layer in self.layers: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description) + dropout_probability = torch.rand([]) + + skip_the_layer = self.training and dropout_probability < self.config.layerdrop + if not skip_the_layer or synced_gpus: + # under fsdp or deepspeed zero3 all gpus must run in sync + layer_outputs = layer( + hidden_states, attention_mask=attention_mask, output_attentions=output_attentions + ) + hidden_states = layer_outputs[0] + + if skip_the_layer: + layer_outputs = (None, None) + + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + hidden_states = self.upsample(hidden_states) + if hidden_states.shape[1] < n_input_timesteps: + hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, n_input_timesteps - hidden_states.shape[1])) + + if not return_dict: + return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + ) + + +@auto_docstring +class SEWPreTrainedModel(PreTrainedModel): + config: SEWConfig + base_model_prefix = "sew" + main_input_name = "input_values" + input_modalities = "audio" + supports_gradient_checkpointing = True + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = False # needs a proper look into the mask creation + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, SEWPositionalConvEmbedding): + init.normal_( + module.conv.weight, + mean=0, + std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)), + ) + init.constant_(module.conv.bias, 0) + elif isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, nn.Conv1d): + if is_deepspeed_zero3_enabled(): + import deepspeed + + if hasattr(module, "weight_v") and hasattr(module, "weight_g"): + with deepspeed.zero.GatheredParameters([module.weight_v, module.weight_g], modifier_rank=0): + init.kaiming_normal_(module.weight) + else: + with deepspeed.zero.GatheredParameters(module.weight, modifier_rank=0): + init.kaiming_normal_(module.weight) + else: + init.kaiming_normal_(module.weight) + + if isinstance(module, (nn.Linear, nn.Conv1d)) and module.bias is not None: + init.zeros_(module.bias) + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor | int): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + return input_lengths + + def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor): + output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long) + batch_size = attention_mask.shape[0] + + attention_mask = torch.zeros( + (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device + ) + # these two operations makes sure that all values before the output lengths idxs are attended to + attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1 + attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() + return attention_mask + + +@auto_docstring +class SEWModel(SEWPreTrainedModel): + def __init__(self, config: SEWConfig): + super().__init__(config) + self.config = config + self.feature_extractor = SEWFeatureEncoder(config) + self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.layer_norm_eps) + + self.project_features = config.conv_dim[-1] != config.hidden_size + if self.project_features: + self.feature_projection = nn.Linear(config.conv_dim[-1], config.hidden_size) + self.feature_dropout = nn.Dropout(config.feat_proj_dropout) + + if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0: + self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_()) + + self.encoder = SEWEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states + def _mask_hidden_states( + self, + hidden_states: torch.FloatTensor, + mask_time_indices: torch.FloatTensor | None = None, + attention_mask: torch.LongTensor | None = None, + ): + """ + Masks extracted features along time axis and/or along feature axis according to + [SpecAugment](https://huggingface.co/papers/1904.08779). + """ + + # `config.apply_spec_augment` can set masking to False + if not getattr(self.config, "apply_spec_augment", True): + return hidden_states + + # generate indices & apply SpecAugment along time axis + batch_size, sequence_length, hidden_size = hidden_states.size() + + if mask_time_indices is not None: + # apply SpecAugment along time axis with given mask_time_indices + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + elif self.config.mask_time_prob > 0 and self.training: + mask_time_indices = _compute_mask_indices( + (batch_size, sequence_length), + mask_prob=self.config.mask_time_prob, + mask_length=self.config.mask_time_length, + attention_mask=attention_mask, + min_masks=self.config.mask_time_min_masks, + ) + mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool) + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + + if self.config.mask_feature_prob > 0 and self.training: + # generate indices & apply SpecAugment along feature axis + mask_feature_indices = _compute_mask_indices( + (batch_size, hidden_size), + mask_prob=self.config.mask_feature_prob, + mask_length=self.config.mask_feature_length, + min_masks=self.config.mask_feature_min_masks, + ) + mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool) + mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1) + hidden_states[mask_feature_indices] = 0 + + return hidden_states + + @auto_docstring + def forward( + self, + input_values: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + mask_time_indices: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + r""" + mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict + masked extracted features in *config.proj_codevector_dim* space. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + extract_features = self.feature_extractor(input_values) + extract_features = extract_features.transpose(1, 2) + extract_features = self.layer_norm(extract_features) + + if self.project_features: + extract_features = self.feature_projection(extract_features) + hidden_states = self.feature_dropout(extract_features) + + if attention_mask is not None: + # compute reduced attention_mask corresponding to feature vectors + attention_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask) + + hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + if not return_dict: + return (hidden_states,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +class SEWForCTC(Wav2Vec2ForCTC): + pass + + +class SEWForSequenceClassification(Wav2Vec2ForSequenceClassification): + pass + + +__all__ = ["SEWForCTC", "SEWForSequenceClassification", "SEWModel", "SEWPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7902aa464a9cfd5b465c2448014e218f4540c9d3 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_sew_d import * + from .modeling_sew_d import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/configuration_sew_d.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/configuration_sew_d.py new file mode 100644 index 0000000000000000000000000000000000000000..8d18745aa234503ccec567a6fc27d93eac445e65 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/configuration_sew_d.py @@ -0,0 +1,196 @@ +# Copyright 2021 ASAPP Inc. and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""SEW-D model configuration""" + +import functools +import operator + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="BAAI/seggpt-vit-large") +@strict +class SEWDConfig(PreTrainedConfig): + r""" + squeeze_factor (`int`, *optional*, defaults to 2): + Sequence length downsampling factor after the encoder and upsampling factor after the transformer. + position_buckets (`int`, *optional*, defaults to 256): + The maximum size of relative position embeddings. + share_att_key (`bool`, *optional*, defaults to `True`): + Whether to share attention key with c2p and p2c. + relative_attention (`bool`, *optional*, defaults to `True`): + Whether to use relative position encoding. + pos_att_type (`tuple[str]`, *optional*, defaults to `("p2c", "c2p")`): + The type of relative position attention, it can be a combination of `("p2c", "c2p")`, e.g. `("p2c")`, + `("p2c", "c2p")`, `("p2c", "c2p")`. + norm_rel_ebd (`str`, *optional*, defaults to `"layer_norm"`): + Whether to use layer norm in relative embedding (`"layer_norm"` if yes) + feat_proj_dropout (`float`, *optional*, defaults to 0.0): + The dropout probability for output of the feature encoder. + final_dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for the final projection layer of [`SEWDForCTC`]. + feature_layer_norm_eps (`float`, *optional*, defaults to 1e-5): + The epsilon used by the layer normalization after the feature encoder. + feat_extract_norm (`str`, *optional*, defaults to `"group"`): + The norm to be applied to 1D convolutional layers in feature encoder. One of `"group"` for group + normalization of only the first 1D convolutional layer or `"layer"` for layer normalization of all 1D + convolutional layers. + feat_extract_activation (`str, `optional`, defaults to `"gelu"`): + The non-linear activation function (function or string) in the 1D convolutional layers of the feature + extractor. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. + conv_dim (`tuple[int]` or `list[int]`, *optional*, defaults to `(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)`): + A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the + feature encoder. The length of *conv_dim* defines the number of 1D convolutional layers. + conv_stride (`tuple[int]` or `list[int]`, *optional*, defaults to `(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)`): + A tuple of integers defining the stride of each 1D convolutional layer in the feature encoder. The length + of *conv_stride* defines the number of convolutional layers and has to match the length of *conv_dim*. + conv_kernel (`tuple[int]` or `list[int]`, *optional*, defaults to `(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the feature encoder. The + length of *conv_kernel* defines the number of convolutional layers and has to match the length of + *conv_dim*. + conv_bias (`bool`, *optional*, defaults to `False`): + Whether the 1D convolutional layers have a bias. + num_conv_pos_embeddings (`int`, *optional*, defaults to 128): + Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional + embeddings layer. + num_conv_pos_embedding_groups (`int`, *optional*, defaults to 16): + Number of groups of 1D convolutional positional embeddings layer. + apply_spec_augment (`bool`, *optional*, defaults to `True`): + Whether to apply *SpecAugment* data augmentation to the outputs of the feature encoder. For reference see + [SpecAugment: A Simple Data Augmentation Method for Automatic Speech + Recognition](https://huggingface.co/papers/1904.08779). + mask_time_prob (`float`, *optional*, defaults to 0.05): + Percentage (between 0 and 1) of all feature vectors along the time axis which will be masked. The masking + procedure generates ''mask_time_prob*len(time_axis)/mask_time_length'' independent masks over the axis. If + reasoning from the probability of each feature vector to be chosen as the start of the vector span to be + masked, *mask_time_prob* should be `prob_vector_start*mask_time_length`. Note that overlap may decrease the + actual percentage of masked vectors. This is only relevant if `apply_spec_augment is True`. + mask_time_length (`int`, *optional*, defaults to 10): + Length of vector span along the time axis. + mask_time_min_masks (`int`, *optional*, defaults to 2),: + The minimum number of masks of length `mask_feature_length` generated along the time axis, each time step, + irrespectively of `mask_feature_prob`. Only relevant if ''mask_time_prob*len(time_axis)/mask_time_length < + mask_time_min_masks'' + mask_feature_prob (`float`, *optional*, defaults to 0.0): + Percentage (between 0 and 1) of all feature vectors along the feature axis which will be masked. The + masking procedure generates ''mask_feature_prob*len(feature_axis)/mask_time_length'' independent masks over + the axis. If reasoning from the probability of each feature vector to be chosen as the start of the vector + span to be masked, *mask_feature_prob* should be `prob_vector_start*mask_feature_length`. Note that overlap + may decrease the actual percentage of masked vectors. This is only relevant if `apply_spec_augment is + True`. + mask_feature_length (`int`, *optional*, defaults to 10): + Length of vector span along the feature axis. + mask_feature_min_masks (`int`, *optional*, defaults to 0),: + The minimum number of masks of length `mask_feature_length` generated along the feature axis, each time + step, irrespectively of `mask_feature_prob`. Only relevant if + ''mask_feature_prob*len(feature_axis)/mask_feature_length < mask_feature_min_masks'' + diversity_loss_weight (`int`, *optional*, defaults to 0.1): + The weight of the codebook diversity loss component. + ctc_zero_infinity (`bool`, *optional*, defaults to `False`): + Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly + occur when the inputs are too short to be aligned to the targets. Only relevant when training an instance + of [`SEWDForCTC`]. + use_weighted_layer_sum (`bool`, *optional*, defaults to `False`): + Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an + instance of [`Wav2Vec2ForSequenceClassification`]. + classifier_proj_size (`int`, *optional*, defaults to 256): + Dimensionality of the projection before token mean-pooling for classification. + + Example: + + ```python + >>> from transformers import SEWDConfig, SEWDModel + + >>> # Initializing a SEW-D asapp/sew-d-tiny-100k style configuration + >>> configuration = SEWDConfig() + + >>> # Initializing a model (with random weights) from the asapp/sew-d-tiny-100k style configuration + >>> model = SEWDModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "sew-d" + vocab_size: int = 32 + hidden_size: int = 768 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + intermediate_size: int = 3072 + squeeze_factor: int = 2 + max_position_embeddings: int = 512 + position_buckets: int = 256 + share_att_key: bool = True + relative_attention: bool = True + pos_att_type: list[str] | tuple[str, ...] = ("p2c", "c2p") + norm_rel_ebd: str = "layer_norm" + hidden_act: str = "gelu_python" + hidden_dropout: float | int = 0.1 + activation_dropout: float | int = 0.1 + attention_dropout: float | int = 0.1 + feat_proj_dropout: float | int = 0.0 + final_dropout: float | int = 0.1 + initializer_range: float = 0.02 + layer_norm_eps: float = 1e-7 + feature_layer_norm_eps: float = 1e-5 + feat_extract_norm: str = "group" + feat_extract_activation: str = "gelu" + conv_dim: list[int] | tuple[int, ...] = (64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) + conv_stride: list[int] | tuple[int, ...] = (5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) + conv_kernel: list[int] | tuple[int, ...] = (10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) + conv_bias: bool = False + num_conv_pos_embeddings: int = 128 + num_conv_pos_embedding_groups: int = 16 + apply_spec_augment: bool = True + mask_time_prob: float | int = 0.05 + mask_time_length: int = 10 + mask_time_min_masks: int = 2 + mask_feature_prob: float | int = 0.0 + mask_feature_length: int = 10 + mask_feature_min_masks: int = 0 + ctc_loss_reduction: str = "mean" + ctc_zero_infinity: bool = False + use_weighted_layer_sum: bool = False + classifier_proj_size: int = 256 + pad_token_id: int | None = 0 + bos_token_id: int | None = 1 + eos_token_id: int | list[int] | None = 2 + + def __post_init__(self, **kwargs): + self.num_feat_extract_layers = len(self.conv_dim) + super().__post_init__(**kwargs) + + def validate_architecture(self): + """Part of `@strict`-powered validation. Validates the architecture of the config.""" + if ( + (len(self.conv_stride) != self.num_feat_extract_layers) + or (len(self.conv_kernel) != self.num_feat_extract_layers) + or (len(self.conv_dim) != self.num_feat_extract_layers) + ): + raise ValueError( + "Configuration for convolutional layers is incorrect. " + "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, " + f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride) " + f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`." + ) + + @property + def inputs_to_logits_ratio(self): + return functools.reduce(operator.mul, self.conv_stride, 1) + + +__all__ = ["SEWDConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/modeling_sew_d.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/modeling_sew_d.py new file mode 100644 index 0000000000000000000000000000000000000000..15323596c71ac4d8ed1ebb5469c2321487619d00 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/sew_d/modeling_sew_d.py @@ -0,0 +1,1621 @@ +# Copyright 2021 ASAPP Inc. and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch SEW model.""" + +import math +from collections.abc import Sequence + +import numpy as np +import torch +from torch import nn +from torch.nn import CrossEntropyLoss, LayerNorm + +from ... import initialization as init +from ...activations import ACT2FN +from ...integrations.deepspeed import is_deepspeed_zero3_enabled +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput +from ...modeling_utils import PreTrainedModel, get_torch_context_manager_or_global_device +from ...utils import auto_docstring, logging +from .configuration_sew_d import SEWDConfig + + +logger = logging.get_logger(__name__) + +_HIDDEN_STATES_START_POSITION = 1 + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2._compute_mask_indices +def _compute_mask_indices( + shape: tuple[int, int], + mask_prob: float, + mask_length: int, + attention_mask: torch.LongTensor | None = None, + min_masks: int = 0, +) -> np.ndarray: + """ + Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for + ASR](https://huggingface.co/papers/1904.08779). Note that this method is not optimized to run on TPU and should be run on + CPU as part of the preprocessing during training. + + Args: + shape: The shape for which to compute masks. This should be of a tuple of size 2 where + the first element is the batch size and the second element is the length of the axis to span. + mask_prob: The percentage of the whole axis (between 0 and 1) which will be masked. The number of + independently generated mask spans of length `mask_length` is computed by + `mask_prob*shape[1]/mask_length`. Note that due to overlaps, `mask_prob` is an upper bound and the + actual percentage will be smaller. + mask_length: size of the mask + min_masks: minimum number of masked spans + attention_mask: A (right-padded) attention mask which independently shortens the feature axis of + each batch dimension. + """ + batch_size, sequence_length = shape + + if mask_length < 1: + raise ValueError("`mask_length` has to be bigger than 0.") + + if mask_length > sequence_length: + raise ValueError( + f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length}" + f" and `sequence_length`: {sequence_length}`" + ) + + # epsilon is used for probabilistic rounding + epsilon = np.random.rand(1).item() + + def compute_num_masked_span(input_length): + """Given input length, compute how many spans should be masked""" + num_masked_span = int(mask_prob * input_length / mask_length + epsilon) + num_masked_span = max(num_masked_span, min_masks) + + # make sure num masked span <= sequence_length + if num_masked_span * mask_length > sequence_length: + num_masked_span = sequence_length // mask_length + + # make sure num_masked span is also <= input_length - (mask_length - 1) + if input_length - (mask_length - 1) < num_masked_span: + num_masked_span = max(input_length - (mask_length - 1), 0) + + return num_masked_span + + # compute number of masked spans in batch + input_lengths = ( + attention_mask.detach().sum(-1).tolist() + if attention_mask is not None + else [sequence_length for _ in range(batch_size)] + ) + + # SpecAugment mask to fill + spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool) + spec_aug_mask_idxs = [] + + max_num_masked_span = compute_num_masked_span(sequence_length) + + if max_num_masked_span == 0: + return spec_aug_mask + + for input_length in input_lengths: + # compute num of masked spans for this input + num_masked_span = compute_num_masked_span(input_length) + + # get random indices to mask + spec_aug_mask_idx = np.random.choice( + np.arange(input_length - (mask_length - 1)), num_masked_span, replace=False + ) + + # pick first sampled index that will serve as a dummy index to pad vector + # to ensure same dimension for all batches due to probabilistic rounding + # Picking first sample just pads those vectors twice. + if len(spec_aug_mask_idx) == 0: + # this case can only happen if `input_length` is strictly smaller then + # `sequence_length` in which case the last token has to be a padding + # token which we can use as a dummy mask id + dummy_mask_idx = sequence_length - 1 + else: + dummy_mask_idx = spec_aug_mask_idx[0] + + spec_aug_mask_idx = np.concatenate( + [spec_aug_mask_idx, np.ones(max_num_masked_span - num_masked_span, dtype=np.int32) * dummy_mask_idx] + ) + spec_aug_mask_idxs.append(spec_aug_mask_idx) + + spec_aug_mask_idxs = np.array(spec_aug_mask_idxs) + + # expand masked indices to masked spans + spec_aug_mask_idxs = np.broadcast_to( + spec_aug_mask_idxs[:, :, None], (batch_size, max_num_masked_span, mask_length) + ) + spec_aug_mask_idxs = spec_aug_mask_idxs.reshape(batch_size, max_num_masked_span * mask_length) + + # add offset to the starting indexes so that indexes now create a span + offsets = np.arange(mask_length)[None, None, :] + offsets = np.broadcast_to(offsets, (batch_size, max_num_masked_span, mask_length)).reshape( + batch_size, max_num_masked_span * mask_length + ) + spec_aug_mask_idxs = spec_aug_mask_idxs + offsets + + # ensure that we cannot have indices larger than sequence_length + if spec_aug_mask_idxs.max() > sequence_length - 1: + spec_aug_mask_idxs[spec_aug_mask_idxs > sequence_length - 1] = sequence_length - 1 + + # scatter indices to mask + np.put_along_axis(spec_aug_mask, spec_aug_mask_idxs, 1, -1) + + return spec_aug_mask + + +def make_log_bucket_position(relative_pos, bucket_size, max_position): + sign = torch.sign(relative_pos) + mid = bucket_size // 2 + abs_pos = torch.where( + (relative_pos < mid) & (relative_pos > -mid), + torch.tensor(mid - 1).type_as(relative_pos), + torch.abs(relative_pos), + ) + log_pos = ( + torch.ceil(torch.log(abs_pos / mid) / torch.log(torch.tensor((max_position - 1) / mid)) * (mid - 1)) + mid + ) + bucket_pos = torch.where(abs_pos <= mid, relative_pos.type_as(log_pos), log_pos * sign) + return bucket_pos + + +def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1, device=None): + """ + Build relative position according to the query and key + + We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key + \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q - + P_k\\) + + Args: + query_size (int): the length of query + key_size (int): the length of key + bucket_size (int): the size of position bucket + max_position (int): the maximum allowed absolute position + device (`torch.device`): the device on which tensors will be created. + + Return: + `torch.LongTensor`: A tensor with shape [1, query_size, key_size] + """ + + q_ids = torch.arange(0, query_size, device=device) + k_ids = torch.arange(0, key_size, device=device) + rel_pos_ids = q_ids[:, None] - k_ids[None, :] + if bucket_size > 0 and max_position > 0: + rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position) + rel_pos_ids = rel_pos_ids.to(torch.long) + rel_pos_ids = rel_pos_ids[:query_size, :] + rel_pos_ids = rel_pos_ids.unsqueeze(0) + return rel_pos_ids + + +@torch.jit.script +def c2p_dynamic_expand(c2p_pos, query_layer, relative_pos): + return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]) + + +@torch.jit.script +def p2c_dynamic_expand(c2p_pos, query_layer, key_layer): + return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)]) + + +@torch.jit.script +def pos_dynamic_expand(pos_index, p2c_att, key_layer): + return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))) + + +def get_mask(input, local_context): + if not isinstance(local_context, DropoutContext): + dropout = local_context + mask = None + else: + dropout = local_context.dropout + dropout *= local_context.scale + mask = local_context.mask if local_context.reuse_mask else None + + if dropout > 0 and mask is None: + mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).to(torch.bool) + + if isinstance(local_context, DropoutContext): + if local_context.mask is None: + local_context.mask = mask + + return mask, dropout + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2NoLayerNormConvLayer with Wav2Vec2->SEWD +class SEWDNoLayerNormConvLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2LayerNormConvLayer with Wav2Vec2->SEWD +class SEWDLayerNormConvLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.layer_norm = nn.LayerNorm(self.out_conv_dim, elementwise_affine=True) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + + hidden_states = hidden_states.transpose(-2, -1) + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states.transpose(-2, -1) + + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2GroupNormConvLayer with Wav2Vec2->SEWD +class SEWDGroupNormConvLayer(GradientCheckpointingLayer): + def __init__(self, config, layer_id=0): + super().__init__() + self.in_conv_dim = config.conv_dim[layer_id - 1] if layer_id > 0 else 1 + self.out_conv_dim = config.conv_dim[layer_id] + + self.conv = nn.Conv1d( + self.in_conv_dim, + self.out_conv_dim, + kernel_size=config.conv_kernel[layer_id], + stride=config.conv_stride[layer_id], + bias=config.conv_bias, + ) + self.activation = ACT2FN[config.feat_extract_activation] + + self.layer_norm = nn.GroupNorm(num_groups=self.out_conv_dim, num_channels=self.out_conv_dim, affine=True) + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.layer_norm(hidden_states) + hidden_states = self.activation(hidden_states) + return hidden_states + + +# Copied from transformers.models.sew.modeling_sew.SEWPositionalConvEmbedding with SEW->SEWD +class SEWDPositionalConvEmbedding(nn.Module): + def __init__(self, config): + super().__init__() + self.conv = nn.Conv1d( + config.hidden_size, + config.hidden_size, + kernel_size=config.num_conv_pos_embeddings, + padding=config.num_conv_pos_embeddings // 2, + groups=config.num_conv_pos_embedding_groups, + stride=config.squeeze_factor, + ) + + weight_norm = nn.utils.weight_norm + if hasattr(nn.utils.parametrizations, "weight_norm"): + weight_norm = nn.utils.parametrizations.weight_norm + + if is_deepspeed_zero3_enabled(): + import deepspeed + + with deepspeed.zero.GatheredParameters(self.conv.weight, modifier_rank=0): + self.conv = weight_norm(self.conv, name="weight", dim=2) + if hasattr(self.conv, "parametrizations"): + weight_g = self.conv.parametrizations.weight.original0 + weight_v = self.conv.parametrizations.weight.original1 + else: + weight_g = self.conv.weight_g + weight_v = self.conv.weight_v + deepspeed.zero.register_external_parameter(self, weight_v) + deepspeed.zero.register_external_parameter(self, weight_g) + else: + self.conv = weight_norm(self.conv, name="weight", dim=2) + + self.padding = SEWDSamePadLayer(config.num_conv_pos_embeddings) + self.activation = ACT2FN[config.feat_extract_activation] + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.padding(hidden_states) + hidden_states = self.activation(hidden_states) + + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2SamePadLayer with Wav2Vec2->SEW +class SEWDSamePadLayer(nn.Module): + def __init__(self, num_conv_pos_embeddings): + super().__init__() + self.num_pad_remove = 1 if num_conv_pos_embeddings % 2 == 0 else 0 + + def forward(self, hidden_states): + if self.num_pad_remove > 0: + hidden_states = hidden_states[:, :, : -self.num_pad_remove] + return hidden_states + + +# Copied from transformers.models.sew.modeling_sew.SEWUpsampling with SEW->SEWD +class SEWDUpsampling(nn.Module): + def __init__(self, config): + super().__init__() + self.projection = nn.Linear(config.hidden_size, config.hidden_size * config.squeeze_factor) + self.activation = ACT2FN[config.feat_extract_activation] + self.squeeze_factor = config.squeeze_factor + + def forward(self, hidden_states): + hidden_states = self.projection(hidden_states) + hidden_states = self.activation(hidden_states) + + if self.squeeze_factor > 1: + # transform embedding channels to sequence length + bsz, src_len, src_embed_dim = hidden_states.size() + tgt_len = src_len * self.squeeze_factor + tgt_embed_dim = src_embed_dim // self.squeeze_factor + hidden_states = hidden_states.reshape(bsz, src_len, self.squeeze_factor, tgt_embed_dim) + hidden_states = hidden_states.reshape(bsz, tgt_len, tgt_embed_dim) + + return hidden_states + + +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2FeatureEncoder with Wav2Vec2->SEWD +class SEWDFeatureEncoder(nn.Module): + """Construct the features from raw audio waveform""" + + def __init__(self, config): + super().__init__() + + if config.feat_extract_norm == "group": + conv_layers = [SEWDGroupNormConvLayer(config, layer_id=0)] + [ + SEWDNoLayerNormConvLayer(config, layer_id=i + 1) for i in range(config.num_feat_extract_layers - 1) + ] + elif config.feat_extract_norm == "layer": + conv_layers = [SEWDLayerNormConvLayer(config, layer_id=i) for i in range(config.num_feat_extract_layers)] + else: + raise ValueError( + f"`config.feat_extract_norm` is {config.feat_extract_norm}, but has to be one of ['group', 'layer']" + ) + self.conv_layers = nn.ModuleList(conv_layers) + self.gradient_checkpointing = False + self._requires_grad = True + + def _freeze_parameters(self): + for param in self.parameters(): + param.requires_grad = False + self._requires_grad = False + + def forward(self, input_values): + hidden_states = input_values[:, None] + + # make sure hidden_states require grad for gradient_checkpointing + if self._requires_grad and self.training: + hidden_states.requires_grad = True + + for conv_layer in self.conv_layers: + hidden_states = conv_layer(hidden_states) + + return hidden_states + + +class ContextPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size) + self.dropout = StableDropout(config.pooler_dropout) + self.config = config + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + + context_token = hidden_states[:, 0] + context_token = self.dropout(context_token) + pooled_output = self.dense(context_token) + pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output) + return pooled_output + + @property + def output_dim(self): + return self.config.hidden_size + + +class XSoftmax(torch.autograd.Function): + """ + Masked Softmax which is optimized for saving memory + + Args: + input (`torch.tensor`): The input tensor that will apply softmax. + mask (`torch.IntTensor`): + The mask matrix where 0 indicate that element will be ignored in the softmax calculation. + dim (int): The dimension that will apply softmax + + Example: + + ```python + >>> import torch + >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax + + >>> # Make a tensor + >>> x = torch.randn([4, 20, 100]) + + >>> # Create a mask + >>> mask = (x > 0).int() + + >>> # Specify the dimension to apply softmax + >>> dim = -1 + + >>> y = XSoftmax.apply(x, mask, dim) + ```""" + + @staticmethod + def forward(ctx, input, mask, dim): + ctx.dim = dim + rmask = ~(mask.to(torch.bool)) + + output = input.masked_fill(rmask, torch.tensor(torch.finfo(input.dtype).min)) + output = torch.softmax(output, ctx.dim) + output.masked_fill_(rmask, 0) + ctx.save_for_backward(output) + return output + + @staticmethod + def backward(ctx, grad_output): + (output,) = ctx.saved_tensors + inputGrad = torch._softmax_backward_data(grad_output, output, ctx.dim, output.dtype) + return inputGrad, None, None + + @staticmethod + def symbolic(g, self, mask, dim): + import torch.onnx.symbolic_helper as sym_help + from torch.onnx.symbolic_opset9 import masked_fill, softmax + + mask_cast_value = g.op("Cast", mask, to_i=sym_help.cast_pytorch_to_onnx["Long"]) + r_mask = g.op( + "Cast", + g.op("Sub", g.op("Constant", value_t=torch.tensor(1, dtype=torch.int64)), mask_cast_value), + to_i=sym_help.cast_pytorch_to_onnx["Bool"], + ) + output = masked_fill( + g, self, r_mask, g.op("Constant", value_t=torch.tensor(torch.finfo(self.type().dtype()).min)) + ) + output = softmax(g, output, dim) + return masked_fill(g, output, r_mask, g.op("Constant", value_t=torch.tensor(0, dtype=torch.bool))) + + +class DropoutContext: + def __init__(self): + self.dropout = 0 + self.mask = None + self.scale = 1 + self.reuse_mask = True + + +class XDropout(torch.autograd.Function): + """Optimized dropout function to save computation and memory by using mask operation instead of multiplication.""" + + @staticmethod + def forward(ctx, input, local_ctx): + mask, dropout = get_mask(input, local_ctx) + ctx.scale = 1.0 / (1 - dropout) + if dropout > 0: + ctx.save_for_backward(mask) + return input.masked_fill(mask, 0) * ctx.scale + else: + return input + + @staticmethod + def backward(ctx, grad_output): + if ctx.scale > 1: + (mask,) = ctx.saved_tensors + return grad_output.masked_fill(mask, 0) * ctx.scale, None + else: + return grad_output, None + + @staticmethod + def symbolic(g: torch._C.Graph, input: torch._C.Value, local_ctx: float | DropoutContext) -> torch._C.Value: + from torch.onnx import symbolic_opset12 + + dropout_p = local_ctx + if isinstance(local_ctx, DropoutContext): + dropout_p = local_ctx.dropout + # StableDropout only calls this function when training. + train = True + # TODO: We should check if the opset_version being used to export + # is > 12 here, but there's no good way to do that. As-is, if the + # opset_version < 12, export will fail with a CheckerError. + # Once https://github.com/pytorch/pytorch/issues/78391 is fixed, do something like: + # if opset_version < 12: + # return torch.onnx.symbolic_opset9.dropout(g, input, dropout_p, train) + return symbolic_opset12.dropout(g, input, dropout_p, train) + + +class StableDropout(nn.Module): + """ + Optimized dropout module for stabilizing the training + + Args: + drop_prob (float): the dropout probabilities + """ + + def __init__(self, drop_prob): + super().__init__() + self.drop_prob = drop_prob + self.count = 0 + self.context_stack = None + + def forward(self, x): + """ + Call the module + + Args: + x (`torch.tensor`): The input tensor to apply dropout + """ + if self.training and self.drop_prob > 0: + return XDropout.apply(x, self.get_context()) + return x + + def clear_context(self): + self.count = 0 + self.context_stack = None + + def init_context(self, reuse_mask=True, scale=1): + if self.context_stack is None: + self.context_stack = [] + self.count = 0 + for c in self.context_stack: + c.reuse_mask = reuse_mask + c.scale = scale + + def get_context(self): + if self.context_stack is not None: + if self.count >= len(self.context_stack): + self.context_stack.append(DropoutContext()) + ctx = self.context_stack[self.count] + ctx.dropout = self.drop_prob + self.count += 1 + return ctx + else: + return self.drop_prob + + +class SEWDSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) + self.dropout = nn.Dropout(config.activation_dropout) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class DisentangledSelfAttention(nn.Module): + """ + Disentangled self-attention module + + Parameters: + config (`DebertaV2Config`): + A model config class instance with the configuration to build a new model. The schema is similar to + *BertConfig*, for more details, please refer [`DebertaV2Config`] + + """ + + def __init__(self, config): + super().__init__() + if config.hidden_size % config.num_attention_heads != 0: + raise ValueError( + f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention " + f"heads ({config.num_attention_heads})" + ) + self.num_attention_heads = config.num_attention_heads + _attention_head_size = config.hidden_size // config.num_attention_heads + self.attention_head_size = getattr(config, "attention_head_size", _attention_head_size) + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) + self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) + self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) + + self.share_att_key = getattr(config, "share_att_key", False) + self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else [] + self.relative_attention = getattr(config, "relative_attention", False) + + if self.relative_attention: + self.position_buckets = getattr(config, "position_buckets", -1) + self.max_relative_positions = getattr(config, "max_relative_positions", -1) + if self.max_relative_positions < 1: + self.max_relative_positions = config.max_position_embeddings + self.pos_ebd_size = self.max_relative_positions + if self.position_buckets > 0: + self.pos_ebd_size = self.position_buckets + + self.pos_dropout = StableDropout(config.activation_dropout) + + if not self.share_att_key: + if "c2p" in self.pos_att_type: + self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True) + if "p2c" in self.pos_att_type: + self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = StableDropout(config.attention_dropout) + + def transpose_for_scores(self, x, attention_heads): + new_x_shape = x.size()[:-1] + (attention_heads, -1) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1)) + + def forward( + self, + hidden_states, + attention_mask, + output_attentions=False, + query_states=None, + relative_pos=None, + rel_embeddings=None, + ): + """ + Call the module + + Args: + hidden_states (`torch.FloatTensor`): + Input states to the module usually the output from previous layer, it will be the Q,K and V in + *Attention(Q,K,V)* + + attention_mask (`torch.BoolTensor`): + An attention mask matrix of shape [*B*, *N*, *N*] where *B* is the batch size, *N* is the maximum + sequence length in which element [i,j] = *1* means the *i* th token in the input can attend to the *j* + th token. + + output_attentions (`bool`, *optional*): + Whether return the attention matrix. + + query_states (`torch.FloatTensor`, *optional*): + The *Q* state in *Attention(Q,K,V)*. + + relative_pos (`torch.LongTensor`): + The relative position encoding between the tokens in the sequence. It's of shape [*B*, *N*, *N*] with + values ranging in [*-max_relative_positions*, *max_relative_positions*]. + + rel_embeddings (`torch.FloatTensor`): + The embedding of relative distances. It's a tensor of shape [\\(2 \\times + \\text{max_relative_positions}\\), *hidden_size*]. + + + """ + if query_states is None: + query_states = hidden_states + query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads) + key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads) + value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads) + + rel_att = None + # Take the dot product between "query" and "key" to get the raw attention scores. + scale_factor = 1 + if "c2p" in self.pos_att_type: + scale_factor += 1 + if "p2c" in self.pos_att_type: + scale_factor += 1 + scale = torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor) + attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2) / scale.to(dtype=query_layer.dtype)) + if self.relative_attention: + rel_embeddings = self.pos_dropout(rel_embeddings) + rel_att = self.disentangled_attention_bias( + query_layer, key_layer, relative_pos, rel_embeddings, scale_factor + ) + + if rel_att is not None: + attention_scores = attention_scores + rel_att + attention_scores = attention_scores.view( + -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1) + ) + + # bsz x height x length x dimension + attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1) + attention_probs = self.dropout(attention_probs) + context_layer = torch.bmm( + attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer + ) + context_layer = ( + context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1)) + .permute(0, 2, 1, 3) + .contiguous() + ) + new_context_layer_shape = context_layer.size()[:-2] + (-1,) + context_layer = context_layer.view(new_context_layer_shape) + if output_attentions: + return (context_layer, attention_probs) + else: + return context_layer + + def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor): + if relative_pos is None: + q = query_layer.size(-2) + relative_pos = build_relative_position( + q, + key_layer.size(-2), + bucket_size=self.position_buckets, + max_position=self.max_relative_positions, + device=query_layer.device, + ) + if relative_pos.dim() == 2: + relative_pos = relative_pos.unsqueeze(0).unsqueeze(0) + elif relative_pos.dim() == 3: + relative_pos = relative_pos.unsqueeze(1) + # bsz x height x query x key + elif relative_pos.dim() != 4: + raise ValueError(f"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}") + + att_span = self.pos_ebd_size + relative_pos = relative_pos.to(device=query_layer.device, dtype=torch.long) + + rel_embeddings = rel_embeddings[0 : att_span * 2, :].unsqueeze(0) + if self.share_att_key: + pos_query_layer = self.transpose_for_scores( + self.query_proj(rel_embeddings), self.num_attention_heads + ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) + pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat( + query_layer.size(0) // self.num_attention_heads, 1, 1 + ) + else: + if "c2p" in self.pos_att_type: + pos_key_layer = self.transpose_for_scores( + self.pos_key_proj(rel_embeddings), self.num_attention_heads + ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) + if "p2c" in self.pos_att_type: + pos_query_layer = self.transpose_for_scores( + self.pos_query_proj(rel_embeddings), self.num_attention_heads + ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1) # .split(self.all_head_size, dim=-1) + + score = 0 + # content->position + if "c2p" in self.pos_att_type: + scale = torch.sqrt(torch.tensor(pos_key_layer.size(-1), dtype=torch.float) * scale_factor) + c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2)) + c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1) + c2p_att = torch.gather( + c2p_att, + dim=-1, + index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]), + ) + score += c2p_att / scale.to(dtype=c2p_att.dtype) + + # position->content + if "p2c" in self.pos_att_type: + scale = torch.sqrt(torch.tensor(pos_query_layer.size(-1), dtype=torch.float) * scale_factor) + if key_layer.size(-2) != query_layer.size(-2): + r_pos = build_relative_position( + key_layer.size(-2), + key_layer.size(-2), + bucket_size=self.position_buckets, + max_position=self.max_relative_positions, + device=query_layer.device, + ) + r_pos = r_pos.unsqueeze(0) + else: + r_pos = relative_pos + + p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1) + p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2)) + p2c_att = torch.gather( + p2c_att, + dim=-1, + index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]), + ).transpose(-1, -2) + score += p2c_att / scale.to(dtype=p2c_att.dtype) + + return score + + +class SEWDAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.self = DisentangledSelfAttention(config) + self.output = SEWDSelfOutput(config) + self.config = config + + def forward( + self, + hidden_states, + attention_mask, + output_attentions=False, + query_states=None, + relative_pos=None, + rel_embeddings=None, + ): + self_output = self.self( + hidden_states, + attention_mask, + output_attentions, + query_states=query_states, + relative_pos=relative_pos, + rel_embeddings=rel_embeddings, + ) + if output_attentions: + self_output, att_matrix = self_output + if query_states is None: + query_states = hidden_states + attention_output = self.output(self_output, query_states) + + if output_attentions: + return (attention_output, att_matrix) + else: + return attention_output + + +# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->SEWD +class SEWDIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class SEWDOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) + self.dropout = nn.Dropout(config.activation_dropout) + self.config = config + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class SEWDLayer(GradientCheckpointingLayer): + def __init__(self, config): + super().__init__() + self.attention = SEWDAttention(config) + self.intermediate = SEWDIntermediate(config) + self.output = SEWDOutput(config) + + def forward( + self, + hidden_states, + attention_mask, + query_states=None, + relative_pos=None, + rel_embeddings=None, + output_attentions=False, + ): + attention_output = self.attention( + hidden_states, + attention_mask, + output_attentions=output_attentions, + query_states=query_states, + relative_pos=relative_pos, + rel_embeddings=rel_embeddings, + ) + if output_attentions: + attention_output, att_matrix = attention_output + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + if output_attentions: + return (layer_output, att_matrix) + else: + return layer_output + + +class ConvLayer(nn.Module): + def __init__(self, config): + super().__init__() + kernel_size = getattr(config, "conv_kernel_size", 3) + groups = getattr(config, "conv_groups", 1) + self.conv_act = getattr(config, "conv_act", "tanh") + self.conv = nn.Conv1d( + config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups + ) + self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps) + self.dropout = StableDropout(config.hidden_dropout_prob) + self.config = config + + def forward(self, hidden_states, residual_states, input_mask): + out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous() + rmask = (1 - input_mask).bool() + out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0) + out = ACT2FN[self.conv_act](self.dropout(out)) + + layer_norm_input = residual_states + out + output = self.LayerNorm(layer_norm_input).to(layer_norm_input) + + if input_mask is None: + output_states = output + else: + if input_mask.dim() != layer_norm_input.dim(): + if input_mask.dim() == 4: + input_mask = input_mask.squeeze(1).squeeze(1) + input_mask = input_mask.unsqueeze(2) + + input_mask = input_mask.to(output.dtype) + output_states = output * input_mask + + return output_states + + +class SEWDTransformerEncoder(nn.Module): + """Modified BertEncoder with relative position bias support""" + + def __init__(self, config): + super().__init__() + + self.layer = nn.ModuleList([SEWDLayer(config) for _ in range(config.num_hidden_layers)]) + self.relative_attention = getattr(config, "relative_attention", False) + + if self.relative_attention: + self.max_relative_positions = getattr(config, "max_relative_positions", -1) + if self.max_relative_positions < 1: + self.max_relative_positions = config.max_position_embeddings + + self.position_buckets = getattr(config, "position_buckets", -1) + pos_ebd_size = self.max_relative_positions * 2 + + if self.position_buckets > 0: + pos_ebd_size = self.position_buckets * 2 + + self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size) + + self.norm_rel_ebd = [x.strip() for x in getattr(config, "norm_rel_ebd", "none").lower().split("|")] + + if "layer_norm" in self.norm_rel_ebd: + self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True) + + self.conv = ConvLayer(config) if getattr(config, "conv_kernel_size", 0) > 0 else None + self.gradient_checkpointing = False + + def get_rel_embedding(self): + rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None + if rel_embeddings is not None and ("layer_norm" in self.norm_rel_ebd): + rel_embeddings = self.LayerNorm(rel_embeddings) + return rel_embeddings + + def get_attention_mask(self, attention_mask): + if attention_mask.dim() <= 2: + extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) + attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1) + elif attention_mask.dim() == 3: + attention_mask = attention_mask.unsqueeze(1) + + return attention_mask + + def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None): + if self.relative_attention and relative_pos is None: + q = query_states.size(-2) if query_states is not None else hidden_states.size(-2) + relative_pos = build_relative_position( + q, + hidden_states.size(-2), + bucket_size=self.position_buckets, + max_position=self.max_relative_positions, + device=hidden_states.device, + ) + return relative_pos + + def forward( + self, + hidden_states, + attention_mask, + output_hidden_states=True, + output_attentions=False, + query_states=None, + relative_pos=None, + return_dict=True, + ): + if attention_mask.dim() <= 2: + input_mask = attention_mask + else: + input_mask = attention_mask.sum(-2) > 0 + attention_mask = self.get_attention_mask(attention_mask) + relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos) + + all_hidden_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + + if isinstance(hidden_states, Sequence): + next_kv = hidden_states[0] + else: + next_kv = hidden_states + rel_embeddings = self.get_rel_embedding() + output_states = next_kv + for i, layer_module in enumerate(self.layer): + if output_hidden_states: + all_hidden_states = all_hidden_states + (output_states,) + + output_states = layer_module( + next_kv, + attention_mask, + query_states=query_states, + relative_pos=relative_pos, + rel_embeddings=rel_embeddings, + output_attentions=output_attentions, + ) + + if output_attentions: + output_states, att_m = output_states + + if i == 0 and self.conv is not None: + output_states = self.conv(hidden_states, output_states, input_mask) + + if query_states is not None: + query_states = output_states + if isinstance(hidden_states, Sequence): + next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None + else: + next_kv = output_states + + if output_attentions: + all_attentions = all_attentions + (att_m,) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (output_states,) + + if not return_dict: + return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions + ) + + +class SEWDEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.pos_conv_embed = SEWDPositionalConvEmbedding(config) + self.pool = nn.AvgPool1d(config.squeeze_factor, config.squeeze_factor) + self.encoder = SEWDTransformerEncoder(config) + self.upsample = SEWDUpsampling(config) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states: torch.tensor, + attention_mask: torch.Tensor | None = None, + output_attentions: bool = False, + output_hidden_states: bool = False, + return_dict: bool = True, + ): + max_encoder_length = hidden_states.shape[1] // self.config.squeeze_factor + if attention_mask is None: + attention_mask = torch.ones( + (hidden_states.shape[0], max_encoder_length), dtype=torch.long, device=hidden_states.device + ) + else: + # make sure padded tokens output 0 + expand_attention_mask = attention_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + hidden_states[~expand_attention_mask.bool()] = 0.0 + + input_lengths = (attention_mask.long()).sum(-1) + # apply pooling formula to get real output_lengths + output_lengths = input_lengths // self.config.squeeze_factor + attention_ids = ( + torch.arange(0, max_encoder_length, device=output_lengths.device) + .view(1, -1) + .expand(output_lengths.shape[0], -1) + ) + attention_mask = (attention_ids < output_lengths.view(-1, 1)).long() + + n_input_timesteps = hidden_states.shape[1] + + hidden_states = hidden_states.transpose(1, 2) + position_embeddings = self.pos_conv_embed(hidden_states) + pooled_hidden_states = self.pool(hidden_states) + min_length = min(position_embeddings.size(-1), pooled_hidden_states.size(-1)) + hidden_states = pooled_hidden_states[..., :min_length] + position_embeddings[..., :min_length] + hidden_states = hidden_states.transpose(1, 2) + + encoder_outputs = self.encoder(hidden_states, attention_mask, output_hidden_states, output_attentions) + + hidden_states = self.upsample(encoder_outputs.last_hidden_state) + if hidden_states.shape[1] < n_input_timesteps: + hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, n_input_timesteps - hidden_states.shape[1])) + + if not return_dict: + return tuple( + v for v in [hidden_states, encoder_outputs.hidden_states, encoder_outputs.attentions] if v is not None + ) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@auto_docstring +class SEWDPreTrainedModel(PreTrainedModel): + config: SEWDConfig + base_model_prefix = "sew-d" + main_input_name = "input_values" + input_modalities = "audio" + supports_gradient_checkpointing = True + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, SEWDPositionalConvEmbedding): + init.normal_( + module.conv.weight, + mean=0, + std=2 * math.sqrt(1 / (module.conv.kernel_size[0] * module.conv.in_channels)), + ) + init.constant_(module.conv.bias, 0) + elif isinstance(module, nn.Linear): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, nn.Conv1d): + if is_deepspeed_zero3_enabled(): + import deepspeed + + if hasattr(module, "weight_v") and hasattr(module, "weight_g"): + with deepspeed.zero.GatheredParameters([module.weight_v, module.weight_g], modifier_rank=0): + init.kaiming_normal_(module.weight) + else: + with deepspeed.zero.GatheredParameters(module.weight, modifier_rank=0): + init.kaiming_normal_(module.weight) + else: + init.kaiming_normal_(module.weight) + elif isinstance(module, nn.Embedding): + init.normal_(module.weight, mean=0.0, std=self.config.initializer_range) + # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag + if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False): + init.zeros_(module.weight[module.padding_idx]) + + if isinstance(module, (nn.Linear, nn.Conv1d)) and module.bias is not None: + init.zeros_(module.bias) + + def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor | int): + """ + Computes the output length of the convolutional layers + """ + + def _conv_out_length(input_length, kernel_size, stride): + # 1D convolutional layer output length formula taken + # from https://pytorch.org/docs/stable/generated/torch.nn.Conv1d.html + return torch.div(input_length - kernel_size, stride, rounding_mode="floor") + 1 + + for kernel_size, stride in zip(self.config.conv_kernel, self.config.conv_stride): + input_lengths = _conv_out_length(input_lengths, kernel_size, stride) + + return input_lengths + + def _get_feature_vector_attention_mask(self, feature_vector_length: int, attention_mask: torch.LongTensor): + output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long) + batch_size = attention_mask.shape[0] + + attention_mask = torch.zeros( + (batch_size, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device + ) + # these two operations makes sure that all values before the output lengths idxs are attended to + attention_mask[(torch.arange(attention_mask.shape[0], device=attention_mask.device), output_lengths - 1)] = 1 + attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() + return attention_mask + + +@auto_docstring +# Copied from transformers.models.sew.modeling_sew.SEWModel with SEW->SEWD, layer_norm_eps->feature_layer_norm_eps +class SEWDModel(SEWDPreTrainedModel): + def __init__(self, config: SEWDConfig): + super().__init__(config) + self.config = config + self.feature_extractor = SEWDFeatureEncoder(config) + self.layer_norm = nn.LayerNorm(config.conv_dim[-1], eps=config.feature_layer_norm_eps) + + self.project_features = config.conv_dim[-1] != config.hidden_size + if self.project_features: + self.feature_projection = nn.Linear(config.conv_dim[-1], config.hidden_size) + self.feature_dropout = nn.Dropout(config.feat_proj_dropout) + + if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0: + self.masked_spec_embed = nn.Parameter(torch.Tensor(config.hidden_size).uniform_()) + + self.encoder = SEWDEncoder(config) + + # Initialize weights and apply final processing + self.post_init() + + # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2Model._mask_hidden_states + def _mask_hidden_states( + self, + hidden_states: torch.FloatTensor, + mask_time_indices: torch.FloatTensor | None = None, + attention_mask: torch.LongTensor | None = None, + ): + """ + Masks extracted features along time axis and/or along feature axis according to + [SpecAugment](https://huggingface.co/papers/1904.08779). + """ + + # `config.apply_spec_augment` can set masking to False + if not getattr(self.config, "apply_spec_augment", True): + return hidden_states + + # generate indices & apply SpecAugment along time axis + batch_size, sequence_length, hidden_size = hidden_states.size() + + if mask_time_indices is not None: + # apply SpecAugment along time axis with given mask_time_indices + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + elif self.config.mask_time_prob > 0 and self.training: + mask_time_indices = _compute_mask_indices( + (batch_size, sequence_length), + mask_prob=self.config.mask_time_prob, + mask_length=self.config.mask_time_length, + attention_mask=attention_mask, + min_masks=self.config.mask_time_min_masks, + ) + mask_time_indices = torch.tensor(mask_time_indices, device=hidden_states.device, dtype=torch.bool) + hidden_states[mask_time_indices] = self.masked_spec_embed.to(hidden_states.dtype) + + if self.config.mask_feature_prob > 0 and self.training: + # generate indices & apply SpecAugment along feature axis + mask_feature_indices = _compute_mask_indices( + (batch_size, hidden_size), + mask_prob=self.config.mask_feature_prob, + mask_length=self.config.mask_feature_length, + min_masks=self.config.mask_feature_min_masks, + ) + mask_feature_indices = torch.tensor(mask_feature_indices, device=hidden_states.device, dtype=torch.bool) + mask_feature_indices = mask_feature_indices[:, None].expand(-1, sequence_length, -1) + hidden_states[mask_feature_indices] = 0 + + return hidden_states + + @auto_docstring + def forward( + self, + input_values: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + mask_time_indices: torch.FloatTensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + **kwargs, + ) -> tuple | BaseModelOutput: + r""" + mask_time_indices (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices to mask extracted features for contrastive loss. When in training mode, model learns to predict + masked extracted features in *config.proj_codevector_dim* space. + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.return_dict + + extract_features = self.feature_extractor(input_values) + extract_features = extract_features.transpose(1, 2) + extract_features = self.layer_norm(extract_features) + + if self.project_features: + extract_features = self.feature_projection(extract_features) + hidden_states = self.feature_dropout(extract_features) + + if attention_mask is not None: + # compute reduced attention_mask corresponding to feature vectors + attention_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask) + + hidden_states = self._mask_hidden_states(hidden_states, mask_time_indices=mask_time_indices) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + if not return_dict: + return (hidden_states,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@auto_docstring( + custom_intro=""" + SEW-D Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC). + """ +) +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC with Wav2Vec2->SEWD, wav2vec2->sew_d, WAV2VEC2->SEWD +class SEWDForCTC(SEWDPreTrainedModel): + def __init__(self, config, target_lang: str | None = None): + r""" + target_lang (`str`, *optional*): + Language id of adapter weights. Adapter weights are stored in the format adapter..safetensors or + adapter..bin. Only relevant when using an instance of [`SEWDForCTC`] with adapters. Uses 'eng' by + default. + """ + super().__init__(config) + + self.sew_d = SEWDModel(config) + self.dropout = nn.Dropout(config.final_dropout) + + self.target_lang = target_lang + + if config.vocab_size is None: + raise ValueError( + f"You are trying to instantiate {self.__class__} with a configuration that " + "does not define the vocabulary size of the language model head. Please " + "instantiate the model as follows: `SEWDForCTC.from_pretrained(..., vocab_size=vocab_size)`. " + "or define `vocab_size` of your model's configuration." + ) + output_hidden_size = ( + config.output_hidden_size if hasattr(config, "add_adapter") and config.add_adapter else config.hidden_size + ) + self.lm_head = nn.Linear(output_hidden_size, config.vocab_size) + + # Initialize weights and apply final processing + self.post_init() + + def tie_weights(self, **kwargs): + """ + This method overwrites [`~PreTrainedModel.tie_weights`] so that adapter weights can be correctly loaded when + passing `target_lang=...` to `from_pretrained(...)`. + + This method is **not** supposed to be called by the user and is prone to be changed in the future. + """ + + if get_torch_context_manager_or_global_device() == torch.device("meta"): + return + + # Note that `tie_weights` is usually used to tie input and output embedding weights. The method is re-purposed to + # correctly load adapter layers for SEWD so that we do not have to introduce a new API to + # [`PreTrainedModel`]. While slightly hacky, SEWD never has to tie input and output embeddings, so that it is + # ok to repurpose this function here. + target_lang = self.target_lang + + if target_lang is not None and getattr(self.config, "adapter_attn_dim", None) is None: + raise ValueError(f"Cannot pass `target_lang`: {target_lang} if `config.adapter_attn_dim` is not defined.") + elif target_lang is None and getattr(self.config, "adapter_attn_dim", None) is not None: + logger.info("By default `target_lang` is set to 'eng'.") + elif target_lang is not None: + self.load_adapter(target_lang, force_load=True) + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.sew_d.feature_extractor._freeze_parameters() + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.sew_d.parameters(): + param.requires_grad = False + + @auto_docstring + def forward( + self, + input_values: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + labels: torch.Tensor | None = None, + **kwargs, + ) -> tuple | CausalLMOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*): + Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to + the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`. + All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ..., + config.vocab_size - 1]`. + """ + return_dict = return_dict if return_dict is not None else self.config.return_dict + + if labels is not None and labels.max() >= self.config.vocab_size: + raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}") + + outputs = self.sew_d( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = outputs[0] + hidden_states = self.dropout(hidden_states) + + logits = self.lm_head(hidden_states) + + loss = None + if labels is not None: + # retrieve loss input_lengths from attention_mask + attention_mask = ( + attention_mask if attention_mask is not None else torch.ones_like(input_values, dtype=torch.long) + ) + input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long) + + # assuming that padded tokens are filled with -100 + # when not being attended to + labels_mask = labels >= 0 + target_lengths = labels_mask.sum(-1) + flattened_targets = labels.masked_select(labels_mask) + + # ctc_loss doesn't support fp16 + log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1) + + with torch.backends.cudnn.flags(enabled=False): + loss = nn.functional.ctc_loss( + log_probs, + flattened_targets, + input_lengths, + target_lengths, + blank=self.config.pad_token_id, + reduction=self.config.ctc_loss_reduction, + zero_infinity=self.config.ctc_zero_infinity, + ) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutput( + loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions + ) + + +@auto_docstring( + custom_intro=""" + SEWD Model with a sequence classification head on top (a linear layer over the pooled output) for tasks like SUPERB + Keyword Spotting. + """ +) +# Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForSequenceClassification with Wav2Vec2->SEWD, wav2vec2->sew_d, WAV2VEC2->SEWD +class SEWDForSequenceClassification(SEWDPreTrainedModel): + def __init__(self, config): + super().__init__(config) + + if hasattr(config, "add_adapter") and config.add_adapter: + raise ValueError( + "Sequence classification does not support the use of SEWD adapters (config.add_adapter=True)" + ) + self.sew_d = SEWDModel(config) + num_layers = config.num_hidden_layers + 1 # transformer layers + input embeddings + if config.use_weighted_layer_sum: + self.layer_weights = nn.Parameter(torch.ones(num_layers) / num_layers) + self.projector = nn.Linear(config.hidden_size, config.classifier_proj_size) + self.classifier = nn.Linear(config.classifier_proj_size, config.num_labels) + + # Initialize weights and apply final processing + self.post_init() + + def freeze_feature_encoder(self): + """ + Calling this function will disable the gradient computation for the feature encoder so that its parameter will + not be updated during training. + """ + self.sew_d.feature_extractor._freeze_parameters() + + def freeze_base_model(self): + """ + Calling this function will disable the gradient computation for the base model so that its parameters will not + be updated during training. Only the classification head will be updated. + """ + for param in self.sew_d.parameters(): + param.requires_grad = False + + @auto_docstring + def forward( + self, + input_values: torch.Tensor | None, + attention_mask: torch.Tensor | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + labels: torch.Tensor | None = None, + **kwargs, + ) -> tuple | SequenceClassifierOutput: + r""" + input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`): + Float values of input raw speech waveform. Values can be obtained by loading a `.flac` or `.wav` audio file + into an array of type `list[float]`, a `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec library + (`pip install torchcodec`) or the soundfile library (`pip install soundfile`). + To prepare the array into `input_values`, the [`AutoProcessor`] should be used for padding and conversion + into a tensor of type `torch.FloatTensor`. See [`SEWDProcessor.__call__`] for details. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + + return_dict = return_dict if return_dict is not None else self.config.return_dict + output_hidden_states = True if self.config.use_weighted_layer_sum else output_hidden_states + + outputs = self.sew_d( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if self.config.use_weighted_layer_sum: + hidden_states = outputs[_HIDDEN_STATES_START_POSITION] + hidden_states = torch.stack(hidden_states, dim=1) + norm_weights = nn.functional.softmax(self.layer_weights, dim=-1) + hidden_states = (hidden_states * norm_weights.view(-1, 1, 1)).sum(dim=1) + else: + hidden_states = outputs[0] + + hidden_states = self.projector(hidden_states) + if attention_mask is None: + pooled_output = hidden_states.mean(dim=1) + else: + padding_mask = self._get_feature_vector_attention_mask(hidden_states.shape[1], attention_mask) + expand_padding_mask = padding_mask.unsqueeze(-1).repeat(1, 1, hidden_states.shape[2]) + hidden_states[~expand_padding_mask] = 0.0 + pooled_output = hidden_states.sum(dim=1) / padding_mask.sum(dim=1).view(-1, 1) + + logits = self.classifier(pooled_output) + + loss = None + if labels is not None: + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1)) + + if not return_dict: + output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = ["SEWDForCTC", "SEWDForSequenceClassification", "SEWDModel", "SEWDPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3eaa894027352f1dd928e853bec4eef131acd8e4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_shieldgemma2 import * + from .modeling_shieldgemma2 import * + from .processing_shieldgemma2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/configuration_shieldgemma2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/configuration_shieldgemma2.py new file mode 100644 index 0000000000000000000000000000000000000000..e00921dfe6936b5027f2bc95e31952bd46c932c2 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/configuration_shieldgemma2.py @@ -0,0 +1,92 @@ +# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import CONFIG_MAPPING, AutoConfig + + +@auto_docstring(checkpoint="google/shieldgemma-2-4b-it") +@strict +class ShieldGemma2Config(PreTrainedConfig): + r""" + tie_word_embeddings (`bool`, *optional*): + Whether to tie the word embeddings. Defaults to the value of `text_config.tie_word_embeddings` if not set. + mm_tokens_per_image (`int`, *optional*, defaults to 256): + The number of tokens per image embedding. + boi_token_index (`int`, *optional*, defaults to 255999): + The begin-of-image token index to wrap the image prompt. + eoi_token_index (`int`, *optional*, defaults to 256000): + The end-of-image token index to wrap the image prompt. + + Example: + + ```python + >>> from transformers import ShieldGemma2ForConditionalGeneration, ShieldGemma2Config, SiglipVisionConfig, ShieldGemma2TextConfig + + >>> # Initializing a Siglip-like vision config + >>> vision_config = SiglipVisionConfig() + + >>> # Initializing a ShieldGemma2 Text config + >>> text_config = ShieldGemma2TextConfig() + + >>> # Initializing a ShieldGemma2 gemma-3-4b style configuration + >>> configuration = ShieldGemma2Config(vision_config, text_config) + + >>> # Initializing a model from the gemma-3-4b style configuration + >>> model = ShieldGemma2TextConfig(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "shieldgemma2" + attribute_map = { + "image_token_id": "image_token_index", + "boi_token_id": "boi_token_index", + "eoi_token_id": "eoi_token_index", + } + sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig} + + text_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + mm_tokens_per_image: int = 256 + boi_token_index: int = 255_999 + eoi_token_index: int = 256_000 + image_token_index: int = 262_144 + initializer_range: float = 0.02 + + def __post_init__(self, **kwargs): + if isinstance(self.vision_config, dict): + self.vision_config["model_type"] = self.vision_config.get("model_type", "siglip_vision_model") + self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config) + elif self.vision_config is None: + self.vision_config = CONFIG_MAPPING["siglip_vision_model"]() + + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "gemma3_text") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) + elif self.text_config is None: + self.text_config = CONFIG_MAPPING["gemma3_text"]() + if kwargs.get("tie_word_embeddings") is None: + self.tie_word_embeddings = getattr(self.text_config, "tie_word_embeddings", True) + + super().__post_init__(**kwargs) + + +__all__ = ["ShieldGemma2Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/modeling_shieldgemma2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/modeling_shieldgemma2.py new file mode 100644 index 0000000000000000000000000000000000000000..04805343076b4860623a6234cb9279bd0c4104b1 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/modeling_shieldgemma2.py @@ -0,0 +1,132 @@ +# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from dataclasses import dataclass + +import torch + +from ...cache_utils import Cache +from ...modeling_outputs import ImageClassifierOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...utils import ( + auto_docstring, + logging, +) +from ..auto import AutoModelForImageTextToText +from .configuration_shieldgemma2 import ShieldGemma2Config + + +logger = logging.get_logger(__name__) + + +@dataclass +class ShieldGemma2ImageClassifierOutputWithNoAttention(ImageClassifierOutputWithNoAttention): + """ShieldGemma2 classifies imags as violative or not relative to a specific policy + Args: + """ + + probabilities: torch.Tensor | None = None + + +@auto_docstring +class ShieldGemma2ForImageClassification(PreTrainedModel): + config: ShieldGemma2Config + input_modalities = ("image", "text") + base_model_prefix = "model" + + def __init__(self, config: ShieldGemma2Config): + super().__init__(config=config) + self.yes_token_index = getattr(config, "yes_token_index", 10_784) + self.no_token_index = getattr(config, "no_token_index", 3771) + self.model = AutoModelForImageTextToText.from_config(config=config) + self.post_init() + + def get_input_embeddings(self): + return self.model.get_decoder().get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.get_decoder().set_input_embeddings(value) + + def get_output_embeddings(self): + return self.model.get_decoder().get_output_embeddings() + + def set_output_embeddings(self, new_embeddings): + self.model.get_decoder().set_output_embeddings(new_embeddings) + + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + token_type_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + output_attentions: bool | None = None, + output_hidden_states: bool | None = None, + return_dict: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **lm_kwargs, + ) -> ShieldGemma2ImageClassifierOutputWithNoAttention: + r""" + Returns: + A `ShieldGemma2ImageClassifierOutputWithNoAttention` instance containing the logits and probabilities + associated with the model predicting the `Yes` or `No` token as the response to that prompt, captured in the + following properties. + + * `logits` (`torch.Tensor` of shape `(batch_size, 2)`): + The first position along dim=1 is the logits for the `Yes` token and the second position along dim=1 is + the logits for the `No` token. + * `probabilities` (`torch.Tensor` of shape `(batch_size, 2)`): + The first position along dim=1 is the probability of predicting the `Yes` token and the second position + along dim=1 is the probability of predicting the `No` token. + + ShieldGemma prompts are constructed such that predicting the `Yes` token means the content *does violate* the + policy as described. If you are only interested in the violative condition, use + `violated = outputs.probabilities[:, 1]` to extract that slice from the output tensors. + + When used with the `ShieldGemma2Processor`, the `batch_size` will be equal to `len(images) * len(policies)`, + and the order within the batch will be img1_policy1, ... img1_policyN, ... imgM_policyN. + """ + outputs = self.model( + input_ids=input_ids, + pixel_values=pixel_values, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + token_type_ids=token_type_ids, + inputs_embeds=inputs_embeds, + labels=labels, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + logits_to_keep=logits_to_keep, + **lm_kwargs, + ) + logits = outputs.logits + selected_logits = logits[:, -1, [self.yes_token_index, self.no_token_index]] + probabilities = torch.softmax(selected_logits, dim=-1) + return ShieldGemma2ImageClassifierOutputWithNoAttention( + logits=selected_logits, + probabilities=probabilities, + ) + + +__all__ = [ + "ShieldGemma2ForImageClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/processing_shieldgemma2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/processing_shieldgemma2.py new file mode 100644 index 0000000000000000000000000000000000000000..d23be1d6f941b104245401cef3f958581b0ad8d9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/shieldgemma2/processing_shieldgemma2.py @@ -0,0 +1,190 @@ +# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. +# +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from collections.abc import Mapping, Sequence + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput +from ...processing_utils import Unpack +from ...utils import logging +from ..gemma3.processing_gemma3 import Gemma3Processor, Gemma3ProcessorKwargs + + +logger = logging.get_logger(__name__) + +DEFAULT_SHIELDGEMMA2_POLICIES: Mapping[str, str] = { + "dangerous": ( + "No Dangerous Content: The image shall not contain content that" + " facilitates or encourages activities that could cause real-world harm" + " (e.g., building firearms and explosive devices, promotion of" + " terrorism, instructions for suicide)." + ), + "sexual": ( + "No Sexually Explicit content: The image shall not contain content that" + " depicts explicit or graphic sexual acts (e.g., pornography, erotic" + " nudity, depictions of rape or sexual assault)." + ), + "violence": ( + "No Violence/Gore content: The image shall not contain content that" + " depicts shocking, sensational, or gratuitous violence (e.g.," + " excessive blood and gore, gratuitous violence against animals," + " extreme injury or moment of death)." + ), +} + + +class ShieldGemma2ProcessorKwargs(Gemma3ProcessorKwargs, total=False): + policies: Sequence[str] | None + custom_policies: Mapping[str, str] | None + _defaults = { + "text_kwargs": { + "padding": True, + }, + "images_kwargs": { + "do_pan_and_scan": False, + }, + } + + +class ShieldGemma2Processor(Gemma3Processor): + def __init__( + self, image_processor, tokenizer, chat_template=None, image_seq_length=256, policy_definitions=None, **kwargs + ): + """A processor for the ShieldGemma 2 model. + + Args: + image_processor: The image processor to use, typically a `Gemma3ImageProcessor` instance. + tokenizer: The tokenizer to use, typically a `GemmaTokenizerFast` instance. + chat_template: The chat template to use with this processor. Typically, this is unset as the processor + configuration on Hugging Face Hub includes this value already. + image_seq_length: The number of soft tokens per image. Typically, this is unset as the processor + configuration on Hugging Face Hub includes this value already. + policy_definitions: A mapping from policy name to its description in text used as the default policies to + classify images against. The policy descriptions are included in the text of the prompts generated by + this processor. Typically, this is unset as the processor configuration on Hugging Face Hub includes + the base policies ShieldGemma was trained on. + """ + super().__init__(image_processor, tokenizer, chat_template, image_seq_length, **kwargs) + if policy_definitions is None: + self.policy_definitions = DEFAULT_SHIELDGEMMA2_POLICIES + else: + self.policy_definitions = policy_definitions + + def __call__( + self, + images: ImageInput | None = None, + text=None, + **kwargs: Unpack[ShieldGemma2ProcessorKwargs], + ) -> BatchFeature: + """Generates a batch of inputs from the provided images. + + ShieldGemma was trained to classify image content for policy compliance using a specific prompt construction. + This processor generates a batch of such prompts from the provided images by: + + 1. Creating a list of conversations, one for each `` pair; + 2. Converting these conversations to text using `self.apply_chat_template()`; and + 3. Encoding the conversations and images using the same techniques as `Gemma3Processor`. + + Args: + images: A single image or a list of images to include in the batch. + text: Not supported. + videos: Not supported. + audio: Not supported. + kwargs: An optional dictionary of keyword arguments to configure the + processor. Possible values include: + + * `custom_policies`: Additional policy definitions that augment the `self.policy_definitions` passed + into the constructor. Note that `custom_policies` that share a key with `self.policy_definitions` + will override the policy description + * `policies`: (Optional) a list of keys in the joint `self.policy_definitions | custom_policies` + dictionary of specific interest for the provided images. If empty or None, prompts will be + generated for every key in the joint dictionary. + + Returns: + A `BatchFeature` containing `input_ids`, `pixel_values`, etc. where each Tensor is of shape + `(len(images) * len(policies), )`, and the order within the batch will be + img1_policy1, ... img1_policyN, ... imgM_policyN. + """ + if not images: + raise ValueError("ShieldGemma 2 needs images to classify") + elif not isinstance(images, Sequence): + images = [images] + + if not self.chat_template: + raise ValueError("ShieldGemma 2 requires the use of a specific chat template") + + common_kwargs = kwargs.setdefault("common_kwargs", {}) + if "return_tensors" in kwargs: + common_kwargs["return_tensors"] = kwargs.pop("return_tensors") + + # Disable pan and scan + images_kwargs = kwargs.setdefault("images_kwargs", {}) + if images_kwargs.get("do_pan_and_scan") is True: + logger.warning_once("ShieldGemma2 does not support pan and scan.") + images_kwargs["do_pan_and_scan"] = False + + # Enable padding on the batch during tokenization + text_kwargs = kwargs.setdefault("text_kwargs", {}) + if "padding" not in text_kwargs: + text_kwargs["padding"] = kwargs.pop("padding", True) + text_kwargs["padding_side"] = kwargs.pop("padding_side", "left") + + policy_definitions: Mapping[str, str] = { + **self.policy_definitions, + **kwargs.get("custom_policies", {}), + } + + if (policies := kwargs.get("policies")) is None: + policies = list(policy_definitions.keys()) + + # TODO(ryanmullins): Support images from PIL or URLs. + messages = [] + expanded_images = [] + for img in images: + if not isinstance(img, list): + img = [img] + elif len(img) > 1: + raise ValueError(f"SheildGemma can process at most one image per sample, but got {len(img)} images") + + for policy in policies: + if img: + messages.append( + [ + { + "role": "user", + "content": [ + {"type": "image"}, + {"type": "text", "text": policy_definitions[policy]}, + ], + } + ] + ) + else: + messages.append( + [ + { + "role": "user", + "content": [ + {"type": "text", "text": policy_definitions[policy]}, + ], + } + ] + ) + expanded_images.append(img) + + text = self.apply_chat_template(messages, tokenize=False) + return super().__call__(images=expanded_images, text=text, **kwargs) + + +__all__ = ["ShieldGemma2Processor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b80f7d13aef410fb8165c6688b09c38b9736864 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/__init__.py @@ -0,0 +1,31 @@ +# Copyright 2024 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_siglip import * + from .image_processing_pil_siglip import * + from .image_processing_siglip import * + from .modeling_siglip import * + from .processing_siglip import * + from .tokenization_siglip import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/configuration_siglip.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/configuration_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..69f5fba89f1e0dbd6dea6a7082fbb0947eb54c27 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/configuration_siglip.py @@ -0,0 +1,153 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Siglip model configuration""" + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="google/siglip-base-patch16-224") +@strict +class SiglipTextConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import SiglipTextConfig, SiglipTextModel + + >>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration + >>> configuration = SiglipTextConfig() + + >>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration + >>> model = SiglipTextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "siglip_text_model" + base_config_key = "text_config" + + vocab_size: int = 32000 + hidden_size: int = 768 + intermediate_size: int = 3072 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + max_position_embeddings: int = 64 + hidden_act: str = "gelu_pytorch_tanh" + layer_norm_eps: float = 1e-6 + attention_dropout: float | int = 0.0 + # This differs from `CLIPTokenizer`'s default and from openai/siglip + # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538 + pad_token_id: int | None = 1 + bos_token_id: int | None = 49406 + eos_token_id: int | list[int] | None = 49407 + projection_size: int | None = None + + def __post_init__(self, **kwargs): + self.projection_size = self.projection_size if self.projection_size is not None else self.hidden_size + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="google/siglip-base-patch16-224") +@strict +class SiglipVisionConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import SiglipVisionConfig, SiglipVisionModel + + >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration + >>> configuration = SiglipVisionConfig() + + >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration + >>> model = SiglipVisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "siglip_vision_model" + base_config_key = "vision_config" + + hidden_size: int = 768 + intermediate_size: int = 3072 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 224 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "gelu_pytorch_tanh" + layer_norm_eps: float = 1e-6 + attention_dropout: float | int = 0.0 + + +@auto_docstring(checkpoint="google/siglip-base-patch16-224") +@strict +class SiglipConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import SiglipConfig, SiglipModel + + >>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration + >>> configuration = SiglipConfig() + + >>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration + >>> model = SiglipModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig + >>> from transformers import SiglipTextConfig, SiglipVisionConfig + + >>> # Initializing a SiglipText and SiglipVision configuration + >>> config_text = SiglipTextConfig() + >>> config_vision = SiglipVisionConfig() + + >>> config = SiglipConfig(text_config=config_text, vision_config=config_vision) + ```""" + + model_type = "siglip" + sub_configs = {"text_config": SiglipTextConfig, "vision_config": SiglipVisionConfig} + + text_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + initializer_factor: float = 1.0 + + def __post_init__(self, **kwargs): + if self.text_config is None: + self.text_config = SiglipTextConfig() + logger.info("`text_config` is `None`. Initializing the `SiglipTextConfig` with default values.") + elif isinstance(self.text_config, dict): + self.text_config = SiglipTextConfig(**self.text_config) + + if self.vision_config is None: + self.vision_config = SiglipVisionConfig() + logger.info("`vision_config` is `None`. initializing the `SiglipVisionConfig` with default values.") + elif isinstance(self.vision_config, dict): + self.vision_config = SiglipVisionConfig(**self.vision_config) + + super().__post_init__(**kwargs) + + +__all__ = ["SiglipConfig", "SiglipTextConfig", "SiglipVisionConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/image_processing_pil_siglip.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/image_processing_pil_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..a7bedc2c7e18b93ca123894d1e2fc94e7337ac2b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/image_processing_pil_siglip.py @@ -0,0 +1,38 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SigLIP.""" + +from ...image_processing_backends import PilBackend +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + PILImageResampling, +) +from ...utils import auto_docstring + + +@auto_docstring(custom_intro="Constructs a SigLIP image processor.") +class SiglipImageProcessorPil(PilBackend): + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 224, "width": 224} + default_to_square = False + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + + +__all__ = ["SiglipImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/image_processing_siglip.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/image_processing_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..81a712f557a342194d58ffdace611c18c768dfe4 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/image_processing_siglip.py @@ -0,0 +1,38 @@ +# Copyright 2024 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SigLIP.""" + +from ...image_processing_backends import TorchvisionBackend +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + PILImageResampling, +) +from ...utils import auto_docstring + + +@auto_docstring(custom_intro="Constructs a SigLIP image processor.") +class SiglipImageProcessor(TorchvisionBackend): + resample = PILImageResampling.BICUBIC + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"height": 224, "width": 224} + default_to_square = False + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + + +__all__ = ["SiglipImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/modeling_siglip.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/modeling_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..af3571242d5b00d0b6876f916310f49a40c5955e --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/modeling_siglip.py @@ -0,0 +1,936 @@ +# Copyright 2024 Google AI and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""PyTorch Siglip model.""" + +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import numpy as np +import torch +from torch import nn + +from ... import initialization as init +from ...activations import ACT2FN +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ( + ModelOutput, + TransformersKwargs, + auto_docstring, + can_return_tuple, + torch_int, +) +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig + + +@auto_docstring( + custom_intro=""" + Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. + """ +) +@dataclass +# Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip +class SiglipVisionModelOutput(ModelOutput): + r""" + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + """ + + image_embeds: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for text model's outputs that also contains a pooling of the last hidden states. + """ +) +# Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Siglip +class SiglipTextModelOutput(ModelOutput): + r""" + text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The text embeddings obtained by applying the projection layer to the pooler_output. + """ + + text_embeds: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@auto_docstring +@dataclass +# Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Siglip +class SiglipOutput(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): + Contrastive loss for image-text similarity. + logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): + The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text + similarity scores. + logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): + The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image + similarity scores. + text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`): + The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`]. + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`): + The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`]. + text_model_output (`BaseModelOutputWithPooling`): + The output of the [`SiglipTextModel`]. + vision_model_output (`BaseModelOutputWithPooling`): + The output of the [`SiglipVisionModel`]. + """ + + loss: torch.FloatTensor | None = None + logits_per_image: torch.FloatTensor | None = None + logits_per_text: torch.FloatTensor | None = None + text_embeds: torch.FloatTensor | None = None + image_embeds: torch.FloatTensor | None = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple(v.to_tuple() if isinstance(v, ModelOutput) else v for v in self.values()) + + +class SiglipVisionEmbeddings(nn.Module): + def __init__(self, config: SiglipVisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches = (self.image_size // self.patch_size) ** 2 + self.num_positions = self.num_patches + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + self.register_buffer("position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False) + + def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor: + """ + This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution + images. This method is also adapted to support torch.jit tracing and no class embeddings. + + Adapted from: + - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and + - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 + """ + + num_patches = embeddings.shape[1] + num_positions = self.position_embedding.weight.shape[0] + + # always interpolate when tracing to ensure the exported model works for dynamic input shapes + if not torch.jit.is_tracing() and num_patches == num_positions and height == width: + return self.position_embedding(self.position_ids) + + patch_pos_embed = self.position_embedding.weight.unsqueeze(0) + + dim = embeddings.shape[-1] + + new_height = height // self.patch_size + new_width = width // self.patch_size + + sqrt_num_positions = torch_int(num_positions**0.5) + patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim) + patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2) + + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + size=(new_height, new_width), + mode="bicubic", + align_corners=False, + ) + + patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim) + return patch_pos_embed + + def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor: + _, _, height, width = pixel_values.shape + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # shape = [*, width, grid, grid] + embeddings = patch_embeds.flatten(2).transpose(1, 2) + + if interpolate_pos_encoding: + embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width) + else: + embeddings = embeddings + self.position_embedding(self.position_ids) + return embeddings + + +# Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->Siglip +class SiglipTextEmbeddings(nn.Module): + def __init__(self, config: SiglipTextConfig): + super().__init__() + embed_dim = config.hidden_size + + self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) + self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + ) -> torch.Tensor: + seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] + max_position_embedding = self.position_embedding.weight.shape[0] + + if seq_length > max_position_embedding: + raise ValueError( + f"Sequence length must be less than max_position_embeddings (got `sequence length`: " + f"{seq_length} and max_position_embeddings: {max_position_embedding}" + ) + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + if inputs_embeds is None: + inputs_embeds = self.token_embedding(input_ids) + + position_embeddings = self.position_embedding(position_ids) + embeddings = inputs_embeds + position_embeddings + + return embeddings + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class SiglipAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + queries = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + keys = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + values = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + queries, + keys, + values, + attention_mask, + is_causal=self.is_causal, + scaling=self.scale, + dropout=0.0 if not self.training else self.dropout, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip +class SiglipMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class SiglipEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: SiglipVisionConfig | SiglipTextConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.self_attn = SiglipAttention(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class SiglipPreTrainedModel(PreTrainedModel): + config: SiglipConfig + base_model_prefix = "siglip" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + + _no_split_modules = [ + "SiglipTextEmbeddings", + "SiglipVisionEmbeddings", + "SiglipEncoderLayer", + "SiglipMultiheadAttentionPoolingHead", + ] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + + _can_record_outputs = { + "hidden_states": SiglipEncoderLayer, + "attentions": SiglipAttention, + } + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, SiglipVisionEmbeddings): + width = ( + self.config.vision_config.hidden_size + if isinstance(self.config, SiglipConfig) + else self.config.hidden_size + ) + init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) + if hasattr(module, "position_ids"): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, nn.Embedding): + init.default_flax_embed_init_(module.weight) + elif isinstance(module, SiglipAttention): + init.xavier_uniform_(module.q_proj.weight) + init.xavier_uniform_(module.k_proj.weight) + init.xavier_uniform_(module.v_proj.weight) + init.xavier_uniform_(module.out_proj.weight) + init.zeros_(module.q_proj.bias) + init.zeros_(module.k_proj.bias) + init.zeros_(module.v_proj.bias) + init.zeros_(module.out_proj.bias) + elif isinstance(module, SiglipMLP): + init.xavier_uniform_(module.fc1.weight) + init.xavier_uniform_(module.fc2.weight) + init.normal_(module.fc1.bias, std=1e-6) + init.normal_(module.fc2.bias, std=1e-6) + elif isinstance(module, SiglipMultiheadAttentionPoolingHead): + init.xavier_uniform_(module.probe) + init.xavier_uniform_(module.attention.in_proj_weight) + init.zeros_(module.attention.in_proj_bias) + elif isinstance(module, SiglipModel): + init.zeros_(module.logit_scale) + init.zeros_(module.logit_bias) + elif isinstance(module, SiglipForImageClassification): + init.normal_( + module.classifier.weight, + std=self.config.vision_config.hidden_size**-0.5 * self.config.initializer_factor, + ) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + init.lecun_normal_(module.weight) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, SiglipTextEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + + +# Copied from transformers.models.altclip.modeling_altclip.AltCLIPEncoder with AltCLIP->Siglip +class SiglipEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`SiglipEncoderLayer`]. + + Args: + config: SiglipConfig + """ + + def __init__(self, config: SiglipConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + # Ignore copy + @auto_docstring + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + **kwargs, + ) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +@auto_docstring( + custom_intro=""" + The text model from SigLIP without any head or projection on top. + """ +) +class SiglipTextModel(SiglipPreTrainedModel): + config: SiglipTextConfig + input_modalities = ("text",) + base_model_prefix = "text_model" + _input_embed_layer = "token_embedding" + + def __init__(self, config: SiglipTextConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + self.embeddings = SiglipTextEmbeddings(config) + self.encoder = SiglipEncoder(config) + self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + self.head = nn.Linear(embed_dim, config.projection_size) + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + r""" + Examples: + + ```python + >>> from transformers import AutoTokenizer, SiglipTextModel + + >>> model = SiglipTextModel.from_pretrained("google/siglip-base-patch16-224") + >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224") + + >>> # important: make sure to set padding="max_length" as that's how the model was trained + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled (EOS token) states + ```""" + if input_ids is None: + raise ValueError("You have to specify input_ids") + + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) + + # note: SigLIP's text model does not use a causal mask, unlike the original CLIP model. + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + ) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.final_layer_norm(last_hidden_state) + + # The model uses the last token's hidden state, which may be padding. + pooled_output = last_hidden_state[:, -1, :] + pooled_output = self.head(pooled_output) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + ) + + +@auto_docstring( + custom_intro=""" + The vision model from SigLIP without any head or projection on top. + """ +) +class SiglipVisionModel(SiglipPreTrainedModel): + config: SiglipVisionConfig + main_input_name = "pixel_values" + input_modalities = ("image",) + base_model_prefix = "vision_model" + _input_embed_layer = "patch_embedding" + + def __init__(self, config: SiglipVisionConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + + self.embeddings = SiglipVisionEmbeddings(config) + self.encoder = SiglipEncoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + self.use_head = True if not hasattr(config, "vision_use_head") else config.vision_use_head + if self.use_head: + self.head = SiglipMultiheadAttentionPoolingHead(config) + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + pixel_values, + interpolate_pos_encoding: bool | None = False, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + r""" + Examples: + + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + >>> from transformers import AutoProcessor, SiglipVisionModel + + >>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled features + ```""" + hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + pooler_output = self.head(last_hidden_state) if self.use_head else None + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooler_output, + ) + + +class SiglipMultiheadAttentionPoolingHead(nn.Module): + """Multihead Attention Pooling.""" + + def __init__(self, config: SiglipVisionConfig): + super().__init__() + + self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True) + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = SiglipMLP(config) + + def forward(self, hidden_state): + batch_size = hidden_state.shape[0] + probe = self.probe.repeat(batch_size, 1, 1) + + hidden_state = self.attention(probe, hidden_state, hidden_state)[0] + + residual = hidden_state + hidden_state = self.layernorm(hidden_state) + hidden_state = residual + self.mlp(hidden_state) + + return hidden_state[:, 0] + + +@auto_docstring +class SiglipModel(SiglipPreTrainedModel): + config: SiglipConfig + + def __init__(self, config: SiglipConfig): + super().__init__(config) + + text_config = config.text_config + vision_config = config.vision_config + + # First, initialize the text and vision models with proper attention implementation + self.text_model = SiglipTextModel._from_config(text_config) + self.vision_model = SiglipVisionModel._from_config(vision_config) + + self.logit_scale = nn.Parameter(torch.randn(1)) + self.logit_bias = nn.Parameter(torch.randn(1)) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, value: nn.Module): + self.text_model.embeddings.token_embedding = value + + @can_return_tuple + @auto_docstring + def get_text_features( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + Examples: + + ```python + >>> from transformers import AutoTokenizer, AutoModel + >>> import torch + + >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") + >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224") + + >>> # important: make sure to set padding="max_length" as that's how the model was trained + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") + >>> with torch.no_grad(): + ... text_features = model.get_text_features(**inputs) + ```""" + return self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + **kwargs, + ) + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + Examples: + + ```python + >>> import torch + >>> from transformers import AutoProcessor, AutoModel + >>> from transformers.image_utils import load_image + + >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = load_image(url) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> with torch.no_grad(): + ... image_features = model.get_image_features(**inputs) + ```""" + return self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + # NOTE: SiglipModel uses Pretrained backbones, so we don't need to add `capture_outputs` here + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + return_loss: bool | None = None, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> SiglipOutput: + r""" + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. + + Examples: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, AutoModel + >>> import torch + + >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"] + >>> # important: we pass `padding=max_length` since the model was trained with this + >>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> logits_per_image = outputs.logits_per_image + >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities + >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") + 31.9% that image 0 is 'a photo of 2 cats' + ```""" + vision_outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values=pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + **kwargs, + ) + + image_embeds = vision_outputs.pooler_output + text_embeds = text_outputs.pooler_output + + # normalized features + image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) + text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) + + # cosine similarity as logits + logits_per_text = torch.matmul(text_embeds, image_embeds.t().to(text_embeds.device)) + + logit_scale, logit_bias = self.logit_scale.to(text_embeds.device), self.logit_bias.to(text_embeds.device) + logits_per_text = logits_per_text * logit_scale.exp() + logit_bias + + logits_per_image = logits_per_text.t() + + loss = None + if return_loss: + # Adapted from https://github.com/google-research/big_vision/blob/01edb81a4716f93a48be43b3a4af14e29cdb3a7f/big_vision/trainers/proj/image_text/siglip.py#L287 + eye = torch.eye(logits_per_text.size(0), device=logits_per_text.device) + m1_diag1 = -torch.ones_like(logits_per_text) + 2 * eye + loglik = torch.nn.functional.logsigmoid(m1_diag1 * logits_per_text) + nll = -torch.sum(loglik, dim=-1) + loss = nll.mean() + + return SiglipOutput( + loss=loss, + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +@auto_docstring( + custom_intro=""" + SigLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of + the patch tokens) e.g. for ImageNet. + """ +) +class SiglipForImageClassification(SiglipPreTrainedModel): + main_input_name = "pixel_values" + input_modalities = ("image",) + + def __init__(self, config: SiglipConfig) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.vision_model = SiglipVisionModel._from_config(config.vision_config) + + # Classifier head + self.classifier = ( + nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_model.embeddings.patch_embedding + + def set_input_embeddings(self, value: nn.Module): + self.vision_model.embeddings.patch_embedding = value + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + interpolate_pos_encoding: bool = False, + **kwargs: Unpack[TransformersKwargs], + ) -> ImageClassifierOutput: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, SiglipForImageClassification + >>> import torch + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> torch.manual_seed(3) # doctest: +IGNORE_RESULT + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> # note: we are loading a `SiglipModel` from the hub here, + >>> # so the head will be randomly initialized, hence the predictions will be random if seed is not set above. + >>> image_processor = AutoImageProcessor.from_pretrained("google/siglip-base-patch16-224") + >>> model = SiglipForImageClassification.from_pretrained("google/siglip-base-patch16-224") + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> logits = outputs.logits + >>> # model predicts one of the two classes + >>> predicted_class_idx = logits.argmax(-1).item() + >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) + Predicted class: LABEL_1 + ```""" + outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values, + interpolate_pos_encoding=interpolate_pos_encoding, + **kwargs, + ) + + sequence_output = outputs.last_hidden_state + + # average pool the patch tokens + sequence_output = torch.mean(sequence_output, dim=1) + # apply classifier + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "SiglipModel", + "SiglipPreTrainedModel", + "SiglipTextModel", + "SiglipVisionModel", + "SiglipForImageClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/processing_siglip.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/processing_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..0e554b22ce3241a056880faece92515460b63c81 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/processing_siglip.py @@ -0,0 +1,28 @@ +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Image/Text processor class for SigLIP. +""" + +from ...processing_utils import ProcessorMixin +from ...utils import auto_docstring + + +@auto_docstring +class SiglipProcessor(ProcessorMixin): + def __init__(self, image_processor, tokenizer): + super().__init__(image_processor, tokenizer) + + +__all__ = ["SiglipProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/tokenization_siglip.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/tokenization_siglip.py new file mode 100644 index 0000000000000000000000000000000000000000..001da7cbb6175b7183e9c2284db02f3c9cbc7bb0 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip/tokenization_siglip.py @@ -0,0 +1,352 @@ +# Copyright 2024 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tokenization class for SigLIP model.""" + +import os +import re +import string +import warnings +from shutil import copyfile +from typing import TYPE_CHECKING, Any + +import sentencepiece as spm + +from ...tokenization_utils_base import AddedToken +from ...tokenization_utils_sentencepiece import SentencePieceBackend + + +if TYPE_CHECKING: + from ...tokenization_utils_base import TextInput +from ...utils import logging, requires_backends +from ...utils.import_utils import requires + + +logger = logging.get_logger(__name__) + +VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} + + +SPIECE_UNDERLINE = "▁" + + +@requires(backends=("sentencepiece",)) +class SiglipTokenizer(SentencePieceBackend): + """ + Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece). + + This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to + this superclass for more information regarding those methods. + + Args: + vocab_file (`str`): + [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that + contains the vocabulary necessary to instantiate a tokenizer. + eos_token (`str`, *optional*, defaults to `""`): + The end of sequence token. + unk_token (`str`, *optional*, defaults to `""`): + The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this + token instead. + pad_token (`str`, *optional*, defaults to `""`): + The token used for padding, for example when batching sequences of different lengths. + additional_special_tokens (`list[str]`, *optional*): + Additional special tokens used by the tokenizer. + sp_model_kwargs (`dict`, *optional*): + Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for + SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things, + to set: + + - `enable_sampling`: Enable subword regularization. + - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout. + + - `nbest_size = {0,1}`: No sampling is performed. + - `nbest_size > 1`: samples from the nbest_size results. + - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) + using forward-filtering-and-backward-sampling algorithm. + + - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for + BPE-dropout. + model_max_length (`int`, *optional*, defaults to 64): + The maximum length (in number of tokens) for model inputs. + do_lower_case (`bool`, *optional*, defaults to `True`): + Whether or not to lowercase the input when tokenizing. + """ + + vocab_files_names = VOCAB_FILES_NAMES + model_input_names = ["input_ids", "attention_mask"] + + def __init__( + self, + vocab_file, + eos_token="", + unk_token="", + pad_token="", + additional_special_tokens=None, + sp_model_kwargs: dict[str, Any] | None = None, + model_max_length=64, + do_lower_case=True, + **kwargs, + ) -> None: + requires_backends(self, "protobuf") + + pad_token = ( + AddedToken(pad_token, rstrip=True, lstrip=True, normalized=False, special=True) + if isinstance(pad_token, str) + else pad_token + ) + unk_token = ( + AddedToken(unk_token, rstrip=True, lstrip=True, normalized=False, special=True) + if isinstance(unk_token, str) + else unk_token + ) + eos_token = ( + AddedToken(eos_token, rstrip=True, lstrip=True, normalized=False, special=True) + if isinstance(eos_token, str) + else eos_token + ) + + self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs + self.do_lower_case = do_lower_case + + super().__init__( + vocab_file=vocab_file, + eos_token=eos_token, + unk_token=unk_token, + pad_token=pad_token, + additional_special_tokens=additional_special_tokens, + sp_model_kwargs=self.sp_model_kwargs, + model_max_length=model_max_length, + do_lower_case=do_lower_case, + **kwargs, + ) + + @property + def vocab_size(self): + return self.sp_model.get_piece_size() + + def get_vocab(self): + vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} + vocab.update(self.added_tokens_encoder) + return vocab + + def get_special_tokens_mask( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None, already_has_special_tokens: bool = False + ) -> list[int]: + """ + Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding + special tokens using the tokenizer `prepare_for_model` method. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + already_has_special_tokens (`bool`, *optional*, defaults to `False`): + Whether or not the token list is already formatted with special tokens for the model. + + Returns: + `list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. + """ + if already_has_special_tokens: + return super().get_special_tokens_mask( + token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True + ) + + # normal case: some special tokens + if token_ids_1 is None: + return ([0] * len(token_ids_0)) + [1] + return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] + + def _add_eos_if_not_present(self, token_ids: list[int]) -> list[int]: + """Do not add eos again if user already added it.""" + if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: + warnings.warn( + f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated" + " eos tokens being added." + ) + return token_ids + else: + return token_ids + [self.eos_token_id] + + def create_token_type_ids_from_sequences( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make + use of token type ids, therefore a list of zeros is returned. + + Args: + token_ids_0 (`list[int]`): + List of IDs. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of zeros. + """ + eos = [self.eos_token_id] + + if token_ids_1 is None: + return len(token_ids_0 + eos) * [0] + return len(token_ids_0 + eos + token_ids_1 + eos) * [0] + + def build_inputs_with_special_tokens( + self, token_ids_0: list[int], token_ids_1: list[int] | None = None + ) -> list[int]: + """ + Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and + adding special tokens. A sequence has the following format: + + - single sequence: `X ` + - pair of sequences: `A B ` + + Args: + token_ids_0 (`list[int]`): + List of IDs to which the special tokens will be added. + token_ids_1 (`list[int]`, *optional*): + Optional second list of IDs for sequence pairs. + + Returns: + `list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. + """ + token_ids_0 = self._add_eos_if_not_present(token_ids_0) + if token_ids_1 is None: + return token_ids_0 + else: + token_ids_1 = self._add_eos_if_not_present(token_ids_1) + return token_ids_0 + token_ids_1 + + def __getstate__(self): + state = self.__dict__.copy() + state["sp_model"] = None + return state + + def __setstate__(self, d): + self.__dict__ = d + + # for backward compatibility + if not hasattr(self, "sp_model_kwargs"): + self.sp_model_kwargs = {} + + self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) + self.sp_model.Load(self.vocab_file) + + def remove_punctuation(self, text: str) -> str: + return text.translate(str.maketrans("", "", string.punctuation)) + + # source: https://github.com/google-research/big_vision/blob/3b8e5ab6ad4f96e32b32826f9e1b8fd277914f9c/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94 + def canonicalize_text(self, text, *, keep_punctuation_exact_string=None): + """Returns canonicalized `text` (puncuation removed). + + Args: + text (`str`): + String to be canonicalized. + keep_punctuation_exact_string (`str`, *optional*): + If provided, then this exact string is kept. For example providing '{}' will keep any occurrences of '{}' + (but will still remove '{' and '}' that appear separately). + """ + if self.do_lower_case: + text = text.lower() + + if keep_punctuation_exact_string: + text = keep_punctuation_exact_string.join( + self.remove_punctuation(part) for part in text.split(keep_punctuation_exact_string) + ) + else: + text = self.remove_punctuation(text) + text = re.sub(r"\s+", " ", text) + text = text.strip() + + return text + + def tokenize(self, text: "TextInput", add_special_tokens=False, **kwargs) -> list[str]: + """ + Converts a string to a list of tokens. + """ + tokens = super().tokenize(SPIECE_UNDERLINE + text.replace(SPIECE_UNDERLINE, " "), **kwargs) + + if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens: + tokens = tokens[1:] + return tokens + + @property + def unk_token_length(self): + return len(self.sp_model.encode(str(self.unk_token))) + + def _tokenize(self, text, **kwargs): + """ + Returns a tokenized string. + + We de-activated the `add_dummy_prefix` option, thus the sentencepiece internals will always strip any + SPIECE_UNDERLINE. + + For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give `['H', 'e', 'y']` instead of `['▁He', 'y']`. + + Thus we always encode `f"{unk_token}text"` and strip the `unk_token`. Here is an example with `unk_token = ""` and `unk_token_length = 4`. + `self.tokenizer.sp_model.encode(" Hey", out_type = str)[4:]`. + """ + text = self.canonicalize_text(text, keep_punctuation_exact_string=None) + tokens = self.sp_model.encode(text, out_type=str) + + # 1. Encode string + prefix ex: " Hey" + tokens = self.sp_model.encode(self.unk_token + text, out_type=str) + # 2. Remove self.unk_token from ['<','unk','>', '▁Hey'] + return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens + + def _convert_token_to_id(self, token): + """Converts a token (str) in an id using the vocab.""" + return self.sp_model.piece_to_id(token) + + def _convert_id_to_token(self, index): + """Converts an index (integer) in a token (str) using the vocab.""" + token = self.sp_model.IdToPiece(index) + return token + + def convert_tokens_to_string(self, tokens): + """Converts a sequence of tokens (string) in a single string.""" + current_sub_tokens = [] + out_string = "" + prev_is_special = False + for token in tokens: + # make sure that special tokens are not decoded using sentencepiece model + if token in self.all_special_tokens: + if not prev_is_special: + out_string += " " + out_string += self.sp_model.decode(current_sub_tokens) + token + prev_is_special = True + current_sub_tokens = [] + else: + current_sub_tokens.append(token) + prev_is_special = False + out_string += self.sp_model.decode(current_sub_tokens) + return out_string.strip() + + def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str]: + if not os.path.isdir(save_directory): + logger.error(f"Vocabulary path ({save_directory}) should be a directory") + return + out_vocab_file = os.path.join( + save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] + ) + + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): + copyfile(self.vocab_file, out_vocab_file) + elif not os.path.isfile(self.vocab_file): + with open(out_vocab_file, "wb") as fi: + content_spiece_model = self.sp_model.serialized_model_proto() + fi.write(content_spiece_model) + + return (out_vocab_file,) + + +__all__ = ["SiglipTokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7c1267a05125b92eecff931d6f415c15a043bdb9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/__init__.py @@ -0,0 +1,31 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_siglip2 import * + from .image_processing_pil_siglip2 import * + from .image_processing_siglip2 import * + from .modeling_siglip2 import * + from .processing_siglip2 import * + from .tokenization_siglip2 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/configuration_siglip2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/configuration_siglip2.py new file mode 100644 index 0000000000000000000000000000000000000000..eb00f285c42219b535e45667156d52734e4d5c9c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/configuration_siglip2.py @@ -0,0 +1,166 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/siglip2/modular_siglip2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_siglip2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="google/siglip2-base-patch16-naflex") +@strict +class Siglip2TextConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Siglip2TextConfig, Siglip2TextModel + + >>> # Initializing a Siglip2TextConfig with google/siglip2-base-patch16-224 style configuration + >>> configuration = Siglip2TextConfig() + + >>> # Initializing a Siglip2TextModel (with random weights) from the google/siglip2-base-patch16-224 style configuration + >>> model = Siglip2TextModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "siglip2_text_model" + base_config_key = "text_config" + + vocab_size: int = 32000 + hidden_size: int = 768 + intermediate_size: int = 3072 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + max_position_embeddings: int = 64 + hidden_act: str = "gelu_pytorch_tanh" + layer_norm_eps: float = 1e-6 + attention_dropout: float | int = 0.0 + # This differs from `CLIPTokenizer`'s default and from openai/siglip2 + # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538 + pad_token_id: int | None = 1 + bos_token_id: int | None = 49406 + eos_token_id: int | list[int] | None = 49407 + projection_size: int | None = None + + def __post_init__(self, **kwargs): + self.projection_size = self.projection_size if self.projection_size is not None else self.hidden_size + super().__post_init__(**kwargs) + + +@auto_docstring(checkpoint="google/siglip2-base-patch16-naflex") +@strict +class Siglip2VisionConfig(PreTrainedConfig): + r""" + num_patches (`int`, *optional*, defaults to 256): + The number of patches in the image with the size of (`patch_size`, `patch_size`). + The image is resized to fill maximum of this number of patches, and to preserve + the aspect ratio. In case the resulted number of patches is lower, the image is + padded in "patch" dimension. + + Example: + + ```python + >>> from transformers import Siglip2VisionConfig, Siglip2VisionModel + + >>> # Initializing a Siglip2VisionConfig with google/siglip2-base-patch16-naflex style configuration + >>> configuration = Siglip2VisionConfig() + + >>> # Initializing a Siglip2VisionModel (with random weights) from the google/siglip2-base-patch16-naflex style configuration + >>> model = Siglip2VisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "siglip2_vision_model" + base_config_key = "vision_config" + + hidden_size: int = 768 + intermediate_size: int = 3072 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "gelu_pytorch_tanh" + layer_norm_eps: float = 1e-6 + attention_dropout: float | int = 0.0 + + num_patches: int = 256 + + +@auto_docstring(checkpoint="google/siglip2-base-patch16-naflex") +@strict +class Siglip2Config(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers import Siglip2Config, Siglip2Model + + >>> # Initializing a Siglip2Config with google/siglip2-base-patch16-224 style configuration + >>> configuration = Siglip2Config() + + >>> # Initializing a Siglip2Model (with random weights) from the google/siglip2-base-patch16-224 style configuration + >>> model = Siglip2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + + >>> # We can also initialize a Siglip2Config from a Siglip2TextConfig and a Siglip2VisionConfig + >>> from transformers import Siglip2TextConfig, Siglip2VisionConfig + + >>> # Initializing a Siglip2Text and Siglip2Vision configuration + >>> config_text = Siglip2TextConfig() + >>> config_vision = Siglip2VisionConfig() + + >>> config = Siglip2Config(text_config=config_text, vision_config=config_vision) + ```""" + + model_type = "siglip2" + sub_configs = {"text_config": Siglip2TextConfig, "vision_config": Siglip2VisionConfig} + + text_config: dict | PreTrainedConfig | None = None + vision_config: dict | PreTrainedConfig | None = None + initializer_factor: float = 1.0 + + def __post_init__(self, **kwargs): + if self.text_config is None: + self.text_config = Siglip2TextConfig() + logger.info("`text_config` is `None`. Initializing the `Siglip2TextConfig` with default values.") + elif isinstance(self.text_config, dict): + self.text_config = Siglip2TextConfig(**self.text_config) + + if self.vision_config is None: + self.vision_config = Siglip2VisionConfig() + logger.info("`vision_config` is `None`. initializing the `Siglip2VisionConfig` with default values.") + elif isinstance(self.vision_config, dict): + self.vision_config = Siglip2VisionConfig(**self.vision_config) + + super().__post_init__(**kwargs) + + +__all__ = ["Siglip2Config", "Siglip2TextConfig", "Siglip2VisionConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/image_processing_pil_siglip2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/image_processing_pil_siglip2.py new file mode 100644 index 0000000000000000000000000000000000000000..8ae926dbe04d0a206bed361a648fea34c2314280 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/image_processing_pil_siglip2.py @@ -0,0 +1,201 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SigLIP2.""" + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput, PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TensorType, + auto_docstring, +) + + +def convert_image_to_patches(image: np.ndarray, patch_size: int) -> np.ndarray: + """ + Convert 3D array image of shape (num_channels, image_height, image_width) into 2D array of patches of shape + (num_patches_height * num_patches_width, patch_size * patch_size * num_channels). + """ + num_channels, image_height, image_width = image.shape + num_patches_height = image_height // patch_size + num_patches_width = image_width // patch_size + patched_image = image.reshape(num_channels, num_patches_height, patch_size, num_patches_width, patch_size) + patched_image = patched_image.transpose(1, 3, 2, 4, 0) + patched_image = patched_image.reshape(num_patches_height * num_patches_width, -1) + return patched_image + + +def pad_along_first_dim(array: np.ndarray, target_length: int, pad_value: int = 0) -> tuple[np.ndarray, np.ndarray]: + """ + Pad the array along the first dimension. + """ + current_length = array.shape[0] + padding_length = target_length - current_length + mask = np.ones((target_length,), dtype=np.int32) + if padding_length > 0: + paddings = [(0, padding_length)] + [(0, 0)] * (array.ndim - 1) + array = np.pad(array, paddings, mode="constant", constant_values=pad_value) + mask[-padding_length:] = 0 + return array, mask + + +# Adapted from transformers.models.siglip2.image_processing_siglip2.Siglip2ImageProcessorKwargs +class Siglip2ImageProcessorKwargs(ImagesKwargs, total=False): + r""" + patch_size (`int`, *optional*, defaults to `self.patch_size`): + The size (resolution) of each patch the image will be split to. + max_num_patches (`int`, *optional*, defaults to `self.max_num_patches`): + The image will be resized to have at most this number of patches, + and then padded in "patch" dimension to match this number exactly. + """ + + patch_size: int + max_num_patches: int + + +# Adapted from transformers.models.siglip2.image_processing_siglip2.get_image_size_for_max_num_patches +def get_image_size_for_max_num_patches( + image_height: int, image_width: int, patch_size: int, max_num_patches: int, eps: float = 1e-5 +) -> tuple[int, int]: + """ + Determine image size based on max number of patches, ensure dimensions are divisible by patch size and image is at least 1 patch. + + Args: + image_height (`int`): + Original image height. + image_width (`int`): + Original image width. + patch_size (`int`): + Patch size for processing. + max_num_patches (`int`): + Maximum number of patches. + eps (`float`): + Small threshold for binary search. + + Returns: + Tuple: (target_height, target_width) + """ + import math + + def get_scaled_image_size(scale: float, size: int, patch_size: int) -> int: + scaled_size = size * scale + scaled_size = math.ceil(scaled_size / patch_size) * patch_size # make divisible by patch_size + scaled_size = max(patch_size, scaled_size) # ensure at least 1 patch + return int(scaled_size) + + # Binary search for optimal scale + scale_min, scale_max = eps / 10, 100.0 + while (scale_max - scale_min) >= eps: + scale = (scale_min + scale_max) / 2 + target_height = get_scaled_image_size(scale, image_height, patch_size) + target_width = get_scaled_image_size(scale, image_width, patch_size) + num_patches = (target_height / patch_size) * (target_width / patch_size) + + if num_patches <= max_num_patches: + scale_min = scale + else: + scale_max = scale + + scale = scale_min + target_height = get_scaled_image_size(scale, image_height, patch_size) + target_width = get_scaled_image_size(scale, image_width, patch_size) + return target_height, target_width + + +@auto_docstring +class Siglip2ImageProcessorPil(PilBackend): + valid_kwargs = Siglip2ImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = [0.5, 0.5, 0.5] + image_std = [0.5, 0.5, 0.5] + do_resize = True + do_rescale = True + do_normalize = True + patch_size = 16 + max_num_patches = 256 + model_input_names = ["pixel_values", "pixel_attention_mask", "spatial_shapes"] + + def __init__(self, **kwargs: Unpack[Siglip2ImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[Siglip2ImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def _validate_preprocess_kwargs(self, **kwargs) -> tuple: + # Remove do_resize from kwargs to not raise an error as size is None (computed dynamically) + kwargs.pop("do_resize", None) + return super()._validate_preprocess_kwargs(**kwargs) + + def _preprocess( + self, + images: list[np.ndarray], + do_resize: bool, + patch_size: int, + max_num_patches: int, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + pixel_masks = [] + pixel_values = [] + spatial_shapes = [] + + for image in images: + if do_resize: + height, width = get_image_size_for_max_num_patches( + image_height=image.shape[-2], + image_width=image.shape[-1], + patch_size=patch_size, + max_num_patches=max_num_patches, + ) + size_dict = SizeDict(height=height, width=width) + image = self.resize(image=image, size=size_dict, resample=resample) + + if do_rescale: + image = self.rescale(image, rescale_factor) + if do_normalize: + image = self.normalize(image, image_mean, image_std) + + # (num_channels, height, width) -> (num_patches, patch_size * patch_size * num_channels) + patches = convert_image_to_patches(image, patch_size) + patches, mask = pad_along_first_dim(patches, max_num_patches) + + num_patches_height = image.shape[-2] // patch_size + num_patches_width = image.shape[-1] // patch_size + + spatial_shapes.append((num_patches_height, num_patches_width)) + pixel_values.append(patches) + pixel_masks.append(mask) + + batch_feature = BatchFeature( + data={ + "pixel_values": pixel_values, + "pixel_attention_mask": pixel_masks, + "spatial_shapes": spatial_shapes, + }, + tensor_type=return_tensors, + ) + return batch_feature + + +__all__ = ["Siglip2ImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/image_processing_siglip2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/image_processing_siglip2.py new file mode 100644 index 0000000000000000000000000000000000000000..5e21a6e34ad71742e0319668345d68eb9413fc2b --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/image_processing_siglip2.py @@ -0,0 +1,199 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for SigLIP2.""" + +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_utils import ImageInput, PILImageResampling, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import ( + TensorType, + auto_docstring, +) + + +class Siglip2ImageProcessorKwargs(ImagesKwargs, total=False): + r""" + patch_size (`int`, *optional*, defaults to `self.patch_size`): + The size (resolution) of each patch the image will be split to. + max_num_patches (`int`, *optional*, defaults to `self.max_num_patches`): + The image will be resized to have at most this number of patches, + and then padded in "patch" dimension to match this number exactly. + """ + + patch_size: int + max_num_patches: int + + +def get_image_size_for_max_num_patches( + image_height: int, image_width: int, patch_size: int, max_num_patches: int, eps: float = 1e-5 +) -> tuple[int, int]: + """ + Determine image size based on max number of patches, ensure dimensions are divisible by patch size and image is at least 1 patch. + + Args: + image_height (`int`): + Original image height. + image_width (`int`): + Original image width. + patch_size (`int`): + Patch size for processing. + max_num_patches (`int`): + Maximum number of patches. + eps (`float`): + Small threshold for binary search. + + Returns: + Tuple: (target_height, target_width) + """ + import math + + def get_scaled_image_size(scale: float, size: int, patch_size: int) -> int: + scaled_size = size * scale + scaled_size = math.ceil(scaled_size / patch_size) * patch_size # make divisible by patch_size + scaled_size = max(patch_size, scaled_size) # ensure at least 1 patch + return int(scaled_size) + + # Binary search for optimal scale + scale_min, scale_max = eps / 10, 100.0 + while (scale_max - scale_min) >= eps: + scale = (scale_min + scale_max) / 2 + target_height = get_scaled_image_size(scale, image_height, patch_size) + target_width = get_scaled_image_size(scale, image_width, patch_size) + num_patches = (target_height / patch_size) * (target_width / patch_size) + + if num_patches <= max_num_patches: + scale_min = scale + else: + scale_max = scale + + scale = scale_min + target_height = get_scaled_image_size(scale, image_height, patch_size) + target_width = get_scaled_image_size(scale, image_width, patch_size) + return target_height, target_width + + +def convert_image_to_patches(image: "torch.Tensor", patch_size: int) -> "torch.Tensor": + """ + Convert 3D tensor image of shape (num_channels, image_height, image_width) into 2D tensor of patches of shape + (num_patches_height * num_patches_width, patch_size * patch_size * num_channels). + """ + num_channels, image_height, image_width = image.shape + num_patches_height = image_height // patch_size + num_patches_width = image_width // patch_size + patched_image = image.reshape(num_channels, num_patches_height, patch_size, num_patches_width, patch_size) + patched_image = patched_image.permute(1, 3, 2, 4, 0) + patched_image = patched_image.reshape(num_patches_height * num_patches_width, -1) + return patched_image + + +def pad_along_first_dim( + tensor: "torch.Tensor", target_length: int, pad_value: int = 0 +) -> tuple["torch.Tensor", "torch.Tensor"]: + """ + Pad the tensor along the first dimension. + """ + current_length = tensor.shape[0] + padding_length = target_length - current_length + mask = torch.ones((target_length,), dtype=torch.int32) + if padding_length > 0: + padding = [0, 0] * (tensor.ndim - 1) + [0, padding_length] + tensor = torch.nn.functional.pad(tensor, padding, mode="constant", value=pad_value) + mask[-padding_length:] = 0 + return tensor, mask + + +@auto_docstring +class Siglip2ImageProcessor(TorchvisionBackend): + valid_kwargs = Siglip2ImageProcessorKwargs + resample = PILImageResampling.BILINEAR + image_mean = [0.5, 0.5, 0.5] + image_std = [0.5, 0.5, 0.5] + do_resize = True + do_rescale = True + do_normalize = True + patch_size = 16 + max_num_patches = 256 + model_input_names = ["pixel_values", "pixel_attention_mask", "spatial_shapes"] + + def __init__(self, **kwargs: Unpack[Siglip2ImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[Siglip2ImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def _validate_preprocess_kwargs(self, **kwargs) -> tuple: + # Remove do_resize from kwargs to not raise an error as size is None (computed dynamically) + kwargs.pop("do_resize", None) + return super()._validate_preprocess_kwargs(**kwargs) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + patch_size: int, + max_num_patches: int, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + pixel_masks = [] + pixel_values = [] + spatial_shapes = [] + + for image in images: + if do_resize: + height, width = get_image_size_for_max_num_patches( + image_height=image.shape[-2], + image_width=image.shape[-1], + patch_size=patch_size, + max_num_patches=max_num_patches, + ) + size_dict = SizeDict(height=height, width=width) + image = self.resize(image=image, size=size_dict, resample=resample) + + image = self.rescale_and_normalize(image, do_rescale, rescale_factor, do_normalize, image_mean, image_std) + + # (num_channels, height, width) -> (num_patches, patch_size * patch_size * num_channels) + patches = convert_image_to_patches(image, patch_size) + patches, mask = pad_along_first_dim(patches, max_num_patches) + + num_patches_height = image.shape[-2] // patch_size + num_patches_width = image.shape[-1] // patch_size + + spatial_shapes.append((num_patches_height, num_patches_width)) + pixel_values.append(patches) + pixel_masks.append(mask) + + batch_feature = BatchFeature( + data={ + "pixel_values": pixel_values, + "pixel_attention_mask": pixel_masks, + "spatial_shapes": spatial_shapes, + }, + tensor_type=return_tensors, + ) + return batch_feature + + +__all__ = ["Siglip2ImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/modeling_siglip2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/modeling_siglip2.py new file mode 100644 index 0000000000000000000000000000000000000000..bb8ed2e89b2ec1f40c13fc0fec0aea41579d1451 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/modeling_siglip2.py @@ -0,0 +1,1028 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/siglip2/modular_siglip2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_siglip2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...activations import ACT2FN +from ...masking_utils import create_bidirectional_mask +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import ModelOutput, TransformersKwargs, auto_docstring, torch_compilable_check +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_siglip2 import Siglip2Config, Siglip2TextConfig, Siglip2VisionConfig + + +@auto_docstring( + custom_intro=""" + Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. + """ +) +@dataclass +class Siglip2VisionOutput(ModelOutput): + r""" + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + """ + + image_embeds: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@dataclass +@auto_docstring( + custom_intro=""" + Base class for text model's outputs that also contains a pooling of the last hidden states. + """ +) +class Siglip2TextOutput(ModelOutput): + r""" + text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The text embeddings obtained by applying the projection layer to the pooler_output. + """ + + text_embeds: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +@auto_docstring +@dataclass +class Siglip2Output(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): + Contrastive loss for image-text similarity. + logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): + The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text + similarity scores. + logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): + The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image + similarity scores. + text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`): + The text embeddings obtained by applying the projection layer to the pooled output of [`Siglip2TextModel`]. + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim`): + The image embeddings obtained by applying the projection layer to the pooled output of [`Siglip2VisionModel`]. + text_model_output (`BaseModelOutputWithPooling`): + The output of the [`Siglip2TextModel`]. + vision_model_output (`BaseModelOutputWithPooling`): + The output of the [`Siglip2VisionModel`]. + """ + + loss: torch.FloatTensor | None = None + logits_per_image: torch.FloatTensor | None = None + logits_per_text: torch.FloatTensor | None = None + text_embeds: torch.FloatTensor | None = None + image_embeds: torch.FloatTensor | None = None + text_model_output: BaseModelOutputWithPooling = None + vision_model_output: BaseModelOutputWithPooling = None + + def to_tuple(self) -> tuple[Any]: + return tuple(v.to_tuple() if isinstance(v, ModelOutput) else v for v in self.values()) + + +class Siglip2VisionEmbeddings(nn.Module): + def __init__(self, config: Siglip2VisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Linear( + in_features=config.num_channels * self.patch_size * self.patch_size, + out_features=self.embed_dim, + ) + + self.num_patches = config.num_patches + self.position_embedding_size = int(self.num_patches**0.5) + self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim) + + @staticmethod + def resize_positional_embeddings( + positional_embeddings: torch.Tensor, + spatial_shapes: torch.LongTensor, + max_length: int, + ) -> torch.Tensor: + """ + Resize positional embeddings to image-specific size and pad to a fixed size. + + Args: + positional_embeddings (`torch.Tensor`): + Position embeddings of shape (height, width, embed_dim) + spatial_shapes (`torch.LongTensor`): + Spatial shapes of shape (batch_size, 2) to resize the positional embeddings to + max_length (`int`): + Maximum length of the positional embeddings to pad resized positional embeddings to + + Returns: + `torch.Tensor`: Embeddings of shape (batch_size, max_length, embed_dim) + """ + batch_size = spatial_shapes.shape[0] + embed_dim = positional_embeddings.shape[-1] + source_dtype = positional_embeddings.dtype + + resulted_positional_embeddings = torch.empty( + (batch_size, max_length, embed_dim), + device=positional_embeddings.device, + dtype=source_dtype, + ) + + # (height, width, embed_dim) -> (1, embed_dim, height, width) for interpolation + positional_embeddings = positional_embeddings.permute(2, 0, 1).unsqueeze(0) + + # Upcast to float32 on CPU because antialias is not supported for bfloat16/float16 on CPU + if positional_embeddings.device.type == "cpu": + positional_embeddings = positional_embeddings.to(torch.float32) + + for i in range(batch_size): + # (1, dim, height, width) -> (1, dim, target_height, target_width) + height, width = spatial_shapes[i].tolist() # will be itemized in F.interpolate either way + torch_compilable_check((width > 0), "Width of resized positional embeddings must be positive.") + torch_compilable_check((height > 0), "Height of resized positional embeddings must be positive.") + torch_compilable_check((height * width) <= max_length, "Resized positional embeddings exceed max_length.") + resized_embeddings = F.interpolate( + positional_embeddings, + size=(height, width), + mode="bilinear", + align_corners=False, + antialias=True, + ) + + # (1, dim, target_height, target_width) -> (target_height * target_width, dim) + resized_embeddings = resized_embeddings.reshape(embed_dim, height * width).transpose(0, 1) + + # Cast to original dtype + resized_embeddings = resized_embeddings.to(source_dtype) + + resulted_positional_embeddings[i, : height * width] = resized_embeddings + resulted_positional_embeddings[i, height * width :] = resized_embeddings[0] + + return resulted_positional_embeddings + + def forward(self, pixel_values: torch.FloatTensor, spatial_shapes: torch.LongTensor) -> torch.Tensor: + """ + Args: + pixel_values (`torch.FloatTensor`): + Pixel values of shape (batch_size, max_num_patches, num_channels * patch_size * patch_size) + spatial_shapes (`list[tuple[int, int]]`): + Spatial shapes of shape (batch_size, 2) to resize the positional embeddings to + """ + + # Apply patch embeddings to already patchified pixel values + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) + + # Get positional resized and padded positional embeddings + positional_embeddings = self.position_embedding.weight.reshape( + self.position_embedding_size, self.position_embedding_size, -1 + ) + resized_positional_embeddings = self.resize_positional_embeddings( + positional_embeddings, spatial_shapes, max_length=pixel_values.shape[1] + ) + + # Add positional embeddings to patch embeddings + embeddings = patch_embeds + resized_positional_embeddings + return embeddings + + +class Siglip2TextEmbeddings(nn.Module): + def __init__(self, config: Siglip2TextConfig): + super().__init__() + embed_dim = config.hidden_size + + self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) + self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer( + "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False + ) + + def forward( + self, + input_ids: torch.LongTensor | None = None, + position_ids: torch.LongTensor | None = None, + inputs_embeds: torch.FloatTensor | None = None, + ) -> torch.Tensor: + seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] + max_position_embedding = self.position_embedding.weight.shape[0] + + if seq_length > max_position_embedding: + raise ValueError( + f"Sequence length must be less than max_position_embeddings (got `sequence length`: " + f"{seq_length} and max_position_embeddings: {max_position_embedding}" + ) + + if position_ids is None: + position_ids = self.position_ids[:, :seq_length] + + if inputs_embeds is None: + inputs_embeds = self.token_embedding(input_ids) + + position_embeddings = self.position_embedding(position_ids) + embeddings = inputs_embeds + position_embeddings + + return embeddings + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class Siglip2Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + self.is_causal = False + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + + input_shape = hidden_states.shape[:-1] + + hidden_shape = (*input_shape, -1, self.head_dim) + queries = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + keys = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + values = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + queries, + keys, + values, + attention_mask, + is_causal=self.is_causal, + scaling=self.scale, + dropout=0.0 if not self.training else self.dropout, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class Siglip2MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class Siglip2EncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: Siglip2VisionConfig | Siglip2TextConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.self_attn = Siglip2Attention(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = Siglip2MLP(config) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +@auto_docstring +class Siglip2PreTrainedModel(PreTrainedModel): + config: Siglip2Config + base_model_prefix = "siglip2" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + + _no_split_modules = [ + "Siglip2TextEmbeddings", + "Siglip2VisionEmbeddings", + "Siglip2EncoderLayer", + "Siglip2MultiheadAttentionPoolingHead", + ] + _supports_flash_attn = False + _supports_sdpa = True + # nn.MultiHeadAttention mask doesn't allow for non 4d mask + _supports_flex_attn = False + _supports_attention_backend = True + + _can_record_outputs = { + "hidden_states": Siglip2EncoderLayer, + "attentions": Siglip2Attention, + } + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + if isinstance(module, Siglip2VisionEmbeddings): + width = ( + self.config.vision_config.hidden_size + if isinstance(self.config, Siglip2Config) + else self.config.hidden_size + ) + init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) + if hasattr(module, "position_ids"): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + elif isinstance(module, nn.Embedding): + init.default_flax_embed_init_(module.weight) + elif isinstance(module, Siglip2Attention): + init.xavier_uniform_(module.q_proj.weight) + init.xavier_uniform_(module.k_proj.weight) + init.xavier_uniform_(module.v_proj.weight) + init.xavier_uniform_(module.out_proj.weight) + init.zeros_(module.q_proj.bias) + init.zeros_(module.k_proj.bias) + init.zeros_(module.v_proj.bias) + init.zeros_(module.out_proj.bias) + elif isinstance(module, Siglip2MLP): + init.xavier_uniform_(module.fc1.weight) + init.xavier_uniform_(module.fc2.weight) + init.normal_(module.fc1.bias, std=1e-6) + init.normal_(module.fc2.bias, std=1e-6) + elif isinstance(module, Siglip2MultiheadAttentionPoolingHead): + init.xavier_uniform_(module.probe) + init.xavier_uniform_(module.attention.in_proj_weight) + init.zeros_(module.attention.in_proj_bias) + elif isinstance(module, Siglip2Model): + init.zeros_(module.logit_scale) + init.zeros_(module.logit_bias) + elif isinstance(module, Siglip2ForImageClassification): + init.normal_( + module.classifier.weight, + std=self.config.vision_config.hidden_size**-0.5 * self.config.initializer_factor, + ) + elif isinstance(module, (nn.Linear, nn.Conv2d)): + init.lecun_normal_(module.weight) + if module.bias is not None: + init.zeros_(module.bias) + elif isinstance(module, nn.LayerNorm): + init.zeros_(module.bias) + init.ones_(module.weight) + elif isinstance(module, Siglip2TextEmbeddings): + init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) + + +class Siglip2Encoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`Siglip2EncoderLayer`]. + + Args: + config: Siglip2Config + """ + + def __init__(self, config: Siglip2Config): + super().__init__() + self.config = config + self.layers = nn.ModuleList([Siglip2EncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + # Ignore copy + @auto_docstring + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutput: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + hidden_states = encoder_layer( + hidden_states, + attention_mask, + **kwargs, + ) + + return BaseModelOutput(last_hidden_state=hidden_states) + + +@auto_docstring( + custom_intro=""" + The vision model from Siglip2 without any head or projection on top. + """ +) +class Siglip2VisionModel(Siglip2PreTrainedModel): + config: Siglip2VisionConfig + main_input_name = "pixel_values" + input_modalities = ("image",) + base_model_prefix = "vision_model" + _input_embed_layer = "patch_embedding" + + def __init__(self, config: Siglip2VisionConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + + self.embeddings = Siglip2VisionEmbeddings(config) + self.encoder = Siglip2Encoder(config) + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + self.use_head = True if not hasattr(config, "vision_use_head") else config.vision_use_head + if self.use_head: + self.head = Siglip2MultiheadAttentionPoolingHead(config) + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_attention_mask: torch.Tensor, + spatial_shapes: torch.LongTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): + Tensor containing the spatial dimensions (height, width) of the input images. + + Examples: + + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + >>> from transformers import AutoProcessor, Siglip2VisionModel + + >>> model = Siglip2VisionModel.from_pretrained("google/siglip2-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled features + ``` + """ + hidden_states = self.embeddings(pixel_values, spatial_shapes) + + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=pixel_attention_mask, + ) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=encoder_attention_mask, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + pooler_output = self.head(last_hidden_state, pixel_attention_mask) if self.use_head else None + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooler_output, + ) + + +@auto_docstring( + custom_intro=""" + The text model from Siglip2 without any head or projection on top. + """ +) +class Siglip2TextModel(Siglip2PreTrainedModel): + config: Siglip2TextConfig + input_modalities = ("text",) + base_model_prefix = "text_model" + _input_embed_layer = "token_embedding" + + def __init__(self, config: Siglip2TextConfig): + super().__init__(config) + self.config = config + embed_dim = config.hidden_size + self.embeddings = Siglip2TextEmbeddings(config) + self.encoder = Siglip2Encoder(config) + self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + self.head = nn.Linear(embed_dim, config.projection_size) + self.post_init() + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + input_ids: torch.Tensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + r""" + Examples: + + ```python + >>> from transformers import AutoTokenizer, Siglip2TextModel + + >>> model = Siglip2TextModel.from_pretrained("google/siglip2-base-patch16-224") + >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip2-base-patch16-224") + + >>> # important: make sure to set padding="max_length" as that's how the model was trained + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled (EOS token) states + ```""" + if input_ids is None: + raise ValueError("You have to specify input_ids") + + input_shape = input_ids.size() + input_ids = input_ids.view(-1, input_shape[-1]) + + hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) + + # note: Siglip2's text model does not use a causal mask, unlike the original CLIP model. + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=attention_mask, + ) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.final_layer_norm(last_hidden_state) + + # The model uses the last token's hidden state, which may be padding. + pooled_output = last_hidden_state[:, -1, :] + pooled_output = self.head(pooled_output) + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooled_output, + ) + + +class Siglip2MultiheadAttentionPoolingHead(nn.Module): + """Multihead Attention Pooling.""" + + def __init__(self, config: Siglip2VisionConfig): + super().__init__() + + self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) + self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True) + self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = Siglip2MLP(config) + + self.config = config + self.num_heads = config.num_attention_heads + + def forward(self, hidden_state: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor: + batch_size = hidden_state.shape[0] + probe = self.probe.repeat(batch_size, 1, 1) + + if attention_mask is not None: + target_len, source_len = probe.shape[1], hidden_state.shape[1] + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=probe, + attention_mask=attention_mask, + encoder_hidden_states=hidden_state, + ) + if attention_mask is not None: + attention_mask = attention_mask.repeat(1, self.num_heads, target_len, 1) + attention_mask = attention_mask.reshape(-1, target_len, source_len) + + # `nn.MultiheadAttention` cannot handle boolean masks (which SDPA can) + if attention_mask.dtype == torch.bool: + attention_mask = torch.where( + attention_mask, + torch.tensor(0.0, device=attention_mask.device, dtype=probe.dtype), + torch.finfo(probe.dtype).min, + ) + + hidden_state = self.attention(probe, hidden_state, hidden_state, attn_mask=attention_mask)[0] + + residual = hidden_state + hidden_state = self.layernorm(hidden_state) + hidden_state = residual + self.mlp(hidden_state) + + return hidden_state[:, 0] + + +@auto_docstring +class Siglip2Model(Siglip2PreTrainedModel): + config: Siglip2Config + + def __init__(self, config: Siglip2Config): + super().__init__(config) + + text_config = config.text_config + vision_config = config.vision_config + + # First, initialize the text and vision models with proper attention implementation + self.text_model = Siglip2TextModel._from_config(text_config) + self.vision_model = Siglip2VisionModel._from_config(vision_config) + + self.logit_scale = nn.Parameter(torch.randn(1)) + self.logit_bias = nn.Parameter(torch.randn(1)) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, value: nn.Module): + self.text_model.embeddings.token_embedding = value + + @can_return_tuple + @auto_docstring + def get_text_features( + self, + input_ids: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + Examples: + + ```python + >>> from transformers import AutoTokenizer, AutoModel + >>> import torch + + >>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224") + >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip2-base-patch16-224") + + >>> # important: make sure to set padding="max_length" as that's how the model was trained + >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") + >>> with torch.no_grad(): + ... text_features = model.get_text_features(**inputs) + ```""" + return self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + **kwargs, + ) + + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor | None = None, + pixel_attention_mask: torch.Tensor | None = None, + spatial_shapes: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): + Tensor containing the spatial dimensions (height, width) of the input images. + + Examples: + + ```python + >>> import torch + >>> from transformers import AutoProcessor, AutoModel + >>> from transformers.image_utils import load_image + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = load_image(url) + + >>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224") + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> with torch.no_grad(): + ... image_features = model.get_image_features(**inputs) + ``` + """ + return self.vision_model( + pixel_values=pixel_values, + pixel_attention_mask=pixel_attention_mask, + spatial_shapes=spatial_shapes, + **kwargs, + ) + + # NOTE: Siglip2Model uses Pretrained backbones, so we don't need to add `capture_outputs` here + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_attention_mask: torch.Tensor | None = None, + spatial_shapes: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + return_loss: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Siglip2Output: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): + Tensor containing the spatial dimensions (height, width) of the input images. + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. + + Examples: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, AutoModel + >>> import torch + + >>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"] + >>> # important: we pass `padding=max_length` since the model was trained with this + >>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> logits_per_image = outputs.logits_per_image + >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities + >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") + 31.9% that image 0 is 'a photo of 2 cats' + ``` + """ + vision_outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values=pixel_values, + pixel_attention_mask=pixel_attention_mask, + spatial_shapes=spatial_shapes, + **kwargs, + ) + + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + **kwargs, + ) + + image_embeds = vision_outputs.pooler_output + text_embeds = text_outputs.pooler_output + + # normalized features + image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) + text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) + + # cosine similarity as logits + logits_per_text = torch.matmul(text_embeds, image_embeds.t().to(text_embeds.device)) + + logit_scale, logit_bias = self.logit_scale.to(text_embeds.device), self.logit_bias.to(text_embeds.device) + logits_per_text = logits_per_text * logit_scale.exp() + logit_bias + + logits_per_image = logits_per_text.t() + + loss = None + if return_loss: + # Adapted from https://github.com/google-research/big_vision/blob/01edb81a4716f93a48be43b3a4af14e29cdb3a7f/big_vision/trainers/proj/image_text/siglip2.py#L287 + eye = torch.eye(logits_per_text.size(0), device=logits_per_text.device) + m1_diag1 = -torch.ones_like(logits_per_text) + 2 * eye + loglik = torch.nn.functional.logsigmoid(m1_diag1 * logits_per_text) + nll = -torch.sum(loglik, dim=-1) + loss = nll.mean() + + return Siglip2Output( + loss=loss, + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +@auto_docstring( + custom_intro=""" + Siglip2 vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of + the patch tokens) e.g. for ImageNet. + """ +) +class Siglip2ForImageClassification(Siglip2PreTrainedModel): + main_input_name = "pixel_values" + input_modalities = ("image",) + + def __init__(self, config: Siglip2Config) -> None: + super().__init__(config) + + self.num_labels = config.num_labels + self.vision_model = Siglip2VisionModel._from_config(config.vision_config) + + # Classifier head + self.classifier = ( + nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity() + ) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self) -> nn.Module: + return self.vision_model.embeddings.patch_embedding + + def set_input_embeddings(self, value: nn.Module): + self.vision_model.embeddings.patch_embedding = value + + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor | None = None, + pixel_attention_mask: torch.Tensor | None = None, + spatial_shapes: torch.LongTensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> ImageClassifierOutput: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): + Tensor containing the spatial dimensions (height, width) of the input images. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, Siglip2ForImageClassification + >>> import torch + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> torch.manual_seed(3) # doctest: +IGNORE_RESULT + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> # note: we are loading a `Siglip2Model` from the hub here, + >>> # so the head will be randomly initialized, hence the predictions will be random if seed is not set above. + >>> image_processor = AutoImageProcessor.from_pretrained("google/siglip2-base-patch16-224") + >>> model = Siglip2ForImageClassification.from_pretrained("google/siglip2-base-patch16-224") + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> logits = outputs.logits + >>> # model predicts one of the two classes + >>> predicted_class_idx = logits.argmax(-1).item() + >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) + Predicted class: LABEL_1 + ``` + """ + outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values, + pixel_attention_mask=pixel_attention_mask, + spatial_shapes=spatial_shapes, + **kwargs, + ) + + sequence_output = outputs.last_hidden_state + + # average pool the patch tokens + if pixel_attention_mask is not None: + pool_mask = pixel_attention_mask[..., None].to(sequence_output.device) + sequence_output = torch.sum(sequence_output * pool_mask, dim=1) / torch.sum(pool_mask, dim=1) + else: + sequence_output = torch.mean(sequence_output, dim=1) + + # apply classifier + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "Siglip2Model", + "Siglip2PreTrainedModel", + "Siglip2TextModel", + "Siglip2VisionModel", + "Siglip2ForImageClassification", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/modular_siglip2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/modular_siglip2.py new file mode 100644 index 0000000000000000000000000000000000000000..fc69c28f99ba3836bbbcec9f8c07910124f806ca --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/modular_siglip2.py @@ -0,0 +1,594 @@ +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict +from tokenizers import normalizers + +from transformers.models.gemma.tokenization_gemma import GemmaTokenizer +from transformers.models.siglip.configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig +from transformers.models.siglip.modeling_siglip import ( + BaseModelOutput, + BaseModelOutputWithPooling, + ImageClassifierOutput, + SiglipForImageClassification, + SiglipModel, + SiglipMultiheadAttentionPoolingHead, + SiglipOutput, + SiglipPreTrainedModel, + SiglipTextModel, + SiglipTextModelOutput, + SiglipVisionModel, + SiglipVisionModelOutput, +) + +from ...masking_utils import create_bidirectional_mask +from ...processing_utils import Unpack +from ...utils import ( + TransformersKwargs, + auto_docstring, + torch_compilable_check, +) +from ...utils.generic import can_return_tuple, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs + + +class Siglip2Tokenizer(GemmaTokenizer): + """ + Gemma tokenizer + SigLIP2 training default: lowercase normalization. + """ + + def __init__( + self, + vocab: str | dict[str, int] | None = None, + merges: str | list[str] | None = None, + unk_token: str = "", + bos_token: str = "", + eos_token: str = "", + pad_token: str = "", + mask_token: str = "", + **kwargs, + ): + super().__init__( + vocab=vocab, + merges=merges, + unk_token=unk_token, + bos_token=bos_token, + eos_token=eos_token, + pad_token=pad_token, + mask_token=mask_token, + **kwargs, + ) + + # Persist for save/load + push_to_hub dynamic tokenizer test + if hasattr(self, "init_kwargs") and isinstance(self.init_kwargs, dict): + self.init_kwargs.setdefault("tokenizer_class", self.__class__.__name__) + + backend = getattr(self, "_tokenizer", None) + if backend is not None and backend.normalizer is not None: + backend.normalizer = normalizers.Sequence([normalizers.Lowercase(), backend.normalizer]) + + +@auto_docstring(checkpoint="google/siglip2-base-patch16-naflex") +@strict +class Siglip2TextConfig(SiglipTextConfig): + pass + + +@auto_docstring(checkpoint="google/siglip2-base-patch16-naflex") +@strict +class Siglip2VisionConfig(SiglipVisionConfig): + r""" + num_patches (`int`, *optional*, defaults to 256): + The number of patches in the image with the size of (`patch_size`, `patch_size`). + The image is resized to fill maximum of this number of patches, and to preserve + the aspect ratio. In case the resulted number of patches is lower, the image is + padded in "patch" dimension. + + Example: + + ```python + >>> from transformers import Siglip2VisionConfig, Siglip2VisionModel + + >>> # Initializing a Siglip2VisionConfig with google/siglip2-base-patch16-naflex style configuration + >>> configuration = Siglip2VisionConfig() + + >>> # Initializing a Siglip2VisionModel (with random weights) from the google/siglip2-base-patch16-naflex style configuration + >>> model = Siglip2VisionModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + num_patches: int = 256 + image_size = AttributeError() + + +@auto_docstring(checkpoint="google/siglip2-base-patch16-naflex") +@strict +class Siglip2Config(SiglipConfig): + pass + + +class Siglip2VisionOutput(SiglipVisionModelOutput): + pass + + +class Siglip2TextOutput(SiglipTextModelOutput): + pass + + +class Siglip2Output(SiglipOutput): + pass + + +class Siglip2VisionEmbeddings(nn.Module): + def __init__(self, config: Siglip2VisionConfig): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Linear( + in_features=config.num_channels * self.patch_size * self.patch_size, + out_features=self.embed_dim, + ) + + self.num_patches = config.num_patches + self.position_embedding_size = int(self.num_patches**0.5) + self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim) + + @staticmethod + def resize_positional_embeddings( + positional_embeddings: torch.Tensor, + spatial_shapes: torch.LongTensor, + max_length: int, + ) -> torch.Tensor: + """ + Resize positional embeddings to image-specific size and pad to a fixed size. + + Args: + positional_embeddings (`torch.Tensor`): + Position embeddings of shape (height, width, embed_dim) + spatial_shapes (`torch.LongTensor`): + Spatial shapes of shape (batch_size, 2) to resize the positional embeddings to + max_length (`int`): + Maximum length of the positional embeddings to pad resized positional embeddings to + + Returns: + `torch.Tensor`: Embeddings of shape (batch_size, max_length, embed_dim) + """ + batch_size = spatial_shapes.shape[0] + embed_dim = positional_embeddings.shape[-1] + source_dtype = positional_embeddings.dtype + + resulted_positional_embeddings = torch.empty( + (batch_size, max_length, embed_dim), + device=positional_embeddings.device, + dtype=source_dtype, + ) + + # (height, width, embed_dim) -> (1, embed_dim, height, width) for interpolation + positional_embeddings = positional_embeddings.permute(2, 0, 1).unsqueeze(0) + + # Upcast to float32 on CPU because antialias is not supported for bfloat16/float16 on CPU + if positional_embeddings.device.type == "cpu": + positional_embeddings = positional_embeddings.to(torch.float32) + + for i in range(batch_size): + # (1, dim, height, width) -> (1, dim, target_height, target_width) + height, width = spatial_shapes[i].tolist() # will be itemized in F.interpolate either way + torch_compilable_check((width > 0), "Width of resized positional embeddings must be positive.") + torch_compilable_check((height > 0), "Height of resized positional embeddings must be positive.") + torch_compilable_check((height * width) <= max_length, "Resized positional embeddings exceed max_length.") + resized_embeddings = F.interpolate( + positional_embeddings, + size=(height, width), + mode="bilinear", + align_corners=False, + antialias=True, + ) + + # (1, dim, target_height, target_width) -> (target_height * target_width, dim) + resized_embeddings = resized_embeddings.reshape(embed_dim, height * width).transpose(0, 1) + + # Cast to original dtype + resized_embeddings = resized_embeddings.to(source_dtype) + + resulted_positional_embeddings[i, : height * width] = resized_embeddings + resulted_positional_embeddings[i, height * width :] = resized_embeddings[0] + + return resulted_positional_embeddings + + def forward(self, pixel_values: torch.FloatTensor, spatial_shapes: torch.LongTensor) -> torch.Tensor: + """ + Args: + pixel_values (`torch.FloatTensor`): + Pixel values of shape (batch_size, max_num_patches, num_channels * patch_size * patch_size) + spatial_shapes (`list[tuple[int, int]]`): + Spatial shapes of shape (batch_size, 2) to resize the positional embeddings to + """ + + # Apply patch embeddings to already patchified pixel values + target_dtype = self.patch_embedding.weight.dtype + patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) + + # Get positional resized and padded positional embeddings + positional_embeddings = self.position_embedding.weight.reshape( + self.position_embedding_size, self.position_embedding_size, -1 + ) + resized_positional_embeddings = self.resize_positional_embeddings( + positional_embeddings, spatial_shapes, max_length=pixel_values.shape[1] + ) + + # Add positional embeddings to patch embeddings + embeddings = patch_embeds + resized_positional_embeddings + return embeddings + + +class Siglip2PreTrainedModel(SiglipPreTrainedModel): + # nn.MultiHeadAttention mask doesn't allow for non 4d mask + _supports_flex_attn = False + _supports_flash_attn = False + + +class Siglip2VisionModel(SiglipVisionModel): + def __init__(self, config: Siglip2VisionConfig): + super().__init__(config) + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + @auto_docstring + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_attention_mask: torch.Tensor, + spatial_shapes: torch.LongTensor, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPooling: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): + Tensor containing the spatial dimensions (height, width) of the input images. + + Examples: + + ```python + >>> import httpx + >>> from io import BytesIO + >>> from PIL import Image + >>> from transformers import AutoProcessor, Siglip2VisionModel + + >>> model = Siglip2VisionModel.from_pretrained("google/siglip2-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + >>> last_hidden_state = outputs.last_hidden_state + >>> pooled_output = outputs.pooler_output # pooled features + ``` + """ + hidden_states = self.embeddings(pixel_values, spatial_shapes) + + encoder_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=pixel_attention_mask, + ) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=encoder_attention_mask, + **kwargs, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + pooler_output = self.head(last_hidden_state, pixel_attention_mask) if self.use_head else None + + return BaseModelOutputWithPooling( + last_hidden_state=last_hidden_state, + pooler_output=pooler_output, + ) + + +class Siglip2TextModel(SiglipTextModel): + pass + + +class Siglip2MultiheadAttentionPoolingHead(SiglipMultiheadAttentionPoolingHead): + def __init__(self, config: Siglip2VisionConfig): + super().__init__(config) + + self.config = config + self.num_heads = config.num_attention_heads + + def forward(self, hidden_state: torch.Tensor, attention_mask: torch.Tensor | None = None) -> torch.Tensor: + batch_size = hidden_state.shape[0] + probe = self.probe.repeat(batch_size, 1, 1) + + if attention_mask is not None: + target_len, source_len = probe.shape[1], hidden_state.shape[1] + attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=probe, + attention_mask=attention_mask, + encoder_hidden_states=hidden_state, + ) + if attention_mask is not None: + attention_mask = attention_mask.repeat(1, self.num_heads, target_len, 1) + attention_mask = attention_mask.reshape(-1, target_len, source_len) + + # `nn.MultiheadAttention` cannot handle boolean masks (which SDPA can) + if attention_mask.dtype == torch.bool: + attention_mask = torch.where( + attention_mask, + torch.tensor(0.0, device=attention_mask.device, dtype=probe.dtype), + torch.finfo(probe.dtype).min, + ) + + hidden_state = self.attention(probe, hidden_state, hidden_state, attn_mask=attention_mask)[0] + + residual = hidden_state + hidden_state = self.layernorm(hidden_state) + hidden_state = residual + self.mlp(hidden_state) + + return hidden_state[:, 0] + + +class Siglip2Model(SiglipModel): + # Update: add `spatial_shapes` and `pixel_attention_mask` + @can_return_tuple + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor | None = None, + pixel_attention_mask: torch.Tensor | None = None, + spatial_shapes: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): + Tensor containing the spatial dimensions (height, width) of the input images. + + Examples: + + ```python + >>> import torch + >>> from transformers import AutoProcessor, AutoModel + >>> from transformers.image_utils import load_image + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = load_image(url) + + >>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224") + + >>> inputs = processor(images=image, return_tensors="pt") + + >>> with torch.no_grad(): + ... image_features = model.get_image_features(**inputs) + ``` + """ + return self.vision_model( + pixel_values=pixel_values, + pixel_attention_mask=pixel_attention_mask, + spatial_shapes=spatial_shapes, + **kwargs, + ) + + # Update: add `spatial_shapes` and `pixel_attention_mask` + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_attention_mask: torch.Tensor | None = None, + spatial_shapes: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + return_loss: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> Siglip2Output: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): + Tensor containing the spatial dimensions (height, width) of the input images. + return_loss (`bool`, *optional*): + Whether or not to return the contrastive loss. + + Examples: + + ```python + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + >>> from transformers import AutoProcessor, AutoModel + >>> import torch + + >>> model = AutoModel.from_pretrained("google/siglip2-base-patch16-224") + >>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224") + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"] + >>> # important: we pass `padding=max_length` since the model was trained with this + >>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") + + >>> with torch.no_grad(): + ... outputs = model(**inputs) + + >>> logits_per_image = outputs.logits_per_image + >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities + >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") + 31.9% that image 0 is 'a photo of 2 cats' + ``` + """ + vision_outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values=pixel_values, + pixel_attention_mask=pixel_attention_mask, + spatial_shapes=spatial_shapes, + **kwargs, + ) + + text_outputs: BaseModelOutputWithPooling = self.text_model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + **kwargs, + ) + + image_embeds = vision_outputs.pooler_output + text_embeds = text_outputs.pooler_output + + # normalized features + image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) + text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) + + # cosine similarity as logits + logits_per_text = torch.matmul(text_embeds, image_embeds.t().to(text_embeds.device)) + + logit_scale, logit_bias = self.logit_scale.to(text_embeds.device), self.logit_bias.to(text_embeds.device) + logits_per_text = logits_per_text * logit_scale.exp() + logit_bias + + logits_per_image = logits_per_text.t() + + loss = None + if return_loss: + # Adapted from https://github.com/google-research/big_vision/blob/01edb81a4716f93a48be43b3a4af14e29cdb3a7f/big_vision/trainers/proj/image_text/siglip2.py#L287 + eye = torch.eye(logits_per_text.size(0), device=logits_per_text.device) + m1_diag1 = -torch.ones_like(logits_per_text) + 2 * eye + loglik = torch.nn.functional.logsigmoid(m1_diag1 * logits_per_text) + nll = -torch.sum(loglik, dim=-1) + loss = nll.mean() + + return Siglip2Output( + loss=loss, + logits_per_image=logits_per_image, + logits_per_text=logits_per_text, + text_embeds=text_embeds, + image_embeds=image_embeds, + text_model_output=text_outputs, + vision_model_output=vision_outputs, + ) + + +class Siglip2ForImageClassification(SiglipForImageClassification): + # Update: add `spatial_shapes` and `pixel_attention_mask` + @can_return_tuple + @auto_docstring + def forward( + self, + pixel_values: torch.Tensor | None = None, + pixel_attention_mask: torch.Tensor | None = None, + spatial_shapes: torch.LongTensor | None = None, + labels: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> ImageClassifierOutput: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): + Tensor containing the spatial dimensions (height, width) of the input images. + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the image classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, Siglip2ForImageClassification + >>> import torch + >>> from PIL import Image + >>> import httpx + >>> from io import BytesIO + + >>> torch.manual_seed(3) # doctest: +IGNORE_RESULT + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> with httpx.stream("GET", url) as response: + ... image = Image.open(BytesIO(response.read())) + + >>> # note: we are loading a `Siglip2Model` from the hub here, + >>> # so the head will be randomly initialized, hence the predictions will be random if seed is not set above. + >>> image_processor = AutoImageProcessor.from_pretrained("google/siglip2-base-patch16-224") + >>> model = Siglip2ForImageClassification.from_pretrained("google/siglip2-base-patch16-224") + + >>> inputs = image_processor(images=image, return_tensors="pt") + >>> outputs = model(**inputs) + >>> logits = outputs.logits + >>> # model predicts one of the two classes + >>> predicted_class_idx = logits.argmax(-1).item() + >>> print("Predicted class:", model.config.id2label[predicted_class_idx]) + Predicted class: LABEL_1 + ``` + """ + outputs: BaseModelOutputWithPooling = self.vision_model( + pixel_values, + pixel_attention_mask=pixel_attention_mask, + spatial_shapes=spatial_shapes, + **kwargs, + ) + + sequence_output = outputs.last_hidden_state + + # average pool the patch tokens + if pixel_attention_mask is not None: + pool_mask = pixel_attention_mask[..., None].to(sequence_output.device) + sequence_output = torch.sum(sequence_output * pool_mask, dim=1) / torch.sum(pool_mask, dim=1) + else: + sequence_output = torch.mean(sequence_output, dim=1) + + # apply classifier + logits = self.classifier(sequence_output) + + loss = None + if labels is not None: + loss = self.loss_function(labels, logits, self.config) + + return ImageClassifierOutput( + loss=loss, + logits=logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +__all__ = [ + "Siglip2Config", + "Siglip2TextConfig", + "Siglip2VisionConfig", + "Siglip2Model", + "Siglip2PreTrainedModel", + "Siglip2TextModel", + "Siglip2VisionModel", + "Siglip2ForImageClassification", + "Siglip2Tokenizer", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/processing_siglip2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/processing_siglip2.py new file mode 100644 index 0000000000000000000000000000000000000000..2315eef2d016d7b233f032c341b0f6e6d80ff691 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/processing_siglip2.py @@ -0,0 +1,44 @@ +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Image/Text processor class for SigLIP2. +""" + +from ...processing_utils import ProcessingKwargs, ProcessorMixin +from ...utils import auto_docstring + + +class Siglip2ProcessorKwargs(ProcessingKwargs, total=False): + _defaults = { + "text_kwargs": { + "padding": "max_length", + "truncation": True, + "max_length": 64, + }, + "images_kwargs": { + "max_num_patches": 256, + "patch_size": 16, + }, + } + + +@auto_docstring +class Siglip2Processor(ProcessorMixin): + valid_processor_kwargs = Siglip2ProcessorKwargs + + def __init__(self, image_processor, tokenizer): + super().__init__(image_processor, tokenizer) + + +__all__ = ["Siglip2Processor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/tokenization_siglip2.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/tokenization_siglip2.py new file mode 100644 index 0000000000000000000000000000000000000000..0a68d80965d861755ab6de4c321796a5ec18f87d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/siglip2/tokenization_siglip2.py @@ -0,0 +1,99 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/siglip2/modular_siglip2.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_siglip2.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers +from tokenizers.models import BPE + +from ...tokenization_utils_tokenizers import TokenizersBackend + + +VOCAB_FILES_NAMES = {"tokenizer_file": "tokenizer.json"} + + +class Siglip2Tokenizer(TokenizersBackend): + """ + Gemma tokenizer + SigLIP2 training default: lowercase normalization. + """ + + vocab_files_names = VOCAB_FILES_NAMES + padding_side = "left" + model_input_names = ["input_ids", "attention_mask"] + model = BPE + + def __init__( + self, + vocab: str | dict[str, int] | None = None, + merges: str | list[str] | None = None, + unk_token: str = "", + bos_token: str = "", + eos_token: str = "", + pad_token: str = "", + mask_token: str = "", + **kwargs, + ): + if vocab is None: + vocab = { + str(pad_token): 0, + str(eos_token): 1, + str(bos_token): 2, + str(unk_token): 3, + str(mask_token): 4, + } + self._vocab = vocab + self._merges = merges or [] + + self._tokenizer = Tokenizer( + BPE( + vocab=self._vocab, + merges=self._merges, + fuse_unk=True, + unk_token=str(unk_token), + dropout=None, + byte_fallback=True, + ) + ) + self._tokenizer.pre_tokenizer = pre_tokenizers.Split( + pattern=" ", behavior="merged_with_previous", invert=False + ) + + self._tokenizer.decoder = decoders.Sequence( + [decoders.Replace("▁", " "), decoders.ByteFallback(), decoders.Fuse()] + ) + self._tokenizer.normalizer = normalizers.Replace(" ", "▁") + super().__init__( + unk_token=unk_token, + bos_token=bos_token, + eos_token=eos_token, + pad_token=pad_token, + mask_token=mask_token, + **kwargs, + ) + + # Persist for save/load + push_to_hub dynamic tokenizer test + if hasattr(self, "init_kwargs") and isinstance(self.init_kwargs, dict): + self.init_kwargs.setdefault("tokenizer_class", self.__class__.__name__) + + backend = getattr(self, "_tokenizer", None) + if backend is not None and backend.normalizer is not None: + backend.normalizer = normalizers.Sequence([normalizers.Lowercase(), backend.normalizer]) + + +__all__ = ["Siglip2Tokenizer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..96d521f4972f899685c4df0590c5cd61e53dba7d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_slanet import * + from .modeling_slanet import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/configuration_slanet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/configuration_slanet.py new file mode 100644 index 0000000000000000000000000000000000000000..74e27df7fbc2da8438f082e18db5e67c22ea2552 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/configuration_slanet.py @@ -0,0 +1,77 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/slanet/modular_slanet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_slanet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...backbone_utils import consolidate_backbone_kwargs_to_config +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring +from ..auto import AutoConfig + + +@auto_docstring(checkpoint="PaddlePaddle/SLANet_plus_safetensors") +@strict +class SLANetConfig(PreTrainedConfig): + r""" + post_conv_out_channels (`int`, *optional*, defaults to 96): + Number of output channels for the post-encoder convolution layer. + out_channels (`int`, *optional*, defaults to 50): + Vocabulary size for the table structure token prediction head, i.e., the number of distinct structure + tokens the model can predict. + hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the hidden states in the attention GRU cell and the structure/location prediction heads. + max_text_length (`int`, *optional*, defaults to 500): + Maximum number of autoregressive decoding steps (tokens) for the structure and location decoder. + csp_kernel_size (`int`, *optional*, defaults to 5): + The kernel size of the Cross Stage Partial (CSP) layer. + csp_num_blocks (`int`, *optional*, defaults to 1): + Number of blocks within the Cross Stage Partial (CSP) layer. + """ + + model_type = "slanet" + + sub_configs = {"backbone_config": AutoConfig} + post_conv_out_channels: int = 96 + out_channels: int = 50 + hidden_size: int = 256 + max_text_length: int = 500 + backbone_config: dict | PreTrainedConfig | None = None + + hidden_act: str = "hardswish" + csp_kernel_size: int = 5 + csp_num_blocks: int = 1 + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="pp_lcnet", + default_config_kwargs={ + "scale": 1, + "out_features": ["stage2", "stage3", "stage4", "stage5"], + "out_indices": [2, 3, 4, 5], + "divisor": 16, + }, + **kwargs, + ) + super().__post_init__(**kwargs) + + +__all__ = ["SLANetConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/modeling_slanet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/modeling_slanet.py new file mode 100644 index 0000000000000000000000000000000000000000..753db1a4f1989121e7ec43c32ecfc03d247859b9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/modeling_slanet.py @@ -0,0 +1,478 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/slanet/modular_slanet.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_slanet.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import Tensor + +from ... import initialization as init +from ...activations import ACT2CLS, ACT2FN +from ...backbone_utils import filter_output_hidden_states, load_backbone +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_slanet import SLANetConfig + + +class SLANetPreTrainedModel(PreTrainedModel): + config: SLANetConfig + base_model_prefix = "backbone" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + _keep_in_fp32_modules_strict = [] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + + # Initialize GRUCell (replicates PyTorch default reset_parameters) + if isinstance(module, nn.GRUCell): + std = 1.0 / math.sqrt(module.hidden_size) if module.hidden_size > 0 else 0 + init.uniform_(module.weight_ih, -std, std) + init.uniform_(module.weight_hh, -std, std) + if module.bias_ih is not None: + init.uniform_(module.bias_ih, -std, std) + if module.bias_hh is not None: + init.uniform_(module.bias_hh, -std, std) + + # Initialize SLAHead layers + if isinstance(module, SLANetSLAHead): + std = 1.0 / math.sqrt(self.config.hidden_size * 1.0) + # Initialize structure_generator and loc_generator layers + for generator in (module.structure_generator,): + for layer in generator.children(): + if isinstance(layer, nn.Linear): + init.uniform_(layer.weight, -std, std) + if layer.bias is not None: + init.uniform_(layer.bias, -std, std) + + +@auto_docstring +@dataclass +class SLANetForTableRecognitionOutput(BaseModelOutputWithNoAttention): + r""" + head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Hidden-states of the SLANetSLAHead at each prediction step, varies up to max `self.config.max_text_length` states (depending on early exits). + head_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Attentions of the SLANetSLAHead at each prediction step, varies up to max `self.config.max_text_length` attentions (depending on early exits). + """ + + head_hidden_states: torch.FloatTensor | None = None + head_attentions: torch.FloatTensor | None = None + + +class SLANetAttentionGRUCell(nn.Module): + def __init__(self, input_size, hidden_size, num_embeddings): + super().__init__() + + self.input_to_hidden = nn.Linear(input_size, hidden_size, bias=False) + self.hidden_to_hidden = nn.Linear(hidden_size, hidden_size) + self.score = nn.Linear(hidden_size, 1, bias=False) + + self.rnn = nn.GRUCell(input_size + num_embeddings, hidden_size) + + def forward( + self, + prev_hidden: torch.FloatTensor, + batch_hidden: torch.FloatTensor, + char_onehots: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ): + batch_hidden_proj = self.input_to_hidden(batch_hidden) + prev_hidden_proj = self.hidden_to_hidden(prev_hidden).unsqueeze(1) + + attention_scores = batch_hidden_proj + prev_hidden_proj + attention_scores = torch.tanh(attention_scores) + attention_scores = self.score(attention_scores) + + attn_weights = F.softmax(attention_scores, dim=1, dtype=torch.float32).to(attention_scores.dtype) + attn_weights = attn_weights.transpose(1, 2) + context = torch.matmul(attn_weights, batch_hidden).squeeze(1) + concat_context = torch.cat([context, char_onehots], 1) + hidden_states = self.rnn(concat_context, prev_hidden) + + return hidden_states, attn_weights + + +class SLANetMLP(nn.Module): + def __init__(self, hidden_size, out_channels, activation=None): + super().__init__() + self.fc1 = nn.Linear(hidden_size, hidden_size) + self.fc2 = nn.Linear(hidden_size, out_channels) + self.act_fn = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_states): + hidden_states = self.fc1(hidden_states) + hidden_states = self.fc2(hidden_states) + hidden_states = self.act_fn(hidden_states) + return hidden_states + + +class SLANetSLAHead(SLANetPreTrainedModel): + _can_record_outputs = { + "attentions": SLANetAttentionGRUCell, + } + + def __init__( + self, + config: dict | None = None, + **kwargs, + ): + super().__init__(config) + + self.structure_attention_cell = SLANetAttentionGRUCell( + config.post_conv_out_channels, config.hidden_size, config.out_channels + ) + self.structure_generator = SLANetMLP(config.hidden_size, config.out_channels) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @filter_output_hidden_states + def forward( + self, + hidden_states: torch.FloatTensor, + targets: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ): + features = torch.zeros( + (hidden_states.shape[0], self.config.hidden_size), dtype=torch.float32, device=hidden_states.device + ) + predicted_chars = torch.zeros(size=[hidden_states.shape[0]], dtype=torch.long, device=hidden_states.device) + + structure_preds_list = [] + structure_ids_list = [] + for _ in range(self.config.max_text_length + 1): + embedding_feature = F.one_hot(predicted_chars, self.config.out_channels).float() + features, _ = self.structure_attention_cell(features, hidden_states.float(), embedding_feature) + structure_step = self.structure_generator(features) + predicted_chars = structure_step.argmax(dim=1) + + structure_preds_list.append(structure_step) + structure_ids_list.append(predicted_chars) + if torch.stack(structure_ids_list, dim=1).eq(self.config.out_channels - 1).any(-1).all(): + break + structure_preds = F.softmax(torch.stack(structure_preds_list, dim=1), dim=-1, dtype=torch.float32).to( + hidden_states.dtype + ) + + return BaseModelOutput(last_hidden_state=structure_preds, hidden_states=structure_preds_list) + + +class SLANetConvLayer(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + stride: int = 1, + activation: str = "hardswish", + groups: int = 1, + ): + super().__init__() + self.convolution = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + bias=False, + groups=groups, + ) + self.normalization = nn.BatchNorm2d(out_channels) + self.activation = ACT2FN[activation] if activation is not None else nn.Identity() + + def forward(self, input: Tensor) -> Tensor: + hidden_state = self.convolution(input) + hidden_state = self.normalization(hidden_state) + hidden_state = self.activation(hidden_state) + return hidden_state + + +class SLANetDepthwiseSeparableConvLayer(GradientCheckpointingLayer): + """ + Depthwise Separable Convolution Layer: Depthwise Conv -> Pointwise Conv + Core component of lightweight models (e.g., MobileNet, PP-LCNet) that significantly reduces + the number of parameters and computational cost. + """ + + def __init__( + self, + in_channels, + out_channels, + stride, + kernel_size, + config, + ): + super().__init__() + self.depthwise_convolution = SLANetConvLayer( + in_channels=in_channels, + out_channels=in_channels, + kernel_size=kernel_size, + stride=stride, + groups=in_channels, + activation=config.hidden_act, + ) + self.squeeze_excitation_module = nn.Identity() + self.pointwise_convolution = SLANetConvLayer( + in_channels=in_channels, + kernel_size=1, + out_channels=out_channels, + stride=1, + activation=config.hidden_act, + ) + + def forward(self, hidden_state): + hidden_state = self.depthwise_convolution(hidden_state) + hidden_state = self.squeeze_excitation_module(hidden_state) + hidden_state = self.pointwise_convolution(hidden_state) + + return hidden_state + + +class SLANetBottleneck(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + activation, + config, + ): + super().__init__() + self.conv1 = SLANetConvLayer( + in_channels=in_channels, out_channels=out_channels, kernel_size=1, activation=activation + ) + self.conv2 = SLANetDepthwiseSeparableConvLayer( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=1, + config=config, + ) + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + hidden_states = self.conv1(hidden_states) + hidden_states = self.conv2(hidden_states) + + return hidden_states + + +class SLANetCSPLayer(nn.Module): + """ + Cross Stage Partial (CSP) network layer. Similar in structure to DFineCSPRepLayer, but with a different forward computation. + """ + + def __init__( + self, + config, + in_channels, + out_channels, + kernel_size=3, + expansion=0.5, + num_blocks=1, + activation="hardswish", + ): + super().__init__() + hidden_channels = int(out_channels * expansion) + self.conv1 = SLANetConvLayer(in_channels, hidden_channels, 1, activation=activation) + self.conv2 = SLANetConvLayer(in_channels, hidden_channels, 1, activation=activation) + self.conv3 = SLANetConvLayer(2 * hidden_channels, out_channels, 1, activation=activation) + self.bottlenecks = nn.ModuleList( + [ + SLANetBottleneck(hidden_channels, hidden_channels, kernel_size, activation, config) + for _ in range(num_blocks) + ] + ) + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + residual = self.conv1(hidden_states) + + hidden_states = self.conv2(hidden_states) + for bottleneck in self.bottlenecks: + hidden_states = bottleneck(hidden_states) + + hidden_states = torch.cat((hidden_states, residual), dim=1) + hidden_states = self.conv3(hidden_states) + + return hidden_states + + +class SLANetCSPPAN(nn.Module): + """ + CSP-PAN: Path Aggregation Network with CSP layers + """ + + def __init__( + self, + config, + in_channel_list, + ): + super().__init__() + out_channels = config.post_conv_out_channels + activation = config.hidden_act + kernel_size = config.csp_kernel_size + csp_num_blocks = config.csp_num_blocks + + self.channel_projector = nn.ModuleList( + [ + SLANetConvLayer( + in_channels=in_channel_list[i], out_channels=out_channels, kernel_size=1, activation=activation + ) + for i in range(len(in_channel_list)) + ] + ) + + # build top-down blocks + self.upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.top_down_blocks = nn.ModuleList( + [ + SLANetCSPLayer( + config, + out_channels * 2, + out_channels, + kernel_size=kernel_size, + num_blocks=csp_num_blocks, + activation=activation, + ) + for _ in range(len(in_channel_list) - 1, 0, -1) + ] + ) + + # build bottom-up blocks + self.downsamples = nn.ModuleList( + [ + SLANetDepthwiseSeparableConvLayer( + out_channels, + out_channels, + kernel_size=kernel_size, + stride=2, + config=config, + ) + for _ in range(len(in_channel_list) - 1) + ] + ) + self.bottom_up_blocks = nn.ModuleList( + [ + SLANetCSPLayer( + config, + out_channels * 2, + out_channels, + kernel_size=kernel_size, + num_blocks=csp_num_blocks, + activation=activation, + ) + for _ in range(len(in_channel_list) - 1) + ] + ) + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + projected_features = [] + for idx in range(len(self.channel_projector)): + projected_features.append(self.channel_projector[idx](hidden_states[idx])) + + top_down_features = [projected_features[-1]] + for top_down_block, low_level_feature in zip(self.top_down_blocks, reversed(projected_features[:-1])): + high_level_feature = top_down_features[-1] + upsampled_feature = F.interpolate( + high_level_feature, + size=low_level_feature.shape[-2:], + mode="nearest", + ) + fused_feature = top_down_block(torch.cat([upsampled_feature, low_level_feature], dim=1)) + top_down_features.append(fused_feature) + + pyramid_features = list(reversed(top_down_features)) + output_feature = pyramid_features[0] + for downsample_layer, bottom_up_block, high_level_feature in zip( + self.downsamples, self.bottom_up_blocks, pyramid_features[1:] + ): + downsampled_feature = downsample_layer(output_feature) + output_feature = bottom_up_block(torch.cat([downsampled_feature, high_level_feature], dim=1)) + + hidden_states = output_feature.flatten(2).transpose(1, 2) + return hidden_states + + +class SLANetBackbone(SLANetPreTrainedModel): + def __init__(self, config: SLANetConfig): + super().__init__(config) + self.vision_backbone = load_backbone(config) + self.post_csp_pan = SLANetCSPPAN(config, self.vision_backbone.num_features[2:]) + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, hidden_states: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.vision_backbone(hidden_states, **kwargs) + hidden_states = self.post_csp_pan(outputs.feature_maps) + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_states, + hidden_states=outputs.hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + SLANet Table Recognition model for table recognition tasks. Wraps the core SLANetPreTrainedModel + and returns outputs compatible with the Transformers table recognition API. + """ +) +class SLANetForTableRecognition(SLANetPreTrainedModel): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + def __init__(self, config: SLANetConfig): + super().__init__(config) + self.backbone = SLANetBackbone(config=config) + self.head = SLANetSLAHead(config=config) + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple[torch.FloatTensor] | SLANetForTableRecognitionOutput: + outputs = self.backbone(pixel_values, **kwargs) + head_outputs = self.head(outputs.last_hidden_state, **kwargs) + # Key difference: no attentions in its vision model + return SLANetForTableRecognitionOutput( + last_hidden_state=head_outputs.last_hidden_state, + hidden_states=outputs.hidden_states, + head_hidden_states=head_outputs.hidden_states, + head_attentions=head_outputs.attentions, + ) + + +__all__ = ["SLANetForTableRecognition", "SLANetPreTrainedModel", "SLANetSLAHead", "SLANetBackbone"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/modular_slanet.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/modular_slanet.py new file mode 100644 index 0000000000000000000000000000000000000000..2c10a49fa8224b3ef7c517cf81356c58c3b19378 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanet/modular_slanet.py @@ -0,0 +1,372 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F +from huggingface_hub.dataclasses import strict + +from ... import initialization as init +from ...backbone_utils import consolidate_backbone_kwargs_to_config, load_backbone +from ...configuration_utils import PreTrainedConfig +from ...modeling_outputs import BaseModelOutputWithNoAttention +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging +from ..auto import AutoConfig +from ..pp_lcnet.modeling_pp_lcnet import PPLCNetConvLayer, PPLCNetDepthwiseSeparableConvLayer +from ..slanext.configuration_slanext import SLANeXtConfig +from ..slanext.modeling_slanext import ( + SLANeXtForTableRecognition, + SLANeXtPreTrainedModel, + SLANeXtSLAHead, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/SLANet_plus_safetensors") +@strict +class SLANetConfig(SLANeXtConfig): + r""" + post_conv_out_channels (`int`, *optional*, defaults to 96): + Number of output channels for the post-encoder convolution layer. + out_channels (`int`, *optional*, defaults to 50): + Vocabulary size for the table structure token prediction head, i.e., the number of distinct structure + tokens the model can predict. + hidden_size (`int`, *optional*, defaults to 256): + Dimensionality of the hidden states in the attention GRU cell and the structure/location prediction heads. + max_text_length (`int`, *optional*, defaults to 500): + Maximum number of autoregressive decoding steps (tokens) for the structure and location decoder. + csp_kernel_size (`int`, *optional*, defaults to 5): + The kernel size of the Cross Stage Partial (CSP) layer. + csp_num_blocks (`int`, *optional*, defaults to 1): + Number of blocks within the Cross Stage Partial (CSP) layer. + """ + + sub_configs = {"backbone_config": AutoConfig} + + vision_config = AttributeError() + backbone_config: dict | PreTrainedConfig | None = None + + post_conv_in_channels = AttributeError() + post_conv_out_channels: int = 96 + hidden_size: int = 256 + + hidden_act: str = "hardswish" + csp_kernel_size: int = 5 + csp_num_blocks: int = 1 + + def __post_init__(self, **kwargs): + self.backbone_config, kwargs = consolidate_backbone_kwargs_to_config( + backbone_config=self.backbone_config, + default_config_type="pp_lcnet", + default_config_kwargs={ + "scale": 1, + "out_features": ["stage2", "stage3", "stage4", "stage5"], + "out_indices": [2, 3, 4, 5], + "divisor": 16, + }, + **kwargs, + ) + PreTrainedConfig.__post_init__(**kwargs) + + +class SLANetPreTrainedModel(SLANeXtPreTrainedModel): + _keep_in_fp32_modules_strict = [] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + PreTrainedModel._init_weights(module) + + # Initialize GRUCell (replicates PyTorch default reset_parameters) + if isinstance(module, nn.GRUCell): + std = 1.0 / math.sqrt(module.hidden_size) if module.hidden_size > 0 else 0 + init.uniform_(module.weight_ih, -std, std) + init.uniform_(module.weight_hh, -std, std) + if module.bias_ih is not None: + init.uniform_(module.bias_ih, -std, std) + if module.bias_hh is not None: + init.uniform_(module.bias_hh, -std, std) + + # Initialize SLAHead layers + if isinstance(module, SLANetSLAHead): + std = 1.0 / math.sqrt(self.config.hidden_size * 1.0) + # Initialize structure_generator and loc_generator layers + for generator in (module.structure_generator,): + for layer in generator.children(): + if isinstance(layer, nn.Linear): + init.uniform_(layer.weight, -std, std) + if layer.bias is not None: + init.uniform_(layer.bias, -std, std) + + +@auto_docstring +@dataclass +class SLANetForTableRecognitionOutput(BaseModelOutputWithNoAttention): + r""" + head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Hidden-states of the SLANetSLAHead at each prediction step, varies up to max `self.config.max_text_length` states (depending on early exits). + head_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Attentions of the SLANetSLAHead at each prediction step, varies up to max `self.config.max_text_length` attentions (depending on early exits). + """ + + head_hidden_states: torch.FloatTensor | None = None + head_attentions: torch.FloatTensor | None = None + + +class SLANetSLAHead(SLANeXtSLAHead): + pass + + +class SLANetConvLayer(PPLCNetConvLayer): + pass + + +class SLANetDepthwiseSeparableConvLayer(PPLCNetDepthwiseSeparableConvLayer): + """ + Depthwise Separable Convolution Layer: Depthwise Conv -> Pointwise Conv + Core component of lightweight models (e.g., MobileNet, PP-LCNet) that significantly reduces + the number of parameters and computational cost. + """ + + def __init__( + self, + in_channels, + out_channels, + stride, + kernel_size, + config, + ): + super().__init__() + self.squeeze_excitation_module = nn.Identity() + + +class SLANetBottleneck(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + activation, + config, + ): + super().__init__() + self.conv1 = SLANetConvLayer( + in_channels=in_channels, out_channels=out_channels, kernel_size=1, activation=activation + ) + self.conv2 = SLANetDepthwiseSeparableConvLayer( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=1, + config=config, + ) + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + hidden_states = self.conv1(hidden_states) + hidden_states = self.conv2(hidden_states) + + return hidden_states + + +class SLANetCSPLayer(nn.Module): + """ + Cross Stage Partial (CSP) network layer. Similar in structure to DFineCSPRepLayer, but with a different forward computation. + """ + + def __init__( + self, + config, + in_channels, + out_channels, + kernel_size=3, + expansion=0.5, + num_blocks=1, + activation="hardswish", + ): + super().__init__() + hidden_channels = int(out_channels * expansion) + self.conv1 = SLANetConvLayer(in_channels, hidden_channels, 1, activation=activation) + self.conv2 = SLANetConvLayer(in_channels, hidden_channels, 1, activation=activation) + self.conv3 = SLANetConvLayer(2 * hidden_channels, out_channels, 1, activation=activation) + self.bottlenecks = nn.ModuleList( + [ + SLANetBottleneck(hidden_channels, hidden_channels, kernel_size, activation, config) + for _ in range(num_blocks) + ] + ) + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + residual = self.conv1(hidden_states) + + hidden_states = self.conv2(hidden_states) + for bottleneck in self.bottlenecks: + hidden_states = bottleneck(hidden_states) + + hidden_states = torch.cat((hidden_states, residual), dim=1) + hidden_states = self.conv3(hidden_states) + + return hidden_states + + +class SLANetCSPPAN(nn.Module): + """ + CSP-PAN: Path Aggregation Network with CSP layers + """ + + def __init__( + self, + config, + in_channel_list, + ): + super().__init__() + out_channels = config.post_conv_out_channels + activation = config.hidden_act + kernel_size = config.csp_kernel_size + csp_num_blocks = config.csp_num_blocks + + self.channel_projector = nn.ModuleList( + [ + SLANetConvLayer( + in_channels=in_channel_list[i], out_channels=out_channels, kernel_size=1, activation=activation + ) + for i in range(len(in_channel_list)) + ] + ) + + # build top-down blocks + self.upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.top_down_blocks = nn.ModuleList( + [ + SLANetCSPLayer( + config, + out_channels * 2, + out_channels, + kernel_size=kernel_size, + num_blocks=csp_num_blocks, + activation=activation, + ) + for _ in range(len(in_channel_list) - 1, 0, -1) + ] + ) + + # build bottom-up blocks + self.downsamples = nn.ModuleList( + [ + SLANetDepthwiseSeparableConvLayer( + out_channels, + out_channels, + kernel_size=kernel_size, + stride=2, + config=config, + ) + for _ in range(len(in_channel_list) - 1) + ] + ) + self.bottom_up_blocks = nn.ModuleList( + [ + SLANetCSPLayer( + config, + out_channels * 2, + out_channels, + kernel_size=kernel_size, + num_blocks=csp_num_blocks, + activation=activation, + ) + for _ in range(len(in_channel_list) - 1) + ] + ) + + def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor: + projected_features = [] + for idx in range(len(self.channel_projector)): + projected_features.append(self.channel_projector[idx](hidden_states[idx])) + + top_down_features = [projected_features[-1]] + for top_down_block, low_level_feature in zip(self.top_down_blocks, reversed(projected_features[:-1])): + high_level_feature = top_down_features[-1] + upsampled_feature = F.interpolate( + high_level_feature, + size=low_level_feature.shape[-2:], + mode="nearest", + ) + fused_feature = top_down_block(torch.cat([upsampled_feature, low_level_feature], dim=1)) + top_down_features.append(fused_feature) + + pyramid_features = list(reversed(top_down_features)) + output_feature = pyramid_features[0] + for downsample_layer, bottom_up_block, high_level_feature in zip( + self.downsamples, self.bottom_up_blocks, pyramid_features[1:] + ): + downsampled_feature = downsample_layer(output_feature) + output_feature = bottom_up_block(torch.cat([downsampled_feature, high_level_feature], dim=1)) + + hidden_states = output_feature.flatten(2).transpose(1, 2) + return hidden_states + + +class SLANetBackbone(SLANetPreTrainedModel): + def __init__(self, config: SLANetConfig): + super().__init__(config) + self.vision_backbone = load_backbone(config) + self.post_csp_pan = SLANetCSPPAN(config, self.vision_backbone.num_features[2:]) + + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, hidden_states: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple[torch.FloatTensor] | BaseModelOutputWithNoAttention: + outputs = self.vision_backbone(hidden_states, **kwargs) + hidden_states = self.post_csp_pan(outputs.feature_maps) + return BaseModelOutputWithNoAttention( + last_hidden_state=hidden_states, + hidden_states=outputs.hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + SLANet Table Recognition model for table recognition tasks. Wraps the core SLANetPreTrainedModel + and returns outputs compatible with the Transformers table recognition API. + """ +) +class SLANetForTableRecognition(SLANeXtForTableRecognition): + _keys_to_ignore_on_load_missing = ["num_batches_tracked"] + + @can_return_tuple + @auto_docstring + def forward( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple[torch.FloatTensor] | SLANetForTableRecognitionOutput: + outputs = self.backbone(pixel_values, **kwargs) + head_outputs = self.head(outputs.last_hidden_state, **kwargs) + # Key difference: no attentions in its vision model + return SLANetForTableRecognitionOutput( + last_hidden_state=head_outputs.last_hidden_state, + hidden_states=outputs.hidden_states, + head_hidden_states=head_outputs.hidden_states, + head_attentions=head_outputs.attentions, + ) + + +__all__ = ["SLANetConfig", "SLANetForTableRecognition", "SLANetPreTrainedModel", "SLANetSLAHead", "SLANetBackbone"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fcd21c1731d44ad05207078647116c7ee86e8502 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/__init__.py @@ -0,0 +1,29 @@ +# Copyright 2026 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_slanext import * + from .image_processing_slanext import * + from .modeling_slanext import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/configuration_slanext.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/configuration_slanext.py new file mode 100644 index 0000000000000000000000000000000000000000..aac603aa4a01583a9350cb82bfe7fac1ce6b2f80 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/configuration_slanext.py @@ -0,0 +1,103 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/slanext/modular_slanext.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_slanext.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="PaddlePaddle/SLANeXt_wired_safetensors") +@strict +class SLANeXtVisionConfig(PreTrainedConfig): + r""" + output_channels (`int`, *optional*, defaults to 256): + Dimensionality of the output channels in the Patch Encoder. + use_abs_pos (`bool`, *optional*, defaults to `True`): + Whether to use absolute position embedding. + use_rel_pos (`bool`, *optional*, defaults to `True`): + Whether to use relative position embedding. + window_size (`int`, *optional*, defaults to 14): + Window size for relative position. + global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`): + The indexes of the global attention layers. + mlp_dim (`int`, *optional*, defaults to 3072): + The dimensionality of the MLP layer in the Transformer encoder. + """ + + base_config_key = "vision_config" + hidden_size: int = 768 + output_channels: int = 256 + num_hidden_layers: int = 12 + num_attention_heads: int = 12 + num_channels: int = 3 + image_size: int = 512 + patch_size: int | list[int] | tuple[int, int] = 16 + hidden_act: str = "gelu" + layer_norm_eps: float = 1e-06 + attention_dropout: float | int = 0.0 + initializer_range: float = 1e-10 + qkv_bias: bool = True + use_abs_pos: bool = True + use_rel_pos: bool = True + window_size: int = 14 + global_attn_indexes: list[int] | tuple[int, ...] = (2, 5, 8, 11) + mlp_dim: int = 3072 + + +@auto_docstring(checkpoint="PaddlePaddle/SLANeXt_wired_safetensors") +@strict +class SLANeXtConfig(PreTrainedConfig): + r""" + vision_config (`dict` or [`SLANeXtVisionConfig`], *optional*): + Configuration for the vision encoder. If `None`, a default [`SLANeXtVisionConfig`] is used. + post_conv_in_channels (`int`, *optional*, defaults to 256): + Number of input channels for the post-encoder convolution layer. + post_conv_out_channels (`int`, *optional*, defaults to 512): + Number of output channels for the post-encoder convolution layer. + out_channels (`int`, *optional*, defaults to 50): + Vocabulary size for the table structure token prediction head, i.e., the number of distinct structure + tokens the model can predict. + hidden_size (`int`, *optional*, defaults to 512): + Dimensionality of the hidden states in the attention GRU cell and the structure/location prediction heads. + max_text_length (`int`, *optional*, defaults to 500): + Maximum number of autoregressive decoding steps (tokens) for the structure and location decoder. + """ + + model_type = "slanext" + sub_configs = {"vision_config": SLANeXtVisionConfig} + + vision_config: dict | SLANeXtVisionConfig | None = None + post_conv_in_channels: int = 256 + post_conv_out_channels: int = 512 + out_channels: int = 50 + hidden_size: int = 512 + max_text_length: int = 500 + + def __post_init__(self, **kwargs): + if self.vision_config is None: + self.vision_config = SLANeXtVisionConfig() + elif isinstance(self.vision_config, dict): + self.vision_config = SLANeXtVisionConfig(**self.vision_config) + super().__post_init__(**kwargs) + + +__all__ = ["SLANeXtConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/image_processing_slanext.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/image_processing_slanext.py new file mode 100644 index 0000000000000000000000000000000000000000..f87aed72137f8871b1a0212bb42b6fef194454ef --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/image_processing_slanext.py @@ -0,0 +1,257 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/slanext/modular_slanext.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_slanext.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torchvision.transforms.v2.functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, SizeDict +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import auto_docstring, is_torchdynamo_compiling, logging +from ...utils.generic import TensorType +from ...utils.import_utils import requires + + +logger = logging.get_logger(__name__) + + +@auto_docstring +@requires(backends=("torch",)) +class SLANeXtImageProcessor(TorchvisionBackend): + resample = 2 # PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 512, "width": 512} + pad_size = {"height": 512, "width": 512} + do_convert_rgb = True + do_resize = True + do_rescale = True + do_normalize = True + do_pad = True + + def _resize( + self, + image: "torch.Tensor", + size: SizeDict, + ) -> "torch.Tensor": + batch_size, channels, height, width = image.shape + image = image.view(batch_size * channels, height, width) + + device = image.device + + scale = max(size.height, size.width) / max(height, width) + target_height = round(height * scale) + target_width = round(width * scale) + + target_col = torch.arange(target_width, dtype=torch.float32, device=device) + src_col = (target_col + 0.5) * (float(width) / float(target_width)) - 0.5 + src_col_floor = src_col.floor().to(torch.int32) + src_col_frac = src_col - src_col_floor.float() + # boundary handling + src_col_frac = torch.where(src_col_floor < 0, torch.zeros_like(src_col_frac), src_col_frac) + src_col_floor = torch.where(src_col_floor < 0, torch.zeros_like(src_col_floor), src_col_floor) + src_col_frac = torch.where(src_col_floor >= width - 1, torch.ones_like(src_col_frac), src_col_frac) + src_col_floor = torch.where( + src_col_floor >= width - 1, torch.full_like(src_col_floor, width - 2), src_col_floor + ) + # fixed-point weights + weight_right = (src_col_frac * 2048 + 0.5).floor().to(torch.int32) # round-to-nearest + weight_left = 2048 - weight_right # (target_w,) + # --- row coordinate tables --- + target_row = torch.arange(target_height, dtype=torch.float32, device=device) + src_row = (target_row + 0.5) * (float(height) / float(target_height)) - 0.5 + src_row_floor = src_row.floor().to(torch.int32) + src_row_frac = src_row - src_row_floor.float() + src_row_frac = torch.where(src_row_floor < 0, torch.zeros_like(src_row_frac), src_row_frac) + src_row_floor = torch.where(src_row_floor < 0, torch.zeros_like(src_row_floor), src_row_floor) + src_row_frac = torch.where(src_row_floor >= height - 1, torch.ones_like(src_row_frac), src_row_frac) + src_row_floor = torch.where( + src_row_floor >= height - 1, torch.full_like(src_row_floor, height - 2), src_row_floor + ) + weight_bottom = (src_row_frac * 2048 + 0.5).floor().to(torch.int32) + weight_top = 2048 - weight_bottom # (target_h,) + + image_uint8 = image.clamp(0, 255).to(torch.uint8) # (C, H, W) + image_int32 = image_uint8.to(torch.int32) # (C, H, W) + col_left = src_col_floor.long() # (target_w,) + col_right = (src_col_floor + 1).long() # (target_w,) safe: src_col_floor <= width-2 + row_top = src_row_floor.long() # (target_h,) + row_bottom = (src_row_floor + 1).long() # (target_h,) + # gather 4 neighbours: (C, target_h, target_w) + pixel_top_left = image_int32[:, row_top[:, None], col_left[None, :]] + pixel_top_right = image_int32[:, row_top[:, None], col_right[None, :]] + pixel_bottom_left = image_int32[:, row_bottom[:, None], col_left[None, :]] + pixel_bottom_right = image_int32[:, row_bottom[:, None], col_right[None, :]] + # fixed-point bilinear: weights broadcast over (C, target_h, target_w) + weight_bottom_3d = weight_bottom.view(1, target_height, 1) + weight_top_3d = weight_top.view(1, target_height, 1) + weight_right_3d = weight_right.view(1, 1, target_width) + weight_left_3d = weight_left.view(1, 1, target_width) + interp = weight_top_3d * ( + weight_left_3d * pixel_top_left + weight_right_3d * pixel_top_right + ) + weight_bottom_3d * (weight_left_3d * pixel_bottom_left + weight_right_3d * pixel_bottom_right) + interp = (interp + (1 << 21)) >> 22 + result = interp.clamp(0, 255).to(torch.uint8) # (B*C, target_h, target_w) + + return result.view(batch_size, channels, target_height, target_width).to(dtype=image.dtype) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + if resample is not None and not is_torchdynamo_compiling(): + logger.warning_once("Resampling is not supported in SLANeXt") + + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self._resize(image=stacked_images, size=size) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + def __init__(self, **kwargs: Unpack[ImagesKwargs]): + super().__init__(**kwargs) + self.init_decoder() + + def init_decoder(self): + """ + Initialize the decoder vocabulary for table structure recognition. + + Builds a character dictionary mapping HTML table structure tokens (e.g., ``, ``, ``, colspan/ + rowspan attributes) to integer indices. The dictionary includes special `"sos"` (start-of-sequence) and + `"eos"` (end-of-sequence) tokens. Merged `` tokens are used in place of standalone `` tokens + when applicable. + """ + dict_character = [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + ] + dict_character += [f' colspan="{i + 2}"' for i in range(19)] + dict_character += [f' rowspan="{i + 2}"' for i in range(19)] + + if "" not in dict_character: + dict_character.append("") + if "" in dict_character: + dict_character.remove("") + + dict_character = ["sos"] + dict_character + ["eos"] + self.dict = {char: i for i, char in enumerate(dict_character)} + self.character = dict_character + self.td_token = ["", ""] + self.bos_id = self.dict["sos"] + self.eos_id = self.dict["eos"] + + def post_process_table_recognition(self, outputs): + """ + Post-process the raw model outputs to decode the predicted table structure into an HTML token sequence. + + Converts the model's predicted probability distributions over the structure vocabulary into a sequence of + HTML tokens representing the table structure. The decoded tokens are wrapped with ``, ``, and + `` tags to form a complete HTML table structure. + + Args: + outputs ([`SLANeXtForTableRecognitionOutput`]): + Raw outputs from the SLANeXt model. The `last_hidden_state` field contains the predicted probability + distributions over the structure vocabulary at each decoding step, with shape + `(batch_size, max_text_length, num_classes)`. + + Returns: + `dict`: A dictionary containing: + - **structure** (`list[str]`): The predicted HTML table structure as a list of tokens, wrapped with + ``, ``, and `
` tags. + - **structure_score** (`float`): The mean confidence score across all predicted tokens. + """ + self.pred = outputs.last_hidden_state + structure_probs = self.pred[0:1] + ignored_tokens = [int(self.bos_id), int(self.eos_id)] + end_idx = int(self.eos_id) + + structure_idx = structure_probs.argmax(dim=2) + structure_probs = structure_probs.max(dim=2).values + + structure_str_list = [] + batch_size = structure_idx.shape[0] + for batch_index in range(batch_size): + structure_list = [] + score_list = [] + for position in range(structure_idx.shape[1]): + char_idx = int(structure_idx[batch_index, position]) + if position > 0 and char_idx == end_idx: + break + if char_idx in ignored_tokens: + continue + text = self.character[char_idx] + structure_list.append(text) + score_list.append(structure_probs[batch_index, position]) + structure_str_list.append(structure_list) + structure_score = torch.stack(score_list).mean().item() + + structure = ["", "", "
"] + structure_str_list[0] + ["
", "", ""] + return {"structure": structure, "structure_score": structure_score} + + +__all__ = ["SLANeXtImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/modeling_slanext.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/modeling_slanext.py new file mode 100644 index 0000000000000000000000000000000000000000..b767efbb8c2558534013a355cf2cfafcd83250bb --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/modeling_slanext.py @@ -0,0 +1,643 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/slanext/modular_slanext.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_slanext.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import collections +import math +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ... import initialization as init +from ...activations import ACT2CLS, ACT2FN +from ...backbone_utils import filter_output_hidden_states +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, ModelOutput +from ...modeling_utils import PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_slanext import SLANeXtConfig, SLANeXtVisionConfig + + +class SLANeXtVisionAttention(nn.Module): + """Multi-head Attention block with relative position embeddings.""" + + def __init__(self, config, window_size): + super().__init__() + input_size = ( + (config.image_size // config.patch_size, config.image_size // config.patch_size) + if window_size == 0 + else (window_size, window_size) + ) + + self.num_attention_heads = config.num_attention_heads + head_dim = config.hidden_size // config.num_attention_heads + self.scale = head_dim**-0.5 + self.dropout = config.attention_dropout + + self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias) + self.proj = nn.Linear(config.hidden_size, config.hidden_size) + + self.use_rel_pos = config.use_rel_pos + if self.use_rel_pos: + if input_size is None: + raise ValueError("Input size must be provided if using relative positional encoding.") + + # initialize relative positional embeddings + self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim)) + self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim)) + + def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor: + """ + Get relative positional embeddings according to the relative positions of + query and key sizes. + + Args: + q_size (int): + size of the query. + k_size (int): + size of key k. + rel_pos (`torch.Tensor`): + relative position embeddings (L, channel). + + Returns: + Extracted positional embeddings according to relative positions. + """ + max_rel_dist = int(2 * max(q_size, k_size) - 1) + # Interpolate rel pos. + rel_pos_resized = F.interpolate( + rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1), + size=max_rel_dist, + mode="linear", + ) + rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0) + + # Scale the coords with short length if shapes for q and k are different. + q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0) + k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0) + relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0) + + return rel_pos_resized[relative_coords.long()] + + def get_decomposed_rel_pos( + self, + query: torch.Tensor, + rel_pos_h: torch.Tensor, + rel_pos_w: torch.Tensor, + q_size: tuple[int, int], + k_size: tuple[int, int], + ) -> torch.Tensor: + """ + Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`. + https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py + + Args: + query (`torch.Tensor`): + query q in the attention layer with shape (batch_size, query_height * query_width, channel). + rel_pos_h (`torch.Tensor`): + relative position embeddings (Lh, channel) for height axis. + rel_pos_w (`torch.Tensor`): + relative position embeddings (Lw, channel) for width axis. + q_size (tuple): + spatial sequence size of query q with (query_height, query_width). + k_size (tuple): + spatial sequence size of key k with (key_height, key_width). + + Returns: + decomposed_rel_pos (`torch.Tensor`): + decomposed relative position embeddings. + """ + query_height, query_width = q_size + key_height, key_width = k_size + relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h) + relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w) + + batch_size, _, dim = query.shape + reshaped_query = query.reshape(batch_size, query_height, query_width, dim) + rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height) + rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width) + + decomposed_rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] + + return decomposed_rel_pos + + def forward(self, hidden_states: torch.Tensor, output_attentions=None) -> tuple[torch.Tensor, torch.Tensor]: + batch_size, height, width, _ = hidden_states.shape + # qkv with shape (3, batch_size, nHead, height * width, channel) + qkv = ( + self.qkv(hidden_states) + .reshape(batch_size, height * width, 3, self.num_attention_heads, -1) + .permute(2, 0, 3, 1, 4) + ) + # q, k, v with shape (batch_size * nHead, height * width, channel) + query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0) + + attn_weights = (query * self.scale) @ key.transpose(-2, -1) + + if self.use_rel_pos: + decomposed_rel_pos = self.get_decomposed_rel_pos( + query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width) + ) + decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights) + attn_weights = attn_weights + decomposed_rel_pos + + attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype) + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1) + attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1) + + attn_output = self.proj(attn_output) + return attn_output, attn_weights + + +class SLANeXtAttentionGRUCell(nn.Module): + def __init__(self, input_size, hidden_size, num_embeddings): + super().__init__() + + self.input_to_hidden = nn.Linear(input_size, hidden_size, bias=False) + self.hidden_to_hidden = nn.Linear(hidden_size, hidden_size) + self.score = nn.Linear(hidden_size, 1, bias=False) + + self.rnn = nn.GRUCell(input_size + num_embeddings, hidden_size) + + def forward( + self, + prev_hidden: torch.FloatTensor, + batch_hidden: torch.FloatTensor, + char_onehots: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ): + batch_hidden_proj = self.input_to_hidden(batch_hidden) + prev_hidden_proj = self.hidden_to_hidden(prev_hidden).unsqueeze(1) + + attention_scores = batch_hidden_proj + prev_hidden_proj + attention_scores = torch.tanh(attention_scores) + attention_scores = self.score(attention_scores) + + attn_weights = F.softmax(attention_scores, dim=1, dtype=torch.float32).to(attention_scores.dtype) + attn_weights = attn_weights.transpose(1, 2) + context = torch.matmul(attn_weights, batch_hidden).squeeze(1) + concat_context = torch.cat([context, char_onehots], 1) + hidden_states = self.rnn(concat_context, prev_hidden) + + return hidden_states, attn_weights + + +class SLANeXtMLP(nn.Module): + def __init__(self, hidden_size, out_channels, activation=None): + super().__init__() + self.fc1 = nn.Linear(hidden_size, hidden_size) + self.fc2 = nn.Linear(hidden_size, out_channels) + self.act_fn = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_states): + hidden_states = self.fc1(hidden_states) + hidden_states = self.fc2(hidden_states) + hidden_states = self.act_fn(hidden_states) + return hidden_states + + +class SLANeXtPreTrainedModel(PreTrainedModel): + config: SLANeXtConfig + base_model_prefix = "backbone" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + _keep_in_fp32_modules_strict = ["structure_attention_cell", "structure_generator"] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + + # Initialize positional embeddings to zero (SLANeXtVisionEncoder holds pos_embed) + if isinstance(module, SLANeXtVisionEncoder): + if module.pos_embed is not None: + init.constant_(module.pos_embed, 0.0) + + # Initialize relative positional embeddings to zero (SLANeXtVisionAttention holds rel_pos_h/w) + if isinstance(module, SLANeXtVisionAttention): + if module.use_rel_pos: + init.constant_(module.rel_pos_h, 0.0) + init.constant_(module.rel_pos_w, 0.0) + + # Initialize GRUCell (replicates PyTorch default reset_parameters) + if isinstance(module, nn.GRUCell): + std = 1.0 / math.sqrt(module.hidden_size) if module.hidden_size > 0 else 0 + init.uniform_(module.weight_ih, -std, std) + init.uniform_(module.weight_hh, -std, std) + if module.bias_ih is not None: + init.uniform_(module.bias_ih, -std, std) + if module.bias_hh is not None: + init.uniform_(module.bias_hh, -std, std) + + # Initialize SLAHead layers + if isinstance(module, SLANeXtSLAHead): + std = 1.0 / math.sqrt(self.config.hidden_size * 1.0) + # Initialize structure_generator and loc_generator layers + for generator in (module.structure_generator,): + for layer in generator.children(): + if isinstance(layer, nn.Linear): + init.uniform_(layer.weight, -std, std) + if layer.bias is not None: + init.uniform_(layer.bias, -std, std) + + +class SLANeXtMLPBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim) + self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size) + self.act = ACT2FN[config.hidden_act] + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.lin1(hidden_states) + hidden_states = self.act(hidden_states) + hidden_states = self.lin2(hidden_states) + return hidden_states + + +class SLANeXtVisionLayer(GradientCheckpointingLayer): + def __init__(self, config, window_size): + super().__init__() + self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.attn = SLANeXtVisionAttention(config, window_size) + self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.mlp = SLANeXtMLPBlock(config) + self.window_size = window_size + + def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> tuple[torch.Tensor, tuple[int, int]]: + """ + Args: + Partition into non-overlapping windows with padding if needed. + hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window + size. + + Returns: + windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel]. + (pad_height, pad_width): padded height and width before partition + """ + batch_size, height, width, channel = hidden_states.shape + + pad_h = (window_size - height % window_size) % window_size + pad_w = (window_size - width % window_size) % window_size + hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h)) + pad_height, pad_width = height + pad_h, width + pad_w + + hidden_states = hidden_states.reshape( + batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel + ) + windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel) + return windows, (pad_height, pad_width) + + def window_unpartition( + self, windows: torch.Tensor, window_size: int, padding_shape: tuple[int, int], original_shape: tuple[int, int] + ) -> torch.Tensor: + """ + Args: + Window unpartition into original sequences and removing padding. + hidden_states (tensor): + input tokens with [batch_size * num_windows, window_size, window_size, channel]. + window_size (int): + window size. + padding_shape (Tuple): + padded height and width (pad_height, pad_width). + original_shape (Tuple): original height and width (height, width) before padding. + + Returns: + hidden_states: unpartitioned sequences with [batch_size, height, width, channel]. + """ + pad_height, pad_width = padding_shape + height, width = original_shape + batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size) + hidden_states = windows.reshape( + batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1 + ) + hidden_states = ( + hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1) + ) + + hidden_states = hidden_states[:, :height, :width, :].contiguous() + return hidden_states + + def forward(self, hidden_states: torch.Tensor) -> tuple[torch.FloatTensor]: + residual = hidden_states + hidden_states = self.layer_norm1(hidden_states) + # Window partition + if self.window_size > 0: + height, width = hidden_states.shape[1], hidden_states.shape[2] + hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size) + + hidden_states, attn_weights = self.attn( + hidden_states=hidden_states, + ) + # Reverse window partition + if self.window_size > 0: + hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width)) + + hidden_states = residual + hidden_states + layernorm_output = self.layer_norm2(hidden_states) + hidden_states = hidden_states + self.mlp(layernorm_output) + return hidden_states + + +@auto_docstring( + custom_intro=""" + Base class for slanext vision model's outputs that also contains image embeddings obtained by applying the projection + layer to the pooler_output. + """ +) +@dataclass +class SLANeXtVisionEncoderOutput(ModelOutput): + r""" + image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): + The image embeddings obtained by applying the projection layer to the pooler_output. + """ + + image_embeds: torch.FloatTensor | None = None + last_hidden_state: torch.FloatTensor | None = None + hidden_states: tuple[torch.FloatTensor, ...] | None = None + attentions: tuple[torch.FloatTensor, ...] | None = None + + +class SLANeXtPatchEmbeddings(nn.Module): + """ + This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial + `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a + Transformer. + """ + + def __init__(self, config): + super().__init__() + image_size, patch_size = config.image_size, config.patch_size + num_channels, hidden_size = config.num_channels, config.hidden_size + image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size) + patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) + num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) + self.image_size = image_size + self.patch_size = patch_size + self.num_channels = num_channels + self.num_patches = num_patches + + self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size) + + def forward(self, pixel_values): + batch_size, num_channels, height, width = pixel_values.shape + if num_channels != self.num_channels: + raise ValueError( + "Make sure that the channel dimension of the pixel values match with the one set in the configuration." + ) + if height != self.image_size[0] or width != self.image_size[1]: + raise ValueError( + f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})." + ) + embeddings = self.projection(pixel_values).permute(0, 2, 3, 1) + return embeddings + + +class SLANeXtLayerNorm(nn.LayerNorm): + r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. + The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, + width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). + """ + + def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs): + super().__init__(normalized_shape, eps=eps, **kwargs) + if data_format not in ["channels_last", "channels_first"]: + raise NotImplementedError(f"Unsupported data format: {data_format}") + self.data_format = data_format + + def forward(self, features: torch.Tensor) -> torch.Tensor: + """ + Args: + features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels) + """ + if self.data_format == "channels_first": + features = features.permute(0, 2, 3, 1) + features = super().forward(features) + features = features.permute(0, 3, 1, 2) + else: + features = super().forward(features) + return features + + +class SLANeXtVisionNeck(nn.Module): + def __init__(self, config: SLANeXtVisionConfig): + super().__init__() + self.config = config + + self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False) + self.layer_norm1 = SLANeXtLayerNorm(config.output_channels, data_format="channels_first") + self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False) + self.layer_norm2 = SLANeXtLayerNorm(config.output_channels, data_format="channels_first") + + def forward(self, hidden_states): + hidden_states = hidden_states.permute(0, 3, 1, 2) + hidden_states = self.conv1(hidden_states) + hidden_states = self.layer_norm1(hidden_states) + + hidden_states = self.conv2(hidden_states) + hidden_states = self.layer_norm2(hidden_states) + return hidden_states + + +class SLANeXtVisionEncoder(SLANeXtPreTrainedModel): + _can_record_outputs = {"hidden_states": SLANeXtVisionLayer, "attentions": SLANeXtVisionAttention} + input_modalities = ("image",) + + def __init__(self, config: SLANeXtVisionConfig): + super().__init__(config) + self.config = config + self.image_size = config.image_size + self.patch_embed = SLANeXtPatchEmbeddings(config) + + self.pos_embed = None + if config.use_abs_pos: + # Initialize absolute positional embedding with pretrain image size. + self.pos_embed = nn.Parameter( + torch.zeros( + 1, + config.image_size // config.patch_size, + config.image_size // config.patch_size, + config.hidden_size, + ) + ) + + self.layers = nn.ModuleList() + for i in range(config.num_hidden_layers): + layer = SLANeXtVisionLayer( + config, + window_size=config.window_size if i not in config.global_attn_indexes else 0, + ) + self.layers.append(layer) + + self.neck = SLANeXtVisionNeck(config) + + self.gradient_checkpointing = False + self.post_init() + + def get_input_embeddings(self): + return self.patch_embed + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, pixel_values: torch.FloatTensor | None = None, **kwargs: Unpack[TransformersKwargs] + ) -> tuple | SLANeXtVisionEncoderOutput: + if pixel_values is None: + raise ValueError("You have to specify pixel_values") + + hidden_states = self.patch_embed(pixel_values) + if self.pos_embed is not None: + hidden_states = hidden_states + self.pos_embed + for layer_module in self.layers: + hidden_states = layer_module(hidden_states) + hidden_states = self.neck(hidden_states) + return SLANeXtVisionEncoderOutput( + last_hidden_state=hidden_states, + ) + + +class SLANeXtBackbone(SLANeXtPreTrainedModel): + def __init__( + self, + config: dict | None = None, + **kwargs, + ): + super().__init__(config) + self.vision_tower = SLANeXtVisionEncoder(config.vision_config) + self.post_conv = nn.Conv2d( + config.post_conv_in_channels, config.post_conv_out_channels, kernel_size=3, stride=2, padding=1, bias=False + ) + self.post_init() + + def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]): + vision_output = self.vision_tower(hidden_states, **kwargs) + hidden_states = self.post_conv(vision_output.last_hidden_state) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=vision_output.hidden_states, + attentions=vision_output.attentions, + ) + + +class SLANeXtSLAHead(SLANeXtPreTrainedModel): + _can_record_outputs = { + "attentions": SLANeXtAttentionGRUCell, + } + + def __init__( + self, + config: dict | None = None, + **kwargs, + ): + super().__init__(config) + + self.structure_attention_cell = SLANeXtAttentionGRUCell( + config.post_conv_out_channels, config.hidden_size, config.out_channels + ) + self.structure_generator = SLANeXtMLP(config.hidden_size, config.out_channels) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @filter_output_hidden_states + def forward( + self, + hidden_states: torch.FloatTensor, + targets: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ): + features = torch.zeros( + (hidden_states.shape[0], self.config.hidden_size), dtype=torch.float32, device=hidden_states.device + ) + predicted_chars = torch.zeros(size=[hidden_states.shape[0]], dtype=torch.long, device=hidden_states.device) + + structure_preds_list = [] + structure_ids_list = [] + for _ in range(self.config.max_text_length + 1): + embedding_feature = F.one_hot(predicted_chars, self.config.out_channels).float() + features, _ = self.structure_attention_cell(features, hidden_states.float(), embedding_feature) + structure_step = self.structure_generator(features) + predicted_chars = structure_step.argmax(dim=1) + + structure_preds_list.append(structure_step) + structure_ids_list.append(predicted_chars) + if torch.stack(structure_ids_list, dim=1).eq(self.config.out_channels - 1).any(-1).all(): + break + structure_preds = F.softmax(torch.stack(structure_preds_list, dim=1), dim=-1, dtype=torch.float32).to( + hidden_states.dtype + ) + + return BaseModelOutput(last_hidden_state=structure_preds, hidden_states=structure_preds_list) + + +@auto_docstring +@dataclass +class SLANeXtForTableRecognitionOutput(BaseModelOutput): + r""" + head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Hidden-states of the SLANeXtSLAHead at each prediction step, varies up to max `self.config.max_text_length` states (depending on early exits). + head_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Attentions of the SLANeXtSLAHead at each prediction step, varies up to max `self.config.max_text_length` attentions (depending on early exits). + """ + + head_hidden_states: torch.FloatTensor | None = None + head_attentions: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + SLANeXt Table Recognition model for table recognition tasks. Wraps the core SLANeXtPreTrainedModel + and returns outputs compatible with the Transformers table recognition API. + """ +) +class SLANeXtForTableRecognition(SLANeXtPreTrainedModel): + def __init__(self, config: SLANeXtConfig): + super().__init__(config) + self.backbone = SLANeXtBackbone(config=config) + self.head = SLANeXtSLAHead(config=config) + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple[torch.FloatTensor] | SLANeXtForTableRecognitionOutput: + backbone_outputs = self.backbone(pixel_values, **kwargs) + head_outputs = self.head(backbone_outputs.last_hidden_state, **kwargs) + return SLANeXtForTableRecognitionOutput( + last_hidden_state=head_outputs.last_hidden_state, + hidden_states=backbone_outputs.hidden_states, + attentions=backbone_outputs.attentions, + head_hidden_states=head_outputs.hidden_states, + head_attentions=head_outputs.attentions, + ) + + +__all__ = ["SLANeXtSLAHead", "SLANeXtBackbone", "SLANeXtForTableRecognition", "SLANeXtPreTrainedModel"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/modular_slanext.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/modular_slanext.py new file mode 100644 index 0000000000000000000000000000000000000000..a63acd1e362d19cecab15acec159897b0918aefa --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/slanext/modular_slanext.py @@ -0,0 +1,539 @@ +# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math +from dataclasses import dataclass + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision.transforms.v2.functional as tvF +from huggingface_hub.dataclasses import strict + +from ... import initialization as init +from ...activations import ACT2CLS +from ...backbone_utils import filter_output_hidden_states +from ...configuration_utils import PreTrainedConfig +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, SizeDict +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging +from ...utils.generic import TensorType, merge_with_config_defaults +from ...utils.import_utils import requires +from ...utils.output_capturing import capture_outputs +from ..got_ocr2.configuration_got_ocr2 import GotOcr2VisionConfig +from ..got_ocr2.modeling_got_ocr2 import ( + GotOcr2VisionAttention, + GotOcr2VisionEncoder, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="PaddlePaddle/SLANeXt_wired_safetensors") +@strict +class SLANeXtVisionConfig(GotOcr2VisionConfig): + image_size: int = 512 + + +class SLANeXtVisionAttention(GotOcr2VisionAttention): + pass + + +@auto_docstring(checkpoint="PaddlePaddle/SLANeXt_wired_safetensors") +@strict +class SLANeXtConfig(PreTrainedConfig): + r""" + vision_config (`dict` or [`SLANeXtVisionConfig`], *optional*): + Configuration for the vision encoder. If `None`, a default [`SLANeXtVisionConfig`] is used. + post_conv_in_channels (`int`, *optional*, defaults to 256): + Number of input channels for the post-encoder convolution layer. + post_conv_out_channels (`int`, *optional*, defaults to 512): + Number of output channels for the post-encoder convolution layer. + out_channels (`int`, *optional*, defaults to 50): + Vocabulary size for the table structure token prediction head, i.e., the number of distinct structure + tokens the model can predict. + hidden_size (`int`, *optional*, defaults to 512): + Dimensionality of the hidden states in the attention GRU cell and the structure/location prediction heads. + max_text_length (`int`, *optional*, defaults to 500): + Maximum number of autoregressive decoding steps (tokens) for the structure and location decoder. + """ + + model_type = "slanext" + sub_configs = {"vision_config": SLANeXtVisionConfig} + + vision_config: dict | SLANeXtVisionConfig | None = None + post_conv_in_channels: int = 256 + post_conv_out_channels: int = 512 + out_channels: int = 50 + hidden_size: int = 512 + max_text_length: int = 500 + + def __post_init__(self, **kwargs): + if self.vision_config is None: + self.vision_config = SLANeXtVisionConfig() + elif isinstance(self.vision_config, dict): + self.vision_config = SLANeXtVisionConfig(**self.vision_config) + super().__post_init__(**kwargs) + + +class SLANeXtAttentionGRUCell(nn.Module): + def __init__(self, input_size, hidden_size, num_embeddings): + super().__init__() + + self.input_to_hidden = nn.Linear(input_size, hidden_size, bias=False) + self.hidden_to_hidden = nn.Linear(hidden_size, hidden_size) + self.score = nn.Linear(hidden_size, 1, bias=False) + + self.rnn = nn.GRUCell(input_size + num_embeddings, hidden_size) + + def forward( + self, + prev_hidden: torch.FloatTensor, + batch_hidden: torch.FloatTensor, + char_onehots: torch.FloatTensor, + **kwargs: Unpack[TransformersKwargs], + ): + batch_hidden_proj = self.input_to_hidden(batch_hidden) + prev_hidden_proj = self.hidden_to_hidden(prev_hidden).unsqueeze(1) + + attention_scores = batch_hidden_proj + prev_hidden_proj + attention_scores = torch.tanh(attention_scores) + attention_scores = self.score(attention_scores) + + attn_weights = F.softmax(attention_scores, dim=1, dtype=torch.float32).to(attention_scores.dtype) + attn_weights = attn_weights.transpose(1, 2) + context = torch.matmul(attn_weights, batch_hidden).squeeze(1) + concat_context = torch.cat([context, char_onehots], 1) + hidden_states = self.rnn(concat_context, prev_hidden) + + return hidden_states, attn_weights + + +class SLANeXtMLP(nn.Module): + def __init__(self, hidden_size, out_channels, activation=None): + super().__init__() + self.fc1 = nn.Linear(hidden_size, hidden_size) + self.fc2 = nn.Linear(hidden_size, out_channels) + self.act_fn = nn.Identity() if activation is None else ACT2CLS[activation]() + + def forward(self, hidden_states): + hidden_states = self.fc1(hidden_states) + hidden_states = self.fc2(hidden_states) + hidden_states = self.act_fn(hidden_states) + return hidden_states + + +class SLANeXtPreTrainedModel(PreTrainedModel): + config: SLANeXtConfig + base_model_prefix = "backbone" + main_input_name = "pixel_values" + input_modalities = ("image",) + supports_gradient_checkpointing = True + _keep_in_fp32_modules_strict = ["structure_attention_cell", "structure_generator"] + + @torch.no_grad() + def _init_weights(self, module): + """Initialize the weights""" + super()._init_weights(module) + + # Initialize positional embeddings to zero (SLANeXtVisionEncoder holds pos_embed) + if isinstance(module, SLANeXtVisionEncoder): + if module.pos_embed is not None: + init.constant_(module.pos_embed, 0.0) + + # Initialize relative positional embeddings to zero (SLANeXtVisionAttention holds rel_pos_h/w) + if isinstance(module, SLANeXtVisionAttention): + if module.use_rel_pos: + init.constant_(module.rel_pos_h, 0.0) + init.constant_(module.rel_pos_w, 0.0) + + # Initialize GRUCell (replicates PyTorch default reset_parameters) + if isinstance(module, nn.GRUCell): + std = 1.0 / math.sqrt(module.hidden_size) if module.hidden_size > 0 else 0 + init.uniform_(module.weight_ih, -std, std) + init.uniform_(module.weight_hh, -std, std) + if module.bias_ih is not None: + init.uniform_(module.bias_ih, -std, std) + if module.bias_hh is not None: + init.uniform_(module.bias_hh, -std, std) + + # Initialize SLAHead layers + if isinstance(module, SLANeXtSLAHead): + std = 1.0 / math.sqrt(self.config.hidden_size * 1.0) + # Initialize structure_generator and loc_generator layers + for generator in (module.structure_generator,): + for layer in generator.children(): + if isinstance(layer, nn.Linear): + init.uniform_(layer.weight, -std, std) + if layer.bias is not None: + init.uniform_(layer.bias, -std, std) + + +class SLANeXtVisionEncoder(GotOcr2VisionEncoder): + pass + + +class SLANeXtBackbone(SLANeXtPreTrainedModel): + def __init__( + self, + config: dict | None = None, + **kwargs, + ): + super().__init__(config) + self.vision_tower = SLANeXtVisionEncoder(config.vision_config) + self.post_conv = nn.Conv2d( + config.post_conv_in_channels, config.post_conv_out_channels, kernel_size=3, stride=2, padding=1, bias=False + ) + self.post_init() + + def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]): + vision_output = self.vision_tower(hidden_states, **kwargs) + hidden_states = self.post_conv(vision_output.last_hidden_state) + hidden_states = hidden_states.flatten(2).transpose(1, 2) + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=vision_output.hidden_states, + attentions=vision_output.attentions, + ) + + +class SLANeXtSLAHead(SLANeXtPreTrainedModel): + _can_record_outputs = { + "attentions": SLANeXtAttentionGRUCell, + } + + def __init__( + self, + config: dict | None = None, + **kwargs, + ): + super().__init__(config) + + self.structure_attention_cell = SLANeXtAttentionGRUCell( + config.post_conv_out_channels, config.hidden_size, config.out_channels + ) + self.structure_generator = SLANeXtMLP(config.hidden_size, config.out_channels) + + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @filter_output_hidden_states + def forward( + self, + hidden_states: torch.FloatTensor, + targets: torch.Tensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ): + features = torch.zeros( + (hidden_states.shape[0], self.config.hidden_size), dtype=torch.float32, device=hidden_states.device + ) + predicted_chars = torch.zeros(size=[hidden_states.shape[0]], dtype=torch.long, device=hidden_states.device) + + structure_preds_list = [] + structure_ids_list = [] + for _ in range(self.config.max_text_length + 1): + embedding_feature = F.one_hot(predicted_chars, self.config.out_channels).float() + features, _ = self.structure_attention_cell(features, hidden_states.float(), embedding_feature) + structure_step = self.structure_generator(features) + predicted_chars = structure_step.argmax(dim=1) + + structure_preds_list.append(structure_step) + structure_ids_list.append(predicted_chars) + if torch.stack(structure_ids_list, dim=1).eq(self.config.out_channels - 1).any(-1).all(): + break + structure_preds = F.softmax(torch.stack(structure_preds_list, dim=1), dim=-1, dtype=torch.float32).to( + hidden_states.dtype + ) + + return BaseModelOutput(last_hidden_state=structure_preds, hidden_states=structure_preds_list) + + +@auto_docstring +@dataclass +class SLANeXtForTableRecognitionOutput(BaseModelOutput): + r""" + head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Hidden-states of the SLANeXtSLAHead at each prediction step, varies up to max `self.config.max_text_length` states (depending on early exits). + head_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Attentions of the SLANeXtSLAHead at each prediction step, varies up to max `self.config.max_text_length` attentions (depending on early exits). + """ + + head_hidden_states: torch.FloatTensor | None = None + head_attentions: torch.FloatTensor | None = None + + +@auto_docstring( + custom_intro=""" + SLANeXt Table Recognition model for table recognition tasks. Wraps the core SLANeXtPreTrainedModel + and returns outputs compatible with the Transformers table recognition API. + """ +) +class SLANeXtForTableRecognition(SLANeXtPreTrainedModel): + def __init__(self, config: SLANeXtConfig): + super().__init__(config) + self.backbone = SLANeXtBackbone(config=config) + self.head = SLANeXtSLAHead(config=config) + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, pixel_values: torch.FloatTensor, **kwargs: Unpack[TransformersKwargs] + ) -> tuple[torch.FloatTensor] | SLANeXtForTableRecognitionOutput: + backbone_outputs = self.backbone(pixel_values, **kwargs) + head_outputs = self.head(backbone_outputs.last_hidden_state, **kwargs) + return SLANeXtForTableRecognitionOutput( + last_hidden_state=head_outputs.last_hidden_state, + hidden_states=backbone_outputs.hidden_states, + attentions=backbone_outputs.attentions, + head_hidden_states=head_outputs.hidden_states, + head_attentions=head_outputs.attentions, + ) + + +@auto_docstring +@requires(backends=("torch",)) +class SLANeXtImageProcessor(TorchvisionBackend): + resample = 2 # PILImageResampling.BILINEAR + image_mean = IMAGENET_DEFAULT_MEAN + image_std = IMAGENET_DEFAULT_STD + size = {"height": 512, "width": 512} + pad_size = {"height": 512, "width": 512} + do_convert_rgb = True + do_resize = True + do_rescale = True + do_normalize = True + do_pad = True + + def _resize( + self, + image: "torch.Tensor", + size: SizeDict, + ) -> "torch.Tensor": + batch_size, channels, height, width = image.shape + image = image.view(batch_size * channels, height, width) + + device = image.device + + scale = max(size.height, size.width) / max(height, width) + target_height = round(height * scale) + target_width = round(width * scale) + + target_col = torch.arange(target_width, dtype=torch.float32, device=device) + src_col = (target_col + 0.5) * (float(width) / float(target_width)) - 0.5 + src_col_floor = src_col.floor().to(torch.int32) + src_col_frac = src_col - src_col_floor.float() + # boundary handling + src_col_frac = torch.where(src_col_floor < 0, torch.zeros_like(src_col_frac), src_col_frac) + src_col_floor = torch.where(src_col_floor < 0, torch.zeros_like(src_col_floor), src_col_floor) + src_col_frac = torch.where(src_col_floor >= width - 1, torch.ones_like(src_col_frac), src_col_frac) + src_col_floor = torch.where( + src_col_floor >= width - 1, torch.full_like(src_col_floor, width - 2), src_col_floor + ) + # fixed-point weights + weight_right = (src_col_frac * 2048 + 0.5).floor().to(torch.int32) # round-to-nearest + weight_left = 2048 - weight_right # (target_w,) + # --- row coordinate tables --- + target_row = torch.arange(target_height, dtype=torch.float32, device=device) + src_row = (target_row + 0.5) * (float(height) / float(target_height)) - 0.5 + src_row_floor = src_row.floor().to(torch.int32) + src_row_frac = src_row - src_row_floor.float() + src_row_frac = torch.where(src_row_floor < 0, torch.zeros_like(src_row_frac), src_row_frac) + src_row_floor = torch.where(src_row_floor < 0, torch.zeros_like(src_row_floor), src_row_floor) + src_row_frac = torch.where(src_row_floor >= height - 1, torch.ones_like(src_row_frac), src_row_frac) + src_row_floor = torch.where( + src_row_floor >= height - 1, torch.full_like(src_row_floor, height - 2), src_row_floor + ) + weight_bottom = (src_row_frac * 2048 + 0.5).floor().to(torch.int32) + weight_top = 2048 - weight_bottom # (target_h,) + + image_uint8 = image.clamp(0, 255).to(torch.uint8) # (C, H, W) + image_int32 = image_uint8.to(torch.int32) # (C, H, W) + col_left = src_col_floor.long() # (target_w,) + col_right = (src_col_floor + 1).long() # (target_w,) safe: src_col_floor <= width-2 + row_top = src_row_floor.long() # (target_h,) + row_bottom = (src_row_floor + 1).long() # (target_h,) + # gather 4 neighbours: (C, target_h, target_w) + pixel_top_left = image_int32[:, row_top[:, None], col_left[None, :]] + pixel_top_right = image_int32[:, row_top[:, None], col_right[None, :]] + pixel_bottom_left = image_int32[:, row_bottom[:, None], col_left[None, :]] + pixel_bottom_right = image_int32[:, row_bottom[:, None], col_right[None, :]] + # fixed-point bilinear: weights broadcast over (C, target_h, target_w) + weight_bottom_3d = weight_bottom.view(1, target_height, 1) + weight_top_3d = weight_top.view(1, target_height, 1) + weight_right_3d = weight_right.view(1, 1, target_width) + weight_left_3d = weight_left.view(1, 1, target_width) + interp = weight_top_3d * ( + weight_left_3d * pixel_top_left + weight_right_3d * pixel_top_right + ) + weight_bottom_3d * (weight_left_3d * pixel_bottom_left + weight_right_3d * pixel_bottom_right) + interp = (interp + (1 << 21)) >> 22 + result = interp.clamp(0, 255).to(torch.uint8) # (B*C, target_h, target_w) + + return result.view(batch_size, channels, target_height, target_width).to(dtype=image.dtype) + + def _preprocess( + self, + images: list["torch.Tensor"], + do_resize: bool, + size: SizeDict, + resample: "tvF.InterpolationMode | int | None", + do_center_crop: bool, + crop_size: SizeDict, + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + pad_size: SizeDict | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + if resample is not None and not is_torchdynamo_compiling(): + logger.warning_once("Resampling is not supported in SLANeXt") + + # Group images by size for batched resizing + grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self._resize(image=stacked_images, size=size) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index) + + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_center_crop: + stacked_images = self.center_crop(stacked_images, crop_size) + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index) + + if do_pad: + processed_images = self.pad(processed_images, pad_size=pad_size, disable_grouping=disable_grouping) + + return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors) + + def __init__(self, **kwargs: Unpack[ImagesKwargs]): + super().__init__(**kwargs) + self.init_decoder() + + def init_decoder(self): + """ + Initialize the decoder vocabulary for table structure recognition. + + Builds a character dictionary mapping HTML table structure tokens (e.g., ``, ``, ``, colspan/ + rowspan attributes) to integer indices. The dictionary includes special `"sos"` (start-of-sequence) and + `"eos"` (end-of-sequence) tokens. Merged `` tokens are used in place of standalone `` tokens + when applicable. + """ + dict_character = [ + "", + "", + "", + "", + "", + "", + "", + "", + "", + ] + dict_character += [f' colspan="{i + 2}"' for i in range(19)] + dict_character += [f' rowspan="{i + 2}"' for i in range(19)] + + if "" not in dict_character: + dict_character.append("") + if "" in dict_character: + dict_character.remove("") + + dict_character = ["sos"] + dict_character + ["eos"] + self.dict = {char: i for i, char in enumerate(dict_character)} + self.character = dict_character + self.td_token = ["", ""] + self.bos_id = self.dict["sos"] + self.eos_id = self.dict["eos"] + + def post_process_table_recognition(self, outputs): + """ + Post-process the raw model outputs to decode the predicted table structure into an HTML token sequence. + + Converts the model's predicted probability distributions over the structure vocabulary into a sequence of + HTML tokens representing the table structure. The decoded tokens are wrapped with ``, ``, and + `` tags to form a complete HTML table structure. + + Args: + outputs ([`SLANeXtForTableRecognitionOutput`]): + Raw outputs from the SLANeXt model. The `last_hidden_state` field contains the predicted probability + distributions over the structure vocabulary at each decoding step, with shape + `(batch_size, max_text_length, num_classes)`. + + Returns: + `dict`: A dictionary containing: + - **structure** (`list[str]`): The predicted HTML table structure as a list of tokens, wrapped with + ``, ``, and `
` tags. + - **structure_score** (`float`): The mean confidence score across all predicted tokens. + """ + self.pred = outputs.last_hidden_state + structure_probs = self.pred[0:1] + ignored_tokens = [int(self.bos_id), int(self.eos_id)] + end_idx = int(self.eos_id) + + structure_idx = structure_probs.argmax(dim=2) + structure_probs = structure_probs.max(dim=2).values + + structure_str_list = [] + batch_size = structure_idx.shape[0] + for batch_index in range(batch_size): + structure_list = [] + score_list = [] + for position in range(structure_idx.shape[1]): + char_idx = int(structure_idx[batch_index, position]) + if position > 0 and char_idx == end_idx: + break + if char_idx in ignored_tokens: + continue + text = self.character[char_idx] + structure_list.append(text) + score_list.append(structure_probs[batch_index, position]) + structure_str_list.append(structure_list) + structure_score = torch.stack(score_list).mean().item() + + structure = ["", "", "
"] + structure_str_list[0] + ["
", "", ""] + return {"structure": structure, "structure_score": structure_score} + + +__all__ = [ + "SLANeXtImageProcessor", + "SLANeXtConfig", + "SLANeXtSLAHead", + "SLANeXtBackbone", + "SLANeXtForTableRecognition", + "SLANeXtPreTrainedModel", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..188d99ef78669b038c92223edb6b0caa4d2bb6be --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/__init__.py @@ -0,0 +1,27 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_smollm3 import * + from .modeling_smollm3 import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/configuration_smollm3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/configuration_smollm3.py new file mode 100644 index 0000000000000000000000000000000000000000..f48c979a1dd3186cb76efc8c81c46222aa71c820 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/configuration_smollm3.py @@ -0,0 +1,118 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/smollm3/modular_smollm3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_smollm3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...modeling_rope_utils import RopeParameters +from ...utils import auto_docstring + + +@auto_docstring(checkpoint="HuggingFaceTB/SmolLM3-3B") +@strict +class SmolLM3Config(PreTrainedConfig): + r""" + no_rope_layers (`List[int]`, *optional*): + List with at least the same length as the number of layers in the model. + A `1` at an index position indicates that the corresponding layer will use RoPE, + while a `0` indicates that it's a NoPE layer. + no_rope_layer_interval (`int`, *optional*, defaults to 4): + If `no_rope_layers` is `None`, it will be created using a NoPE layer every + `no_rope_layer_interval` layers. + + ```python + >>> from transformers import SmolLM3Model, SmolLM3Config + + >>> # Initializing a SmolLM3 style configuration + >>> configuration = SmolLM3Config() + + >>> # Initializing a model from the SmolLM3 style configuration + >>> model = SmolLM3Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "smollm3" + keys_to_ignore_at_inference = ["past_key_values"] + default_theta = 2000000.0 + + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 128256 + hidden_size: int = 2048 + intermediate_size: int = 11008 + num_hidden_layers: int = 36 + num_attention_heads: int = 16 + num_key_value_heads: int | None = 4 + hidden_act: str = "silu" + max_position_embeddings: int = 32768 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-6 + use_cache: bool = True + pad_token_id: int | None = 128004 + bos_token_id: int | None = 128000 + eos_token_id: int | list[int] | None = 128001 + rope_parameters: RopeParameters | dict | None = None + use_sliding_window: bool = False + sliding_window: int | None = None + no_rope_layers: list[int] | None = None + no_rope_layer_interval: int = 4 + layer_types: list[str] | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + mlp_bias: bool = False + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.no_rope_layers is None: + self.no_rope_layers = [ + int((layer_idx + 1) % self.no_rope_layer_interval != 0) for layer_idx in range(self.num_hidden_layers) + ] + + if self.layer_types is None: + self.layer_types = [] + for layer_idx in range(self.num_hidden_layers): + has_rope = self.no_rope_layers[layer_idx] + if self.use_sliding_window and self.sliding_window is not None and not has_rope: + self.layer_types.append("sliding_attention") + else: + self.layer_types.append("full_attention") + + super().__post_init__(**kwargs) + + +__all__ = ["SmolLM3Config"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/modeling_smollm3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/modeling_smollm3.py new file mode 100644 index 0000000000000000000000000000000000000000..8d911e414b0f3176aa923a3247ea681216f0a11f --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/modeling_smollm3.py @@ -0,0 +1,538 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/smollm3/modular_smollm3.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_smollm3.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable +from typing import Optional + +import torch +from torch import nn + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationMixin +from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func +from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import ( + GenericForQuestionAnswering, + GenericForSequenceClassification, + GenericForTokenClassification, + GradientCheckpointingLayer, +) +from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple +from ...utils.generic import maybe_autocast, merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from .configuration_smollm3 import SmolLM3Config + + +class SmolLM3RotaryEmbedding(nn.Module): + inv_freq: torch.Tensor # fix linting for `register_buffer` + + def __init__(self, config: SmolLM3Config, device=None): + super().__init__() + self.max_seq_len_cached = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + + self.rope_type = self.config.rope_parameters["rope_type"] + rope_init_fn: Callable = self.compute_default_rope_parameters + if self.rope_type != "default": + rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + inv_freq, self.attention_scaling = rope_init_fn(self.config, device) + + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False) + + @staticmethod + def compute_default_rope_parameters( + config: SmolLM3Config | None = None, + device: Optional["torch.device"] = None, + seq_len: int | None = None, + ) -> tuple["torch.Tensor", float]: + """ + Computes the inverse frequencies according to the original RoPE implementation + Args: + config ([`~transformers.PreTrainedConfig`]): + The model configuration. + device (`torch.device`): + The device to use for initialization of the inverse frequencies. + seq_len (`int`, *optional*): + The current sequence length. Unused for this type of RoPE. + Returns: + Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the + post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE). + """ + base = config.rope_parameters["rope_theta"] + dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads + + attention_factor = 1.0 # Unused in this type of RoPE + + # Compute the inverse frequencies + inv_freq = 1.0 / ( + base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim) + ) + return inv_freq, attention_factor + + @torch.no_grad() + @dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope) + def forward(self, x, position_ids): + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu" + with maybe_autocast(device_type=device_type, enabled=False): # Force float32 + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() * self.attention_scaling + sin = emb.sin() * self.attention_scaling + + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +@use_kernel_func_from_hub("rotary_pos_emb") +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs: Unpack[TransformersKwargs], +): + key_states = repeat_kv(key, module.num_key_value_groups) + value_states = repeat_kv(value, module.num_key_value_groups) + + attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + attn_output = torch.matmul(attn_weights, value_states) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +@use_kernelized_func(apply_rotary_pos_emb) +class SmolLM3Attention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config: SmolLM3Config, layer_idx: int): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads) + self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads + self.scaling = self.head_dim**-0.5 + self.attention_dropout = config.attention_dropout + self.is_causal = True + + self.q_proj = nn.Linear( + config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias + ) + self.k_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.v_proj = nn.Linear( + config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias + ) + self.o_proj = nn.Linear( + config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias + ) + + self.use_rope = config.no_rope_layers[layer_idx] + self.sliding_window = ( + config.sliding_window + if config.use_sliding_window and config.layer_types[layer_idx] == "sliding_attention" + else None + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + if self.use_rope: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +@use_kernel_forward_from_hub("RMSNorm") +class SmolLM3RMSNorm(nn.Module): + def __init__(self, hidden_size, eps: float = 1e-6) -> None: + """ + SmolLM3RMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +class SmolLM3MLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + return down_proj + + +class SmolLM3DecoderLayer(GradientCheckpointingLayer): + def __init__(self, config: SmolLM3Config, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = SmolLM3Attention(config=config, layer_idx=layer_idx) + + self.mlp = SmolLM3MLP(config) + self.input_layernorm = SmolLM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.post_attention_layernorm = SmolLM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + use_cache: bool | None = False, + position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.Tensor: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + # Self Attention + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + position_embeddings=position_embeddings, + **kwargs, + ) + hidden_states = residual + hidden_states + + # Fully Connected + residual = hidden_states + hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + return hidden_states + + +@auto_docstring +class SmolLM3PreTrainedModel(PreTrainedModel): + config: SmolLM3Config + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["SmolLM3DecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + + _can_compile_fullgraph = True + _supports_attention_backend = True + _can_record_outputs = { + "hidden_states": SmolLM3DecoderLayer, + "attentions": SmolLM3Attention, + } + + +@auto_docstring +class SmolLM3Model(SmolLM3PreTrainedModel): + def __init__(self, config: SmolLM3Config): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [SmolLM3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + self.norm = SmolLM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.rotary_emb = SmolLM3RotaryEmbedding(config=config) + self.gradient_checkpointing = False + self.has_sliding_layers = "sliding_attention" in self.config.layer_types + + # Initialize weights and apply final processing + self.post_init() + + @merge_with_config_defaults + @capture_outputs + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> BaseModelOutputWithPast: + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if position_ids is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens + position_ids = position_ids.unsqueeze(0) + + # It may already have been prepared by e.g. `generate` + if not isinstance(causal_mask_mapping := attention_mask, dict): + # Prepare mask arguments + mask_kwargs = { + "config": self.config, + "inputs_embeds": inputs_embeds, + "attention_mask": attention_mask, + "past_key_values": past_key_values, + "position_ids": position_ids, + } + # Create the masks + causal_mask_mapping = { + "full_attention": create_causal_mask(**mask_kwargs), + } + # The sliding window alternating layers are not always activated depending on the config + if self.has_sliding_layers: + causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) + + hidden_states = inputs_embeds + position_embeddings = self.rotary_emb(hidden_states, position_ids) + + for i, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]): + hidden_states = decoder_layer( + hidden_states, + attention_mask=causal_mask_mapping[self.config.layer_types[i]], + position_embeddings=position_embeddings, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = self.norm(hidden_states) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=past_key_values if use_cache else None, + ) + + +@auto_docstring +class SmolLM3ForCausalLM(SmolLM3PreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"} + _tp_plan = {"lm_head": "colwise_gather_output"} + _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} + + def __init__(self, config): + super().__init__(config) + self.model = SmolLM3Model(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> CausalLMOutputWithPast: + r""" + Example: + + ```python + >>> from transformers import AutoTokenizer, SmolLM3ForCausalLM + + >>> model = SmolLM3ForCausalLM.from_pretrained("meta-smollm3/SmolLM3-2-7b-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("meta-smollm3/SmolLM3-2-7b-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." + ```""" + outputs: BaseModelOutputWithPast = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + **kwargs, + ) + + hidden_states = outputs.last_hidden_state + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + + +class SmolLM3ForSequenceClassification(GenericForSequenceClassification, SmolLM3PreTrainedModel): + pass + + +class SmolLM3ForTokenClassification(GenericForTokenClassification, SmolLM3PreTrainedModel): + pass + + +class SmolLM3ForQuestionAnswering(GenericForQuestionAnswering, SmolLM3PreTrainedModel): + base_model_prefix = "transformer" # For BC, where `transformer` was used instead of `model` + + +__all__ = [ + "SmolLM3PreTrainedModel", + "SmolLM3Model", + "SmolLM3ForCausalLM", + "SmolLM3ForSequenceClassification", + "SmolLM3ForTokenClassification", + "SmolLM3ForQuestionAnswering", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/modular_smollm3.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/modular_smollm3.py new file mode 100644 index 0000000000000000000000000000000000000000..f75017ad264543c11294216b774ee2faf7ab8138 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smollm3/modular_smollm3.py @@ -0,0 +1,228 @@ +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable + +import torch +from huggingface_hub.dataclasses import strict + +from ...cache_utils import Cache +from ...configuration_utils import PreTrainedConfig +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_rope_utils import RopeParameters +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS +from ...processing_utils import Unpack +from ...utils import auto_docstring, logging +from ..llama.modeling_llama import ( + LlamaAttention, + LlamaDecoderLayer, + LlamaForCausalLM, + LlamaForQuestionAnswering, + LlamaForSequenceClassification, + LlamaForTokenClassification, + LlamaPreTrainedModel, + apply_rotary_pos_emb, + eager_attention_forward, +) +from ..qwen2.modeling_qwen2 import Qwen2Model, Qwen2RotaryEmbedding + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="HuggingFaceTB/SmolLM3-3B") +@strict +class SmolLM3Config(PreTrainedConfig): + r""" + no_rope_layers (`List[int]`, *optional*): + List with at least the same length as the number of layers in the model. + A `1` at an index position indicates that the corresponding layer will use RoPE, + while a `0` indicates that it's a NoPE layer. + no_rope_layer_interval (`int`, *optional*, defaults to 4): + If `no_rope_layers` is `None`, it will be created using a NoPE layer every + `no_rope_layer_interval` layers. + + ```python + >>> from transformers import SmolLM3Model, SmolLM3Config + + >>> # Initializing a SmolLM3 style configuration + >>> configuration = SmolLM3Config() + + >>> # Initializing a model from the SmolLM3 style configuration + >>> model = SmolLM3Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "smollm3" + keys_to_ignore_at_inference = ["past_key_values"] + default_theta = 2000000.0 + + base_model_tp_plan = { + "layers.*.self_attn.q_proj": "colwise", + "layers.*.self_attn.k_proj": "colwise", + "layers.*.self_attn.v_proj": "colwise", + "layers.*.self_attn.o_proj": "rowwise", + "layers.*.mlp.gate_proj": "colwise", + "layers.*.mlp.up_proj": "colwise", + "layers.*.mlp.down_proj": "rowwise", + } + base_model_pp_plan = { + "embed_tokens": (["input_ids"], ["inputs_embeds"]), + "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), + "norm": (["hidden_states"], ["hidden_states"]), + } + + vocab_size: int = 128256 + hidden_size: int = 2048 + intermediate_size: int = 11008 + num_hidden_layers: int = 36 + num_attention_heads: int = 16 + num_key_value_heads: int | None = 4 + hidden_act: str = "silu" + max_position_embeddings: int = 32768 + initializer_range: float = 0.02 + rms_norm_eps: float = 1e-6 + use_cache: bool = True + pad_token_id: int | None = 128004 + bos_token_id: int | None = 128000 + eos_token_id: int | list[int] | None = 128001 + rope_parameters: RopeParameters | dict | None = None + use_sliding_window: bool = False + sliding_window: int | None = None + no_rope_layers: list[int] | None = None + no_rope_layer_interval: int = 4 + layer_types: list[str] | None = None + attention_bias: bool = False + attention_dropout: float | int = 0.0 + mlp_bias: bool = False + tie_word_embeddings: bool = True + + def __post_init__(self, **kwargs): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + if self.no_rope_layers is None: + self.no_rope_layers = [ + int((layer_idx + 1) % self.no_rope_layer_interval != 0) for layer_idx in range(self.num_hidden_layers) + ] + + if self.layer_types is None: + self.layer_types = [] + for layer_idx in range(self.num_hidden_layers): + has_rope = self.no_rope_layers[layer_idx] + if self.use_sliding_window and self.sliding_window is not None and not has_rope: + self.layer_types.append("sliding_attention") + else: + self.layer_types.append("full_attention") + + super().__post_init__(**kwargs) + + +class SmolLM3RotaryEmbedding(Qwen2RotaryEmbedding): + pass + + +class SmolLM3Attention(LlamaAttention): + def __init__(self, config: SmolLM3Config, layer_idx: int): + super().__init__(config, layer_idx) + + self.use_rope = config.no_rope_layers[layer_idx] + self.sliding_window = ( + config.sliding_window + if config.use_sliding_window and config.layer_types[layer_idx] == "sliding_attention" + else None + ) + + def forward( + self, + hidden_states: torch.Tensor, + position_embeddings: tuple[torch.Tensor, torch.Tensor], + attention_mask: torch.Tensor | None, + past_key_values: Cache | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple[torch.Tensor, torch.Tensor | None]: + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + + query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + if self.use_rope: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_values is not None: + key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + query_states, + key_states, + value_states, + attention_mask, + dropout=0.0 if not self.training else self.attention_dropout, + scaling=self.scaling, + sliding_window=self.sliding_window, + **kwargs, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights + + +class SmolLM3DecoderLayer(LlamaDecoderLayer): + pass + + +class SmolLM3PreTrainedModel(LlamaPreTrainedModel): + pass + + +class SmolLM3Model(Qwen2Model): + pass + + +class SmolLM3ForCausalLM(LlamaForCausalLM): + pass + + +class SmolLM3ForSequenceClassification(LlamaForSequenceClassification): + pass + + +class SmolLM3ForTokenClassification(LlamaForTokenClassification): + pass + + +class SmolLM3ForQuestionAnswering(LlamaForQuestionAnswering): + pass + + +__all__ = [ + "SmolLM3Config", + "SmolLM3PreTrainedModel", + "SmolLM3Model", + "SmolLM3ForCausalLM", + "SmolLM3ForSequenceClassification", + "SmolLM3ForTokenClassification", + "SmolLM3ForQuestionAnswering", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/__init__.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0ddab5b2b0dbd89521fcadfcd299bd835b4deb1d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/__init__.py @@ -0,0 +1,30 @@ +# Copyright 2025 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import _LazyModule +from ...utils.import_utils import define_import_structure + + +if TYPE_CHECKING: + from .configuration_smolvlm import * + from .image_processing_pil_smolvlm import * + from .image_processing_smolvlm import * + from .modeling_smolvlm import * + from .processing_smolvlm import * +else: + import sys + + _file = globals()["__file__"] + sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__) diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/configuration_smolvlm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/configuration_smolvlm.py new file mode 100644 index 0000000000000000000000000000000000000000..067db68e129eb35ecf742eaabe3b0079704c7508 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/configuration_smolvlm.py @@ -0,0 +1,118 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/smolvlm/modular_smolvlm.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_smolvlm.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# Written by Orr Zohar +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from huggingface_hub.dataclasses import strict + +from ...configuration_utils import PreTrainedConfig +from ...utils import auto_docstring, logging +from ..auto import CONFIG_MAPPING, AutoConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="HuggingFaceTB/SmolVLM2-2.2B-Instruct") +@strict +class SmolVLMVisionConfig(PreTrainedConfig): + r""" + Example: + + ```python + >>> from transformers.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer + >>> from transformers.models.smolvlm.configuration_smolvlm import SmolVLMVisionConfig + + >>> # Initializing a SmolVLMVisionConfig with google/siglip-so400m-patch14-384 style configuration + >>> configuration = SmolVLMVisionConfig() + + >>> # Initializing a SmolVLMVisionTransformer (with random weights) from the google/siglip-so400m-patch14-384 style configuration + >>> model = SmolVLMVisionTransformer(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "smolvlm_vision" + base_config_key = "vision_config" + + hidden_size: int = 1152 + intermediate_size: int = 3072 + num_hidden_layers: int = 12 + num_attention_heads: int = 16 + num_channels: int = 3 + image_size: int | list[int] | tuple[int, int] = 224 + patch_size: int | list[int] | tuple[int, int] = 32 + hidden_act: str = "gelu_pytorch_tanh" + layer_norm_eps: float = 1e-6 + attention_dropout: float | int = 0.0 + initializer_range: float = 0.02 + + +@auto_docstring(checkpoint="HuggingFaceTB/SmolVLM2-2.2B-Instruct") +@strict +class SmolVLMConfig(PreTrainedConfig): + r""" + scale_factor (`int`, *optional*, defaults to 2): + The scale factor for the image encoder. + + Example: + ```python + >>> from transformers import SmolVLMModel, SmolVLMConfig + >>> # Initializing configuration + >>> configuration = SmolVLMConfig() + >>> # Initializing a model from the configuration + >>> model = SmolVLMModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "smolvlm" + sub_configs = {"text_config": AutoConfig, "vision_config": SmolVLMVisionConfig} + + use_cache: bool = True + image_token_id: int = 128257 + tie_word_embeddings: bool = False + vision_config: dict | PreTrainedConfig | None = None + text_config: dict | PreTrainedConfig | None = None + scale_factor: int = 2 + pad_token_id: int | None = 128_002 + + def __post_init__(self, **kwargs): + if self.vision_config is None: + self.vision_config = SmolVLMVisionConfig() + logger.info("vision_config is None, using default vision config") + elif isinstance(self.vision_config, dict): + self.vision_config = SmolVLMVisionConfig(**self.vision_config) + + if isinstance(self.text_config, dict): + self.text_config["model_type"] = self.text_config.get("model_type", "llama") + self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config) + elif self.text_config is None: + logger.info("text_config is None, using default Llama text config") + self.text_config = CONFIG_MAPPING["llama"]( + rms_norm_eps=1e-5, + pad_token_id=self.pad_token_id, + ) + + super().__post_init__(**kwargs) + + +__all__ = ["SmolVLMVisionConfig", "SmolVLMConfig"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/image_processing_pil_smolvlm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/image_processing_pil_smolvlm.py new file mode 100644 index 0000000000000000000000000000000000000000..3d53ed09c11fd66a4cd1de2d4bd62d7e5ce62f7d --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/image_processing_pil_smolvlm.py @@ -0,0 +1,481 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/smolvlm/modular_smolvlm.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_smolvlm.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# Written by Orr Zohar +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math + +import numpy as np + +from ...image_processing_backends import PilBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import PaddingMode, pad +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ImageInput, + PILImageResampling, + SizeDict, + make_nested_list_of_images, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +class SmolVLMImageProcessorKwargs(ImagesKwargs, total=False): + """ + do_image_splitting (`bool`, *optional*, defaults to `True`): + Whether to split the image into sub-images concatenated with the original image. They are split into patches + such that each patch has a size of `max_image_size["height"]` x `max_image_size["width"]`. + max_image_size (`Dict`, *optional*, defaults to `{"longest_edge": 364}`): + Maximum resolution of the patches of images accepted by the model. This is a dictionary containing the key "longest_edge". + return_row_col_info (`bool`, *optional*, defaults to `False`): + Whether to return the row and column information of the images. + """ + + do_image_splitting: bool + max_image_size: dict[str, int] + return_row_col_info: bool + + +def _make_pixel_mask(image: np.ndarray, output_size: tuple[int, int]) -> np.ndarray: + """Make pixel mask: 1=valid, 0=padding. Images are CHW.""" + h, w = image.shape[-2:] + mask = np.zeros(output_size, dtype=np.int64) + mask[:h, :w] = 1 + return mask + + +# Adapted from transformers.models.smolvlm.image_processing_smolvlm.MAX_IMAGE_SIZE +MAX_IMAGE_SIZE = 4096 # 4k resolution as absolute maximum + + +# Adapted from transformers.models.smolvlm.image_processing_smolvlm._resize_output_size_rescale_to_max_len +def _resize_output_size_rescale_to_max_len( + height: int, width: int, min_len: int | None = 1, max_len: int | None = None +) -> tuple[int, int]: + """ + Get the output size of the image after resizing given a dictionary specifying the max and min sizes. + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + min_len (`int`, *optional*, defaults to 1): + Minimum size of the output image. + max_len (`int`, *optional*, defaults to the maximum size of the image): + Maximum size of the output image. + Returns: + The output size of the image after resizing. + """ + max_len = max(height, width) if max_len is None else max_len + aspect_ratio = width / height + + if width >= height: + width = max_len + height = int(width / aspect_ratio) + if height % 2 != 0: + height += 1 + elif height > width: + height = max_len + width = int(height * aspect_ratio) + if width % 2 != 0: + width += 1 + + # Avoid resizing to a size smaller than min_len + height = max(height, min_len) + width = max(width, min_len) + return height, width + + +# Adapted from transformers.models.smolvlm.image_processing_smolvlm._resize_output_size_scale_below_upper_bound +def _resize_output_size_scale_below_upper_bound( + height: int, width: int, max_len: dict[str, int] | None = None +) -> tuple[int, int]: + """ + Get the output size of the image after resizing given a dictionary specifying the max and min sizes. + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + max_len (`Dict[str, int]`, *optional*, defaults to the maximum size of the image): + Defines the maximum dimensions of the image. + Returns: + The output size of the image after resizing. + """ + max_len = max(height, width) if max_len is None else max_len + + aspect_ratio = width / height + if width >= height and width > max_len: + width = max_len + height = int(width / aspect_ratio) + elif height > width and height > max_len: + height = max_len + width = int(height * aspect_ratio) + + # Avoid resizing to a size smaller than 1 + height = max(height, 1) + width = max(width, 1) + return height, width + + +def get_max_height_width(images_list: list[list[np.ndarray]]) -> tuple[int, int]: + """ + Get the maximum height and width across all images in a batch. + """ + image_sizes = [] + for images in images_list: + for image in images: + image_sizes.append(image.shape[-2:]) + + max_height = max(size[0] for size in image_sizes) + max_width = max(size[1] for size in image_sizes) + return (max_height, max_width) + + +def get_num_channels(images_list: list[list[np.ndarray]]) -> int: + """ + Get the number of channels across all images in a batch. Handle empty sublists like in [[], [image]]. + """ + for images in images_list: + if images: + return images[0].shape[0] + + raise ValueError("No images found in the batch.") + + +def get_resize_output_image_size( + image: np.ndarray, + resolution_max_side: int, +) -> tuple[int, int]: + """ + Get the output size of the image after resizing given a dictionary specifying the max and min sizes. + Args: + image (`np.ndarray`): + Image to resize. + resolution_max_side (`int`): + The longest edge of the image will be resized to this value. The shortest edge will be resized to keep the + input aspect ratio. + Returns: + The output size of the image after resizing. + """ + height, width = image.shape[-2:] + + # Find the output size, when rescaling the longest edge to max_len and preserving the aspect ratio + height, width = _resize_output_size_rescale_to_max_len(height, width, max_len=resolution_max_side) + # Find the output size when scaling the image to be below the MAX_IMAGE_SIZE + height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=MAX_IMAGE_SIZE) + return height, width + + +@auto_docstring +class SmolVLMImageProcessorPil(PilBackend): + resample = PILImageResampling.LANCZOS + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"longest_edge": 4 * 364} + max_image_size = {"longest_edge": 364} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + do_image_splitting = True + do_pad = True + return_row_col_info = False + valid_kwargs = SmolVLMImageProcessorKwargs + model_input_names = ["pixel_values", "pixel_attention_mask"] + + def __init__(self, **kwargs: Unpack[SmolVLMImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[SmolVLMImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def _prepare_images_structure(self, images: ImageInput, expected_ndims: int = 3) -> ImageInput: + images = self.fetch_images(images) + return make_nested_list_of_images(images, expected_ndims=expected_ndims) + + def resize( + self, + image: np.ndarray, + size: SizeDict, + resample: PILImageResampling = PILImageResampling.LANCZOS, + **kwargs, + ) -> np.ndarray: + if size.longest_edge: + new_size = get_resize_output_image_size(image, resolution_max_side=size.longest_edge) + elif size.height and size.width: + new_size = (size.height, size.width) + else: + raise ValueError("size must be a dictionary with key 'longest_edge' or 'height' and 'width'.") + return super().resize(image, SizeDict(height=new_size[0], width=new_size[1]), resample=resample, **kwargs) + + def split_images( + self, + image: np.ndarray, + max_image_size: dict[str, int], + resample: "PILImageResampling | None" = None, + ): + """Split an image into patches (mirrors TorchvisionBackend.split_images). Images are always CHW.""" + num_channels, height, width = image.shape + max_height = max_width = max_image_size["longest_edge"] + + if height > max_height or width > max_width: + num_splits_h = (height - max_height) // max_height + 1 + num_splits_w = (width - max_width) // max_width + 1 + + frames = [] + for r in range(num_splits_h): + for c in range(num_splits_w): + start_y = r * max_height + start_x = c * max_width + end_y = start_y + max_height + end_x = start_x + max_width + crop = image[:, start_y:end_y, start_x:end_x] + frames.append(crop) + + global_image_height, global_image_width = max_height, max_width + image = self.resize( + image, SizeDict(height=global_image_height, width=global_image_width), resample=resample + ) + frames.append(image) + else: + num_splits_h, num_splits_w = 0, 0 + frames = [image] + + return frames, num_splits_h, num_splits_w + + def resize_for_vision_encoder( + self, + image: np.ndarray, + vision_encoder_max_size: int, + resample: "PILImageResampling | None" = None, + ): + """Resize images to be multiples of vision_encoder_max_size. Images are always CHW.""" + height, width = image.shape[-2:] + aspect_ratio = width / height + if width >= height: + width = math.ceil(width / vision_encoder_max_size) * vision_encoder_max_size + height = int(width / aspect_ratio) + height = math.ceil(height / vision_encoder_max_size) * vision_encoder_max_size + elif height > width: + height = math.ceil(height / vision_encoder_max_size) * vision_encoder_max_size + width = int(height * aspect_ratio) + width = math.ceil(width / vision_encoder_max_size) * vision_encoder_max_size + new_size = SizeDict(height=height, width=width) + return self.resize(image, size=new_size, resample=resample) + + def pad( + self, + image: np.ndarray, + padded_size: tuple[int, int], + fill: int = 0, + return_pixel_mask: bool = True, + ): + """Pad image to padded_size. Mirrors TorchvisionBackend.pad. Images are always CHW.""" + original_size = image.shape[-2:] + padding_bottom = padded_size[0] - original_size[0] + padding_right = padded_size[1] - original_size[1] + + if padding_bottom < 0 or padding_right < 0: + raise ValueError( + f"Padding dimensions are negative. Please make sure that the padded size is larger than the " + f"original size. Got padded size: {padded_size}, original size: {original_size}." + ) + + pixel_mask = _make_pixel_mask(image, output_size=padded_size) if return_pixel_mask else None + + if original_size != padded_size: + padding = ((0, padding_bottom), (0, padding_right)) + image = pad( + image, + padding, + mode=PaddingMode.CONSTANT, + constant_values=fill, + data_format="channels_first", + input_data_format="channels_first", + ) + + return image, pixel_mask + + def _preprocess( + self, + images: list[list[np.ndarray]], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + do_image_splitting: bool | None, + max_image_size: dict[str, int] | None, + return_row_col_info: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """Process a batch of images. Mirrors TorchvisionBackend._preprocess with per-image loops instead of batching.""" + # Resize + if do_resize: + images = [ + [self.resize(image=img, size=size, resample=resample) for img in batch_images] + for batch_images in images + ] + + # Image splitting + if do_image_splitting: + images = [ + [ + self.resize_for_vision_encoder(image, max_image_size["longest_edge"], resample=resample) + for image in batch_images + ] + for batch_images in images + ] + images_split_arrays = [] + images_rows = [] + images_cols = [] + for batch_images in images: + split_image_arrays = [] + image_rows = [] + image_cols = [] + for image in batch_images: + split_image_array, rows, cols = self.split_images( + image, max_image_size=max_image_size, resample=resample + ) + split_image_arrays.extend(split_image_array) + image_rows.append(rows) + image_cols.append(cols) + images_split_arrays.append(split_image_arrays) + images_rows.append(image_rows) + images_cols.append(image_cols) + images = images_split_arrays + rows = images_rows + cols = images_cols + else: + images = [ + [ + self.resize( + image=image, + size=SizeDict(height=max_image_size["longest_edge"], width=max_image_size["longest_edge"]), + resample=resample, + ) + for image in batch_images + ] + for batch_images in images + ] + rows = [[0] * len(batch_images) for batch_images in images] + cols = [[0] * len(batch_images) for batch_images in images] + + # Rescale and normalize + if do_rescale: + images = [[self.rescale(img, rescale_factor) for img in batch_images] for batch_images in images] + if do_normalize: + images = [[self.normalize(img, image_mean, image_std) for img in batch_images] for batch_images in images] + + # Pad + if do_pad: + max_num_images = max(len(images_) for images_ in images) + max_height, max_width = get_max_height_width(images) + num_channels = get_num_channels(images) + + padded_images_list = [ + [np.zeros((num_channels, max_height, max_width), dtype=np.float32) for _ in range(max_num_images)] + for _ in range(len(images)) + ] + pixel_attention_masks = [ + [np.zeros((max_height, max_width), dtype=np.int64) for _ in range(max_num_images)] + for _ in range(len(images)) + ] + + for i, batch_images in enumerate(images): + for j, image in enumerate(batch_images): + padded_images_list[i][j], pixel_attention_masks[i][j] = self.pad(image, (max_height, max_width)) + images = padded_images_list + + if do_pad: + data = { + "pixel_values": np.array(images), + "pixel_attention_mask": np.array(pixel_attention_masks), + } + elif return_tensors == "pt": + data = {"pixel_values": np.asarray(images)} + else: + data = {"pixel_values": images} + + encoding = BatchFeature(data=data, tensor_type=return_tensors) + if return_row_col_info: + encoding["rows"] = rows + encoding["cols"] = cols + + return encoding + + def to_dict(self): + encoder_dict = super().to_dict() + encoder_dict.pop("_valid_processor_keys", None) + encoder_dict.pop("return_row_col_info", None) + return encoder_dict + + def get_number_of_image_patches(self, height: int, width: int, images_kwargs: dict): + """ + A utility that returns number of image patches for a given image size. + + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + images_kwargs (`dict`) + Any kwargs to override defaults of the image processor. + Returns: + `int`: Number of patches per image. + """ + do_image_splitting = images_kwargs.get("do_image_splitting", self.do_image_splitting) + max_image_size = images_kwargs.get("max_image_size", self.max_image_size) + size = images_kwargs.get("size", self.size) + + num_patches = num_rows = num_cols = 0 + if do_image_splitting: + height, width = _resize_output_size_rescale_to_max_len(height, width, max_len=size["longest_edge"]) + height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=MAX_IMAGE_SIZE) + aspect_ratio = width / height + + if width >= height: + resized_width = math.ceil(width / max_image_size["longest_edge"]) * max_image_size["longest_edge"] + resized_height = int(width / aspect_ratio) + resized_height = math.ceil(height / max_image_size["longest_edge"]) * max_image_size["longest_edge"] + elif height > width: + resized_height = math.ceil(height / max_image_size["longest_edge"]) * max_image_size["longest_edge"] + resized_width = int(height * aspect_ratio) + resized_width = math.ceil(width / max_image_size["longest_edge"]) * max_image_size["longest_edge"] + + max_height = max_width = max_image_size["longest_edge"] + if resized_height > max_height or resized_width > max_width: + num_rows = math.ceil(resized_height / max_height) + num_cols = math.ceil(resized_width / max_width) + num_patches = num_rows * num_cols + 1 + + return num_patches, num_rows, num_cols + + +__all__ = ["SmolVLMImageProcessorPil"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/image_processing_smolvlm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/image_processing_smolvlm.py new file mode 100644 index 0000000000000000000000000000000000000000..6c22e496c6697387931240a5c6f2f1222a73cd41 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/image_processing_smolvlm.py @@ -0,0 +1,540 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/smolvlm/modular_smolvlm.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_smolvlm.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# Written by Orr Zohar +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import math + +import numpy as np +import torch +from torchvision.transforms.v2 import functional as tvF + +from ...image_processing_backends import TorchvisionBackend +from ...image_processing_utils import BatchFeature +from ...image_transforms import group_images_by_shape, reorder_images +from ...image_utils import ( + IMAGENET_STANDARD_MEAN, + IMAGENET_STANDARD_STD, + ImageInput, + PILImageResampling, + SizeDict, + make_nested_list_of_images, +) +from ...processing_utils import ImagesKwargs, Unpack +from ...utils import TensorType, auto_docstring + + +class SmolVLMImageProcessorKwargs(ImagesKwargs, total=False): + """ + do_image_splitting (`bool`, *optional*, defaults to `True`): + Whether to split the image into sub-images concatenated with the original image. They are split into patches + such that each patch has a size of `max_image_size["height"]` x `max_image_size["width"]`. + max_image_size (`Dict`, *optional*, defaults to `{"longest_edge": 364}`): + Maximum resolution of the patches of images accepted by the model. This is a dictionary containing the key "longest_edge". + return_row_col_info (`bool`, *optional*, defaults to `False`): + Whether to return the row and column information of the images. + """ + + do_image_splitting: bool + max_image_size: dict[str, int] + return_row_col_info: bool + + +MAX_IMAGE_SIZE = 4096 # 4k resolution as absolute maximum + + +def _resize_output_size_rescale_to_max_len( + height: int, width: int, min_len: int | None = 1, max_len: int | None = None +) -> tuple[int, int]: + """ + Get the output size of the image after resizing given a dictionary specifying the max and min sizes. + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + min_len (`int`, *optional*, defaults to 1): + Minimum size of the output image. + max_len (`int`, *optional*, defaults to the maximum size of the image): + Maximum size of the output image. + Returns: + The output size of the image after resizing. + """ + max_len = max(height, width) if max_len is None else max_len + aspect_ratio = width / height + + if width >= height: + width = max_len + height = int(width / aspect_ratio) + if height % 2 != 0: + height += 1 + elif height > width: + height = max_len + width = int(height * aspect_ratio) + if width % 2 != 0: + width += 1 + + # Avoid resizing to a size smaller than min_len + height = max(height, min_len) + width = max(width, min_len) + return height, width + + +def _resize_output_size_scale_below_upper_bound( + height: int, width: int, max_len: dict[str, int] | None = None +) -> tuple[int, int]: + """ + Get the output size of the image after resizing given a dictionary specifying the max and min sizes. + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + max_len (`Dict[str, int]`, *optional*, defaults to the maximum size of the image): + Defines the maximum dimensions of the image. + Returns: + The output size of the image after resizing. + """ + max_len = max(height, width) if max_len is None else max_len + + aspect_ratio = width / height + if width >= height and width > max_len: + width = max_len + height = int(width / aspect_ratio) + elif height > width and height > max_len: + height = max_len + width = int(height * aspect_ratio) + + # Avoid resizing to a size smaller than 1 + height = max(height, 1) + width = max(width, 1) + return height, width + + +def get_resize_output_image_size( + image: "torch.Tensor", + resolution_max_side: int, +) -> tuple[int, int]: + """ + Get the output size of the image after resizing given a dictionary specifying the max and min sizes. + Args: + image (`torch.Tensor`): + Image to resize. + resolution_max_side (`int`): + The longest edge of the image will be resized to this value. The shortest edge will be resized to keep the + input aspect ratio. + Returns: + The output size of the image after resizing. + """ + height, width = image.shape[-2:] + + # Find the output size, when rescaling the longest edge to max_len and preserving the aspect ratio + height, width = _resize_output_size_rescale_to_max_len(height, width, max_len=resolution_max_side) + # Find the output size when scaling the image to be below the MAX_IMAGE_SIZE + height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=MAX_IMAGE_SIZE) + return height, width + + +def get_max_height_width(images_list: list[list["torch.Tensor|np.ndarray"]]) -> tuple[int, int]: + """ + Get the maximum height and width across all images in a batch. + """ + image_sizes = [] + for images in images_list: + for image in images: + image_sizes.append(image.shape[-2:]) + + max_height = max(size[0] for size in image_sizes) + max_width = max(size[1] for size in image_sizes) + return (max_height, max_width) + + +def get_num_channels(images_list: list[list["torch.Tensor|np.ndarray"]]) -> int: + """ + Get the number of channels across all images in a batch. Handle empty sublists like in [[], [image]]. + """ + for images in images_list: + if images: + return images[0].shape[0] + + raise ValueError("No images found in the batch.") + + +def get_device_from_images(images_list: list[list["torch.Tensor"]]) -> "torch.device": + """ + Get the device from the first non-empty element in a nested list of images. + Handle empty sublists like in [[], [image]]. + """ + for images in images_list: + if images: + return images[0].device + + +@auto_docstring +class SmolVLMImageProcessor(TorchvisionBackend): + resample = PILImageResampling.LANCZOS + image_mean = IMAGENET_STANDARD_MEAN + image_std = IMAGENET_STANDARD_STD + size = {"longest_edge": 4 * 364} + max_image_size = {"longest_edge": 364} + do_resize = True + do_rescale = True + do_normalize = True + do_convert_rgb = True + do_image_splitting = True + do_pad = True + return_row_col_info = False + valid_kwargs = SmolVLMImageProcessorKwargs + model_input_names = ["pixel_values", "pixel_attention_mask"] + + def __init__(self, **kwargs: Unpack[SmolVLMImageProcessorKwargs]): + super().__init__(**kwargs) + + @auto_docstring + def preprocess(self, images: ImageInput, **kwargs: Unpack[SmolVLMImageProcessorKwargs]) -> BatchFeature: + return super().preprocess(images, **kwargs) + + def _prepare_images_structure(self, images: ImageInput, expected_ndims: int = 3) -> ImageInput: + """ + Prepare a nested images structure for processing. + """ + # Checks for `str` in case of URL/local path and optionally loads images + images = self.fetch_images(images) + return make_nested_list_of_images(images, expected_ndims=expected_ndims) + + def resize( + self, + image: "torch.Tensor", + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + **kwargs, + ) -> "torch.Tensor": + """ + Resize an image. The longest edge of the image is resized to size.longest_edge, with the shortest edge + resized to keep the input aspect ratio. Can also be used with size.height and size.width. + Args: + image (`torch.Tensor`): + Image to resize. + size (`SizeDict`): + Size of the output image. + resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): + Resampling filter to use when resizing the image. + """ + if size.longest_edge: + new_size = get_resize_output_image_size(image, resolution_max_side=size.longest_edge) + elif size.height and size.width: + new_size = (size.height, size.width) + else: + raise ValueError("size must be a dictionary with key 'longest_edge' or 'height' and 'width'.") + + return super().resize(image, SizeDict(height=new_size[0], width=new_size[1]), resample=resample, **kwargs) + + def split_images( + self, + images: torch.Tensor, + max_image_size: dict[str, int], + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + ): + """ + Split an image into squares of side max_image_size and the original image resized to max_image_size. + That means that a single image becomes a sequence of images. + This is a "trick" to spend more compute on each image with no changes in the vision encoder. + 1) If one side of the original image is larger than `max_image_size`, resize it to `max_image_size` while preserving the aspect ratio. + 2) Divide the resulting image into `ceil(height / max_image_size)` x `ceil(width / max_image_size)` + sub-images of the same size each (image_size, image_size). Typically, 364x364. + 3) Returns the list of the crops and the original image, in addition to the number of splits for the height and the width. + Args: + images (`torch.Tensor`): + Images to split. + max_image_size (`Dict[str, int]`): + Maximum size of the output image. If the image is larger than this size, it will be split into + patches of this size, and the original image will be concatenated with the patches, resized to max_size. + resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): + Resampling filter to use when resizing the image. + """ + batch_size, num_channels, height, width = images.size() + height_dim, width_dim = 2, 3 + + max_height = max_width = max_image_size["longest_edge"] + + frames = [] + if height > max_height or width > max_width: + # Calculate the number of splits + num_splits_h = math.ceil(height / max_height) + num_splits_w = math.ceil(width / max_width) + + # Split the images by height, then by width + frames = ( + images.unfold(height_dim, size=max_height, step=max_height) + .unfold(width_dim, size=max_width, step=max_width) + .contiguous() + .view(batch_size, num_channels, -1, max_height, max_width) + .permute(0, 2, 1, 3, 4) + ) # batch_size x n_frames x num_channels x height x width + + # For the global image at the end, we resize it to match the max_image_size, for cpu memory efficiency + global_image_height, global_image_width = max_height, max_width + images = self.resize( + images, SizeDict(height=global_image_height, width=global_image_width), resample=resample + ) + + frames = torch.cat((frames, images.unsqueeze(1)), dim=1) + else: + num_splits_h, num_splits_w = 0, 0 + frames = images.unsqueeze(1) + + num_splits_h = [num_splits_h] * batch_size + num_splits_w = [num_splits_w] * batch_size + + return frames, num_splits_h, num_splits_w + + def resize_for_vision_encoder( + self, + image: torch.Tensor, + vision_encoder_max_size: int, + resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None, + ): + """ + Resize images to be multiples of `vision_encoder_max_size` while preserving the aspect ratio. + Args: + image (`torch.Tensor`): + Images to resize. + vision_encoder_max_size (`int`): + Maximum size of the output image. If the image is larger than this size, it will be split into + patches of this size, and the original image will be concatenated with the patches, resized to max_size. + resample (`PILImageResampling | tvF.InterpolationMode | int | None`, *optional*): + Resampling filter to use when resizing the image. + """ + height, width = image.size()[-2:] + + aspect_ratio = width / height + if width >= height: + width = math.ceil(width / vision_encoder_max_size) * vision_encoder_max_size + height = int(width / aspect_ratio) + height = math.ceil(height / vision_encoder_max_size) * vision_encoder_max_size + elif height > width: + height = math.ceil(height / vision_encoder_max_size) * vision_encoder_max_size + width = int(height * aspect_ratio) + width = math.ceil(width / vision_encoder_max_size) * vision_encoder_max_size + new_size = SizeDict(height=height, width=width) + return self.resize(image, size=new_size, resample=resample) + + def pad( + self, + image: torch.Tensor, + padded_size: tuple[int, int], + fill: int = 0, + return_pixel_mask: bool = True, + ): + original_size = image.shape[-2:] + padding_bottom = padded_size[0] - original_size[0] + padding_right = padded_size[1] - original_size[1] + + if padding_bottom < 0 or padding_right < 0: + raise ValueError( + f"Padding dimensions are negative. Please make sure that the padded size is larger than the " + f"original size. Got padded size: {padded_size}, original size: {original_size}." + ) + + # Only pad if necessary + if original_size != padded_size: + padding = (0, 0, padding_right, padding_bottom) + image = tvF.pad(image, padding, fill=fill, padding_mode="constant") + + # Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. + pixel_mask = None + if return_pixel_mask: + pixel_mask = torch.zeros_like(image[..., 0, :, :], dtype=torch.int64) + pixel_mask[: original_size[0], : original_size[1]] = 1 + + return image, pixel_mask + + def _preprocess( + self, + images: list[list["torch.Tensor"]], + do_resize: bool, + size: SizeDict, + resample: "PILImageResampling | tvF.InterpolationMode | int | None", + do_rescale: bool, + rescale_factor: float, + do_normalize: bool, + image_mean: float | list[float] | None, + image_std: float | list[float] | None, + do_pad: bool | None, + do_image_splitting: bool | None, + max_image_size: dict[str, int] | None, + return_row_col_info: bool | None, + disable_grouping: bool | None, + return_tensors: str | TensorType | None, + **kwargs, + ) -> BatchFeature: + """ + Process a batch of images for the model. + """ + + grouped_images, grouped_images_index = group_images_by_shape( + images, is_nested=True, disable_grouping=disable_grouping + ) + resized_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + if do_resize: + stacked_images = self.resize(stacked_images, size, resample=resample) + resized_images_grouped[shape] = stacked_images + resized_images = reorder_images(resized_images_grouped, grouped_images_index, is_nested=True) + + grouped_images, grouped_images_index = group_images_by_shape( + resized_images, is_nested=True, disable_grouping=disable_grouping + ) + split_images_grouped = {} + if do_image_splitting: + rows_grouped = {} + cols_grouped = {} + for shape, stacked_images in grouped_images.items(): + stacked_images = self.resize_for_vision_encoder( + stacked_images, max_image_size["longest_edge"], resample=resample + ) + stacked_images, rows, cols = self.split_images( + stacked_images, max_image_size=max_image_size, resample=resample + ) + split_images_grouped[shape] = stacked_images + rows_grouped[shape] = rows + cols_grouped[shape] = cols + processed_images = reorder_images(split_images_grouped, grouped_images_index, is_nested=True) + rows = reorder_images(rows_grouped, grouped_images_index, is_nested=True) + cols = reorder_images(cols_grouped, grouped_images_index, is_nested=True) + # flattenened the doubly nested list to a nested list + for i, group_images in enumerate(processed_images): + processed_images[i] = [image for sublist in group_images for image in sublist] + else: + for shape, stacked_images in grouped_images.items(): + # We square the images to max_image_size + stacked_images = self.resize( + image=stacked_images, + size=SizeDict(height=max_image_size["longest_edge"], width=max_image_size["longest_edge"]), + resample=resample, + ) + split_images_grouped[shape] = stacked_images + processed_images = reorder_images(split_images_grouped, grouped_images_index, is_nested=True) + rows = [[0] * len(images) for images in processed_images] + cols = [[0] * len(images) for images in processed_images] + # Group images by size for further processing + # Needed in case do_resize is False, or resize returns images with different sizes + grouped_images, grouped_images_index = group_images_by_shape( + processed_images, is_nested=True, disable_grouping=disable_grouping + ) + processed_images_grouped = {} + for shape, stacked_images in grouped_images.items(): + # Fused rescale and normalize + stacked_images = self.rescale_and_normalize( + stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std + ) + processed_images_grouped[shape] = stacked_images + processed_images = reorder_images(processed_images_grouped, grouped_images_index, is_nested=True) + if do_pad: + # Get max images per batch + max_num_images = max(len(images_) for images_ in processed_images) + max_height, max_width = get_max_height_width(processed_images) + num_channels = get_num_channels(processed_images) + device = get_device_from_images(processed_images) + + processed_images_padded = torch.zeros( + len(processed_images), + max_num_images, + *(num_channels, max_height, max_width), + device=device, + ) + pixel_attention_masks = torch.zeros( + len(processed_images), + max_num_images, + *(max_height, max_width), + device=device, + ) + for i, images in enumerate(processed_images): + for j, image in enumerate(images): + processed_images_padded[i, j], pixel_attention_masks[i, j] = self.pad( + image, (max_height, max_width) + ) + processed_images = processed_images_padded + + if do_pad: + data = {"pixel_values": processed_images, "pixel_attention_mask": pixel_attention_masks} + elif return_tensors == "pt": + data = {"pixel_values": torch.stack([torch.stack(images) for images in processed_images])} + else: + data = {"pixel_values": processed_images} + # This is needed for generating correct text inputs in the processor - we don't pad to the max number of images + encoding = BatchFeature(data=data, tensor_type=return_tensors) + + if return_row_col_info: + encoding["rows"] = rows + encoding["cols"] = cols + + return encoding + + def to_dict(self): + encoder_dict = super().to_dict() + encoder_dict.pop("_valid_processor_keys", None) + encoder_dict.pop("return_row_col_info", None) + return encoder_dict + + def get_number_of_image_patches(self, height: int, width: int, images_kwargs: dict): + """ + A utility that returns number of image patches for a given image size. + + Args: + height (`int`): + Height of the input image. + width (`int`): + Width of the input image. + images_kwargs (`dict`) + Any kwargs to override defaults of the image processor. + Returns: + `int`: Number of patches per image. + """ + do_image_splitting = images_kwargs.get("do_image_splitting", self.do_image_splitting) + max_image_size = images_kwargs.get("max_image_size", self.max_image_size) + size = images_kwargs.get("size", self.size) + + num_patches = num_rows = num_cols = 0 + if do_image_splitting: + height, width = _resize_output_size_rescale_to_max_len(height, width, max_len=size["longest_edge"]) + height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=MAX_IMAGE_SIZE) + aspect_ratio = width / height + + if width >= height: + resized_width = math.ceil(width / max_image_size["longest_edge"]) * max_image_size["longest_edge"] + resized_height = int(width / aspect_ratio) + resized_height = math.ceil(height / max_image_size["longest_edge"]) * max_image_size["longest_edge"] + elif height > width: + resized_height = math.ceil(height / max_image_size["longest_edge"]) * max_image_size["longest_edge"] + resized_width = int(height * aspect_ratio) + resized_width = math.ceil(width / max_image_size["longest_edge"]) * max_image_size["longest_edge"] + + max_height = max_width = max_image_size["longest_edge"] + if resized_height > max_height or resized_width > max_width: + # Calculate the number of splits + num_rows = math.ceil(resized_height / max_height) + num_cols = math.ceil(resized_width / max_width) + num_patches = num_rows * num_cols + 1 + + return num_patches, num_rows, num_cols + + +__all__ = ["SmolVLMImageProcessor"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/modeling_smolvlm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/modeling_smolvlm.py new file mode 100644 index 0000000000000000000000000000000000000000..c9573c27a1776e1d834db94f3a275f3347846e8c --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/modeling_smolvlm.py @@ -0,0 +1,871 @@ +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# This file was automatically generated from src/transformers/models/smolvlm/modular_smolvlm.py. +# Do NOT edit this file manually as any edits will be overwritten by the generation of +# the file from the modular. If any change should be done, please apply the change to the +# modular_smolvlm.py file directly. One of our CI enforces this. +# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# Written by Orr Zohar +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +from collections.abc import Callable +from dataclasses import dataclass + +import torch +from torch import nn + +from ...activations import ACT2FN +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationConfig, GenerationMixin +from ...masking_utils import create_bidirectional_mask +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_layers import GradientCheckpointingLayer +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ModelOutput +from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check +from ...utils.generic import merge_with_config_defaults +from ...utils.output_capturing import capture_outputs +from ..auto import AutoModel +from .configuration_smolvlm import SmolVLMConfig, SmolVLMVisionConfig + + +logger = logging.get_logger(__name__) + + +@auto_docstring +class SmolVLMPreTrainedModel(PreTrainedModel): + config: SmolVLMConfig + base_model_prefix = "model" + input_modalities = ("image", "text") + supports_gradient_checkpointing = True + _no_split_modules = ["SmolVLMVisionAttention", "SmolVLMDecoderLayer"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn = True + _supports_sdpa = True + _supports_flex_attn = True + _supports_attention_backend = True + + +class SmolVLMVisionEmbeddings(nn.Module): + """ + This is a modified version of `siglip.modelign_siglip.SiglipVisionEmbeddings` to enable images of variable + resolution. + + The modifications are adapted from [Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution](https://huggingface.co/papers/2307.06304) + which allows treating images in their native aspect ratio and without the need to resize them to the same + fixed size. In particular, we start from the original pre-trained SigLIP model + (which uses images of fixed-size square images) and adapt it by training on images of variable resolutions. + """ + + def __init__(self, config: SmolVLMVisionConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.image_size = config.image_size + self.patch_size = config.patch_size + + self.patch_embedding = nn.Conv2d( + in_channels=config.num_channels, + out_channels=self.embed_dim, + kernel_size=self.patch_size, + stride=self.patch_size, + padding="valid", + ) + + self.num_patches_per_side = self.image_size // self.patch_size + self.num_patches = self.num_patches_per_side**2 + self.num_positions = self.num_patches + self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) + + def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor: + batch_size, _, max_im_h, max_im_w = pixel_values.shape + + patch_embeds = self.patch_embedding(pixel_values) + embeddings = patch_embeds.flatten(2).transpose(1, 2) + + max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size + boundaries = torch.arange( + 1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side, device=pixel_values.device + ) + position_ids = torch.full( + size=(batch_size, max_nb_patches_h * max_nb_patches_w), fill_value=0, device=pixel_values.device + ) + + nb_patches_h = patch_attention_mask[:, :, 0].sum(dim=1) # (batch_size,) + nb_patches_w = patch_attention_mask[:, 0, :].sum(dim=1) # (batch_size,) + + step_h = 1.0 / nb_patches_h # (batch_size,) + step_w = 1.0 / nb_patches_w # (batch_size,) + + max_patches_h = patch_attention_mask.size(1) + max_patches_w = patch_attention_mask.size(2) + h_indices = torch.arange(max_patches_h, device=position_ids.device, dtype=torch.float32) + w_indices = torch.arange(max_patches_w, device=position_ids.device, dtype=torch.float32) + + fractional_coords_h = h_indices[None, :] * step_h[:, None] + fractional_coords_w = w_indices[None, :] * step_w[:, None] + + fractional_coords_h = torch.clamp(fractional_coords_h, max=(1.0 - 1e-6)) + fractional_coords_w = torch.clamp(fractional_coords_w, max=(1.0 - 1e-6)) + + fractional_coords_h = fractional_coords_h.to(pixel_values.dtype) + fractional_coords_w = fractional_coords_w.to(pixel_values.dtype) + + bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True) + bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True) + + pos_ids = bucket_coords_h[:, :, None] * self.num_patches_per_side + bucket_coords_w[:, None, :] + pos_ids = pos_ids.reshape(batch_size, -1) + + position_ids[patch_attention_mask.view(batch_size, -1)] = pos_ids[patch_attention_mask.view(batch_size, -1)] + + embeddings = embeddings + self.position_embedding(position_ids) + return embeddings + + +def eager_attention_forward( + module: nn.Module, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attention_mask: torch.Tensor | None, + scaling: float, + dropout: float = 0.0, + **kwargs, +): + attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) + + attn_output = torch.matmul(attn_weights, value) + attn_output = attn_output.transpose(1, 2).contiguous() + + return attn_output, attn_weights + + +class SmolVLMVisionAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + def __init__(self, config): + super().__init__() + self.config = config + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {self.num_heads})." + ) + self.scale = self.head_dim**-0.5 + self.dropout = config.attention_dropout + + self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) + + # Ignore copy + self.is_causal = False + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + **kwargs, + ) -> tuple[torch.Tensor, torch.Tensor | None]: + """Input shape: Batch x Time x Channel""" + input_shape = hidden_states.shape[:-1] + hidden_shape = (*input_shape, -1, self.head_dim) + queries = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) + keys = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) + values = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) + + attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface( + self.config._attn_implementation, eager_attention_forward + ) + + attn_output, attn_weights = attention_interface( + self, + queries, + keys, + values, + attention_mask, + is_causal=self.is_causal, + scaling=self.scale, + dropout=0.0 if not self.training else self.dropout, + ) + + attn_output = attn_output.reshape(*input_shape, -1).contiguous() + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights + + +class SmolVLMVisionMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.activation_fn = ACT2FN[config.hidden_act] + self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) + self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + hidden_states = self.fc1(hidden_states) + hidden_states = self.activation_fn(hidden_states) + hidden_states = self.fc2(hidden_states) + return hidden_states + + +class SmolVLMEncoderLayer(GradientCheckpointingLayer): + def __init__(self, config: SmolVLMVisionConfig): + super().__init__() + self.embed_dim = config.hidden_size + self.self_attn = SmolVLMVisionAttention(config) + self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + self.mlp = SmolVLMVisionMLP(config) + self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) + + @auto_docstring + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + **kwargs: Unpack[TransformersKwargs], + ) -> torch.FloatTensor: + residual = hidden_states + + hidden_states = self.layer_norm1(hidden_states) + hidden_states, _ = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + **kwargs, + ) + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.layer_norm2(hidden_states) + hidden_states = self.mlp(hidden_states) + hidden_states = residual + hidden_states + + return hidden_states + + +class SmolVLMEncoder(nn.Module): + """ + Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a + [`SmolVLMEncoderLayer`]. + + Args: + config: SmolVLMConfig + """ + + def __init__(self, config: SmolVLMConfig): + super().__init__() + self.config = config + self.layers = nn.ModuleList([SmolVLMEncoderLayer(config) for _ in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + # Ignore copy + @auto_docstring + def forward( + self, + inputs_embeds, + attention_mask: torch.Tensor | None = None, + ) -> tuple | BaseModelOutput: + hidden_states = inputs_embeds + for encoder_layer in self.layers: + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + ) + + hidden_states = layer_outputs + + return BaseModelOutput(last_hidden_state=hidden_states) + + +@auto_docstring( + custom_intro=""" + The SmolVLM Vision Transformer Model outputting raw image embedding. + """ +) +class SmolVLMVisionTransformer(SmolVLMPreTrainedModel): + config: SmolVLMVisionConfig + input_modalities = ("image",) + _can_record_outputs = { + "hidden_states": SmolVLMEncoderLayer, + "attentions": SmolVLMVisionAttention, + } + + def __init__(self, config: SmolVLMVisionConfig): + super().__init__(config) + embed_dim = config.hidden_size + + self.embeddings = SmolVLMVisionEmbeddings(config) + self.encoder = SmolVLMEncoder(config) + self.patch_size = config.patch_size + self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) + + self.post_init() + + def get_input_embeddings(self): + return self.embeddings + + def set_input_embeddings(self, value): + self.embeddings = value + + @merge_with_config_defaults + @capture_outputs(tie_last_hidden_states=False) + def forward( + self, + pixel_values, + patch_attention_mask: torch.BoolTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutput: + batch_size = pixel_values.size(0) + if patch_attention_mask is None: + patch_size = self.patch_size + patch_attention_mask = torch.ones( + ( + batch_size, + pixel_values.size(2) // patch_size, + pixel_values.size(3) // patch_size, + ) + ) + patch_attention_mask = patch_attention_mask.to(dtype=torch.bool, device=pixel_values.device) + + hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask) + + patch_attention_mask = patch_attention_mask.view(batch_size, -1) + # Create the correct attention mask based on the attention implementation + patch_attention_mask = create_bidirectional_mask( + config=self.config, + inputs_embeds=hidden_states, + attention_mask=patch_attention_mask, + ) + + encoder_outputs: BaseModelOutput = self.encoder( + inputs_embeds=hidden_states, + attention_mask=patch_attention_mask, + ) + + last_hidden_state = encoder_outputs.last_hidden_state + last_hidden_state = self.post_layernorm(last_hidden_state) + + return BaseModelOutput( + last_hidden_state=last_hidden_state, + ) + + +@auto_docstring( + custom_intro=""" + Base class for SmolVLM model's outputs that may also contain a past key/values (to speed up sequential decoding). + """ +) +@dataclass +class SmolVLMBaseModelOutputWithPast(ModelOutput): + r""" + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1, + hidden_size)` is output. + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if + `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values` + input) to speed up sequential decoding. + image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, + sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder + """ + + last_hidden_state: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + image_hidden_states: tuple[torch.FloatTensor] | None = None + + +class SmolVLMSimpleMLP(nn.Module): + def __init__(self, config): + super().__init__() + input_size = config.vision_config.hidden_size * (config.scale_factor**2) + output_size = config.text_config.hidden_size + self.proj = nn.Linear(input_size, output_size, bias=False) + + def forward(self, x): + return self.proj(x) + + +class SmolVLMConnector(nn.Module): + def __init__(self, config): + super().__init__() + self.scale_factor = config.scale_factor + self.modality_projection = SmolVLMSimpleMLP(config) + + def pixel_shuffle(self, x, scale_factor=2): + bsz, seq, embed_dim = x.size() + height = width = int(seq**0.5) + x = x.view(bsz, height, width, embed_dim) + x = x.view(bsz, height, int(width / scale_factor), embed_dim * scale_factor) + x = x.permute(0, 2, 1, 3) + x = x.reshape(bsz, int(width / scale_factor), int(height / scale_factor), embed_dim * (scale_factor**2)) + x = x.permute(0, 2, 1, 3) + x = x.reshape(bsz, int(seq / (scale_factor**2)), embed_dim * (scale_factor**2)) + return x + + def forward(self, image_hidden_states): + image_hidden_states = self.pixel_shuffle(image_hidden_states, self.scale_factor) + image_hidden_states = self.modality_projection(image_hidden_states) + return image_hidden_states + + +@auto_docstring( + custom_intro=""" + SmolVLM model consisting of a SIGLIP vision encoder and Llama3 language decoder + """ +) +class SmolVLMModel(SmolVLMPreTrainedModel): + """ + A subclass of Idefics3Model. We do *not* remove or block the call to inputs_merger + in forward. Instead, we override inputs_merger here with custom logic. + """ + + def __init__(self, config: SmolVLMConfig): + super().__init__(config) + self.padding_idx = self.config.text_config.pad_token_id + self.vocab_size = self.config.text_config.vocab_size + + self.vision_model = SmolVLMVisionTransformer._from_config(config.vision_config) + self.connector = SmolVLMConnector(config) + self.text_model = AutoModel.from_config(config.text_config) + + self.image_seq_len = int( + ((config.vision_config.image_size // config.vision_config.patch_size) ** 2) / (config.scale_factor**2) + ) + self.image_token_id = self.config.image_token_id + + self.post_init() + + def get_input_embeddings(self): + return self.text_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.text_model.set_input_embeddings(value) + + def inputs_merger( + self, input_ids: torch.LongTensor, inputs_embeds: torch.Tensor, image_hidden_states: torch.Tensor + ): + """ + This method aims at merging the token embeddings with the image hidden states into one single sequence of vectors that are fed to the transformer LM. + The merging happens as follows: + - The text token sequence is: `tok_1 tok_2 tok_3 ... tok_4`. + - We get the image hidden states for the image through the vision encoder and that hidden state, after a pixel shuffle operation, is then projected into the text embedding space. + We thus have a sequence of image hidden states of size (1, image_seq_len, hidden_dim), where 1 is for batch_size of 1 image and hidden_dim is the hidden_dim of the LM transformer. + - The merging happens so that we obtain the following sequence: `vector_tok_1 vector_tok_2 vector_tok_3 vector_fake_tok_around_image {sequence of image_seq_len image hidden states} vector_fake_toke_around_image vector_tok_4`. That sequence is fed to the LM. + - To fit the format of that sequence, `input_ids`, `inputs_embeds`, `attention_mask` are all 3 adapted to insert the image hidden states. + """ + _, patch_size, _ = image_hidden_states.shape + + if input_ids is None: + image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + image_mask = image_mask[..., 0] # slice off the hidden dim + else: + image_mask = input_ids == self.config.image_token_id + + num_image_tokens = image_mask.sum(dim=1) + torch_compilable_check( + torch.all(num_image_tokens % patch_size == 0), + "At least one sample has tokens not divisible by patch_size.", + ) + blocks_per_sample = num_image_tokens // patch_size + + offsets = torch.nn.functional.pad(blocks_per_sample.cumsum(dim=0), (1, 0), value=0) + block_offset = offsets[:-1] + row_cum = image_mask.cumsum(dim=-1) + chunk_idx = (row_cum - 1) // patch_size + local_idx = (row_cum - 1) % patch_size + block_idx = block_offset.unsqueeze(1) + chunk_idx + + image_embeds = torch.zeros_like(inputs_embeds) + image_embeds[image_mask] = image_hidden_states[block_idx[image_mask], local_idx[image_mask], :] + + merged_embeds = torch.where(image_mask.unsqueeze(-1), image_embeds, inputs_embeds) + return merged_embeds + + @can_return_tuple + @auto_docstring( + custom_intro="Encodes images into continuous embeddings that can be forwarded to the language model." + ) + def get_image_features( + self, + pixel_values: torch.FloatTensor, + pixel_attention_mask: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + pixel_attention_mask (`torch.LongTensor`, *optional*): + The attention mask indicating padded regions in the image. + """ + batch_size, num_images, num_channels, height, width = pixel_values.shape + pixel_values = pixel_values.to(dtype=self.dtype) # fp16 compatibility + pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:]) + + # Remove padding images - padding images are full 0. + nb_values_per_image = pixel_values.shape[1:].numel() + real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image + + # If no images, leave one empty image. + real_images_inds[0] |= ~torch.any(real_images_inds) + + pixel_values = pixel_values[real_images_inds].contiguous() + # Handle the vision attention mask + if pixel_attention_mask is None: + pixel_attention_mask = torch.ones( + size=[pixel_values.shape[i] for i in (0, 2, 3)], + dtype=torch.bool, + device=pixel_values.device, + ) + else: + # Remove padding images from the mask + pixel_attention_mask = pixel_attention_mask.view(batch_size * num_images, *pixel_attention_mask.shape[2:]) + pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous() + patch_size = self.config.vision_config.patch_size + patches_subgrid = pixel_attention_mask.unfold(dimension=1, size=patch_size, step=patch_size) + patches_subgrid = patches_subgrid.unfold(dimension=2, size=patch_size, step=patch_size) + patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool() + + # Get sequence from the vision encoder + image_outputs = self.vision_model( + pixel_values=pixel_values, patch_attention_mask=patch_attention_mask, return_dict=True, **kwargs + ) + image_hidden_states = image_outputs.last_hidden_state + + # Modality projection & resampling + image_features = self.connector(image_hidden_states) + image_outputs.pooler_output = image_features + + return image_outputs + + @can_return_tuple + @auto_docstring( + custom_intro=""" + Inputs fed to the model can have an arbitrary number of images. To account for this, pixel_values fed to + the model have image padding -> (batch_size, max_num_images, 3, max_heights, max_widths) where + max_num_images is the maximum number of images among the batch_size samples in the batch. + Padding images are not needed beyond padding the pixel_values at the entrance of the model. + For efficiency, we only pass through the vision_model's forward the real images by + discarding the padding images i.e. pixel_values of size (image_batch_size, 3, height, width) where + image_batch_size would be 7 when num_images_per_sample=[1, 3, 1, 2] and max_num_images would be 3. + """ + ) + @can_return_tuple + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_attention_mask: torch.BoolTensor | None = None, + image_hidden_states: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple | SmolVLMBaseModelOutputWithPast: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The hidden states of the image encoder after modality projection. + """ + if self.training and self.text_model.gradient_checkpointing and use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.text_model.get_input_embeddings()(input_ids).to(input_ids.device) + + if pixel_values is not None and image_hidden_states is not None: + raise ValueError("You cannot specify both pixel_values and image_hidden_states at the same time") + + if pixel_values is not None: + image_hidden_states = self.get_image_features( + pixel_values, pixel_attention_mask, return_dict=True + ).pooler_output + image_hidden_states = image_hidden_states.to(inputs_embeds.device) + elif image_hidden_states is not None: + image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=inputs_embeds.device) + + if image_hidden_states is not None: + inputs_embeds = self.inputs_merger( + input_ids=input_ids, + inputs_embeds=inputs_embeds, + image_hidden_states=image_hidden_states, + ) + + outputs = self.text_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + return SmolVLMBaseModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=image_hidden_states, + ) + + +@auto_docstring( + custom_intro=""" + Base class for Idefics causal language model (or autoregressive) outputs. + """ +) +@dataclass +class SmolVLMCausalLMOutputWithPast(ModelOutput): + r""" + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Language modeling loss (for next-token prediction). + logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): + Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). + past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): + It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). + + Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see + `past_key_values` input) to speed up sequential decoding. + image_hidden_states (`tuple(torch.FloatTensor)`, *optional*): + Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images, + sequence_length, hidden_size)`. + image_hidden_states of the model produced by the vision encoder + """ + + loss: torch.FloatTensor | None = None + logits: torch.FloatTensor | None = None + past_key_values: Cache | None = None + hidden_states: tuple[torch.FloatTensor] | None = None + attentions: tuple[torch.FloatTensor] | None = None + image_hidden_states: tuple[torch.FloatTensor] | None = None + + +@auto_docstring( + custom_intro=""" + The SmolVLM Model with a language modeling head. It is made up a SigLIP vision encoder, with a language modeling head on top. + """ +) +class SmolVLMForConditionalGeneration(SmolVLMPreTrainedModel, GenerationMixin): + _tied_weights_keys = {"lm_head.weight": "model.text_model.embed_tokens.weight"} + + def __init__(self, config): + super().__init__(config) + self.model = SmolVLMModel(config) + self.image_token_id = self.config.image_token_id + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.vocab_size = config.text_config.vocab_size + self.model.text_model.generation_config = GenerationConfig.from_model_config(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.text_model.get_input_embeddings() + + def set_input_embeddings(self, value): + self.model.text_model.set_input_embeddings(value) + + @auto_docstring + def get_image_features( + self, + pixel_values: torch.FloatTensor, + pixel_attention_mask: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + pixel_attention_mask (`torch.LongTensor`, *optional*): + The attention mask indicating padded regions in the image. + """ + return self.model.get_image_features( + pixel_values=pixel_values, pixel_attention_mask=pixel_attention_mask, **kwargs + ) + + @can_return_tuple + @auto_docstring + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_attention_mask: torch.BoolTensor | None = None, + image_hidden_states: torch.FloatTensor | None = None, + labels: torch.LongTensor | None = None, + use_cache: bool | None = None, + logits_to_keep: int | torch.Tensor = 0, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | SmolVLMCausalLMOutputWithPast: + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The hidden states of the image encoder after modality projection. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or `model.image_token_id`. Tokens with indices set to `model.image_token_id` are + ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> import httpx + >>> from io import BytesIO + >>> import torch + >>> from PIL import Image + >>> from io import BytesIO + + >>> from transformers import AutoProcessor, AutoModelForImageTextToText + >>> from transformers.image_utils import load_image + + >>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible + >>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg") + >>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg") + >>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg") + + >>> processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct") + >>> model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", dtype=torch.bfloat16, device_map="auto") + + >>> # Create inputs + >>> messages = [ + ... { + ... "role": "user", + ... "content": [ + ... {"type": "video", "path": path/to/video}, + ... {"type": "text", "text": "What is happening in this video?"}, + ... ] + ... } + ... ] + + >>> inputs = processor.apply_chat_template([messages], add_generation_prompt=True) + + >>> # Generate + >>> generated_ids = model.generate(**inputs, max_new_tokens=256) + >>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True) + + >>> print(generated_texts) + ```""" + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + pixel_values=pixel_values, + pixel_attention_mask=pixel_attention_mask, + image_hidden_states=image_hidden_states, + use_cache=use_cache, + return_dict=True, + **kwargs, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep + logits = self.lm_head(hidden_states[:, slice_indices, :]) + + loss = None + if labels is not None: + loss = self.loss_function( + logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs + ) + + return SmolVLMCausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=outputs.image_hidden_states, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + pixel_values=None, + pixel_attention_mask=None, + image_hidden_states=None, + logits_to_keep=None, + is_first_iteration=False, + use_cache=False, + **kwargs, + ): + # Overwritten -- there are mutually exclusive inputs (if the logic to make `image_hidden_states` take + # precedence is moved to the model, we can remove this fn) + + model_inputs = super().prepare_inputs_for_generation( + input_ids, + past_key_values=past_key_values, + attention_mask=attention_mask, + inputs_embeds=inputs_embeds, + pixel_values=pixel_values, + pixel_attention_mask=pixel_attention_mask, + image_hidden_states=image_hidden_states, + logits_to_keep=logits_to_keep, + is_first_iteration=is_first_iteration, + use_cache=use_cache, + **kwargs, + ) + + if image_hidden_states is not None or (use_cache and not is_first_iteration): + model_inputs["pixel_values"] = None + model_inputs["pixel_attention_mask"] = None + + return model_inputs + + +__all__ = ["SmolVLMForConditionalGeneration", "SmolVLMPreTrainedModel", "SmolVLMModel", "SmolVLMVisionTransformer"] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/modular_smolvlm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/modular_smolvlm.py new file mode 100644 index 0000000000000000000000000000000000000000..cf91863c56a7c00bb72c9eafd2e876afacae9be8 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/modular_smolvlm.py @@ -0,0 +1,354 @@ +# Copyright 2025 the HuggingFace Inc. team. All rights reserved. +# Written by Orr Zohar +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +from huggingface_hub.dataclasses import strict +from torch import nn + +from ...cache_utils import Cache, DynamicCache +from ...generation import GenerationConfig +from ...modeling_flash_attention_utils import FlashAttentionKwargs +from ...modeling_outputs import BaseModelOutputWithPooling +from ...processing_utils import Unpack +from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check +from ..idefics3.configuration_idefics3 import Idefics3Config, Idefics3VisionConfig +from ..idefics3.image_processing_idefics3 import Idefics3ImageProcessor +from ..idefics3.image_processing_pil_idefics3 import Idefics3ImageProcessorPil +from ..idefics3.modeling_idefics3 import ( + Idefics3BaseModelOutputWithPast, + Idefics3ForConditionalGeneration, + Idefics3Model, + Idefics3PreTrainedModel, + Idefics3VisionTransformer, +) + + +logger = logging.get_logger(__name__) + + +@auto_docstring(checkpoint="HuggingFaceTB/SmolVLM2-2.2B-Instruct") +@strict +class SmolVLMVisionConfig(Idefics3VisionConfig): + r""" + Example: + + ```python + >>> from transformers.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer + >>> from transformers.models.smolvlm.configuration_smolvlm import SmolVLMVisionConfig + + >>> # Initializing a SmolVLMVisionConfig with google/siglip-so400m-patch14-384 style configuration + >>> configuration = SmolVLMVisionConfig() + + >>> # Initializing a SmolVLMVisionTransformer (with random weights) from the google/siglip-so400m-patch14-384 style configuration + >>> model = SmolVLMVisionTransformer(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "smolvlm_vision" + + +class SmolVLMPreTrainedModel(Idefics3PreTrainedModel): + pass + + +class SmolVLMVisionTransformer(Idefics3VisionTransformer): + pass + + +@auto_docstring(checkpoint="HuggingFaceTB/SmolVLM2-2.2B-Instruct") +@strict +class SmolVLMConfig(Idefics3Config): + r""" + scale_factor (`int`, *optional*, defaults to 2): + The scale factor for the image encoder. + + Example: + ```python + >>> from transformers import SmolVLMModel, SmolVLMConfig + >>> # Initializing configuration + >>> configuration = SmolVLMConfig() + >>> # Initializing a model from the configuration + >>> model = SmolVLMModel(configuration) + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "smolvlm" + + +class SmolVLMImageProcessor(Idefics3ImageProcessor): + pass + + +class SmolVLMImageProcessorPil(Idefics3ImageProcessorPil): + pass + + +class SmolVLMBaseModelOutputWithPast(Idefics3BaseModelOutputWithPast): + pass + + +class SmolVLMModel(Idefics3Model): + """ + A subclass of Idefics3Model. We do *not* remove or block the call to inputs_merger + in forward. Instead, we override inputs_merger here with custom logic. + """ + + def inputs_merger( + self, input_ids: torch.LongTensor, inputs_embeds: torch.Tensor, image_hidden_states: torch.Tensor + ): + _, patch_size, _ = image_hidden_states.shape + + if input_ids is None: + image_mask = inputs_embeds == self.get_input_embeddings()( + torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device) + ) + image_mask = image_mask[..., 0] # slice off the hidden dim + else: + image_mask = input_ids == self.config.image_token_id + + num_image_tokens = image_mask.sum(dim=1) + torch_compilable_check( + torch.all(num_image_tokens % patch_size == 0), + "At least one sample has tokens not divisible by patch_size.", + ) + blocks_per_sample = num_image_tokens // patch_size + + offsets = torch.nn.functional.pad(blocks_per_sample.cumsum(dim=0), (1, 0), value=0) + block_offset = offsets[:-1] + row_cum = image_mask.cumsum(dim=-1) + chunk_idx = (row_cum - 1) // patch_size + local_idx = (row_cum - 1) % patch_size + block_idx = block_offset.unsqueeze(1) + chunk_idx + + image_embeds = torch.zeros_like(inputs_embeds) + image_embeds[image_mask] = image_hidden_states[block_idx[image_mask], local_idx[image_mask], :] + + merged_embeds = torch.where(image_mask.unsqueeze(-1), image_embeds, inputs_embeds) + return merged_embeds + + @can_return_tuple + @auto_docstring( + custom_intro="Encodes images into continuous embeddings that can be forwarded to the language model." + ) + def get_image_features( + self, + pixel_values: torch.FloatTensor, + pixel_attention_mask: torch.LongTensor | None = None, + **kwargs: Unpack[TransformersKwargs], + ) -> tuple | BaseModelOutputWithPooling: + r""" + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The tensors corresponding to the input images. + pixel_attention_mask (`torch.LongTensor`, *optional*): + The attention mask indicating padded regions in the image. + """ + batch_size, num_images, num_channels, height, width = pixel_values.shape + pixel_values = pixel_values.to(dtype=self.dtype) # fp16 compatibility + pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:]) + + # Remove padding images - padding images are full 0. + nb_values_per_image = pixel_values.shape[1:].numel() + real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image + + # If no images, leave one empty image. + real_images_inds[0] |= ~torch.any(real_images_inds) + + pixel_values = pixel_values[real_images_inds].contiguous() + # Handle the vision attention mask + if pixel_attention_mask is None: + pixel_attention_mask = torch.ones( + size=[pixel_values.shape[i] for i in (0, 2, 3)], + dtype=torch.bool, + device=pixel_values.device, + ) + else: + # Remove padding images from the mask + pixel_attention_mask = pixel_attention_mask.view(batch_size * num_images, *pixel_attention_mask.shape[2:]) + pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous() + patch_size = self.config.vision_config.patch_size + patches_subgrid = pixel_attention_mask.unfold(dimension=1, size=patch_size, step=patch_size) + patches_subgrid = patches_subgrid.unfold(dimension=2, size=patch_size, step=patch_size) + patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool() + + # Get sequence from the vision encoder + image_outputs = self.vision_model( + pixel_values=pixel_values, patch_attention_mask=patch_attention_mask, return_dict=True, **kwargs + ) + image_hidden_states = image_outputs.last_hidden_state + + # Modality projection & resampling + image_features = self.connector(image_hidden_states) + image_outputs.pooler_output = image_features + + return image_outputs + + @can_return_tuple + @auto_docstring( + custom_intro=""" + Inputs fed to the model can have an arbitrary number of images. To account for this, pixel_values fed to + the model have image padding -> (batch_size, max_num_images, 3, max_heights, max_widths) where + max_num_images is the maximum number of images among the batch_size samples in the batch. + Padding images are not needed beyond padding the pixel_values at the entrance of the model. + For efficiency, we only pass through the vision_model's forward the real images by + discarding the padding images i.e. pixel_values of size (image_batch_size, 3, height, width) where + image_batch_size would be 7 when num_images_per_sample=[1, 3, 1, 2] and max_num_images would be 3. + """ + ) + @can_return_tuple + def forward( + self, + input_ids: torch.LongTensor | None = None, + attention_mask: torch.Tensor | None = None, + position_ids: torch.LongTensor | None = None, + past_key_values: Cache | None = None, + inputs_embeds: torch.FloatTensor | None = None, + pixel_values: torch.FloatTensor | None = None, + pixel_attention_mask: torch.BoolTensor | None = None, + image_hidden_states: torch.FloatTensor | None = None, + use_cache: bool | None = None, + **kwargs: Unpack[FlashAttentionKwargs], + ) -> tuple | SmolVLMBaseModelOutputWithPast: + if self.training and self.text_model.gradient_checkpointing and use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if input_ids is not None: + batch_size, seq_length = input_ids.shape + elif inputs_embeds is not None: + batch_size, seq_length, _ = inputs_embeds.shape + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + if use_cache and past_key_values is None: + past_key_values = DynamicCache(config=self.config) + + if inputs_embeds is None: + inputs_embeds = self.text_model.get_input_embeddings()(input_ids).to(input_ids.device) + + if pixel_values is not None and image_hidden_states is not None: + raise ValueError("You cannot specify both pixel_values and image_hidden_states at the same time") + + if pixel_values is not None: + image_hidden_states = self.get_image_features( + pixel_values, pixel_attention_mask, return_dict=True + ).pooler_output + image_hidden_states = image_hidden_states.to(inputs_embeds.device) + elif image_hidden_states is not None: + image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=inputs_embeds.device) + + if image_hidden_states is not None: + inputs_embeds = self.inputs_merger( + input_ids=input_ids, + inputs_embeds=inputs_embeds, + image_hidden_states=image_hidden_states, + ) + + outputs = self.text_model( + inputs_embeds=inputs_embeds, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + use_cache=use_cache, + **kwargs, + ) + + return SmolVLMBaseModelOutputWithPast( + last_hidden_state=outputs.last_hidden_state, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + image_hidden_states=image_hidden_states, + ) + + +class SmolVLMForConditionalGeneration(Idefics3ForConditionalGeneration): + _tied_weights_keys = {"lm_head.weight": "model.text_model.embed_tokens.weight"} + + def __init__(self, config): + super().__init__(config) + self.model = SmolVLMModel(config) + self.model.text_model.generation_config = GenerationConfig.from_model_config(config) + self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False) + self.post_init() + + def forward(self, **super_kwargs): + r""" + pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*): + Mask to avoid performing attention on padding pixel indices. + image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): + The hidden states of the image encoder after modality projection. + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or `model.image_token_id`. Tokens with indices set to `model.image_token_id` are + ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + Example: + + ```python + >>> import httpx + >>> from io import BytesIO + >>> import torch + >>> from PIL import Image + >>> from io import BytesIO + + >>> from transformers import AutoProcessor, AutoModelForImageTextToText + >>> from transformers.image_utils import load_image + + >>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible + >>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg") + >>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg") + >>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg") + + >>> processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct") + >>> model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", dtype=torch.bfloat16, device_map="auto") + + >>> # Create inputs + >>> messages = [ + ... { + ... "role": "user", + ... "content": [ + ... {"type": "video", "path": path/to/video}, + ... {"type": "text", "text": "What is happening in this video?"}, + ... ] + ... } + ... ] + + >>> inputs = processor.apply_chat_template([messages], add_generation_prompt=True) + + >>> # Generate + >>> generated_ids = model.generate(**inputs, max_new_tokens=256) + >>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True) + + >>> print(generated_texts) + ```""" + super().forward(**super_kwargs) + + +__all__ = [ + "SmolVLMVisionConfig", + "SmolVLMConfig", + "SmolVLMImageProcessor", + "SmolVLMImageProcessorPil", + "SmolVLMForConditionalGeneration", + "SmolVLMPreTrainedModel", + "SmolVLMModel", + "SmolVLMVisionTransformer", +] diff --git a/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/processing_smolvlm.py b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/processing_smolvlm.py new file mode 100644 index 0000000000000000000000000000000000000000..0107ae31cbfe7ae81c16adf42ed1b7745f03d9e9 --- /dev/null +++ b/micromamba_root/envs/pytorch_env/Lib/site-packages/transformers/models/smolvlm/processing_smolvlm.py @@ -0,0 +1,353 @@ +# Copyright 2025 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Processor class for SmolVLM. +""" + +from datetime import timedelta +from typing import TYPE_CHECKING, Union + +from ...feature_extraction_utils import BatchFeature +from ...image_utils import ImageInput, make_nested_list_of_images +from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack +from ...tokenization_utils_base import BatchEncoding, TextInput +from ...utils import auto_docstring, is_num2words_available, logging +from ...video_utils import VideoInput + + +# Adapted from transformers.models.smolvlm.video_processing_smolvlm.DEFAULT_VIDEO_INTRO +DEFAULT_VIDEO_INTRO = ( + "You are provided the following series of {frame_count} frames from a {video_duration} [H:MM:SS] video.\n" +) +# Adapted from transformers.models.smolvlm.video_processing_smolvlm.DEFAULT_MEDIA_OUTTRO +DEFAULT_MEDIA_OUTTRO = "\n\n" +# Adapted from transformers.models.smolvlm.video_processing_smolvlm.FRAME_TIMESTAMP_MESSAGE +FRAME_TIMESTAMP_MESSAGE = "\nFrame from {timestamp}:" + +if TYPE_CHECKING: + from ...tokenization_utils_base import PreTokenizedInput + +logger = logging.get_logger(__name__) + + +if is_num2words_available(): + from num2words import num2words +else: + num2words = None + + +# The correct chat template to be used for videos after #38105 +DEFAULT_CHAT_TEMPLATE = "<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '' }}{% elif line['type'] == 'video' %}{{ '