text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
class Conv2dSamePadding(nn.Conv2d):
"""
Conv2d layer with padding="same" support. Source:
https://gist.github.com/sumanmichael/4de9dee93f972d47c80c4ade8e149ea6
"""
def __init__(self, *args, **kwargs):
super(Conv2dSamePadding, self).__init__(*args, **kwargs)
self.zero_pad_2d = nn.Zer... | class_definition | 113,104 | 113,660 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,100 |
class Conv2DDownsample(nn.Module):
"""Downsamples 4x by applying a 2D convolution and doing max pooling."""
def __init__(
self,
num_layers: int = 1,
in_channels: int = 3,
out_channels: int = 64,
use_batchnorm: bool = True,
):
"""
Constructs a Conv2DDo... | class_definition | 113,663 | 114,918 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,101 |
class PerceiverAbstractPositionEncoding(nn.Module, metaclass=abc.ABCMeta):
"""Perceiver abstract position encoding."""
@property
@abc.abstractmethod
def num_dimensions(self) -> int:
raise NotImplementedError
@abc.abstractmethod
def output_size(self, *args, **kwargs) -> int:
rai... | class_definition | 118,262 | 118,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,102 |
class PerceiverTrainablePositionEncoding(PerceiverAbstractPositionEncoding):
"""Trainable position encoding."""
def __init__(self, index_dims, num_channels=128):
super().__init__()
self._num_channels = num_channels
self._index_dims = index_dims
index_dim = np.prod(index_dims)
... | class_definition | 118,706 | 120,838 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,103 |
class PerceiverFourierPositionEncoding(PerceiverAbstractPositionEncoding):
"""Fourier (Sinusoidal) position encoding."""
def __init__(self, num_bands, max_resolution, concat_pos=True, sine_only=False):
super().__init__()
self.num_bands = num_bands
self.max_resolution = max_resolution
... | class_definition | 122,175 | 123,610 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,104 |
class AbstractPreprocessor(nn.Module):
@property
def num_channels(self) -> int:
"""Returns size of preprocessor output."""
raise NotImplementedError() | class_definition | 123,613 | 123,787 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,105 |
class PerceiverTextPreprocessor(AbstractPreprocessor):
"""
Text preprocessing for Perceiver Encoder. Can be used to embed `inputs` and add positional encodings.
The dimensionality of the embeddings is determined by the `d_model` attribute of the configuration.
Args:
config ([`PerceiverConfig`]... | class_definition | 123,790 | 125,068 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,106 |
class PerceiverEmbeddingDecoder(nn.Module):
"""
Module to decode embeddings (for masked language modeling).
Args:
config ([`PerceiverConfig`]):
Model configuration.
"""
def __init__(self, config: PerceiverConfig) -> None:
super().__init__()
self.config = config
... | class_definition | 125,071 | 125,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,107 |
class PerceiverMultimodalPostprocessor(nn.Module):
"""
Multimodal postprocessing for Perceiver. Can be used to combine modality-specific postprocessors into a single
postprocessor.
Args:
modalities (`Mapping[str, PostprocessorType]`):
Dictionary mapping modality name to postproces... | class_definition | 125,902 | 127,415 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,108 |
class PerceiverClassificationPostprocessor(nn.Module):
"""
Classification postprocessing for Perceiver. Can be used to convert the decoder output to classification logits.
Args:
config ([*PerceiverConfig*]):
Model configuration.
in_channels (`int`):
Number of channel... | class_definition | 127,418 | 128,109 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,109 |
class PerceiverAudioPostprocessor(nn.Module):
"""
Audio postprocessing for Perceiver. Can be used to convert the decoder output to audio features.
Args:
config ([*PerceiverConfig*]):
Model configuration.
in_channels (`int`):
Number of channels in the input.
p... | class_definition | 128,112 | 129,193 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,110 |
class PerceiverProjectionPostprocessor(nn.Module):
"""
Projection postprocessing for Perceiver. Can be used to project the channels of the decoder output to a lower
dimension.
Args:
in_channels (`int`):
Number of channels in the input.
out_channels (`int`):
Numbe... | class_definition | 129,196 | 129,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,111 |
class PerceiverImagePreprocessor(AbstractPreprocessor):
"""
Image preprocessing for Perceiver Encoder.
Note: the *out_channels* argument refers to the output channels of a convolutional layer, if *prep_type* is set to
"conv1x1" or "conv". If one adds absolute position embeddings, one must make sure the... | class_definition | 129,897 | 140,349 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,112 |
class PerceiverOneHotPreprocessor(AbstractPreprocessor):
"""
One-hot preprocessor for Perceiver Encoder. Can be used to add a dummy index dimension to the input.
Args:
config ([`PerceiverConfig`]):
Model configuration.
"""
def __init__(self, config: PerceiverConfig) -> None:
... | class_definition | 140,352 | 141,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,113 |
class PerceiverAudioPreprocessor(AbstractPreprocessor):
"""
Audio preprocessing for Perceiver Encoder.
Args:
config ([*PerceiverConfig*]):
Model configuration.
prep_type (`str`, *optional*, defaults to `"patches"`):
Preprocessor type to use. Only "patches" is support... | class_definition | 141,175 | 145,227 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,114 |
class PerceiverMultimodalPreprocessor(AbstractPreprocessor):
"""
Multimodal preprocessing for Perceiver Encoder.
Inputs for each modality are preprocessed, then padded with trainable position embeddings to have the same number
of channels.
Args:
modalities (`Mapping[str, PreprocessorType]`... | class_definition | 145,230 | 148,917 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/modeling_perceiver.py | null | 9,115 |
class PerceiverImageProcessor(BaseImageProcessor):
r"""
Constructs a Perceiver image processor.
Args:
do_center_crop (`bool`, `optional`, defaults to `True`):
Whether or not to center crop the image. If the input size if smaller than `crop_size` along any edge, the
image wil... | class_definition | 1,406 | 17,447 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/image_processing_perceiver.py | null | 9,116 |
class PerceiverConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PerceiverModel`]. It is used to instantiate an
Perceiver model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield... | class_definition | 1,108 | 9,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/configuration_perceiver.py | null | 9,117 |
class PerceiverOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 9,285 | 12,153 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/configuration_perceiver.py | null | 9,118 |
class PerceiverFeatureExtractor(PerceiverImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use PerceiverImageProcessor instead.",
Futu... | class_definition | 824 | 1,206 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/feature_extraction_perceiver.py | null | 9,119 |
class PerceiverTokenizer(PreTrainedTokenizer):
"""
Construct a Perceiver tokenizer. The Perceiver simply uses raw bytes utf-8 encoding.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding thos... | class_definition | 831 | 8,017 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/perceiver/tokenization_perceiver.py | null | 9,120 |
class InstructBlipVideoVisionConfig(InstructBlipVisionConfig):
pass | class_definition | 1,330 | 1,401 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modular_instructblipvideo.py | null | 9,121 |
class InstructBlipVideoQFormerConfig(InstructBlipQFormerConfig):
pass | class_definition | 1,404 | 1,477 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modular_instructblipvideo.py | null | 9,122 |
class InstructBlipVideoConfig(PretrainedConfig):
r"""
[`InstructBlipVideoConfig`] is the configuration class to store the configuration of a
[`InstructBlipVideoForConditionalGeneration`]. It is used to instantiate a Instructblipvideo model according to the specified
arguments, defining the vision model,... | class_definition | 1,480 | 6,699 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modular_instructblipvideo.py | null | 9,123 |
class InstructBlipVideoForConditionalGenerationModelOutput(InstructBlipForConditionalGenerationModelOutput):
pass | class_definition | 6,713 | 6,830 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modular_instructblipvideo.py | null | 9,124 |
class InstructBlipVideoForConditionalGeneration(InstructBlipForConditionalGeneration):
def forward(
self,
pixel_values: torch.FloatTensor,
qformer_input_ids: torch.FloatTensor,
qformer_attention_mask: Optional[torch.LongTensor] = None,
input_ids: Optional[torch.FloatTensor] =... | class_definition | 6,833 | 22,838 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modular_instructblipvideo.py | null | 9,125 |
class InstructBlipVideoForConditionalGenerationModelOutput(ModelOutput):
"""
Class defining the outputs of [`InstructBlipVideoForConditionalGeneration`].
Args:
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Language model... | class_definition | 2,492 | 3,941 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,126 |
class InstructBlipVideoVisionEmbeddings(nn.Module):
def __init__(self, config: InstructBlipVideoVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.cla... | class_definition | 3,944 | 7,380 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,127 |
class InstructBlipVideoAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_... | class_definition | 7,383 | 10,629 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,128 |
class InstructBlipVideoMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.h... | class_definition | 10,632 | 11,215 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,129 |
class InstructBlipVideoEncoderLayer(nn.Module):
def __init__(self, config: InstructBlipVideoConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = InstructBlipVideoAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
... | class_definition | 11,218 | 13,108 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,130 |
class InstructBlipVideoPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = InstructBlipVideoConfig
base_model_prefix = "blip"
supports_gradient_checkpointing = True
... | class_definition | 13,111 | 14,733 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,131 |
class InstructBlipVideoEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`InstructBlipVideoEncoderLayer`].
Args:
config (`InstructBlipVideoConfig`):
The corresponding vision configuration for the `InstructBli... | class_definition | 14,736 | 18,545 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,132 |
class InstructBlipVideoVisionModel(InstructBlipVideoPreTrainedModel):
main_input_name = "pixel_values"
config_class = InstructBlipVideoVisionConfig
def __init__(self, config: InstructBlipVideoVisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size... | class_definition | 19,544 | 22,123 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,133 |
class InstructBlipVideoQFormerMultiHeadAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
... | class_definition | 22,126 | 28,784 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,134 |
class InstructBlipVideoQFormerSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_drop... | class_definition | 28,787 | 29,413 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,135 |
class InstructBlipVideoQFormerAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.attention = InstructBlipVideoQFormerMultiHeadAttention(config, is_cross_attention)
self.output = InstructBlipVideoQFormerSelfOutput(config)
self.pruned_he... | class_definition | 29,416 | 31,584 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,136 |
class InstructBlipVideoQFormerIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
... | class_definition | 31,587 | 32,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,137 |
class InstructBlipVideoQFormerOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dr... | class_definition | 32,175 | 32,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,138 |
class InstructBlipVideoQFormerLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = InstructBlipVideoQFormerAttention(config)
self.layer_idx = layer_idx
... | class_definition | 32,806 | 36,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,139 |
class InstructBlipVideoQFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[InstructBlipVideoQFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_ch... | class_definition | 36,838 | 40,354 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,140 |
class InstructBlipVideoQFormerEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position... | class_definition | 40,357 | 42,361 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,141 |
class InstructBlipVideoQFormerModel(InstructBlipVideoPreTrainedModel):
"""
Querying Transformer (Q-Former), used in InstructBlipVideo. Slightly modified from BLIP-2 as it also takes the
instruction as input.
"""
def __init__(self, config: InstructBlipVideoQFormerConfig):
super().__init__(co... | class_definition | 42,364 | 52,219 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,142 |
class InstructBlipVideoForConditionalGeneration(InstructBlipVideoPreTrainedModel, GenerationMixin):
config_class = InstructBlipVideoConfig
main_input_name = "pixel_values"
def __init__(self, config: InstructBlipVideoConfig):
super().__init__(config)
self.vision_model = InstructBlipVideoVis... | class_definition | 57,217 | 77,069 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py | null | 9,143 |
class InstructBlipVideoProcessor(ProcessorMixin):
r"""
Constructs an InstructBLIPVideo processor which wraps a InstructBLIP image processor and a LLaMa/T5 tokenizer into a single
processor.
[`InstructBlipVideoProcessor`] offers all the functionalities of [`InstructBlipVideoImageProcessor`] and [`AutoTo... | class_definition | 1,191 | 11,107 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/processing_instructblipvideo.py | null | 9,144 |
class InstructBlipVideoVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipVideoVisionModel`]. It is used to
instantiate a InstructBlipVideo vision encoder according to the specified arguments, defining the model architecture.
Instantiating ... | class_definition | 1,718 | 5,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/configuration_instructblipvideo.py | null | 9,145 |
class InstructBlipVideoQFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InstructBlipVideoQFormerModel`]. It is used to
instantiate a InstructBlipVideo Querying Transformer (Q-Former) model according to the specified arguments, defining the
model arc... | class_definition | 5,715 | 11,541 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/configuration_instructblipvideo.py | null | 9,146 |
class InstructBlipVideoConfig(PretrainedConfig):
r"""
[`InstructBlipVideoConfig`] is the configuration class to store the configuration of a
[`InstructBlipVideoForConditionalGeneration`]. It is used to instantiate a Instructblipvideo model according to the specified
arguments, defining the vision model,... | class_definition | 11,544 | 16,763 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/configuration_instructblipvideo.py | null | 9,147 |
class InstructBlipVideoImageProcessor(BaseImageProcessor):
r"""
Constructs a InstructBLIPVideo image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by the
... | class_definition | 2,304 | 17,363 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/instructblipvideo/image_processing_instructblipvideo.py | null | 9,148 |
class GPT2Tokenizer(PreTrainedTokenizer):
"""
Construct a GPT-2 tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (wi... | class_definition | 2,304 | 13,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2.py | null | 9,149 |
class GPT2Attention(nn.Module):
def __init__(self, config, is_cross_attention=False, layer_idx=None):
super().__init__()
self.config = config
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max... | class_definition | 6,065 | 15,556 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,150 |
class GPT2MLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activation_function]
... | class_definition | 15,559 | 16,250 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,151 |
class GPT2Block(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self... | class_definition | 16,253 | 19,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,152 |
class GPT2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
load_tf_weights = load_tf_weights_in_gpt2
base_model_prefix = "transformer"
is_paralleli... | class_definition | 19,681 | 21,942 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,153 |
class GPT2DoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
mc_loss (`torch.Float... | class_definition | 21,956 | 24,554 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,154 |
class GPT2Model(GPT2PreTrainedModel):
_supports_param_buffer_assignment = False
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embed... | class_definition | 31,836 | 45,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,155 |
class GPT2LMHeadModel(GPT2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = GPT2Model(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
# Model parallel... | class_definition | 45,676 | 51,977 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,156 |
class GPT2DoubleHeadsModel(GPT2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 1
self.transformer = GPT2Model(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bi... | class_definition | 52,402 | 60,800 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,157 |
class GPT2ForSequenceClassification(GPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPT2Model(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Model parallel
... | class_definition | 61,590 | 67,000 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,158 |
class GPT2ForTokenClassification(GPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPT2Model(config)
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
classi... | class_definition | 67,229 | 71,154 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,159 |
class GPT2ForQuestionAnswering(GPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = GPT2Model(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Model parallel
self.model_parallel ... | class_definition | 71,457 | 75,836 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_gpt2.py | null | 9,160 |
class TFGPT2Tokenizer(keras.layers.Layer):
"""
This is an in-graph tokenizer for GPT2. It should be initialized similarly to other tokenizers, using the
`from_pretrained()` method. It can also be initialized with the `from_tokenizer()` method, which imports settings
from an existing standard tokenizer o... | class_definition | 255 | 3,832 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_tf.py | null | 9,161 |
class GPT2TokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" GPT-2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be enco... | class_definition | 1,061 | 5,246 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/tokenization_gpt2_fast.py | null | 9,162 |
class FlaxConv1D(nn.Module):
features: int
use_bias: bool = True
dtype: Any = jnp.float32
precision: Any = None
@nn.compact
def __call__(self, inputs):
inputs = jnp.asarray(inputs, self.dtype)
kernel = self.param("kernel", jax.nn.initializers.normal(stddev=0.02), (self.features,... | class_definition | 5,541 | 6,251 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,163 |
class FlaxGPT2Attention(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
causal: bool = True
is_cross_attention: bool = False
def setup(self):
config = self.config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_... | class_definition | 6,254 | 13,200 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,164 |
class FlaxGPT2MLP(nn.Module):
config: GPT2Config
intermediate_size: int
dtype: jnp.dtype = jnp.float32
def setup(self):
embed_dim = self.config.hidden_size
self.c_fc = FlaxConv1D(self.intermediate_size, dtype=self.dtype)
self.c_proj = FlaxConv1D(embed_dim, dtype=self.dtype)
... | class_definition | 13,203 | 13,967 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,165 |
class FlaxGPT2Block(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
hidden_size = self.config.hidden_size
inner_dim = self.config.n_inner if self.config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(epsilon=self.config.layer_norm_e... | class_definition | 13,970 | 17,135 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,166 |
class FlaxGPT2PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
base_model_prefix = "transformer"
module_class: nn.Module = None
def __init__(
... | class_definition | 17,138 | 23,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,167 |
class FlaxGPT2BlockCollection(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxGPT2Block(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def __call__(
self,
hidden_... | class_definition | 23,421 | 25,281 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,168 |
class FlaxGPT2Module(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.embed_dim = self.config.hidden_size
self.wte = nn.Embed(
self.config.vocab_size,
self.embed_dim,
embedding_init=jax.nn.initializers.normal(stddev=sel... | class_definition | 25,284 | 27,823 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,169 |
class FlaxGPT2Model(FlaxGPT2PreTrainedModel):
module_class = FlaxGPT2Module | class_definition | 27,979 | 28,058 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,170 |
class FlaxGPT2LMHeadModule(nn.Module):
config: GPT2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.transformer = FlaxGPT2Module(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,
... | class_definition | 28,211 | 30,105 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,171 |
class FlaxGPT2LMHeadModel(FlaxGPT2PreTrainedModel):
module_class = FlaxGPT2LMHeadModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values = self.i... | class_definition | 30,306 | 31,865 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_flax_gpt2.py | null | 9,172 |
class TFAttention(keras.layers.Layer):
def __init__(self, nx, config, scale=False, is_cross_attention=False, **kwargs):
super().__init__(**kwargs)
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
... | class_definition | 1,802 | 7,745 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,173 |
class TFMLP(keras.layers.Layer):
def __init__(self, n_state, config, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
self.c_fc = TFConv1D(n_state, nx, initializer_range=config.initializer_range, name="c_fc")
self.c_proj = TFConv1D(nx, n_state, initializer_range=config.initia... | class_definition | 7,748 | 8,909 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,174 |
class TFBlock(keras.layers.Layer):
def __init__(self, config, scale=False, **kwargs):
super().__init__(**kwargs)
nx = config.n_embd
inner_dim = config.n_inner if config.n_inner is not None else 4 * nx
self.ln_1 = keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name... | class_definition | 8,912 | 12,960 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,175 |
class TFGPT2MainLayer(keras.layers.Layer):
config_class = GPT2Config
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
self.config = config
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states... | class_definition | 12,983 | 23,836 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,176 |
class TFGPT2PreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPT2Config
base_model_prefix = "transformer"
# names with a '.' represents the authorized unexpecte... | class_definition | 23,839 | 24,883 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,177 |
class TFGPT2DoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
logits (`tf.Tensor` of shape `(batch_size, num_choices, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head ... | class_definition | 24,897 | 26,998 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,178 |
class TFGPT2Model(TFGPT2PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPT2MainLayer(config, name="transformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@add_code_samp... | class_definition | 33,987 | 37,807 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,179 |
class TFGPT2LMHeadModel(TFGPT2PreTrainedModel, TFCausalLanguageModelingLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.transformer = TFGPT2MainLayer(config, name="transformer")
def get_output_embeddings(self):
return self.get_input_... | class_definition | 38,008 | 44,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,180 |
class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
config.num_labels = 1
self.transformer = TFGPT2MainLayer(config, name="transformer")
self.multiple_choice_head = TFSequenceSummary(
... | class_definition | 44,718 | 50,937 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,181 |
class TFGPT2ForSequenceClassification(TFGPT2PreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.score = keras.layers.Dense(
config.num_labels,
... | class_definition | 51,729 | 56,595 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/modeling_tf_gpt2.py | null | 9,182 |
class GPT2Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 1,056 | 8,952 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py | null | 9,183 |
class GPT2OnnxConfig(OnnxConfigWithPast):
def __init__(
self,
config: PretrainedConfig,
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_pas... | class_definition | 8,955 | 11,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt2/configuration_gpt2.py | null | 9,184 |
class IBertEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.embedding_bit = 8
self.embedding_act_bit = 16
self.act_b... | class_definition | 1,820 | 6,915 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,185 |
class IBertSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the numbe... | class_definition | 6,918 | 13,044 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,186 |
class IBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32
self.dense = QuantLinear(
... | class_definition | 13,047 | 14,929 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,187 |
class IBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.self = IBertSelfAttention(config)
self.output = IBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) ... | class_definition | 14,932 | 16,888 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,188 |
class IBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.dense = QuantLinear(
config.hidden_size,
config.intermediate_siz... | class_definition | 16,891 | 18,277 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,189 |
class IBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.weight_bit = 8
self.bias_bit = 32
self.ln_input_bit = 22
self.ln_output_bit = 32
self.dense = QuantLinear(
... | class_definition | 18,280 | 20,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,190 |
class IBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.act_bit = 8
self.seq_len_dim = 1
self.attention = IBertAttention(config)
self.intermediate = IBertIntermediate(config)
self.output = IBertOutp... | class_definition | 20,167 | 22,392 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,191 |
class IBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.quant_mode = config.quant_mode
self.layer = nn.ModuleList([IBertLayer(config) for _ in range(config.num_hidden_layers)])
def forward(
self,
hidden_states,
... | class_definition | 22,395 | 24,594 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,192 |
class IBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.quant_mode = config.quant_mode
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply t... | class_definition | 24,597 | 25,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,193 |
class IBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = IBertConfig
base_model_prefix = "ibert"
def _init_weights(self, module):
"""Initialize the we... | class_definition | 25,174 | 26,440 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,194 |
class IBertModel(IBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/... | class_definition | 30,111 | 35,611 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,195 |
class IBertForMaskedLM(IBertPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.bias", "lm_head.decoder.weight"]
def __init__(self, config):
super().__init__(config)
self.ibert = IBertModel(config, add_pooling_layer=False)
self.lm_head = IBertLMHead(config)
# Initialize w... | class_definition | 35,719 | 38,984 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,196 |
class IBertLMHead(nn.Module):
"""I-BERT Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder... | class_definition | 38,987 | 40,058 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,197 |
class IBertForSequenceClassification(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.ibert = IBertModel(config, add_pooling_layer=False)
self.classifier = IBertClassificationHead(config)
# Initialize weigh... | class_definition | 40,283 | 44,040 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,198 |
class IBertForMultipleChoice(IBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.ibert = IBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initialize weights and apply f... | class_definition | 44,274 | 47,853 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/ibert/modeling_ibert.py | null | 9,199 |
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