text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return causal_mask | 3,273 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeForCausalLM(OlmoePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = OlmoeModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.voc... | 3,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
def get_decoder(self):
return self.model | 3,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
@add_start_docstrings_to_model_forward(OLMOE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optio... | 3,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
... | 3,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 3,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
'Hey, are you conscious? Can you talk to me?\nI’m not sure if you’re conscious of this, but I’m'
```... | 3,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 3,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits if return_dict else outputs[-1],
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not ... | 3,274 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/modeling_olmoe.py |
class OlmoeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OlmoeModel`]. It is used to instantiate an OLMoE
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
Args:
vocab_size (`int`, *optional*, defaults to 50304):
Vocabulary size of the OLMoE model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`OlmoeModel`]
hidden_size (`int`, *optional*, defaults to 2048):
Dimens... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaul... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
If not `None`, elements of query, key and value attention states are clipped so that their
absolute value does not exceed this value.
num_experts_per_tok (`int`, *optional*, defaults to 8):
Number of selected experts.
num_experts (`int`, *optional*, defaults to 64):
N... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
```python
>>> from transformers import OlmoeModel, OlmoeConfig
>>> # Initializing a OLMoE 7B A1B style configuration
>>> configuration = OlmoeConfig()
>>> # Initializing a model from the OLMoE 7B A1B style configuration
>>> model = OlmoeModel(configuration)
>>> # Accessing the model configura... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
def __init__(
self,
vocab_size=50304,
hidden_size=2048,
intermediate_size=2048,
num_hidden_layers=16,
num_attention_heads=16,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=4096,
initializer_range=0.02,
rms_nor... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = a... | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) | 3,275 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmoe/configuration_olmoe.py |
class Speech2TextTokenizer(PreTrainedTokenizer):
"""
Construct an Speech2Text tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods. | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
Args:
vocab_file (`str`):
File containing the vocabulary.
spm_file (`str`):
Path to the [SentencePiece](https://github.com/google/sentencepiece) model file
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sentence token.
eos_token (... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
Whether or not to lowercase the input when tokenizing.
tgt_lang (`str`, *optional*):
A string representing the target language.
sp_model_kwargs (`dict`, *optional*):
Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
SentencePiec... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
- `enable_sampling`: Enable subword regularization.
- `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
- `nbest_size = {0,1}`: No sampling is performed.
- `nbest_size > 1`: samples from the nbest_size results.
- `nbest_size < 0`: assuming tha... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
def __init__(
self,
vocab_file,
spm_file,
bos_token="<s>",
eos_token="</s>",
pad_token="<pad>",
unk_token="<unk>",
do_upper_case=False,
do_lower_case=False,
tgt_lang=None,
lang_codes=None,
additional_special_tokens=None,
... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
if lang_codes is not None:
self.lang_codes = lang_codes
self.langs = LANGUAGES[lang_codes]
self.lang_tokens = [f"<lang:{lang}>" for lang in self.langs]
self.lang_code_to_id = {lang: self.sp_model.PieceToId(f"<lang:{lang}>") for lang in self.langs}
if additiona... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
super().__init__(
bos_token=bos_token,
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
do_upper_case=do_upper_case,
do_lower_case=do_lower_case,
tgt_lang=tgt_lang,
lang_codes=lang_codes,
sp_model_k... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
"""Reset the special tokens to the target language setting. prefix=[eos, tgt_lang_code] and suffix=[eos]."""
lang_code_id = self.lang_code_to_id[tgt_lang]
self.prefix_tokens = [lang_code_id]
def _tokenize(self, text: str) -> List... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Converts a sequence of tokens (strings for sub-words) in a single string."""
current_sub_tokens = []
out_string = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""Build model inputs from a sequence by appending eos_token_id."""
if token_ids_1 is None:
return self.prefix_tokens + token_ids_0 + [self.eos_token_id]
# We don't expect to process pairs, but le... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the token list is ... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
def __getstate__(self) -> Dict:
state = self.__dict__.copy()
state["sp_model"] = None
return state
def __setstate__(self, d: Dict) -> None:
self.__dict__ = d
# for backward compatibility
if not hasattr(self, "sp_model_kwargs"):
self.sp_model_kwargs = {}
... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file):
copyfile(self.spm_file, spm_save_path)
elif not os.path.isfile(self.spm_file):
with open(spm_save_path, "wb") as fi:
content_spiece_model = self.sp_model.serialized_mode... | 3,276 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/tokenization_speech_to_text.py |
class Speech2TextFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a Speech2Text feature extractor.
This feature extractor inherits from [`Speech2TextFeatureExtractor`] which contains most of the main methods. Users
should refer to this superclass for more information regarding those methods.... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
Args:
feature_size (`int`, *optional*, defaults to 80):
The feature dimension of the extracted features.
sampling_rate (`int`, *optional*, defaults to 16000):
The sampling rate at which the audio files should be digitalized expressed in hertz (Hz).
num_mel_bins (`int`, *o... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
model_input_names = ["input_features", "attention_mask"]
def __init__(
self,
feature_size=80,
sampling_rate=16000,
num_mel_bins=80,
padding_value=0.0,
do_ceptral_normalize=True,
normalize_means=True,
normalize_vars=True,
**kwargs,
):
... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
if not is_speech_available():
mel_filters = mel_filter_bank(
num_frequency_bins=256,
num_mel_filters=self.num_mel_bins,
min_frequency=20,
max_frequency=sampling_rate // 2,
sampling_rate=sampling_rate,
norm=None,
... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
def _extract_fbank_features(
self,
waveform: np.ndarray,
) -> np.ndarray:
"""
Get mel-filter bank features using TorchAudio. Note that TorchAudio requires 16-bit signed integers as inputs
and hence the waveform should not be normalized before feature extraction.
"""
... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
mel_filters=self.mel_filters,
log_mel="log",
mel_floor=1.192092955078125e-07,
remove_dc_offset=True,
).T
return features | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
@staticmethod
def utterance_cmvn(
x: np.ndarray,
input_length: int,
normalize_means: Optional[bool] = True,
normalize_vars: Optional[bool] = True,
padding_value: float = 0.0,
) -> np.ndarray:
# make sure we normalize float32 arrays
if normalize_means:
... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
def normalize(
self, input_features: List[np.ndarray], attention_mask: Optional[np.ndarray] = None
) -> List[np.ndarray]:
lengths = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(x, n, self.normalize_mea... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
padding: Union[bool, str, PaddingStrategy] = False,
max_length: Optional[int] = None,
truncation: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_te... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
Args:
raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
values, a list of numpy arrays or a list of list of float values. Must be mono channel... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that ... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
`>= 7.5` (Volta), or on TPUs which benefit from having sequence lengths be a multiple of 128.
return_attention_mask (`bool`, *optional*):
Whether to return the attention mas... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return Numpy `np.ndarray` objects.
sampling_rate (`int`, *optional*):
The sampling rate at which the `raw_speech` input was sampled. It is strongly re... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
f" {self.sampling_rate}. Please make sure that the provided `raw_s... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
if is_batched_numpy and len(raw_speech.shape) > 2:
raise ValueError(f"Only mono-channel audio is supported for input to {self}")
is_batched = is_batched_numpy or (
isinstance(raw_speech, (list, tu... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
# convert into correct format for padding
encoded_inputs = BatchFeature({"input_features": features})
padded_inputs = self.pad(
encoded_inputs,
padding=padding,
max_length=max_length,
truncation=truncation,
pad_to_multiple_of=pad_to_multiple_o... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
attention_mask = (
np.array(attention_mask, dtype=np.int32)
if self._get_padding_strategies(padding, max_length=max_length) is not PaddingStrategy.DO_NOT_PAD
else ... | 3,277 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/feature_extraction_speech_to_text.py |
class Conv1dSubsampler(nn.Module):
"""
Convolutional subsampler: a stack of 1D convolution (along temporal dimension) followed by non-linear activation
via gated linear units (https://arxiv.org/abs/1911.08460)
"""
def __init__(self, config):
super(Conv1dSubsampler, self).__init__()
... | 3,278 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
def forward(self, input_features):
hidden_states = input_features.transpose(1, 2).contiguous() # -> B x (C x D) x T
for conv in self.conv_layers:
hidden_states = conv(hidden_states)
hidden_states = nn.functional.glu(hidden_states, dim=1)
hidden_states = hidden_states.tra... | 3,278 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextSinusoidalPositionalEmbedding(nn.Module):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__()
self.offset = 2
self.embedding_dim = em... | 3,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
@staticmethod
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
"""
Build sinusoidal embeddings. This matches the implementation in tensor2tensor, but differs slightly from the
description in Section 3.5 of "Attention Is All You Need".
"""... | 3,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
@torch.no_grad()
def forward(self, input_ids: torch.Tensor, past_key_values_length: int = 0):
bsz, seq_len = input_ids.size()
# Create the position ids from the input token ids. Any padded tokens remain padded.
position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_id... | 3,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
def create_position_ids_from_input_ids(
self, input_ids: torch.Tensor, padding_idx: int, past_key_values_length: Optional[int] = 0
):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding
symbols are ignored. This is modified fr... | 3,279 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
# `past_key_value[0].shape[2] == key_value_states.shape[1]`
# is checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
else:
# self_attention
key_states = self._shape(self.k_proj(hidden_sta... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (d... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if layer_head_mask is not None:
if layer_head_mask.size() != (self.num_heads,):
raise ValueError(
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
f" {layer_head_mask.size()}"
)
attn_weights = la... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_we... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
... | 3,280 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextEncoderLayer(nn.Module):
def __init__(self, config: Speech2TextConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = SPEECH_TO_TEXT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.enco... | 3,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq... | 3,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, traini... | 3,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
... | 3,281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextDecoderLayer(nn.Module):
def __init__(self, config: Speech2TextConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = SPEECH_TO_TEXT_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.deco... | 3,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = SPEECH_TO_TEXT_ATTENTION_CLASSES[config._attn_implementation](
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
config=config... | 3,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_l... | 3,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_a... | 3,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_w... | 3,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states) | 3,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_st... | 3,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self... | 3,282 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextPreTrainedModel(PreTrainedModel):
config_class = Speech2TextConfig
base_model_prefix = "model"
main_input_name = "input_features"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.... | 3,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
def _get_feature_vector_attention_mask(self, feature_vector_length, attention_mask):
# generate creates 3D attention mask, because of the shape of input_features
# convert it to 2D if thats the case
if len(attention_mask.shape) > 2:
attention_mask = attention_mask[:, :, -1]
... | 3,283 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextEncoder(Speech2TextPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`Speech2TextEncoderLayer`].
Args:
config: Speech2TextConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
self.embed_positions = Speech2TextSinusoidalPositionalEmbedding(
self.max_source_positions,
embed_dim,
self.padding_idx,
)
self.layers = nn.ModuleList([Speech2TextEncoderLayer(config) for _ in range(config.encoder_layers)])
self.layer_norm = nn.LayerNorm(confi... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
def forward(
self,
input_features,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_features (`torch.LongTensor` of shape `(batch_size, sequence_length, fe... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing convolution and attention on padding token indices. Mask values selected in
`[0, 1]`: | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
Mask to nullify selected heads of ... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of a... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
inputs_embeds = self.embed_scale * inputs_embeds | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# subsample attention mask if necessary
if attention_mask is not None:
attention_mask = self._get_feature_vector_attention_mask(inputs_embeds.shape[1], attention_mask)
padding_mask = attention_mask.ne(1).long()
else:
padding_mask = torch.zeros(inputs_embeds.shape[:2],... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
assert head_mask.size()[0] == (
len(self.layers)
), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
for idx... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
if to_drop:
layer_outputs = (None, None)
else:
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
encoder_layer.__call__,
hidden_states,
... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
la... | 3,284 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
class Speech2TextDecoder(Speech2TextPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`Speech2TextDecoderLayer`]
Args:
config: Speech2TextConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: Speec... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
self.layers = nn.ModuleList([Speech2TextDecoderLayer(config) for _ in range(config.decoder_layers)])
self.layer_norm = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of ... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, ... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
input... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_a... | 3,285 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/modeling_speech_to_text.py |
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