text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def embed_questions(
self,
input_ids,
attention_mask=None,
checkpoint_batch_size=-1,
):
q_reps = self.embed_sentences_checkpointed(
input_ids,
attention_mask,
self.bert_query,
checkpoint_batch_size,
)
return self... | 10,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py |
def forward(
self,
input_ids_query: torch.LongTensor,
attention_mask_query: Optional[torch.FloatTensor],
input_ids_doc: torch.LongTensor,
attention_mask_doc: Optional[torch.FloatTensor],
checkpoint_batch_size: int = -1,
) -> torch.FloatTensor:
r"""
Arg... | 10,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
input_ids_doc (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary... | 10,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py |
Return:
`torch.FloatTensor``: The bidirectional cross-entropy loss obtained while trying to match each query to its
corresponding document and each document to its corresponding query in the batch
"""
device = input_ids_query.device
q_reps = self.embed_questions(input_ids... | 10,432 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/retribert/modeling_retribert.py |
class Speech2Text2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Speech2Text2ForCausalLM`]. It is used to
instantiate an Speech2Text2 model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defa... | 10,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py |
Args:
vocab_size (`int`, *optional*, defaults to 50265):
Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`Speech2TextModel`]
d_model (`int`, *optional*, defaults to 1024):
... | 10,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py |
`"silu"` and `"gelu_new"` are supported.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabiliti... | 10,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py |
Whether or not the model should return the last key/values attentions (not used by all models).
max_target_positions (`int`, *optional*, defaults to 1024):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 o... | 10,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py |
Example:
```python
>>> from transformers import Speech2Text2Config, Speech2Text2ForCausalLM
>>> # Initializing a Speech2Text2 s2t_transformer_s style configuration
>>> configuration = Speech2Text2Config()
>>> # Initializing a model (with random weights) from the s2t_transformer_s style configurat... | 10,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py |
def __init__(
self,
vocab_size=10000,
decoder_layers=6,
decoder_ffn_dim=2048,
decoder_attention_heads=4,
decoder_layerdrop=0.0,
use_cache=True,
activation_function="relu",
d_model=256,
dropout=0.1,
attention_dropout=0.0,
act... | 10,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py |
self.decoder_layerdrop = decoder_layerdrop
self.use_cache = use_cache
self.num_hidden_layers = decoder_layers
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
self.max_target_positions = max_target_positions | 10,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
decoder_start_token_id=decoder_start_token_id,
**kwargs,
) | 10,433 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/configuration_speech_to_text_2.py |
class Speech2Text2Tokenizer(PreTrainedTokenizer):
"""
Constructs a Speech2Text2Tokenizer.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains some of the main methods. Users should refer to
the superclass for more information regarding such methods.
Args:
vocab_file (`str`)... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
**kwargs
Additional keyword arguments passed along to [`PreTrainedTokenizer`]
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
bos_token="<s>",
pad_token="<pad>",
eos_token=... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
merges = [tuple(merge.split()[:2]) for merge in merges]
self.bpe_ranks = dict(zip(merges, range(len(merges))))
self.cache = {}
super().__init__(
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
if self.bpe_ranks is None:
raise ValueError(
"This tokenizer was instantiated without a `merges.txt` file, so"
" that it can only be used for decoding, not for encoding. "
"Make sure to provide `merges.txt` file at instantiation to enable "
"en... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the vocab."""
result = self.decoder.get(index, self.unk_token)
return result
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""
Converts a list of output t... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
vocab_file = os.path.join(
save_directory, (file... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
with open(merges_file, "w", encoding="utf-8") as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Saving vocabulary to {merges_file}: BPE merge indices are not c... | 10,434 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/tokenization_speech_to_text_2.py |
class Speech2Text2SinusoidalPositionalEmbedding(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 = e... | 10,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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".
"""... | 10,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,435 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
class Speech2Text2Attention(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,
... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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() | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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"
... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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.
... | 10,436 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
class Speech2Text2DecoderLayer(nn.Module):
def __init__(self, config: Speech2Text2Config):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = Speech2Text2Attention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropo... | 10,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim) | 10,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
# Fully Connected
residual = 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)
hidden_states = nn.functional.dro... | 10,437 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
class Speech2Text2PreTrainedModel(PreTrainedModel):
config_class = Speech2Text2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.d... | 10,438 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
class Speech2Text2Decoder(Speech2Text2PreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`Speech2Text2DecoderLayer`]
Args:
config: Speech2Text2Config
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: S... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
self.layers = nn.ModuleList([Speech2Text2DecoderLayer(config) for _ in range(config.decoder_layers)])
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embed_tokens
def set_input_emb... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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=... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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**,
... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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 ... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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, ... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=in... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
next_decoder_cache = () if use_cache else None | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != (len(self.layers)):
... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = layer_outputs[0] | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# a... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPa... | 10,439 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
class Speech2Text2DecoderWrapper(Speech2Text2PreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__... | 10,440 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
class Speech2Text2ForCausalLM(Speech2Text2PreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config = copy.deepcopy(config)
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = Speech2Text2Dec... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
e... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it. | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
Indices can be obtained using [`Speech2Text2Tokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0,... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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, ... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.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)`.
label... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
ou... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
```python
>>> from transformers import (
... SpeechEncoderDecoderModel,
... Speech2Text2ForCausalLM,
... Wav2Vec2Model,
... Speech2Text2Config,
... Wav2Vec2Config,
... Wav2Vec2FeatureExtractor,
... Speech2Text2Tokenizer,
... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(
... ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
... )
>>> input_values = inputs.input_values
>>> dec... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
inp... | 10,441 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py |
class Speech2Text2Processor(ProcessorMixin):
r"""
Constructs a Speech2Text2 processor which wraps a Speech2Text2 feature extractor and a Speech2Text2 tokenizer into
a single processor.
[`Speech2Text2Processor`] offers all the functionalities of [`AutoFeatureExtractor`] and [`Speech2Text2Tokenizer`].
... | 10,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py |
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
self._in_target_context_manager = False
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards al... | 10,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py |
if "raw_speech" in kwargs:
warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.")
audio = kwargs.pop("raw_speech")
else:
audio = kwargs.pop("audio", None)
sampling_rate = kwargs.pop("sampling_rate", None)
text = kwargs.po... | 10,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py |
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to Speech2Text2Tokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
de... | 10,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py |
@contextmanager
def as_target_processor(self):
"""
Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning
Speech2Text2.
"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transform... | 10,442 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/speech_to_text_2/processing_speech_to_text_2.py |
class EfficientFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of an [`EfficientFormerModel`]. It is used to
instantiate an EfficientFormer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the ... | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
Args:
depths (`List(int)`, *optional*, defaults to `[3, 2, 6, 4]`)
Depth of each stage.
hidden_sizes (`List(int)`, *optional*, defaults to `[48, 96, 224, 448]`)
Dimensionality of each stage.
downsamples (`List(bool)`, *optional*, defaults to `[True, True, True, True]`)
... | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
num_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the 3D MetaBlock.
mlp_expansion_ratio (`int`, *optional*, defaults to 4):
Ratio of size of the hidden dimensionality of an MLP to the dimensionality of its input.
hidden_... | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
The stride of convolution kernels in downsampling layers.
downsample_pad (`int`, *optional*, defaults to 1):
Padding in downsampling layers.
drop_path_rate (`int`, *optional*, defaults to 0):
Rate at which to increase dropout probability in DropPath.
num_meta3d_blocks (`i... | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
`"relu"`, `"selu"` and `"gelu_new"` are supported.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used... | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
Example:
```python
>>> from transformers import EfficientFormerConfig, EfficientFormerModel
>>> # Initializing a EfficientFormer efficientformer-l1 style configuration
>>> configuration = EfficientFormerConfig()
>>> # Initializing a EfficientFormerModel (with random weights) from the efficientfor... | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
def __init__(
self,
depths: List[int] = [3, 2, 6, 4],
hidden_sizes: List[int] = [48, 96, 224, 448],
downsamples: List[bool] = [True, True, True, True],
dim: int = 448,
key_dim: int = 32,
attention_ratio: int = 4,
resolution: int = 7,
num_hidden_lay... | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
batch_norm_eps: float = 1e-05,
**kwargs,
) -> None:
super().__init__(**kwargs) | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.hidden_sizes = hidden_sizes
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.initializer_range = initializer_range
self.layer_norm_eps = layer_no... | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
self.use_layer_scale = use_layer_scale
self.layer_scale_init_value = layer_scale_init_value
self.image_size = image_size
self.batch_norm_eps = batch_norm_eps | 10,443 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/configuration_efficientformer.py |
class EfficientFormerPatchEmbeddings(nn.Module):
"""
This class performs downsampling between two stages. For the input tensor with the shape [batch_size, num_channels,
height, width] it produces output tensor with the shape [batch_size, num_channels, height/stride, width/stride]
"""
def __init__(s... | 10,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configur... | 10,444 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerSelfAttention(nn.Module):
def __init__(self, dim: int, key_dim: int, num_heads: int, attention_ratio: int, resolution: int):
super().__init__() | 10,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
self.num_heads = num_heads
self.key_dim = key_dim
self.attention_ratio = attention_ratio
self.scale = key_dim**-0.5
self.total_key_dim = key_dim * num_heads
self.expanded_key_dim = int(attention_ratio * key_dim)
self.total_expanded_key_dim = int(self.expanded_key_dim * nu... | 10,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
self.attention_biases = torch.nn.Parameter(torch.zeros(num_heads, len(attention_offsets)))
self.register_buffer("attention_bias_idxs", torch.LongTensor(idxs).view(num_points, num_points)) | 10,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
@torch.no_grad()
def train(self, mode=True):
super().train(mode)
if mode and hasattr(self, "ab"):
del self.ab
else:
self.ab = self.attention_biases[:, self.attention_bias_idxs]
def forward(self, hidden_states: torch.Tensor, output_attentions: bool = False) -> Tup... | 10,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
# set `model.to(torch_device)` won't change `self.ab.device`, if there is no follow-up `train` or `eval` call.
# Let's do it manually here, so users won't have to do this everytime.
if not self.training:
self.ab = self.ab.to(self.attention_biases.device)
attention_probs = (torch.matm... | 10,445 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerConvStem(nn.Module):
def __init__(self, config: EfficientFormerConfig, out_channels: int):
super().__init__()
self.convolution1 = nn.Conv2d(config.num_channels, out_channels // 2, kernel_size=3, stride=2, padding=1)
self.batchnorm_before = nn.BatchNorm2d(out_channels //... | 10,446 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerPooling(nn.Module):
def __init__(self, pool_size: int):
super().__init__()
self.pool = nn.AvgPool2d(pool_size, stride=1, padding=pool_size // 2, count_include_pad=False)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
output = self.pool(hidden_states... | 10,447 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
class EfficientFormerDenseMlp(nn.Module):
def __init__(
self,
config: EfficientFormerConfig,
in_features: int,
hidden_features: Optional[int] = None,
out_features: Optional[int] = None,
):
super().__init__()
out_features = out_features or in_features
... | 10,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/deprecated/efficientformer/modeling_efficientformer.py |
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