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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch M-CTC-T model."""
import math
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ....activations import ACT2FN
from ....file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ....integrations.deepspeed import is_deepspeed_zero3_enabled
from ....modeling_outputs import BaseModelOutput, CausalLMOutput
from ....modeling_utils import (
PreTrainedModel,
apply_chunking_to_forward,
find_pruneable_heads_and_indices,
prune_linear_layer,
)
from ....utils import logging
from .configuration_mctct import MCTCTConfig
logger = logging.get_logger(__name__)
_HIDDEN_STATES_START_POSITION = 1
_CONFIG_FOR_DOC = "MCTCTConfig"
# Base docstring
_CHECKPOINT_FOR_DOC = "speechbrain/m-ctc-t-large"
_EXPECTED_OUTPUT_SHAPE = [1, 195, 1536]
# CTC docstring
_CTC_EXPECTED_OUTPUT = '"Mr. Quilter is the apostle of the middle classes, and we\'re glad to welcome his gospel."'
_CTC_EXPECTED_LOSS = 1885.65
MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"speechbrain/m-ctc-t-large",
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
]
# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
class MCTCTConv1dSubsampler(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().__init__()
self.config = config
self.glu_dim = config.conv_glu_dim
self.dropout = nn.Dropout(config.conv_dropout)
self.num_layers = config.num_conv_layers
self.in_channels = config.input_feat_per_channel * config.input_channels
if self.num_layers > 1:
if config.conv_channels is None:
raise ValueError(
"Need to specify `conv_channels` configuration in `MCTCTConfig` to use multiple convolution"
" layers."
)
self.mid_channels = config.conv_channels
else:
self.mid_channels = None
self.out_channels = config.hidden_size * 2 # considering GLU halving
self.kernel_size = config.conv_kernel
self.stride = config.conv_stride
# NOTE: MCTCT by construction only uses one convolution kernel. I've made this flexible to allow for
# multiple layers of convolutions, but not sure if this model definition should just restrict it
# to one layer. This becomes especially relevant when considering the padding like line 1 of forward().
self.conv_layers = nn.ModuleList(
nn.Conv1d(
self.in_channels if i == 0 else self.mid_channels[i],
self.mid_channels[i] if i < self.num_layers - 1 else self.out_channels,
kernel_size=k,
stride=self.stride[i],
padding="valid",
)
for i, k in enumerate(self.kernel_size)
)
def forward(self, input_features):
# NOTE: in reference to the NOTE in __init__, right now it just calculates padding as if
# there will be just one conv layer.
padding = sum([size // 2 for size in self.kernel_size]) # (7, 7) -> (3, 3)
input_features = torch.nn.functional.pad(input_features, (0, 0, padding, padding), "constant", 0)
hidden_states = input_features.transpose(1, 2).contiguous() # -> Batch x Frame x Time
for conv in self.conv_layers:
hidden_states = conv(hidden_states)
hidden_states = nn.functional.glu(hidden_states, dim=self.glu_dim)
hidden_states = self.dropout(hidden_states)
hidden_states = hidden_states.transpose(1, 2).contiguous() # -> Batch x Time x Frame
return hidden_states
class MCTCTEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type 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_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
# self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.LayerNorm = MCTCTLayerNorm()
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
self.register_buffer(
"token_type_ids",
torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device),
persistent=False,
)
def forward(
self, input_features=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
input_shape = input_features.size() if input_features is not None else inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
# Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
# when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
# issue #5664
if token_type_ids is None:
if hasattr(self, "token_type_ids"):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
token_type_ids = buffered_token_type_ids_expanded
else:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_features)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class MCTCTSelfAttention(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 number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = config.attention_head_dim
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def reshape_fortran(self, x, shape):
if len(x.shape) > 0:
x = x.permute(*reversed(range(len(x.shape))))
return x.reshape(*reversed(shape)).permute(*reversed(range(len(shape))))
def relative_position_embedding_rotate(self, scores):
# NOTE: should re-evaluate whether this re-implementation was truly necessary
# or the reason why my complete re-haul worked was due to some other part
# of the code. Adding this and the reshape fortrain code seems very undesirable.
scores = scores.permute(0, 2, 3, 1) # e.g. [10, 1839, 14, 4]
batch, hidden_state, seq_len, heads = scores.shape
# e.g. [10, 1853, 14, 4]
scores = torch.cat((scores, torch.zeros((batch, seq_len, seq_len, heads), device=scores.device)), dim=1)
# e.g. [10, 25942, 1, 4]
scores = self.reshape_fortran(scores, [batch, (hidden_state + seq_len) * seq_len, 1, heads])
# e.g. [10, 25928, 1, 4]
scores = scores[:, : (seq_len + hidden_state - 1) * seq_len]
# e.g. [10, 1852, 14, 4]
scores = self.reshape_fortran(scores, [batch, hidden_state + seq_len - 1, seq_len, heads])
halfpoint = hidden_state // 2
scores = scores[:, halfpoint : halfpoint + seq_len].transpose(1, 2) # e.g. [10, 14, 14, 4]
return scores.permute(0, 3, 1, 2)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
mixed_query_layer = self.query(hidden_states)
mixed_query_layer = mixed_query_layer / math.sqrt(self.attention_head_size)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
# relative key position embeddings
positional_embedding = self.distance_embedding.weight
relative_position_scores = torch.einsum("lh, bche -> bcle", positional_embedding, query_layer.transpose(2, 3))
relative_position_scores = self.relative_position_embedding_rotate(relative_position_scores)
attention_scores = attention_scores + relative_position_scores
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in MCTCTModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).flatten(start_dim=-2)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class MCTCTLayerNorm(nn.Module):
def __init__(self):
super().__init__()
self.singleton_weight = nn.Parameter(torch.ones(1))
self.singleton_bias = nn.Parameter(torch.zeros(1))
def forward(self, hidden_states):
return (hidden_states * self.singleton_weight) + self.singleton_bias
class MCTCTSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MCTCTAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = MCTCTSelfAttention(config)
self.output = MCTCTSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
output_attentions,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class MCTCTIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class MCTCTOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class MCTCTLayer(nn.Module):
def __init__(self, config: MCTCTConfig):
super().__init__()
self.seq_len_dim = 1
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.intermediate = MCTCTIntermediate(config)
self.attention = MCTCTAttention(config)
self.is_decoder = config.is_decoder
self.output = MCTCTOutput(config)
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
):
self_attention_outputs = self.attention(
hidden_states, attention_mask, head_mask, output_attentions=output_attentions
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class MCTCTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = MCTCTConfig
base_model_prefix = "mctct"
main_input_name = "input_features"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
std = self.config.initializer_range
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, MCTCTLayerNorm):
module.singleton_weight.data.fill_(1.0)
module.singleton_bias.data.zero_()
if isinstance(module, (nn.Linear, nn.Conv1d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers
"""
dilation = 1
for _, kernel_sz, stride in zip(
range(self.config.num_conv_layers), self.config.conv_kernel, self.config.conv_stride
):
padding = kernel_sz // 2
input_lengths = input_lengths + 2 * padding - dilation * (kernel_sz - 1) - 1
input_lengths = torch.div(input_lengths, stride, rounding_mode="trunc") + 1
return input_lengths
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]
# subsampled_lengths = attention_mask.sum(-1)
subsampled_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1))
bsz = attention_mask.size()[0]
attention_mask = torch.zeros(
(bsz, feature_vector_length), dtype=attention_mask.dtype, device=attention_mask.device
)
# these two operations makes sure that all values
# before the output lengths indices are attended to
attention_mask[(torch.arange(bsz, device=attention_mask.device), subsampled_lengths - 1)] = 1
attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).long()
return attention_mask
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (MCTCTEncoder)):
module.gradient_checkpointing = value
MCTCT_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MCTCTConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
MCTCT_INPUTS_DOCSTRING = r"""
Args:
input_features (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`Wav2Vec2CTCTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **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.
output_hidden_states (`bool`, *optional*):
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 [`~file_utils.ModelOutput`] instead of a plain tuple.
"""
class MCTCTEncoder(MCTCTPreTrainedModel):
def __init__(self, config: MCTCTConfig):
super().__init__(config)
self.hidden_dropout_prob = config.hidden_dropout_prob
self.layer_norm = MCTCTLayerNorm()
self.conv = MCTCTConv1dSubsampler(config)
self.layers = nn.ModuleList([MCTCTLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
input_features: torch.Tensor,
attention_mask: torch.Tensor,
head_mask: torch.Tensor,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
input_features = self.layer_norm(input_features)
inputs_embeds = self.conv(input_features)
# 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)
hidden_states = nn.functional.dropout(inputs_embeds, p=self.hidden_dropout_prob, training=self.training)
# expand attention_mask
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, "
f"but it is for {head_mask.size()[0]}."
)
deepspeed_zero3_is_enabled = is_deepspeed_zero3_enabled()
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = torch.rand([])
skip_the_layer = True if self.training and (dropout_probability < self.config.layerdrop) else False
if not skip_the_layer or deepspeed_zero3_is_enabled:
# under deepspeed zero3 all gpus must run in sync
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(encoder_layer),
hidden_states,
attention_mask,
(head_mask[idx] if head_mask is not None else None),
)
else:
layer_outputs = encoder_layer(
hidden_states=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if skip_the_layer:
layer_outputs = (None, None)
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
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(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
@add_start_docstrings(
"The bare M-CTC-T Model transformer outputting raw hidden-states without any specific head on top.",
MCTCT_START_DOCSTRING,
)
class MCTCTModel(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.encoder = MCTCTEncoder(config)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
modality="audio",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def forward(
self,
input_features: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_features is None:
raise ValueError("You have to specify input_features.")
encoder_outputs = self.encoder(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutput(
last_hidden_state=sequence_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@add_start_docstrings(
"""MCTCT Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
MCTCT_START_DOCSTRING,
)
class MCTCTForCTC(MCTCTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.mctct = MCTCTModel(config)
if config.vocab_size is None:
raise ValueError(
f"You are trying to instantiate {self.__class__} with a configuration that "
"does not define the vocabulary size of the language model head. Please "
"instantiate the model as follows: `MCTCTForCTC.from_pretrained(..., vocab_size=vocab_size)`. "
"or define `vocab_size` of your model's configuration."
)
output_hidden_size = config.hidden_size
self.ctc_head = nn.Linear(output_hidden_size, config.vocab_size)
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(MCTCT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_CTC_EXPECTED_OUTPUT,
expected_loss=_CTC_EXPECTED_LOSS,
)
def forward(
self,
input_features: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size - 1]`.
All labels set to `-100` are ignored (masked), the loss is only computed for labels in `[0, ...,
config.vocab_size - 1]`.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.mctct(
input_features,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.ctc_head(hidden_states)
loss = None
if labels is not None:
if labels.max() >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
# retrieve loss input_lengths from attention_mask
attention_mask = (
attention_mask
if attention_mask is not None
else torch.ones(input_features.shape[:-1], dtype=torch.long)
)
input_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)).to(torch.long)
# assuming that padded tokens are filled with -100
# when not being attended to
labels_mask = labels >= 0
target_lengths = labels_mask.sum(-1)
flattened_targets = labels.masked_select(labels_mask)
# ctc_loss doesn't support fp16
log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)
with torch.backends.cudnn.flags(enabled=False):
loss = nn.functional.ctc_loss(
log_probs,
flattened_targets,
input_lengths,
target_lengths,
blank=self.config.pad_token_id,
reduction=self.config.ctc_loss_reduction,
zero_infinity=self.config.ctc_zero_infinity,
)
if not return_dict:
output = (logits,) + outputs[_HIDDEN_STATES_START_POSITION:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)