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from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from torch.nn import CrossEntropyLoss
import torch.utils.checkpoint
import torch.utils.checkpoint
from transformers.modeling_outputs import Seq2SeqLMOutput
from transformers.models.speech_encoder_decoder.modeling_speech_encoder_decoder import (
shift_tokens_right,
)
from transformers.models.whisper.modeling_whisper import (
WhisperEncoder,
)
from transformers.models.whisper.modeling_whisper import (
WhisperForConditionalGeneration,
shift_tokens_right,
WhisperModel,
)
from transformers.models.whisper.modeling_whisper import sinusoids
from transformers.utils import logging
from .config import Seq2SeqLMOutputLosses, Seq2SeqModelOutputLogit, DiCoWConfig
from .encoder import DiCoWEncoder
from .FDDT import FDDT
from .layers import CustomLinear, CustomDiagonalLinear, Gate
from .generation import DiCoWGenerationMixin
logging.set_verbosity_debug()
logger = logging.get_logger("transformers")
class DiCoW(WhisperModel):
def __init__(self, config: DiCoWConfig):
super().__init__(config)
self.encoder = DiCoWEncoder(config)
def forward(
self,
input_features: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
stno_mask: Optional[torch.FloatTensor] = None,
per_group_sizes: Optional[torch.LongTensor] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutputLosses]:
r"""
Returns:
Example:
```python
>>> import torch
>>> from transformers import AutoFeatureExtractor, WhisperModel
>>> from datasets import load_dataset
>>> model = WhisperModel.from_pretrained("openai/whisper-base")
>>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
>>> input_features = inputs.input_features
>>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
>>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
>>> list(last_hidden_state.shape)
[1, 2, 512]
```"""
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
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
input_features = self._mask_input_features(input_features, attention_mask=attention_mask)
encoder_outputs = self.encoder(
input_features,
output_attentions=output_attentions,
output_hidden_states=True,
head_mask=head_mask,
return_dict=return_dict,
stno_mask=stno_mask,
per_group_sizes=per_group_sizes
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
# elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
# raise ValueError("encoder_outputs should be of type BaseModelOutput when return_dict=True.")
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs.hidden_states[-1],
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
position_ids=decoder_position_ids,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutputLogit(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.hidden_states[-1],
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
encoder_logits=encoder_outputs.logits,
)
class DiCoWForConditionalGeneration(DiCoWGenerationMixin, WhisperForConditionalGeneration):
config_class = DiCoWConfig
def __init__(self, config: DiCoWConfig):
super().__init__(config)
self.model = DiCoW(config)
self.encoder_logits = None
self.tokenizer = None
self.vad_seek_callback = None
self.stno_mask = None
self.stno_mask_seek = None
# We need this setter as we can't pass a function/method as a config argument.
# JSON serialization fails at that point.
def set_vad_seek_callback(self, vad_seek_callback):
self.vad_seek_callback = vad_seek_callback
def set_tokenizer(self, tokenizer):
self.tokenizer = tokenizer
def _init_weights(self, module):
std = self.config.init_std
fddt_init = self.config.fddt_init
if isinstance(module, CustomLinear):
with torch.no_grad():
if fddt_init == 'random':
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.normal_(mean=0.0, std=std)
elif fddt_init == 'non-disturbing':
module.weight.data = torch.eye(*module.weight.shape).data
if module.bias is not None:
module.bias.data.zero_()
elif fddt_init == 'disparagement':
eye = torch.eye(*module.weight.shape)
eye *= module.init_eye_val
module.weight.data = eye.data
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, CustomDiagonalLinear):
with torch.no_grad():
if fddt_init == 'random':
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.normal_(mean=0.0, std=std)
elif fddt_init == 'non-disturbing':
module.weight.data = torch.ones_like(module.weight.data).data
if module.bias is not None:
module.bias.data.zero_()
elif fddt_init == 'disparagement':
module.weight.data = module.init_eye_val * torch.ones_like(module.weight.data).data
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, FDDT):
if module.bias_only:
if fddt_init == 'random':
module.target_linear.data.normal_(mean=0.0, std=std)
module.non_target_linear.data.normal_(mean=0.0, std=std)
module.overlap_linear.data.normal_(mean=0.0, std=std)
module.silence_linear.data.normal_(mean=0.0, std=std)
module.scb.data.normal_(mean=0.0, std=std)
else:
module.target_linear.data.zero_()
module.non_target_linear.data.zero_()
module.overlap_linear.data.zero_()
module.silence_linear.data.zero_()
module.scb.data.zero_()
elif 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_()
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, WhisperEncoder):
with torch.no_grad():
embed_positions = module.embed_positions.weight
embed_positions.copy_(sinusoids(*embed_positions.shape))
elif isinstance(module, nn.LayerNorm):
module.reset_parameters()
elif isinstance(module, nn.MultiheadAttention):
module._reset_parameters()
elif isinstance(module, nn.ConvTranspose1d):
module.reset_parameters()
elif isinstance(module, Gate):
module.gate.data = module.init_val * torch.ones_like(module.gate.data).data
def forward(
self,
input_features: Optional[torch.FloatTensor] = None,
stno_mask: Optional[torch.FloatTensor] = None,
per_group_sizes: Optional[torch.LongTensor] = None,
attention_mask_enc: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
decoder_position_ids: Optional[Tuple[torch.LongTensor]] = None,
labels: Optional[torch.LongTensor] = None,
upp_labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
is_valid: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the 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 (masked), the loss is
only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Returns:
Example:
```python
>>> import torch
>>> from transformers import AutoProcessor, WhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
>>> input_features = inputs.input_features
>>> generated_ids = model.generate(inputs=input_features)
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_features,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
decoder_inputs_embeds=decoder_inputs_embeds,
decoder_position_ids=decoder_position_ids,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
stno_mask=stno_mask,
per_group_sizes=per_group_sizes
)
dec_lm_logits = self.proj_out(outputs.last_hidden_state)
enc_lm_logits = outputs.encoder_logits
loss = None
ctc_loss = 0
# remove fake inputs from labels and logits given per group sizes
if is_valid is not None:
if self.config.ctc_weight > 0.0:
enc_lm_logits = enc_lm_logits[is_valid]
dec_lm_logits = dec_lm_logits[is_valid]
labels = labels[is_valid]
upp_labels = upp_labels[is_valid]
if labels is not None and self.config.ctc_weight > 0.0:
enc_labels = labels.clone()
for token in self.tokenizer.prefix_tokens:
if (enc_labels[:, 0] == token).all():
enc_labels = enc_labels[:, 1:]
enc_labels[enc_labels == self.config.eos_token_id] = -100
ctc_loss = self.get_encoder().get_loss(enc_lm_logits, enc_labels)
if labels is not None:
loss_fct = CrossEntropyLoss(reduction='none')
# move labels to correct device to enable PP
labels = labels.to(dec_lm_logits.device)
dec_loss1 = loss_fct(dec_lm_logits.view(-1, self.config.vocab_size), labels.reshape(-1))
dec_loss2 = loss_fct(dec_lm_logits.view(-1, self.config.vocab_size), upp_labels.reshape(-1))
dec_loss = torch.hstack((dec_loss1[..., None], dec_loss2[..., None])).min(dim=-1).values.mean()
loss = (1 - self.config.ctc_weight) * dec_loss + self.config.ctc_weight * ctc_loss
if not return_dict:
output = (dec_lm_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return Seq2SeqLMOutputLosses(
loss=loss,
logits=dec_lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
encoder_logits=enc_lm_logits,
)
def _get_feat_extract_output_lengths(self, attention_mask: torch.Tensor) -> torch.Tensor:
return (self.model.encoder._get_feat_extract_output_lengths(attention_mask) / 4).ceil()
def freeze_except(self, prefixes_to_preheat):
for name, param in self.named_parameters():
param.requires_grad = False
for prefix in prefixes_to_preheat:
if name.startswith(prefix):
param.requires_grad = True
def suppress_interactions(self):
"""This method suppress final projection in CoAttention blocks to let the original information flow through"""
for name, param in self.named_parameters():
if "interaction" in name and "cat_proj" in name:
with torch.no_grad():
if "bias" in name:
param[:] = 0.
else:
param[:] *= 0.001
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