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from typing import Optional
import torch
from torch import nn
from transformers.modeling_outputs import CausalLMOutput, BaseModelOutput
from transformers.models.whisper.modeling_whisper import WhisperEncoder, WhisperEncoderLayer, WHISPER_ATTENTION_CLASSES
from .config import DiCoWConfig
class CustomLinear(nn.Linear):
def __init__(self, *args, init_eye_val=0.0, is_diagonal=False, **kwargs):
super().__init__(*args, **kwargs)
self.init_eye_val = init_eye_val
class CustomDiagonalLinear(nn.Module):
def __init__(self, d_model, bias=True, init_eye_val=0.0):
super().__init__()
self.init_eye_val = init_eye_val
self.weight = nn.Parameter(torch.full((d_model,), init_eye_val))
self.bias = nn.Parameter(torch.zeros(d_model)) if bias else None
def forward(self, input):
out = input * self.weight
if self.bias is not None:
out += self.bias
return out
class FDDT(nn.Module):
def __init__(self, d_model, non_target_rate=0.01, is_diagonal=False, bias_only=False, use_silence=True,
use_target=True, use_overlap=True, use_non_target=True, use_interaction=False,
scb_module: Optional[nn.Module] = None, ):
super().__init__()
if use_target:
self.target_linear = nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, init_eye_val=1.0) if is_diagonal else CustomLinear(d_model,
d_model,
bias=True,
init_eye_val=1.0))
if use_non_target:
self.non_target_linear = nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, init_eye_val=non_target_rate) if is_diagonal else CustomLinear(
d_model, d_model, bias=True, init_eye_val=non_target_rate))
if use_overlap:
self.overlap_linear = nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, init_eye_val=1.0) if is_diagonal else CustomLinear(d_model,
d_model,
bias=True,
init_eye_val=1.0))
if use_silence:
self.silence_linear = nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, init_eye_val=non_target_rate) if is_diagonal else CustomLinear(
d_model, d_model, bias=True, init_eye_val=non_target_rate))
if use_interaction:
self.scb = scb_module if scb_module is not None else (nn.Parameter(torch.zeros(d_model)) if bias_only else (
CustomDiagonalLinear(d_model, bias=True, init_eye_val=1.0) if is_diagonal else CustomLinear(
d_model, d_model, bias=True, init_eye_val=1.0)))
self.use_silence = use_silence
self.use_target = use_target
self.use_overlap = use_overlap
self.use_non_target = use_non_target
self.use_interaction = use_interaction
self.bias_only = bias_only
@staticmethod
def mask_out_non_interaction_signal(hidden_states, mask):
mask = torch.round(mask).bool()
masked_hidden_states = hidden_states * mask
return masked_hidden_states
def forward(self, hidden_states, stno_mask):
stno_mask = stno_mask.to(hidden_states.device)[..., None]
if self.bias_only:
if self.use_silence:
hidden_states += stno_mask[:, 0, ...] * self.silence_linear
if self.use_target:
hidden_states += stno_mask[:, 1, ...] * self.target_linear
if self.use_non_target:
hidden_states += stno_mask[:, 2, ...] * self.non_target_linear
if self.use_overlap:
hidden_states += stno_mask[:, 3, ...] * self.overlap_linear
if self.use_interaction:
hidden_states += stno_mask[:, 4, ...] * self.scb
else:
orig_hidden_states = hidden_states
hidden_states = (self.silence_linear(
orig_hidden_states) if self.use_silence else orig_hidden_states) * stno_mask[:, 0, :] + \
(self.target_linear(
orig_hidden_states) if self.use_target else orig_hidden_states) * stno_mask[:, 1, :] + \
(self.non_target_linear(
orig_hidden_states) if self.use_non_target else orig_hidden_states) * stno_mask[:, 2,
:] + \
(self.overlap_linear(
orig_hidden_states) if self.use_overlap else orig_hidden_states) * stno_mask[:, 3, :] + \
(self.scb(
self.mask_out_non_interaction_signal(orig_hidden_states,
stno_mask[:, 4, :])) * stno_mask[:, 4,
:] if self.use_interaction else (
0 if stno_mask.size(
1) == 4 else orig_hidden_states * stno_mask[:, 4,
:]))
return hidden_states
class DiCoWEncoder(WhisperEncoder):
config_class = DiCoWConfig
def __init__(self, config: DiCoWConfig):
super().__init__(config)
self.ctc_weight = config.ctc_weight
if config.additional_layer and self.ctc_weight > 0.0:
self.additional_layer = WhisperEncoderLayer(config)
if config.additional_self_attention_layer and self.ctc_weight > 0.0:
self.additional_self_attention_layer = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=config.d_model,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
config=config,
)
if config.sub_sample and self.ctc_weight > 0.0:
self.subsample_conv1 = nn.Conv1d(
in_channels=config.d_model,
out_channels=config.d_model,
kernel_size=3,
stride=2,
padding=1,
bias=False,
)
self.subsample_conv2 = nn.Conv1d(
in_channels=config.d_model,
out_channels=config.d_model,
kernel_size=3,
stride=2,
padding=1,
bias=False,
)
if self.ctc_weight > 0.0:
self.lm_head = nn.Linear(config.d_model, config.vocab_size + 1, bias=False)
self.final_dropout = nn.Dropout(config.final_dropout)
if config.use_fddt:
num_fddts = self.config.apply_fddt_to_n_layers if self.config.apply_fddt_to_n_layers != -1 else len(
self.layers)
self.initial_fddt = FDDT(config.d_model,
non_target_rate=config.non_target_fddt_value,
is_diagonal=config.fddt_is_diagonal,
bias_only=config.fddt_bias_only,
use_silence=config.fddt_use_silence,
use_target=config.fddt_use_target,
use_overlap=config.fddt_use_overlap,
use_non_target=config.fddt_use_non_target)
is_mt = config.mt_num_speakers > 1
num_scbs = (self.config.scb_layers if self.config.scb_layers != -1 else len(
self.layers)) if is_mt else 0
self.scbs_identity_layers = config.encoder_layers - num_scbs
self.fddts = nn.ModuleList([
FDDT(config.d_model,
non_target_rate=1.0,
is_diagonal=config.fddt_is_diagonal,
bias_only=config.fddt_bias_only,
use_silence=config.fddt_use_silence,
use_target=config.fddt_use_target,
use_overlap=config.fddt_use_overlap,
use_non_target=config.fddt_use_non_target,
use_interaction=is_mt,
)
for i in range(num_fddts)
])
self.first_task_token = self.config.vocab_size - 30 * 50 - 1 - 6 # 30 seconds of 50 Hz timestamps -1 to get to 0.0 and -6 number of tasks
self.post_init()
@classmethod
def _load_pretrained_model(
cls,
model,
state_dict,
loaded_keys,
resolved_archive_file,
pretrained_model_name_or_path,
**kwargs
):
for key in list(state_dict.keys()):
if key.startswith("encoder."):
state_dict[key[8:]] = state_dict.pop(key)
loaded_keys.remove(key)
loaded_keys.append(key[8:])
output = super()._load_pretrained_model(
model,
state_dict,
loaded_keys,
resolved_archive_file,
pretrained_model_name_or_path,
**kwargs
)
return output
def get_loss(self, logits, labels):
if labels.max() >= self.config.vocab_size:
raise ValueError(f"Label values must be <= vocab_size: {self.config.vocab_size}")
if self.config.remove_timestamps_from_ctc:
labels = torch.nn.utils.rnn.pad_sequence([label[label < self.first_task_token] for label in labels],
padding_value=-100).T
input_lengths = torch.full((logits.shape[0],), fill_value=logits.shape[1],
device=logits.device)
# 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_enc.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=True):
ctc_loss = nn.functional.ctc_loss(
log_probs,
labels,
input_lengths,
target_lengths,
blank=logits.shape[-1] - 1,
reduction=self.config.ctc_loss_reduction,
zero_infinity=True,
)
return ctc_loss
def forward(
self,
input_features,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
stno_mask=None,
per_group_sizes=None
):
# For MT-ASR the input has shape (B X S) x F x T
# we can use torch.view(B, S, F, -1) to obtain
# new tensor with speaker dim
expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
if input_features.shape[-1] != expected_seq_length:
if input_features.shape[-1] > expected_seq_length:
return CausalLMOutput(
logits=None,
hidden_states=None,
attentions=None,
)
else:
raise ValueError(
f"Whisper expects the mel input features to be of length {expected_seq_length}, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
)
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
inputs_embeds = nn.functional.gelu(self.conv1(input_features))
inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
inputs_embeds = inputs_embeds.permute(0, 2, 1)
embed_pos = self.embed_positions.weight
if hasattr(self, "shift_embeds") and self.shift_embeds:
embed_pos = embed_pos[
torch.clamp(((stno_mask[:, 1, :] + stno_mask[:, 3, :]).cumsum(dim=-1) - 1), min=0).to(torch.long)]
if self.config.use_fddt:
inputs_embeds = self.initial_fddt(inputs_embeds, stno_mask)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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:
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, 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)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if self.config.use_fddt and idx < len(self.fddts):
hidden_states = self.fddts[idx](hidden_states, stno_mask)
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,
None,
(head_mask[idx] if head_mask is not None else None),
output_attentions,
)
else:
layer_outputs = encoder_layer(
hidden_states,
None,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layer_norm(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
outputs = tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
else:
outputs = BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
if hasattr(self, "additional_layer"):
inter_output, = self.additional_layer(
outputs.last_hidden_state,
attention_mask=None,
output_attentions=output_attentions,
layer_head_mask=None,
)
elif hasattr(self, "additional_self_attention_layer"):
inter_output, _, __ = self.additional_self_attention_layer(
outputs.last_hidden_state,
attention_mask=None,
output_attentions=output_attentions,
layer_head_mask=None,
)
else:
inter_output = outputs.last_hidden_state
inter_output = self.final_dropout(inter_output)
if hasattr(self, "subsample_conv2"):
inter_output = self.subsample_conv2(self.subsample_conv1(inter_output.transpose(1, 2))).transpose(1, 2)
if self.ctc_weight > 0.0:
logits = self.lm_head(inter_output)
else:
logits = None
return CausalLMOutput(
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
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