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Enable T2U Finetuning for UnitYNART2UModel in SeamlessM4T_v2_large and Refactor Context Management #563

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Enable T2U Finetuning for UnitYNART2UModel in SeamlessM4T_v2_large and Refactor Context Management #563

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74 changes: 50 additions & 24 deletions src/seamless_communication/cli/m4t/finetune/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@
from seamless_communication.models.unity import (
UnitYModel,
UnitYT2UModel,
UnitYNART2UModel,
)

logger = logging.getLogger(__name__)
Expand Down Expand Up @@ -100,7 +101,8 @@ def __init__(self, model: UnitYModel, mode: FinetuneMode, device: Device):
def forward(
self, batch: dataloader.MultimodalSeqsBatch
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
dummy_context = contextmanager(lambda: iter([None]))()
# dummy_context = contextmanager(lambda: iter([None]))()
dummy_context = nullcontext()
with torch.no_grad() if self.freeze_s2t else dummy_context: # type:ignore
assert batch.speech_to_text.src_tokens is not None
seqs = batch.speech_to_text.src_tokens.to(self.device)
Expand All @@ -125,30 +127,54 @@ def forward(
return (text_logits, None)
assert self.model.t2u_model is not None
assert batch.text_to_units.prev_output_tokens is not None
dummy_context = contextmanager(lambda: iter([None]))()
with torch.no_grad() if self.freeze_t2u else dummy_context: # type:ignore
if not isinstance(self.model.t2u_model, UnitYT2UModel):
dummy_context = nullcontext()
with torch.no_grad() if self.freeze_t2u else nullcontext():
if isinstance(self.model.t2u_model, UnitYNART2UModel):
assert batch.speech_to_text.target_tokens is not None
text_seqs = batch.speech_to_text.target_tokens.to(self.device)
unit_output, unit_decoder_padding_mask, durations = self.model.t2u_model.forward(
text_decoder_output=text_decoder_out,
text_decoder_padding_mask=text_decoder_padding_mask,
text_seqs=text_seqs,
duration_factor=1.0,
)
unit_logits = unit_output.logits
target_length = batch.text_to_units.target_tokens.size(1)
generated_length = unit_logits.size(1)
if generated_length != target_length:
if generated_length < target_length:
pad_size = target_length - generated_length
unit_logits = torch.nn.functional.pad(
unit_logits,
(0, 0, 0, pad_size),
value=0
)
else:
unit_logits = unit_logits[:, :target_length, :]

elif isinstance(self.model.t2u_model, UnitYT2UModel):
(
unit_encoder_out,
unit_encoder_padding_mask,
) = self.model.t2u_model.encode(
seqs=text_decoder_out,
padding_mask=text_decoder_padding_mask,
)
seqs = batch.text_to_units.prev_output_tokens.to(self.device)
assert batch.text_to_units.target_lengths is not None
seq_lens = batch.text_to_units.target_lengths.to(self.device)
unit_decoder_out, _ = self.model.t2u_model.decode(
seqs=seqs,
padding_mask=PaddingMask(seq_lens, seqs.size(1)),
encoder_output=unit_encoder_out,
encoder_padding_mask=unit_encoder_padding_mask,
)
unit_logits = self.model.t2u_model.final_proj(unit_decoder_out)
else:
raise NotImplementedError(
"T2U finetuning implemented only for UnitYT2UModel"
f"T2U finetuning not implemented for {type(self.model.t2u_model).__name__}. "
f"Supported models: UnitYT2UModel (v1), UnitYNART2UModel (v2)"
)
(
unit_encoder_out,
unit_encoder_padding_mask,
) = self.model.t2u_model.encode(
seqs=text_decoder_out,
padding_mask=text_decoder_padding_mask,
)
seqs = batch.text_to_units.prev_output_tokens.to(self.device)
assert batch.text_to_units.target_lengths is not None
seq_lens = batch.text_to_units.target_lengths.to(self.device)
unit_decoder_out, _ = self.model.t2u_model.decode(
seqs=seqs,
padding_mask=PaddingMask(seq_lens, seqs.size(1)),
encoder_output=unit_encoder_out,
encoder_padding_mask=unit_encoder_padding_mask,
)
unit_logits = self.model.t2u_model.final_proj(unit_decoder_out)

return (text_logits, unit_logits)


Expand Down Expand Up @@ -436,4 +462,4 @@ def run(self) -> None:
)
break

self.epoch_idx += 1
self.epoch_idx += 1