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Comment out hack for now

This commit is contained in:
Daniel Walmsley 2024-07-08 15:55:33 -07:00
parent 61ec4322d4
commit e8663dd3f8
1 changed files with 32 additions and 27 deletions
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59
TTS/tts/layers/xtts/stream_generator.py
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@ -195,36 +195,41 @@ class NewGenerationMixin(GenerationMixin):
generation_config.pad_token_id,
generation_config.eos_token_id,
)
eos_token_tensor = (
torch.tensor([generation_config.eos_token_id], device=inputs_tensor.device)
if generation_config.eos_token_id is not None
else None
)
# pad_token_tensor = (
# torch.tensor([generation_config.pad_token_id], device=inputs_tensor.device)
# if generation_config.pad_token_id is not None
# else None
# )
# eos_token_tensor = (
# torch.tensor([generation_config.eos_token_id], device=inputs_tensor.device)
# if generation_config.eos_token_id is not None
# else None
# )
# hack to produce attention mask for mps devices since transformers bails but pytorch supports torch.isin on mps now
# for this to work, you must run with PYTORCH_ENABLE_MPS_FALLBACK=1 and call model.to(mps_device) on the XttsModel
if inputs_tensor.device.type == "mps":
default_attention_mask = torch.ones(inputs_tensor.shape[:2], dtype=torch.long, device=inputs_tensor.device)
# # hack to produce attention mask for mps devices since transformers bails but pytorch supports torch.isin on mps now
# # for this to work, you must run with PYTORCH_ENABLE_MPS_FALLBACK=1 and call model.to(mps_device) on the XttsModel
# if inputs_tensor.device.type == "mps":
# default_attention_mask = torch.ones(inputs_tensor.shape[:2], dtype=torch.long, device=inputs_tensor.device)
is_pad_token_in_inputs = (pad_token_tensor is not None) and (
custom_isin(elements=inputs_tensor, test_elements=pad_token_tensor).any()
)
is_pad_token_not_equal_to_eos_token_id = (eos_token_tensor is None) or ~(
custom_isin(elements=eos_token_tensor, test_elements=pad_token_tensor).any()
)
can_infer_attention_mask = is_pad_token_in_inputs * is_pad_token_not_equal_to_eos_token_id
attention_mask_from_padding = inputs_tensor.ne(pad_token_tensor).long()
# is_pad_token_in_inputs = (pad_token_tensor is not None) and (
# custom_isin(elements=inputs_tensor, test_elements=pad_token_tensor).any()
# )
# is_pad_token_not_equal_to_eos_token_id = (eos_token_tensor is None) or ~(
# custom_isin(elements=eos_token_tensor, test_elements=pad_token_tensor).any()
# )
# can_infer_attention_mask = is_pad_token_in_inputs * is_pad_token_not_equal_to_eos_token_id
# attention_mask_from_padding = inputs_tensor.ne(pad_token_tensor).long()
model_kwargs["attention_mask"] = (
attention_mask_from_padding * can_infer_attention_mask
+ default_attention_mask * ~can_infer_attention_mask
)
else:
model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
inputs_tensor,
pad_token_tensor,
eos_token_tensor,
)
# model_kwargs["attention_mask"] = (
# attention_mask_from_padding * can_infer_attention_mask
# + default_attention_mask * ~can_infer_attention_mask
# )
# else:
# model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
# inputs_tensor,
# pad_token_tensor,
# eos_token_tensor,
# )
# decoder-only models should use left-padding for generation
if not self.config.is_encoder_decoder: