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fix #175

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hiyouga
2023-07-17 18:07:17 +08:00
parent 1e1358431d
commit 85c2210452
5 changed files with 38 additions and 12 deletions
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33
src/llmtuner/tuner/sft/trainer.py
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@@ -32,17 +32,40 @@ class Seq2SeqPeftTrainer(PeftTrainer):
Subclass and override to inject custom behavior.
"""
prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1)
if self.tokenizer.padding_side == "right": # pads the labels to the same length as the inputs
inputs["labels"] = torch.cat((inputs["labels"], torch.zeros_like(inputs["input_ids"])[:, label_len:]), dim=-1)
else:
inputs["labels"] = torch.cat((torch.zeros_like(inputs["input_ids"])[:, label_len:], inputs["labels"]), dim=-1)
if prompt_len > label_len:
inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"])
if label_len > prompt_len:
inputs["input_ids"] = self._pad_tensors_to_target_len(inputs["input_ids"], inputs["labels"])
loss, generated_tokens, labels = super().prediction_step(
model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
)
generated_tokens = generated_tokens[:, prompt_len:] if generated_tokens is not None else None
generated_tokens = generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
return (loss, generated_tokens, labels)
def _pad_tensors_to_target_len(self, src_tensor: torch.Tensor, tgt_tensor: torch.Tensor) -> torch.Tensor:
r"""
Pads the tensor to the same length as the target tensor.
Should only be called when predict_with_generate=True.
"""
if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
# If PAD token is not defined at least EOS token has to be defined
pad_token_id = (
self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
)
else:
if self.model.config.pad_token_id is not None:
pad_token_id = self.model.config.pad_token_id
else:
raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")
padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding
return padded_tensor
def save_predictions(
self,
predict_results: PredictionOutput