108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
import os
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import json
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import torch
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import numpy as np
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import torch.nn as nn
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from typing import Any, Dict, List, Optional, Tuple, Union
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from transformers.trainer import PredictionOutput
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from llmtuner.extras.constants import IGNORE_INDEX
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from llmtuner.extras.logging import get_logger
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from llmtuner.tuner.core.trainer import PeftTrainer
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logger = get_logger(__name__)
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class Seq2SeqPeftTrainer(PeftTrainer):
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r"""
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Inherits PeftTrainer to compute generative metrics such as BLEU and ROUGE.
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"""
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def prediction_step(
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self,
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model: nn.Module,
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inputs: Dict[str, Union[torch.Tensor, Any]],
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prediction_loss_only: bool,
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ignore_keys: Optional[List[str]] = None,
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) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
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r"""
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Removes the prompt part in the generated tokens.
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Subclass and override to inject custom behavior.
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"""
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prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1)
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if prompt_len > label_len:
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inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"])
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if label_len > prompt_len:
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inputs["input_ids"] = self._pad_tensors_to_target_len(inputs["input_ids"], inputs["labels"])
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if "attention_mask" in inputs:
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inputs["attention_mask"] = self._pad_tensors_to_target_len(
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inputs["attention_mask"], inputs["labels"], pad_token_id=0
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)
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if "position_ids" in inputs:
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inputs["position_ids"] = self._pad_tensors_to_target_len(
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inputs["position_ids"], inputs["labels"], pad_token_id=0
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)
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loss, generated_tokens, labels = super().prediction_step(
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model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
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)
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generated_tokens = (
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generated_tokens[:, max(prompt_len, label_len):] if generated_tokens is not None else None
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)
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return (loss, generated_tokens, labels)
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def _pad_tensors_to_target_len(
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self,
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src_tensor: torch.Tensor,
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tgt_tensor: torch.Tensor,
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pad_token_id: Optional[int] = None
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) -> torch.Tensor:
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r"""
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Pads the tensor to the same length as the target tensor.
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Should only be called when predict_with_generate=True.
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"""
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if pad_token_id is None:
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if self.tokenizer is not None and hasattr(self.tokenizer, "pad_token_id"):
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assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
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pad_token_id = self.tokenizer.pad_token_id
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else:
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if self.model.config.pad_token_id is not None:
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pad_token_id = self.model.config.pad_token_id
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else:
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raise ValueError("Pad_token_id must be set in the configuration of the model.")
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padded_tensor = pad_token_id * torch.ones_like(tgt_tensor)
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padded_tensor[:, -src_tensor.shape[-1]:] = src_tensor # adopt left-padding
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return padded_tensor
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def save_predictions(
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self,
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predict_results: PredictionOutput
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) -> None:
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r"""
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Saves model predictions to `output_dir`.
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A custom behavior that not contained in Seq2SeqTrainer.
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"""
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if not self.is_world_process_zero():
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return
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output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
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logger.info(f"Saving prediction results to {output_prediction_file}")
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preds = np.where(predict_results.predictions != IGNORE_INDEX, predict_results.predictions, self.tokenizer.pad_token_id)
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labels = np.where(predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id)
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decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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with open(output_prediction_file, "w", encoding="utf-8") as writer:
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res: List[str] = []
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for pred, label in zip(decoded_preds, decoded_labels):
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res.append(json.dumps({"label": label, "predict": pred}, ensure_ascii=False))
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writer.write("\n".join(res))
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