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