modity code structure

src/llmtuner/tuner/sft/trainer.py

@@ -0,0 +1,71 @@
+import os
+import json
+import torch
+import numpy as np
+import torch.nn as nn
+from typing import Any, Dict, List, Optional, Tuple, Union
+from transformers.trainer import PredictionOutput
+
+from llmtuner.extras.constants import IGNORE_INDEX
+from llmtuner.extras.logging import get_logger
+from llmtuner.tuner.core.trainer import PeftTrainer
+
+
+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 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)
+        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
+
+        return (loss, generated_tokens, labels)
+
+    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))