fix seq2seq predictions

Former-commit-id: 65e9ce2cdda3924170774f7b3c6e3fbdbdd87b7f

README.md

@@ -89,9 +89,9 @@ huggingface-cli login
 
 And **powerful GPUs**!
 
-If you want to enable LoRA(QLoRA) or Freeze quantization on Windows, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1.
+If you want to enable quantized LoRA (QLoRA) on the Windows platform, you should install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1.
 
-```
+```bash
 pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
 ```
 

src/utils/seq2seq.py

@@ -1,8 +1,10 @@
 import os
 import json
+import torch
 import numpy as np
+import torch.nn as nn
 from dataclasses import dataclass
-from typing import Dict, List, Sequence, Tuple, Union
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
 from transformers.trainer import PredictionOutput
 from transformers.tokenization_utils import PreTrainedTokenizer
@@ -34,11 +36,10 @@ class ComputeMetrics:
         preds, labels = eval_preds
         score_dict = {"rouge-1": [], "rouge-2": [], "rouge-l": [], "bleu-4": []}
 
-        for pred, label in zip(preds, labels):
+        preds = np.where(preds != IGNORE_INDEX, preds, self.tokenizer.pad_token_id)
-            pred_pad_len, label_pad_len = np.sum(pred == IGNORE_INDEX), np.sum(label == IGNORE_INDEX)
+        labels = np.where(labels != IGNORE_INDEX, labels, self.tokenizer.pad_token_id)
-            pred = pred[len(label) - label_pad_len : len(pred) - pred_pad_len] # remove prompts
-            label = label[:len(label) - label_pad_len]
 
+        for pred, label in zip(preds, labels):
             hypothesis = list(jieba.cut(self.tokenizer.decode(pred, skip_special_tokens=True)))
             reference = list(jieba.cut(self.tokenizer.decode(label, skip_special_tokens=True)))
 
@@ -63,6 +64,25 @@ class Seq2SeqPeftTrainer(PeftTrainer):
     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.
+        """
+        input_ids = inputs["input_ids"]
+        loss, generated_tokens, labels = super().prediction_step(
+            model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
+        )
+        generated_tokens = generated_tokens[:, input_ids.size(-1):] if generated_tokens is not None else None
+        return (loss, generated_tokens, labels)
+
     def save_predictions(
             self,
             predict_results: PredictionOutput
@@ -77,13 +97,13 @@ class Seq2SeqPeftTrainer(PeftTrainer):
 
         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)
+
         with open(output_prediction_file, "w", encoding="utf-8") as writer:
             res: List[str] = []
-            for pred, label in zip(predict_results.predictions, predict_results.label_ids):
+            for pred, label in zip(preds, labels):
-                pred_pad_len, label_pad_len = np.sum(pred == IGNORE_INDEX), np.sum(label == IGNORE_INDEX)
-                pred = pred[len(label) - label_pad_len : len(pred) - pred_pad_len] # remove prompts
-                label = label[:len(label) - label_pad_len]
-
                 pred = self.tokenizer.decode(pred, skip_special_tokens=True)
                 label = self.tokenizer.decode(label, skip_special_tokens=True)