Upload logos_reward_ft_job.py with huggingface_hub

logos_reward_ft_job.py

@@ -44,7 +44,6 @@ from transformers import (
     WhisperForConditionalGeneration,
     Seq2SeqTrainer,
     Seq2SeqTrainingArguments,
-    DataCollatorForSeq2Seq,
 )
 from peft import LoraConfig, get_peft_model, TaskType
 from huggingface_hub import login
@@ -297,7 +296,23 @@ class RewardWeightedTrainer(Seq2SeqTrainer):
 
         return (weighted_loss, outputs) if return_outputs else weighted_loss
 
-collator = DataCollatorForSeq2Seq(processor.tokenizer, model=model, padding=True)
+class WhisperRewardCollator:
+    """Stack input_features, pad labels with -100, pass reward_weight through."""
+    def __call__(self, features):
+        input_features = torch.tensor(
+            np.array([f["input_features"] for f in features]), dtype=torch.float32
+        )
+        max_len = max(len(f["labels"]) for f in features)
+        labels = torch.full((len(features), max_len), -100, dtype=torch.long)
+        for i, f in enumerate(features):
+            ids = torch.tensor(f["labels"], dtype=torch.long)
+            labels[i, :len(ids)] = ids
+        reward_weight = torch.tensor(
+            [f["reward_weight"] for f in features], dtype=torch.float32
+        )
+        return {"input_features": input_features, "labels": labels, "reward_weight": reward_weight}
+
+collator = WhisperRewardCollator()
 
 training_args = Seq2SeqTrainingArguments(
     output_dir                  = OUTPUT_DIR,