[trainer] fix vlm loss for transformers 4.49 (#7448)

src/llamafactory/data/mm_plugin.py

@@ -143,6 +143,9 @@ class MMPluginMixin:
     ) -> None:
         r"""Validate if this model accepts the input modalities."""
         image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
+        video_processor: BaseImageProcessor = getattr(
+            processor, "video_processor", getattr(processor, "image_processor", None)
+        )
         feature_extractor: SequenceFeatureExtractor = getattr(processor, "feature_extractor", None)
         if len(images) != 0 and self.image_token is None:
             raise ValueError(
@@ -165,6 +168,9 @@ class MMPluginMixin:
         if self.image_token is not None and image_processor is None:
             raise ValueError("Image processor was not found, please check and update your processor config.")
 
+        if self.video_token is not None and video_processor is None:
+            raise ValueError("Video processor was not found, please check and update your processor config.")
+
         if self.audio_token is not None and feature_extractor is None:
             raise ValueError("Audio feature extractor was not found, please check and update your processor config.")
 

src/llamafactory/train/dpo/trainer.py

@@ -128,9 +128,9 @@ class CustomDPOTrainer(DPOTrainer):
         return super()._get_train_sampler()
 
     @override
-    def get_batch_samples(self, epoch_iterator, num_batches, *args, **kwargs):
+    def get_batch_samples(self, *args, **kwargs):
         r"""Replace the method of DPO Trainer with the one of the standard Trainer."""
-        return Trainer.get_batch_samples(self, epoch_iterator, num_batches, *args, **kwargs)
+        return Trainer.get_batch_samples(self, *args, **kwargs)
 
     def odds_ratio_loss(self, chosen_logps: "torch.Tensor", rejected_logps: "torch.Tensor") -> "torch.Tensor":
         r"""Compute ORPO's odds ratio (OR) loss for batched log probabilities of the policy model."""

src/llamafactory/train/kto/trainer.py

@@ -127,9 +127,9 @@ class CustomKTOTrainer(KTOTrainer):
         return Trainer._get_train_sampler(self)
 
     @override
-    def get_batch_samples(self, epoch_iterator, num_batches, *args, **kwargs):
+    def get_batch_samples(self, *args, **kwargs):
         r"""Replace the method of KTO Trainer with the one of the standard Trainer."""
-        return Trainer.get_batch_samples(self, epoch_iterator, num_batches, *args, **kwargs)
+        return Trainer.get_batch_samples(self, *args, **kwargs)
 
     @override
     def forward(

src/llamafactory/train/pt/trainer.py

@@ -70,3 +70,7 @@ class CustomTrainer(Trainer):
             return torch.utils.data.SequentialSampler(self.train_dataset)
 
         return super()._get_train_sampler()
+
+    @override
+    def compute_loss(self, model, inputs, *args, **kwargs):
+        return super().compute_loss(model, inputs, *args, **kwargs)

src/llamafactory/train/sft/trainer.py

@@ -59,6 +59,9 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
             self.processing_class: PreTrainedTokenizer = kwargs.get("tokenizer")
 
         super().__init__(**kwargs)
+        if processor is not None:
+            self.model_accepts_loss_kwargs = False
+
         self.finetuning_args = finetuning_args
         if gen_kwargs is not None:
             # https://github.com/huggingface/transformers/blob/v4.45.0/src/transformers/trainer_seq2seq.py#L287
@@ -93,6 +96,10 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
 
         return super()._get_train_sampler()
 
+    @override
+    def compute_loss(self, model, inputs, *args, **kwargs):
+        return super().compute_loss(model, inputs, *args, **kwargs)
+
     @override
     def prediction_step(
         self,