Merge pull request #6465 from hiyouga/hiyouga/fix_eval_loss

[trainer] fix eval loss

src/llamafactory/cli.py

@@ -121,5 +121,6 @@ def main():
     else:
         raise NotImplementedError(f"Unknown command: {command}.")
 
+
 if __name__ == "__main__":
     main()

src/llamafactory/data/collator.py

@@ -105,7 +105,10 @@ def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, "torch.Tenso
             fake_messages = [{"role": "user", "content": IMAGE_PLACEHOLDER}]
             fake_images = [Image.new("RGB", (64, 64), (255, 255, 255))]
             fake_messages = self.template.mm_plugin.process_messages(fake_messages, fake_images, [], self.processor)
-            fake_input_ids = self.processor.tokenizer.encode(fake_messages[0]["content"], add_special_tokens=False)
+            fake_input_ids = self.tokenizer.encode(fake_messages[0]["content"], add_special_tokens=False)
+            fake_input_ids, _ = self.template.mm_plugin.process_token_ids(
+                fake_input_ids, None, fake_images, [], self.tokenizer, self.processor
+            )
             if self.tokenizer.padding_side == "right":
                 features[0]["input_ids"] = features[0]["input_ids"] + fake_input_ids
                 features[0]["attention_mask"] = features[0]["attention_mask"] + [0] * len(fake_input_ids)
@@ -116,6 +119,7 @@ def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, "torch.Tenso
                 features[0]["labels"] = [IGNORE_INDEX] * len(fake_input_ids) + features[0]["labels"]
 
             batch_images = fake_images
+            batch_imglens[0] = 1
             batch_input_ids[0] = features[0]["input_ids"]
 
         mm_inputs = self.template.mm_plugin.get_mm_inputs(

src/llamafactory/train/dpo/trainer.py

@@ -79,6 +79,7 @@ def __init__(
         self.simpo_gamma = finetuning_args.simpo_gamma
 
         Trainer.__init__(self, model=model, **kwargs)
+        self.model_accepts_loss_kwargs = False  # overwrite trainer's default behavior
         if not hasattr(self, "accelerator"):
             raise AttributeError("Please update `transformers`.")
 
@@ -274,15 +275,14 @@ def compute_loss(
         self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
     ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
         r"""
-        Fixes the loss value for transformers 4.46.0.
-        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
+        Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
         """
         loss = super().compute_loss(model, inputs, return_outputs)
-        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
+        if is_transformers_version_equal_to_4_46() and kwargs.get("num_items_in_batch"):
             if return_outputs:
-                return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
+                loss = (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
             else:
-                return loss / self.args.gradient_accumulation_steps
+                loss = loss / self.args.gradient_accumulation_steps
 
         return loss
 

src/llamafactory/train/kto/trainer.py

@@ -77,6 +77,7 @@ def __init__(
         self.ftx_gamma = finetuning_args.pref_ftx
 
         Trainer.__init__(self, model=model, **kwargs)
+        self.model_accepts_loss_kwargs = False  # overwrite trainer's default behavior
         if not hasattr(self, "accelerator"):
             raise AttributeError("Please update `transformers`.")
 
@@ -252,15 +253,14 @@ def compute_loss(
         self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
     ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
         r"""
-        Fixes the loss value for transformers 4.46.0.
-        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
+        Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
         """
         loss = super().compute_loss(model, inputs, return_outputs)
-        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
+        if is_transformers_version_equal_to_4_46() and kwargs.get("num_items_in_batch"):
             if return_outputs:
-                return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
+                loss = (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
             else:
-                return loss / self.args.gradient_accumulation_steps
+                loss = loss / self.args.gradient_accumulation_steps
 
         return loss
 

src/llamafactory/train/pt/trainer.py

@@ -19,7 +19,7 @@
 from transformers import Trainer
 from typing_extensions import override
 
-from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
+from ...extras.packages import is_transformers_version_greater_than
 from ..callbacks import SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
@@ -78,15 +78,13 @@ def compute_loss(
         self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
     ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
         r"""
-        Fixes the loss value for transformers 4.46.0.
-        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
+        Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
         """
         loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
-        if is_transformers_version_equal_to_4_46() and not getattr(self, "model_accepts_loss_kwargs", False):
-            # other model should not scale the loss
+        if kwargs.get("num_items_in_batch") and not getattr(self, "model_accepts_loss_kwargs", False):
             if return_outputs:
-                return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
+                loss = (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
             else:
-                return loss / self.args.gradient_accumulation_steps
+                loss = loss / self.args.gradient_accumulation_steps
 
         return loss

src/llamafactory/train/rm/trainer.py

@@ -52,6 +52,7 @@ def __init__(
             kwargs["processing_class"] = kwargs.pop("tokenizer")
 
         super().__init__(**kwargs)
+        self.model_accepts_loss_kwargs = False  # overwrite trainer's default behavior
         self.finetuning_args = finetuning_args
         self.can_return_loss = True  # override property to return eval_loss
         self.add_callback(FixValueHeadModelCallback)
@@ -107,8 +108,8 @@ def compute_loss(
 
         loss = -torch.nn.functional.logsigmoid(chosen_scores.float() - rejected_scores.float()).mean()
 
-        if is_transformers_version_equal_to_4_46() and kwargs.pop("num_items_in_batch", False):
-            loss /= self.args.gradient_accumulation_steps  # fixes the loss value for transformers 4.46.0
+        if is_transformers_version_equal_to_4_46() and kwargs.get("num_items_in_batch"):
+            loss /= self.args.gradient_accumulation_steps  # fixes the loss value for transformers 4.46.0-4.46.1
 
         if return_outputs:
             return loss, (loss, chosen_scores, rejected_scores)

src/llamafactory/train/sft/trainer.py

@@ -27,7 +27,7 @@
 
 from ...extras import logging
 from ...extras.constants import IGNORE_INDEX
-from ...extras.packages import is_transformers_version_equal_to_4_46, is_transformers_version_greater_than
+from ...extras.packages import is_transformers_version_greater_than
 from ..callbacks import SaveProcessorCallback
 from ..trainer_utils import create_custom_optimizer, create_custom_scheduler
 
@@ -93,16 +93,14 @@ def compute_loss(
         self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False, **kwargs
     ) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
         r"""
-        Fixes the loss value for transformers 4.46.0.
-        https://github.com/huggingface/transformers/blob/v4.46.0/src/transformers/trainer.py#L3605
+        Fixes the loss value. See https://github.com/huggingface/transformers/pull/35438 for details.
         """
         loss = super().compute_loss(model, inputs, return_outputs, **kwargs)
-        if is_transformers_version_equal_to_4_46() and not getattr(self, "model_accepts_loss_kwargs", False):
-            # other model should not scale the loss
+        if kwargs.get("num_items_in_batch") and not getattr(self, "model_accepts_loss_kwargs", False):
             if return_outputs:
-                return (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
+                loss = (loss[0] / self.args.gradient_accumulation_steps, *loss[1:])
             else:
-                return loss / self.args.gradient_accumulation_steps
+                loss = loss / self.args.gradient_accumulation_steps
 
         return loss