Repository: huggingface/transformers
Pull request: fix(trainer): Avoid moving model with device_map

Fix #41013 

When a model is loaded with `device_map="auto"` and is too large to fit on a single GPU, `accelerate` will offload some layers to the CPU or disk. The `Trainer` would previously attempt to move the entire model to the specified device, causing a `RuntimeError` because a model dispatched with `accelerate` hooks cannot be moved.

This commit fixes the issue by adding a check in `_move_model_to_device` to see if the model has an `hf_device_map` attribute. If it does, the device placement is assumed to be handled by `accelerate`, and the `model.to(device)` call is skipped.

A regression test is added to ensure the `Trainer` can be initialized with a model that has a `hf_device_map` that simulates offloading without raising an error.

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Changed files:
filename: src/transformers/trainer.py
patch: @@ -894,6 +894,11 @@ def remove_callback(self, callback):
         self.callback_handler.remove_callback(callback)
 
     def _move_model_to_device(self, model, device):
+        if getattr(model, "hf_device_map", None) is not None:
+            logger.warning(
+                "The model is already on multiple devices. Skipping the move to device specified in `args`."
+            )
+            return
         model = model.to(device)
         # Moving a model to an XLA device disconnects the tied weights, so we have to retie them.
         if self.args.parallel_mode == ParallelMode.TPU and hasattr(model, "tie_weights"):

filename: tests/trainer/test_trainer.py
patch: @@ -1428,6 +1428,24 @@ def test_trainer_works_with_dict(self):
         _ = trainer.evaluate()
         _ = trainer.predict(eval_dataset)
 
+    def test_init_with_offloaded_model(self):
+        # Test that Trainer can be initialized with a model that has been offloaded to CPU
+        config = RegressionModelConfig(a=1.5, b=2.5)
+        model = RegressionPreTrainedModel(config)
+
+        # Simulate a model with some parts offloaded to CPU
+        device_map = {"a": "cpu"}
+        if torch.cuda.is_available():
+            device_map["b"] = "cuda:0"
+        else:
+            device_map["b"] = "cpu"
+        model.hf_device_map = device_map
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            args = TrainingArguments(output_dir=tmp_dir, report_to="none")
+            # This should not raise an error.
+            _ = Trainer(model, args=args, train_dataset=RegressionDataset())
+
     def test_training_arguments_are_left_untouched(self):
         tmp_dir = self.get_auto_remove_tmp_dir()
         trainer = get_regression_trainer(output_dir=tmp_dir)