Merge bbbdfac392 into 32111085d8

src/chronos/chronos2/trainer.py

@@ -74,6 +74,28 @@ class Chronos2Trainer(Trainer):
 
         return DataLoader(train_dataset, **dataloader_params)  # type: ignore
 
+    def _move_model_to_device(self, model, device):
+        """
+        Keep the model on its existing CUDA device when fine-tuning a single-device model.
+
+        `Trainer` may otherwise move a model loaded on e.g. `cuda:5` to `args.device` (often `cuda:0`).
+        """
+        model_device = getattr(model, "device", None)
+        model_device_type = getattr(model_device, "type", None)
+        target_device_type = getattr(device, "type", None)
+        has_hf_device_map = getattr(model, "hf_device_map", None) is not None
+
+        if (
+            not has_hf_device_map
+            and model_device is not None
+            and model_device_type == "cuda"
+            and target_device_type == "cuda"
+            and model_device != device
+        ):
+            device = model_device
+
+        super()._move_model_to_device(model, device)
+
     def get_eval_dataloader(self, eval_dataset: str | Dataset | None = None) -> DataLoader:
         if self.eval_dataset is None:
             raise ValueError("Trainer: evaluation requires an eval_dataset.")

test/test_chronos2_trainer.py

@@ -0,0 +1,64 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+from transformers import Trainer
+
+from chronos.chronos2.trainer import Chronos2Trainer
+
+
+class _DummyModel:
+    def __init__(self, device: torch.device, hf_device_map=None):
+        self.device = device
+        self.hf_device_map = hf_device_map
+
+
+def test_move_model_to_device_preserves_loaded_cuda_device(monkeypatch):
+    """When model is on a single CUDA device, keep that device instead of forcing cuda:0."""
+    captured = {}
+
+    def fake_move(self, model, device):
+        captured["device"] = device
+
+    monkeypatch.setattr(Trainer, "_move_model_to_device", fake_move)
+
+    trainer = object.__new__(Chronos2Trainer)
+    model = _DummyModel(torch.device("cuda:5"))
+
+    Chronos2Trainer._move_model_to_device(trainer, model, torch.device("cuda:0"))
+
+    assert captured["device"] == torch.device("cuda:5")
+
+
+def test_move_model_to_device_keeps_requested_cpu_device(monkeypatch):
+    """CPU fine-tuning should preserve existing Trainer behavior."""
+    captured = {}
+
+    def fake_move(self, model, device):
+        captured["device"] = device
+
+    monkeypatch.setattr(Trainer, "_move_model_to_device", fake_move)
+
+    trainer = object.__new__(Chronos2Trainer)
+    model = _DummyModel(torch.device("cpu"))
+
+    Chronos2Trainer._move_model_to_device(trainer, model, torch.device("cpu"))
+
+    assert captured["device"] == torch.device("cpu")
+
+
+def test_move_model_to_device_keeps_requested_device_for_hf_device_map(monkeypatch):
+    """Do not override device movement for models managed via hf_device_map."""
+    captured = {}
+
+    def fake_move(self, model, device):
+        captured["device"] = device
+
+    monkeypatch.setattr(Trainer, "_move_model_to_device", fake_move)
+
+    trainer = object.__new__(Chronos2Trainer)
+    model = _DummyModel(torch.device("cuda:5"), hf_device_map={"": "cuda:5"})
+
+    Chronos2Trainer._move_model_to_device(trainer, model, torch.device("cuda:0"))
+
+    assert captured["device"] == torch.device("cuda:0")