[test] add allreduce test on npu (#9619)

Co-authored-by: frozenleaves <frozen@Mac.local>

src/llamafactory/train/test_utils.py

@@ -20,7 +20,6 @@ from transformers import AutoModelForCausalLM
 from trl import AutoModelForCausalLMWithValueHead
 
 from ..data import get_dataset, get_template_and_fix_tokenizer
-from ..extras.misc import get_current_device
 from ..hparams import get_infer_args, get_train_args
 from ..model import load_model, load_tokenizer
 
@@ -81,17 +80,16 @@ def load_reference_model(
     is_trainable: bool = False,
     add_valuehead: bool = False,
 ) -> Union["PreTrainedModel", "LoraModel"]:
-    current_device = get_current_device()
     if add_valuehead:
         model: AutoModelForCausalLMWithValueHead = AutoModelForCausalLMWithValueHead.from_pretrained(
-            model_path, torch_dtype=torch.float16, device_map=current_device
+            model_path, torch_dtype=torch.float16, device_map="auto"
         )
         if not is_trainable:
             model.v_head = model.v_head.to(torch.float16)
 
         return model
 
-    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map=current_device)
+    model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, device_map="auto")
     if use_lora or use_pissa:
         model = PeftModel.from_pretrained(
             model, lora_path, subfolder="pissa_init" if use_pissa else None, is_trainable=is_trainable

src/llamafactory/v1/accelerator/helper.py

@@ -103,6 +103,36 @@ def is_torch_xpu_available():
     return get_current_accelerator().type == DeviceType.XPU
 
 
+def get_current_device() -> "torch.device":
+    r"""Get the current available device."""
+    if is_torch_xpu_available():
+        device = "xpu:{}".format(os.getenv("LOCAL_RANK", "0"))
+    elif is_torch_npu_available():
+        device = "npu:{}".format(os.getenv("LOCAL_RANK", "0"))
+    elif is_torch_mps_available():
+        device = "mps:{}".format(os.getenv("LOCAL_RANK", "0"))
+    elif is_torch_cuda_available():
+        device = "cuda:{}".format(os.getenv("LOCAL_RANK", "0"))
+    else:
+        device = "cpu"
+
+    return torch.device(device)
+
+
+def get_device_count() -> int:
+    r"""Get the number of available devices."""
+    if is_torch_xpu_available():
+        return torch.xpu.device_count()
+    elif is_torch_npu_available():
+        return torch.npu.device_count()
+    elif is_torch_mps_available():
+        return torch.mps.device_count()
+    elif is_torch_cuda_available():
+        return torch.cuda.device_count()
+    else:
+        return 0
+
+
 def all_gather(tensor: Tensor, group: Optional[ProcessGroup] = None) -> Tensor:
     """Gathers the tensor from all ranks and concats them along the first dim."""
     world_size = get_world_size()

src/llamafactory/v1/utils/utils.py

@@ -0,0 +1,34 @@
+# Copyright 2025 the LlamaFactory team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import socket
+
+
+def find_available_port() -> int:
+    r"""Find an available port on the local machine."""
+    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    sock.bind(("", 0))
+    port = sock.getsockname()[1]
+    sock.close()
+    return port
+
+
+def is_env_enabled(env_var: str, default: str = "0") -> bool:
+    r"""Check if the environment variable is enabled."""
+    return os.getenv(env_var, default).lower() in ["true", "y", "1"]
+
+
+if __name__ == "__main__":
+    print(find_available_port())