[misc]refactor Platform.set_device method (#20262)

Signed-off-by: Kunshang Ji <kunshang.ji@intel.com>

vllm/platforms/cpu.py

@@ -75,6 +75,13 @@ class CpuPlatform(Platform):
     def get_device_total_memory(cls, device_id: int = 0) -> int:
         return psutil.virtual_memory().total
 
+    @classmethod
+    def set_device(cls, device: torch.device) -> None:
+        """
+        Set the device for the current platform.
+        """
+        torch.cpu.set_device(device)
+
     @classmethod
     def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
         return False

vllm/platforms/cuda.py

@@ -77,7 +77,7 @@ class CudaPlatformBase(Platform):
         """
         Set the device for the current platform.
         """
-        super().set_device(device)
+        torch.cuda.set_device(device)
         # With this trick we can force the device to be set eagerly
         # see https://github.com/pytorch/pytorch/issues/155668
         # for why and when it is needed

vllm/platforms/hpu.py

@@ -45,6 +45,13 @@ class HpuPlatform(Platform):
     def inference_mode(cls):
         return torch.no_grad()
 
+    @classmethod
+    def set_device(cls, device: torch.device) -> None:
+        """
+        Set the device for the current platform.
+        """
+        torch.hpu.set_device(device)
+
     @classmethod
     def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
 

vllm/platforms/interface.py

@@ -305,7 +305,7 @@ class Platform:
         """
         Set the device for the current platform.
         """
-        torch.cuda.set_device(device)
+        raise NotImplementedError
 
     @classmethod
     def pre_register_and_update(cls,

vllm/platforms/rocm.py

@@ -241,6 +241,17 @@ class RocmPlatform(Platform):
         logger.info("Using ROCmFlashAttention backend.")
         return "vllm.attention.backends.rocm_flash_attn.ROCmFlashAttentionBackend"  # noqa: E501
 
+    @classmethod
+    def set_device(cls, device: torch.device) -> None:
+        """
+        Set the device for the current platform.
+        """
+        torch.cuda.set_device(device)
+        # With this trick we can force the device to be set eagerly
+        # see https://github.com/pytorch/pytorch/issues/155668
+        # for why and when it is needed
+        _ = torch.zeros(1, device=device)
+
     @classmethod
     @lru_cache(maxsize=8)
     def get_device_capability(cls,

vllm/platforms/tpu.py

@@ -55,6 +55,13 @@ class TpuPlatform(Platform):
         logger.info("Using Pallas V1 backend.")
         return "vllm.v1.attention.backends.pallas.PallasAttentionBackend"
 
+    @classmethod
+    def set_device(cls, device: torch.device) -> None:
+        """
+        Set the device for the current platform.
+        """
+        torch.tpu.set_device(device)
+
     @classmethod
     def get_device_name(cls, device_id: int = 0) -> str:
         chip_type, _ = device.get_local_chips()

vllm/platforms/xpu.py

@@ -45,6 +45,13 @@ class XPUPlatform(Platform):
         logger.info("Using Flash Attention backend on V1 engine.")
         return "vllm.v1.attention.backends.flash_attn.FlashAttentionBackend"
 
+    @classmethod
+    def set_device(cls, device: torch.device) -> None:
+        """
+        Set the device for the current platform.
+        """
+        torch.xpu.set_device(device)
+
     @classmethod
     def get_device_capability(
         cls,

vllm/v1/worker/gpu_worker.py

@@ -130,7 +130,7 @@ class Worker(WorkerBase):
             # This env var set by Ray causes exceptions with graph building.
             os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
             self.device = torch.device(f"cuda:{self.local_rank}")
-            torch.cuda.set_device(self.device)
+            current_platform.set_device(self.device)
 
             _check_if_gpu_supports_dtype(self.model_config.dtype)
             gc.collect()

vllm/v1/worker/xpu_worker.py

@@ -132,7 +132,7 @@ class XPUWorker(Worker):
         if self.device_config.device.type == "xpu" and current_platform.is_xpu(
         ):
             self.device = torch.device(f"xpu:{self.local_rank}")
-            torch.xpu.set_device(self.device)
+            current_platform.set_device(self.device)
             torch.xpu.empty_cache()
             self.init_gpu_memory = torch.xpu.get_device_properties(
                 self.local_rank).total_memory