[misc] use nvml to get consistent device name (#7582)

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -11,6 +11,7 @@ import triton.language as tl
 import vllm.envs as envs
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
+from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
 
@@ -287,7 +288,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor, B: torch.Tensor, C: torch.Tensor,
 
 
 def get_config_file_name(E: int, N: int, dtype: Optional[str]) -> str:
-    device_name = torch.cuda.get_device_name().replace(" ", "_")
+    device_name = current_platform.get_device_name().replace(" ", "_")
     dtype_selector = "" if not dtype else f",dtype={dtype}"
     return f"E={E},N={N},device_name={device_name}{dtype_selector}.json"
 

vllm/platforms/cuda.py

@@ -44,6 +44,35 @@ def get_physical_device_capability(device_id: int = 0) -> Tuple[int, int]:
     return pynvml.nvmlDeviceGetCudaComputeCapability(handle)
 
 
+@lru_cache(maxsize=8)
+@with_nvml_context
+def get_physical_device_name(device_id: int = 0) -> str:
+    handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
+    return pynvml.nvmlDeviceGetName(handle)
+
+
+@with_nvml_context
+def warn_if_different_devices():
+    device_ids: int = pynvml.nvmlDeviceGetCount()
+    if device_ids > 1:
+        device_names = [get_physical_device_name(i) for i in range(device_ids)]
+        if len(set(device_names)) > 1 and os.environ.get(
+                "CUDA_DEVICE_ORDER") != "PCI_BUS_ID":
+            logger.warning(
+                "Detected different devices in the system: \n%s\nPlease"
+                " make sure to set `CUDA_DEVICE_ORDER=PCI_BUS_ID` to "
+                "avoid unexpected behavior.", "\n".join(device_names))
+
+
+try:
+    from sphinx.ext.autodoc.mock import _MockModule
+
+    if not isinstance(pynvml, _MockModule):
+        warn_if_different_devices()
+except ModuleNotFoundError:
+    warn_if_different_devices()
+
+
 def device_id_to_physical_device_id(device_id: int) -> int:
     if "CUDA_VISIBLE_DEVICES" in os.environ:
         device_ids = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
@@ -61,6 +90,11 @@ class CudaPlatform(Platform):
         physical_device_id = device_id_to_physical_device_id(device_id)
         return get_physical_device_capability(physical_device_id)
 
+    @staticmethod
+    def get_device_name(device_id: int = 0) -> str:
+        physical_device_id = device_id_to_physical_device_id(device_id)
+        return get_physical_device_name(physical_device_id)
+
     @staticmethod
     @with_nvml_context
     def is_full_nvlink(physical_device_ids: List[int]) -> bool:

vllm/platforms/interface.py

@@ -27,6 +27,10 @@ class Platform:
     def get_device_capability(device_id: int = 0) -> Tuple[int, int]:
         raise NotImplementedError
 
+    @staticmethod
+    def get_device_name(device_id: int = 0) -> str:
+        raise NotImplementedError
+
     @staticmethod
     def inference_mode():
         """A device-specific wrapper of `torch.inference_mode`.

vllm/platforms/rocm.py

@@ -13,3 +13,8 @@ class RocmPlatform(Platform):
     @lru_cache(maxsize=8)
     def get_device_capability(device_id: int = 0) -> Tuple[int, int]:
         return torch.cuda.get_device_capability(device_id)
+
+    @staticmethod
+    @lru_cache(maxsize=8)
+    def get_device_name(device_id: int = 0) -> str:
+        return torch.cuda.get_device_name(device_id)

vllm/worker/worker.py

@@ -368,7 +368,7 @@ def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
     if torch_dtype == torch.bfloat16:
         compute_capability = current_platform.get_device_capability()
         if compute_capability[0] < 8:
-            gpu_name = torch.cuda.get_device_name()
+            gpu_name = current_platform.get_device_name()
             raise ValueError(
                 "Bfloat16 is only supported on GPUs with compute capability "
                 f"of at least 8.0. Your {gpu_name} GPU has compute capability "