NvmlCudaPlatform: remove some internal methods

Signed-off-by: Conroy Cheers <[email protected]>

vllm/platforms/cuda.py

@@ -4,7 +4,7 @@
 
 import os
 from functools import lru_cache, wraps
-from typing import TYPE_CHECKING, Callable, List, Tuple, TypeVar
+from typing import TYPE_CHECKING, Callable, List, TypeVar
 
 import pynvml
 import torch
@@ -57,6 +57,19 @@ def device_id_to_physical_device_id(device_id: int) -> int:
         return device_id
 
 
+def with_nvml_context(fn: Callable[_P, _R]) -> Callable[_P, _R]:
+
+    @wraps(fn)
+    def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
+        pynvml.nvmlInit()
+        try:
+            return fn(*args, **kwargs)
+        finally:
+            pynvml.nvmlShutdown()
+
+    return wrapper
+
+
 class CudaPlatformBase(Platform):
     _enum = PlatformEnum.CUDA
     device_type: str = "cuda"
@@ -103,36 +116,32 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
 # the major benefit of using NVML is that it will not initialize CUDA
 class NvmlCudaPlatform(CudaPlatformBase):
 
-    @staticmethod
-    def with_nvml_context(fn: Callable[_P, _R]) -> Callable[_P, _R]:
-
-        @wraps(fn)
-        def wrapper(*args: _P.args, **kwargs: _P.kwargs) -> _R:
-            pynvml.nvmlInit()
-            try:
-                return fn(*args, **kwargs)
-            finally:
-                pynvml.nvmlShutdown()
-
-        return wrapper
-
     @classmethod
+    @lru_cache(maxsize=8)
+    @with_nvml_context
     def get_device_capability(cls, device_id: int = 0) -> DeviceCapability:
         physical_device_id = device_id_to_physical_device_id(device_id)
-        major, minor = cls._get_physical_device_capability(physical_device_id)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
+        major, minor = pynvml.nvmlDeviceGetCudaComputeCapability(handle)
         return DeviceCapability(major=major, minor=minor)
 
     @classmethod
+    @lru_cache(maxsize=8)
+    @with_nvml_context
     def get_device_name(cls, device_id: int = 0) -> str:
         physical_device_id = device_id_to_physical_device_id(device_id)
         return cls._get_physical_device_name(physical_device_id)
 
     @classmethod
+    @lru_cache(maxsize=8)
+    @with_nvml_context
     def get_device_total_memory(cls, device_id: int = 0) -> int:
         physical_device_id = device_id_to_physical_device_id(device_id)
-        return cls._get_physical_device_total_memory(physical_device_id)
+        handle = pynvml.nvmlDeviceGetHandleByIndex(physical_device_id)
+        return int(pynvml.nvmlDeviceGetMemoryInfo(handle).total)
 
     @classmethod
+    @with_nvml_context
     def is_full_nvlink(cls, physical_device_ids: List[int]) -> bool:
         """
         query if the set of gpus are fully connected by nvlink (1 hop)
@@ -159,27 +168,10 @@ def is_full_nvlink(cls, physical_device_ids: List[int]) -> bool:
         return True
 
     @classmethod
-    @lru_cache(maxsize=8)
-    @with_nvml_context
-    def _get_physical_device_capability(cls,
-                                        device_id: int = 0) -> Tuple[int, int]:
-        handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
-        return pynvml.nvmlDeviceGetCudaComputeCapability(handle)
-
-    @classmethod
-    @lru_cache(maxsize=8)
-    @with_nvml_context
     def _get_physical_device_name(cls, device_id: int = 0) -> str:
         handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
         return pynvml.nvmlDeviceGetName(handle)
 
-    @classmethod
-    @lru_cache(maxsize=8)
-    @with_nvml_context
-    def _get_physical_device_total_memory(cls, device_id: int = 0) -> int:
-        handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
-        return int(pynvml.nvmlDeviceGetMemoryInfo(handle).total)
-
     @classmethod
     @with_nvml_context
     def log_warnings(cls):