[Platform] Refactor current_memory_usage() function in DeviceMemoryProfiler to Platform

vllm/platforms/cuda.py

@@ -143,6 +143,13 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         if cache_config and cache_config.block_size is None:
             cache_config.block_size = 16
 
+    @classmethod
+    def get_current_memory_usage(cls,
+                                 device: Optional[torch.types.Device] = None
+                                 ) -> float:
+        torch.cuda.reset_peak_memory_stats(device)
+        return torch.cuda.max_memory_allocated(device)
+
     @classmethod
     def get_attn_backend_cls(cls, selected_backend, head_size, dtype,
                              kv_cache_dtype, block_size, use_v1) -> str:

vllm/platforms/interface.py

@@ -276,6 +276,15 @@ def is_pin_memory_available(cls) -> bool:
             return False
         return True
 
+    @classmethod
+    def get_current_memory_usage(cls,
+                                 device: Optional[torch.types.Device] = None
+                                 ) -> float:
+        """
+        Return the memory usage in bytes.
+        """
+        raise NotImplementedError
+
     @classmethod
     def get_punica_wrapper(cls) -> str:
         """

vllm/platforms/rocm.py

@@ -157,3 +157,10 @@ def verify_quantization(cls, quant: str) -> None:
     @classmethod
     def get_punica_wrapper(cls) -> str:
         return "vllm.lora.punica_wrapper.punica_gpu.PunicaWrapperGPU"
+
+    @classmethod
+    def get_current_memory_usage(cls,
+                                 device: Optional[torch.types.Device] = None
+                                 ) -> float:
+        torch.cuda.reset_peak_memory_stats(device)
+        return torch.cuda.max_memory_allocated(device)

vllm/platforms/xpu.py

@@ -94,3 +94,10 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
     def is_pin_memory_available(cls):
         logger.warning("Pin memory is not supported on XPU.")
         return False
+
+    @classmethod
+    def get_current_memory_usage(cls,
+                                 device: Optional[torch.types.Device] = None
+                                 ) -> float:
+        torch.xpu.reset_peak_memory_stats(device)
+        return torch.xpu.max_memory_allocated(device)

vllm/utils.py

@@ -710,13 +710,7 @@ def __init__(self, device: Optional[torch.types.Device] = None):
     def current_memory_usage(self) -> float:
         # Return the memory usage in bytes.
         from vllm.platforms import current_platform
-        if current_platform.is_cuda_alike():
-            torch.cuda.reset_peak_memory_stats(self.device)
-            mem = torch.cuda.max_memory_allocated(self.device)
-        elif current_platform.is_xpu():
-            torch.xpu.reset_peak_memory_stats(self.device)  # type: ignore
-            mem = torch.xpu.max_memory_allocated(self.device)  # type: ignore
-        return mem
+        return current_platform.get_current_memory_usage(self.device)
 
     def __enter__(self):
         self.initial_memory = self.current_memory_usage()