[Hardware] correct method signatures for HPU,ROCm,XPU

vllm/platforms/__init__.py

@@ -42,7 +42,6 @@ def tpu_platform_plugin() -> Optional[str]:
         logger.debug("Confirmed TPU platform is available.")
     except Exception as e:
         logger.debug("TPU platform is not available because: %s", str(e))
-        pass
 
     return "vllm.platforms.tpu.TpuPlatform" if is_tpu else None
 
@@ -112,7 +111,6 @@ def rocm_platform_plugin() -> Optional[str]:
             amdsmi.amdsmi_shut_down()
     except Exception as e:
         logger.debug("ROCm platform is not available because: %s", str(e))
-        pass
 
     return "vllm.platforms.rocm.RocmPlatform" if is_rocm else None
 
@@ -130,7 +128,6 @@ def hpu_platform_plugin() -> Optional[str]:
                          "habana_frameworks is not found.")
     except Exception as e:
         logger.debug("HPU platform is not available because: %s", str(e))
-        pass
 
     return "vllm.platforms.hpu.HpuPlatform" if is_hpu else None
 
@@ -148,7 +145,6 @@ def xpu_platform_plugin() -> Optional[str]:
             logger.debug("Confirmed XPU platform is available.")
     except Exception as e:
         logger.debug("XPU platform is not available because: %s", str(e))
-        pass
 
     return "vllm.platforms.xpu.XPUPlatform" if is_xpu else None
 
@@ -170,7 +166,6 @@ def cpu_platform_plugin() -> Optional[str]:
 
     except Exception as e:
         logger.debug("CPU platform is not available because: %s", str(e))
-        pass
 
     return "vllm.platforms.cpu.CpuPlatform" if is_cpu else None
 
@@ -222,8 +217,11 @@ def resolve_current_platform_cls_qualname() -> str:
             platform_cls_qualname = func()
             if platform_cls_qualname is not None:
                 activated_plugins.append(name)
+                logger.info("Platform plugin %s loaded.", name)
+            logger.warning(
+                "Platform plugin %s function's return value is None", name)
         except Exception:
-            pass
+            logger.exception("Failed to load platform plugin %s", name)
 
     activated_builtin_plugins = list(
         set(activated_plugins) & set(builtin_platform_plugins.keys()))

vllm/platforms/hpu.py

@@ -39,8 +39,8 @@ def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
     def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
         return True
 
-    @staticmethod
-    def inference_mode():
+    @classmethod
+    def inference_mode(cls):
         return torch.no_grad()
 
     @classmethod

vllm/platforms/rocm.py

@@ -217,9 +217,9 @@ def get_device_capability(cls,
         major, minor = torch.cuda.get_device_capability(device_id)
         return DeviceCapability(major=major, minor=minor)
 
-    @staticmethod
+    @classmethod
     @with_amdsmi_context
-    def is_fully_connected(physical_device_ids: list[int]) -> bool:
+    def is_fully_connected(cls, physical_device_ids: list[int]) -> bool:
         """
         Query if the set of gpus are fully connected by xgmi (1 hop)
         """

vllm/platforms/xpu.py

@@ -37,15 +37,17 @@ def get_attn_backend_cls(cls, selected_backend: _Backend, head_size: int,
         logger.info("Using IPEX attention backend.")
         return "vllm.attention.backends.ipex_attn.IpexAttnBackend"
 
-    @staticmethod
+    @classmethod
     def get_device_capability(
-            device_id: int = 0) -> Optional[DeviceCapability]:
+        cls,
+        device_id: int = 0,
+    ) -> Optional[DeviceCapability]:
         # capacity format differs from cuda's and will cause unexpected
         # failure, so use None directly
         return None
 
-    @staticmethod
-    def get_device_name(device_id: int = 0) -> str:
+    @classmethod
+    def get_device_name(cls, device_id: int = 0) -> str:
         return torch.xpu.get_device_name(device_id)
 
     @classmethod
@@ -57,8 +59,8 @@ def get_device_total_memory(cls, device_id: int = 0) -> int:
     def is_async_output_supported(cls, enforce_eager: Optional[bool]) -> bool:
         return True
 
-    @staticmethod
-    def inference_mode():
+    @classmethod
+    def inference_mode(cls):
         return torch.no_grad()
 
     @classmethod