[Hardware][XPU] using current_platform.is_xpu (#9605)

vllm/utils.py

@@ -327,29 +327,6 @@ def is_openvino() -> bool:
         return False
 
 
-@lru_cache(maxsize=None)
-def is_xpu() -> bool:
-    from importlib.metadata import PackageNotFoundError, version
-    try:
-        is_xpu_flag = "xpu" in version("vllm")
-    except PackageNotFoundError:
-        return False
-    # vllm is not build with xpu
-    if not is_xpu_flag:
-        return False
-    try:
-        import intel_extension_for_pytorch as ipex  # noqa: F401
-        _import_ipex = True
-    except ImportError as e:
-        logger.warning("Import Error for IPEX: %s", e.msg)
-        _import_ipex = False
-    # ipex dependency is not ready
-    if not _import_ipex:
-        logger.warning("not found ipex lib")
-        return False
-    return hasattr(torch, "xpu") and torch.xpu.is_available()
-
-
 @lru_cache(maxsize=None)
 def get_max_shared_memory_bytes(gpu: int = 0) -> int:
     """Returns the maximum shared memory per thread block in bytes."""
@@ -379,7 +356,7 @@ def seed_everything(seed: int) -> None:
     if current_platform.is_cuda_alike():
         torch.cuda.manual_seed_all(seed)
 
-    if is_xpu():
+    if current_platform.is_xpu():
         torch.xpu.manual_seed_all(seed)
 
 
@@ -774,7 +751,7 @@ def is_pin_memory_available() -> bool:
         print_warning_once("Using 'pin_memory=False' as WSL is detected. "
                            "This may slow down the performance.")
         return False
-    elif is_xpu():
+    elif current_platform.is_xpu():
         print_warning_once("Pin memory is not supported on XPU.")
         return False
     elif current_platform.is_neuron():
@@ -795,7 +772,7 @@ class DeviceMemoryProfiler:
         if current_platform.is_cuda_alike():
             torch.cuda.reset_peak_memory_stats(self.device)
             mem = torch.cuda.max_memory_allocated(self.device)
-        elif is_xpu():
+        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