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

vllm/attention/selector.py

@@ -10,7 +10,7 @@ import vllm.envs as envs
 from vllm.attention.backends.abstract import AttentionBackend
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import STR_BACKEND_ENV_VAR, is_hip, is_openvino, is_xpu
+from vllm.utils import STR_BACKEND_ENV_VAR, is_hip, is_openvino
 
 logger = init_logger(__name__)
 
@@ -136,7 +136,7 @@ def get_attn_backend(
         from vllm.attention.backends.openvino import OpenVINOAttentionBackend
         return OpenVINOAttentionBackend
     elif backend == _Backend.IPEX:
-        assert is_xpu(), RuntimeError(
+        assert current_platform.is_xpu(), RuntimeError(
             "IPEX attention backend is only used for the XPU device.")
         logger.info("Using IPEX attention backend.")
         from vllm.attention.backends.ipex_attn import IpexAttnBackend
@@ -198,7 +198,7 @@ def which_attn_to_use(
             logger.info("Cannot use %s backend on OpenVINO.", selected_backend)
         return _Backend.OPENVINO
 
-    if is_xpu():
+    if current_platform.is_xpu():
         if selected_backend != _Backend.IPEX:
             logger.info("Cannot use %s backend on XPU.", selected_backend)
         return _Backend.IPEX

vllm/config.py

@@ -17,7 +17,7 @@ from vllm.transformers_utils.config import (ConfigFormat, get_config,
                                             get_hf_image_processor_config,
                                             get_hf_text_config)
 from vllm.utils import (GiB_bytes, cuda_device_count_stateless, get_cpu_memory,
-                        is_hip, is_openvino, is_xpu, print_warning_once)
+                        is_hip, is_openvino, print_warning_once)
 
 if TYPE_CHECKING:
     from ray.util.placement_group import PlacementGroup
@@ -1121,7 +1121,7 @@ class DeviceConfig:
                 self.device_type = "tpu"
             elif current_platform.is_cpu():
                 self.device_type = "cpu"
-            elif is_xpu():
+            elif current_platform.is_xpu():
                 self.device_type = "xpu"
             else:
                 raise RuntimeError("Failed to infer device type")

vllm/executor/ray_utils.py

@@ -10,7 +10,7 @@ from vllm.executor.msgspec_utils import decode_hook, encode_hook
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.sequence import ExecuteModelRequest, IntermediateTensors
-from vllm.utils import get_ip, is_hip, is_xpu
+from vllm.utils import get_ip, is_hip
 from vllm.worker.worker_base import WorkerWrapperBase
 
 logger = init_logger(__name__)
@@ -231,7 +231,7 @@ def initialize_ray_cluster(
     assert_ray_available()
 
     # Connect to a ray cluster.
-    if is_hip() or is_xpu():
+    if is_hip() or current_platform.is_xpu():
         ray.init(address=ray_address,
                  ignore_reinit_error=True,
                  num_gpus=parallel_config.world_size)

vllm/model_executor/custom_op.py

@@ -7,7 +7,7 @@ import vllm.envs as envs
 from vllm.compilation.levels import CompilationLevel
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
-from vllm.utils import is_hip, is_xpu, print_warning_once
+from vllm.utils import is_hip, print_warning_once
 
 logger = init_logger(__name__)
 
@@ -78,7 +78,7 @@ class CustomOp(nn.Module):
             return self.forward_cpu
         elif current_platform.is_tpu():
             return self.forward_tpu
-        elif is_xpu():
+        elif current_platform.is_xpu():
             return self.forward_xpu
         else:
             return self.forward_cuda

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

vllm/worker/xpu_worker.py

@@ -17,7 +17,7 @@ from vllm.distributed import (ensure_model_parallel_initialized,
 from vllm.distributed.parallel_state import get_pp_group
 from vllm.logger import init_logger
 from vllm.model_executor import set_random_seed
-from vllm.utils import is_xpu
+from vllm.platforms import current_platform
 from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.worker import Worker
 from vllm.worker.worker_base import LoraNotSupportedWorkerBase
@@ -53,7 +53,7 @@ class XPUWorker(LoraNotSupportedWorkerBase, Worker):
         observability_config: Optional[ObservabilityConfig] = None,
     ) -> None:
         assert device_config.device_type == "xpu"
-        assert is_xpu()
+        assert current_platform.is_xpu()
 
         self.model_config = model_config
         self.parallel_config = parallel_config
@@ -91,7 +91,8 @@ class XPUWorker(LoraNotSupportedWorkerBase, Worker):
         self.gpu_cache: Optional[List[List[torch.Tensor]]]
 
     def init_device(self) -> None:
-        if self.device_config.device.type == "xpu" and is_xpu():
+        if self.device_config.device.type == "xpu" and current_platform.is_xpu(
+        ):
             self.device = torch.device(f"xpu:{self.local_rank}")
             torch.xpu.set_device(self.device)
             torch.xpu.empty_cache()