[Hardware][Intel GPU] Add intel GPU pipeline parallel support. (#7810)

vllm/executor/multiproc_gpu_executor.py

@@ -30,16 +30,12 @@ class MultiprocessingGPUExecutor(DistributedGPUExecutor):
     uses_ray: bool = False
 
     def _init_executor(self) -> None:
+        self._check_executor_parameters()
+
         # Create the parallel GPU workers.
         world_size = self.parallel_config.world_size
         tensor_parallel_size = self.parallel_config.tensor_parallel_size
 
-        # Set CUDA_VISIBLE_DEVICES for the driver, inherited by workers
-        if "CUDA_VISIBLE_DEVICES" not in os.environ:
-            update_environment_variables({
-                "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
-            })
-
         # Ensure that VLLM_INSTANCE_ID is set, to be inherited by workers
         os.environ["VLLM_INSTANCE_ID"] = get_vllm_instance_id()
 
@@ -68,16 +64,6 @@ class MultiprocessingGPUExecutor(DistributedGPUExecutor):
         if world_size > 1:
             maybe_set_triton_cache_manager()
 
-        cuda_device_count = cuda_device_count_stateless()
-        # Use confusing message for more common TP-only case.
-        assert tensor_parallel_size <= cuda_device_count, (
-            f"please set tensor_parallel_size ({tensor_parallel_size}) "
-            f"to less than max local gpu count ({cuda_device_count})")
-
-        assert world_size <= cuda_device_count, (
-            f"please ensure that world_size ({world_size}) "
-            f"is less than than max local gpu count ({cuda_device_count})")
-
         # Multiprocessing-based executor does not support multi-node setting.
         # Since it only works for single node, we can use the loopback address
         # 127.0.0.1 for communication.
@@ -139,6 +125,26 @@ class MultiprocessingGPUExecutor(DistributedGPUExecutor):
                           max_concurrent_workers=self.parallel_config.
                           max_parallel_loading_workers)
 
+    def _check_executor_parameters(self):
+        world_size = self.parallel_config.tensor_parallel_size
+        tensor_parallel_size = self.parallel_config.tensor_parallel_size
+
+        # Set CUDA_VISIBLE_DEVICES for the driver, inherited by workers
+        if "CUDA_VISIBLE_DEVICES" not in os.environ:
+            update_environment_variables({
+                "CUDA_VISIBLE_DEVICES": (",".join(map(str, range(world_size))))
+            })
+
+        cuda_device_count = cuda_device_count_stateless()
+        # Use confusing message for more common TP-only case.
+        assert tensor_parallel_size <= cuda_device_count, (
+            f"please set tensor_parallel_size ({tensor_parallel_size}) "
+            f"to less than max local gpu count ({cuda_device_count})")
+
+        assert world_size <= cuda_device_count, (
+            f"please ensure that world_size ({world_size}) "
+            f"is less than than max local gpu count ({cuda_device_count})")
+
     def shutdown(self):
         if (worker_monitor := getattr(self, "worker_monitor",
                                       None)) is not None:

vllm/executor/multiproc_xpu_executor.py

@@ -0,0 +1,26 @@
+import vllm.envs as envs
+from vllm.executor.multiproc_gpu_executor import (
+    MultiprocessingGPUExecutor, MultiprocessingGPUExecutorAsync)
+from vllm.executor.xpu_executor import XPUExecutor
+from vllm.logger import init_logger
+from vllm.utils import make_async
+
+logger = init_logger(__name__)
+
+
+class MultiprocessingXPUExecutor(MultiprocessingGPUExecutor, XPUExecutor):
+    """Python multiprocessing-based multi-XPU executor"""
+
+    def _check_executor_parameters(self):
+        mp_method = envs.VLLM_WORKER_MULTIPROC_METHOD
+        if mp_method != "spawn":
+            raise RuntimeError(
+                "XPU multiprocess executor only support spawn as mp method")
+
+
+class MultiprocessingXPUExecutorAsync(MultiprocessingXPUExecutor,
+                                      MultiprocessingGPUExecutorAsync):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.driver_exec_model = make_async(self.driver_worker.execute_model)