[Hardware] [Intel GPU] refactor xpu worker/executor (vllm-project#7686)

vllm/executor/xpu_executor.py

@@ -1,16 +1,16 @@
-from typing import List, Optional
+from typing import List, Optional, Tuple, Union
 
 import torch
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
-                         ModelConfig, ParallelConfig, PromptAdapterConfig,
-                         SchedulerConfig, SpeculativeConfig)
+                         ModelConfig, ObservabilityConfig, ParallelConfig,
+                         PromptAdapterConfig, SchedulerConfig,
+                         SpeculativeConfig)
 from vllm.executor.executor_base import ExecutorAsyncBase
 from vllm.executor.gpu_executor import GPUExecutor
 from vllm.logger import init_logger
-from vllm.sequence import ExecuteModelRequest, SamplerOutput
+from vllm.sequence import ExecuteModelRequest, PoolerOutput, SamplerOutput
 from vllm.utils import make_async
-from vllm.worker.worker_base import WorkerWrapperBase
 
 logger = init_logger(__name__)
 
@@ -30,6 +30,7 @@ def __init__(
         lora_config: Optional[LoRAConfig],
         prompt_adapter_config: Optional[PromptAdapterConfig],
         speculative_config: Optional[SpeculativeConfig],
+        observability_config: Optional[ObservabilityConfig],
     ) -> None:
         assert device_config.device_type == "xpu"
         assert (not speculative_config
@@ -46,32 +47,23 @@ def __init__(
         self.device_config = device_config
         self.prompt_adapter_config = prompt_adapter_config
         self.speculative_config = None
+        self.observability_config = observability_config
 
         # Instantiate the worker and load the model to GPU.
         self._init_executor()
 
-    def _create_worker(self,
-                       local_rank: int = 0,
-                       rank: int = 0,
-                       distributed_init_method: Optional[str] = None):
-        if self.speculative_config is None:
-            worker_module_name = "vllm.worker.xpu_worker"
-            worker_class_name = "XPUWorker"
-        else:
+    def _get_worker_module_and_class(self) -> Tuple[str, str]:
+        if self.speculative_config is not None:
             raise NotImplementedError(
                 "XPU does not support speculative decoding")
-
-        wrapper = WorkerWrapperBase(
-            worker_module_name=worker_module_name,
-            worker_class_name=worker_class_name,
-        )
-        wrapper.init_worker(**self._get_worker_kwargs(local_rank, rank,
-                                                      distributed_init_method))
-        return wrapper.worker
+        else:
+            worker_module_name = "vllm.worker.xpu_worker"
+            worker_class_name = "XPUWorker"
+        return (worker_module_name, worker_class_name)
 
     def execute_model(
-            self,
-            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
+        self, execute_model_req: ExecuteModelRequest
+    ) -> Optional[List[Union[SamplerOutput, PoolerOutput]]]:
         output = self.driver_worker.execute_model(execute_model_req)
         return output
 

vllm/worker/xpu_model_runner.py

@@ -137,7 +137,6 @@ def load_model(self) -> None:
                 device_config=self.device_config,
                 load_config=self.load_config,
                 lora_config=self.lora_config,
-                multimodal_config=self.multimodal_config,
                 parallel_config=self.parallel_config,
                 scheduler_config=self.scheduler_config,
                 cache_config=self.cache_config,

vllm/worker/xpu_worker.py

@@ -9,8 +9,8 @@
 import torch.distributed
 
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
-                         ModelConfig, MultiModalConfig, ParallelConfig,
-                         PromptAdapterConfig, SchedulerConfig,
+                         ModelConfig, MultiModalConfig, ObservabilityConfig,
+                         ParallelConfig, PromptAdapterConfig, SchedulerConfig,
                          SpeculativeConfig)
 from vllm.distributed import (ensure_model_parallel_initialized,
                               init_distributed_environment)
@@ -50,6 +50,7 @@ def __init__(
         speculative_config: Optional[SpeculativeConfig] = None,
         prompt_adapter_config: Optional[PromptAdapterConfig] = None,
         is_driver_worker: bool = False,
+        observability_config: Optional[ObservabilityConfig] = None,
     ) -> None:
         assert device_config.device_type == "xpu"
         assert is_xpu()
@@ -67,8 +68,10 @@ def __init__(
         self.lora_config = lora_config
         self.prompt_adapter_config = prompt_adapter_config
         self.is_driver_worker = is_driver_worker
-        if self.is_driver_worker:
-            assert self.rank == 0, "The driver worker must have rank 0."
+        self.observability_config = observability_config
+        if parallel_config and is_driver_worker:
+            assert rank % parallel_config.tensor_parallel_size == 0, \
+                   "Driver worker should be rank 0 of tensor parallel group."
 
         self.multimodal_config = multimodal_config
 
@@ -183,7 +186,11 @@ def init_worker_distributed_environment(self) -> None:
             # dependency (libdrm and drm headers) on your system.
             ENV_CCL_ZE_IPC_EXCHANGE = os.getenv("CCL_ZE_IPC_EXCHANGE",
                                                 "sockets")
+            ENV_LOCAL_WORLD_SIZE = os.getenv("LOCAL_WORLD_SIZE",
+                                             str(parallel_config.world_size))
             os.environ['CCL_ZE_IPC_EXCHANGE'] = ENV_CCL_ZE_IPC_EXCHANGE
+            os.environ["LOCAL_WORLD_SIZE"] = ENV_LOCAL_WORLD_SIZE
+            os.environ["LOCAL_RANK"] = str(self.local_rank)
             init_distributed_environment(
                 world_size=parallel_config.world_size,
                 rank=rank,