add qwen3

vllm-v0.6.2/vllm/executor/gpu_executor.py

@@ -0,0 +1,182 @@
+from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
+
+from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
+from vllm.logger import init_logger
+from vllm.lora.request import LoRARequest
+from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.prompt_adapter.request import PromptAdapterRequest
+from vllm.sequence import ExecuteModelRequest, PoolerOutput
+from vllm.utils import (get_distributed_init_method, get_ip, get_open_port,
+                        make_async)
+from vllm.worker.worker_base import WorkerBase, WorkerWrapperBase
+
+logger = init_logger(__name__)
+
+
+def create_worker(worker_module_name: str, worker_class_name: str,
+                  worker_class_fn: Optional[Callable[[], Type[WorkerBase]]],
+                  **kwargs):
+    wrapper = WorkerWrapperBase(
+        worker_module_name=worker_module_name,
+        worker_class_name=worker_class_name,
+        worker_class_fn=worker_class_fn,
+    )
+    wrapper.init_worker(**kwargs)
+    return wrapper.worker
+
+
+class GPUExecutor(ExecutorBase):
+
+    uses_ray: bool = False
+
+    def _init_executor(self) -> None:
+        """Initialize the worker and load the model.
+        """
+        assert self.parallel_config.world_size == 1, (
+            "GPUExecutor only supports single GPU.")
+
+        self.driver_worker = self._create_worker()
+        self.driver_worker.init_device()
+        self.driver_worker.load_model()
+
+    def _get_worker_kwargs(
+            self,
+            local_rank: int = 0,
+            rank: int = 0,
+            distributed_init_method: Optional[str] = None) -> Dict[str, Any]:
+        """Return worker init args for a given rank."""
+        if distributed_init_method is None:
+            distributed_init_method = get_distributed_init_method(
+                get_ip(), get_open_port())
+        return dict(
+            vllm_config=self.vllm_config,
+            local_rank=local_rank,
+            rank=rank,
+            distributed_init_method=distributed_init_method,
+            is_driver_worker=(not self.parallel_config)
+            or (rank % self.parallel_config.tensor_parallel_size == 0),
+        )
+
+    def _get_worker_module_and_class(
+            self) -> Tuple[str, str, Optional[Callable[[], Type[WorkerBase]]]]:
+        worker_class_fn = None
+        if self.scheduler_config.is_multi_step:
+            worker_module_name = "vllm.worker.multi_step_worker"
+            worker_class_name = "MultiStepWorker"
+        elif self.speculative_config:
+            worker_module_name = "vllm.spec_decode.spec_decode_worker"
+            worker_class_name = "create_spec_worker"
+        else:
+            worker_module_name = "vllm.worker.worker"
+            worker_class_name = "Worker"
+        return (worker_module_name, worker_class_name, worker_class_fn)
+
+    def _get_create_worker_kwargs(
+            self,
+            local_rank: int = 0,
+            rank: int = 0,
+            distributed_init_method: Optional[str] = None) -> Dict:
+        worker_kwargs = self._get_worker_kwargs(local_rank, rank,
+                                                distributed_init_method)
+
+        (worker_module_name, worker_class_name,
+         worker_class_fn) = self._get_worker_module_and_class()
+        worker_kwargs.update(
+            worker_module_name=worker_module_name,
+            worker_class_name=worker_class_name,
+            worker_class_fn=worker_class_fn,
+        )
+
+        return worker_kwargs
+
+    def _create_worker(self,
+                       local_rank: int = 0,
+                       rank: int = 0,
+                       distributed_init_method: Optional[str] = None):
+        return create_worker(**self._get_create_worker_kwargs(
+            local_rank=local_rank,
+            rank=rank,
+            distributed_init_method=distributed_init_method))
+
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
+        """Determine the number of available KV blocks by invoking the
+        underlying worker.
+        """
+        return self.driver_worker.determine_num_available_blocks()
+
+    def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks) -> None:
+        """Initialize the KV cache by invoking the underlying worker.
+        """
+        # NOTE: This is logged in the executor because there can be >1 worker
+        # with other executors. We could log in the engine level, but work
+        # remains to abstract away the device for non-GPU configurations.
+        logger.info("# GPU blocks: %d, # CPU blocks: %d", num_gpu_blocks,
+                    num_cpu_blocks)
+        max_concurrency = (num_gpu_blocks * self.cache_config.block_size /
+                           self.model_config.max_model_len)
+        logger.info("Maximum concurrency for %s tokens per request: %.2fx",
+                    self.model_config.max_model_len, max_concurrency)
+
+        self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
+
+    def execute_model(
+        self, execute_model_req: ExecuteModelRequest
+    ) -> Optional[List[Union[SamplerOutput, PoolerOutput]]]:
+        output = self.driver_worker.execute_model(execute_model_req)
+        return output
+
+    def add_lora(self, lora_request: LoRARequest) -> bool:
+        assert lora_request.lora_int_id > 0, "lora_id must be greater than 0."
+        return self.driver_worker.add_lora(lora_request)
+
+    def remove_lora(self, lora_id: int) -> bool:
+        assert lora_id > 0, "lora_id must be greater than 0."
+        return self.driver_worker.remove_lora(lora_id)
+
+    def pin_lora(self, lora_id: int) -> bool:
+        assert lora_id > 0, "lora_id must be greater than 0."
+        return self.driver_worker.pin_lora(lora_id)
+
+    def list_loras(self) -> Set[int]:
+        return self.driver_worker.list_loras()
+
+    def add_prompt_adapter(
+            self, prompt_adapter_request: PromptAdapterRequest) -> bool:
+        assert prompt_adapter_request.prompt_adapter_id > 0, \
+            "prompt_adapter_id must be greater than 0."
+        return self.driver_worker.add_prompt_adapter(prompt_adapter_request)
+
+    def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
+        assert prompt_adapter_id > 0, \
+            "prompt_adapter_id must be greater than 0."
+        return self.driver_worker.remove_prompt_adapter(prompt_adapter_id)
+
+    def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool:
+        assert prompt_adapter_id > 0, \
+                "prompt_adapter_id must be greater than 0."
+        return self.driver_worker.pin_prompt_adapter(prompt_adapter_id)
+
+    def list_prompt_adapters(self) -> Set[int]:
+        return self.driver_worker.list_prompt_adapters()
+
+    def check_health(self) -> None:
+        # GPUExecutor will always be healthy as long as
+        # it's running.
+        return
+
+    def start_profile(self) -> None:
+        self.driver_worker.start_profile()
+
+    def stop_profile(self) -> None:
+        self.driver_worker.stop_profile()
+
+
+class GPUExecutorAsync(GPUExecutor, ExecutorAsyncBase):
+
+    async def execute_model_async(
+        self,
+        execute_model_req: ExecuteModelRequest,
+    ) -> List[Union[SamplerOutput, PoolerOutput]]:
+        output = await make_async(self.driver_worker.execute_model
+                                  )(execute_model_req=execute_model_req)
+        return output