181 lines
7.1 KiB
Python
181 lines
7.1 KiB
Python
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.prompt_adapter.request import PromptAdapterRequest
|
|
from vllm.sequence import ExecuteModelRequest, PoolerOutput, SamplerOutput
|
|
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(
|
|
model_config=self.model_config,
|
|
parallel_config=self.parallel_config,
|
|
scheduler_config=self.scheduler_config,
|
|
device_config=self.device_config,
|
|
cache_config=self.cache_config,
|
|
load_config=self.load_config,
|
|
local_rank=local_rank,
|
|
rank=rank,
|
|
distributed_init_method=distributed_init_method,
|
|
lora_config=self.lora_config,
|
|
speculative_config=self.speculative_config,
|
|
prompt_adapter_config=self.prompt_adapter_config,
|
|
is_driver_worker=(not self.parallel_config)
|
|
or (rank % self.parallel_config.tensor_parallel_size == 0),
|
|
observability_config=self.observability_config,
|
|
)
|
|
|
|
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)
|
|
|
|
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
|
|
|
|
|
|
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
|