214 lines
8.1 KiB
Python
214 lines
8.1 KiB
Python
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from typing import List, Set, Tuple
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import openvino as ov
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import openvino.properties.hint as hints
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import torch
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import vllm.envs as envs
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from vllm.config import CacheConfig, ModelConfig
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from vllm.executor.executor_base import ExecutorAsyncBase, ExecutorBase
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.model_executor.layers.sampler import SamplerOutput
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from vllm.sequence import ExecuteModelRequest
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from vllm.utils import (GiB_bytes, get_distributed_init_method, get_ip,
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get_open_port, make_async)
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logger = init_logger(__name__)
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def is_openvino_cpu() -> bool:
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return "CPU" in envs.VLLM_OPENVINO_DEVICE
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def is_openvino_gpu() -> bool:
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return "GPU" in envs.VLLM_OPENVINO_DEVICE
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class OpenVINOExecutor(ExecutorBase):
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uses_ray: bool = False
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def _init_executor(self) -> None:
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assert self.device_config.device_type == "openvino"
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assert self.lora_config is None, "OpenVINO backend doesn't support LoRA"
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assert is_openvino_cpu() or is_openvino_gpu(), \
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"OpenVINO backend supports only CPU and GPU devices"
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self.ov_core = ov.Core()
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self.model_config = _verify_and_get_model_config(self.model_config)
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self.cache_config = _verify_and_get_cache_config(
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self.ov_core, self.cache_config)
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# Instantiate the worker and load the model to CPU.
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self._init_worker()
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def _init_worker(self):
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from vllm.worker.openvino_worker import OpenVINOWorker
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assert (
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self.parallel_config.world_size == 1
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), "OpenVINOExecutor only supports single CPU socket currently."
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distributed_init_method = get_distributed_init_method(
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get_ip(), get_open_port())
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self.driver_worker = OpenVINOWorker(
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ov_core=self.ov_core,
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model_config=self.model_config,
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parallel_config=self.parallel_config,
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scheduler_config=self.scheduler_config,
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device_config=self.device_config,
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cache_config=self.cache_config,
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load_config=self.load_config,
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local_rank=0,
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rank=0,
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distributed_init_method=distributed_init_method,
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lora_config=self.lora_config,
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kv_cache_dtype=self.cache_config.cache_dtype,
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is_driver_worker=True,
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)
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self.driver_worker.init_device()
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self.driver_worker.load_model()
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def determine_num_available_blocks(self) -> Tuple[int, int]:
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"""Determine the number of available KV blocks by invoking the
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underlying worker.
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"""
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return self.driver_worker.determine_num_available_blocks()
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def initialize_cache(self, num_gpu_blocks: int,
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num_cpu_blocks: int) -> None:
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"""Initialize the KV cache by invoking the underlying worker."""
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# NOTE: We log here to avoid multiple logs when number of workers is
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# greater than one. We could log in the engine, but not all executors
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# have GPUs.
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# NOTE: In case of a CPU device, `cpu block` for OpenVINO backend
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# is located on CPU memory but is referred as `gpu block`.
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# Because we want to reuse the existing block management procedure.
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device_blocks = num_gpu_blocks
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swap_blocks = num_cpu_blocks
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logger.info("OpenVINO %s: # device blocks: %d; # swap blocks: %d",
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envs.VLLM_OPENVINO_DEVICE, device_blocks, swap_blocks)
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self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)
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def execute_model(
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self,
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execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
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output = self.driver_worker.execute_model(execute_model_req)
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return output
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def add_lora(self, lora_request: LoRARequest) -> bool:
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return self.driver_worker.add_lora(lora_request)
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def remove_lora(self, lora_id: int) -> bool:
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return self.driver_worker.remove_lora(lora_id)
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def pin_lora(self, lora_id: int) -> bool:
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return self.driver_worker.pin_lora(lora_id)
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def list_loras(self) -> Set[int]:
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return self.driver_worker.list_loras()
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def add_prompt_adapter(self, prompt_adapter_request) -> bool:
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raise NotImplementedError(
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"Soft prompt is currently not supported by the OPENVINO backend.")
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def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
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raise NotImplementedError(
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"Soft prompt is currently not supported by the OPENVINO backend.")
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def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool:
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raise NotImplementedError(
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"Soft prompt is currently not supported by the OPENVINO backend.")
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def list_prompt_adapters(self) -> Set[int]:
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raise NotImplementedError(
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"Soft prompt is currently not supported by the OPENVINO backend.")
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def check_health(self) -> None:
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# OpenVINOExecutor will always be healthy as long as
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# it's running.
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return
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class OpenVINOExecutorAsync(OpenVINOExecutor, ExecutorAsyncBase):
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async def execute_model_async(
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self,
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execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
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output = await make_async(self.driver_worker.execute_model
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)(execute_model_req=execute_model_req, )
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return output
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async def check_health_async(self) -> None:
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# OpenVINOExecutor will always be healthy as long as
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# it's running.
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return
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def _verify_and_get_model_config(config: ModelConfig) -> ModelConfig:
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if config.dtype != torch.float32:
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logger.warning(
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f"Only float32 dtype is supported on OpenVINO, casting from {config.dtype}." # noqa: G004, E501
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)
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config.dtype = torch.float32
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if not config.enforce_eager:
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logger.warning(
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"CUDA graph is not supported on OpenVINO backend, fallback to the "
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"eager mode.")
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config.enforce_eager = True
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return config
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def _verify_and_get_cache_config(ov_core: ov.Core,
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config: CacheConfig) -> CacheConfig:
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if envs.VLLM_OPENVINO_CPU_KV_CACHE_PRECISION == "u8":
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if not is_openvino_cpu():
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logger.info("VLLM_OPENVINO_CPU_KV_CACHE_PRECISION is"
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"ignored for GPU, f16 data type will be used.")
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config.cache_dtype = ov.Type.f16
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else:
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logger.info("KV cache type is overridden to u8 via "
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"VLLM_OPENVINO_CPU_KV_CACHE_PRECISION env var.")
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config.cache_dtype = ov.Type.u8
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else:
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if is_openvino_cpu():
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ov_device = envs.VLLM_OPENVINO_DEVICE
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inference_precision = ov_core.get_property(
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ov_device, hints.inference_precision)
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if inference_precision == ov.Type.bf16:
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config.cache_dtype = ov.Type.bf16
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else:
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config.cache_dtype = ov.Type.f16
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else:
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config.cache_dtype = ov.Type.f16
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if is_openvino_cpu():
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if config.block_size != 32:
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logger.info(
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f"OpenVINO CPU optimal block size is 32, overriding currently set {config.block_size}" # noqa: G004, E501
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)
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config.block_size = 32
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else:
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if config.block_size != 16:
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logger.info(
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f"OpenVINO GPU optimal block size is 16, overriding currently set {config.block_size}" # noqa: G004, E501
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)
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config.block_size = 16
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kv_cache_space = envs.VLLM_OPENVINO_KVCACHE_SPACE
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if kv_cache_space >= 0:
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if kv_cache_space == 0 and is_openvino_cpu():
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config.openvino_kvcache_space_bytes = 4 * GiB_bytes # type: ignore
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logger.warning(
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"Environment variable VLLM_OPENVINO_KVCACHE_SPACE (GB) "
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"for OpenVINO backend is not set, using 4 by default.")
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else:
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config.openvino_kvcache_space_bytes = kv_cache_space * GiB_bytes # type: ignore
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else:
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raise RuntimeError(
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"Invalid environment variable VLLM_OPENVINO_KVCACHE_SPACE"
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f" {kv_cache_space}, expect a positive integer value.")
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return config
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