aphrodite-engine/aphrodite/executor/openvino_executor.py

from typing import List, Set, Tuple

import openvino as ov
import openvino.properties.hint as hints
import torch
from loguru import logger

import aphrodite.common.envs as envs
from aphrodite.common.config import CacheConfig, ModelConfig
from aphrodite.common.sequence import ExecuteModelRequest
from aphrodite.common.utils import (GiB_bytes, get_distributed_init_method,
                                    get_ip, get_open_port, make_async)
from aphrodite.executor.executor_base import ExecutorAsyncBase, ExecutorBase
from aphrodite.lora.request import LoRARequest
from aphrodite.modeling.layers.sampler import SamplerOutput

APHRODITE_OPENVINO_KVCACHE_SPACE = envs.APHRODITE_OPENVINO_KVCACHE_SPACE
APHRODITE_OPENVINO_CPU_KV_CACHE_PRECISION = (
    envs.APHRODITE_OPENVINO_CPU_KV_CACHE_PRECISION)


class OpenVINOExecutor(ExecutorBase):

    uses_ray: bool = False

    def _init_executor(self) -> None:
        assert self.device_config.device_type == "openvino"
        assert self.lora_config is None, "OpenVINO backend doesn't support LoRA"
        self.model_config = _verify_and_get_model_config(self.model_config)
        self.cache_config = _verify_and_get_cache_config(self.cache_config)

        # Instantiate the worker and load the model to CPU.
        self._init_worker()

    def _init_worker(self):
        from aphrodite.worker.openvino_worker import OpenVINOWorker

        assert (
            self.parallel_config.world_size == 1
        ), "OpenVINOExecutor only supports single CPU socket currently."

        distributed_init_method = get_distributed_init_method(
            get_ip(), get_open_port())
        self.driver_worker = OpenVINOWorker(
            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=0,
            rank=0,
            distributed_init_method=distributed_init_method,
            lora_config=self.lora_config,
            kv_cache_dtype=self.cache_config.cache_dtype,
            is_driver_worker=True,
        )
        self.driver_worker.init_device()
        self.driver_worker.load_model()

    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: int) -> None:
        """Initialize the KV cache by invoking the underlying worker."""
        # NOTE: We log here to avoid multiple logs when number of workers is
        # greater than one. We could log in the engine, but not all executors
        # have GPUs.
        # NOTE: `cpu block` for OpenVINO backend is located on CPU memory but is
        # referred as `gpu block`. Because we want to reuse the existing block
        # management procedure.
        logger.info(f"# CPU blocks: {num_gpu_blocks}")
        logger.info(
            f"Minimum concurrency: {num_gpu_blocks * self.cache_config.block_size / self.scheduler_config.max_model_len:.2f}x"  # noqa: E501
        )
        self.driver_worker.initialize_cache(num_gpu_blocks, num_cpu_blocks)

    def execute_model(
            self,
            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
        output = self.driver_worker.execute_model(execute_model_req)
        return output

    def add_lora(self, lora_request: LoRARequest) -> bool:
        return self.driver_worker.add_lora(lora_request)

    def remove_lora(self, lora_id: int) -> bool:
        return self.driver_worker.remove_lora(lora_id)

    def pin_lora(self, lora_id: int) -> bool:
        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) -> bool:
        raise NotImplementedError(
            "Soft prompt is currently not supported by the OPENVINO backend.")

    def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
        raise NotImplementedError(
            "Soft prompt is currently not supported by the OPENVINO backend.")

    def pin_prompt_adapter(self, prompt_adapter_id: int) -> bool:
        raise NotImplementedError(
            "Soft prompt is currently not supported by the OPENVINO backend.")

    def list_prompt_adapters(self) -> Set[int]:
        raise NotImplementedError(
            "Soft prompt is currently not supported by the OPENVINO backend.")

    def check_health(self) -> None:
        # OpenVINOExecutor will always be healthy as long as
        # it's running.
        return


class OpenVINOExecutorAsync(OpenVINOExecutor, ExecutorAsyncBase):

    async def execute_model_async(
            self,
            execute_model_req: ExecuteModelRequest) -> List[SamplerOutput]:
        output = await make_async(self.driver_worker.execute_model
                                  )(execute_model_req=execute_model_req, )
        return output

    async def check_health_async(self) -> None:
        # OpenVINOExecutor will always be healthy as long as
        # it's running.
        return


def _verify_and_get_model_config(config: ModelConfig) -> ModelConfig:
    if config.dtype != torch.float32:
        logger.warning(
            f"Only float32 dtype is supported on OpenVINO, casting from {config.dtype}."  # noqa: G004, E501
        )
        config.dtype = torch.float32
    if not config.enforce_eager:
        logger.warning(
            "CUDA graph is not supported on OpenVINO backend, fallback to the "
            "eager mode.")
        config.enforce_eager = True
    return config


def _verify_and_get_cache_config(config: CacheConfig) -> CacheConfig:
    if APHRODITE_OPENVINO_CPU_KV_CACHE_PRECISION == "u8":
        logger.info("KV cache type is overried to u8 via "
                    "APHRODITE_OPENVINO_CPU_KV_CACHE_PRECISION env var.")
        config.cache_dtype = ov.Type.u8
    else:
        core = ov.Core()
        inference_precision = core.get_property("CPU",
                                                hints.inference_precision)
        if inference_precision == ov.Type.bf16:
            config.cache_dtype = ov.Type.bf16
        else:
            config.cache_dtype = ov.Type.f16

    if config.block_size != 32:
        logger.info(
            f"OpenVINO optimal block size is 32, overriding currently set {config.block_size}"  # noqa: G004, E501
        )
        config.block_size = 32

    kv_cache_space = APHRODITE_OPENVINO_KVCACHE_SPACE
    if kv_cache_space >= 0:
        if kv_cache_space == 0:
            config.openvino_kvcache_space_bytes = 4 * GiB_bytes  # type: ignore
            logger.warning(
                "Environment variable APHRODITE_OPENVINO_KVCACHE_SPACE (GB) "
                "for OpenVINO backend is not set, using 4 by default.")
        else:
            config.openvino_kvcache_space_bytes = kv_cache_space * GiB_bytes  # type: ignore
    else:
        raise RuntimeError(
            "Invalid environment variable APHRODITE_OPENVINO_KVCACHE_SPACE"
            f" {kv_cache_space}, expect a positive integer value.")

    return config