vLLM/vllm/worker/xpu_worker.py

# SPDX-License-Identifier: Apache-2.0
"""A XPU worker class."""
import gc
import os
from typing import List, Optional, Tuple

import intel_extension_for_pytorch  # noqa: F401
import oneccl_bindings_for_pytorch  # noqa: F401
import torch
import torch.distributed

from vllm.config import VllmConfig
from vllm.distributed import (ensure_model_parallel_initialized,
                              init_distributed_environment)
from vllm.distributed.parallel_state import get_pp_group
from vllm.logger import init_logger
from vllm.model_executor import set_random_seed
from vllm.platforms import current_platform
from vllm.worker.cache_engine import CacheEngine
from vllm.worker.worker import Worker
from vllm.worker.worker_base import LoraNotSupportedWorkerBase, WorkerBase
from vllm.worker.xpu_model_runner import XPUModelRunner

logger = init_logger(__name__)


class XPUWorker(LoraNotSupportedWorkerBase, Worker):
    """A worker class that executes (a partition of) the model on a GPU.
    
    Each worker is associated with a single XPU device. The worker is 
    responsible for maintaining the KV cache and executing the model on the 
    XPU. In case of distributed inference, each worker is assigned a partition
    of the model.
    """

    def __init__(
        self,
        vllm_config: VllmConfig,
        local_rank: int,
        rank: int,
        distributed_init_method: str,
        is_driver_worker: bool = False,
    ) -> None:
        WorkerBase.__init__(self, vllm_config=vllm_config)
        device_config = self.device_config
        parallel_config = self.parallel_config
        assert device_config.device_type == "xpu"
        assert current_platform.is_xpu()

        self.parallel_config.rank = rank

        self.local_rank = local_rank
        self.rank = rank
        self.distributed_init_method = distributed_init_method
        self.is_driver_worker = is_driver_worker
        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.model_runner = XPUModelRunner(  # type: ignore
            vllm_config=vllm_config,
            kv_cache_dtype=self.cache_config.cache_dtype,
            is_driver_worker=is_driver_worker,
        )
        # Uninitialized cache engine. Will be initialized by
        # initialize_cache.
        self.cache_engine: List[CacheEngine]
        self.gpu_cache: Optional[List[List[torch.Tensor]]]

    def init_device(self) -> None:
        if self.device_config.device.type == "xpu" and current_platform.is_xpu(
        ):
            self.device = torch.device(f"xpu:{self.local_rank}")
            torch.xpu.set_device(self.device)
            torch.xpu.empty_cache()
            self.init_gpu_memory = torch.xpu.get_device_properties(
                self.local_rank).total_memory
        else:
            raise RuntimeError(
                f"Not support device type: {self.device_config.device}")
        # Initialize the distributed environment.
        self.init_worker_distributed_environment()
        # Initialize the model.
        set_random_seed(self.model_config.seed)

    # keep this method for `empty_cache` and `synchronize` api
    @torch.inference_mode()
    def determine_num_available_blocks(self) -> Tuple[int, int]:
        """Profiles the peak memory usage of the model to determine how many
        KV blocks may be allocated without OOMs.

        The engine will first conduct a profiling of the existing memory usage.
        Then, it calculate the maximum possible number of GPU and CPU blocks
        that can be allocated with the remaining free memory.

        .. tip::
            You may limit the usage of GPU memory
            by adjusting the `gpu_memory_utilization` parameter.
        """
        # Profile the memory usage of the model and get the maximum number of
        # cache blocks that can be allocated with the remaining free memory.
        torch.xpu.empty_cache()

        # Execute a forward pass with dummy inputs to profile the memory usage
        # of the model.
        self.model_runner.profile_run()

        # Calculate the number of blocks that can be allocated with the
        # profiled peak memory.
        torch.xpu.synchronize()
        used_memory = torch.xpu.memory_allocated()
        total_gpu_memory = torch.xpu.get_device_properties(
            self.local_rank).total_memory
        free_gpu_memory = total_gpu_memory - used_memory

        # NOTE(woosuk): Here we assume that the other processes using the same
        # GPU did not change their memory usage during the profiling.
        peak_memory = self.init_gpu_memory - free_gpu_memory
        assert peak_memory > 0, (
            "Error in memory profiling. "
            f"Initial free memory {self.init_gpu_memory}, current free memory"
            f" {free_gpu_memory}. This happens when the GPU memory was "
            "not properly cleaned up before initializing the vLLM instance.")

        cache_block_size = self.get_cache_block_size_bytes()
        num_gpu_blocks = int(
            (total_gpu_memory * self.cache_config.gpu_memory_utilization -
             peak_memory) // cache_block_size)
        num_cpu_blocks = int(self.cache_config.swap_space_bytes //
                             cache_block_size)
        num_gpu_blocks = max(num_gpu_blocks, 0)
        num_cpu_blocks = max(num_cpu_blocks, 0)
        gc.collect()
        torch.xpu.empty_cache()
        return num_gpu_blocks, num_cpu_blocks

    def _warm_up_model(self) -> None:
        # IPEX don't support capture graph yet
        pass

    def init_worker_distributed_environment(self) -> None:
        """Initialize the distributed environment."""

        parallel_config = self.parallel_config
        rank = self.rank
        distributed_init_method = self.distributed_init_method

        if torch.distributed.is_initialized():
            torch_world_size = torch.distributed.get_world_size()
            if torch_world_size != parallel_config.world_size:
                raise RuntimeError(
                    "torch.distributed is already initialized but the torch "
                    "world size does not match parallel_config.world_size "
                    f"({torch_world_size} vs. {parallel_config.world_size}).")
        elif not distributed_init_method:
            raise ValueError(
                "distributed_init_method must be set if torch.distributed "
                "is not already initialized")
        else:
            # use sockets as default Level zero IPC exchange backend. By
            # default oneccl will use `drmfd` as mechanism which need extra
            # dependency (libdrm and drm headers) on your system.
            ENV_CCL_ATL_TRANSPORT = os.getenv("CCL_ATL_TRANSPORT", "ofi")
            ENV_LOCAL_WORLD_SIZE = os.getenv("LOCAL_WORLD_SIZE",
                                             str(parallel_config.world_size))
            os.environ["CCL_ATL_TRANSPORT"] = ENV_CCL_ATL_TRANSPORT
            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,
                distributed_init_method=distributed_init_method,
                local_rank=self.local_rank,
                backend="ccl")

        ensure_model_parallel_initialized(
            parallel_config.tensor_parallel_size,
            parallel_config.pipeline_parallel_size)
        # global all_reduce needed for overall oneccl warm up
        torch.distributed.all_reduce(torch.zeros(1).xpu())

        if parallel_config.pipeline_parallel_size > 1:
            # Add pp group init to avoid
            # p2p communication as the first call
            get_pp_group().all_reduce(torch.zeros(1).xpu())