gpu_worker.py

"""A GPU worker class."""
import gc
import os
from typing import TYPE_CHECKING, Optional

import torch
import torch.distributed
import torch.nn as nn

import vllm.envs as envs
from vllm.config import CacheConfig, ModelConfig, ParallelConfig, VllmConfig
from vllm.distributed import (ensure_model_parallel_initialized,
                              init_distributed_environment,
                              set_custom_all_reduce)
from vllm.logger import init_logger
from vllm.model_executor import set_random_seed
from vllm.platforms import current_platform
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, LayerBlockType, get_dtype_size
from vllm.v1.core.scheduler import SchedulerOutput
from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
from vllm.v1.outputs import ModelRunnerOutput
from vllm.v1.worker.gpu_model_runner import GPUModelRunner

logger = init_logger(__name__)

if TYPE_CHECKING:
    from vllm.v1.core.scheduler import SchedulerOutput


class Worker:

    def __init__(
        self,
        vllm_config: VllmConfig,
        local_rank: int,
        rank: int,
        distributed_init_method: str,
        is_driver_worker: bool = False,
    ):

        # TODO: use WorkerBase.__init__(self, vllm_config=vllm_config)
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.cache_config = vllm_config.cache_config
        self.lora_config = vllm_config.lora_config
        self.load_config = vllm_config.load_config
        self.parallel_config = vllm_config.parallel_config
        self.scheduler_config = vllm_config.scheduler_config
        self.device_config = vllm_config.device_config
        self.speculative_config = vllm_config.speculative_config
        self.prompt_adapter_config = vllm_config.prompt_adapter_config
        self.observability_config = vllm_config.observability_config

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

        if self.model_config.trust_remote_code:
            # note: lazy import to avoid importing torch before initializing
            from vllm.utils import init_cached_hf_modules
            init_cached_hf_modules()

        # Torch profiler. Enabled and configured through env vars:
        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
        if envs.VLLM_TORCH_PROFILER_DIR:
            torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
            logger.info("Profiling enabled. Traces will be saved to: %s",
                        torch_profiler_trace_dir)
            self.profiler = torch.profiler.profile(
                activities=[
                    torch.profiler.ProfilerActivity.CPU,
                    torch.profiler.ProfilerActivity.CUDA,
                ],
                with_stack=True,
                on_trace_ready=torch.profiler.tensorboard_trace_handler(
                    torch_profiler_trace_dir, use_gzip=True))
        else:
            self.profiler = None

    def init_device(self):
        if self.device_config.device.type == "cuda":
            # torch.distributed.all_reduce does not free the input tensor until
            # the synchronization point. This causes the memory usage to grow
            # as the number of all_reduce calls increases. This env var disables
            # this behavior.
            # Related issue:
            # https://discuss.pytorch.org/t/cuda-allocation-lifetime-for-inputs-to-distributed-all-reduce/191573
            os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"

            # This env var set by Ray causes exceptions with graph building.
            os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
            self.device = torch.device(f"cuda:{self.local_rank}")
            torch.cuda.set_device(self.device)

            _check_if_gpu_supports_dtype(self.model_config.dtype)
            gc.collect()
            torch.cuda.empty_cache()
            self.init_gpu_memory = torch.cuda.mem_get_info()[0]
        else:
            raise RuntimeError(
                f"Not support device type: {self.device_config.device}")
        # Initialize the distributed environment.
        init_worker_distributed_environment(self.parallel_config, self.rank,
                                            self.distributed_init_method,
                                            self.local_rank)
        # Set random seed.
        set_random_seed(self.model_config.seed)

        # Construct the model runner
        self.model_runner = GPUModelRunner(self.vllm_config, self.device)

    def load_model(self) -> None:
        self.model_runner.load_model()

    @torch.inference_mode()
    def determine_available_memory(self) -> int:
        """Profiles the peak memory usage of the model to determine how much 
        memory can be used for KV cache without OOMs.

        The engine will first conduct a profiling of the existing memory usage.
        Then, it calculate the free memory that can be used for KV cache in
        bytes.

        .. tip::
            You may limit the usage of GPU memory
            by adjusting the `gpu_memory_utilization` parameter.
        """
        torch.cuda.empty_cache()
        torch.cuda.reset_peak_memory_stats()

        _, total_gpu_memory = torch.cuda.mem_get_info()
        # Execute a forward pass with dummy inputs to profile the memory usage
        # of the model.
        self.model_runner.profile_run()

        free_gpu_memory, _ = torch.cuda.mem_get_info()
        # NOTE(woosuk): Here we assume that the other processes using the same
        # GPU did not change their memory usage during the profiling.
        assert self.init_gpu_memory > free_gpu_memory, (
            "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.")

        # Get the peak memory allocation recorded by torch
        peak_memory = torch.cuda.memory_stats()["allocated_bytes.all.peak"]

        # Check for any memory left around that may have been allocated on the
        # gpu outside of `torch`. NCCL operations, for example, can use a few
        # GB during a forward pass
        torch.cuda.empty_cache()
        torch_allocated_bytes = torch.cuda.memory_stats(
        )["allocated_bytes.all.current"]
        total_allocated_bytes = torch.cuda.mem_get_info(
        )[1] - torch.cuda.mem_get_info()[0]
        non_torch_allocations = total_allocated_bytes - torch_allocated_bytes
        if non_torch_allocations > 0:
            peak_memory += non_torch_allocations
        available_kv_cache_memory = (
            total_gpu_memory * self.cache_config.gpu_memory_utilization -
            peak_memory)

        return int(available_kv_cache_memory)

    def get_kv_cache_spec(self) -> KVCacheSpec:
        return self.model_runner.get_kv_cache_spec()

    def initialize_cache(self, kv_cache_config: KVCacheConfig) -> None:
        """Allocate GPU KV cache with the specified kv_cache_config."""
        self.model_runner.initialize_kv_cache(kv_cache_config)

    def compile_or_warm_up_model(self) -> None:
        if not self.model_config.enforce_eager:
            self.model_runner.capture_model()
        # Reset the seed to ensure that the random state is not affected by
        # the model initialization and profiling.
        set_random_seed(self.model_config.seed)

    def get_model(self) -> nn.Module:
        return self.model_runner.get_model()

    @torch.inference_mode()
    def execute_model(
        self,
        scheduler_output: "SchedulerOutput",
    ) -> Optional[ModelRunnerOutput]:
        output = self.model_runner.execute_model(scheduler_output)
        return output if self.rank == 0 else None

    def profile(self, is_start: bool = True):
        if self.profiler is None:
            raise RuntimeError("Profiler is not enabled.")
        if is_start:
            self.profiler.start()
        else:
            self.profiler.stop()

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


def init_worker_distributed_environment(
    parallel_config: ParallelConfig,
    rank: int,
    distributed_init_method: Optional[str] = None,
    local_rank: int = -1,
) -> None:
    """Initialize the distributed environment."""
    set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)

    init_distributed_environment(parallel_config.world_size, rank,
                                 distributed_init_method, local_rank)

    ensure_model_parallel_initialized(parallel_config.tensor_parallel_size,
                                      parallel_config.pipeline_parallel_size)


def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
    # Check if the GPU supports the dtype.
    if torch_dtype == torch.bfloat16:  # noqa: SIM102
        if not current_platform.has_device_capability(80):
            capability = current_platform.get_device_capability()
            gpu_name = current_platform.get_device_name()

            if capability is None:
                compute_str = "does not have a compute capability"
            else:
                version_str = capability.as_version_str()
                compute_str = f"has compute capability {version_str}"

            raise ValueError(
                "Bfloat16 is only supported on GPUs with compute capability "
                f"of at least 8.0. Your {gpu_name} GPU {compute_str}. "
                "You can use float16 instead by explicitly setting the"
                "`dtype` flag in CLI, for example: --dtype=half.")


def _get_cache_block_size(
    cache_config: CacheConfig,
    model_config: ModelConfig,
    parallel_config: ParallelConfig,
) -> int:
    head_size = model_config.get_head_size()
    num_heads = model_config.get_num_kv_heads(parallel_config)
    num_attention_layers = model_config.get_num_layers_by_block_type(
        parallel_config, LayerBlockType.attention)

    key_cache_block = cache_config.block_size * num_heads * head_size
    value_cache_block = key_cache_block
    total = num_attention_layers * (key_cache_block + value_cache_block)
    if cache_config.cache_dtype == "auto":
        dtype = model_config.dtype
    else:
        dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
    dtype_size = get_dtype_size(dtype)
    return dtype_size * total