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https://git.datalinker.icu/vllm-project/vllm.git
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890 lines
35 KiB
Python
890 lines
35 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""A GPU worker class."""
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import copy
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import gc
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import os
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from contextlib import AbstractContextManager, nullcontext
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from types import NoneType
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from typing import TYPE_CHECKING, Any
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import torch
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import torch.distributed
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import torch.nn as nn
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import vllm.envs as envs
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from vllm.config import VllmConfig
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from vllm.distributed import (
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ensure_model_parallel_initialized,
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init_distributed_environment,
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set_custom_all_reduce,
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)
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from vllm.distributed.kv_transfer import (
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ensure_kv_transfer_initialized,
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get_kv_transfer_group,
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has_kv_transfer_group,
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)
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from vllm.distributed.parallel_state import (
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get_pp_group,
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get_tp_group,
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)
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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 import set_random_seed
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from vllm.model_executor.models.interfaces import is_mixture_of_experts
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from vllm.model_executor.warmup.kernel_warmup import kernel_warmup
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from vllm.platforms import current_platform
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from vllm.profiler.gpu_profiler import CudaProfilerWrapper
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from vllm.sequence import IntermediateTensors
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from vllm.tasks import SupportedTask
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from vllm.utils.mem_constants import GiB_bytes
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from vllm.utils.mem_utils import MemorySnapshot, memory_profiling
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from vllm.v1.core.sched.output import GrammarOutput
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from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
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from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
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from vllm.v1.outputs import (
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EMPTY_MODEL_RUNNER_OUTPUT,
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AsyncModelRunnerOutput,
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DraftTokenIds,
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ModelRunnerOutput,
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)
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from vllm.v1.utils import report_usage_stats
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from vllm.v1.worker.gpu_model_runner import GPUModelRunner
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from vllm.v1.worker.worker_base import WorkerBase
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logger = init_logger(__name__)
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if TYPE_CHECKING:
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from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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from vllm.v1.core.sched.output import SchedulerOutput
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class Worker(WorkerBase):
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def __init__(
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self,
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vllm_config: VllmConfig,
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local_rank: int,
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rank: int,
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distributed_init_method: str,
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is_driver_worker: bool = False,
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):
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super().__init__(
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vllm_config=vllm_config,
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local_rank=local_rank,
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rank=rank,
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distributed_init_method=distributed_init_method,
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is_driver_worker=is_driver_worker,
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)
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if self.model_config.trust_remote_code:
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# note: lazy import to avoid importing torch before initializing
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from vllm.utils.import_utils import init_cached_hf_modules
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init_cached_hf_modules()
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# Buffers saved before sleep
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self._sleep_saved_buffers: dict[str, torch.Tensor] = {}
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# Torch profiler. Enabled and configured through env vars:
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# VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
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if envs.VLLM_TORCH_PROFILER_DIR:
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torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
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worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
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logger.info(
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"Profiling enabled. Traces will be saved to: %s",
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torch_profiler_trace_dir,
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)
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logger.debug(
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"Profiler config: record_shapes=%s,"
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"profile_memory=%s,with_stack=%s,with_flops=%s",
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envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
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envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
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envs.VLLM_TORCH_PROFILER_WITH_STACK,
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envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
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)
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self.profiler = torch.profiler.profile(
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activities=[
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torch.profiler.ProfilerActivity.CPU,
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torch.profiler.ProfilerActivity.CUDA,
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],
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record_shapes=envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
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profile_memory=envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
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with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
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with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
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on_trace_ready=torch.profiler.tensorboard_trace_handler(
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torch_profiler_trace_dir, worker_name=worker_name, use_gzip=True
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),
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)
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elif envs.VLLM_TORCH_CUDA_PROFILE:
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self.profiler = CudaProfilerWrapper()
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else:
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self.profiler = None
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def sleep(self, level: int = 1) -> None:
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from vllm.device_allocator.cumem import CuMemAllocator
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free_bytes_before_sleep = torch.cuda.mem_get_info()[0]
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# Save the buffers before level 2 sleep
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if level == 2:
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model = self.model_runner.model
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self._sleep_saved_buffers = {
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name: buffer.cpu().clone() for name, buffer in model.named_buffers()
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}
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allocator = CuMemAllocator.get_instance()
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allocator.sleep(offload_tags=("weights",) if level == 1 else tuple())
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free_bytes_after_sleep, total = torch.cuda.mem_get_info()
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freed_bytes = free_bytes_after_sleep - free_bytes_before_sleep
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used_bytes = total - free_bytes_after_sleep
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assert freed_bytes >= 0, "Memory usage increased after sleeping."
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logger.info(
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"Sleep mode freed %.2f GiB memory, %.2f GiB memory is still in use.",
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freed_bytes / GiB_bytes,
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used_bytes / GiB_bytes,
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)
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def wake_up(self, tags: list[str] | None = None) -> None:
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from vllm.device_allocator.cumem import CuMemAllocator
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allocator = CuMemAllocator.get_instance()
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allocator.wake_up(tags)
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# Restore the buffers after level 2 sleep
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if len(self._sleep_saved_buffers):
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model = self.model_runner.model
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for name, buffer in model.named_buffers():
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if name in self._sleep_saved_buffers:
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buffer.data.copy_(self._sleep_saved_buffers[name].data)
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self._sleep_saved_buffers = {}
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def _maybe_get_memory_pool_context(self, tag: str) -> AbstractContextManager:
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if self.vllm_config.model_config.enable_sleep_mode:
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from vllm.device_allocator.cumem import CuMemAllocator
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allocator = CuMemAllocator.get_instance()
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if tag == "weights":
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assert allocator.get_current_usage() == 0, (
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"Sleep mode can only be used for one instance per process."
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)
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context = allocator.use_memory_pool(tag=tag)
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else:
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context = nullcontext()
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return context
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def initialize_cache(self, num_gpu_blocks: int, num_cpu_blocks: int) -> None:
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self.cache_config.num_gpu_blocks = num_gpu_blocks
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self.cache_config.num_cpu_blocks = num_cpu_blocks
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def init_device(self):
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if self.device_config.device.type == "cuda":
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# This env var set by Ray causes exceptions with graph building.
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os.environ.pop("NCCL_ASYNC_ERROR_HANDLING", None)
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if (
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self.parallel_config.data_parallel_size > 1
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and self.parallel_config.data_parallel_size_local > 0
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and self.parallel_config.distributed_executor_backend
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not in ["ray", "external_launcher"]
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and self.vllm_config.parallel_config.data_parallel_backend != "ray"
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):
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# Use local DP rank if available, otherwise use global DP rank.
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dp_local_rank = self.parallel_config.data_parallel_rank_local
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if dp_local_rank is None:
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dp_local_rank = self.parallel_config.data_parallel_rank
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tp_pp_world_size = (
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self.parallel_config.pipeline_parallel_size
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* self.parallel_config.tensor_parallel_size
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)
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# DP_LOCAL_RANK * TP_PP_WORLD_SIZE + TP_LOCAL_RANK
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self.local_rank += dp_local_rank * tp_pp_world_size
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assert self.local_rank < torch.cuda.device_count(), (
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f"DP adjusted local rank {self.local_rank} is out of bounds. "
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)
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self.device = torch.device(f"cuda:{self.local_rank}")
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current_platform.set_device(self.device)
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current_platform.check_if_supports_dtype(self.model_config.dtype)
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# Initialize the distributed environment BEFORE taking
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# memory snapshot
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# This ensures NCCL buffers are allocated before we measure
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# available memory
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init_worker_distributed_environment(
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self.vllm_config,
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self.rank,
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self.distributed_init_method,
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self.local_rank,
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current_platform.dist_backend,
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)
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# Set random seed.
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set_random_seed(self.model_config.seed)
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# Now take memory snapshot after NCCL is initialized
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gc.collect()
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torch.cuda.empty_cache()
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# take current memory snapshot
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self.init_snapshot = MemorySnapshot()
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self.requested_memory = (
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self.init_snapshot.total_memory
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* self.cache_config.gpu_memory_utilization
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)
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if self.init_snapshot.free_memory < self.requested_memory:
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GiB = lambda b: round(b / GiB_bytes, 2)
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raise ValueError(
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f"Free memory on device "
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f"({GiB(self.init_snapshot.free_memory)}/"
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f"{GiB(self.init_snapshot.total_memory)} GiB) on startup "
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f"is less than desired GPU memory utilization "
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f"({self.cache_config.gpu_memory_utilization}, "
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f"{GiB(self.requested_memory)} GiB). Decrease GPU memory "
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f"utilization or reduce GPU memory used by other processes."
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)
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else:
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raise RuntimeError(f"Not support device type: {self.device_config.device}")
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# Construct the model runner
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self.model_runner: GPUModelRunner = GPUModelRunner(
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self.vllm_config, self.device
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)
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if self.rank == 0:
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# If usage stat is enabled, collect relevant info.
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report_usage_stats(self.vllm_config)
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# FIXME(youkaichao & ywang96): Use TorchDispatchMode instead of memory pool
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# to hijack tensor allocation.
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def load_model(self) -> None:
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eep_scale_up = os.environ.get("VLLM_ELASTIC_EP_SCALE_UP_LAUNCH") == "1"
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with self._maybe_get_memory_pool_context(tag="weights"):
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self.model_runner.load_model(eep_scale_up=eep_scale_up)
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def update_config(self, overrides: dict[str, Any]) -> None:
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self.model_runner.update_config(overrides)
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def reload_weights(self) -> None:
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self.model_runner.reload_weights()
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@torch.inference_mode()
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def determine_available_memory(self) -> int:
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"""Profiles the peak memory usage of the model to determine how much
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memory can be used for KV cache without OOMs.
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The engine will first conduct a profiling of the existing memory usage.
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Then, it calculates the free memory that can be used for KV cache in
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bytes.
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Tip:
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You may limit the usage of GPU memory
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by adjusting the `gpu_memory_utilization` parameter.
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"""
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GiB = lambda b: b / GiB_bytes
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if kv_cache_memory_bytes := self.cache_config.kv_cache_memory_bytes:
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# still need a profile run which compiles the model for
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# max_num_batched_tokens
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self.model_runner.profile_run()
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msg = (
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f"Initial free memory {GiB(self.init_snapshot.free_memory):.2f} "
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f"GiB, reserved {GiB(kv_cache_memory_bytes):.2f} GiB memory for "
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"KV Cache as specified by kv_cache_memory_bytes config and "
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"skipped memory profiling. This does not respect the "
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"gpu_memory_utilization config. Only use kv_cache_memory_bytes "
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"config when you want manual control of KV cache memory "
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"size. If OOM'ed, check the difference of initial free "
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"memory between the current run and the previous run "
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"where kv_cache_memory_bytes is suggested and update it "
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"correspondingly."
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)
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logger.info(msg)
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return kv_cache_memory_bytes
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torch.cuda.empty_cache()
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torch.cuda.reset_peak_memory_stats()
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# Execute a forward pass with dummy inputs to profile the memory usage
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# of the model.
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with memory_profiling(
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self.init_snapshot,
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weights_memory=int(self.model_runner.model_memory_usage),
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) as profile_result:
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self.model_runner.profile_run()
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self.non_torch_memory = profile_result.non_torch_increase
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self.peak_activation_memory = profile_result.torch_peak_increase
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free_gpu_memory = profile_result.after_profile.free_memory
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# NOTE(woosuk): Here we assume that the other processes using the same
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# GPU did not change their memory usage during the profiling.
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assert self.init_snapshot.free_memory > free_gpu_memory, (
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"Error in memory profiling. "
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f"Initial free memory {GiB(self.init_snapshot.free_memory)} GiB, "
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f"current free memory {GiB(free_gpu_memory)} GiB. "
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"This happens when other processes sharing the same container "
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"release GPU memory while vLLM is profiling during initialization. "
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"To fix this, ensure consistent GPU memory allocation or "
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"isolate vLLM in its own container."
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)
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self.available_kv_cache_memory_bytes = (
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self.requested_memory - profile_result.non_kv_cache_memory
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)
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unrequested_memory = self.init_snapshot.free_memory - self.requested_memory
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logger.debug(
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"Initial free memory: %.2f GiB; Requested memory: %.2f (util), %.2f GiB",
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GiB(self.init_snapshot.free_memory),
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self.cache_config.gpu_memory_utilization,
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GiB(self.requested_memory),
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)
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logger.debug(
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"Free memory after profiling: %.2f GiB (total), "
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"%.2f GiB (within requested)",
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GiB(free_gpu_memory),
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GiB(free_gpu_memory - unrequested_memory),
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)
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logger.debug(profile_result)
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logger.info_once(
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"Available KV cache memory: %.2f GiB",
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GiB(self.available_kv_cache_memory_bytes),
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scope="local",
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)
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gc.collect()
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return int(self.available_kv_cache_memory_bytes)
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def get_kv_connector_handshake_metadata(self) -> dict | None:
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"""Get KV connector metadata from this worker if available."""
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if not has_kv_transfer_group():
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return None
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connector = get_kv_transfer_group()
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# Return None for connectors that don't need to exchange handshake
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# metadata across workers.
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if (metadata := connector.get_handshake_metadata()) is None:
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return None
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tp_rank = get_tp_group().rank_in_group
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return {tp_rank: metadata}
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def get_kv_cache_spec(self) -> dict[str, KVCacheSpec]:
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return self.model_runner.get_kv_cache_spec()
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def initialize_from_config(self, kv_cache_config: KVCacheConfig) -> None:
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"""Allocate GPU KV cache with the specified kv_cache_config."""
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# Init kv cache connector here, because it requires
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# `kv_cache_config`.
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# NOTE(Kuntai): This need to be done before `initialize_kv_cache`,
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# because `initialize_kv_cache` will inject kv cache groups not
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# related to kv cache connector (e.g. kv cache sharing layers).
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ensure_kv_transfer_initialized(self.vllm_config, kv_cache_config)
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if self.vllm_config.model_config.enable_sleep_mode:
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from vllm.device_allocator.cumem import CuMemAllocator
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allocator = CuMemAllocator.get_instance()
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context = allocator.use_memory_pool(tag="kv_cache")
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else:
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context = nullcontext()
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with context:
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self.model_runner.initialize_kv_cache(kv_cache_config)
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def compile_or_warm_up_model(self) -> None:
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# warm up sizes that are not in cudagraph capture sizes,
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# but users still want to compile for better performance,
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# e.g. for the max-num-batched token size in chunked prefill.
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warmup_sizes = self.vllm_config.compilation_config.compile_sizes.copy()
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if not self.model_config.enforce_eager:
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warmup_sizes = [
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x
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for x in warmup_sizes
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if x not in self.vllm_config.compilation_config.cudagraph_capture_sizes
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]
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# We skip EPLB here since we don't want to record dummy metrics
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for size in sorted(warmup_sizes, reverse=True):
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logger.info("Compile and warming up model for size %d", size)
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self.model_runner._dummy_run(size, skip_eplb=True, remove_lora=False)
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self.model_runner.maybe_remove_all_loras(self.model_runner.lora_config)
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# Warmup and tune the kernels used during model execution before
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# cuda graph capture.
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kernel_warmup(self)
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cuda_graph_memory_bytes = 0
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if not self.model_config.enforce_eager:
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cuda_graph_memory_bytes = self.model_runner.capture_model()
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if self.cache_config.kv_cache_memory_bytes is None and hasattr(
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self, "peak_activation_memory"
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):
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# Suggests optimal kv cache memory size if we rely on
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# memory_profiling to guess the kv cache memory size which
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# provides peak_activation_memory and a few other memory
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# consumption. `memory_profiling` does not consider
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# CUDAGraph memory size and may not utilize all gpu memory.
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# Users may want fine-grained control to specify kv cache
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# memory size.
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GiB = lambda b: round(b / GiB_bytes, 2)
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# empirically observed that the memory profiling may
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# slightly underestimate the memory consumption.
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# So leave a small buffer (=150MiB) to avoid OOM.
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redundancy_buffer_memory = 150 * (1 << 20)
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non_kv_cache_memory = (
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self.model_runner.model_memory_usage
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+ self.peak_activation_memory
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+ self.non_torch_memory
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+ cuda_graph_memory_bytes
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)
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kv_cache_memory_bytes_to_gpu_limit = (
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self.init_snapshot.free_memory
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- non_kv_cache_memory
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- redundancy_buffer_memory
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)
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kv_cache_memory_bytes_to_requested_limit = (
|
|
int(self.requested_memory)
|
|
- non_kv_cache_memory
|
|
- redundancy_buffer_memory
|
|
)
|
|
|
|
msg = (
|
|
f"Free memory on device "
|
|
f"({GiB(self.init_snapshot.free_memory)}/"
|
|
f"{GiB(self.init_snapshot.total_memory)} GiB) on startup. "
|
|
f"Desired GPU memory utilization is "
|
|
f"({self.cache_config.gpu_memory_utilization}, "
|
|
f"{GiB(self.requested_memory)} GiB). "
|
|
f"Actual usage is {GiB(self.model_runner.model_memory_usage)} "
|
|
f"GiB for weight, {GiB(self.peak_activation_memory)} GiB "
|
|
f"for peak activation, {GiB(self.non_torch_memory)} GiB "
|
|
f"for non-torch memory, and {GiB(cuda_graph_memory_bytes)} "
|
|
f"GiB for CUDAGraph memory. Replace gpu_memory_utilization "
|
|
f"config with `--kv-cache-memory="
|
|
f"{kv_cache_memory_bytes_to_requested_limit}` "
|
|
f"({GiB(kv_cache_memory_bytes_to_requested_limit)} GiB) to fit "
|
|
f"into requested memory, or `--kv-cache-memory="
|
|
f"{kv_cache_memory_bytes_to_gpu_limit}` "
|
|
f"({GiB(kv_cache_memory_bytes_to_gpu_limit)} GiB) to fully "
|
|
f"utilize gpu memory. Current kv cache memory in use is "
|
|
f"{GiB(self.available_kv_cache_memory_bytes)} GiB."
|
|
)
|
|
|
|
logger.debug(msg)
|
|
|
|
# Warm up sampler and preallocate memory buffer for logits and other
|
|
# sampling related tensors of max possible shape to avoid memory
|
|
# fragmentation issue.
|
|
# NOTE: This is called after `capture_model` on purpose to prevent
|
|
# memory buffers from being cleared by `torch.cuda.empty_cache`.
|
|
if get_pp_group().is_last_rank:
|
|
max_num_reqs = min(
|
|
self.scheduler_config.max_num_seqs,
|
|
self.scheduler_config.max_num_batched_tokens,
|
|
)
|
|
|
|
# We skip EPLB here since we don't want to record dummy metrics
|
|
hidden_states, last_hidden_states = self.model_runner._dummy_run(
|
|
num_tokens=max_num_reqs,
|
|
skip_eplb=True,
|
|
)
|
|
if self.model_runner.is_pooling_model:
|
|
self.model_runner._dummy_pooler_run(hidden_states)
|
|
else:
|
|
self.model_runner._dummy_sampler_run(hidden_states=last_hidden_states)
|
|
|
|
# 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 reset_mm_cache(self) -> None:
|
|
self.model_runner.reset_mm_cache()
|
|
|
|
def get_model(self) -> nn.Module:
|
|
return self.model_runner.get_model()
|
|
|
|
def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
|
|
return self.model_runner.get_supported_tasks()
|
|
|
|
def annotate_profile(self, scheduler_output):
|
|
# add trace annotation so that we can easily distinguish
|
|
# new/cached request numbers in each iteration
|
|
if not self.profiler:
|
|
return nullcontext()
|
|
|
|
num_new = len(scheduler_output.scheduled_new_reqs)
|
|
num_cached = len(scheduler_output.scheduled_cached_reqs.req_ids)
|
|
|
|
return torch.profiler.record_function(
|
|
f"execute_new_{num_new}_cached_{num_cached}"
|
|
)
|
|
|
|
@torch.inference_mode()
|
|
def sample_tokens(
|
|
self, grammar_output: "GrammarOutput | None"
|
|
) -> ModelRunnerOutput | AsyncModelRunnerOutput:
|
|
return self.model_runner.sample_tokens(grammar_output)
|
|
|
|
@torch.inference_mode()
|
|
def execute_model(
|
|
self, scheduler_output: "SchedulerOutput"
|
|
) -> ModelRunnerOutput | None:
|
|
intermediate_tensors = None
|
|
forward_pass = scheduler_output.total_num_scheduled_tokens > 0
|
|
num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
|
|
num_input_tokens = self.model_runner._get_num_input_tokens(num_scheduled_tokens)
|
|
all_gather_tensors = {
|
|
"residual": not is_residual_scattered_for_sp(
|
|
self.vllm_config, num_input_tokens
|
|
)
|
|
}
|
|
if forward_pass and not get_pp_group().is_first_rank:
|
|
intermediate_tensors = IntermediateTensors(
|
|
get_pp_group().recv_tensor_dict(
|
|
all_gather_group=get_tp_group(),
|
|
all_gather_tensors=all_gather_tensors,
|
|
)
|
|
)
|
|
|
|
with self.annotate_profile(scheduler_output):
|
|
output = self.model_runner.execute_model(
|
|
scheduler_output, intermediate_tensors
|
|
)
|
|
if isinstance(output, (ModelRunnerOutput, NoneType)):
|
|
return output
|
|
|
|
assert isinstance(output, IntermediateTensors)
|
|
parallel_config = self.vllm_config.parallel_config
|
|
assert (
|
|
parallel_config.distributed_executor_backend != "external_launcher"
|
|
and not get_pp_group().is_last_rank
|
|
)
|
|
|
|
get_pp_group().send_tensor_dict(
|
|
output.tensors,
|
|
all_gather_group=get_tp_group(),
|
|
all_gather_tensors=all_gather_tensors,
|
|
)
|
|
|
|
kv_connector_output = output.kv_connector_output
|
|
if not kv_connector_output:
|
|
return None
|
|
|
|
# In case of PP with kv transfer, we need to pass through the
|
|
# kv_connector_output
|
|
if kv_connector_output.is_empty():
|
|
return EMPTY_MODEL_RUNNER_OUTPUT
|
|
|
|
output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
|
|
output.kv_connector_output = kv_connector_output
|
|
return output
|
|
|
|
def take_draft_token_ids(self) -> DraftTokenIds | None:
|
|
return self.model_runner.take_draft_token_ids()
|
|
|
|
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()
|
|
# only print profiler results on rank 0
|
|
if (
|
|
isinstance(self.profiler, torch.profiler.profile)
|
|
and self.local_rank == 0
|
|
):
|
|
print(
|
|
self.profiler.key_averages().table(sort_by="self_cuda_time_total")
|
|
)
|
|
|
|
def execute_dummy_batch(self) -> None:
|
|
self.model_runner._dummy_run(1, uniform_decode=True)
|
|
|
|
def add_lora(self, lora_request: LoRARequest) -> bool:
|
|
return self.model_runner.add_lora(lora_request)
|
|
|
|
def remove_lora(self, lora_id: int) -> bool:
|
|
return self.model_runner.remove_lora(lora_id)
|
|
|
|
def list_loras(self) -> set[int]:
|
|
return self.model_runner.list_loras()
|
|
|
|
def pin_lora(self, lora_id: int) -> bool:
|
|
return self.model_runner.pin_lora(lora_id)
|
|
|
|
def check_health(self) -> None:
|
|
# worker will always be healthy as long as it's running.
|
|
return
|
|
|
|
def _eplb_before_scale_down(self, old_ep_size: int, new_ep_size: int) -> None:
|
|
from vllm.distributed.parallel_state import get_ep_group
|
|
|
|
if get_ep_group().rank == 0:
|
|
logger.info(
|
|
"[Elastic EP] Starting expert resharding before scaling down..."
|
|
)
|
|
rank_mapping = {
|
|
old_ep_rank: old_ep_rank if old_ep_rank < new_ep_size else -1
|
|
for old_ep_rank in range(old_ep_size)
|
|
}
|
|
assert self.model_runner.eplb_state is not None
|
|
self.model_runner.eplb_state.rearrange(
|
|
execute_shuffle=True,
|
|
global_expert_load=None,
|
|
rank_mapping=rank_mapping,
|
|
)
|
|
torch.cuda.synchronize()
|
|
if get_ep_group().rank == 0:
|
|
logger.info("[Elastic EP] Expert resharding completed!")
|
|
|
|
def _eplb_after_scale_up(
|
|
self,
|
|
old_ep_size: int,
|
|
new_ep_size: int,
|
|
global_expert_loads: list[torch.Tensor] | None,
|
|
) -> None:
|
|
from vllm.distributed.parallel_state import get_ep_group
|
|
|
|
if get_ep_group().rank == 0:
|
|
logger.info("[Elastic EP] Starting expert resharding after scaling up...")
|
|
rank_mapping = {old_ep_rank: old_ep_rank for old_ep_rank in range(old_ep_size)}
|
|
assert self.model_runner.eplb_state is not None
|
|
self.model_runner.eplb_state.rearrange(
|
|
execute_shuffle=True,
|
|
global_expert_loads=global_expert_loads,
|
|
rank_mapping=rank_mapping,
|
|
)
|
|
if get_ep_group().rank == 0:
|
|
logger.info("[Elastic EP] Expert resharding completed!")
|
|
|
|
def _reconfigure_parallel_config(
|
|
self, reconfig_request: ReconfigureDistributedRequest
|
|
) -> None:
|
|
"""
|
|
Update parallel config with provided reconfig_request
|
|
"""
|
|
parallel_config = self.vllm_config.parallel_config
|
|
parallel_config.data_parallel_size = reconfig_request.new_data_parallel_size
|
|
if (
|
|
reconfig_request.new_data_parallel_rank
|
|
!= ReconfigureRankType.KEEP_CURRENT_RANK
|
|
):
|
|
parallel_config.data_parallel_rank = reconfig_request.new_data_parallel_rank
|
|
if (
|
|
reconfig_request.new_data_parallel_rank_local
|
|
!= ReconfigureRankType.KEEP_CURRENT_RANK
|
|
):
|
|
parallel_config.data_parallel_rank_local = (
|
|
reconfig_request.new_data_parallel_rank_local
|
|
)
|
|
parallel_config.data_parallel_master_ip = (
|
|
reconfig_request.new_data_parallel_master_ip
|
|
)
|
|
parallel_config.data_parallel_master_port = (
|
|
reconfig_request.new_data_parallel_master_port
|
|
)
|
|
|
|
def _reconfigure_moe(
|
|
self, old_ep_size: int, new_ep_size: int
|
|
) -> torch.Tensor | None:
|
|
"""
|
|
Reconfigure MoE modules with provided reconfig_request
|
|
|
|
Return the global expert load if new_ep_size > old_ep_size,
|
|
otherwise None
|
|
"""
|
|
from vllm.distributed.parallel_state import (
|
|
get_dp_group,
|
|
get_ep_group,
|
|
prepare_communication_buffer_for_model,
|
|
)
|
|
from vllm.model_executor.layers.fused_moe.layer import (
|
|
FusedMoE,
|
|
FusedMoEParallelConfig,
|
|
)
|
|
|
|
parallel_config = self.vllm_config.parallel_config
|
|
|
|
def get_moe_modules(model: torch.nn.Module) -> list[FusedMoE]:
|
|
return [
|
|
module
|
|
for module in model.modules()
|
|
if (
|
|
module.__class__.__name__ == "FusedMoE"
|
|
or module.__class__.__name__ == "SharedFusedMoE"
|
|
)
|
|
]
|
|
|
|
def update_moe_modules(moe_modules: list[FusedMoE], num_local_experts: int):
|
|
assert all(
|
|
module.moe_config.num_local_experts == num_local_experts
|
|
for module in moe_modules
|
|
), "All MoE modules must have the same number of experts"
|
|
for module in moe_modules:
|
|
module.moe_config.num_experts = num_local_experts * new_ep_size
|
|
module.global_num_experts = module.moe_config.num_experts
|
|
module.moe_parallel_config = FusedMoEParallelConfig.make(
|
|
tp_size_=get_tp_group().world_size,
|
|
dp_size_=get_dp_group().world_size,
|
|
vllm_parallel_config=parallel_config,
|
|
)
|
|
module.moe_config.moe_parallel_config = module.moe_parallel_config
|
|
return moe_modules
|
|
|
|
model_moe_modules = get_moe_modules(self.model_runner.model)
|
|
num_local_experts = model_moe_modules[0].moe_config.num_local_experts
|
|
|
|
update_moe_modules(model_moe_modules, num_local_experts)
|
|
drafter_model = None
|
|
if hasattr(self.model_runner, "drafter") and hasattr(
|
|
self.model_runner.drafter, "model"
|
|
):
|
|
drafter_model = self.model_runner.drafter.model
|
|
if drafter_model is not None and is_mixture_of_experts(drafter_model):
|
|
drafter_moe_modules = get_moe_modules(drafter_model)
|
|
# Check if drafter and model have matching configs
|
|
assert (
|
|
drafter_moe_modules[0].moe_config.num_local_experts == num_local_experts
|
|
), "Drafter and model configs should be the same"
|
|
update_moe_modules(drafter_moe_modules, num_local_experts)
|
|
|
|
if new_ep_size < old_ep_size:
|
|
num_local_physical_experts = num_local_experts
|
|
assert self.model_runner.eplb_state is not None
|
|
new_physical_experts = (
|
|
self.model_runner.eplb_state.physical_to_logical_map.shape[1]
|
|
)
|
|
parallel_config.eplb_config.num_redundant_experts = (
|
|
new_physical_experts
|
|
- self.model_runner.eplb_state.logical_replica_count.shape[1]
|
|
)
|
|
global_expert_loads = None
|
|
else:
|
|
num_local_physical_experts = torch.tensor(
|
|
[num_local_experts], dtype=torch.int32, device="cpu"
|
|
)
|
|
torch.distributed.broadcast(
|
|
num_local_physical_experts, group=get_ep_group().cpu_group, group_src=0
|
|
)
|
|
num_local_physical_experts = num_local_physical_experts.item()
|
|
new_physical_experts = num_local_physical_experts * new_ep_size
|
|
assert self.model_runner.eplb_state is not None
|
|
global_expert_loads = self.model_runner.eplb_state.rearrange(
|
|
execute_shuffle=False
|
|
)
|
|
parallel_config.eplb_config.num_redundant_experts = (
|
|
new_physical_experts - global_expert_loads[0].shape[1]
|
|
)
|
|
prepare_communication_buffer_for_model(self.model_runner.model)
|
|
if drafter_model is not None:
|
|
prepare_communication_buffer_for_model(drafter_model)
|
|
self.model_runner.model.update_physical_experts_metadata(
|
|
num_physical_experts=new_physical_experts,
|
|
num_local_physical_experts=num_local_physical_experts,
|
|
)
|
|
return global_expert_loads
|
|
|
|
def reinitialize_distributed(
|
|
self, reconfig_request: ReconfigureDistributedRequest
|
|
) -> None:
|
|
from vllm.config import set_current_vllm_config
|
|
from vllm.distributed.parallel_state import (
|
|
cleanup_dist_env_and_memory,
|
|
get_ep_group,
|
|
)
|
|
|
|
old_ep_size = get_ep_group().world_size
|
|
old_ep_rank = get_ep_group().rank
|
|
new_ep_size = (
|
|
reconfig_request.new_data_parallel_size
|
|
* get_tp_group().world_size
|
|
* get_pp_group().world_size
|
|
)
|
|
if new_ep_size < old_ep_size:
|
|
self._eplb_before_scale_down(old_ep_size, new_ep_size)
|
|
|
|
cleanup_dist_env_and_memory()
|
|
|
|
if (
|
|
reconfig_request.new_data_parallel_rank
|
|
== ReconfigureRankType.SHUTDOWN_CURRENT_RANK
|
|
):
|
|
assert old_ep_rank >= new_ep_size
|
|
# shutdown
|
|
return
|
|
|
|
self._reconfigure_parallel_config(reconfig_request)
|
|
|
|
with set_current_vllm_config(self.vllm_config):
|
|
init_worker_distributed_environment(
|
|
self.vllm_config,
|
|
self.rank,
|
|
self.distributed_init_method,
|
|
self.local_rank,
|
|
)
|
|
|
|
global_expert_loads = self._reconfigure_moe(old_ep_size, new_ep_size)
|
|
|
|
if new_ep_size > old_ep_size:
|
|
assert global_expert_loads is not None
|
|
self._eplb_after_scale_up(old_ep_size, new_ep_size, global_expert_loads)
|
|
|
|
def save_sharded_state(
|
|
self,
|
|
path: str,
|
|
pattern: str | None = None,
|
|
max_size: int | None = None,
|
|
) -> None:
|
|
from vllm.model_executor.model_loader import ShardedStateLoader
|
|
|
|
ShardedStateLoader.save_model(
|
|
self.model_runner.model,
|
|
path,
|
|
pattern=pattern,
|
|
max_size=max_size,
|
|
)
|
|
|
|
def save_tensorized_model(
|
|
self,
|
|
tensorizer_config: "TensorizerConfig",
|
|
) -> None:
|
|
self.model_runner.save_tensorized_model(
|
|
tensorizer_config=tensorizer_config,
|
|
)
|
|
|
|
def shutdown(self) -> None:
|
|
if runner := getattr(self, "model_runner", None):
|
|
runner.ensure_kv_transfer_shutdown()
|
|
|
|
|
|
def init_worker_distributed_environment(
|
|
vllm_config: VllmConfig,
|
|
rank: int,
|
|
distributed_init_method: str | None = None,
|
|
local_rank: int = -1,
|
|
backend: str = "nccl",
|
|
) -> None:
|
|
"""Initialize the distributed environment."""
|
|
parallel_config = vllm_config.parallel_config
|
|
from vllm.model_executor.layers.batch_invariant import init_batch_invariance
|
|
|
|
init_batch_invariance()
|
|
set_custom_all_reduce(not parallel_config.disable_custom_all_reduce)
|
|
|
|
init_distributed_environment(
|
|
parallel_config.world_size, rank, distributed_init_method, local_rank, backend
|
|
)
|
|
|
|
ensure_model_parallel_initialized(
|
|
parallel_config.tensor_parallel_size,
|
|
parallel_config.pipeline_parallel_size,
|
|
parallel_config.decode_context_parallel_size,
|
|
)
|