639 lines
27 KiB
Python
639 lines
27 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
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# SPDX-License-Identifier: Apache-2.0
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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, Optional
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import torch
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import torch.distributed
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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.parallel_state import get_tp_group, get_pp_group
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from vllm.distributed.kv_transfer import (ensure_kv_transfer_initialized,
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has_kv_transfer_group)
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from vllm.logger import init_logger
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from vllm.model_executor import set_random_seed
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from vllm.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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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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from vllm.v1.outputs import EMPTY_MODEL_RUNNER_OUTPUT, ModelRunnerOutput
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from vllm.v1.utils import report_usage_stats
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from vllm.v1.engine import ReconfigureDistributedRequest, ReconfigureRankType
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from vllm.v1.worker.gpu_worker import Worker, init_worker_distributed_environment
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.utils.mem_constants import GiB_bytes
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if TYPE_CHECKING:
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm_mlu.model_executor.warmup.kernel_warmup import kernel_warmup
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from vllm_mlu.profiler.mlu_profiler import MluProfilerWrapper
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from vllm_mlu.utils import MemorySnapshot, memory_profiling
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from vllm_mlu._mlu_utils import VLLM_DUMP_MLU_INFO_EN
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from vllm_mlu.device_allocator.cnmem import CnMemAllocator
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from vllm_mlu.v1.worker.mlu_quant import MLUWorkerQuant
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from vllm_mlu.v1.worker.gpu_model_runner import MLUModelRunner
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from vllm_mlu.v1.worker.dp_gpu_model_runner import DPMLUModelRunner
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logger = init_logger(__name__)
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class MLUWorker(Worker, MLUWorkerQuant):
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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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WorkerBase.__init__(self, 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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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.MLU,
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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 = MluProfilerWrapper()
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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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free_bytes_before_sleep = torch.mlu.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 = CnMemAllocator.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.mlu.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, "
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"%.2f GiB memory is still in use.", freed_bytes / GiB_bytes,
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used_bytes / GiB_bytes)
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def wake_up(self, tags: Optional[list[str]] = None) -> None:
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allocator = CnMemAllocator.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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allocator = CnMemAllocator.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 init_device(self):
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if self.device_config.device.type == "mlu":
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# This env var set by Ray causes exceptions with graph building.
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os.environ.pop("CNCL_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.mlu.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"mlu:{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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gc.collect()
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torch.mlu.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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model_runner_cls = (DPMLUModelRunner
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if self._enable_moe_dp_opt() else MLUModelRunner)
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self.model_runner: MLUModelRunner = model_runner_cls(
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self.vllm_config, self.device)
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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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@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 calculate 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.mlu.empty_cache()
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torch.mlu.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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GiB = lambda b: b / GiB_bytes
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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(
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self.model_runner.model_memory_usage)) as profile_result:
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self.model_runner.profile_run()
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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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self.peak_memory = profile_result.non_kv_cache_memory
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self.block_memory = self.available_kv_cache_memory_bytes
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return int(self.available_kv_cache_memory_bytes)
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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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allocator = CnMemAllocator.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 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 = (
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int(self.requested_memory)
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- non_kv_cache_memory
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- redundancy_buffer_memory
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)
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msg = (
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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"Desired GPU memory utilization is "
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f"({self.cache_config.gpu_memory_utilization}, "
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f"{GiB(self.requested_memory)} GiB). "
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f"Actual usage is {GiB(self.model_runner.model_memory_usage)} "
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f"GiB for weight, {GiB(self.peak_activation_memory)} GiB "
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f"for peak activation, {GiB(self.non_torch_memory)} GiB "
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f"for non-torch memory, and {GiB(cuda_graph_memory_bytes)} "
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f"GiB for CUDAGraph memory. Replace gpu_memory_utilization "
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f"config with `--kv-cache-memory="
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f"{kv_cache_memory_bytes_to_requested_limit}` "
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f"({GiB(kv_cache_memory_bytes_to_requested_limit)} GiB) to fit "
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f"into requested memory, or `--kv-cache-memory="
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f"{kv_cache_memory_bytes_to_gpu_limit}` "
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f"({GiB(kv_cache_memory_bytes_to_gpu_limit)} GiB) to fully "
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f"utilize gpu memory. Current kv cache memory in use is "
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f"{GiB(self.available_kv_cache_memory_bytes)} GiB."
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)
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logger.debug(msg)
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# Warm up sampler and preallocate memory buffer for logits and other
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# sampling related tensors of max possible shape to avoid memory
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# fragmentation issue.
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# 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)
|
|
|
|
@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,
|
|
)
|
|
|
|
return None
|
|
|
|
def _enable_moe_dp_opt(self):
|
|
'''
|
|
We will enable the MLU-optimized DP scheme for the specified MoE models,
|
|
otherwise the native DP implementation will be used.
|
|
'''
|
|
# case0 enable data parallel
|
|
enable_dp = self.parallel_config.data_parallel_size > 1
|
|
# case1 ds mla
|
|
is_ds_mla = self.model_config.is_deepseek_mla
|
|
# case2 qwen3 moe
|
|
is_supported_moe_model = hasattr(self.model_config.hf_text_config, "model_type") and \
|
|
self.model_config.hf_text_config.model_type in ('qwen3_moe', 'glm4_moe')
|
|
# case 3, private model
|
|
is_private_model = getattr(self.model_config.hf_config, "is_private", False)
|
|
return enable_dp and (is_ds_mla or is_supported_moe_model or is_private_model)
|
|
|
|
def execute_dummy_batch(self) -> None:
|
|
if self._enable_moe_dp_opt():
|
|
self.model_runner.moe_dp_execute_dummy_batch(1)
|
|
else:
|
|
self.model_runner._dummy_run(1, uniform_decode=True)
|
|
|
|
def response_remote_alloc_once(self) -> None:
|
|
self.model_runner.response_remote_alloc_once()
|
|
|
|
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.mlu.synchronize()
|
|
if get_ep_group().rank == 0:
|
|
logger.info("[Elastic EP] Expert resharding completed!")
|
|
|
|
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,
|
|
current_platform.dist_backend,
|
|
)
|
|
|
|
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 get_hfu_info(self, batch, input_len, output_len):
|
|
try:
|
|
self.model_runner.model.collect_hfu_io_effciency_info(batch, input_len, output_len)
|
|
if VLLM_DUMP_MLU_INFO_EN:
|
|
return self.model_runner.model.hfu_info, self.model_runner.model.io_efficiency
|
|
else:
|
|
return self.model_runner.model.flops_info, 0.0
|
|
except Exception as e:
|
|
raise RuntimeError(
|
|
"Model match failure when get HFU info, please check if an init method was registed."
|
|
)
|
|
|
|
def _get_latency(self, time_markers):
|
|
total_latency = 0
|
|
if not isinstance(time_markers, list):
|
|
time_markers = [time_markers]
|
|
for time_marker in time_markers:
|
|
start, end = time_marker
|
|
latency = start.elapsed_time(end)
|
|
total_latency += latency
|
|
return total_latency
|
|
|
|
def get_latency(self):
|
|
return self._get_latency(self.model_runner.time_markers)
|
|
|
|
def get_mm_encoder_latency(self):
|
|
if not hasattr(self.model_runner, "mm_time_markers"):
|
|
return None
|
|
mm_time_markers = self.model_runner.mm_time_markers
|
|
return None if len(mm_time_markers) == 0 else\
|
|
self._get_latency(mm_time_markers)
|
|
|
|
def get_memory_usage(self):
|
|
return (self.peak_memory, self.block_memory)
|
|
|
|
def recapture_model(self,
|
|
prefill_enable_mlugraph: bool,
|
|
batch_size: int,
|
|
input_len: int):
|
|
# Reset history capture context
|
|
self.model_runner.reset_capture_context(
|
|
prefill_enable_mlugraph, batch_size, input_len)
|
|
# Re-capture decode graph(full graph or peicewise graph)
|
|
self.compile_or_warm_up_model()
|